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Impaired insulin secretion is a hallmark of type 2 diabetes ( T2D ) . Epigenetics may affect disease susceptibility . To describe the human methylome in pancreatic islets and determine the epigenetic basis of T2D , we analyzed DNA methylation of 479 , 927 CpG sites and the transcriptome in pancreatic islets from T2D and non-diabetic donors . We provide a detailed map of the global DNA methylation pattern in human islets , β- and α-cells . Genomic regions close to the transcription start site showed low degrees of methylation and regions further away from the transcription start site such as the gene body , 3′UTR and intergenic regions showed a higher degree of methylation . While CpG islands were hypomethylated , the surrounding 2 kb shores showed an intermediate degree of methylation , whereas regions further away ( shelves and open sea ) were hypermethylated in human islets , β- and α-cells . We identified 1 , 649 CpG sites and 853 genes , including TCF7L2 , FTO and KCNQ1 , with differential DNA methylation in T2D islets after correction for multiple testing . The majority of the differentially methylated CpG sites had an intermediate degree of methylation and were underrepresented in CpG islands ( ∼7% ) and overrepresented in the open sea ( ∼60% ) . 102 of the differentially methylated genes , including CDKN1A , PDE7B , SEPT9 and EXOC3L2 , were differentially expressed in T2D islets . Methylation of CDKN1A and PDE7B promoters in vitro suppressed their transcriptional activity . Functional analyses demonstrated that identified candidate genes affect pancreatic β- and α-cells as Exoc3l silencing reduced exocytosis and overexpression of Cdkn1a , Pde7b and Sept9 perturbed insulin and glucagon secretion in clonal β- and α-cells , respectively . Together , our data can serve as a reference methylome in human islets . We provide new target genes with altered DNA methylation and expression in human T2D islets that contribute to perturbed insulin and glucagon secretion . These results highlight the importance of epigenetics in the pathogenesis of T2D .
Type 2 diabetes ( T2D ) is a complex multifactorial disorder characterized by chronic hyperglycemia due to impaired insulin secretion from pancreatic β-cells , elevated glucagon secretion from pancreatic α-cells and insulin resistance in target tissues . As a result of aging populations and an increasing prevalence of obesity and physical inactivity , the number of patients with T2D has dramatically increased worldwide [1] . Family studies together with genome-wide association studies ( GWAS ) have shown that the genetic background also influences the risk of T2D [2] , [3] . The majority of T2D single nucleotide polymorphisms ( SNPs ) identified by GWAS are associated with impaired insulin secretion rather than insulin action , pointing to pancreatic islet defects as key mechanisms in the pathogenesis of T2D [3]–[5] . However , the identified SNPs only explain a small proportion of the estimated heritability of T2D , suggesting that additional genetic factors remain to be identified [3] . Genetic variants can interact with environmental factors and thereby modulate the risk for T2D through gene-environment interactions [6] . The interaction between genes and environment may also happen through direct chemical modifications of the genome by so called epigenetic modifications , including DNA methylation and histone modifications [7] . These are known to influence the chromatin structure and DNA accessibility and can thereby regulate gene expression [8] , [9] . Epigenetic alterations may subsequently influence phenotype transmission and the development of different diseases , including T2D [7] , [10] . Our group has recently found increased DNA methylation in parallel with decreased expression of PPARGC1A , PDX-1 and INS in human pancreatic islets from patients with T2D by using a candidate gene approach [11]–[13] . Another group has analyzed DNA methylation of ∼0 . 1% of the CpG sites in the human genome in pancreatic islets from five T2D and 11 non-diabetic donors [14] . Animal studies further support the hypothesis that epigenetic modifications in pancreatic islets may lead to altered gene expression , impaired insulin secretion and subsequently diabetes [15]–[17] . Although these studies point towards a key role for epigenetic modifications in the growing incidence of T2D , comprehensive human epigenetic studies , covering most genes and regions in the genome in pancreatic islets from diabetic and non-diabetic donors , are still lacking . Human studies further need to link T2D associated epigenetic modifications with islet gene expression and eventually impaired insulin and/or glucagon secretion . Moreover , the human methylome has previously not been described in human pancreatic islets . In the present study , we analyzed the genome-wide DNA methylation pattern in pancreatic islets from patients with T2D and non-diabetic donors using the Infinium HumanMethylation450 BeadChip , which covers ∼480 , 000 CpG sites in 21 , 231 ( 99% ) RefSeq genes . The degree of DNA methylation was further related to the transcriptome in the same set of islets . A number of genes that exhibited both differential DNA methylation and gene expression in human T2D islets were then selected for functional follow up studies; insulin and glucagon secretion were analyzed in clonal β- and α-cells , respectively where selected candidate genes had been either overexpressed or silenced . Also , reporter gene constructs were used to study the direct effect of DNA methylation on the transcriptional activity . Together , our study provides the first detailed map of the human methylome in pancreatic islets and it provides new target genes with altered DNA methylation and expression in human T2D islets that contribute to perturbed insulin and glucagon secretion .
To describe the methylome in pancreatic islets and unravel the epigenetic basis of T2D , DNA methylation of a total of 485 , 577 sites were analyzed in human pancreatic islets from 15 T2D and 34 non-diabetic donors by using the Infinium HumanMethylation450 BeadChip . The characteristics of the islet donors included in the genome-wide analysis of DNA methylation are described in Table 1 . T2D donors had higher HbA1c levels , nominally higher BMI and lower glucose-stimulated insulin secretion compared with non-diabetic donors ( Table 1 ) . There were no differences in islet purity ( P = 0 . 97; Figure S1A ) or β-cell content ( P = 0 . 43; Figure S1B ) between T2D and non-diabetic islets . A stringent quality control procedure was then performed and 2 , 546 ( 0 . 5% ) sites were excluded for having a mean detection P-value>0 . 01 and as a result 483 , 031 sites generated reliable DNA methylation data and were used for further analysis . These 483 , 031 sites included 479 , 927 CpG sites , 3 , 039 non-CpG sites and 65 SNPs related to 21 , 231 RefSeq genes . Additional quality control steps were performed and all samples showed high bisulfite conversion efficiency ( materials and methods ) . The probes included on the Infinium HumanMethylation450 BeadChip have been annotated based on their relation to the nearest gene and the probes may belong to any of the following genomic elements: TSS1500 , TSS200 , 5′UTR , 1st exon , gene body , 3′UTR or intergenic regions ( Figure 1A ) . To describe the overall methylome in human pancreatic islets and to test whether there are global differences in DNA methylation in T2D islets , we calculated the average degree of DNA methylation of different genomic elements in T2D and non-diabetic islets . While genomic regions close to the transcription start site showed relatively low degrees of methylation ( 27 . 5±1 . 5% for TSS1500 , 12 . 3±0 . 06% for TSS200 , 23 . 0±1 . 2% for 5′UTR and 14 . 0±0 . 08% for 1st exon in non-diabetic islets ) , there was a higher degree of methylation in regions further away from the transcription start site ( 60 . 2±1 . 9% for gene body , 71 . 3±1 . 8% for 3′UTR and 57 . 6±2 . 0% for intergenic regions ) ( Figure 1B ) . The probes on the Infinium HumanMethylation450 BeadChip have also been annotated based on their genomic location relative to CpG islands as shown in Figure 1A , where CpG island shores cover regions 0–2 kb from CpG islands and shelves cover regions 2–4 kb from CpG islands . North and south are used to determine whether the CpG site is upstream or downstream from a CpG island and open sea are isolated CpG sites in the genome . We found that CpG islands are hypomethylated ( 14 . 9±0 . 07% ) , shelves and open sea are hypermethylated ( 72 . 9±1 . 8% for N shelf , 73 . 5±1 . 8% for S shelf and 69 . 2±1 . 9% for open sea ) , while shores show an intermediate degree of methylation ( 42 . 5±2 . 1% for N shore and 41 . 1±2 . 0% for S shore ) in human islets ( Figure 1C ) . The average degree of DNA methylation for any of the genomic regions did not differ in T2D versus non-diabetic islets ( Figure 1B–C ) . We next examined if any individual sites exhibit differential DNA methylation in pancreatic islets from T2D compared with non-diabetic donors . After correcting for multiple testing using a false discovery rate ( FDR ) analysis we identified 3 , 116 CpG sites that were differentially methylated between T2D and non-diabetic islets with FDR less than 5% ( q<0 . 05 ) , which means that 156 false positives are expected by chance [18] . The distribution of the absolute differences in DNA methylation between T2D and non-diabetic islets is shown in Figure 1D . To gain biological relevance , we filtered our DNA methylation results requiring absolute differences in methylation ≥5% between T2D and non-diabetic islets . We found 1 , 649 CpG sites , of which 1 , 008 are located in or near 853 unique genes and 561 are intergenic , that had absolute differences in methylation ≥5% in T2D versus non-diabetic islets and these were used for all further analyses ( Figure 1D and Table S1 ) . When the differences in DNA methylation between diabetic and non-diabetic islets of these 1 , 649 CpG sites are expressed as fold-change instead of absolute differences , we observe differences in methylation ranging from 6 to 59% . While 1 , 596 of these 1 , 649 CpG sites ( 97% ) showed decreased DNA methylation , only 53 sites ( 3% ) showed increased DNA methylation in T2D compared with non-diabetic islets ( Figure 1E ) . The majority of CpG sites showing decreased DNA methylation in T2D versus non-diabetic islets had an intermediate degree of methylation and they were overrepresented among CpG sites with 20–70% methylation ( Figure 1F ) . On the other hand , CpG sites showing increased DNA methylation in T2D versus non-diabetic islets had a lower degree of methylation and they were overrepresented among CpG sites with 20–40% methylation ( Figure 1G ) . Our data suggest that CpG sites with an intermediate degree of DNA methylation are more dynamic to change in human islets . There is an accumulation of genetic variation on certain chromosomes associated with disease [19] , [20] . However , it remains unknown if there is an over- or underrepresentation of differential DNA methylation on certain chromosomes linked to diabetes . We therefore determined the chromosomal distribution of the 1 , 649 sites that exhibit differential DNA methylation in human T2D islets ( Figure 2A ) . Using Chi2 tests , we found that the number of differentially methylated sites were overrepresented on chromosomes 1 and 2 , and underrepresented on chromosome 19 , in comparison to the chromosomal distribution of all analyzed sites on the Infinium HumanMethylation450 BeadChip . These results are not explained by the distribution of analyzed sites on the array or gene density on the chromosomes . Previous cancer studies suggest that differential DNA methylation mainly occurs in CpG island shores rather than in CpG islands and promoter regions [21] , [22] . However it remains unknown if this is also the case in T2D patients . We therefore evaluated the distribution of differentially methylated sites in T2D versus non-diabetic islets , either based on their relation to the nearest gene and functional genome distribution ( Figure 2B ) or based on the CpG content and neighbourhood content ( Figure 2C ) . We found that differentially methylated CpG sites were overrepresented in intergenic regions and underrepresented in the TSS200 , 1st exon and 3′UTR in comparison to the probe distribution on the Infinium HumanMethylation450 BeadChip ( Figure 2B ) . The distribution of the probes on the Infinium HumanMethylation450 BeadChip as well as the differentially methylated CpG sites in T2D islets in relation to a CpG island is further shown in Figure 2C . We found that ∼60% of the differentially methylated CpG sites in T2D islets are located in the open sea while ∼25% are located in the CpG island shores and only ∼7% are located in CpG islands ( Figure 2C ) . Moreover , differentially methylated CpG sites were overrepresented in the open sea and underrepresented in CpG islands in comparison to the probe distribution on the Infinium HumanMethylation450 BeadChip ( Figure 2C ) . The Infinium HumanMethylation450 BeadChip covers 16 , 232 previously known differentially methylated regions ( DMRs ) that were selected based on the previously described criteria [21] , [23] . We found 156 CpG sites with decreased and 4 CpG sites with increased DNA methylation in known DMRs in T2D compared with non-diabetic islets ( Table S2 ) , which is more than expected ( P<0 . 001 ) . It has previously been established that DNA methylation in differentiated mammalian cells mainly occurs on cytosines in CG dinucleotides [24] . While the Infinium HumanMethylation450 BeadChip mainly analyzes DNA methylation in CpG sites , it also generated methylation data for 3 , 039 non-CpG sites in the human islets . However , only 1 , 189 of these non-CpG probes can be mapped with a perfect match to the correct genomic location annotated by Illumina [25] and methylation data of those probes were subsequently used for further analysis . These 1 , 189 sites were most predominant in the intergenic region , gene body and open sea ( Figure . S2A–B ) . To test if non-CpG sites are methylated in human islets , we calculated the average degree of DNA methylation of the analyzed non-CpG sites . The average degree of methylation of non-CpG sites was 5 . 9–21 . 7% in human islets ( Figure 2D and Table S3 ) . Out of these non-CpG sites , only two sites were significant after correcting for multiple testing ( q<0 . 05 ) and none of these sites had an absolute difference in methylation >5% in T2D versus non-diabetic islets . We next performed a KEGG pathway analysis to identify biological pathways with enrichment of genes that exhibit differential DNA methylation in T2D versus non-diabetic islets . A total of 853 genes , represented by CpG sites with differential DNA methylation ≥5% in T2D islets ( Table S1 ) , were analyzed using WebGestalt . Relevant enriched KEGG pathways in T2D islets include pathways in cancer , axon guidance , MAPK signaling pathway , focal adhesion , ECM-receptor interaction and regulation of actin cytoskeleton ( Table 2 ) . We further performed a separate KEGG pathway analysis only including genes that exhibit increased DNA methylation in T2D islets and we then found an enrichment of genes in the complement and coagulation cascades; C4A and C4B ( observed number of genes = 2 , expected number of genes = 0 . 13 and Padjusted = 0 . 0175 ) . We also tested if any of the genes in Table 2 exhibit differential expression in human β- compared with α-cell fractions using published data by Dorrell et al [26] . However , among the genes included in Table 2 , there were no significant differences in expression in β- versus α-cells . Previous GWAS have identified SNPs associated with T2D and/or obesity [3] . These SNPs have been linked to candidate genes , representing genes closest to respective risk SNPs . However , the SNPs identified in GWAS only explain a small proportion of the estimated heritability for T2D , proposing that there are additional genetic factors left to be discovered . These may include genetic factors interacting with epigenetics [27] . We therefore tested if any of 40 T2D candidate genes and 53 obesity genes identified by GWAS were differentially methylated in the human T2D islets [3] . The Infinium HumanMethylation450 BeadChip covers 1 , 525 CpG sites representing 39 of the T2D candidate genes and 1 , 473 CpG sites representing all 53 obesity genes . However , one should keep in mind that for a number of these SNPs it still remains unknown if the closest gene is the gene involved in T2D or obesity and if the identified SNP is the functional SNP . Therefore , to cover most regions harboring a genetic variant associated with T2D or obesity , we also investigated the level of DNA methylation for all CpG sites in a region 10 kb up- and downstream of intergenic SNPs associated with T2D ( n = 28 ) and obesity ( n = 41 ) ( www . genome . gov/gwastudies . Accessed: March 18 , 2013 ) . We identified 44 methylation sites , representing 17 T2D candidate genes and one intergenic SNP that were differentially methylated in T2D versus non-diabetic islets with a FDR less than 5% ( q<0 . 05 , Table 3 ) . Twenty-one of these sites , representing ten genes , had absolute differences in methylation >5% in T2D versus non-diabetic islets , which correspond to a fold change ranging from 7 to 28% . Only three sites in three different obesity genes were differentially methylated in T2D islets and one of these sites had an absolute difference in methylation >5% ( Table 3 ) . Finally , based on literature search , genes with known functions in pancreatic islets and/or β-cells [28]–[40] ( Figure 2E ) , the exocytotic process [41]–[44] ( Figure 2F ) and apoptosis [45] ( Figure 2G ) were found among the genes that showed differential DNA methylation in T2D islets . DNA methylation of certain genomic regions may silence gene transcription [8] , [12] . We therefore used microarray mRNA expression data to examine if any of the 853 genes that exhibit differential DNA methylation in T2D islets also exhibit differential mRNA expression in islets from the same donors . We found that 102 of the 853 differentially methylated genes were also differentially expressed in T2D compared with non-diabetic islets ( Table S4 ) . While 77 ( ∼75% ) of the differentially expressed genes had an inverse relationship with DNA methylation , e . g . decreased DNA methylation was associated with increased gene expression in T2D islets , 26 ( ∼25% ) had a positive relationship with DNA methylation , e . g . decreased DNA methylation was associated with decreased expression ( Figure 3A and Table S4 ) . Figure 3B–C describes the genomic distribution of the differentially methylated CpG sites that are located in/near genes that also exhibit differential expression in T2D islets . Interestingly , there was an overrepresentation of CpG sites in the 5′UTR only when differential DNA methylation and gene expression show an inverse relationship ( Figure 3B ) . In addition , CpG sites in the open sea and northern shore were overrepresented while sites in the CpG islands were underrepresented when DNA methylation and gene expression show an inverse relationship ( Figure 3C ) . These data suggest that differential DNA methylation in certain genomic regions may contribute to an inverse regulation of gene expression . In addition , we found an overrepresentation of differentially methylated CpG sites in the gene body regardless of whether methylation and gene expression show a positive or inverse relationship ( Figure 3B ) , this is known as the DNA methylation paradox which still remains unexplained [46] . We continued to functionally test if DNA methylation affects gene expression . We selected two genes with differential DNA methylation of multiple CpG sites and in which DNA methylation showed an inverse relationship with gene expression for luciferase experiments ( Table S4 and Figure 3D–E ) . Reporter gene constructs were subsequently produced for CDKN1A and PDE7B . Promoter sequences of the selected genes were inserted into a luciferase expression plasmid that completely lacks CpG dinucleotides . The constructs could thereby be used to study the effect of DNA methylation on luciferase activity in transfection assays . Each construct was mock methylated or methylated with the methyltransferases SssI or HhaI that methylates all CpG sites or the internal cytosine residue in a GCGC sequence only , respectively . SssI methylation thereby results in highly methylated constructs and HhaI methylation gives point methylated constructs in which only a fraction of the CpG sites are methylated . The number of CpG sites that may be methylated by these enzymes in each respective construct is shown in Figure 3F . Clonal β-cells were then transfected with the mock-methylated or methylated constructs . The highest reporter gene expression was generated by the mock-methylated control constructs including the promoter regions ( Figure 3F ) . Furthermore , methylation of the human CDKN1A and PDE7B promoter regions suppressed reporter expression significantly ( P<0 . 05 ) . While methylation of the promoter regions by SssI suppressed reporter gene expression to 28 . 0±11 . 8% for CDKN1A and to 22 . 1±2 . 9% for PDE7B , point methylation by HhaI suppressed reporter expression to 47 . 2±7 . 1% for CDKN1A and to 55 . 9±9 . 4 for PDE7B ( Figure 3F ) . We identified 75 genes that exhibit decreased DNA methylation and increased gene expression in pancreatic islets of T2D compared with non-diabetic donors when performing the genome-wide DNA methylation analysis ( Table S4 ) . To model the situation in humans and elucidate the mechanisms whereby these genes may contribute to impaired β-cell function and the development of T2D , we overexpressed three of these genes; Cdkn1a , Pde7b and Sept9 , in clonal β-cells ( Figure 4A ) . These genes were selected based on their potential role in diabetes and islet function and because they showed both differential DNA methylation of multiple CpG sites and differential gene expression in T2D islets . Overexpression of Cdkn1a and Pde7b led to a significant decrease in glucose-stimulated insulin secretion in clonal β-cells , while Sept9 had no significant effect ( Figure 4B and Figure S3A ) . Moreover , while the direct response to the membrane-depolarizing agent KCl was unaffected , the fold-change of insulin secretion at KCl-stimulation divided by insulin secretion at low glucose was decreased in clonal β-cells overexpressing Cdkn1a , Pde7b or Sept9 ( Figure S3B–C ) . Cdkn1a ( also known as p21 ) encodes a potent cyclin-dependent kinase inhibitor that regulates cell cycle progression [31] and we therefore tested if overexpression of this gene would affect cell proliferation in clonal β-cells . Indeed increased Cdkn1a levels resulted in decreased β-cell proliferation ( Figure 4C ) . Pde7b encodes a cAMP-specific phosphodiesterase [47] and cAMP potentiates insulin secretion [48] . We next stimulated Pde7b overexpressing cells with glucose in combination with IBMX , a general phosphodiesterase inhibitor . Addition of IBMX normalized glucose-stimulated insulin secretion in Pde7b overexpressing β-cells ( Figure 4D ) suggesting that the cAMP hydrolyzing activity of Pde7b underlies the secretory defect . EXOC3L2 is one of the genes that exhibit increased DNA methylation and decreased gene expression in pancreatic islets of T2D compared with non-diabetic donors ( Figure 4E and Table S4 ) . The protein encoded by EXOC3L2 is part of the exocyst complex [42] and may consequently affect exocytosis of insulin from pancreatic β-cells . To model the situation in human T2D islets , and to examine if decreased levels of EXOC3L2 affect β-cell exocytosis , Exoc3l was silenced in clonal β-cells using siRNA . This resulted in a 60 . 7% reduction of Exoc3l levels ( Figure 4F ) . Next , exocytosis was measured as changes in membrane capacitance using the patch-clamp technique . Silencing of Exoc3l resulted in decreased β-cell exocytosis ( Figure 4G–H ) . In particular , the two first depolarizations , representing the rapid first-phase insulin secretion , were decreased in Exoc3l deficient β-cells ( Figure 4H ) . Moreover , the Ca2+ current was unaffected in Exoc3l deficient β-cells ( Figure 4I–J ) , demonstrating a direct effect of Exoc3l on the exocytosis machinery rather than on the Ca2+ current . We also observed decreased voltage dependent Na+ current in Exoc3l deficient β-cells ( Figure 4I , K ) . As elevated glucagon levels in the fasted state contribute to hyperglycemia in patients with T2D [49] , we next tested if selected candidate genes that exhibit decreased DNA methylation and increased expression in pancreatic islets of T2D compared with non-diabetic donors contribute to increased glucagon secretion in pancreatic α-cells ( αTC1-6 cells ) . While overexpression of Cdkn1a and Pde7b resulted in increased glucagon release at 1 mM glucose compared to control transfected α-cells , Sept9 overexpression led to a borderline significant increase in glucagon secretion ( Figure S3D and Figure 4L ) . Additionally , when stimulated with 16 . 7 mM glucose , Pde7b and Sept9 overexpressing α-cells secreted more glucagon than control transfected cells ( Figure 4L ) . As a technical replication of the Infinium HumanMethylation450 BeadChip data , one islet sample was bisulfite converted and analyzed on the Infinium chips at two different occasions . The correlation for DNA methylation of 100 000 randomly chosen CpG sites on the two chips was then calculated and the methylation data showed a strong positive correlation ( R2 = 0 . 99 , P = 2 . 2×10−16; Figure S4A ) . In addition , seven of the CpG sites that exhibit differential DNA methylation in T2D islets were selected for technical validation of the Infinium HumanMethylation450 BeadChip data with pyrosequencing . The CpG sites selected for technical validation include cg21091547 ( CDKN1A ) , cg27306443 ( PDE7B ) , cg19654743 ( SEPT9 ) , cg04751089 ( IRS1 ) , cg20995304 ( HDAC7 ) , cg01649611 ( THADA ) and cg15572489 ( PTPRN2 ) ( Table 4 ) . In agreement with the Infinium HumanMethylation450 BeadChip data , all seven CpG sites showed differential DNA methylation in T2D versus non-diabetic islets when analyzed with pyrosequencing with differences in methylation of a similar magnitude between the two groups ( Table 4 ) . Furthermore , the DNA methylation data generated with Infinium HumanMethylation450 BeadChip and pyrosequencing for these seven CpG sites correlated strongly ( rho = 0 . 84–0 . 94 , P≤1 . 2×10−13; Table 4 and Figure S4B ) . To further validate our results , we tested if any of the CpG sites that exhibit differential DNA methylation in T2D islets of a recent study by Volkmar et al [14] , also exhibit differential methylation in T2D islets in our study . While our study analyzed DNA methylation of 479 , 927 CpG sites distributed across the entire genome and 99% of RefSeq genes , the study by Volkmar et al . only analyzed 27 , 578 CpG sites distributed mainly in CpG islands in a subset of RefSeq genes and it was therefore only possible to compare some of our studied CpG sites . Nevertheless , our array covers 264 of the 276 CpG sites that exhibit differential DNA methylation in the study by Volkmar et al and 71 of these sites ( ∼27% ) were differentially methylated in our study with P<0 . 05 , which is more than expected by chance ( Chi2 = 66 . 7 and P<0 . 0001; Table S5 ) . The data by Volkmar et al have not been corrected for multiple testing and it may therefore include false positive results , which may explain the difficulty to replicate some of their results . Yet , the DNA methylation data of the 264 CpG sites analyzed in both studies correlated significantly for both non-diabetic ( rho = 0 . 66 , P<0 . 0001; Figure S5A ) and diabetic ( rho = 0 . 68 , P<0 . 0001; Figure S5B ) islets . As an additional control , we compared the results from the present study with our previous studies where we found differential DNA methylation of PPARGC1A , INS and PDX1 in human pancreatic islets of T2D versus non-diabetic donors by using a candidate gene approach [11]–[13] . For PPARGC1A we were unable to compare the methylation data between the two studies as our previous study was based on the average degree of methylation of multiple CpG sites in an analyzed genomic region [11] . We have previously found increased DNA methylation in four CpG sites of the INS gene in human pancreatic islets from T2D compared with non-diabetic donors [13] . Two of these four CpG sites ( +63 and −180 ) were covered by probes on the Infinium HumanMethylation450 BeadChip and in agreement with the results in our previous study these two CpG sites show increased DNA methylation in T2D versus non-diabetic islets also in the present analysis ( cg00613255 , P = 0 . 008 and cg25336198 , P = 0 . 003 , Figure S5C ) . In the present study there were 13 additional CpG sites annotated to the INS gene with increased DNA methylation in T2D islets and P<0 . 05 ( Figure S5C ) . On the other hand , none of the individual CpG sites showing differential methylation in PDX1 in T2D islets in our previous study [12] were covered by probes on the Infinium HumanMethylation450 BeadChip . However , using EpiTYPER , we could validate our previous PDX1 data [12] , in the islet samples included in the present study ( Figure S5D ) . As a final control , we examined the degree of methylation in four known imprinted genes; SNRPN , MEST , KCNQ1 and IGF2 . As expected these genes showed approximately 50% methylation in islets from non-diabetic donors ( Figure S5E ) . We then tested if known risk factors for T2D , including hyperglycemia , aging and obesity affect DNA methylation of the 1 , 649 CpG sites differentially methylated in T2D islets , already in non-diabetic subjects . The impact of HbA1c levels , age and BMI on DNA methylation of these 1 , 649 CpG sites was examined in pancreatic islets of 87 non-diabetic donors with HbA1c levels , age and BMI spanning between 4 . 3–6 . 4% , 26–74 years and 17 . 6–40 . 1 kg/m2 respectively ( Table S6 ) . HbA1c levels were associated with differential DNA methylation of 142 CpG sites ( Table S7 ) . Moreover , age and BMI were associated with differential DNA methylation of 28 and 16 CpG sites , respectively ( Tables S8 , S9 ) . Interestingly , increased age was associated with decreased methylation of CDKN1A and increased methylation of EXOC3L2 ( Tables S8 ) , which is in agreement with the results seen in T2D islets ( Figure 3d and Figure 4e ) . Moreover , ∼92% of the CpG sites that exhibit differential DNA methylation due to increased HbA1c , age or BMI in non-diabetic islets changed in the same direction as methylation in T2D islets . These data suggest that increased HbA1c levels , aging and/or obesity may affect DNA methylation of CpG sites which are differentially methylated in T2D islets already in islets of non-diabetic subjects . We also tested if there are significant associations between DNA methylation and gene expression of the 102 genes that exhibit both differential DNA methylation and gene expression in T2D versus non-diabetic islets , already in non-diabetic subjects . A linear regression model was used including batch , age , gender , BMI , HbA1c , islet purity and days of culture as covariates . In islets of the 87 non-diabetic donors , we found significant associations between DNA methylation and gene expression for 55 CpG sites in/near 43 genes ( Table S10 ) out of the 149 CpG sites in/near 102 genes that exhibit both differential DNA methylation and gene expression in T2D versus non-diabetic islets ( Table S4 ) . The association between DNA methylation and gene expression for these 43 genes was in the same direction in both the 87 non-diabetic donors as in the T2D versus non-diabetic donors ( Table S10 and Table S4 ) . We further tested for associations between DNA methylation and gene expression in non-diabetic islets including all analyzed CpG sites on the Infinium HumanMethylation450 BeadChip in/near the 102 genes that show both differential DNA methylation and gene expression in T2D versus non-diabetic islets . We then found significant associations between DNA methylation and gene expression for 663 CpG sites in/near 57 genes out of the 102 studied genes . To identify factors that may contribute to differential DNA methylation in human pancreatic islets , we further tested if risk factors for T2D affect islet expression of a number of enzymes which are known to regulate DNA methylation and demethylation in mammals [50] . We found that the islet expression of DNMT3b , which is involved in de novo DNA methylation , correlated negatively with age ( rho = −0 . 25 , P = 0 . 02 ) . In addition , exposure to lipids ( 1 mM palmitate ) for 48 hours in vitro decreased the expression of two methyltransferases , DNMT3a ( control islets 111 . 3±2 . 3 vs . lipid treated islets 107 . 7±2 . 0 , P = 0 . 039 , n = 13 ) and DNMT1 ( control islets 182 . 4±5 . 0 vs . lipid treated islets 154 . 6±6 . 3 , P = 0 . 00005 , n = 13 ) , in human pancreatic islets . Lipid exposure also increased islet expression of GADD45A ( control islets 489 . 4±23 . 7 vs . lipid treated islets 612 . 8±56 . 3 , P = 0 . 010 , n = 13 ) , which encodes a protein involved in demethylation [50] . We next compared the DNA methylation pattern in whole human islets ( n = 4 ) with DNA methylation in FACS sorted β- ( n = 3 ) and α-cell ( n = 2 ) fractions of non-diabetic donors . The donors of whole human islets are different from the donors of FACS sorted β- and α-cells . However , the whole islet donors and the donors of FACS sorted β- and α-cells were matched for age and BMI . To describe the overall methylome , we first calculated the average degree of DNA methylation in different genomic elements ( Figure 1A ) in human islets as well as in FACS sorted β- and α-cells . The global methylation pattern was similar in human islets and in FACS sorted β- and α-cells ( Figure 5A–B ) . In both whole human islets and FACS sorted islet cells , the genomic regions close to the transcription start site showed relatively low degrees of methylation ( TSS1500 , TSS200 , 5′UTR and 1st exon ) and there was a higher degree of methylation in regions further away from the transcription start site ( gene body , 3′UTR and intergenic regions ) ( Figure 5A ) . Also in relation to CpG islands , the global methylation pattern was similar in human islets and in FACS sorted β- and α-cells ( Figure 5B ) . We found that CpG islands were hypomethylated , shelves and open sea were hypermethylated , while shores showed an intermediate degree of methylation in whole human islets as well as in FACS sorted islet cells ( Figure 5B ) . We then tested if any of the 1 , 649 CpG sites that were differentially methylated in T2D versus non-diabetic islets ( Table S1 ) exhibit a different degree of methylation in whole human islets compared with purified human β-cells . Without correction for multiple testing , 132 of 1 , 649 CpG sites showed differential DNA methylation in whole islets compared with purified human β-cells at P<0 . 05 , ( Table S11 ) . Moreover , among the candidate genes for T2D and obesity shown in Table 3 , there was elevated DNA methylation in β-cells compared with human islets for three CpG sites at P<0 . 05 ( cg26979504 in HHEX , cg03257822 in HMGA2 and cg04920032 in FAIM2 ) . However , no differences remained significant after correction for multiple testing .
Our study provides a detailed map of the human methylome in pancreatic islets from T2D and non-diabetic donors . We identified 1 , 649 individual CpG sites and 853 unique genes that exhibit differential DNA methylation , with absolute differences in methylation larger than 5% , representing a fold-change between 6–59% , in diabetic compared with non-diabetic islets . These include genes with previous known functions in pancreatic islets , the exocytosis process and apoptosis [28]–[45] . Recent GWAS have identified SNPs near/in genes that affect the risk for T2D [3] . We found differential DNA methylation of 17 T2D candidate genes , including TCF7L2 , THADA , KCNQ1 , FTO and IRS1 in T2D islets . Several of these genes affect islet function and insulin secretion [4] , [5] . It is possible that epigenetic modifications of T2D candidate genes in combination with genetic variation influence disease susceptibility [27] . Indeed , we have previously shown that ∼50% of SNPs associated with T2D are CpG-SNPs that introduce or delete possible DNA methylations sites [27] . Additionally , these T2D associated CpG-SNPs were associated with altered DNA methylation , gene expression , alternative splicing events and hormone secretion in human islets from non-diabetic donors [27] . Here , we demonstrate for the first time altered DNA methylation patterns of T2D candidate genes in human islets from patients with T2D . Our data propose that genetic and epigenetic mechanisms may interact to affect diabetes susceptibility and they show the importance of not just considering either genetics or epigenetics when dissecting factors that contribute to T2D . Our study also identified enrichments of differentially methylated genes in pathways in cancer , axon guidance , MAPK signaling pathway , focal adhesion and actin cytoskeleton in T2D islets . The significance of our data is supported by recent epidemiological studies that point to a close link between insulin resistance , T2D and cancer [51] as well as studies demonstrating central functions of MAPK signaling in pancreatic islets and diabetes [52] . Moreover , while focal adhesions play an important role in many signaling pathways and affect the ability of cells to interact with the extracellular matrix and respond efficiently to the dynamic microenvironment in age-related disease [53] , it has been shown that genes involved in axon guidance , regulation of actin cytoskeleton and complement and coagulation cascades are differentially expressed in T2D patients [54] . Additionally , we found altered DNA methylation of members of the Plexin and Semaphorin families , which play a role in axon guidance and are suggested to control glucose homeostasis via regulating communication between pancreatic β-cells [55] . Some genes with differential DNA methylation in T2D islets e . g . EGF and VEGFA are part of several of the significant KEGG pathways . While EGF is a growth factor important for postnatal expansion of β-cell mass and for the survival of β-cells following stress induced apoptosis [56] , VEGFA is responsible for dense islet vascularization and is expressed more in the endocrine than the exocrine part of the pancreas [57] . Hypermethylation of certain genomic regions may lead to suppressed transcription [8] . By combining genome-wide DNA methylation data with transcriptome profiles , we identified 102 genes that exhibit both differential DNA methylation and gene expression in diabetic islets . The majority of these genes showed decreased DNA methylation and increased gene expression in T2D islets . Our functional data further showed that increased DNA methylation of the human CDKN1A and PDE7B promoters decreased the transcriptional activity in clonal β-cells in vitro . However , based on these in vitro experiments it is still difficult to conclude whether altered DNA methylation in vivo has direct effects on gene expression . Moreover , DNA methylation may also regulate alternative splicing events [58] and/or transcription elongation efficiency via alternative promoters [59] when methylation takes place within gene bodies [27] . We found an overrepresentation of differentially methylated CpG sites in the gene body regardless of whether methylation and gene expression show a positive or inverse relationship . This is known as the DNA methylation paradox , which remains unexplained [46] . Interestingly , a recent study suggests a model by which the relationship between gene body DNA methylation and expression is bell shaped and varies depending on the transcriptional activity of the gene , meaning that high levels of gene body methylation are observed in genes with moderate expression levels while low levels of gene body methylation are observed in genes with low and high expression [60] . We also found an overrepresentation of CpG sites located within the open sea and the northern shore showing an inverse relationship between differential DNA methylation and gene expression in human T2D islets . Our data are supported by cancer studies where CpG island shore methylation was strongly related to gene expression [22] . The majority ( 97% ) of the differentially methylated CpG sites showed decreased DNA methylation in diabetic islets . This may be explained by an increased expression and/or activity of proteins controlling demethylation . It could also be explained by a decreased activity/expression of the methyltransferase DNMT1 during cell replication or a decreased activity of DNMT3a and 3b , which are responsible for de novo methylation [50] . Interestingly , we found decreased expression of two methyltransferases and increased expression of GADD45A , which affects demethylation , in human pancreatic islets exposed to lipotoxicity , a risk factor for T2D . Another explanation for hypomethylation may be decreased levels of methyl donors [61] . T2D is a multifactorial polygenic disease and previous GWAS have identified more than 40 SNPs strongly associated with the disease . However , the effect size of these variants is modest and each SNP only explains a small proportion of the heritability for T2D [3] . Identified differences in gene expression of diabetic cases and controls have also been modest , but although alterations in each gene may only contribute with a small biological effect , together multiple changes in specific metabolic pathways are likely to increase the risk for disease [62] , [63] . Moreover , in contrast to the big differences in DNA methylation that are found when comparing cancer and normal cells and which is probably due to the presence of an abnormal clone of cells , most of the absolute differences in DNA methylation that have been reported in non-cancer studies are modest in magnitude ranging from 0 . 13–11% [64]–[66] . In accordance with these studies , we also found that the absolute differences in methylation between the diabetic and non-diabetic islets ranged from 5–15% , which corresponds to a fold change of 6–59% . However , recent studies have shown that modest differences in DNA methylation of individual CpG sites can have big effects on gene expression and that in late onset diseases such as T2D small changes in gene expression may have a big effect on disease over long periods of time [64] . This is further supported by our in vitro experiments , where methylating only 2 or 6 CpG sites , respectively , in 1500 bp regions resulted in a profound decrease in gene activity . To find support for the role of the identified genes in the pathogenesis of T2D , we manipulated the expression of selected genes in clonal β- and α-cells . Over-expression of Cdkn1a and Pde7b resulted in decreased glucose-stimulated insulin secretion in clonal β-cells . These experimental results are in agreement with our human data , where T2D islets show impaired glucose-stimulated insulin secretion in parallel with increased expression and decreased DNA methylation of CDKN1A and PDE7B . Together , our experimental and human data support a model where diabetes associated epigenetic modifications may lead to altered gene expression and subsequently impaired insulin secretion . CDKN1A , also known as p21 , is a well characterized tumor suppressor and a negative regulator of the cell cycle [67] . Dependent on its cellular location , CDKN1A may also affect other cellular processes [31] , [67] . However , there is limited information on its role and regulation in pancreatic islets . PDE7B is a cAMP-specific phosphodiesterase that regulates cellular cAMP levels [47] . Although this is the first study showing that PDE7B affects insulin secretion , other members of this family are known to control cAMP and insulin secretion in β-cells [48] . A deficient exocytosis machinery may result in perturbed insulin secretion [41] . Our study demonstrates for the first time that T2D islets exhibit decreased expression and increased DNA methylation of EXOC3L2 , a member of the exocyst complex . Our in vitro experiments further show how Exoc3l deficiency results in decreased β-cell exocytosis . Even though the expression changes seen in clonal β-cells in vitro are bigger than the ones identified in human islets in vivo , it is likely that modest expression changes of multiple genes contribute to the disease phenotype in humans . Since both decreased insulin and increased glucagon secretion from pancreatic islets are known to contribute to hyperglycemia in patients with T2D [49] , we investigated whether the identified candidate genes also affect glucagon secretion in clonal α-cells . Interestingly , overexpression of Cdkn1a and Pde7b resulted in elevated glucagon secretion at low glucose levels . Together , our functional data propose a model where identified candidate genes may contribute to hyperglycemia in T2D patients by both lowering insulin and increasing glucagon secretion from pancreatic islets . A previous study analyzed DNA methylation of ∼27 , 000 CpG sites representing ∼0 . 1% of the CpG sites in the human genome in pancreatic islets from five T2D and 11 non-diabetic donors [14] . Volkmar et al used an array that mainly covers CpG islands in promoter regions and they found 276 CpG sites with differential DNA methylation with P<0 . 01 in T2D islets . However , since their data was not corrected for multiple testing , it may include false positives . Yet , we could replicate 71 of the differentially methylated sites identified by Volkmar et al and methylation data from the two islet studies correlated significantly [14] . In the present study , we could also validate data from our previous study where T2D was associated with differential DNA methylation of INS in human islets [13] . Our ability to confirm previous data support that T2D is associated with differential DNA methylation at specific sites . However , it should be noted that we used different bioinformatic and statistical analysis compared to Volkmar et al , e . g . we used quantile normalization and M-values and we analyzed differences in DNA methylation between T2D and non-diabetic islets using a linear regression model including batch , gender , BMI , age , islet purity and days of culture as covariates . Moreover , the array used in our study analyzes DNA methylation genome-wide of ∼480 , 000 CpG sites , covering most gene regions including promoters , 5′UTR , gene body and 3′UTR in ∼99% of RefSeq genes , as well as intergenic regions [68] . The array also covers CpG islands , shores and shelves as well as the open sea . Interestingly , we found that differentially methylated CpG sites were underrepresented in CpG islands and overrepresented in the open sea , which are isolated CpG sites throughout the genome . It may thereby be difficult to identify differentially methylated CpG sites in T2D islets using an array that mainly covers CpG islands such as the Infinium 27k array . While some researchers have found a decreased β-cell number and increased α-cell number in human T2D compared with non-diabetic islets , others have not found any differences [69]–[71] . We found no differences in β-cell content in T2D versus non-diabetic islets , nor did Volkmar et al [14] , suggesting that the differences seen in DNA methylation are not due to altered β-cell composition in islets from T2D patients . Although it would be of interest to analyze DNA methylation in isolated insulin-producing human β-cells , the sorting of these cells is technically difficult , which results in loss of many cells and in addition , may affect their function [72] . Indeed , there are only a few studies to date that have used isolated human β-cells with a maximum number of 16 donors [12] , [13] , [73]–[75] . While we and others have found epigenetic differences between human β- and α-cells from a modest number of non-diabetic donors [12] , [13] , [74] , [75] , to our knowledge there is no available data showing differential DNA methylation in human β-cells from patients with T2D compared with non-diabetic donors , nor have any studies analyzed gene expression genome-wide in human β- and α-cells from patients with T2D . Here , we analyzed for the first time DNA methylation genome-wide in purified β-and α-cell fractions from non-diabetic human islets donors . The global DNA methylation pattern was similar in purified human β-cells and whole islets . Moreover , for the 1 , 649 CpG sites showing differential DNA methylation in T2D versus non-diabetic human islets , we could not detect any significant differences in methylation between purified human β-cells and whole islets . Importantly , most of the other islet cell types such as glucagon-producing α-cells , somatostatin-producing δ-cells and pancreatic polypeptide producing PP-cells also have key effects on whole body glucose homeostasis [76] . Differential DNA methylation and gene expression in the majority of islet cells may thereby affect the pathogenesis of T2D and studying epigenetic modifications in whole human pancreatic islets is therefore physiologically warranted . Indeed , our functional studies show effects of candidate genes identified in whole islets in both clonal β- and α-cells . However , future studies should also address if there are epigenetic differences in sorted β- and α-cells from diabetic versus non-diabetic donors . Interestingly , risk factors for T2D such as hyperglycemia ( HbA1c ) , aging and BMI were associated with differential DNA methylation already in islets of non-diabetic human donors . It is hence possible that differential DNA methylation in islets predisposes to disease . Nevertheless , we cannot rule out that some of the identified epigenetic differences are secondary to disease or epiphenomenon [77] . However , excluding epiphenomenon would require a longitudinal study taking pancreatic biopsies at different time points which is not ethically possible in humans . Nevertheless , previous animal studies support that epigenetic modifications , taking place in pancreatic islets at an early age due to an unfavorable fetal environment , can predispose to diabetes in adult life [15]–[17] , [78] . Importantly , Thompson et al found differential DNA methylation of ∼1 , 400 CpG sites together with altered gene expression in islets of seven week old rats exposed to an intrauterine growth restriction , a model that causes diabetes in elderly rats [78] . Our recent studies further support that altered DNA methylation may play a role in the pathogenesis of T2D as we found positive correlations between HbA1c levels and DNA methylation of INS and PDX-1 in human islets and elevated glucose levels had direct effects on DNA methylation of INS and PDX-1 in clonal β-cells [12] , [13] . Additionally , DNA methylation of INS and PDX-1 was increased in islets from T2D patients compared with controls . Also , as epigenetic modifications are tissue specific , using a surrogate tissue like blood is unlikely to give the same result , which has been shown in previous studies [13] , [14] . Moreover , our functional data support an important role of identified candidate genes on islet function and in the pathogenesis of T2D . Recent studies demonstrate that some probes on Illumina's DNA methylation chip can cross-react to multiple locations in the genome [25] . However , none of the 1 , 649 probes used to detect differential methylation in our study have a perfect match elsewhere in the genome and only 14 probes have a near-perfect match . Overall , our study identified novel epigenetic modifications in T2D patients that contribute to differential gene expression and perturbed insulin secretion , a key characteristic of T2D . Our genome-wide DNA methylation data can furthermore serve as a reference methylome for human pancreatic islets .
The pancreatic islet donor or her/his relatives had given their consent to donate organs for medical research upon admission to intensive care unit . All procedures were approved by ethics committees at Uppsala and Lund Universities . Human pancreatic islets from 15 donors with T2D and 34 donors not diagnosed with diabetes were included in the genome-wide analysis of DNA methylation and mRNA expression . Donors were considered to have T2D if they had been diagnosed with the disease prior to their death . Selection criteria for non-diabetic donors were to have an HbA1c below 6 . 0% . Clinical characteristics of these donors are given in Table 1 . Moreover , the impact of HbA1c levels , age and BMI on DNA methylation was studied in pancreatic islets from 87 non-diabetic donors . Their characteristics are given in Table S6 . Human pancreatic islets were provided by the Nordic Network for Islet Transplantation , Uppsala University , Sweden . Human islets were prepared by collagenase digestion and density gradient purification . Prior to nucleic acid purification , islets were cultured for 2 . 7±0 . 15 days as previously described [63] . DNA and RNA were extracted with the All Prep DNA/RNA kit ( Qiagen , Hilden , Germany ) and purity and concentration were determined by using a nanodrop ( NanoDrop Technologies , Wilmington , USA ) . The purity of the islet preparations was determined by expression of endocrine ( somatostatin and glucagon ) and exocrine ( pancreatic lipase , amylase α2A , chymotrypsin 2 ) markers and dithizone staining [63] . β-cell content in human islets of donors with available embedded islets ( 6 T2D and 13 non-diabetic donors ) was analyzed using transmission electron microscopy . Hand-picked islets where fixed in 2 . 5% glutaraldehyde in freshly prepared Millonig and post-fixed in 1% osmium tetroxide before being dehydrated and embedded in AGAR 100 ( Oxford Instruments Nordiska , Lidingö , Sweden ) and cut into ultrathin sections as described [5] . The sections were put on Cu-grids and contrasted using uranyl acetate and lead citrate . The islet containing sections were examined in a JEM 1230 electron microscope ( JEOL-USA . Inc . , MA ) . Micrographs were analyzed for β-cell content with ImageJ and in-house software programmed in Matlab using methods previously described [14] , [79] . Islet cell types were distinguished by means of granular appearance: β-cell granules have a dense core surrounded by a white halo and α-cells have small dense granules . The ratio of β-cells in each islet was calculated by division of the total number of β-cells by the sum of the β-cell and α-cell numbers . Furthermore , glucose-stimulated insulin secretion from human pancreatic islets was measured as described [80] . β- and α-cells were purified from pancreatic islets of three human donors ( aged 54 , 55 and 74 years old , with BMI 21 . 5–23 . 1 kg/m2 ) , different from the donors described in Table 1 and Table S1 , by using a method previously described by Parnaud et al [73] . Dissociation of islet cells was achieved by incubation with constant agitation for 3 minutes at 37°C in 0 . 05% trypsin-EDTA ( Invitrogen ) supplemented with 3 mg/ml DNAse I ( Roche , Mannheim , Germany ) followed by vigorous pipetting . Labelling and FACS sorting of the β- and α-cell fractions was performed as previously described [73] . Sorted β- and α-cells were applied to microscope slides and co-immunostained for insulin and glucagon in order to detect the amount of α-cells in the β-cell fraction , and vice versa . Using this method , a cell purity of 89±9% ( mean ± SD ) was achieved [81] . After DNA isolation using Qiagen DNEasy blood and tissue kit ( Qiagen ) and ethanol precipitation , DNA for genome-wide methylation profiling was available for β- and α-cell fractions from 3 and 2 donors , respectively . Genome-wide DNA methylation profiling of human pancreatic islets and purified human β- and α-cell fractions was performed at the SCIBLU genomics center at Lund University with the Infinium HumanMethylation450 BeadChip ( Illumina , Inc . , San Diego , CA , USA ) which interrogates 482 , 421 CpG sites , 3091 non-CpG sites and 65 random SNPs and covers 21 , 231 RefSeq genes [68] . 500 ng DNA from human pancreatic islets was bisulfite converted using the EZ DNA Methylation Kit D5001 ( Zymo Research , Orange , CA , USA ) according to the manufacturer's instructions . Bisulfite converted DNA was amplified , fragmented and hybridized to the BeadChips following the standard Infinium protocol . T2D islet samples were randomized across the chips and all samples were analyzed on the same machine by the same technician to reduce batch effects . After single base extension and staining , the BeadChips were imaged with the Illumina iScan . Raw fluorescence intensities of the scanned images were extracted with the GenomeStudio ( V2011 . 1 ) Methylation module ( 1 . 9 . 0 ) ( Illumina ) . The fluorescence intensity ratio was used to calculate a β-value which corresponds to the methylation score for each analyzed site according to the following equation: β-value = intensity of the Methylated allele ( M ) / ( intensity of the Unmethylated allele ( U ) +intensity of the Methylated allele ( M ) +100 ) . DNA methylation β-values range from 0 ( completely unmethylated ) to 1 ( completely methylated ) . All samples had high bisulfite conversion efficiency ( intensity signal >4000 ) and they were included for further analysis based on GenomeStudio quality control steps where control probes for staining , hybridization , extension and specificity were examined . The intensity of both sample dependent and sample independent built in controls was checked for the red and green channels using GenomeStudio . We next exported the DNA methylation data from GenomeStudio and used Bioconductor [82] and the lumi package [83] for further analyses . Individual probes were filtered based on their mean detection P-value and those with a P-value>0 . 01 were excluded from further analysis . As a result , DNA methylation data for 483 , 031 ( 99 . 5% ) probes , including 479 , 927 CpG sites and 3 , 039 non-CpG sites were used for further analysis . Because M-values are more statistically valid [84] , β-values were converted to M-values using the following equation: M = log2 β-value/ ( 1−β-value ) . M-values were then used for further statistical analysis [84] . In order to correct for background fluorescence the median M-value of the built in negative controls was subtracted from M-values . Next a quantile normalization was performed as described [85] . The Universal Methylated Human DNA standard D5011 ( Zymo Research ) , which is human DNA that has been enzymatically methylated in the CpG sites by M . SssI methyltransferase , was used as a positive control in every batch and it showed high levels of methylation ( 87 . 7±0 . 6% ) . The low intensity of Y chromosome loci in female samples was used as an additional control . Moreover , one human pancreatic islet sample was run in two different batches on two different days and used as a technical replicate . As the β-value is easier to interpret biologically , M-values were reconverted to β-values when describing the results and creating the figures . The enrichment of KEGG pathways among genes that exhibit differential DNA methylation in T2D compared with non-diabetic islets was tested using WebGestalt ( http://bioinfo . vanderbilt . edu/webgestalt , March 2012 ) . mRNA expression of the human pancreatic islets was analyzed using the GeneChip Human Gene 1 . 0 ST array from Affymetrix ( Santa Clara , CA , USA ) as previously described [63] . 1500 bp of the human CDKN1A or PDE7B promoters ( sequences are given in Table S12 ) were inserted into a CpG-free firefly luciferase reporter vector ( pCpGL-basic ) kindly provided by Dr Klug and Dr Rehli [86] . Amplification of CDKN1A and PDE7B DNA sequences and insertion into the pCpGL-basic vector was done by GenScript ( Piscataway , NJ , USA ) . The constructs were either mock-methylated or methylated using two different DNA methyltransferases; SssI and HhaI ( New England Biolabs , Frankfurt am Main , Germany ) . While SssI methylates all cytosine residues within the double stranded dinucleotide recognition sequence CG , HhaI only methylates the internal cytosine residue in GCGC sequence . INS-1 832/13 β-cells were co-transfected with 100 ng pCpGL-vector either with or without respective insert together with 2 ng of pRL renilla luciferase control reporter vector ( pRL-CMV vector , Promega , Madison , USA ) as a control for transfection efficiency and luciferase activity was measured as previously described [12] . INS-1 832/13 β-cells were cultured as previously described [87] and αTC1-6 cells were cultured according to ATCC's instructions ( ATCC , Manassas , VA ) . pcDNA3 . 1 expression vectors with rat cDNA for either Cdkn1a , Pde7b or Sept9 , or the empty vector , were transfected into β- or α-cells with Lipofectamine LTX and Plus Reagent ( Life Technologies , Paisley , UK ) , according to the manufacturer's instructions ( sequences for Cdkn1a , Pde7b or Sept9 are given in Table S13 ) . Overexpression was verified with real-time PCR using an ABI 7900 system ( Applied Biosystems , Foster City , CA , USA ) and a SYBR Green assay for Cdkn1a ( fwd-primer: ATGTCCGACCTGTTCCACAC , rev-primer: CAGACGTAGTTGCCCTCCAG ) or TaqMan assays ( Life Technologies ) for Pde7b ( Rn00590117_m1 ) and Sept9 ( Rn00582942_m1 ) . Cyclophilin B ( Rn03302274_m1 and Mm00478295_m1 ) was used as an endogenous control . Expression levels were calculated with the ΔΔCt method . Overexpression was also verified by Western Blot analysis and cells transfected with HA-tagged cDNAs for Cdkn1a , Pde7b and Sept9 were lysed in RIPA buffer ( 50 mM Tris pH 7 . 6 , 150 mM NaCl , 0 . 1% SDS , 0 . 5% sodium deoxycholate , 1% Triton×100 and 1× protease inhibitor cocktail ( P8340 , Sigma-Aldrich , USA ) and boiled with 6× sample buffer ( 60 mM Tris pH 6 . 8 , 10% glycerol , 2% SDS , 10% β-mercaptoethanol and bromophenol blue ) . Samples were separated on gradient Mini-PROTEAN® TGX gels ( Bio-Rad , Hercules , CA , USA ) and transferred onto Hybond-LFP PVDF membranes ( GE Healthcare , Piscataway , NJ , USA ) . Protein expression was detected with primary antibodies against HA tag ( ab9110 , Abcam Cambridge , UK ) and β-actin ( A5441 , Sigma-Aldrich ) and secondary DyLight 680/800 conjugated anti-mouse and anti-rabbit antibodies ( 35518 and 35571 , Thermo Scientific , Rockford , USA ) and blots were scanned in an ODYSSEY ( Licor , Lincoln , NE , USA ) . 48 hours post transfection of INS-1 832/13 β-cells , insulin secretion with indicated secretagogues was determined during 1 hour static incubations as previously described [87] . Insulin content of cells was determined after acid ethanol extraction of the hormone . Insulin secretion was normalized to total insulin content . αTC1-6 cells were transfected as described above . 48 hours post transfection clonal α-cells were pre-incubated in HEPES balanced salt solution ( HBSS , 114 mM NaCl; 4 . 7 mM KCl; 1 . 2 mM KH2PO4; 1 . 16 mM MgSO4; 20 mM HEPES; 2 . 5 mM CaCl2; 25 . 5 mM NaHCO3; 0 . 2% BSA , pH 7 . 2 ) supplemented with 5 . 5 mM glucose . Secretion was then stimulated in 1 hour static incubation with HBSS supplemented with 1 or 16 . 7 mM glucose . Secreted glucagon was measured with a glucagon ELISA ( Mercodia , Uppsala , Sweden ) and normalized to total protein as determined by a BCA assay ( Thermo Scientific ) . INS-1 832/13 β-cells were transfected as described above . 72 hours post transfection the β-cells were washed with PBS and stained with 0 . 1% crystal violet in 0 . 15 M NaCl . Cells were then washed with water and allowed to dry . Methanol was added to wells and absorbance measured at 600 nm in an Infinite M200 plate reader ( Tecan , Männerdorf , Switzerland ) . INS-1 832/13 β-cells were transfected with Lipofectamine RNAiMAX ( LifeTechnologies ) according to the manufacturer's instructions with siRNA targeting Exoc3l ( LifeTechnologies , ID: s146127 ) or negative control siRNA ( 5′-GAGACCCUAUCCGUGAUUAUU-3′ ) . Following 24 hours incubation , cells were transferred onto Petri dishes and cultured another 24 hours . Exoc3l knock-down was verified with real-time PCR using an ABI 7900 system and assays for Exoc3l ( Rn01432027_m1 ) and endogenous controls ( Cyclophilin B , Rn03302274_m1 and Hprt , Rn01527840_m1 ) ( Life Technologies ) . Electrophysiological measurements of exocytosis were performed on INS-1 832/13 β-cells as described [88] . Pyrosequencing was used to technically validate the Infinium HumanMethylation450 BeadChip DNA methylation data . EpiTect Bisulfite Kit ( Qiagen ) was used for bisulfite conversion of human islet DNA . Primers were designed using the PyroMark Assay design Software 2 . 0 ( Qiagen ) . Sequences are included in Table S14 . Bisulfite converted DNA was amplified with the PyroMark PCR kit . Pyrosequencing was performed with PyroMark ID 96 and PyroMark Gold Q96 reagents ( Qiagen ) according to the manufacturer's instructions . Data were analyzed with the PyroMark Q96 2 . 5 . 7 software program . Sequenom's MassARRAY EpiTYPER protocol ( Sequenom , San Diego , CA , USA ) was used to measure DNA methylation of PDX1 in its distal promoter and enhancer regions according to our previous study [12] . A principle component analysis was performed to examine batch effects and other possible sources of variation on the DNA methylation data . To identify differences in DNA methylation and mRNA expression between T2D and non-diabetic islets a linear regression model was used including batch , gender , BMI , age , islet purity and days of culture as covariates and DNA methylation or mRNA expression as quantitative variables . A false discovery rate ( FDR ) analysis was used to correct for multiple testing [18] , [89] , [90] . Chi2 tests were used to compare the expected number of probes on the Infinium HumanMethylation450 BeadChip with observed number of differentially methylated probes in T2D islets .
|
Epigenetic modifications such as DNA methylation are implicated in the development of human disease . However , genome-wide epigenetic analyses in patients with type 2 diabetes ( T2D ) remain scarce . In this study we aimed to unravel the epigenetic basis of T2D by analyzing DNA methylation of 479 , 927 CpG sites in human pancreatic islets from T2D and non-diabetic donors . We identified 1 , 649 CpG sites and 853 genes with differential DNA methylation ( fold change 6–59% ) in T2D islets . These include reported diabetes loci , such as TCF7L2 , FTO and KCNQ1 . Furthermore , we found 102 genes that showed both differential DNA methylation and gene expression in T2D islets , including CDKN1A , PDE7B , SEPT9 and EXOC3L2 . Finally , we provide functional proof that identified candidate genes directly affect insulin secretion and exocytosis in pancreatic β-cells as well as glucagon secretion in α-cells . Overall , this study provides a detailed map of the methylome in human pancreatic islets and demonstrates that altered DNA methylation in human islets contributes to perturbed hormone secretion and the pathogenesis of T2D .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"genome",
"analysis",
"tools",
"diabetes",
"mellitus",
"type",
"2",
"diabetic",
"endocrinology",
"gastroenterology",
"and",
"hepatology",
"endocrinology",
"gene",
"expression",
"genetics",
"human",
"genetics",
"molecular",
"genetics",
"epigenetics",
"biology",
"genomics",
"pancreas",
"computational",
"biology",
"transcriptomes"
] |
2014
|
Genome-Wide DNA Methylation Analysis of Human Pancreatic Islets from Type 2 Diabetic and Non-Diabetic Donors Identifies Candidate Genes That Influence Insulin Secretion
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Arsenic contamination of drinking water is a major public health issue in many countries , increasing risk for a wide array of diseases , including cancer . There is inter-individual variation in arsenic metabolism efficiency and susceptibility to arsenic toxicity; however , the basis of this variation is not well understood . Here , we have performed the first genome-wide association study ( GWAS ) of arsenic-related metabolism and toxicity phenotypes to improve our understanding of the mechanisms by which arsenic affects health . Using data on urinary arsenic metabolite concentrations and approximately 300 , 000 genome-wide single nucleotide polymorphisms ( SNPs ) for 1 , 313 arsenic-exposed Bangladeshi individuals , we identified genome-wide significant association signals ( P<5×10−8 ) for percentages of both monomethylarsonic acid ( MMA ) and dimethylarsinic acid ( DMA ) near the AS3MT gene ( arsenite methyltransferase; 10q24 . 32 ) , with five genetic variants showing independent associations . In a follow-up analysis of 1 , 085 individuals with arsenic-induced premalignant skin lesions ( the classical sign of arsenic toxicity ) and 1 , 794 controls , we show that one of these five variants ( rs9527 ) is also associated with skin lesion risk ( P = 0 . 0005 ) . Using a subset of individuals with prospectively measured arsenic ( n = 769 ) , we show that rs9527 interacts with arsenic to influence incident skin lesion risk ( P = 0 . 01 ) . Expression quantitative trait locus ( eQTL ) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT ( P = 10−12 ) and neighboring gene C10orf32 ( P = 10−44 ) , which are involved in C10orf32-AS3MT read-through transcription . This is the largest and most comprehensive genomic investigation of arsenic metabolism and toxicity to date , the only GWAS of any arsenic-related trait , and the first study to implicate 10q24 . 32 variants in both arsenic metabolism and arsenical skin lesion risk . The observed patterns of associations suggest that MMA% and DMA% have distinct genetic determinants and support the hypothesis that DMA is the less toxic of these two methylated arsenic species . These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide .
Over 100 million individuals worldwide are exposed to arsenic through drinking water , including approximately 56 million people in Bangladesh [1] and 13 million in the United States [2] . Arsenic is a class I human carcinogen , and chronic exposure to high levels of arsenic ( >300 µg/L ) is associated with substantial increased risk for a wide array of diseases including cancers of the lung [3] , bladder [4] , liver [5] , skin [6] , and kidney [7] , [8] , as well as neurological [9] , [10] and cardiovascular [11] diseases . Emerging evidence suggests that arsenic may have adverse effects on health even at concentrations as low as 10–50 µg/L , as recent studies in Bangladesh have observed dose-response relationships with mortality [12] , [13] and arsenical skin lesion risk [14] in populations with low to moderate arsenic exposure over many years . Arsenical skin lesions are a classical sign of arsenic toxicity , an indicator of susceptibility to arsenic-related disease , and a precursor to arsenic-induced skin cancers [6] . Once individuals are chronically exposed to arsenic , risk for arsenic-related diseases and mortality remains high for several decades even after cessation of exposure [15] , [16] . Consumed arsenic enters the blood as AsV and AsIII , known collectively as inorganic arsenic ( iAs ) . Once consumed , iAs is methylated using S-Adenosyl methionine ( SAM ) as the methyl donor , producing monomethylarsonic acid ( MMA ) and then dimethylarsinic acid ( DMA ) . MMA is believed to be the more toxic of these metabolites , with the DMA/MMA ratio showing an inverse association with arsenic toxicity in several studies [17]–[20] and DMA being more readily excreted in urine and expelled from the body . Arsenic metabolite concentrations are often expressed as percentages of all arsenic species present in urine ( i . e . , iAs% , MMA% , DMA% ) or as ratios that reflect methylation efficiency ( e . g . , DMA%/MMA% , MMA%/iAs% ) . There is considerable inter-individual variation in arsenic metabolism , as some individuals are able to methylate , and thus excrete , arsenic more efficiently than others [21] , [22] . Similarly , because high inter-individual variability in toxicity is observed among individuals with similar levels of exposure to arsenic , genetic susceptibility factors for arsenical skin lesions are believed to exist [23] . In light of the enormous global health impact of arsenic exposure and the remarkable inter-individual variability in arsenic metabolism and toxicity , we performed the first genome-wide association study ( GWAS ) of common arsenic-related phenotypes . We identified multiple genetic variants in the 10q24 . 32 region near AS3MT ( arsenite methyltransferase , previously known as CYT19 ) that show robust associations with urinary concentrations of arsenic metabolites , risk for arsenical skin lesions , and local gene expression , including transcript levels of AS3MT .
We assessed genome-wide associations for the three arsenic metabolites measured in urine ( iAs% , MMA% , and DMA% ) using high-quality data on 259 , 597 single-nucleotide polymorphisms ( SNPs ) from 1 , 313 individuals randomly selected from a large population-based cohort of Bangladeshi individuals exposed to a wide range of arsenic concentrations through drinking water . Associations were assessed using mixed linear models [24] to account for existence of related individuals in our sample ( Figure S1 ) . The strongest association signals , genome-wide , for both DMA% and MMA% were in the 10q24 . 32 region ( P<5×10−8 ) ( Figures S2 and S3 ) , which contains the AS3MT gene and substantial LD spanning ∼1 Mb ( Figure S4 ) . For DMA% , the strongest 10q24 . 32 association was for rs9527 ( P = 2 . 7×10−9; Figure 1 ) . After conditioning on rs9527 , a strong residual association signal remained ( rs11191527; P = 8 . 0×10−8 ) , the strength of which was weaker without adjustment for rs9527 ( P = 2 . 3×10−5 ) due to mild LD between these SNPs ( D′ = 0 . 26 , r2 = 0 . 03 in our data; D′ = 0 . 27; r2 = 0 . 03 in HapMap GIH ) . After conditioning on both SNPs , there was very little evidence of additional association in the region . Analyses of imputed and measured genotypes produced the same two association signals , but with imputed SNPs rs3740394 and rs17115073 showing slightly stronger association than rs9527 and rs11191527 , respectively ( Figure S5 ) . The strongest association observed for MMA% was rs4919694 ( P = 2 . 9×10−8 ) ( Figure 2 ) . After conditioning on rs4919694 , residual association was still observed ( rs4290163; P = 7 . 0×10−5 ) . This association is much weaker without adjustment for rs4919694 ( P = 0 . 03 ) due to LD between rs4919694 and rs4290163 ( D′ = 0 . 80 , r2 = 0 . 09 in our data; D′ = 0 . 80 , r2 = 0 . 04 in HapMap GIH ) . Aftern conditioning on both SNPs , residual association was observed for rs11191659 ( P = 0 . 0009 ) , a SNP in moderate LD with rs9527 , the top SNP from the %DMA analysis ( D′ = 0 . 66 , r2 = 0 . 23 in our data; D′ = 0 . 82 , r2 = 0 . 30 in HapMap GIH ) . Conditioning on all three SNPs eliminated the 10q24 . 32 association signal . Imputation of unobserved genotypes in the region did not reveal associations stronger than those observed for the measured genotypes ( Figure S6 ) . Multivariate models for %DMA and %MMA including all five of the above-mentioned SNPs are described in Table 1 . Outside of the 10q24 . 32 region , there was no genome-wide significant ( P<5×10−8 ) association signal for DMA% or MMA% . The 10q24 . 32 association results for the DMA%/MMA% ratio ( the “secondary methylation index” , log-transformed ) , were very similar to the MMA% results , as these phenotypes were strongly correlated ( r = −0 . 84; Table S1 ) . Associations for 10q24 . 32 SNPs with iAs% and MMA%/iAs% ( the “primary methylation index” ( PMI ) log-transformed ) were much weaker than for DMA% and MMA%; The strongest association in the 10q24 . 32 region observed for iAs% was rs9527 ( P = 0 . 0009 ) and no association of P<0 . 001 was observed for log ( PMI ) . In genome-wide analyses of iAs% and PMI , no SNP reached genome-wide significance ( Figure S7 ) . Because variants influencing arsenic metabolism may alter susceptibility to arsenic toxicity , we investigated the roles of metabolite-associated SNPs in arsenic-induced premalignant skin lesions , the hallmark of chronic arsenic toxicity . For our five lead SNPs , we tested association with skin lesion status among 1 , 085 skin lesion cases and 1 , 794 population controls , using the ROADTRIPS method that was developed for case-control association testing in the presence of cryptic relatedness [25] . The rs9527 allele associated with decreased DMA% ( A ) was associated with increased skin lesion risk ( P = 0 . 0005 ) , consistent with the hypothesis that DMA is less toxic than MMA ( Table 2 ) . rs11191659 showed suggestive association ( P = 0 . 02 ) , also consistent with this hypothesis . To confirm that these associations with skin lesions were due to gene-arsenic interaction , we tested the interaction between rs9527 and arsenic exposure using a subset of 69 incident skin lesion cases and 700 controls with prospectively-measured arsenic exposure ( measured in both water and urine at baseline , prior to skin lesion incidence and arsenic mitigation efforts [26] ) . We found SNP-arsenic interaction for both rs9527 ( multiplicative interaction P = 0 . 01; additive interaction P = 0 . 004 ) and rs11191659 ( multiplicative interaction P = 0 . 02; additive interaction P = 0 . 001 ) , where water arsenic exposure showed stronger association with skin lesions in the presence of the risk allele ( Table 2 and Table S2 ) . There were no significant main effects for either of these SNPs in the context of models that included SNP-arsenic interaction terms . For the subset of individuals with available genotype , arsenic metabolite , and skin lesion data ( 82 cases , 1211 controls ) , DMA% showed evidence of partial mediation of the association between rs9527 and skin lesions ( accounting for 13% of the observed association ) . To investigate the role of our lead SNPs in gene regulation , used genome-wide expression data derived from lymphocyte RNA obtained at baseline for 950 participants ( Illumina HumanHT-12 array ) and examined SNP-expression associations for all 30 genes in the 10q24 . 32 LD region ( Table S3 ) . Several of our lead SNPs showed association with AS3MT expression at P<5×10−5 ( rs4919694 , rs9527 , rs4290163 ) . However , after examining associations for all SNPs in this region , C10orf32 intronic SNP rs7096169 showed the strongest association with AS3MT expression ( P = 8×10−12; Figure 3 and Figure S8 ) , and conditioning on rs7096169 eliminated the eQTL signal . rs7096169 was not one of our lead SNPs , but it was associated with DMA% ( P = 0 . 001; MMA% P = 0 . 28 ) . Interestingly , the rs9527 risk allele ( A ) was associated with decreased C10orf32 expression ( P = 2 . 6×10−41; Figure S8 ) , the strongest eQTL signal for C10orf32 expression in the region ( Figure 3 ) and the strongest genome-wide eQTL effect for rs9527 ( Figure S9 ) . C10orf32 is ∼4 kb upstream of AS3MT , and these genes are involved in C10orf32-AS3MT read-through transcription , producing a transcript that is a candidate for nonsense-mediated mRNA decay . Thus , it is possible that the eQTL signal observed for C10orf32 represents a regulatory mechanism that influences read-through transcript production . After conditioning on rs9527 , the residual eQTL signal was best represented by rs11083790 ( P = 10−5 ) . Conditioning on both SNPs eliminated the eQTL signal . Interestingly , C10orf32 expression was also associated with arsenic exposure ( measured as total arsenic in urine , collected at the same time as blood; P = 0 . 001 ) , while AS3MT expression was not ( P = 0 . 37 ) . None of our lead SNPs modified the association between arsenic exposure and C10orf32 expression . Our lead SNPs were also associated with USMG5 expression ( Table S3 ) , a gene ∼500 kb downstream of AS3MT , but these associations appear to be due to moderate LD with downstream variants showing very strong association with USMG5 expression ( e . g . , rs12220267; P = 10−210; Figure S8 ) .
The role of AS3MT in arsenic metabolism has been described [27] , and several prior studies have evaluated associations between candidate AS3MT variants arsenic-related traits in Bangladesh and elsewhere [28]–[34] . A recent review [35] highlighted two AS3MT SNPs , rs11191439 ( Met287Thr ) and rs3740393 ( intronic ) , as being consistently related to arsenic metabolism across diverse populations . The most recent and comprehensive Bangladeshi study of AS3MT SNPs [28] reported three association signals for arsenic metabolites , best represented by HapMap3 SNPs rs1046778 ( for MMA% ) , rs11191439 ( DMA% and iAs% ) , and rs3740390 ( DMA% and iAs% ) , a proxy for rs3740393 ( r2 = 0 . 91 ) . After imputation , we were able to replicate rs11191439 ( DMA% P = 4 . 2×10−6; MMA% P = 5 . 8×10−7 ) and rs1046778 ( MMA% P = 8 . 9×10−7; DMA% P = 0 . 0002 ) , which were strongly correlated with lead SNPs rs4919694 ( r2 = 0 . 69 ) and rs4290163 ( r2 = 0 . 63 ) , respectively . After conditioning on our lead SNPs , these associations were no longer significant . The evidence for rs3740390 was less convincing ( DMA% P = 0 . 54; MMA% P = 0 . 007 ) , as this SNP was not strongly correlated with any of our lead SNPs ( Figure S10 ) . We identified two novel 10q24 . 32 association signals , represented by rs9527 and rs11191527 , which were not strongly correlated with any previously-reported SNP ( Figure S10 ) . These SNPs were likely missed in prior studies due to limited coverage of the SNPs in this region . The identities of the functional variants in this region remain unclear . rs9527 lies in the 5′ UTR of C10orf32 , a transcription factor binding region ( GATA-1 and TAL1 ( SC-12984 ) ) and a DNase hypersensitivity site . If causal , rs9527 could also exert its effects through regulation of AS3MT-C10orf32 read-through transcription . However , the LD block represented by rs9527 includes transcription factor binding site SNP rs12416687 and miRNA SNPs rs11191401 , rs12573077 , rs7904252 , and rs9527 . Detailed information on potential functional variants from HapMap3 ( GIH ) for each of the 5 SNPs identified is contained in Tables S4 , S5 , S6 , S7 , S8 . However , genetic variation in this population has not been comprehensively characterized ( especially rare variation ) , and the underlying functional variants may not be present in HapMap3 . It is also possible that the underlying causal variants have implications for surrounding genes . For example , rs4919694 and rs11191527 are intronic SNPs within the CNNM2 gene , which is involved in magnesium reabsorption by the kidney [36] . It is possible that magnesium and iAs interact [37] , [38] , influencing the amount of free arsenic available for methylation . To our knowledge , this study is the largest genetic association study of arsenic metabolites to date , the only GWAS of arsenic-related traits , the first study to implicate 10q24 . 32 SNPs in both arsenic metabolism and arsenical skin lesion risk , and one of the earliest GWAS conducted in the developing country setting . Our results suggest that MMA% and DMA% have distinct genetic determinants and highlight the importance of conditional analyses , as LD among alleles with opposing effects can mask associations in univariate analyses . The associations observed in this study are likely due to the effects of unmeasured , potentially rare variants in LD with the measured SNPs and/or substantial allelic heterogeneity , whereby multiple 10q24 . 32 variants influence arsenic metabolism . Considering the substantial LD in this region [39] , the variation in allele frequencies and LD patterns among the various arsenic-exposed populations under study [40] , and the apparent allelic heterogeneity with respect to arsenic metabolism , future DNA sequencing studies are needed to help identify causal variants in the 10q24 . 32 region . Identifying these variants will help clarify the links between the association signals observed for %DMA , %MMA , and AS3MT/C10orf32 expression . These association signals appear largely independent in our dataset , but perhaps there are underlying causal variants that influence all of these phenotypes . Developing a better understanding the effects of functional variation related to AS3MT will also provide a more nuanced understanding of the biology of arsenic methylation , which can in turn help us better understand how variation in methylation efficiency affects health . Finally , knowledge of this causal variation and the methylation processes that they influence could potentially be exploited for intervention strategies that aim to prevent large numbers of deaths arsenic-exposed populations , by defining susceptibility subgroups and exploiting the biological processes uncovered by genomics for developing pharmacological treatments .
The DNA samples genotyped in this study were obtained at baseline recruitment from individuals participating in one of the following studies: The Health Effects of Arsenic Longitudinal Study ( HEALS ) [41] or the Bangladesh Vitamin E and Selenium Trial ( BEST ) [42] . GWAS analyses of arsenic metabolites were conducted using urinary arsenic metabolite and SNP data on 1 , 313 individuals randomly selected from the HEALS study . Analyses of skin lesion data were conducted using genotype data from 1 , 085 skin lesion cases and 1 , 794 controls drawn from both studies , including the 1 , 313 HEALS individuals with metabolite data . Skin lesion cases included individuals with keratosis , melanosis , and leukomelanosis . Gene expression analyses were based on lymphocyte RNA extracted at baseline recruitment for the first 950 BEST participants . A summary of these overlapping sets of samples is provided in Figure S11 . The Health Effects of Arsenic Longitudinal Study ( HEALS [41] ) is a prospective investigation of health outcomes associated with arsenic exposure through drinking water in a cohort of adults in Araihazar , Bangladesh , a rural area east of the capital city , Dhaka . Between October 2000 and May 2002 , we recruited healthy married individuals ( age 18–75 years ) who were residents of the study area for at least five years and primarily consumed drinking water from a local well . We enumerated 65 , 876 individuals residing in Araihazar , from which we identified a sampling frame of 14 , 828 eligible residents . Of these 14 , 828 individuals , 11 , 746 men and women were enrolled . During 2006–2008 , additional recruitment of 8 , 287 participants from the same underlying source population expanded the cohort size to over 20 , 000 individuals . All 5 , 966 wells in the study area were tested for arsenic using graphite furnace atomic absorption spectrometry and individuals reported the primary well from which they drank . At baseline , trained study physicians , blinded to the arsenic measurements , conducted in-person interviews and clinical evaluations and collected spot urine and blood samples from participants in their homes using structured protocols . Similar in-person follow-up interviews were conducted biennially for the entire cohort during the following periods: follow-up 1 during September 2002 to May 2004 , follow-up 2 during June 2004 to August 2006 , and follow-up 3 during January 2007 to February 2009 . At baseline and each follow-up interview , a structured protocol was used to ascertain skin lesions by the study physicians , who had undergone training for the detection and diagnosis of skin lesions [43] . The study protocol was approved by the Institutional Review Boards of The University of Chicago , Columbia University , and the Bangladesh Medical Research Council . Informed consent was obtained from all participants . The Bangladesh Vitamin E and Selenium Trial ( BEST ) is a 2×2 factorial randomized chemoprevention trial evaluating the long-term effects of vitamin E and selenium supplementation on non-melanoma skin cancer ( NMSC ) risk . BEST participants are residents of Araihazar ( the same geographic area as HEALS participants with 132 overlapping participants ) , Matlab , and surrounding areas . BEST uses many of the same study protocols as does HEALS , especially arsenic exposure assessment and biospecimen collection protocols . All participants were required to have existing arsenic-related skin lesions to be eligible . A total of 7 , 000 individuals have been randomized to one of the four treatment arms: vitamin E only ( 100 IU/day ) , L-selenomethionine only ( 200 µg/day ) , both vitamin E and selenium , and placebo . Participants have been actively followed for 6 years and systematic ascertainment of histopathologically-confirmed NMSC has been conducted ( including BCC and SCC ) . For all participants , biological samples , including all fractions of blood including DNA and RNA , urine , toenails , and tumor samples have been collected at baseline , along with clinical and covariate data , creating a biological and data repository that is available for research purposes . The study protocol was approved by the Ethical Review Committee of International Center for Diarrheal Disease Research , Bangladesh , the Bangladesh Medical Research Council , and the Institutional Review Boards of The University of Chicago and Columbia University . Informed consent was obtained from all participants . In each study , urinary arsenic was measured using graphite furnace atomic absorption spectrometry in a single laboratory [44] . Urinary creatinine was measured by a colorimetric diagnostics kit ( Sigma , St Louis , MO , USA ) . Total urinary arsenic concentration was divided by creatinine to obtain creatinine-adjusted total arsenic concentration ( µg/g creatinine ) [45] . Urinary arsenic metabolites ( arsenobetaine , arsenocholine , arsenite , arsenate , monomethylarseonous acid , and dimethylarsenic acid ) were distinguished as described by Ahsan et al . [17] , using a high-performance liquid chromatography method for separation of arsenic metabolites , followed by detection using inductively coupled plasma-mass spectrometry with dynamic reaction cell . The percentage of iAs , MMA and DMA in total arsenic was calculated after subtracting asenobetaine and arsenocholine ( i . e . , nontoxic organic arsenic from dietary sources ) . Because these metabolites lie on the same biological pathway and are expressed as a percentage of arsenic species , their values show substantial correlation ( Table S1 ) . For BEST samples , DNA extraction was carried out from the whole blood using the QIAamp 96 DNA Blood Kit ( cat # 51161 ) from Qiagen , Valencia , USA . For HEALS samples , DNA was extracted from clot blood using Flexigene DNA kit ( Cat # 51204 ) from Qiagen . Concentration and quality of all extracted DNA were checked by Nanodrop 1000 . As starting material , 250 ng of DNA was used on the Illumina Infinium HD SNP array according to Illumina's protocol . Samples were processed on HumanCytoSNP-12 v2 . 1 chips with 299 , 140 markers and read on the BeadArray Reader . Image data was processed in BeadStudio software to generate genotype calls . Prior to genotype QC , our genotype data consisted of 2 , 920 samples typed for 299 , 140 SNPs . First , we removed DNA samples with very poor call rates ( <90%; n = 8 ) and SNPs that were poorly called ( <90% ) or monomorphic ( n = 39 , 276 ) . Individuals with gender mismatches were removed ( n = 10 ) , as were technical replicate DNA samples run to assure high genotyping accuracy ( n = 21 ) . No individuals had outlying autosomal heterozygosity or inbreeding values . After inspecting distributions of SNP and samples call rates , we excluded samples with call rates <97% ( n = 2 ) and SNPs with call rates <95% ( n = 103 ) . SNPs with HWE p-values<10−7 were excluded ( n = 164 ) . This QC resulted in 2 , 879 individuals with high-quality genotype data for 259 , 597 SNPs . All QC was performed using PLINK [46] . RNA was extracted from mononuclear cells preserved in buffer RLT , stored at −86°C using RNeasy Micro Kit ( cat# 74004 ) from Qiagen , Valencia , USA . Concentration and quality of all extracted RNA were checked on Nanodrop 1000 . cRNA synthesis was done from 250 ng of RNA using Illumina TotalPrep 96 RNA Amplification kit . As recommended by Illumina we used 750 ng of cRNA on HumanHT-12-v4 for gene expression . The chip contains a total of 47 , 231 probes covering 31 , 335 genes . Pair-wise kinship coefficients were estimated using PLINK [46] and their distribution is shown in Figure S1 . To assess population structure that was unrelated to the relative pairs present in our dataset , we removed one individual from each related pair ( kinship coefficient >0 . 05 ) and assessed population structure in this dataset of 403 individuals using principal components analysis as implemented in EIGENSTRAT [47] . We found very little evidence of population stratification ( Figure S12 ) , with the eigenvalues from the first ten principle components being between 1 . 123 and 1 . 184 . All SNP association tests for urinary metabolites were conducted using a mixed model that accounted for cryptic relatedness as implemented in EMMAX [24] ( rather than principle components ) , adjusting for water arsenic , sex , and age . All regional association plots were generated using LocusZoom [48] . Association testing for skin lesion status was conducting using PLINK [46] and the ROADTRIPS [25] software developed for case-control association testing in samples with unknown population and pedigree structure . We conducted local imputation for the 10q24 . 32 region using MACH , the GIH reference panel , and imputation parameters suggested by the developers [49] . The estimated genotype and allele error rates were 0 . 034 and 0 . 017 , respectively . LD structure in the 10q24 . 32 region was visualized using Haploview [50] . Information on the potential functional consequences of SNPs in the 10q24 . 32 regions was obtained using the NIEHS's SNPinfo Web Server [51] . Interaction analyses was conducted using only HEALS incident cases ( n = 69 ) and controls ( n = 701 ) . For BEST participants and some HEALS participants arsenic exposure ( based on water and urine ) was not measured prior to arsenic mitigation efforts [26] , so the measured exposure status for these individuals is not likely to reflect long-term arsenic exposure status . Interactions were tested using the SAS 9 . 2 PROC MIXED procedure , using the “bn” matrix derived using EMMAX . To assess mediation of the association between SNPs and skin lesions , we used the “proportion explained” ( PE ) equation for odds ratios ( PEOR = ( ORxy−ORxy|m ) / ( ORxy−1 ) where x is an exposure , y is a binary outcome , and m is a potential mediating factor [52] ) . Genome-wide eQTL analysis for our five lead SNPs was performed using the significance of microarray method as implemented in BRB Array Tools . Promising eQTL effects were then examined using EMMAX as described above , treating the expression values as a quantitative trait . In a similar fashion , arsenic exposure was tested for association with expression traits of interest and for interaction with SNPs in relation to expression traits using PROC MIXED .
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Exposure to arsenic through drinking water is a serious public health issue in many countries , including Bangladesh and the United States . Although there is substantial inter-individual variation in arsenic metabolism and toxicity , the biological basis of this variation is not well understood . Here , we have conducted the first genome-wide association study of arsenic-related traits within a unique population cohort of arsenic-exposed Bangladeshi individuals . Using data on 1 , 313 well-characterized individuals , we identify multiple association signals for urinary arsenic metabolite concentrations in the 10q24 . 32 regions , near the AS3MT ( arsenite methyltransferase ) gene . In a subsequent analysis of >2 , 000 individuals , we show for the first time that variants that influence arsenic metabolism can also influence risk for arsenical skin lesions ( the classical sign of arsenic toxicity ) through interaction with arsenic exposure . Using array-based genome-wide gene expression data , we show that several of our lead genetic variants are associated with expression of AS3MT and neighboring gene C10orf32 , providing a potential mechanism by which 10q24 . 32 variants influence arsenic metabolism and toxicity . Knowledge of variation in this region and associated biological processes could be used to develop intervention and pharmacological strategies aimed at preventing large numbers of arsenic-related deaths in arsenic-exposed populations .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"medicine",
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2012
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Genome-Wide Association Study Identifies Chromosome 10q24.32 Variants Associated with Arsenic Metabolism and Toxicity Phenotypes in Bangladesh
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Diverse soil-resident bacteria can contribute to plant growth and health , but the molecular mechanisms enabling them to effectively colonize their plant hosts remain poorly understood . We used randomly barcoded transposon mutagenesis sequencing ( RB-TnSeq ) in Pseudomonas simiae , a model root-colonizing bacterium , to establish a genome-wide map of bacterial genes required for colonization of the Arabidopsis thaliana root system . We identified 115 genes ( 2% of all P . simiae genes ) with functions that are required for maximal competitive colonization of the root system . Among the genes we identified were some with obvious colonization-related roles in motility and carbon metabolism , as well as 44 other genes that had no or vague functional predictions . Independent validation assays of individual genes confirmed colonization functions for 20 of 22 ( 91% ) cases tested . To further characterize genes identified by our screen , we compared the functional contributions of P . simiae genes to growth in 90 distinct in vitro conditions by RB-TnSeq , highlighting specific metabolic functions associated with root colonization genes . Our analysis of bacterial genes by sequence-driven saturation mutagenesis revealed a genome-wide map of the genetic determinants of plant root colonization and offers a starting point for targeted improvement of the colonization capabilities of plant-beneficial microbes .
Plant health is intimately influenced by a diverse community of microorganisms inhabiting the root surface ( rhizoplane ) and endophytic compartment [1] . This root microbiome is recruited from surrounding soil communities [2–4] and is thought to be modulated by host plant immune function , root exudate-mediated signaling and metabolic compatibility , as well as intermicrobial interactions within the rhizosphere [5–7] . These interactions , especially during the initial colonization period , are critical for establishment of a root-associated bacterial community that is distinct from that of the surrounding soil . Extensive studies of plant pathogens have established the role of plant genetic factors , including immune phytohormone pathways , in controlling the ability of bacteria to colonize plants [5 , 8–10] . Although there is increasing recognition that root microbiomes , in particular plant growth-promoting rhizobacteria ( PGPR ) , may be harnessed to improve plant fitness in agricultural applications , progress toward this goal requires a more thorough understanding of the bacterial genetic factors contributing to root colonization and fitness in the root microbiome [11] . Root-associated bacterial communities have been defined for several plants , including A . thaliana , using culture-independent 16S rRNA sequencing strategies [3 , 4] . Bacterial communities across diverse plant species show similar dominant representation of Proteobacteria , Actinobacteria , and Bacteroidetes phyla [1] . The Pseudomonadaceae ( within the Proteobacteria phylum ) , in particular , comprise many genera capable of plant association , with the best studied examples ( e . g . , Pseudomonas fluorescens and P . syringae ) being commensals or pathogens , respectively [12 , 13] . Many other isolates within the Pseudomonadaceae family are characterized as PGPR , which can enhance plant growth and viability through beneficial immune stimulation ( induced systemic resistance [ISR] ) [14] , by improvement of soil nutrient acquisition , or by directly triggering plant growth pathways through phytohormone production [1 , 15] . Additionally , P . fluorescens spp . have been shown to actively protect crops from a variety of fungal pathogens [16] . P . simiae WCS417r was originally characterized as a biocontrol isolate on wheat [17] . This strain was originally characterized as a member of the P . fluorescens group but was reclassified based on its genome sequence homology to the P . simiae-type strain [18] and is a well-studied example of a PGPR [18] . WCS417r displays other PGPR activities , including ISR induction , siderophore production , lateral root growth stimulation , and activation of auxin signaling pathways [19] . Importantly , WCS417r can colonize the roots of many plant species including Arabidopsis [20] . These features make colonization of Arabidopsis roots by WCS417r an ideal system for identifying generalized bacterial colonization traits . Conventional , nonsaturation screens of transposon mutagenesis libraries of P . fluorescens and P . putida strains led to the identification of genes required for root and rhizosphere colonization [12 , 21] . To enable the generation of a comprehensive genome-wide map of root colonization genes , we used randomly barcoded transposon mutagenesis sequencing ( RB-TnSeq ) , a barcode-enabled extension of transposon mutagenesis coupled to high-throughput sequencing ( transposon mutagenesis sequencing [TnSeq] ) [22] that allows for the generation of reusable libraries of unique , mapped , and barcoded insertion mutant strains [23 , 24] . We adopted RB-TnSeq to construct a genome-wide map of P . simiae WCS417r plant-association factors in an in vivo screen using Arabidopsis as the host plant . This screen revealed mutations in 115 genes that have a negative impact on the ability of P . simiae WCS417r to colonize roots . In addition to genes linked to well-known colonization traits such as motility and carbon metabolism , our mutant screen revealed additional , previously uncharacterized genes . Our screen also identified 243 genes , the loss of function of which enhances colonization fitness . Many of the genes identified in each class are clustered into predicted operons . Integration of the genome-wide colonization data with RB-TnSeq phenotypes from more than 90 different in vitro growth conditions [23] highlighted motility , stress response , amino acid metabolism , as well as potentially unknown pathways as being functionally important for root/bacterial interactions .
To enable the generation of a genome-wide map of genes required for plant root colonization in P . simiae , we used RB-TnSeq with a mariner transposon to create a saturation mutagenesis library of P . simiae WCS417r [22–25] . We selected WCS417r based on its plant growth promoting potential , its ease of transformation at high efficiency , and its tractability for lab manipulation . By high-throughput sequence analysis of barcoded insertion mutants , we identified and mapped 110 , 142 unique transposon insertion sites , distributed throughout the genome [18] at an average of approximately 18 insertions per 1 , 000 bp ( S1 Fig , S1 Data ) . Most insertions ( 59 . 5% ) mapped to a gene body , with 84% of genes harboring at least 1 insertion event ( median insertions per gene: 9; S1 Fig ) . Of the remaining 827 genes with no insertion mutant detected , nearly half shared significant homology ( Materials and methods ) to genes known to be essential in other species ( 385; 55 . 6% of such genes in the WCS417r genome; S1 Fig ) , suggesting that insertions in these genes are lethal in P . simiae . Furthermore , 146 of the untargeted genes contained fewer than 3 potential thymine-adenine dinucleotide mariner transposon insertion sites , representing 59% of such genes in the WCS417r genome . Thus , our library includes null mutations in the vast majority of nonessential genes in the P . simiae WCS417r genome , supporting its utility for large-scale genetic screening for various phenotypes . To determine which genes are necessary for root association , we designed a competitive colonization screen in which P . simiae mutant strains migrate from support medium through a porous nylon filter toward the root system of Arabidopsis seedlings , where they can attach and propagate ( Fig 1 and S2 Fig ) . After a colonization period and removal of loosely adhering bacteria , root-associated bacteria were isolated as a combined rhizoplane and endophytic sample . Controls required for data analysis included a “no root initial” ( NRI ) sample ( i . e . , an empty nylon filter incubated on a plate containing the mutant library , harvested the same day as the library was inoculated ) and a “no root final” ( NRF ) sample ( i . e . , a filter incubated on the plate in the absence of plants for a full week ) . In total , we analyzed samples and controls harvested from approximately 15 , 000 seedlings . We sequenced approximately 181 , 300 unique barcodes from each pooled root sample ( see Materials and methods ) , corresponding to approximately 240 colonization events per individual root . Of all barcode sequence reads , we mapped 70% to known barcoded insertion sites . We used sequenced barcode read counts to quantify the representation of mutants in each sample and compared barcode frequencies across samples [24] ( Fig 1; Materials and methods ) . After normalization of total counts across samples , we determined 3 separate derived fitness scores for each gene . Each of these scores measures a different potential effect influencing microbial growth in these experimental conditions: a “mesh fitness score” comparing the NRF and NRI samples and thus measuring changes in the ability to growth on the nylon mesh alone; a “root + mesh fitness score” comparing the “root” and the NRI samples , which measures the overall ability to grow on the root and the nylon mesh; and a “root fitness score , ” comparing the root and NRF samples directly , which represents the “root + mesh fitness score” corrected for the “mesh fitness score” to quantify the ability to grow on the root after correction for mesh-related effects . We used this root fitness score ( Materials and methods ) to identify mutant strains corresponding to 358 genes as significantly depleted or enriched in the root-associated sample , which included 115 colonization-depleted genes ( that , when mutated , results in reduced colonization ability ) and 243 colonization-enriched genes ( that , when mutated , increased colonization ability , S3 Fig ) . We used the colonization fitness scores of individual genes to create a genome-wide map of the root colonization trait ( Fig 2 ) . Genes significantly contributing to colonization fitness were distributed throughout the P . simiae genome , with many clustering together ( Fig 2 , Table 1 ) . Strikingly , 45 of the 115 genes mutated in colonization-depleted strains are clustered into 8 predicted operons , each containing at least 3 genes that decrease colonization fitness when mutated ( Table 1 ) . Similarly , 62 of the 243 genes mutated in colonization-enriched strains were located within 14 predicted operons containing at least 3 genes that significantly increased colonization fitness when mutated ( Table 1 ) . Thus , 22 predicted operons contained 3 or more genes with significant fitness scores corresponding to enhanced or reduced colonization ability . In 21 of these operons , all genes with significant fitness scores contributed to colonization in a consistent direction within the operon , with 14 operons exhibiting >50% of the constituent genes as significant . We examined predicted functions of the identified colonization genes and operons based on clusters of orthologous groups ( COG ) of proteins annotations [26 , 27] . Among colonization-depleted genes , motility was the most common COG category ( P < 1 . 88 e-20; hypergeometric test ) , followed by cell wall/membrane/envelope biogenesis ( P < 2 e-3 ) and carbohydrate transport and metabolism ( P < 2 . 43 e-2; Fig 2 ) , consistent with the known roles of motility , lipopolysaccharide production , and sugar metabolism in root colonization and activity[12] . Among colonization-enriched genes , common COG categories included amino acid metabolism and transport ( P < 1 . 38 e-2 ) , cell wall/membrane biogenesis ( P < 5 . 99 e-3 ) , and transcription ( P < 4 . 18 e-3; Fig 2 ) . Taken together , our genome-wide colonization screen allowed for the simultaneous functional assessment of nearly all genes within the WCS417r genome for their contribution to plant colonization , and we identified a substantial number of genes and operons likely to be important for this process . To evaluate the robustness of our screen , we isolated individual insertion mutant strains from sequence-informed WCS417r library arrays ( Materials and methods ) . We selected 22 insertion mutant strains to validate ( using a single insertion mutant strain per gene ) covering a diversity of potentially interesting putative functions , with some representing operons containing multiple genes with significant fitness scores ( Table 1 ) , and others representing individual genes with a broad range of negative or positive fitness score effects ( S1 Data ) . The selected mutants included 9 predicted to have compromised colonization fitness and 13 predicted to have increased colonization ability ( S1 Data ) . We designed a competitive colonization screen in which individual mutants compete against a luminescent , but otherwise wild-type ( WT ) P . simiae WCS417r strain , and direct luminescence quantification of roots can be used to measure competitive fitness ( Fig 3 , Materials and methods ) . We observed that 7 out of 9 colonization-depleted insertion mutants were out-competed by the luciferase-producing ( Lux+ ) strain ( Fig 3 ) . Similarly , all 13 colonization-enriched insertion mutants competed either as well or better than the Lux+ strain at the root tip ( Fig 3 ) . Overall , the direction of fitness change as assessed in luminescence-based competition assays was consistent with the direction predicted by RB-TnSeq in 20 of 22 cases ( 91% , chi-squared P < 0 . 00012 ) . Selected insertion mutants were further validated using an analogous LacZ blue/white screening approach , as well as through colonization assays with corresponding loss-of-function mutants generated by targeted mutagenesis ( Materials and methods; S1 Data; S6 Fig ) . Although the magnitude of estimated fitness changes for individual genes varied across validation methods , the results were largely consistent with luciferase-based validation screens and confirmed in particular the impact of mutations in predicted colonization-depleted genes on colonization fitness ( Materials and methods , S6 Fig , S1 Data ) . We also explored dynamic aspects of root colonization , as the overall fitness of root colonizers might change over the days it takes to establish colonies on the root . To test whether fitness is static across a time course , we selected 4 predicted poor colonizers ( PS417_00160 , PS417_01955 , PS417_22145 , and PS417_22775 ) and 4 predicted enhanced colonizers ( PS417_08165 , PS417_21035 , PS417_03095 , and PS417_10720 ) to inoculate Arabidopsis seedlings in competition with the LuxABCDE expressing P . simiae strain as above but sampled at 1 , 3 , 5 , and 7 days after inoculation . We measured the proportion of Luciferase-negative cells ( i . e . , mutant strain ) from each root sample . Although all poor colonizers tended to grow more slowly once present on the roots , 1 ( PS417_22775 ) failed to colonize very early on ( S7 Fig ) . Most predicted enhanced colonizers appeared to grow more quickly than their poor colonizing counterparts , especially on later days ( S7 Fig ) , although were still present in reduced numbers than expected , indicating a possible bias towards measuring luciferase-positive cells in this assay . Together , these detailed validation efforts support that the RB-TnSeq method applied to plant-bacteria interactions robustly defines both pronounced as well as subtle colonization defects . Many of the genes identified by our screen have no or at best vague annotations . To explore the physiological functions of the identified colonization genes in more detail , we compared our data to RB-TnSeq results of the same insertion mutant library tested under 90 distinct in vitro conditions , including 48 conditions using a defined compound as a sole carbon source in otherwise minimal media , 11 conditions using a defined nitrogen source , 29 stress conditions , and 2 in vitro motility conditions ( inner and outer cuts of a soft agar motility assay ) [23] . Although the complexity of individual phenotypes measured by these in vitro assays is considerably lower than that of root colonization processes , these assays are scalable and can thus be used to rapidly assess many metabolic or stress responsive functions . Within the large dataset covering genome-wide fitness across 90 conditions , we specifically examined the in vitro phenotypes of mutations in all 115 colonization-depleted and 243 colonization-enriched genes ( Materials and methods , Fig 4 and S8–S10 Figs , S1 Data ) .
We developed a genome-wide map of microbial genes required for colonization of plant roots in a plant/microbial system . Building on the successful application of RB-TnSeq for the large-scale assessment of in vitro phenotypes [23] , the present study demonstrates the utility of this experimental paradigm for studies of bacterial plant root colonization in vivo , thus applying it to a process that considerably exceeds in vitro assays in terms of complexity . By using the colonization of Arabidopsis roots by the biocontrol bacterium , P . simiae WCS417r , as a model of colonization , we observed a substantial variety of genes conferring altered survivability to the bacteria when mutated , mirroring the complex nature of this interaction system . One challenge of TnSeq assays in general , and TnSeq assays targeting colonization phenotypes in particular , is the reliance on a diverse population of insertion mutants in the colonized host after coincubation . During Arabidopsis colonization by P . simiae , we found that only 100 to 1 , 000 independent colonization events occur per individual root , creating a potential bottleneck for downstream analysis . We mitigated this effect by sampling large numbers of plants ( approximately 1 , 000 pooled seedlings per sample ) , resulting in the recovery of most constituent mutant strain barcodes in every pooled sample . Additional confounding factors include environmental or community considerations , namely that survival of individual mutants on the plant support medium and filter prior to root colonization might be reduced , which need to be corrected with appropriate controls ( S3 Fig , Materials and methods ) . Certain functional deficiencies , especially those associated with secreted or extracellular activity , of some colonization genes might be effectively rescued by a largely WT population for that function . Furthermore , some mutants identified by RB-TnSeq showed quantitatively weaker or no significant phenotypes in validation screens , raising the possibility that their fitness is higher when they are rare members of a diverse mutant population , as opposed to validation experiments where these mutants represent 50% of the population . Notwithstanding these limitations , the high-validation rate of colonization genes in secondary validation assays supports the robustness of our genome-wide map of root colonization . Many genes with significant fitness scores clustered within operons , further reinforcing the validity of RB-TnSeq-derived results . Indeed , colonization genes within the operons shown in Table 1 represent the majority of genes included within these operons . Additionally , 98 colonization genes were not predicted to be part of an operon or were part of an operon of only 2 to 3 genes . Some colonization genes occurred in operons in which only 1 or a small subset of genes showed significant fitness scores . For these operons , it is possible that not every gene is required for the given function , or that individual enzymes are shared across alternative pathways . These results , along with the observation that many of the genes identified from our screen are involved in processes well known to be vital to colonization of plants ( e . g . , motility , carbohydrate utilization ) are consistent with the notion that fitness scores from genome-wide colonization reflect valid , biologically relevant genes and pathways . We also compared the list of genes significantly affecting colonization to known colonization genes based on a number of smaller-scale mutant screens in P . putida [21] and found that 20 out of 87 P . putida homologues with colonization data in our screen showed altered fitness ( S1 Data ) . Although this limited overlap is expected due to the heterogeneous nature of the assays performed across multiple studies in P . putida , as well as known differences between organisms and hosts , they further strengthen the biological validity of the genome-wide colonization map generated in the present study . We observed a surprisingly large number of genes with positive colonization fitness scores ( 243 positive versus 115 negative ) . While most of these mutants showed quantitatively less pronounced phenotypes in luciferase-based screens than predicted by the initial RB-TnSeq scores , in almost all cases the direction of the effect was confirmed ( Fig 3 ) . This observation , along with the propensity of colonization-enriched genes to colocate in operons , supports the conclusion that the predicted phenotypes for these genes are biologically relevant . A large proportion of these genes encode proteins involved in amino acid transport and metabolism ( Figs 2 and 4 ) , suggesting that auxotrophy for certain amino acids confers a selective advantage for survival in the plant-associated environment rich with exuded amino acids and sugars . Two of these genes ( PS417_01565 and PS417_21035 ) were assayed in our luciferase-based competitive colonization screen , and behaved as predicted by the RB-TnSeq data . This poses an intriguing technological opportunity: engineering strains to be more dependent on their plant hosts may have the dual effect of improving colonization while simultaneously restricting the survivability of engineered strains outside the context of the root . Lastly , we identified 44 genes that had vague or no annotation information . A particularly noteworthy subset of these genes lie within operons with multiple mutants with colonization phenotypes , yet without clear functional annotation . These may represent truly novel genes or pathways contributing to colonization of and survival on roots and the results from our in vivo and in vitro screens pave the way for their targeted functional and biochemical characterization . In summary , the genome-wide map of plant colonization genes described in the present study highlights diverse metabolic and physiological functions that support or hinder plant-microbe association and points to novel functions mediating this process .
A . thaliana Col-0 seeds were surface-sterilized in 70% ethanol for 5 minutes , followed by 10% bleach plus 0 . 1% Triton-X100 for an additional 5 to 10 minutes . Sterilized seeds were washed 5 times in sterile water , and stratified in the dark for 2 to 3 days at 4°C . After stratification , 100 seeds were plated on a nylon mesh filter ( 100 micron pore size , cut to an area of approximately 8 cm2 [B0043D1XRE Amazon . com Inc , Seattle , WA] ) placed on top of plant growth media ( 0 . 5X Murashige and Skoog basal salts [MSP01 , Caisson Laboratories , Smithfield , UT] , 2 . 5 mM MES [M3671 , Sigma-Aldrich , St . Louis , MO] , 0 . 6% phytagel [P8169 , Sigma-Aldrich] , pH 5 . 7 ) in a 10 cm square petri dish . Seedlings were grown upright in a Percival incubator ( CU-36L5 , Geneva Scientific , Williams Bay , WI ) for 7 days prior to treatment . A cultured isolate of P . simiae ( WCS417r ) was obtained from Dr . Corné Pieterse ( Utrecht University ) . The barcoded insertion library for this strain was generated by transposon mutagenesis with a barcoded mariner transposon library , followed by TnSeq mapping and barcode association , as previously described [23 , 24] . Glycerol stocks of this library were used for subsequent experiments , stored in 1 mL aliquots containing approximately 4 x 10^8 cells/mL . On average , this represents greater than 1 , 000-fold excess of each individual strain and should avoid any filtration or passage effects associated with recovery from glycerol stocks . A LuxABCDE-expressing transformant ( WCS417r:Lux+ ) was generated by inserting an IPTG-inducible expression cassette using a mariner transposase system , such that Luciferase expression could be visualized following IPTG induction . The insertion site of the LuxABCDE transgene was determined to be at approximately position 1628942 . WCS417r , WCS417r:Lux+ cultures were grown in LB Lennox media at 28°C in a shaking incubator at 200 rpm . The insertion mutant library and single insertion mutants were grown in LB Lennox supplemented with 100 μg/mL kanamycin at 28°C in a shaking incubator at 200 rpm . LacZ-expressing WCS417r was created using a previously engineered miniCTX-lacZ vector driven by the Vibrio cholera lacZ promoter[36] . Briefly , lacZ was transferred to the neutral phage attachment site ( attB ) of WCS417r via biparental mating using Escherichia coli SM10 and selected on LB plates containing 75 μg/mL tetracycline . Each colonization experiment was comprised of 5 replicates of each sample type . Three colonization experiments were performed at the DOE Joint Genome Institute ( sets A , B , and C ) . For a single colonization experiment , a glycerol stock containing the transposon library was inoculated in 50 mL fresh LB and grown for approximately 6 hours until the culture reached the midlog phase ( OD between 0 . 2 and 0 . 6 ) . Cells were then harvested by centrifugation ( 3 , 000 g for 3 minutes ) and washed 3 times by resuspending in 1 mL of 0 . 5X MS media and pelleting the cells . After washing , the cells were resuspended in 1 mL , 0 . 5X MS , and the OD of the resuspension was calculated by spectrophotometer ( using a 1:10 dilution ) . Cells were then normalized to OD 0 . 5 , and 50 μl ( corresponding to approximately 1 . 0 x 10^7 cells ) was spread onto 0 . 5X MS phytagel ( 0 . 6% ) plates using sterile glass beads . Seven-day-old Arabidopsis seedlings grown on a sterile nylon mesh filter ( 110 μm pore size ) laid on top of 0 . 5X MS phytagel ( 0 . 6% ) plates were transferred by lifting and replacing the filter onto the inoculated plates . Five aliquots of the OD 0 . 5 culture were saved as an input ( IPT ) culture for each experimental replicate ( set ) . Ten plates were inoculated with bacteria and exposed to a nylon mesh filter without seedlings . Five such filters ( NRI ) were allowed to contact the bacteria plate for 1 hour before being used to inoculate 50 mL LB + Kanamycin ( 100 μg/L ) overnight . The remaining 5 filters ( NRF ) along with the plate/filters containing Col-0 seedlings were incubated vertically in a Percival growth chamber for 7 days under short-day ( 8 hour light/16 hour dark ) conditions ( 22°C ) . Following cocultivation , the NRF filters were used to inoculate a 50 mL LB Lennox + Kanamycin ( 100 μg/L ) culture , and grown overnight . Seedlings on plates containing bacteria were then cut just below the root/shoot junction , and the isolated roots were placed into 10 mL , 0 . 5X MS liquid . Ten plates of roots were pooled into a single sample . The pooled roots were vortexed for 15 seconds to wash loosely adhered cells from the surface of the roots , and the buffer removed . The washing procedure was repeated 5 more times ( total 6 washes ) . The washed roots were then cut into thirds , placed into 2 mL eppendorf tubes with 2 metal beads and 200 μl , 0 . 5X MS liquid . The roots were ground in a TissueLyser bead mill for 2 cycles of 5-minute grinds at 30 Hz ( Qiagen , Hilden , Germany ) , inverting the tubes between cycles . Ground roots ( rhizoplane + endophytic compartment; RPL ) were used to inoculate 50 mL LB Lennox + Kanamycin ( 100 μg/L ) cultures overnight . Two mL samples from all overnight cultures ( IPT , NRI , NRF , RPL ) were harvested after 12 to 16 hours of growth , pelleted , and stored at −80°C prior to DNA extraction . DNA from frozen pellets was isolated using the DNeasy Blood and Tissue kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s instructions . DNA was quantified with a Qubit fluorometer ( Thermo Scientific , Raleigh , NC ) according to the manufacturer’s instructions and normalized to 10 ng/μl . Samples with ( DNA ) less than 10 ng/μl were not diluted . Twenty microliters of the normalized ( or undiluted , in the case of low concentration samples ) DNA was used as template in a PCR using primers flanking the transposon barcode region , each containing an Illumina adapter and multiplexing index sequence ( BarSeq ) [23 , 24] . PCR was performed using Q5 DNA polymerase with Q5 GC enhancer ( New England Biolabs , Ipswich , MA ) for 25 cycles of 30 seconds at 98°C , 30 seconds at 55°C , and 30 seconds at 72°C , followed by a final extension at 72°C for 5 minutes . Following PCR , 10 μl of each reaction was pooled into 3 sets of 25 ( sets A , B , and C; see S1 Data ) amplicon libraries , corresponding to each experimental set ( see previous section ) . Three pooled libraries ( representing sets A , B , and C ) were then purified using the DNA Clean & Concentrate Kit ( Zymo , Irvine , CA ) according to the manufacturer’s instructions . Each set was sequenced on its own lane on an Illumina HiSeq 2500 machine using the 1T paired-end , 2 x 101 cycle protocol , producing an average of 3 to 8 million reads per sample . We used barcode sequencing to quantify the representation of mutants in each sample and compared barcode frequencies across samples [24] . Raw sequence reads were initially processed by looking for the 6 nt adapter sequences on either side of a 20 nt random barcode . Reads with exactly 20 nt barcode sequences , no mismatches between mate-pair barcodes , and high-quality scores ( Q > 30 ) from each of the 60 libraries ( 15 IPT , 15 NRI , 15 NRF , and 15 RPL ) were then saved into filtered fastq files and used as input into the BarSeqR pipeline [24] . For this analysis , the NRI samples were set as “Time0” controls , with all samples normalized to these samples . The NRI samples were used as the normalization controls to factor out any amplification effects caused by overnight culturing . To assess the saturation of our sampling method , we quantified the number of barcodes ( and genes mutated ) in all 60 samples . On average , for each sample , we recovered >80% of the insertion mutants that we had mapping information for . We also quantified the number of barcodes and genes with mutations when samples were considered together ( combining the unique barcode and gene count represented by any of the samples within a given sample type , e . g . , NRF or RPL ) . With replication , our recovery rates approach saturation for each of the 4 sample types ( S11 Fig ) . The BarSeqR scripts report per-gene fitness scores ( normalized log-ratio ) and t-like test statistics indicating the relative effect size and significance between each sample and the average of the Time0 controls , respectively , for each of the 60 samples , including the Time0 controls ( NRI samples ) [24] . After normalization of total counts across samples , we determined 3 separate derived fitness scores for each gene . Each of these scores measures a different potential effect influencing microbial growth in these experimental conditions: A mesh fitness score comparing the NRF and NRI samples and thus measuring changes in the ability to growth on the nylon mesh alone; a root + mesh fitness score comparing the RPL and the NRI samples , which measures the overall ability to grow on the root and the nylon mesh; and a root fitness score , comparing the RPL and NRF samples directly , which represents the root + mesh fitness score corrected for the mesh fitness score to quantify the ability to grow on the root after correction for mesh-related effects . To classify genes based on their mesh phenotype , we compared fitness scores from the various sample types and computed 3 derived fitness scores , looking for significant differences based on an empirical P value corresponding to an FDR of 0 . 05 ( Student t test ) and an effect size ( absolute difference between the means ) of > 0 . 5: a root + mesh fitness score ( comparing RPL to NRI; P < 0 . 014 ) , a mesh fitness score ( comparing NRF to NRI; P < 0 . 01 ) and a root fitness score ( comparing RPL to NRF; P < 0 . 013 ) . Considering that weak colonization fitness scores may not be biologically meaningful , we chose a threshold effect size cutoff of 0 . 5 , which eliminated nearly half of the genes that were significant based on P value alone . We binned these genes into 2 main groups ( S3 Fig ) . Genes in group 1 ( gray , S3 Fig; 149 genes ) had significant root + mesh fitness scores , but the quantitative magnitude of this effect was largely explained by changes to the ability to survive on the nylon mesh alone ( mesh fitness score ) . Consequently , genes in group 1 were considered low-confidence candidate genes for root colonization , despite their significant root fitness scores . Genes in group 2 ( blue and cyan , S3 Fig; 358 genes ) exhibited significant root fitness scores of at least moderate effect sizes ( root fitness score absolute value >0 . 5 ) . A subset of these ( group 2b cyan , S3 Fig; 75 genes ) additionally did not exhibit significant root + mesh fitness scores” . Mutations in these genes likely confer altered fitness on mesh , but this phenotype was at least partially compensated for by the presence of roots . Despite this more complicated phenotype , we included these genes in group 2 because they are likely to be involved in root colonization . Putative essential genes in the P . simiae genome were identified by a BLAST homology search using as a query protein sequences of P . simiae genes , and as a subject database genes characterized as essential from representative gamma-proteobacteria within the Database of Essential Genes [37] . Alignments between P . simiae and DEG protein sequences having more than 50% identity across at least 80% of both subject and query sequences , and having an e-value <1e-50 were considered to be significant , and those P . simiae proteins were classified as homologous to an essential gene . For RB-TnSeq growth assays in defined media , we incorporated the P . simiae WCS417r strain into a larger microbial functional genomics effort to be published soon [23] . As part of this effort , growth assays with carbon sources , nitrogen sources , and inhibitors and soft agar motility assays were performed as previously described [23 , 24] . For comparing colonization to in vitro assays , we used a simplistic threshold to assign a phenotype”of abs ( in vitro assay fitness score ) >1 , and a strong phenotype of abs ( in vitro assay fitness score ) >2 , as described [24] . An aliquot of the RB-TnSeq library was grown to an optical density of approximately 0 . 2 , diluted approximately 1 to 50 , 000 , and plated on LB + kanamycin agar plates . These plates were incubated for 64 hours at 18°C , with limited lighting until single colonies were apparent . Colonies were then selected by a Qpix460 ( Molecular Devices , Sunnyvale , CA ) and arrayed into 384-well plates ( 64 plates total ) containing 90 μL LB with 7 . 5% glycerol and kanamycin and were grown overnight in the dark at 29°C until they reached an OD450 of 0 . 5 . Aliquots were taken for downstream processes and the 384-well plate was kept at −80°C . To unambiguously determine the barcode identity and plate address of all the mutants in this collection , 25 nL from each well of the 384-well plates was transferred using an Echo525 instrument ( Labcyte , San Jose , CA ) to a new well on a 96-well plate based on a multiplexing strategy involving pooling of rows , columns , and plates . 1 . 6–4 . 8 μl of each pooled culture within the 96-well plate was used as input for RB-TnSeq . Following sequencing of the 96 RB-TnSeq libraries on a MiSeq v2 instrument , an in-house script for determining the address for each barcode within the clone library ( based on the pooling schematic ) was used . To isolate specific barcoded insertion mutants , the 384-well glycerol stock plate containing the clone of interest was removed from −80°C and kept on dry ice . A sterile loop was used to scrape the surface of each frozen stock well and streak LB+kanamycin agar plates . Following an overnight incubation at 28°C , 3 to 6 colonies were individually picked and grown in 100 μl LB with Kanamycin for 6 to 8 hours at 28°C . An aliquot of this culture was diluted 1:10 in water , and used as input for qPCR ( 40 cycles ) using primers flanking the barcode region . Positive qPCR wells were cleaned with ExoSAP-IT and sent for Sanger Sequencing to confirm the correct barcode sequence . Cultures started from individually-picked colonies that were confirmed with Sanger data were then used for phenotypic analysis in planta . To independently test whether isolated mutants had altered colonization ability , we designed a bioluminescence assay using the Lux+ strain described above . When mixed at a defined ratio with individual nonluminescent mutant strains , this Lux+ strain allows for a direct , luminescence-based in planta quantification of competitive colonization , in which luminescence intensity inversely correlates with the proportion of the unlabeled strain on the root ( Fig 3 and S5 Fig ) . This ratio gradient can be used to derive a standard curve with which we can normalize root colonization measurements ( by luminescence ) with mutant strains . In this assay , 24 bacterial cultures , including the 22 insertion mutant strains , the parent WCS417r strain , and the engineered Lux+ strain , were grown overnight , and subcultured the following morning into 5 mL LB ( + Kan for the insertion mutant strains ) . After reaching an OD of between 0 . 2 and 0 . 6 , the cultures were pelleted at 3 , 000 g , and washed 3 times with 1 mL , 0 . 5X MS . After the third wash , each culture was resuspended in 1 mL , 0 . 5X MS , and measured for optical density . Each culture was then normalized to an OD of 0 . 5 , and 30 mixtures were made: a WT/Lux+ standard curve series ( 100% WT/0% Lux+ , 80% WT/20% Lux+ , 60% WT/40% Lux+ , 50%WT/50% Lux+ , 40% WT/60% Lux+ , 20%WT/80% Lux+ , and 0%WT/100% Lux+ ) , a buffer control mixture ( 50% Lux+ , 50% 0 . 5X MS ) , and 22 mixtures of 50%Lux+/50% insertion mutant strain . 50 μL of each mixture was then spread onto 2 sets of separate 0 . 5XMS ( 100 μm IPTG , 0 . 6% phytagel ) plates ( 60 plates total ) using sterile glass beads . Squares of sterile nylon mesh were placed on one set of 30 plates , while 7-day-old Arabidopsis seedlings grown on nylon mesh were transferred to the other set of 30 plates . All 60 plates were sealed with micropore tape and incubated vertically in a short-day light chamber for 7 days as for the initial colonization screen . On day 7 , the plates containing only nylon mesh ( and bacteria ) were opened , and the filter was vortexed in a 15 mL tube with 5 mL , 0 . 5X MS for 10 seconds . An aliquot was sampled from this mixture , and 5 serial dilutions were prepared . Twenty microliters from each dilution were spotted into single a cell of a 6 x 6 gridded LB agar plate and incubated overnight ( to estimate the ratio of the luciferase strain to the WT luciferase strain on mesh alone ) . Each seedling plate was then removed from the growth chamber , and 5 seedlings from each plate were placed onto a large 0 . 5X MS plate ( 100 μM IPTG , 0 . 6% phytagel ) and imaged using an epi-white illumination for a 2 second exposure and in complete darkness for a separate 30 minute exposure using a GelDoc gel imager ( Bio-Rad , Hercules , CA ) . The following day , the colonies from the LB plate spotted with the mesh-derived dilution series were imaged in a GelDoc imaging platform , taking both transblue images ( approximately 0 . 01 second exposure ) and dark ( no illumination ) exposures . Bioluminescence from each seedling imaged was quantified by determining the integrated pixel density within a 10 x 30 pixel rectangle surrounding the basal region of the root , with the lower boundary of each rectangle positioned just above the root tip , and then subtracting from this value the integrated density of a 10 x 30 pixel rectangle just below the root ( Fig 3 ) without overlapping the root region . The resulting values from each of the 5 seedlings per sample type ( e . g . , WT/Lux+ standard curve or mutant/Lux+ mixture ) were then used to determine the mean root intensity for that sample . The estimated root ratio of mutant/Lux+ was determined by normalizing the root intensity for each of the mutant/Lux+ roots to a linear regression model of the WT/Lux+ ratio series . To determine the ratio of each unlabeled ( i . e . , WT and mutant strains ) to the Lux+ strain in all the mixtures derived from the mesh filter alone , colonies were counted from dilutions where typically between 30 and 300 colonies were visible on a single cell in the 6 x 6 gridded plate . These values ( mesh ratios ) are expressed as the ratio of unlabeled colonies to the total number of colonies . A least-squares linear regression was then computed from the mesh ratios and root intensities of the WT/Lux+ series . The slope and intercept of this regression was used to determine the estimated ratio of unlabeled strains to Lux+ strains on the root . The colonization index was defined as the natural log of the estimated root ratio minus the natural log of the observed mesh ratio . This procedure was performed independently 3 times . Eight strains were selected for additional validation experiments over a 7-day time course: 4 with insertions in predicted depleted colonization genes ( PS417_00160 , PS417_01955 , PS417_22145 , and PS417_22775 ) and 4 with insertions in predicted enhanced colonization genes ( PS417_08165 , PS417_21035 , PS417_03095 , and PS417_10720 ) . Briefly , 5 mL cultures of each strain , the WT strain , and the Lux+ strain were grown individually in LB Lennox overnight to midlog phase , and then pelleted , washed 3 times , and normalized to OD 0 . 5 in 0 . 5X MS media . Sixteen populations were then created: 8 1:1 mixtures of the Lux+ strain with the mutant strains ( 1 mixture each ) , and 8 mixtures of WT and Lux+ strains at different ratios ( 100%/0% , 85%/15% , 70%/30% , 50%/50% , 40%/60% , 25%/75% , 10%/90% and 0%/100% WT/Lux+ strains ) . Each population was inoculated onto 2 separate , 0 . 5X MS/0 . 6% phytagel plates ( 50 μl used for each inoculation ) , totaling 32 plates for each experiment . Four squares of nylon mesh ( approximately 3 cm2 each ) were added to each of 16 out of the 32 plates . Nylon mesh ( approximately 8 cm2 each ) supporting approximately one hundred 7-day-old Arabidopsis seedlings were transferred to the remaining 16 plates . After 1 , 3 , 5 , and 7 days following inoculation , 1 nylon mesh square from each mesh-containing plate was vortexed in 1 mL 0 . 5X MS media for 30 seconds , and then placed onto a large square petri dish containing 0 . 5X MS and 0 . 6% phytagel and 10 μm IPTG ( to induce luciferase expression ) , leaving behind 1 mL of the mesh population . Additionally , 3 seedlings from each of the plant-containing plates were transferred to same large plate and allowed to incubate at room temperature for 2 hours prior to imaging as described in the previous section . Root length and mesh area were measured from each image , along with total luciferase intensity ( though this was not a reliable metric especially on days 1 and 3 when the intensity was very dim ) . Following imaging , the 3 selected roots were excised , placed into 300 μl 0 . 5X MS in 1 . 5 mL tubes , sonicated at low power ( 160W at 20 kHz for 30 seconds ) , and vortexed for 30 seconds at high speed , resulting in the root population . 20 μl of the root and mesh populations were diluted separately ( for each mutant and WT/Lux ratio mixture ) into 6 serial 10-fold dilutions , plated ( 15 μl each ) onto LB Lennox and 10 μm IPTG and 1 . 5% agar plates , and incubated for 24 hours . Each plate was then imaged with transblue illumination and with no illumination ( 60 second exposure ) to visualize all colonies and Lux+ colonies . Both images were overlaid , and Lux+ and Lux- CFUs were counted for dilutions in which single colonies could be visualized . Root CFUs ( Lux+ and Lux- ) were used to compute the competitive colonization ability for each mutant , while the mesh CFUs and total CFUs from the root sample were used to compute the overall population sizes per unit length ( for roots ) or area ( for mesh ) . Deletion mutation alleles for PS417_08425 , PS417_19755 , PS417_08190 were constructed using splice-overlap extension PCR and WCS417r genomic DNA as a template . Deletion alleles were cloned into suicide vector pDONRX via Gateway cloning [38] . Resultant plasmids were confirmed by Sanger sequencing and introduced into WCS417r via biparental mating with SM10 . Unmarked double-crossover mutants were then isolated using sucrose-mediated counter-selection as previously described [39] and confirmed by PCR . WCS417r:LacZ+ and either unmarked WCS417r or mutant derivatives were mixed 1:1 , as determined by OD600 , before 2 x 104 CFU were applied to the surface of 0 . 5X Murashige and Skoog plates containing 0 . 6% phytagel . Approximately fifty 1-week-old Arabidopsis seedlings germinated on nylon mesh were transferred to phytagel plates containing bacterial mixes and allowed to coincubate for 24 hours . Roots were separated from shoots , and washed 3 to 4 times prior to lysis . LacZ positive colonies were enumerated by plating serial dilutions of root homogenates and nylon mesh washes on LB media containing 40 μg/mL X-gal . Competitive indices were computed based on the white:blue ratio . Although the results from assaying many of the mutant strains under the LacZ and Lux+ assays agree , there was variability between assays . This was largely expected , given that the validation assays have higher degrees of variability compared to the RB-TnSeq screen , and the scale of the data is vastly different ( tens of colonies per replicate for the validation assay versus hundreds to thousands of counts for the RB-TnSeq screen ) .
|
Plants fix carbon to create an abundance of sugars and amino acids , thus providing an enticing environment for microorganisms that reside in soil . Once these microorganisms have colonized the root environment , they can dramatically influence plant growth and development . We set out to identify a comprehensive set of microbial genes that control or influence root colonization , using a genome-wide transposon mutagenesis approach ( randomly barcoded transposon sequencing [RB-TnSeq] ) . By using this method , we identified several hundred genes that , when mutated , affect the ability of the bacterium P . simiae to competitively colonize the root system of the model plant A . thaliana . These included many genes purported to be involved in carbohydrate metabolism , cell wall biosynthesis , and motility , underscoring the notion that sugar metabolism , defense , and motility are all key features of a root-colonizing microbe . We also identified several amino acid transport and metabolism genes with mutations that confer a fitness advantage in root colonization . Lastly , we identified several genes with no known function that significantly alter root colonization ability when mutated . These findings suggest novel engineering strategies to improve biological product development , and will facilitate the mechanistic exploration of the root colonization process .
|
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2017
|
Genome-wide identification of bacterial plant colonization genes
|
Animals living in groups make movement decisions that depend , among other factors , on social interactions with other group members . Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations , with less emphasis in obtaining first-principles approaches that allow their derivation . Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty . We build a decision-making model with two stages: Bayesian estimation and probabilistic matching . In the first stage , each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals . In the probability matching stage , each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one . This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters , one that each animal assigns to the other animals and another given by the quality of the non-social information . We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks , Gasterosteus aculeatus , a shoaling fish species . The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging , mate selection , neurobiology and psychology , and gives predictions for experiments directly testing the relationship between estimation and collective behavior .
Animals need to make decisions without certainty in which option is best . This uncertainty is due to the ambiguity of sensory data but also to limited processing capabilities , and is an intrinsic and general property of the representation that animals can build about the world . A general way to make decisions in uncertain situations is to make probabilistic estimations [1] , [2] . There is evidence that animals use probabilistic estimations , for example in the early stages of sensory perception [3]–[11] , sensory-motor transformations [12]–[14] , learning [15]–[17] and behaviors in an ecological context such as strategies for food patch exploitation [18]–[20] and mate selection [21] , among others [13] , [17] , [21] , [22] . An additional source of information about the environment may come from the behavior of other animals ( social information ) [23]–[28] . This information can have different degrees of ambiguity . In particular cases , the behavior of conspecifics directly reveals environmental characteristics ( for example , food encountered by another individual informs about the quality of a food patch ) . Cases in which social information correlates well with the environmental characteristic of interest have been very well studied [29]–[37] . But in most cases social information is ambiguous and potentially misleading [26] , [38] . In spite of this ambiguity , there is evidence that in some cases such as predator avoidance [39] , [40] and mate choice [41] , animals use this kind of information . Social animals have a continuous flow of information about the environment coming from the behaviours of other animals . It is therefore possible that social animals use it at all times , making probabilistic estimations to counteract its ambiguity . If this is the case , estimation of the environment using both non-social and social information might be a major determinant of the structure of animal collectives . In order to test this hypothesis , we have developed a Bayesian decision-making model that includes both personal and social information , that naturally weights them according to their reliability in order to get a better estimate of the environment . All members of the group can then use these improved estimations to make better decisions , and collective patterns of decisions then emerge from these individuals interacting through their perceptual systems . We show that this model derives social rules that economically explain detailed experiments of decision-making in animal groups [42] , [43] . This approach should complement the empirical approach used in the study of animal groups [42]–[47] , finding which mathematical functions should correspond to each experimental problem and to propose experiments relating estimation and collective motion . The Bayesian structure of our model also builds a bridge between the field of collective behavior and other fields of animal behavior , such as optimal foraging theory [18]–[22] and others [21] , [22] . Further , it explicitly includes in a natural way different cognitive abilities , making more direct contact with neurobiology and psychology [3]–[10] , [17] .
We derived a model in which each individual decides from an estimation of which behavior is best to perform . These behaviors can be to go to one of several different places , to choose among some behaviors like forage , explore or run away , or any other set of options . For clarity , here we particularize to the case of choosing the best of two spatial locations , and ( see Text S1 for more than two options ) . ‘Best’ may correspond to the safest , the one with highest food density or most interesting for any other reasons . We assume that each decision maker uses in the estimation of the best location both non-social and social information . Non-social information may include sensory information about the environment ( i . e . shelter properties , potential predators , food items ) , memory of previous experiences and internal states . Social information consists of the behaviors performed by other decision-makers . Each individual estimates the probability that each location , say , is the best one , using its non-social information ( ) and the behavior of the other individuals ( ) , ( 1 ) where stands for ‘ is the best location’ . , because there are only two locations to choose from . We can compute the probability in Eq . 1 using Bayes' theorem , ( 2 ) By simply dividing numerator and denominator by the numerator we find an interesting structure , ( 3 ) where ( 4 ) and ( 5 ) Note that does not contain any social information so it can be understood as the “non-social term” of the estimation . We can also understand as the “social term” because it contains all the social information , although is also depends on the non-social information . The non-social term is the likelihood ratio for the two options given only the non-social information . This kind of likelihood ratio is the basis of Bayesian decision-making in the absence of social information [5] , [11]–[14] . Eq . 3 now tells us that this well known term interacts with the social term simply through multiplication . We are seeking a model based on probabilistic estimation that can simultaneously give us insight into social decision-making and fit experimental data . For this reason we simplify the model by assuming that the focal individual does not make use of the correlations among the behaviour of others , but instead assumes their behaviours to be independent of each other . This is a strong hypothesis but allows us to derive simple explicit expressions with important insights . The section ‘Model including dependencies’ at the end of Results shows that this assumption gives a very good approximation to a more complete model that takes into account these correlations . The assumption of independence translates in that the probability of a given set of behaviors is just the product of the probabilities of the individual behaviors . We apply it to the probabilities needed to compute in Eq . 5 , getting ( 6 ) where is the set of all the behaviors of the other animals at the time the focal individual chooses , , and denotes the behavior of one of them , individual . is a combinatorial term counting the number of possible decision sequences that lead to the set of behaviors , that will cancel out in the next step . Substituting Eq . 6 and the corresponding expression for into Eq . 5 , we get ( 7 ) Instead of an expression in terms of as many behaviors as individuals , it may be more useful to consider a discrete set of behavioral classes . For example , in our two-choice example , these behavioral classes may be ‘go to ’ ( denoted ) , ‘go to ’ ( ) and ‘remain undecided’ ( ) . Frequently , these behavioral classes ( or simply ‘behaviors’ ) will be directly related to the choices , so that each behavior will consist of choosing one option . For example , behaviors and are directly related to choices and , respectively . But there may be behaviors not related to any option as the case of indecision , , or related to choices in an indirect way . These behaviors can still be informative because they may be more consistent with one of the options being better than the other ( for example , indecision may increase when there is a predator , so the presence of undecided individuals may bias the decision against the place where the non-social information suggests the presence of a predator ) . Let us consider different behavioral classes , . We do not here consider individual differences for animals performing the same behavior ( say , behavior ) , so they have the same probabilities and . Thus , if for example the first individuals are performing behavior , we have that . We can then write Eq . 7 as ( 8 ) where is the number of individuals performing behavior , and ( 9 ) The term is the probability that an individual performs behavior when is the best option , over the probability that it performs the same behavior when is the best choice . The higher the more reliably behavior indicates that is better than , so we can understand as the reliability parameter of behavior . If , observing behavior indicates with complete certainty that is the best option , while for behavior gives no information . For , observing behavior favors as the best option , and more so the closer it is to 0 . Note that and are not the actual probabilities of performing behavior , but estimates of these probabilities that the deciding animal uses to assess the reliability of the other decision-makers . These estimates may be ‘hard-wired’ as a result of evolutionary adaptation , but may also be subject to change due to learning . To summarize , using Eqs . 3 and 8 , the probability that is the best choice , given both social and non-social information is ( 10 ) with in Eq . 4 and in Eq . 9 . We have so far only considered the perceptual stage of decision-making , in which the deciding individual estimates the probability that each behavior is the best one . Now it must decide according to this estimation . A simple decision rule would be to go to when is above a certain threshold . This rule maximizes the amount of correct choices when the probabilities do not change [48] , but is not consistent with the experimental data considered in this paper . Applying this deterministic rule strictly , without any noise sources , one would obtain that all individuals behave exactly in the same way when facing the same stimuli , but in the experiments considered here this is not the case . Instead , we used a different decision rule called probability matching , that has been experimentally observed in many species , from insects to humans [49]–[55] . According to this rule an individual chooses each option with a probability that is equal to the probability that it is the best choice . Therefore , in our case the probability of going to ( ) , is the same as the estimated probability that is the best location ( ) , so ( 11 ) Probability matching does not maximize the amount of right choices if we assume that the probabilities stay always the same , but in many circumstances it can be the optimal behavior , such as when there is competition for resources [56] , [57] , when the estimated probabilities are expected to change due to learning [53] , [55] , or for other reasons [53] , [58] . Finally , using Eqs . 10 and 11 we have that the probability that the deciding individual goes to is ( 12 ) The assumption of probability matching has the advantage that the final expression for the decision in Eq . 12 is identical to the one given by Bayesian estimation in Eq . 10 , with no extra parameters . Alternative decision rules could be noisy versions of the threshold rule , but at the price of adding at least one extra parameter to describe the noise . Also , decision rules might not depend on estimation alone , but also on other factors or constraints . These more complicated rules fall beyond the scope of this paper . In the following sections , we particularize Eq . 12 to different experimental settings to test its results against existing rich experimental data sets that have previously been fitted to different mathematical expressions [42] , [43] . We first considered the simple case of two identical equidistant sites , and , Fig . 1A . For a set-up made symmetric by experimental design there is no true best option . But deciding individuals must act , like for any other case , using only their incomplete sensory data to make the best possible decision . Even when non-social sensory data indicates no relevant difference between the two sites , the social information can bias the estimation of the best option to one of the two sites . Using Eq . 12 and that the three possible behaviors are ‘go to ’ ( ) , ‘go to ’ ( ) and ‘remain undecided’ ( ) , we obtain ( 13 ) where and are the number of individuals that have already chosen and , respectively , and is the size of the group containing our focal individual and other animals . As the set-up is symmetric , the sensory information available to the deciding individual is the same for both options so and then according to Eq . 4 . Also , since indecision is not related to any particular choice , symmetry imposes , so indecision is not informative , ( Eq . 9 ) . For the other two behaviors , going to ( ) and going to ( ) , Eq . 9 gives ( 14 ) and are the estimated probabilities of making the right choice , that is , going to when is the best option , or going to when is the best option . Since in this case the sensory information is identical for both options , the probability of making the correct choice must be the same for both options , . An analogous argument holds for the incorrect choices , , giving ( 15 ) In cases in which , we find it convenient to express reliability more generally as ( 16 ) which is the ratio of the probability of making the correct choice and the probability of making a mistake , for both behaviors . Using this definition and given that , Eq . 13 reduces to ( 17 ) with the variable . Eq . 17 describes a sigmoidal function that is steeper the higher the higher the value of ( Fig . 1B ) . Therefore , for very reliable behaviors ( high , meaning individuals that are much more likely to make correct choices than erroneous ones ) , grows fast with and the deciding individual then goes to with high probability when taking into account the behaviors of only very few individuals . The behavior of the group is obtained by applying the decision rule in Eq . 17 sequentially to each individual ( see Methods ) . After each behavioural choice , we update the number of individuals at and , using the new and for the next deciding individual ( Fig . 1C , bottom ) . Repeating this procedure for all the individuals in the group , we can compute the probability for each possible final outcome of the experiment ( Fig . 1C , top ) . The relevance of the symmetric case is that the model has a single parameter and a single variable , enabling a powerful comparison against experimental data . We tested the model using an existing rich data set of collective decisions in three-spined sticklebacks [42] , a shoaling fish species . This data set was obtained using a group of fish choosing between two identical refugia , one on their left and another one on their right ( Fig . 2A ) , equivalent to locations and in the model ( Fig . 1A ) . At the start of the experiment , ( ) replica fish made of resin were moved along lines on the left ( right ) towards the refugia ( Fig . 2A ) . The experimental results consisted on the statistics of collective decisions between the two refugia for 19 different cases using different group sizes = 2 , 4 or 8 and different numbers of replicas going left and right , = {1∶1 , 2∶2 , 0∶1 , 1∶2 , 0∶2 , 1∶3 , 0∶3} ( Fig . 2B , blue histograms ) . To compare against these experimental data , we calculated the probability of finding a collective pattern applying the individual behavioural rule in Eq . 17 iteratively over each fish for the 19 experimental settings . We found a good fit of the model to the experimental data using for the 19 graphs the same value ( Fig . 2B , red line ) . The model is robust , with good fits in the interval ( Fig . 3 , red line ) . Despite the simplicity of the behavioral rule in Eq . 17 , it reproduces the experimental results , including the dependence on the total number of fish , even though the rule is independent of this parameter , except for determining the range of possible values of . The dependence of the final distributions on emerges from the application of the rule to the individuals in the group , as is illustrated in Fig . 4 . Each small box represents a state of the system in which fish have already decided to go to and , respectively . The lines connecting each box with another two boxes on top represent the decision made by the next deciding individual , that takes the system to the next state . The width of the lines is proportional to the probability of the decision . As more individuals decide , the central states become less likely simply because they accumulate more unlikely decisions . Therefore , the U-shape or J-shape becomes more pronounced for larger groups , even though the individual decision rule in Eq . 17 is independent of the total number of individuals . Group decision-making in three-spined sticklebacks shows a single type of distribution in which probability is minimum at the center and increases monotonically towards the edges , denoted here as U-shaped distribution ( or J-shaped when there is a bias to one of the two options ) . However , the model in Eq . 17 also gives two other types of distributions , Fig . 5A . For non-social behavior ( ) the histogram is bell-shaped due to combinatorial effects . However , a bell-shape is also compatible with social animals for a certain range of and group size ( white region on the bottom-left of Fig . 5A ) . For higher values of , the histograms are M-shaped , with two maxima located between the center and the sides ( region coloured in black and blue in Fig . 5A ) . However , the M shape becomes clear only with enough number of bins because the drop in probability near the edge or at the center of the distribution disappears when binning is too coarse , producing a bell-shaped or U-shaped histogram , Fig . 5B . This is an important practical issue , because the amount of data that can be collected rarely allows for more than 5 bins . The colorscale in Fig . 5A reflects the number of bins needed to observe the M shape ( black has been reserved for exactly 5 bins ) . For high values of , the histograms are U-shaped ( white region on the top of Fig . 5A ) . Also , all the M-region above the black zone becomes of type U when the binning is too coarse . An interesting prediction of our model is that , for a given number of bins , the shape of the distribution of choices changes with the number of decided individuals , and the dynamics of this change depends on . For high values of , the probability is U-shaped from the beginning and becomes steeper as more individuals decide ( as is the case for the stickleback dataset ) , Fig . 5C . For lower values of , we observe M-shaped distributions for the first individuals and then U-shaped ones when more individuals decide , Fig . 5D . For even lower values of , we observe bell-shaped distributions for the first individuals , then M-shaped and finally U-shaped , Fig . 5E , F . An interesting modification of the experimental set-up consists in using replicas of the animals that we can modify to potentially alter their reliability estimated by the animals . We considered the particular case , motivated by experiments in [43] , of two types of modified replicas with different characteristics ( for example , fat or thin ) , Fig . 6A . We considered 7 behaviors: ‘animal goes to ’ ( ) , ‘animal goes to ’ ( ) , ‘most attractive replica goes to ’ ( ) , ‘most attractive replica goes to ’ ( ) ‘least attractive replica goes to ’ ( ) , ‘least attractive replica goes to ’ ( ) , and ‘animal remains undecided’ ( ) . The probability of going to in Eq . 12 then reduces to ( 18 ) where subindex ‘f’ refers to real fish and ‘R’ ( ‘r’ ) to replicas of the most ( least ) attractive type . As in the previous section , symmetry imposes that and . It also imposes the following relations between the reliability parameters , , , . Therefore , ( 19 ) where , and . In the particular case of only two different replicas , one going to and the other to and for notational simplicity taking the convention that the most ( least ) attractive replica goes to ( ) , we have and . Therefore , ( 20 ) Note that the probability in Eq . 20 does not depend on and separately , but only on their ratio . Therefore , in this case the model uses only two parameters ( and ) . We compared the model with the stickleback data set from [43] , Fig . 6 . The data in Fig . 6B has a different type of replica pair in each row , so in principle we would fit a different ratio for each row . But note that the first three rows correspond to experiments with the same three replicas ( large , medium and small ) , combined in different pairs . The same can be said for the second and third threesomes of rows . Therefore , there are only two free parameters for each three rows . On the other hand , should have the same value for all cases . The model again reproduces the experimental results reported in reference [43] , obtaining the best fit for ( Fig . 6B ) . The result is robust , with good fits for ( Fig . 3 , green line ) in accord with the value obtained for the case shown in Fig . 2B . We finally considered the case in which sites and are different and the three behaviors are ‘go to ’ ( ) , ‘go to ’ ( ) and ‘remain undecided’ ( ) . Eq . 12 reduces to ( 21 ) The term represents the non-social information and in general because the set-up is asymmetric by design . This asymmetry might also affect how a deciding animal takes into account the behaviours of other animals depending on which side they chose , making in general . Also , indecision might be informative . For example , if non-social information indicates the possible presence of a predator at , the indecision of other animals might confirm this to the deciding individual , further biasing the decision towards . Therefore , we may have . But it may also be the case that the set-up's asymmetry does not affect the social terms , so we also tested a simpler model in which and , giving ( 22 ) The stickleback dataset reported in reference [42] is ideally suited to test the asymmetric model for the experiments that were performed with a replica predator at the right arm ( Fig . 7A ) . The model in Eq . 22 fits best the data with ( Fig . 7B ) and it is robust with a good fit in ( Fig . 3 , blue line ) . The more complex model in Eq . 21 gives fits very similar to those of simpler model . Specifically , parameter was rejected by the Bayes Information Criterion [59] , [60] , suggesting that fish do not rely on undecided individuals . The fact that fish rely differently on other fish depending on the option they have taken could not be ruled out by the Bayes Information Criterion , but in any case the impact of this difference on the data is small . In the experiments in Fig . 2 and Fig . 7 , we have assumed that the replicas are perceived by fish as real animals . However , it is reasonable to think that fish might perceive the difference , and rely differently on replicas and real fish . To test this , we considered different behaviors for fish and replicas , such as ‘fish goes to ’ and ‘replica goes to ’ . Making that distinction , we get that Eq . 12 reduces to ( 23 ) The Bayes Information Criterion rejects only parameter . However , the addition of the new parameters that distinguish replica from real fish give very small improvements in the fits compared to results of the simpler models in Eq . 17 and Eq . 22 ( see Fig . S1 and S3 ) , suggesting that fish follow replicas as much as they follow real fish . In this section we will remove the hypothesis of independence among the behaviors of the other individuals ( Eq . 6 ) . We now consider that the focal individual not only takes into account the behaviors of the other animals at the time of decision but the specific sequence of decisions that has taken place before , , being the number of individuals that have decided before the focal one . For example , the sequence may give different information to the focal individual than the sequence . This is illustrated in Fig . 8A , where there are two possible paths leading to states labeled as 1∶1 , but these two states are in different branches of the tree ( in contrast with Fig . 4 , in which these two states were collapsed in a single one ) . To calculate the probability of the observed sequence of behaviors provided that is the correct choice , ( 24 ) one can apply repeatedly to obtain ( 25 ) This expression substitutes the assumption of independence in Eq . 6 . Each of the terms in the product is simply the probability that the individual makes its decision , given the previous decisions , and also given that is the correct choice . This result was expected since if we look at the tree in Fig . 8A we see that the probability of reaching a given state is simply the product of the probabilities of choosing the adequate branches in each step . So the problem reduces to computing the individual decision probabilities . We assume in the following that these probabilities are calculated by the focal individual by assuming that all animals use the same rules to make a decision . The rule for the focal individual is , as in previous sections , ( 26 ) where the non-social and social terms are ( 27 ) and ( 28 ) respectively , and where we have added subscript to , and to reflect that they apply to the focal individual , that makes its decision in the place . The assumption that all animals apply the same rules translates into the following . To apply an equation like Eq . 26 but on a different individual ( say , individual ) it is necessary to know the non-social information . Remember that all these computations are made from the point of view of the focal individual , and obviously the focal individual does not have access to the non-social information of the other individuals . It may seem reasonable for the focal animal to assume that all the other individuals have the same non-social information ( ) , but this would result in no social behavior at all ( if the other individuals have the same non-social information , their behaviors will not give any extra information ) . Instead , one can assume that the other individuals may have a different non-social information , . Furthermore , this non-social information depends on which is the best choice , because if for example is the best choice the other individuals have some probability of detecting it , and therefore their non-social information will be on average biased towards . We approximate this average bias by assuming that , if ( ) is the best choice , all the other individuals will have non-social information ( ) that will bias the decision towards ( ) . It is therefore the same to assume that ( ) is the best option as to assume that all the other individuals have non-social information ( ) . Therefore , for the probabilities of individual behaviors in Eq . 25 , we have that ( 29 ) where now applies to the individual , so we can compute this probability simply by applying Eq . 26 to the individual , ( 30 ) where ( 31 ) Then , if we denote , we have that ( 32 ) These are the individual probabilities needed in Eq . 25 , that takes into account the correlations among the other individuals . So we can already calculate using Eq . 28 , ( 33 ) Eqs . 30 and 33 have a recursive relation , because we need the probabilities up to step to compute , and then we need to compute the probabilities in step . At the beginning no individual has made any choices , so we start with and work recursively from there until we obtain the probabilities for individual , that allow to compute . Then , we can already use Eq . 26 to compute the decision probability of the focal individual , this time using its actual non-social term ( which is 1 for the symmetric cases , and fitted to the data in the non-symmetric case ) . The equations above constitute the model taking into account dependencies . The new parameters of this model are and , which substitute and in the previous models , so the number of parameters is exactly the same . In the symmetrical case we must have that , so the model has a single parameter . For the non-symmetrical case these parameters may be independent of each other , but we find good results even assuming that they are not , as was the case for the simplified model . So for simplicity we always assume that ( 34 ) For the case with different replicas at each side , each of them has a different value of , thus making one replica more attractive than the other . The new model also matches very well with the experimental data discussed in this paper . Results for the case of two different replicas are shown in Fig . 9 , for the symmetric case in Fig . S4 and for the case with predator in Fig . S5 . Fits are robust , and all cases are well explained by the model with the same value of , Fig . S6 . See Figs . S1 , S2 , S3 for a comparison of all models . We now ask how different is the model including dependencies from the model that neglects them . To compare the two models , we plot the probability of going to as a function of for the new model , as we did in Fig . 1B for the old one . The inclusion of dependencies has the consequence that the probability of going to does not depend only on , since now different states with the same may have different probabilities . Therefore , when we plot the probability of going to as a function of we obtain different values of the probability for each value of . This is shown by the black dots in Fig . 8B , where the size of the dots is proportional to the probability of observing each state when starting from 0∶0 . The red line shows the average probability for each , taking into account the probability of each state . Both the dots and this line correspond to , which is the one that fits best the data . The green line corresponds to the probability for the simplest model neglecting dependencies , with the value that best fits to the data ( ) . This line is close to the mean probability for the new model and to the values with highest probability of occurrence , so the simple model is as a good approximation to the model with dependencies . We find an interesting prediction of the new model: There are some states in which the most likely option is to choose the option chosen by fewer individuals ( for example , note in Fig . 8D that some points with are above 0 . 5 ) . This surprising result comes from the fact that , as more fish accumulate at one side , their choices become less and less informative ( because it is very likely that they are simply following the others ) . If then one fish goes to the opposite side , its behavior is very informative , because it is contradicting its social information . This effect can be so strong that it may beat the effect of all the other individuals , resulting in a higher probability of following this last individual than all the individuals that decided before .
We have shown that probabilistic estimation in the presence of uncertainty can explain collective animal decisions . This approach generated a new expression for each experimental manipulation , Eq . 17–22 , and was naturally extended to test for more refined cognitive capacities , Eq . 23 . The model was found to have a good correspondence with the data in three experimental settings ( Figs . 2 , 6 and 7 ) , always giving a good fit with the social reliability parameter in the interval 2–4 . Indeed , all the data have a very good fit with ( Figs . 2 , 6 and 7 , green lines ) . According to Eq . 9 , this value for has the interpretation that , for the behaviors relevant for these experiments , the fish assume that their conspecifics make the right choice 2 . 5 times more often than the wrong choice . For the data used in this paper , previous empirical fits used more parameters [42] ( Figs . S1 , S2 , S3 , blue line ) , and added more complex behavioral rules when the basic model failed [43] ( Fig . S2 , blue line ) . Our approach thus gains in simplicity . It also finds an expression for each set-up with expressions for complex set-ups obtained with add-ons to those of simpler set-ups , making the model scalable and easier to understand in terms of simpler experiments . Also , taking the models as fits to experimental data , the bayesian information criterion finds our models to be better than those in [42] and [43] ( see captions in Figs . S1 , S2 , S3 for details ) . Collective animal behavior has been subject to a particularly careful quantitative analysis . Previous studies have given descriptions led by the powerful idea that complex collective behaviors can emerge from simple individual rules . In fact , some systems have been found empirically to obey rules that are mathematically similar or the same as some of the ones presented in this paper , further supporting the idea that probabilistic estimation might underlie collective decision rules in many species . For example , a function like the one in Eq . 17 has been used to describe the behavior of Pharaoh's ant [61] , a function like Eq . 22 for mosquito fish [62] , and a function like the one in the right-hand-side of Eq . 22 for meerkats [63] . But despite the importance of group decisions in animals , little is known about the origin of such simple individual rules . This paper argues that probabilistic estimation can be an underlying substrate for the rules explaining collective decisions , thus helping in their evolutionary explanation . Also , this connection between patterns in animal collectives and a cognitive process helps to explain the similarities that exist between decision-making processes at the level of the brain and at the level of animal collectives [64] , [65] . Our model is naturally compatible with other theories that use a Bayesian formalism to study different aspects of behavior and neurobiology , thus contributing to a unified approach of information processing in animals . For example , it may be combined with the formalism of Bayesian foraging theory [18] , through an expansion of the non-social reliability . Related to this case , a very well studied example of use of social information is the one in which one individual can observe directly the food collected by another individual [29]–[33] . In this case the social information is as unambiguous as the non-social one , so in this case both types of information should have a similar mathematical form [29]–[33] . This is consistent with our model , that in this case will give a similar expression for and . Other kinds of social information ( such as another individual's decision to leave a food patch or choices of females in mating [41] ) would enter naturally in our reliability terms . In discussing these and similar problems , it has been proposed that animals should use social information when their personal information is poor , and ignore it otherwise [25] , [26] , [41] . Our model provides a quantitative framework for this problem , predicting that social information is always used , only with different weights with respect to other sources of information . Bayesian estimation is also a prominent approach to study decisions in neurobiology and psychology [3]–[17] and it would be of interest to explore the mechanisms and role played by the multiplicative relation between non-social and social terms . Our approach also makes a number of predictions . For example , it derives the probability of choosing among options ( see Eq . S16 of the Text S1 ) , that for the symmetric case reduces to ( 35 ) predicted also to fit the data for cases with options . We also predict a quantitative link between estimation and collective behavior . The parameters and in our model are in fact not merely fitting parameters , but true experimental variables . Manipulations of and should allow to test that changes in collective behavior follow the predictions of the model . A counterintuitive prediction about the manipulation of is that external factors unrelated to the social component can nevertheless modify it . For example , a fish that usually finds food in a given environment should interpret a sudden turn of one of his mates as an indication that it has found food , and therefore will follow it . In contrast , another fish that is not expected to find food in that environment will not interpret the sudden turn as indicative of food , and will not follow . Thus , the model predicts that the a priori probability of finding food ( to which each fish can be trained in isolation ) will modify its propensity to follow conspecifics . An alternative approach that would not need manipulation of the reliabilities would consist in showing that the probability of copying a behavior increases with how reliably the behavior informs about the environment . We can also extend the estimation model to use , instead of the location of animals , their predicted location . We would then find expressions like the ones in this paper but for the number or density of individuals estimated for a later time . Consider for example the case without non-social information , described in Eq . 17 for two options and in Eq . 35 for more options . We can rewrite these equations as with one of the options and is the normalization , , where is the number of options . Then , we would have for the continuous case using prediction . Future positions at times ( where does not need to be constant ) in terms of variables at present time would be given by for animals moving at constant velocity . Consider then a simple case of an animal located at and estimating the future position of a compact group at and moving with velocity . The deciding animal would be predicted to move with a high probability in the direction . Estimation of future locations thus naturally predicts in this simple case a particular form of ‘attraction’ and ‘alignment’ forces of dynamical empirical models [46] , [66] as attraction to future positions , but in the general also deviations from these simple rules .
The estimation rules presented in this paper refer to a single individual . To simulate the behavior of a group , we use the following algorithm: The current individual decides between and . After the decision , we recompute the relevant parameters of the model and use the new values for the next deciding individual . The undecided individuals are only those that are waiting for their turn to decide . We tested an alternative implementation in which individuals may remain undecided or in which two individuals can decide simultaneously , obtaining no relevant differences . For the case of the model including dependencies , the model always starts at state 0∶0 , with . Most experiments have initial conditions in which several replicas are already going to either side , and the fish have no information about the path followed to reach this state . In these cases , we average the probabilities of all the paths that might have possibly led to the initial state to compute the initial value of . Protocol S1 and Protocol S2 , contain Matlab functions that run the models ( extensions of the files must be changed from . txt to . m to make them operative ) . Protocol S1 corresponds to the model without dependencies , and Protocol S2 corresponds to the model with dependencies . These functions have been used to generate all the theoretical results presented in this paper . We computed log likelihood as the logarithm of the probability that the histograms come from the model . We searched for the model parameters giving a higher value of log likelihood , corresponding to a better fit . This search was performed by optimizing each parameter separately ( keeping the rest constant ) and iterating through all parameters until convergence . In all cases convergence was rapidly achieved . We performed multiple searches for best fitting parameters starting from random initial conditions and always found convergence to the same values , suggesting there are no local maxima . Indeed , we observed that log-likelihood is smooth and with a single maximum in all the cases with 1 or 2 parameters ( see Fig . 3 for an example ) . For model comparison we used the Bayesian Information Criterion ( BIC ) [59] , [60] , which takes into account both goodness of fit and the number of parameters . According to this criterion , among several models that have been fitted to maximize log likelihood , one should select the one for which ( 36 ) is largest , where is the logarithm of the probability that the data comes from the model once its parameters have been optimized to maximize this probability , is its number of parameters of the model and is the number of measurements ( which in our case is the same for all models ) . More intuitive than the direct values in Eq . 36 are the BIC weights , defined as [60] ( 37 ) when we assume that all models are a priori equally likely . Roughly speaking , can be interpreted as the probability that model is the most correct one [60] . We used BIC to compare different versions of our model , and also to compare our model with those of references [42] , [43] ( see Figs . S1 , S2 , S3 ) . The models of refs . [42] , [43] were originally fitted by minimizing the mean squared error instead of by maximizing logprob . For this reason , they score very poorly in BIC with their reported parameters . For this reason , we re-optimized for maximum logprob all their model parameters ( these parameters are , using the notation of refs . [42] , [43] , , , , and , with only applicable in the case of predator present ) . For the case of different replicas going to each side , parameter takes a different value for each row in the figure , adding up to 10 parameters . The model in ref . [43] is computationally expensive , so it is not feasible to re-optimize these many parameters . Therefore , we treated them as if they were independently measured: we fixed in each case so that the results of the trials with a single individual matched exactly the model's prediction ( as reported in [43] ) . We also followed this procedure with the ratios of our model without dependencies , and the pairs in our model with dependencies . Then , we performed BIC taking into account neither these parameters ( the ratios and the pairs ) nor the data from trials using single individuals .
|
Animals need to act on uncertain data and with limited cognitive abilities to survive . It is well known that our sensory and sensorimotor processing uses probabilistic estimation as a means to counteract these limitations . Indeed , the way animals learn , forage or select mates is well explained by probabilistic estimation . Social animals have an interesting new opportunity since the behavior of other members of the group provides a continuous flow of indirect information about the environment . This information can be used to improve their estimations of environmental factors . Here we show that this simple idea can derive basic interaction rules that animals use for decisions in social contexts . In particular , we show that the patterns of choice of Gasterosteus aculeatus correspond very well to probabilistic estimation using the social information . The link found between estimation and collective behavior should help to design experiments of collective behavior testing for the importance of estimation as a basic property of how brains work .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"animal",
"behavior",
"theoretical",
"biology",
"ecology",
"biology",
"zoology",
"neuroscience",
"behavioral",
"ecology"
] |
2011
|
Collective Animal Behavior from Bayesian Estimation and Probability Matching
|
Comparisons between diverse vertebrate genomes have uncovered thousands of highly conserved non-coding sequences , an increasing number of which have been shown to function as enhancers during early development . Despite their extreme conservation over 500 million years from humans to cartilaginous fish , these elements appear to be largely absent in invertebrates , and , to date , there has been little understanding of their mode of action or the evolutionary processes that have modelled them . We have now exploited emerging genomic sequence data for the sea lamprey , Petromyzon marinus , to explore the depth of conservation of this type of element in the earliest diverging extant vertebrate lineage , the jawless fish ( agnathans ) . We searched for conserved non-coding elements ( CNEs ) at 13 human gene loci and identified lamprey elements associated with all but two of these gene regions . Although markedly shorter and less well conserved than within jawed vertebrates , identified lamprey CNEs are able to drive specific patterns of expression in zebrafish embryos , which are almost identical to those driven by the equivalent human elements . These CNEs are therefore a unique and defining characteristic of all vertebrates . Furthermore , alignment of lamprey and other vertebrate CNEs should permit the identification of persistent sequence signatures that are responsible for common patterns of expression and contribute to the elucidation of the regulatory language in CNEs . Identifying the core regulatory code for development , common to all vertebrates , provides a foundation upon which regulatory networks can be constructed and might also illuminate how large conserved regulatory sequence blocks evolve and become fixed in genomic DNA .
Comparisons between diverse vertebrate genomes have uncovered thousands of very highly conserved sequences that show little or no evidence of coding for proteins [1]–[3] . Many of these sequences are found in the proximity of genes that co-ordinate early development , and an increasing number have been shown to function as enhancers in both zebrafish [2] and mouse embryos [4] , [5] . Fish-mammal comparisons have allowed the detection of functional elements that are likely to be of importance to all bony vertebrates . Recently , the extraordinary level of conservation displayed by CNEs has been shown to extend through to the Chondrichthyes ( sharks , rays and chimaeras ) [3] dating these sequences prior to the divergence of cartilaginous and bony fish , over 500 million years ago [6] . Furthermore , the repertoire of CNEs in the genome of the elephant shark ( Callorhinchus milii ) generally encompasses those conserved between mammals and teleost fish [3] indicating that these sequences evolved and became fixed very early in the vertebrate lineage . By contrast , vertebrate CNEs were not detected at the sequence level in any invertebrate genomes [2] , including the urochordates , which are now considered to be the closest relatives to vertebrates [6] , [7] . However , analyses suggest that urochordates are evolving very rapidly [6] , [8] , [9] which may have resulted in loss of CNE-like sequences in this lineage . More recently , the sequencing of the genome of the cephalocordate , amphioxus ( Branchiostoma floridae ) , has uncovered traces of the origins of a very small number of vertebrate CNEs [10] , [11] , further fuelling speculation that their evolution into a large set of highly conserved non-coding sequences represents a key , defining characteristic of the ancestral vertebrate body plan [2] . Invertebrate groups have been found to possess their own sets of CNEs [12] , [13] and interestingly there is a correlation between the classes of genes around which both vertebrate and invertebrate CNEs cluster suggesting parallel evolution of CNE networks [13] . Consequently , whereas the steady evolution of coding sequences can be charted readily across the invertebrate/vertebrate boundary , the evolution of CNEs appears to have been shaped in an entirely different way , with some form of rapid expansion very early in vertebrate evolution . In order to begin to investigate the origin and evolution of CNEs in the early vertebrate lineage we focus here on the lamprey , Petromyzon marinus . The lamprey is an agnathan ( jawless ) fish , and represents , along with hagfish , the earliest diverging vertebrate group , separating from the jawed ( gnathostome ) lineage between 550 and 650 million years ago [6] , [14] , [15] . It can be assumed that the common ancestor of agnathans and gnathostomes possessed the developmental mechanisms and characteristics that remain shared by both groups . After their divergence further innovations specific to each lineage would have evolved . As such , comparative studies of the lamprey and its genome will complement existing efforts that examine the evolution of developmental gene regulatory networks [16] and provide valuable insights into the ancestral vertebrate state and the common underlying sequences that determined it . It is becoming increasingly accepted that the early vertebrate genome was shaped by large-scale/whole genome duplication events , with gnathostomes possessing multiple paralogous copies of genes that are single copy in invertebrates such as amphioxus [10] . The timing of these events is still unclear , but there is evidence to suggest that the emergence of lampreys coincided with one , or both , of the duplications [10] and that the first round occurred before , and the second shortly after , the divergence of lampreys and gnathostomes [17] . However , without an assembled lamprey genome , this is difficult to resolve . Although most CNEs appear to be unique within the human genome , we recently identified a fraction that are duplicated and found in the vicinity of paralogous genes [18] . Many of these duplicated CNEs ( dCNEs ) are found in all bony vertebrates and therefore it is likely that they were present ( as single copies ) in an ancestral genome prior to at least one of the whole genome duplication events early in the vertebrate radiation . This predicts that at least some CNEs might be found in lampreys . It is currently unknown if lampreys have undergone any large scale lineage-specific duplications , although analysis of the HOX cluster suggests that at least this region has duplicated in agnathans independently of gnathostomes [19] . A growing number of CNEs have been tested for their ability to up-regulate gene expression in vivo in a number of different animal systems , including zebrafish [2] , [18] , [20]–[24] , medaka [25] , mouse [4] , [5] , [22] , [26] , [27] , chick [28] and Xenopus [21] . Generally , a reporter gene under the control of a minimal promoter is injected alongside a CNE , and embryos are screened for tissue specific up-regulation . Results from these assays can then be compared with the endogenous pattern of expression of the gene ( usually generated through in situ hybridisation ) with which the CNE is thought to be associated . Whilst there is a generally good agreement between the two types of expression analyses , the CNE reporter assays often define additional regions of expression not detected for the gene itself [2] , [6] , [21] . Generally , CNE sequences have been tested in relatively closely related model systems ( e . g . human sequences in mouse , Fugu sequences in zebrafish ) but given their ubiquity in vertebrates , and to support the notion that they represent a common and fundamental regulatory language , it is critical to establish that CNEs from highly divergent vertebrates function in a similar manner . Recently , the genome of the sea lamprey , Petromyzon marinus , has been targeted for a high quality draft and assembly . As a result , over 18 million whole genome shotgun ( WGS ) reads , equivalent to approximately 6-fold coverage of the genome , have been made publicly available . Here , we are able to exploit the lamprey data to investigate an ancient epoch prior to , or coinciding with , the emergence of the large repertoire of CNEs currently observed throughout the gnathostome lineage and identify those ancient CNEs that are common to all extant vertebrates . We amplify representative CNEs from both the human and the lamprey genomes , and test them for enhancer activity in zebrafish embryos , thereby assaying regulatory potential spanning over a billion years of evolutionary divergence .
We searched the lamprey whole genome shotgun sequence with a dataset of 1205 CNEs from 13 gene loci that are distributed across 27Mb of the human genome . These regions include 108 duplicated CNEs ( dCNEs ) , the most ancient CNEs for which we have a date of origin [18] and 46 ultraconserved elements ( UCEs ) , sequences that retain 100% identity over at least 200bp between mouse , rat and human genomes [29] ( see Methods and Table S1 ) . 73 lamprey CNEs were identified ( Table S2 ) , including hits to 38 dCNEs and 14 UCEs , with matches to gnathostome CNEs in all but two of the gene regions ( Table 1 ) , implying a widespread distribution of CNEs across developmental regulators in lamprey . Interestingly , the proportion of lamprey hits to dCNEs ( 38/73 = 52% ) is considerably higher than expected ( dCNEs only make up 9% of the total CNEs across the 13 regions ) demonstrating nearly six-fold enrichment for this particular set of ancient elements ( Table 1 ) . Although UCEs are also enriched ( 30 . 4% detection rate ) it should be noted that this is largely due to the fact that 10/14 of the UCEs are also dCNEs . The high retention rate of dCNEs is of particular note , as it corroborates the notion that an ancient subset of CNEs were present prior to the divergence of the lamprey and gnathostome lineages . The length of the lamprey matches with human CNEs is on average considerably lower than comparisons within gnathostomes ( Table S3 ) . Lamprey sequences match on average only 47% of the length of CNEs defined through alignments between mammals and fish using the same BLAST [30] parameters . However , sequence conservation remains high across these shorter core regions , with an average identity of 80% , compared with approximately 90% between fish and mammals ( Figure S1 and Figure S2 ) . We searched the pre-Ensembl lamprey release ( PMAL3 ) for assembled contigs that encompass more than one lamprey CNE . The longest ( contig 1709 ) encompasses 33 . 7kb of contiguous lamprey sequence with just three very short unresolved regions . This contig not only contains a number of CNEs , but it also covers an uncharacterised gene , C15orf41 , which lies directly downstream of the Meis2 gene in the human and other vertebrate genomes , oriented in a tail to tail fashion ( Figure 1A ) . The identified lamprey CNEs reside directly adjacent to , or within the introns of , the C15orf41 gene , and in the human genome form part of a much larger , complex regulatory architecture that covers nearly 3 . 5Mb of the Meis2 locus , containing over 200 CNEs ( Figure 1A ) . Multiple alignment approaches using the conserved coding exons as anchors throughout [31] , reveal the organisation of this region , allowing us to identify which gnathostome CNEs are detectable in the lamprey genome . Given the conserved positional relationship of CNEs in all other vertebrates , we assume that if lamprey CNEs are present , they will also be co-linear . From Figure 1B it is apparent that while some CNEs are clearly detectable in the lamprey genome , others are absent , or at least not detectable using sequence similarity . Furthermore , BLAST searches of the WGS reads do not identify these CNE sequences elsewhere in the lamprey genome . The pattern of lamprey CNE occurrence is intriguing; a majority of CNEs across a particular region are present , whereas in the neighbouring region , which harbours some large gnathostome CNEs , no lamprey sequence homology is detected . One of the most highly conserved CNEs in our data set is found within the sixth intron of the human EBF3 gene region and extends to 491 bp at greater than 90% identity between Fugu and human . The corresponding region identifiable in the lamprey genome is just 211 bp long ( at 79% identity ) . A second representative CNE , approximately 84kb upstream of the human PAX2 gene is 85% identical across 425 bp between Fugu and human , but only 123 bp is conserved in lamprey ( at 73% identity ) . We hypothesised that these much shorter but persistently conserved regions of sequence conservation , retained across the extremes of the vertebrate lineage , might comprise critical core cis-regulatory modules common to all vertebrates . In order to test this we amplified the core regions from the EBF3 and PAX2 CNEs from both the human and lamprey genomes , and used our functional assay [2] to test their ability to up-regulate GFP reporter expression in zebrafish embryos . EBF3 is a member of the COE ( Col-Olf-Ebf ) gene family , which consists of the vertebrate orthologues of the Drosophila collier gene [32] and the C . elegans unc-3 gene [33] . Present as a single copy gene in Amphioxus [34] , there are four family members in man and mouse , with considerable conservation of function across animal lineages . EBF3 is expressed in the developing central nervous system ( CNS ) [35] and adult brain and recent evidence suggests it acts as a tumour suppressor [36] . Although little is known of the precise function of the EBF3 gene , it appears to be a key regulator of neurogenesis , associated with the maturation of specific neuronal cell types in the spinal cord and brain [32] . On day two of zebrafish embryo development , at 24–30 hours post-fertilisation ( hpf ) , both the lamprey and human elements direct expression of a GFP reporter gene predominantly in the forebrain , with the human element directing particularly specific expression ( Figure 2 ) . However , expression is more widespread on day three , 48–54 hpf , encompassing the spinal cord as well as the fore- , mid- and hindbrain regions . Interestingly , both the lamprey and the human EBF3 elements appear to up-regulate GFP expression specifically in a particular set of neurons in the zebrafish embryo ( Figure 3 ) , demonstrating precise functional conservation between the lamprey element and the equivalent core region of the human CNE , when assayed in a teleost fish model . PAX2 is thought to be the most ancient member of the vertebrate PAX2/5/8 family of genes that arose from early duplications in , or prior to , the vertebrate lineage [37] . It is a transcription factor that plays an important role during the development of the eye , ear , pronephros and midbrain-hindbrain boundary . It is also involved in interneuron specification in the hindbrain and spinal cord [38] . In lamprey , it has been demonstrated that the expression pattern of pax2 in each region is remarkably similar to that of gnathostomes [39] . Injection of PAX2 elements derived from both lamprey and human genomic DNA resulted in GFP expression in additional regions to the CNS on day two , but with a more organised pattern of neuronal expression , particularly the hindbrain , on day three ( Figure 2 ) . Once again , similar sets of neurons are up-regulated by the human and lamprey elements ( Figure 3 ) . In all four cases the core elements up-regulate GFP expression in a temporal and tissue specific manner that reflects aspects of the endogenous pattern of expression of the associated gene . Furthermore , there is excellent spatial and temporal concordance between the lamprey and human elements .
The ancestry of vertebrate CNEs is clearly identifiable through their abundance in sharks [3] , whereas the identification of duplicated CNEs ( dCNEs ) suggests that some elements were present even earlier [18] . Indeed , within the invertebrates , and despite its earlier radiation from the vertebrate lineage than Ciona , the amphioxus genome contains traces of a very small number of CNEs [10] . A total of 56 non-coding , non-repetitive amphioxus sequences were identified with similarity to the human genome [11] , displaying on average 64% identity across regions of 50–70bp . Although only a few of these elements overlap with previously identified vertebrate CNEs , they associate once again with genes that regulate development , indicating that the very beginnings of vertebrate CNEs existed in the chordate ancestor of amphioxus and vertebrates . Just 5 of the proposed amphioxus CNEs fall within gene loci covered in our study ( 3 close to BARHL2 , one near BCL11A and one near ZNF503 ) , and none of these have sufficient sequence identity or length to be detected in any vertebrate genome , including lamprey , using unaligned genome wide searches . Consequently , critical questions remain as to when , how and why such a large repertoire of very highly conserved sequences became fixed in the chordate lineage . Given their proposed regulatory involvement in development , it is essential that there is an understanding of how CNEs evolved and to what extent they contribute to the gene regulatory networks ( GRNs ) responsible for orchestrating the patterning of the early vertebrate embryo . Unfortunately , there is a dearth of extant organisms that occupy an evolutionary position between the chordate radiation and the emergence of jawed vertebrates . Only lampreys and hagfish survive from this period , and it is therefore both timely and convenient that the genome sequence of the sea lamprey , Petromyzon marinus , is being generated . Currently , there is only a limited assembly , yet the public availability of over 18 million sequence traces allows a preliminary foray into the CNE architecture of the genome . CNEs from 11 out of the 13 regions chosen for this study have matches in the lamprey whole genome shotgun reads , indicating that CNEs are widespread in lamprey . Only the DACH and BARHL2 gene CNEs gave no matches against the lamprey sequence , although there is evidence that DACH-like and BARHL-like coding sequence is present in lamprey . It is not yet apparent how uniform the WGS sequence coverage is for the lamprey genome although our mapping of over 20 , 000 lamprey ESTs back to the WGS reads predicts greater than 95% coverage ( Methods ) . Nevertheless , gaps may exist to account for the absence of CNEs around some genes . It is evident that gnathostome CNEs have evolved extremely slowly , given their near identity between sharks and mammals [3] , species which are thought to have diverged over 500 million years ago [6] , [15] . This may be a reflection , given their proposed function , of a stable and shared gene/genome copy number and a common bilateral body plan . Indeed , CNEs within the gnathostomes show most variation in teleost fish , a lineage that has undergone its own genome duplication event [40] . Lamprey CNEs on the other hand , whilst widespread throughout the genome , are considerably shorter and somewhat less well conserved at the nucleotide level than their gnathostome counterparts ( Table S2 , Figure S1 , Figure S2 , Figure S3 ) . This is in part a function of the increased evolutionary divergence between agnathans and gnathostomes but it may also reflect a particularly dynamic and unstable era , early in the vertebrate radiation , during which one or possibly two whole genome duplications occurred . Given their unique developmental characteristics , we suggest that lampreys may have diverged at a time when genomes and CNEs were rapidly evolving , and the vertebrate body plan itself was taking shape ( Figure 4 ) . Hence the contemporary repertoire of lamprey CNEs retains only a core set of conserved regulatory signatures that act to specify common features within rather different body plans . This further supports the theory that lampreys separated from the vertebrate lineage prior to at least one whole genome duplication that occurred in the ancestor of all other vertebrates [10] , [17] . The strong enrichment for dCNEs in our lamprey data is interesting . Firstly it confirms that dCNEs are ancient , being present in the ancestral vertebrate genome prior to the divergence of lampreys and gnathostomes . The fact that over half the detected lamprey CNE sequences are dCNEs in gnathostomes also supports the notion that the smaller repertoire of CNEs in lamprey is due to the separation of its lineage during a time when CNE sequences were evolving and becoming fixed , such that the stem group of the lamprey lineage only had a relatively small number of CNEs for lamprey to inherit . The alternative scenario , in which all CNE sequences were present in the ancestor to both the agnathans and gnathostomes followed by considerable divergence only in agnathans , struggles to explain the high ratio of dCNEs present in the lamprey genome ( i . e . why should the dCNEs not evolve at the same rate as non-dCNEs , thereby preserving the ratio of CNE:dCNE ) . Additionally , this result suggests that the emergence of many CNEs in vertebrate genomes coincided with , and was perhaps facilitated by rounds of whole genome duplication . The identification of the C15orf41gene contig allows an insight into the CNE landscape of the lamprey genome . At one end of the gene region , a majority of gnathostome CNEs are detectable in lamprey , yet in the other half of this region , there are no lamprey CNEs present . This could indicate that sets of CNEs co-operate locally across relatively large distances in order to function as modules , something that has not been considered to date , although without further examples , it is not possible to draw any more general conclusions . We chose two very highly conserved CNEs for functional analysis . The first , a CNE near the EBF3 gene , is over 90% identical across almost 500bp in jawed vertebrates , yet the lamprey identity extends to just over 200bp across the centre of this CNE . We reasoned that this shorter region , given its presence across all vertebrates , might be able to drive reporter gene expression in zebrafish embryos and might therefore define a core region of the human element . Both the human core and the lamprey element drove very specific , near identical , patterns of GFP expression in the developing zebrafish brain , confirming that the shorter region of reduced conservation still retains the basic instructions for this enhancer function . A similar result was obtained for a second CNE , from the PAX2 region , which shows an even more dramatic reduction in length in lamprey , being less than 30% of the length of the gnathostome CNE . Up to now , the long length and high sequence identity of CNEs has made them recalcitrant to analyses that aim to identify regulatory language encoded within them . The lamprey sequence , combined with functional assays , provides a new angle to this approach and may identify important functional motifs within CNEs . The shorter regions of identity defined by the lamprey sequences appear , at least in the case of the two elements tested here , to be sufficient to drive a highly specific pattern of reporter gene expression in a limited number of structures . By contrast , the majority of the expression data generated from CNEs defined by fish-mammal comparisons , tends to be less specific and often encompasses a range of tissues [2] , [5] , [21] . Thus the flanking regions of a gnathostome CNE , not conserved in lamprey , might encode additional functional signatures which have evolved since the agnathan divergence , but which are still common , and therefore conserved , within all bony vertebrates . This suggests that CNEs are multi-functional modules that are built from the middle out , and might explain the unusual size of CNEs for cis-regulatory sequences . CNEs found in common between lampreys and other vertebrates are likely to represent the most ancient regulatory instructions for the ancestral vertebrate body plan . The completion and assembly of the lamprey genome will provide an outstanding resource from which many facets of our vertebrate ancestry may be traced . Investigation of the role and function of lamprey CNEs on a whole genome scale will provide a critical starting point for the building of gene regulatory networks and for understanding the most fundamental language of vertebrate development .
In a previous analysis , we identified a set of 256 human-Fugu CNEs that have been duplicated and retained in the vicinity of paralogous genes . Approximately two thirds of these duplicated CNEs ( dCNEs ) appear to have arisen very early in the vertebrate lineage , pre-dating a whole genome duplication event that occurred before the divergence of fish and tetrapods making them a useful starting point for identifying conserved elements in lamprey . We selected the 13 gene regions containing the largest numbers of dCNEs ( 3 or more ) as identified in [18] , and sensitively aligned them using the program Multi-LAGAN [31] using human , mouse , rat ( or dog if either mouse or rat not available ) and Fugu regions with a window size of 40bp and a cut-off of 65% identity ( data available at http://condor . fugu . biology . qmul . ac . uk/ ) . Regions extend to the nearest neighbouring gene , outside of which there are no CNEs . A further filter was applied to eliminate smaller elements by using an “LPC” score of > = 50 , a score based on the sequence length and identity across the four species ( http://condor . fugu . biology . qmul . ac . uk/tutorial . html#lpc ) . In order to not have a redundant CNE set ( containing both copies of a dCNE ) , only the region with the highest total number of CNEs from each set of regions containing paralogous genes was selected for further analysis . SALL1 is included in the IRX5 region and so was not analysed separately . The resulting 13 gene regions encompass a total of 27 Mb of sequence and contain a total of 1205 elements ( Table S1 ) . The 1205 CNEs identified were searched against the chicken and Fugu genomes in Ensembl , and against the lamprey reads ( downloaded from the NCBI trace server: http://www . ncbi . nlm . nih . gov/Traces/trace . cgi ) using BLAST [30] ( -W 8 –q -1 –e 5e-4 ) . A parallel analysis of the lamprey reads using discontiguous MegaBLAST [41] gives essentially identical results . Details of sequence hits can be found in Table S2 . Contamination was suspected in the Petromyzon data as some hits were identical to chicken when compared to all vertebrates in Ensembl using BLAST . The full length clones for each of the Petromyzon hits were retrieved and compared to the chicken genome using BLAST ( default parameters ) and reads that had the highest match to chicken , with over 90% identity across most of their length and extending into non-conserved regions were removed ( 35 lamprey hits ) . As the lamprey sequence is not assembled , many lamprey hits were to multiple redundant reads . Consensus sequences were generated for each hit if sequences were more than 95% identical . Overlapping hits were joined to make the longest contiguous hit . UCEs [29] were selected if they fall within one of the 13 regions , and then searched against the chicken , Fugu and lamprey genomes as for the CNEs using default BLAST parameters . The Petromyzon trace data was checked for genome coverage using EST sequences downloaded from NCBI . 20 , 732 EST sequences were BLAST searched against the trace data using stringent parameters ( word size = 25 , E-value = 1×10−10 ) and only 969 were found to have no significant hit , indicating that there is approximately 95% coverage . Core CNE sequences conserved between human and lamprey for the two selected elements ( Figure S3 ) were PCR amplified from the genomic DNA of each species . This was then purified using QIAquick columns ( Qiagen ) and co-injected with a GFP reporter into zebrafish embryos . This assay and the subsequent imaging are described previously [2] .
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Recent comparative analyses of vertebrate genomes has resulted in the identification of highly conserved non-coding sequences near genes that coordinate early development . Many of these sequences can activate gene expression and are thought to be important regulatory elements . Surprisingly , a large set of these long , near-identical sequences is found in every jawed vertebrate , including sharks , yet almost completely absent in non-vertebrates . This study looks for this set of sequences in the lamprey , a representative of our most distant vertebrate relatives , in order to determine when and how such a large set of important non-coding regulatory sequences became established in the genome . Although the lamprey divergence is only a little older than the divergence of cartilaginous fish ( including sharks ) , relatively few , and considerably shorter , conserved non-coding sequences are identifiable . Nevertheless , these shorter lamprey sequences are capable of driving gene expression in a precise spatial pattern in zebrafish embryos in the same way as the equivalent human elements . This analysis has shed light on the emergence of these regulatory sequences during early vertebrate evolution , at a time of whole-genome duplications and considerable morphological variation , consistent with a role for these sequences in directing gene regulatory networks for vertebrate development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/genomics",
"genetics",
"and",
"genomics/functional",
"genomics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"developmental",
"biology/developmental",
"evolution",
"computational",
"biology/genomics",
"evolutionary",
"biology/developmental",
"evolution"
] |
2009
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Early Evolution of Conserved Regulatory Sequences Associated with Development in Vertebrates
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The human gut harbours a large and genetically diverse population of symbiotic microbes that both feed and protect the host . Evolutionary theory , however , predicts that such genetic diversity can destabilise mutualistic partnerships . How then can the mutualism of the human microbiota be explained ? Here we develop an individual-based model of host-associated microbial communities . We first demonstrate the fundamental problem faced by a host: The presence of a genetically diverse microbiota leads to the dominance of the fastest growing microbes instead of the microbes that are most beneficial to the host . We next investigate the potential for host secretions to influence the microbiota . This reveals that the epithelium–microbiota interface acts as a selectivity amplifier: Modest amounts of moderately selective epithelial secretions cause a complete shift in the strains growing at the epithelial surface . This occurs because of the physical structure of the epithelium–microbiota interface: Epithelial secretions have effects that permeate upwards through the whole microbial community , while lumen compounds preferentially affect cells that are soon to slough off . Finally , our model predicts that while antimicrobial secretion can promote host epithelial selection , epithelial nutrient secretion will often be key to host selection . Our findings are consistent with a growing number of empirical papers that indicate an influence of host factors upon microbiota , including growth-promoting glycoconjugates . We argue that host selection is likely to be a key mechanism in the stabilisation of the mutualism between a host and its microbiota .
Many microbial species live on or are associated with epithelia of multicellular organisms . Examples range from plants and soil bacteria interactions in the rhizosphere where plant secretions affect the composition of bacterial communities [1] , [2] , through the light organs of marine animals in which specialised symbiotic bacteria are cultivated by the host [3]–[5] to many surfaces of the mammalian body [6] . Every human is home to roughly 100 trillion bacterial cells , collectively called the microbiota . The majority of these cells reside in the human gastrointestinal tract and , in particular , in the large intestine [7] . Here , bacteria can have beneficial effects such as the digestion of complex carbohydrates , colonisation resistance against invading pathogens , maturation of the adaptive mucosal immune system and immune cells , and the production of secondary metabolites , including vitamins [8]–[10] . However , these activities are not performed by all species , and the species composition of the microbiota in a healthy human is clearly distinct from bacterial communities in other environments [11] . Moreover , various diseases correlate with disturbances in the species composition of the microbiota [6] , [10] . It is clear then that the gut community has the ability to both help and harm the host . Despite the potential for harmful effects of the gut microbiota , the major class of interaction with the host appears to be one of mutualism , whereby both sides benefit from the interaction . The evidence for host benefits comes both from our understanding of the metabolic services that the gut microbiota provides and studies of germ-free animal models [6] , [12]–[18] . There is a growing literature on the evolution of mutualisms among species , both theoretical and empirical , which emphasizes a number of key factors required for the evolutionary stability of mutualisms [19]–[22] . Most relevant for the gut microbiota is the issue of having multiple genetically different individuals on one side of the mutualism ( microbes ) involved in a single interaction with the other ( host ) [19] , [20] , [22] . On the side with multiple genotypes , this can lead to the loss of helpful mutualistic genotypes , whenever non-helpful genotypes are more competitive . How is such potential conflict among partner species resolved in other systems ? Theory predicts a central role for partner choice: the selection of the best mutualistic partners by a focal species [22] . Moreover , partner choice is widespread in nature with evidence from many different systems [21] , [22] including leaf cutter ants and their fungus [23] , legumes and rhizobia [24] , and the mutualism between the bobtail squid and the luminescent bacterium Vibrio fischeri [25] . The predominance of partner choice mechanisms in other systems begs the question: What is the role of partner choice in the mammalian gut ? The sheer diversity of microbial species in the mammalian gut shows that hosts do not select for one or two partner species , as occurs in some mutualisms . In addition , there is a clear environmental effect on microbial species composition in the form of host nutrient intake [26] , [27] . Nevertheless , there are also a range of mechanisms by which vertebrate hosts affect their microbes more directly . In particular , the intestinal epithelium produces a wide range of secretions that help to maintain the barrier between the gut lumen and host tissues [9] , [28]–[31] . Central to this barrier is mucus secretion [32]–[35] that limits the direct access of bacteria to the epithelium [36] . The mucus becomes less dense , however , as it moves upwards away from the epithelium and bacteria grow in the upper layers that can feed on carbohydrates such as fucose , which the host adds to the mucus proteins [37]–[40] . The host also secretes a range of antimicrobials into the mucus , including defensins . Mucosal community composition has been studied in mice that lack an enzyme required for murine alpha-defensins but secrete human alpha-defensin [31] . The observed changes in community composition , in combination with other studies , led to the conclusion that defensins are essential regulators of intestinal microbial ecology ( for a review , see [41] ) . More work is now required to understand the exact role of defensins as a selective agent of the microbiota . In particular , the defensins of the small intestine have been the primary focus of research , and the effect of defensins in the large intestine is less well understood . Moreover , studies have shown that production and activation of defensins can themselves be dependent on the resident microbiota [42] , [43] , which opens the way for feedback loops between the host and its microbiota . In addition to defensins , the adaptive immune system also has the potential for selective effects . B-cell-derived immunoglobulin A ( IgA ) is considered the most likely host secretion to affect the localization , growth , and composition of the microbiota [29] , [44] , [45] . While it is clear that epithelial secretions can affect the microbiota , the primary role is often assumed to be as a simple barrier between the lumen and host tissues [46] , [47] . However , there is evidence that epithelial secretions differentially affect different strains and species . Sugars like fucose are more easily utilized by some microbial species than others [37] , [38] , [40] , and defensins and IgA have biased effects on the microbiota [14] , [29] , [31] , [45] . Such findings suggest that host secretions might help to control the composition of the resident microbiota [41] , [48] . Indeed , it has even been suggested that control over a wide array of non-pathogenic microbes is the primary reason why adaptive immunity first evolved [49] . Despite this , we understand very little about how the host might in practice select for particular microbial strains or species . Here we build a model to evaluate the potential of a host to select their microbiota . Ecologies like the mammalian gut are extremely complex dynamical systems and will require a central role for theoretical approaches if we are to dissect their complexity [50] , [51] . We have , therefore , developed a new model of host-associated microbial communities with the goal of bringing an evolutionary perspective to the study of host–microbiota interaction . Our model is relatively complex in that it includes realistic features such as mechanistic interactions among cells , spatial structure , and chemical gradients . However , it greatly simplifies the full complexity of the gut and is not intended as a complete description . We hope to show , nevertheless , that one can gain new understanding by the application of such simplifications to the problem of the host–microbiota interaction . In particular , our study reveals three key findings . First , we demonstrate the problem of multiple genotypes on one side of a mutualistic partnership , which renders the host–microbiota mutualism intrinsically fragile . Second , we show that a solution to this fragility is host selection: The epithelium–microbiota interface acts as a selectivity amplifier that can quickly shift the composition of the microbiota at the interface . Finally , we show that central to the selectivity is the provision of nutrients , and not just antimicrobial factors , by the host . Our results suggest a host's epithelium is a remarkable environment for partner choice , which is well suited to control bacterial community composition .
Our first goal is to evaluate the potential effects of differences in growth rates between strains under the simplest of conditions , and then build in increasing complexity in order to understand the key factors at play . We denote two bacterial competitors A and B , where B divides more rapidly than A ( Figure 1B ) . These two strains can either represent two members of one species that differ only in their interaction with the host or two different species that differ in other ways . As such , the model can be viewed from either an evolutionary ( genotypes within a species ) or ecological ( species within a community ) perspective . We return to the differences between these two scenarios in the Discussion . While we only model two strains , the model also approximates more diverse communities in which there is selection for a set of beneficial ecotypes where each “strain” would then represent multiple strains with similar phenotypes . These simple models show the potential power of competition in a host-associated microbial community . Figure 1C shows the increase in frequency of the fittest species over time in the epithelial community . Here and in the majority of subsequent figures , we show time as an axis . One reason we do this is because it is impractical to run all simulations until the final frequencies of the two strains have been established , especially for very small differences in fitness . Nevertheless , we expect in the majority of cases that one strain will ultimately dominate the system ( see following section and Text S1 for exceptions ) . Indeed , even for a modest difference in the growth rate among strains ( e . g . , 10% ) , a faster growing strain rapidly reduces the slower growing strain to negligible frequency in tens of generations ( Figure 1C ) . This corresponds to a few days for species like E . coli in a mammalian gut [52] . The constant removal of cells leads to thinning out and eventual eradication of the slower growing strain A near the epithelium ( Figure 1B , C ) . For larger difference in growth rate , such as B doubling at twice the rate of A , the eradication of A occurs in a few generations . This demonstrates the fundamental problem faced by a host when having multiple possible genotypes competing for a niche where a mutualistic species could exist . Whenever the most beneficial bacteria do not grow the fastest , competition between bacterial genotypes will lead to the loss of mutualistic strains within the host and thus a suboptimal microbiota composition ( Figure 2 ) . But is it possible that mutualistic species are , without exception , intrinsically faster growing than non-mutualist species ? If anything , the reverse is expected . Recent phylogenetic work shows that species from healthy guts tend to cluster with species from complex and relatively slow-growing communities [53] . By contrast , bacteria of infants and unhealthy guts tend to cluster with bacteria from fast-growing pioneer communities . In an entirely neutral host that does not exert any control over the bacterial composition , therefore , our model predicts that the mutualism between bacteria and a host is intrinsically fragile . So far , there is little spatial structure in our model , and we confirmed that our first results correspond to a well-mixed ( no spatial resolution ) ordinary differential equation model of evolutionary competition ( Text S1 , Figure S2 ) . We next extend the simulations to introduce more realism and calculate nutrient levels as a function of space and time . As cells divide , they use up nutrients such that nutrient concentration is depressed as one moves away from the nutrient source and into a group of dividing cells . These solute gradients are known to be important in natural bacterial groups and can have strong influences on community structure and composition [54]–[56] . In our case , there is the potential for two solute gradients , one from the lumen direction and one from the host epithelium direction . Our question is then: How do selective compounds from the epithelium and from the lumen influence the composition of this bacterial community ? Compared to the well-mixed case , the ability of nutrients to select for one strain over the other is reduced in the presence of solute gradients because not all cells have access to nutrients . With less reproduction , natural selection is less powerful . However , more striking is that lumen nutrients exert a much weaker selective effect than epithelial nutrients . This suggests a bias that may empower the host to affect the microbial communities growing on the epithelial surface ( Figure 3 ) . What causes this difference ? When the epithelium secretes nutrients , growth occurs at the base of the bacterial colony , which can affect the whole bacterial community . By contrast , lumen selection from the opposite direction preferentially affects cells that are about to be sloughed off , which limits the effect of lumen nutrients on cells at the base of the bacterial community . The inhibition of lumen selection only occurs beyond a certain thickness of the bacterial community ( Text S1 , Figure S2 ) . While it is difficult to measure the thickness of these bacterial communities in vivo , the range of thicknesses used in our model are consistent with the outer mucus layer of mice and rats [32] . A corollary of these results is that selection from the lumen should be weakened by growth near the epithelium . Hence , we further show that the addition of non-selective nutrients at the epithelium strongly inhibits lumen selection ( Figure S3B ) . By contrast , additional non-selective lumen nutrients do not affect the ability of epithelial nutrients to select for one strain over the other . Our model predicts that the physical layout of the gut epithelium environment allows host secretions to have disproportionately strong effects . We next test this by pitting the two sources of nutrients against one another . We assume that epithelial nutrients select for strain A , whereas lumen nutrients select for strain B , simulating a scenario in which the slow growing strain A would be lost without host selection . We present a conservative case in which epithelial nutrients are both less abundant and less selective . Specifically , lumen nutrient concentrations are five times higher than epithelial nutrients and the growth rate advantage of strain B on lumen nutrients ( 100% ) is always higher than or equal to the ( varied ) growth rate advantage of strain A on epithelial nutrients ( Figure 4A ) . Initially , strain B outgrows strain A as the former's overall growth rate advantage from the nutrients is much greater than that of strain A . However , the advantage of strain B diminishes as the microbial community grows and the effects of lumen nutrients and epithelial nutrients separate into distinct regions . This allows strain A to establish itself at the epithelial surface , and for all but the weakest selection by the host , strain B is eliminated eventually . In fact , in this example , the host need only provide a 5% growth rate advantage to strain A to counter the 100% growth rate advantage and five times higher concentrations that lumen nutrients provide to strain B . In summary , we find that a fast growing strain , which would rapidly replace slow growing strains in a well-mixed environment , can be eliminated by moderate counter-selection at the gut epithelium . This process is also effective when strain A is initially rare ( Figure S4 ) . Host selection at the epithelium , therefore , can effectively operate on an initially rare strain or species that is a minor member of a diverse community . We next tested the effects of epithelial selection using antimicrobials that tend to harm strain B more than strain A . In our model , selection with antimicrobials is slower than with nutrients , because the antimicrobials kill both strains , which reduces the rate at which one strain outgrows the other . Antimicrobials could , in principle , select more quickly than nutrients if they could instantly kill only one of the two strains . In the absence of such extreme selectivity , however , nutrient selection is more powerful . Indeed , for a wide range of conditions , we find that it is critical that the host also supplies nutrients ( Figure 4B ) . These do not need to be selective if selective antimicrobials are secreted . However , nutrients are required because the selective effects of antimicrobials will not permeate up through the community unless there is net positive growth at the epithelial surface . With antimicrobials alone , cell death can easily outweigh the birth of new cells at the epithelial surface because lumen nutrients are at their lowest concentrations . This means that although the host kills more cells of strain B than of strain A ( depending on the specificity of the antimicrobial ) , if growth is limited by nutrients at the epithelium , no net positive growth of strain A will occur either . For this reason , providing nutrients at the epithelial surface greatly widens the range of conditions under which antimicrobials can be used as a selectivity mechanism by allowing sufficient growth in this critical region . One challenging case for the host is when lumen nutrient levels are so great as to remove all nutrient gradients in the bacterial community and hence nutrients are available at high concentrations throughout the colony . However , even here , the host can use the epithelium as a selectivity amplifier ( Figures 5 , S5 ) . Selectivity amplification occurs whenever the host can maintain a thin region next to the epithelium that favours strain A over strain B and allows for net positive growth . With this , strain A will eventually take over the community even though it is counter-selected in the vast majority of the community ( Figure 5 ) . As a control , we show in Figure 5 how the same amount of solutes evenly distributed throughout the system would strongly select against A , which contrasts with the selectivity amplification seen when solute gradients are present . Finally , our results are robust to fluctuations in lumen nutrient concentrations , which are inevitable in organisms that have discontinuous food intake . As our model predicts , the effects of epithelial secretions are strongest during starvation periods , because lumen nutrient concentrations are highest after feeding [37] . However , implementing a feast–famine cycle that increases the variance in lumen nutrient concentration ( but does not affect the mean ) suggests that the net effect of these cycles is modest ( Figure S6 ) .
The gut is a competitive environment where the potential for high growth rates and population turnover means that slower-growing bacterial strains can be rapidly lost . This presents a problem for hosts . Natural selection of microbial phenotypes based upon intrinsic growth rate will disadvantage any microbes that grow more slowly ( Figures 1 , 2 ) . Our model predicts that a host can compensate for this effect using epithelial secretions that promote relatively slow-growing strains . Importantly , these effects do not require a highly specific selection mechanism akin to the full force of adaptive immunity . In our model , moderate selectivities that allow poorly growing strains to grow 5% to 10% faster at the epithelial surface are sufficient to reverse their fate . Epithelial selection may occur either through growth-promoting secretions or toxic growth inhibitors , but we find that growth promotion is often critical because selectivity amplification requires net growth of the microbial community near the epithelial surface . In this context , it is interesting that host epithelial secretions include growth promoters , particularly mucosal glycans [57] , [58] , in addition to the growth inhibitors of the immune system . Positive growth at the epithelium surface is important because it causes a flow of microbial cells towards the lumen that limits the effects of lumen nutrients on the community . Cells nearest the lumen are least likely to persist due to detachment and sloughing deeper in the lumen . In our model , this motion is driven by pushing and shoving of dividing bacterial cells . In the mammalian gut , the flow towards the surface is likely to be further promoted by the constant release of mucin polymers from the epithelial surface [57] , [59] . Furthermore , the diffusion of IgA—a key secretion known to influence the microbiota—is inhibited by mucins [34] . Our work suggests that this diffusion limitation will maximize not only the residence time of IgA in the gut but particularly the residence time close to the epithelium , where IgA will have an amplified effect . Our model requires that a host has mechanisms to differentially affect the net growth rate of different bacterial strains or species . Are such effects always possible , particularly in the face of bacterial coevolution to evade the negative effects of host selection ? The greatest challenge for host selection will occur when the strains involved are variants of a single species that differ only in their cooperativity towards the host ( as opposed to different species that differ in many ways ) . However , even here , host selection is possible if the host can select directly on the beneficial phenotype in the bacteria [60] , [61] . This appears to occur in the mutualism between bioluminescent Vibrio fischeri bacteria and the bobtail squid . It is thought that the squid creates an oxidizing environment in the light organ that selects for cells using the luminescence reaction because this reaction uses up oxygen [4] . We believe comparable mechanisms to those seen in the bobtail squid may exist in the gut . Mammalian cells produce glycoconjugates of a remarkable structural complexity and diversity , which are known to favour , or disfavour , the attachment and growth of different microbial species [62] . These compounds may represent an evolutionarily stable way to select for bacteria , like Bacteroides thetaiotaomicron , which are carbohydrate specialists that convert complex carbohydrates for the host: B . thetaiotaomicron has over five times the number of glycoside hydrolases as species like Salmonella enterica or Shigella flexneri [63] . Indeed , human milk contains polysaccharides that cannot be digested by the infant , suggesting that mothers may also be exercising this simple but effective form of selection [64] . But is host secretion of complex carbohydrates vulnerable to exploitation by a variant that receives benefits but does not provide any help to the host ? The use of complex carbohydrates as a selective mechanism is likely to greatly constrain the evolutionary options for bacterial species by demanding that bacteria use the glycoside hydrolases that also help the host with digestion . Of course , these species might still attempt to invade the epithelial layer . Our model is not intended to capture direct attacks by pathogens , but the detection of tissue damage is a relatively simple problem for a host as compared to selecting among more or less metabolically useful symbionts . And we know that hosts possess mechanisms to counter direct attacks , such as the inflammation response . However effective , host selection will not preclude bacterial coevolution in the gut . Indeed , long-term bacterial evolution in the gut may allow mutualists to achieve gains in competitiveness both in the presence and absence of host selection . Consistent with such adaptation , B . thetaiotaomicron can induce carbohydrate secretion by the host [37] . Coevolution also brings the potential for arms-races with pathogens that adapt and use host-provided nutrients or evade host-secreted antimicrobials . For example , l-Fucose utilization provides Campylobacter jejuni with a competitive advantage [65] . More generally , the possibility of bacterial counter-adaptation to host selection mechanisms leaves interesting questions to be answered . These include the issue of how antimicrobial secretions can remain selective when bacteria are known to rapidly develop resistances to many antimicrobials . In addition , the fitness of a bacterial cell will be influenced by cells that possess different secretion , motility , or adhesion phenotypes [66] . We do not yet understand how the potential for complex social interactions among cells will influence host selection . In sum , hosts may be forced to modify or increase their exact selection criteria , either during the life of the host via adaptive immunity or over evolutionary time . Interestingly , recent work has shown how the use of multiple selective mechanisms can allow a host to stay ahead in evolutionary arms races with parasites [67] . Can multiple strains coexist within the epithelial community ? We do not find evidence that coexistence is a stable state in our model in the sense that multiple strains will persist indefinitely . This can be seen in Figure 5 , where despite lumen selection being much stronger than epithelial selection , the lumen-favoured strain does not persist . The reason for this is that epithelial selection generates a ratchet-like effect whereby the epithelial-favoured population expands and gradually pushes any other strains up and out of the community . If host selection is weak and/or growth in the community is slow , however , favoured and disfavoured strains may both persist for long periods . Moreover , a number of other processes in the gut will counter any winnowing by host selection and help to maintain bacterial diversity . This includes the existence of multiple niches , both at different positions along the epithelial surface but also within the lumen proper . Community diversity will be further influenced by the influx rates of different species [68] and diet [26] , [27] . Host epithelial selection is not the only process that influences the microbial species composition of the gut . Nevertheless , our model predicts that the control of epithelium-associated microbial communities is much easier for a host than expected from unstructured environments . Selection of particular microbial species and strains at this position is likely to pay dividends both metabolically but also in terms of the competitive exclusion of undesirable species . Furthermore , epithelium-associated communities are relatively unlikely to be washed out and may represent a stable source community for the rest of the gut . We conclude that host influence on the composition of microbiota is both likely and likely to be powerful .
The study centres upon an individual-based simulation framework that captures bacterial growth and the concentration gradients of solutes , such as nutrients , that originate from bacterial activity while they are growing near to an epithelial host layer . While the model can capture a wide range of conditions , our analysis focuses upon a relatively nutrient-rich environment where cells grow rapidly ( Table S1 ) and slough off at a fixed height above the epithelial surface , which is intended to reflect microbial growth in an animal intestine [32] , [52] . In the mammalian gut , these cells will typically grow in the loose upper mucus layer of the epithelial surface , which continually detaches and sloughs off into the lumen [32] , [36] , [57] . We do not explicitly model the effects of these mucin polymers but implicitly include the protection from sloughing they provide for adherent bacteria in the loose layer . Note that we are only explicitly modelling the bacteria at the surface of the epithelium and not those in the lumen . Of course , selection at the epithelial surface will influence the lumen to some degree ( discussion ) , but we do not explicitly model this process . The model is an extension of an established framework that has been developed and tested over the last 15 years to understand and predict the behaviour of bacterial communities growing on inert surfaces [54] , [69]–[73] . While originally developed for problems in bioengineering , it has most recently been applied to understand the evolution and ecology of microbial groups [55] , [66] , [73]–[75] . Subsequent empirical validation of these models has demonstrated the ability of the framework to both describe bacterial communities and identify new biological mechanisms [76] , [77] . The model assumptions , justifications , and implementation are extensively discussed elsewhere [69] , [70] , [72] , [78] . In brief , bacterial cells are modelled as solid spheres that metabolise nutrients in a continuous concentration field . At each iteration , the concentration field is updated solving the two- or three-dimensional reaction-diffusion equations using multigrid solvers . This takes into account local sinks , such as a bacterium utilising the solutes around it as a nutrient source or local sources , such as secretions from a cell . Cells increase in diameter and eventually divide pushing aside neighbouring cells . The model focuses upon the resident bacterial communities that grow in the loose upper mucus layer at the interface of the lumen and epithelium , which are most likely to be affected by host selection [79] . We inoculate our simulations with a total of 250 cells in varying frequencies . This is a simplification as initial assembly of the microbiota has been shown to be more complex and may depend on interbacterial cross-talk as well as other yet unknown factors [80] . Bacteria reside above a layer of host cells that secrete solutes at varying rates . We assume that this epithelial layer and the dense mucus layer immediately above it is impenetrable to the bacterial cells [32] , [81] . This is supported by data on the healthy gut with a few notable exceptions , such as segmented filamentous bacteria in mice that live in the dense mucus layer [41] . Accordingly , we do not consider host responses to invasion of a pathogen or breach of the mucus layer , such as inflammation ( but see Discussion ) . The bacteria grow and divide utilising nutrients diffusing in from the lumen or the epithelium . At a certain height above the epithelium , cells are sloughed and excluded from the simulation . Bacteria utilise nutrients ( N ) and convert them into biomass at the rate μ following Monod-kinetics:where Ks is the Monod constant . Competing strains in our simulations differ in their maximum growth rates , μmax . Bacteria may switch between different substrates , ensuring that the maximum growth rate cannot be exceeded , where switching is based upon a recent analysis of optimal foraging in microbes [82] . Death of cells through antimicrobials is modelled using a similar equation as for growth:where p is the probability of death for a cell , T is the local concentration of the antimicrobial , and S the concentration at which cell death within 1 h occurs with a probability of 50% . Different strains may have different susceptibilities to the antimicrobial and hence different probabilities for cell death at a given concentration . Most of our understanding of host-secreted antimicrobials stems from secretions of the epithelium in the small intestine , whereas secretions in the larger intestine are less well understood [41] , [48] .
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The cells of our bodies are greatly outnumbered by the bacteria that live on us and , in particular , in our gut . It is now clear that many gut bacteria are highly beneficial , protecting us from pathogens and helping us with digestion . But what prevents beneficial bacteria from going bad ? Why don't bacteria evolve to shirk on the help that they provide and simply use us as a food source ? Here we explore this problem using a computer model that reduces the problem to its key elements . We first illustrate the basic problem faced by a host: Whenever beneficial bacteria grow slowly , the host will lose them to fast-growing species that provide no benefit . We then propose a solution to the host's problem: The host can use secretions—nutrients and toxins—to control the bacteria that grow on the epithelial cell layer of the gut . In particular , our model predicts that the epithelial surface acts as a “selectivity amplifier” . The host can thereby maintain beneficial bacteria with only small amounts of weakly selective secretions . Our model fits with a growing body of experimental data showing that hosts have diverse and important influences on their gut bacteria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology",
"microbiology",
"microbial",
"evolution",
"theoretical",
"biology",
"evolutionary",
"modeling",
"biology",
"evolutionary",
"theory",
"microbial",
"ecology",
"systems",
"biology",
"ecosystem",
"modeling",
"immunity",
"innate",
"immunity",
"computational",
"biology",
"evolutionary",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
The Evolution of Mutualism in Gut Microbiota Via Host Epithelial Selection
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We previously developed a panel of neutralizing monoclonal antibodies against Dengue virus ( DENV ) -1 , of which few exhibited inhibitory activity against all DENV-1 genotypes . This finding is consistent with reports observing variable neutralization of different DENV strains and genotypes using serum from individuals that experienced natural infection or immunization . Herein , we describe the crystal structures of DENV1-E111 bound to a novel CC′ loop epitope on domain III ( DIII ) of the E protein from two different DENV-1 genotypes . Docking of our structure onto the available cryo-electron microscopy models of DENV virions revealed that the DENV1-E111 epitope was inaccessible , suggesting that this antibody recognizes an uncharacterized virus conformation . While the affinity of binding between DENV1-E111 and DIII varied by genotype , we observed limited correlation with inhibitory activity . Instead , our results support the conclusion that potent neutralization depends on genotype-dependent exposure of the CC′ loop epitope . These findings establish new structural complexity of the DENV virion , which may be relevant for the choice of DENV strain for induction or analysis of neutralizing antibodies in the context of vaccine development .
Dengue viruses ( DENV ) are mosquito-borne viruses of the Flavivirus genus , which include other significant human pathogens such as West Nile ( WNV ) , Japanese encephalitis , and yellow fever viruses . Infection with DENV can cause symptoms in humans ranging from a mild febrile illness ( Dengue Fever , DF ) to a more severe hemorrhagic fever ( DHF ) and life-threatening dengue shock syndrome ( DSS ) . Currently , it is estimated that DENV infects ∼50 to 100 million people per year resulting in ∼250 , 000 to 500 , 000 cases of DHF/DSS [1] . The four serotypes of DENV ( DENV-1 , DENV-2 , DENV-3 , and DENV-4 ) comprise a genetically related yet antigenically distinct serocomplex , varying from one another by 25 to 40% at the amino acid level . Each DENV serotype is further divided into genotypes , which can vary up to 3% [2] , [3] . Currently , there are no specific antiviral therapies or vaccines approved for use in humans , and treatment of severe disease remains supportive in a tertiary care setting . The humoral response contributes to protection and also , paradoxically to the pathogenesis of severe DENV disease . Infection with a given serotype is believed to induce durable levels of neutralizing antibodies that provide life-long immunity against subsequent challenge by a strain of the same serotype [4] . However , secondary infection with a heterologous serotype increases the relative risk of developing DHF and DSS [5] . A favored hypothesis is that during secondary infection poorly neutralizing cross-reactive antibodies from the primary infection enhance infection of the heterologous virus in cells bearing Fc-γ receptors [6] . Recent studies in non-human primates and mice have confirmed that passive transfer of anti-DENV monoclonal or polyclonal antibodies can augment replication of a heterologous DENV in challenge models , and in some cases cause a lethal vascular leakage syndrome that resembles DSS [7]–[9] . Humoral protection against DENV correlates with the induction of a neutralizing antibody response against the envelope ( E ) protein on the surface of the virion ( [10] and reviewed in [11] ) . The ectodomain of the DENV E protein is composed of three domains [12]: DI is a central nine-stranded β-barrel domain , DII consists of two finger-like protrusions from DI and contains the hydrophobic peptide required for virus fusion , and DIII is an immunoglobulin-like domain on the other side of DI that has been proposed to interact with as yet uncharacterized host receptor ( s ) . Neutralizing monoclonal antibodies ( MAbs ) against the different DENV serotypes map to all three domains [13]–[19] , although many potently inhibitory mouse MAbs localize to DIII [13] . To date , three epitopes have been established on DIII [7] , [20] , [21]: MAbs binding the lateral ridge or A-strand epitope are relatively inhibitory , whereas MAbs recognizing the AB loop neutralize infection less efficiently or not at all [22] , presumably due to poor epitope accessibility on the virion . Cryo-electron microscopy ( cryo-EM ) studies have revealed that the E proteins of mature flavivirus virions form anti-parallel dimers that lie flat against the surface of the virion and are arranged with T = 3 quasi-icosahedral symmetry [23] , [24] . In this configuration , E proteins exist in three distinct chemical environments defined by their proximity to the 2- , 3- , or 5-fold axis of symmetry [23] , [24] . While 180 copies of the E protein are present on all flavivirus virions , the different environments imposed by the quasi-icosahedral symmetry make some epitopes unequally accessible . Epitope exposure also may be affected by neighboring E proteins in adjacent symmetry units , or by the presence or absence of prM in the case of immature , partially mature , and mature virions [25]–[30] . The arrangement of the E proteins on the surface of the virion can be modulated over time and across a range of temperatures due to the intrinsic conformational heterogeneity of virions [31] , [32] Consequently , the accessibility of epitopes can vary across structurally distinct epitopes , ultimately affecting the number of sites available for antibody binding and neutralization . Recently , we generated a panel of 79 MAbs against DENV-1 to define how antibodies neutralized different DENV-1 genotypes [17] . Within this panel , 15 MAbs were potently neutralizing , and most mapped to previously identified epitopes in DIII , although few retained strong inhibitory activity against heterologous DENV-1 genotypes . Prior studies have described disparate neutralization of DENV strains corresponding to different genotypes within a serotype with serum from natural infection [33]–[35] or after immunization with live-attenuated tetravalent vaccine candidates [34] , [36]–[38] . One such DIII-specific neutralizing MAb from our panel , DENV1-E111 ( henceforth termed E111 ) potently neutralized infection of a genotype 2 DENV-1 ( strain 16007 , EC50 of ∼4 ng/ml ) , but inhibited infection of a genotype 4 virus poorly ( strain Western Pacific-74 ( West Pac-74 ) , EC50 of ∼15 , 200 ng/ml ) . Sequence analysis of the variation between residues 296–400 of DIII for 16007 or West Pac-74 revealed only two differences ( amino acids 339 and 345 ) , with amino acid 345 as the only residue that varied in all five DENV-1 genotypes . Here , we determined the crystal structures of an E111 single-chain variable fragment ( scFv ) in complex with DIII of 16007 and the E111 Fab in complex with DIII from West Pac-74 . E111 bound to a previously uncharacterized epitope centered on the CC′ loop of DIII , which should not be exposed on the virion according to existing flavivirus atomic models; our structural data defining the CC′ loop epitope of E111 was supported by extensive mutagenesis and binding analyses . While E111 showed a higher affinity and longer half-life of binding to DIII of 16007 ( genotype 2 ) compared to DIII from several other DENV-1 genotypes , this did not explain the disparity in neutralization potency for viruses from all five genotypes . Mutation at position 345 of West Pac-74 DIII to the corresponding residue in 16007 resulted in increased E111 binding , but only a small improvement in neutralization potency , suggesting that differences in amino acids within the epitope among genotypes could not account for the phenotype . However , neutralization of DENV-1 West Pac-74 with E111 was enhanced by incubating virus-antibody complexes at higher temperature or for longer times , whereas this treatment failed to equivalently impact inhibition of strain 16007 by E111 . Our experiments suggest that the conformational ensemble of DENV virion structures differs in a genotype-dependent manner , which impacts the neutralizing activity of antibodies that recognize nominally cryptic epitopes .
We initially formed complexes of E111 Fab with soluble , bacterially expressed DIII ( residues 293–399 ) cloned from 16007 and West Pac-74 DENV-1 strains ( Figure S1 , and data not shown ) . Several conditions yielded diffracting crystals of the 16007 DIII-E111 Fab complex but failed to diffract better than ∼6 . 0 Å resolution . As an alternative strategy , we cloned the heavy ( VH ) and light chain ( VL ) variable domain sequences from the E111 hybridoma to create an scFv . Two constructs of the E111 scFv were generated , with either the VL or VH sequence at the N-terminus , separated with a ( GGGGS ) 3 linker , and a C-terminal hexahistadine tag for affinity purification . These inserts were cloned into the pAK400 vector that contains a pelB leader sequence for targeting the polyprotein transcript to the oxidative environment of the bacterial periplasm [39] . Sequential purification by nickel affinity and size exclusion chromatography revealed two species of scFv: a non-disulfide domain-swapped dimer and a monomer . For our structural analysis , we used the monomeric species with VL at the N-terminus ( Figure S1A ) . We determined the structure of E111 scFv in complex with DIII of DENV-1 16007 to 2 . 5 Å resolution ( model statistics are in Table 1 ) . There were no major structural perturbations to the immunoglobulin-like β-sandwich topology of DIII found in other flavivirus E proteins , with a root mean squared displacement of 0 . 7 Å compared to unbound DIII . The E111 scFv adopted the predicted variable domain assembly ( Figure 1A ) . The binding interface had an average degree of shape complementarity ( Sc = 0 . 68 , with Sc = 1 . 0 a perfect score ) for antibody-antigen interactions and 2 , 095 Å2 of combined surface area . The light and heavy chains engaged DIII equivalently , with a combined buried surface area of 1 , 017 Å2 ( 460 Å2 of DIII and 557 Å2 of the light chain ) versus 1 , 078 Å2 ( 550 Å2 of DIII and 528 Å2 of the heavy chain ) ( Figure 1C ) . The interaction between E111 and DIII of strain 16007 was dominated by hydrophobic interactions , in addition to six direct hydrogen bonds and fourteen water-mediated networks at the interface of the complex ( Table S1 ) . We obtained crystals of DIII of West Pac-74 in complex with E111 Fab that diffracted to 3 . 8 Å resolution ( Figure S1B ) . There were two complexes in the asymmetric unit , and non-crystallographic symmetry restraints were applied in the refinement ( model statistics are in Table 1 ) . The two Fabs have essentially identical structures with the notable exception of the elbow angles between the variable and constant domains ( ( 149 . 0° versus 133 . 5° as calculated by the RBOW server [40] ) . As with the co-crystal structure with the E111 scFv and DIII of 16007 , there were limited conformational changes in the West Pac-74 DIII upon E111 Fab ligation ( R . m . s . d = 0 . 8 Å ) . The E111 Fab-DIII interface also had a similar average degree of shape complementarity ( Sc = 0 . 65 ) and total buried surface area ( 2 , 076 . 5 Å2 ) . Overall , the E111 scFv and Fab structures ( DIII and Fv domains ) varied little from one another in terms of structure ( R . m . s . d = 0 . 3 Å ) or orientation of engagement of DIII ( Figure S1C ) . E111 engaged discontinuous segments of DIII of 16007 and West Pac-74 including the N-terminal linker ( residues 300–301 ) , C-strand , CC′ loop , C′-strand ( residues 334–351 ) , EF loop ( 372 ) , and FG loop ( residues 382–384 ) ( Figure 1D ) ; together , these form a single convex surface patch of 25 residues . A total of 20 residues of E111 contacted DIII: 8 from the light chain and 12 from the heavy chain . The heavy and light chains both formed contacts with three of the same amino acid residues ( S338 , G344 , and A345 ) . The E111 binding site was centered on the CC′ loop ( 7 of 25 residues ) , a previously uncharacterized epitope for flavivirus neutralizing MAbs ( Figure 1E ) . Analysis of the CC′ loop sequences from other DENV serotypes revealed significant variation ( Figure 1F ) , which likely explains the type-specificity ( i . e . , does not bind or neutralize other DENV serotypes ) of E111 [17] . We previously observed reduced binding of E111 to DENV-1 West Pac-74 strain ( genotype 4 ) in a virus capture ELISA , which correlated with a ∼4 , 100-fold decrease in neutralization efficiency [17] . E111 contacted every residue in the CC′ loop of 16007 DIII , as well as residues on the adjacent C- and C′-strands . Sequence variation between 16007 ( genotype 2 ) and West Pac-74 ( genotype 4 ) occurs at two DIII positions , 339 and 345 , both of which are directly contacted by E111 . As variation within the CC′ loop and surrounding regions might affect the differential binding and neutralization of E111 for different DENV-1 genotypes , we generated a library of soluble DENV-1 DIII proteins based upon natural sequence variation of all five DENV-1 genotypes and tested their binding kinetics at 25°C to E111 by surface plasmon resonance ( SPR ) ( Figure 2A , 2F , and Table 2 ) . Whereas E111 had a KD of 18 . 0±0 . 08 nM and a half-life of 194 seconds for DIII from 16007 , its interaction with West Pac-74 DIII was weaker with a KD of 415±24 nM and half-life of 6 . 5 seconds ( Figure 2B and 2C ) . Mutagenesis of a T→S at position 339 had a similar affinity as wild type 16007 DIII ( KD of 16 . 6±0 . 17 nM; t1/2 = 222 . 4 seconds ( Figure 2D ) ) . The crystal structures show that the additional methyl group present in the 16007 Thr residue does not contact E111 , whereas the Ser/Thr hydroxyl groups both make equivalent hydrogen bonds with TyrL30B in CDR1 of the E111 light chain ( see Figure 1E ) . In contrast , an A→V change at position 345 of 16007 DIII ( to the residue in West Pac-74 ) decreased the affinity of binding such that kinetics became comparable to West Pac-74 DIII with a KD of 1143±60 nM and half-life of 5 . 3 seconds ( Figure 2E ) . The side chain of Ala 345 of 16007 makes limited contact with E111 , and the additional two methyl groups in Val 345 of West Pac-74 appear to be tolerated sterically at the E111 interface with only minor structural perturbations . However , position 345 and the adjacent CC′ loop residues participate in an extensive network of hydrogen bonds with E111 ( Figure S1D ) , and we speculate that Val 345 leads to a dramatically faster off-rate by subtle destabilization of this interface . Due to the low resolution of the E111 Fab-West Pac-74 DIII structure , ordered water molecules could not be modeled , making precise comparison of the interfaces difficult . While the on-rates for E111-DIII interactions were relatively constant , the off-rate governed the differences in affinity for the DIII variants ( Figure 2F ) . DIII from strain 3146 SL varies in six positions from 16007 , including a valine at position 345 . Kinetic analysis with 3146 SL DIII revealed a decreased half-life ( 5 . 5 seconds ) as compared to 16007 . Substitution of individual amino acids corresponding to variation in strain 3146L ( D341N ( CC′ loop ) or A369T ( E-strand ) ) had little negative effect on the half-life of strain 16007 ( t1/2 of 173 seconds and 182 seconds , respectively ) . One amino acid difference ( V380I ( F-strand ) ) in 3146 SL caused a small increase in the half-life ( t1/2 of 296 seconds ) when inserted into DIII of 16007 , despite its side chain location ∼10 Å from DIII . An A→I change at position 345 was the only difference in DIII sequence between strains 16007 ( genotype 2 ) and TVP-5175 ( genotype 3 ) ; this single amino acid substitution reduced the half-life ( t1/2 of 2 . 2 seconds ) and affinity of binding ( KD of 1981±441 nM ) . Precise kinetic measurements with DIII from TVP-2130 ( genotype 1 ) were limited by non-specific interactions , and thus not analyzed ( data not shown ) . However , a DIII variant of 16007 that included the unique variation of strain TVP-2130 in the CC′ loop ( L351V ) showed a decreased half-life with E111 ( t1/2 of 32 . 9 seconds ) likely due to the disruption of optimal hydrophobic contact with Tyr30B of the variable light chain CDR1 loop . As a control , insertion of a triple mutation of K310E/T329E/K361T into the 16007 DIII lateral ridge and A-strand epitopes did not alter significantly E111 binding affinity or half-life ( t1/2 of 155 seconds ) . SPR binding studies with the E111 scFv showed a similarly reduced half-life for West Pac-74 DIII compared to 16007 DIII ( Table 2 ) . A similar kinetic pattern of E111-DIII interactions also was observed at 37°C ( data not shown ) . Overall , our genetic and biophysical studies support the crystallographic analysis and establish the CC′ loop as important for recognition of DIII by E111 . We determined the inhibitory activity of E111 against strains representing the three other genotypes of DENV-1: TVP-2130 ( genotype 1 ) , TVP-5175 ( genotype 3 ) , and 3146 SL ( genotype 5 ) . Only strains corresponding to genotypes 2 and 5 ( 16007 and 3146 SL ) were neutralized potently by E111 with EC50 values of 3 . 8±2 . 0 ng/ml and 22±10 ng/ml , respectively ( Figure 3A and Table 3 ) . In comparison , E111 neutralized strains of genotype 1 , 3 , and 4 poorly with EC50 values ranging from 9 , 700±3 , 200 ng/ml to greater than 25 , 000 ng/ml . Although rather extreme differences in E111-mediated neutralization were observed with different genotypes , this pattern failed to correlate with the KD or half-life of binding with recombinant DIII by SPR ( see Table 2 ) . These results suggest that DIII epitope sequence-independent factors ( e . g . , CC′ epitope accessibility on the virion or secondary binding sites in other domains ) likely contribute to the differential genotype neutralization by E111 . Our SPR data demonstrated that substitution of a single residue ( A→V ) at position 345 of soluble 16007 DIII reduced the E111 binding half-life to that observed with DIII of West Pac-74 . To test the effect of a reciprocal V→A change at position 345 in the West Pac-74 strain , we used a reverse genetic system: DENV-1 West Pac-74 reporter virus particles ( RVP ) [32] , [41] incorporating a single V345A mutation were analyzed for sensitivity to MAb neutralization . Whereas DENV1-E103 MAb ( which maps to residues T303 , G328 , T329 , D330 , and P332 on the lateral ridge of DIII [17] ) neutralized both wild-type and V345A DENV-1 West Pac-74 RVP equivalently ( Figure 3B ) , E111 showed only moderately enhanced neutralization of the V345A-containing RVP ( 3 . 6-fold , P<0 . 05 , Figure 3C ) . Although the V345A change improved neutralization of West Pac-74 by E111 , it failed to restore the sensitivity seen with DENV-16007 RVP or the fully infectious virus . Thus , either additional amino acid residues accounted for the genotypic difference in neutralization or the epitope was not displayed equivalently on the two viruses . Because of the differential neutralization of West Pac-74 and 16007 by E111 , we evaluated its epitope in the context of full-length E protein structures . We docked our scFv–DIII complex onto the available structure of the pre-fusion DENV E protein dimer ( PDB ID 1OAN [12] ) , and compared this to other characterized DIII-specific anti-flavivirus neutralizing MAbs ( Figure 4A ) . E111 engaged the face of DIII opposite to the one seen previously with 1A1D-2 and 4E11 ( A-strand ) [31] , [42] or WNV-E16 ( lateral ridge ) [25] . The E111 Fab was rotated in a downward orientation compared to the WNV E16 Fab ( Figure 4A ) or the A-strand DENV Fabs docked onto the same structure ( data not shown ) . Based on this docking it appears that E111 does not bind the outer exposed surface of the DENV-1 E protein but rather a determinant that is localized to the interior of the virus . Antibody neutralization of flaviviruses can occur by blocking attachment , internalization , and/or endosomal fusion . Prior to viral fusion , the E proteins on the surface of the virus dissociate from their dimeric hairpin arrangement to form trimeric spikes upon acidification in the late endosome . This rearrangement is essential to allow the newly exposed fusion loop to insert into the endosomal membrane . While the exact structural transitions of the E proteins from dimer to trimer remain unknown , DIII is displaced ∼70° from the pre-fusion structure and settles adjacent to DI in the post-fusion state [43]–[45] . We examined structurally how E111 could disrupt a post-attachment step by docking our Fab-DIII complex onto the structure of the DENV-1 post-fusion trimer [45] . While the Fab does not clash with the adjacent E protein in the DENV-2 pre-fusion dimer structure ( Figure 4A ) , the light chain of E111 sterically would inhibit formation of the post-fusion trimer ( Figure 4B ) by clashing with the neighboring DI of an adjacent E protein . Thus , from a structural perspective , E111 likely hinders the necessary conformational change from E protein homodimer to homotrimer , and limits viral fusion and infection . To begin to understand the mechanism of E111-mediated neutralization , we performed pre- and post-attachment neutralization assays [10] , [18] , [25] , [46] . E111 MAb was incubated with DENV-1 16007 before or after virus binding to BHK21-15 cells , and infection was measured by the plaque reduction assay . E111 efficiently neutralized DENV-1 when premixed with the virus before cell attachment or when added after the virus had attached to the cell surface ( Figure 4C ) . This result suggests that E111 has the capacity to neutralize infection after virus attachment has occurred , and is consistent with previously observed patterns of inhibition seen for potently neutralizing DIII-specific antibodies [18] , [25] , [46] , [47] . We next docked our E111-DIII structures onto the cryo-EM-derived model of the mature DENV virion [23] . With three envelope glycoproteins in the asymmetric unit , there are three potential E111-binding environments . However , in the mature DENV model , the E111 epitope was not accessible on the surface in any of the three symmetry environments ( Figure 5A ) . Instead , the E111 epitope was buried in E protein contacts on the virion surface ( Figure S2A–E ) . Because some anti-flavivirus MAbs ( e . g . , DII fusion loop-specific ) show differential neutralization of mature and partially mature virions , we hypothesized that intrinsic differences in particle maturation among different DENV-1 genotypes might impact neutralization by E111 . However , the distinct neutralization profiles by E111 of DENV-1 16007 and West Pac-74 were not explained by differential epitope accessibility due to variation in the maturation state of the viruses ( Figure S3 ) . As in the mature virion model , the E111 epitope also appeared inaccessible on the cryo-EM model of the immature DENV virion [48] , due to the trimeric arrangement of prM-E , which positions the epitope farther into the virus interior ( Figure 5B and Figure S2 F–H ) . The cryo-EM model of DENV-2 in complex with the 1A1D-2 Fab describes one conformational ensemble that is a consequence of “breathing” of a virus particle [31] . The 1A1D-2 epitope is partially inaccessible in the unbound conformation of the mature virion , and an increase in temperature allows for dissociation of E protein homodimers and greater exposure of the A-strand of DIII , a major component of the 1A1D-2 epitope . Although there are major rearrangements of the E proteins in this structure , E111 binding still would be prohibited by steric clashes of adjacent E protein monomers ( Figure 5C and Figure S2 I–K ) at the 3- and 5-fold axes of symmetry . While access of the 2-fold axis is not hindered by contacts with neighboring E proteins , its orientation would inhibit an immunoglobulin from binding this site . Based on these models , it appears unlikely that E111 binds to DENV-1 in the conformations that have been described by cryo-EM to date . Changes in time and temperature of binding can expose otherwise cryptic epitopes and enhance neutralizing activity of some MAbs [31] , [32] , [49] . Given our structural , biophysical , genetic , and virological data , we hypothesized that the CC′ epitope on West Pac-74 ( genotype 4 ) was less well exposed compared to 16007 ( genotype 2 ) . Alternatively , a difference in the range of the ensemble structures sampled by the two viruses could contribute to the differential neutralization by E111 . We compared the time- and temperature-dependence of neutralization of E111 with 16007 and observed little change in EC50 values after incubation of 16007 in the presence of antibody at 37°C or 40°C from 1 to 7 or 4 . 5 hours , respectively ( Figure 6A and E ) ; this suggests that the CC′ loop epitope generally is accessible among the ensemble of conformations sampled by 16007 under steady-state conditions . Similarly , a modest change in the pattern of neutralization was observed with E111 and West Pac-74 RVP after incubation at 37°C up through 7 hours ( Figure 6B ) . However , we observed a marked increase in neutralization when E111 and West Pac-74 RVP were incubated at 40°C for 4 . 5 hours , with a 20-fold ( P<0 . 001 ) reduction in the EC50 value ( Figure 6F ) . By comparison , 16007 exhibited only a 3 . 5-fold fold increase in potency over the same interval . A shift in EC50 was observed with both 16007 and West Pac-74 after 22 hours at 37°C suggesting that over time , a greater number of E111 epitopes become exposed for binding ( Figure 6A and B ) . Because our SPR binding and structural data ( Figures 1 and 2 ) did not correlate with neutralization experiments in which amino acids of 16007 and West Pac-74 were exchanged ( Figure 3 ) , we speculated that the interaction between E111 and amino acid 345 on DIII might be modulated by epitope accessibility in a genotype-dependent manner . We evaluated the effects on time and temperature on E111 neutralization of the reciprocal pair of DENV-1 RVP , V345A West Pac-74 and A345V 16007 . We observed enhanced E111 neutralization of V345A West Pac-74 as a function of increased time and temperature ( Figure 6C and G ) ; by 22 hours at 37°C or 7 hours at 40°C , the EC50 value of V345A West Pac-74 RVP neutralization approached that of the wild type 16007 RVP ( Figure S4 ) . Neutralization of the A345V 16007 RVP by E111 also increased with time and temperature ( Figure 6D and H ) , although there was no difference in EC50 value compared with wild type 16007 RVP . Overall , these experiments suggest that under steady-state conditions , the ensemble of structures with respect to exposure of the CC′ loop epitope are different between strains 16007 and West Pac-74 . Virion conformations sampled by individual DENV-1 genotypes likely vary with temperature and differ from those described in existing cryo-electron microscopy models .
Antibody neutralization of flaviviruses requires multiple antibodies to bind a single virion until a neutralization threshold is reached . The ability of a MAb to bind a given viral epitope depends on its concentration , the affinity of its interaction with the infectious virus particle , and the accessibility of the epitope on the virion [11] . While some epitopes are readily accessible on the surface of mature DENV , others are partially or completely inaccessible [27] , [29] , [31] , [50] . However , antibodies that recognize partially or completely occluded sites on the mature virion can still neutralize flavivirus infection because of particle heterogeneity with respect to maturation [29] , [50] and/or by sampling of alternate ensemble structures or “breathing” , which allows for intermittent display of cryptic epitopes [31] , [32] . Here , our structural studies show that E111 binds to a novel CC′ loop epitope on DIII that does not appear to be affected by particle maturation . Although the CC′ epitope is predicted to be inaccessible on both the mature and immature virion , E111 still potently neutralizes some but not all DENV-1 genotypes . While the amino acid sequence of DIII varies among genotypes in and around the CC′ loop , which affects E111 binding by SPR , there was a limited relationship between the kinetics of binding in vitro and the potency of genotypic neutralization in cell culture . Thus , some aspect of E111 recognition and neutralization appears independent of the epitope sequence . While E111-mediated neutralization of strain 16007 was less affected by changes in time or temperature of incubation , neutralization of West Pac-74 was enhanced substantially after incubation with E111 at higher temperatures and for longer times . These experiments suggest that at steady state , DENV-1 16007 has a broader ensemble of conformations compared to West Pac-74 , allowing for enhanced exposure of particular DIII-specific epitopes for MAb neutralization . This phenomenon could explain in part why so many ( 13 of 15 ) of our DIII-specific MAbs strongly neutralized infection of strain 16007 but not West Pac-74 despite the relatively few amino acid changes in DIII [17] . Several of our other anti-DENV-1 MAbs map to the lateral ridge epitope on DIII , which should be fully exposed on the virion [51] , and , in principle , not require temperature or time-dependent changes in structure for enhanced epitope accessibility . Nonetheless , in on-going studies , DIII lateral ridge epitope-specific MAbs ( e . g . , DENV1-E102 , DENV1-E103 , DENV1-E105 , and DENV1-E106 ) all neutralized infection by DENV-1 West Pac-74 more efficiently after an increase of temperature and duration of incubation ( K . Dowd and T . Pierson , unpublished results ) . Thus , for DENV-1 , structural perturbations to the virion may influence neutralization by MAbs recognizing ostensibly more and less exposed epitopes in a strain-dependent manner . Within DIII of different DENV-1 genotypes , the greatest sequence variation occurs within and surrounding the CC′ loop ( 4 of 9 sites ) . In comparison , the CC′ loop residues of other DENV serotypes are highly conserved: for DENV-2 and DENV-3 genotypes , only 2 of 8 and 1 of 11 sites , respectively , show amino acid variation within or proximal to the CC′ loop . Neutralizing antibodies that localize to the CC′ loop are not restricted to DENV-1 . We recently mapped four inhibitory DENV-2 MAbs to residues within the CC′ loop by yeast surface display [18] . Several of our DENV-2-specific CC′ loop MAbs protected against DENV-2 challenge both as pre-exposure prophylaxis and post-exposure therapy in mice . Due to the lack of a reproducible mouse model for DENV-1 16007 infection , we have not assessed directly the therapeutic efficacy of E111 under conditions where it is highly neutralizing . Nonetheless , E111 was effective as prophylaxis against DENV-1 West Pac-74 in an immunocompromised AG129 mouse model of infection [17] , despite its relatively poor EC50 value in cell culture . Flavivirus virions can undergo structural re-arrangements with an increase of temperature , which can facilitate binding of antibodies to epitopes with limited accessibility [31] , [32] . Indeed , DENV-1 RVP showed markedly enhanced neutralization by E111 that was dependent on both time and temperature of incubation with antibody . While these pre-incubation conditions alone improved neutralization of West Pac-74 RVP by E111 , insertion of the V345A substitution ( from 16007 into West Pac-74 ) was required to shift the neutralization curve to achieve an EC50 value of wild type 16007 . This observation is consistent with a role for amino acid 345 in E111 engagement and correlates with differences in the binding of V345A and wild type DIII of West Pac-74 observed by SPR . Thus , while a lack of E111 epitope accessibility explains why West Pac-74 was not efficiently neutralized under steady-state conditions , prolonged time and higher temperature of incubation promoted sampling of a broader ensemble of structures that revealed the differential effect of residue 345 on neutralization of West Pac-74 . Interestingly , the reciprocal mutation , A345V , when substituted into 16007 had essentially no impact on neutralization by E111 , regardless of the time and temperature of incubation . While wild type DIII of 16007 binds E111 with a 37-fold longer half-life than the A345V variant , the on-rates were equivalent . Thus , E111 binding and neutralization may be preferentially determined by the on-rate kinetics of antibody attachment through stabilization of a potentially transient/infrequent conformation present in the 16007 ensemble of structures . Indeed , a mutant DIII of 16007 ( K343I ) , which showed a substantially enhanced half-life of binding interaction ( ∼45 minutes ) with E111 , did not affect neutralization potency ( S . K . Austin , M . Diamond , and D . Fremont , unpublished results ) . Currently , there are no cryo-electron microscopy models of DENV-1 , whereas several models of DENV-2 have been described [23] , [31] , [52] . These models were used as surrogates of DENV-1 in an attempt to understand how E111 engaged its epitope in the context of a virion . Due to the packing of individual E protein monomers in the particle , there are limitations of accessibility of antibodies to portions of the E protein depending upon its particular symmetry environment . Examination of the available cryo-electron microscopy models of DENV failed to explain how E111 binds to the CC′ loop on the virion , as it is completely inaccessible in all models , in all symmetry environments . DENV-2 particles are believed to sample ensemble of conformations [32] , as shown in the captured intermediate of the cryo-electron microscopy reconstruction of DENV-2 with the 1A1D-2 Fab [31] . Despite a sizable increase in the relative E protein surface area exposed in the 1A1D-2 captured intermediate , from a structural perspective there was still insufficient accessibility to allow engagement by E111 . Our crystallographic , kinetic , and functional data all support a role for the CC′ loop in E111 recognition yet the existing atomic models cannot explain how it engages the virion . We speculate that a particular structural ensemble allows exposure of the CC′ loop and binding of E111 for certain DENV-1 genotypes . Indeed , we know little about the alternate conformational states sampled by flaviviruses , as only two cryo-electron microscopy models of transitional flavivirus states exist: a low pH model of WNV E16 Fab and WNV , and the 1A1D-2 Fab binding to DENV-2 at physiological pH [31] , [53] . Further investigation using antibody captured virus conformations are needed to explore the breadth of structures sampled by flaviviruses . Our structural and functional characterization of E111 has implications for vaccine development and assessment . While natural infection with DENV is believed to confer durable protective immunity against homologous DENV serotypes , several papers have reported disparate neutralization titers of homotypic strains and genotypes after natural infection or immunization . The neutralization potency of patient sera during the course of an DENV-3 epidemic varied substantially for DENV-3 strains corresponding to distinct genotypes [33] . A study of sera from individuals experiencing DENV-1 infections also showed variable neutralizing activity against different DENV-1 strains [35] . Moreover , pooled sera from monkeys immunized with a tetravalent chimeric live attenuated DENV vaccine revealed a range ( e . g . , ∼12-fold for DENV-1 strains ) of variability in EC50 neutralization titers against individual strains of a given DENV serotype [38] . It remains possible that the differences are even larger , as full neutralization profiles or EC90 values were not reported in this latter study . Studies examining how genotypic variation affects neutralization with MAbs [16]–[18] , [54] , [55] suggest that natural sequence variation among genotypes of a DENV serotype impacts the potency of antibody neutralization . Analogously , many neutralizing antibodies against HIV , influenza , and hepatitis C viruses fail to inhibit related stains and/or serotypes [56] . While the cryptic nature of the CC′ loop may be a special case [17] , we propose that disparate neutralization of DENV-1 strains by monoclonal or polyclonal antibodies could be due to or at least be affected by differences in the ensemble of conformations sampled by the virion . Selection of DENV strains that sample a greater diversity of conformations as vaccine candidates could broaden the repertoire of neutralizing antibodies against DENV . Such strains could better expose and present the spectrum of epitopes available , and thereby induce a more diverse neutralizing antibody repertoire . Alternatively , the use of DENV strains or formulations with a limited structural ensemble could focus the neutralizing antibody response on specific epitopes whose accessibility is independent of time and temperature , and thus , more effective at neutralizing a diverse range of strains , regardless of particle conformation . Although a monovalent formalin-fixed DENV-2 vaccine induced strongly neutralizing antibodies against the parent strain in mice and monkeys , it was never evaluated for activity against a range of strains corresponding to different genotypes [57] . Clearly , further empirical studies are necessary to assess directly how virion ensembles affect immunogenicity as well as pathogenesis . Finally , the conformational diversity of DENV strains used for diagnostic evaluation of polyclonal serum could affect the interpretation of its neutralizing potential; for example , the choice of a DENV strain that cycles through limited structural conformations at 37°C for neutralization assays could underestimate the quality of the inhibitory activity of the antibody response in human serum . In summary , we have defined a novel structural epitope on the CC′ loop of DIII of DENV , which is not accessible in the existing cryo-electron microscopy reconstruction models of DENV particles . Our experiments also suggest that the ensemble of conformations of the DENV virion structure varies in a genotype-dependent manner , which impacts the neutralizing activity of antibodies and has direct implications for the development and analysis of candidate DENV tetravalent vaccines .
An untagged form of DENV-1 DIII ( strain 16007 , residues 293 to 399 ) was cloned into the pET21a vector ( Novagen ) and expressed by autoinduction [58] in BL21 bacterial cells ( Agilent ) . Isolated inclusion bodies were solubilized and oxidatively re-folded , as previously described [59] . Variants of the DENV-1 16007 strain ( residues 293–399 ) were generated by site-directed mutagenesis ( QuikChange , Agilent ) using unique primer sets ( Table S2 ) . E111 scFv was engineered with a ( GGGGS ) 3 linker between the VL and VH and domains and a C-terminal hexahistadine tag , cloned into the pAK400 vector , and expressed in the periplasm of bacteria . The bacteria were lysed and the E111 scFv was purified by nickel affinity and size exclusion chromatography . The scFv was complexed with excess DIII and purified by size exclusion chromatography . The E111 scFv-DIII complexes were crystallized at 10 mg/ml by sitting-drop vapor diffusion at 20°C using 20% polyethylene glycol ( PEG ) 3350 , 0 . 2 M potassium sulfate , and 5% glycerol . Crystals were cryo-protected in a solution containing 35% glycerol and cooled in liquid nitrogen . After protein A affinity purification , the E111 IgG was cleaved with immobilized papain ( Pierce Biotechnology ) , and Fabs were recovered , as the Fc and uncleaved IgG were removed by passage over a second protein A affinity column . West Pac-74 DIII and E111 Fab were mixed and isolated by size exclusion chromatography on a S75 Superdex column . The E111 Fab-DIII complexes were crystallized at 15 . 8 mg/ml by sitting-drop vapor diffusion at 20°C using 0 . 1 M MES pH 5 . 3 , 20% PEG 6000 ( final pH 6 . 0 ) with 1% glycerol . The crystals were cryo-protected in the mother solution supplemented with 20% ethylene glycol and cooled in liquid nitrogen . Data were collected at APS beamline 19–ID ( Argonne National Laboratories ) at 293° K and at a wavelength of 1 . 007 Å using a CCD detector . Data were processed , scaled , and merged with HKL-2000 [60] . Crystallographic phasing for the E111 scFv-DIII complex was obtained by molecular replacement ( PHENIX [61] ) using the predicted scFv model given by the PIGS server [62] and the atomic structure of DENV-1 16007 DIII ( PBD accession number 3IRC [17] ) . The crystals belong to the space group P43212 with the unit cell dimensions of a = b = 135 . 224 and c = 52 . 221 , with one E111 scFv-DIII complex per asymmetric unit . An atomic model was iteratively built in COOT [63] and refined in PHENIX , and contained 328 amino acids ( residues 298–396 from DIII , 1–114 of the E111 VH , and 1–107 of the E111 VL , ( Chothia numbering ) , 147 water molecules , four chloride ions and one sulfate and glycol molecule each . The final 2 . 5 Å resolution model was refined to an Rwork = 19 . 6% and Rfree = 23 . 9% for all F>0 , with excellent geometry and Ramachandran angles ( 97 . 4% favored and 0 . 3% outliers ) . Data for the E111 Fab-West Pac-74 DIII complex were initially processed with centered orthorhombic symmetry with subsequent identification of pseudo-merohedral twinning . The crystals actually belong to space group P21 and suffer ∼30% twinning with the operator h , -k , -h-l . The data was successfully phased by molecular replacement using the E111 scFv-DIII complex and the constant domains from PDB ID 4AEH with two molecules per asymmetric unit . The atomic model was iteratively build in COOT and refined in REFMAC [64] and PHENIX using jelly body and reference model restrains , respectively . The structure contained 1060 amino acids ( residues 299–395 from DIII , 1–212 from the light chain , and 1–212 from the heavy chain ( Chothia numbering ) . The final 3 . 8 Å resolution model was refined to an Rwork = 23 . 7% and Rfree = 27 . 8% for all F>0 , with excellent geometry and Ramachandran angles ( 97 . 0% favored and 0 . 4% outliers ) . The atomic coordinates and structure factors have been deposited in the Protein Data Bank ( www . rcsb . org ) under accession numbers 4FFY and 4FFZ for the scFv and Fab complexes , respectively . Kinetic information on the interaction between E111 and DIII variants was obtained using a Biacore T100 instrument . Approximately 500 response units ( RU ) of E111 or control MAb/scFv ( WNV E16 ) was immobilized using amine coupling to a Series S CM5 chip . Once stabilized , a two-fold dilution series of the DENV DIII variants were injected over the chip at a flow rate of 65 ml/min for 180 seconds and allowed to dissociate for 1 , 000 seconds . DIII had dissociated over this time period and additional regeneration was not necessary . Data was processed using the Biacore Evaluation Software ( Version 1 . 1 . 1 ) by double referencing and a 1∶1 Langmuir fit of the curves . All curves were reference subtracted from a flow cell containing the negative control WNV E16 MAb/scFv . Maximum response units were plotted versus concentration and this curve was fitted to determine the KD . Results were generated from at least three independent experiments , with a minimum of six binding curves per experiment . PRNT were performed with the five DENV-1 genotype strains with E111 on Vero cells as described previously [17] . In some experiments , pre- or post-attachment studies were performed as a variation [18] , [46] . Briefly , serially diluted MAbs were mixed 1∶1 with 102 PFU of 16007 DENV-1 virus in DMEM containing 10% FBS and incubated for one hour at 4°C . The virus-MAb mixture was then added to the cells at 4°C , and after washing , incubated at 37°C for one additional hour . Alternatively , cells and media were chilled to 4°C before 102 PFU of virus was added and incubated for one hour . Unbound virus was washed away with chilled media before the addition of E111 MAb . After one hour at 4°C , cells were washed with warm media and overlaid with 2% low-melt agarose ( SeaPlaque ) in modified Eagle medium and 4% FBS and incubated at 37°C for 6 days . PRNT50 values were determined using non-linear regression analysis ( Graph Pad Prism4 ) . DENV-1 RVP were generated as described previously [32] , [41] . Plasmids expressing the wild type or mutant capsid ( C ) -prM-E genes of DENV-1 ( strain 16007 or West Pac-74 ) were co-transfected into HEK293T cells with a plasmid encoding a sub-genomic WNV replicon expressing GFP . E protein variants were engineered by site-directed mutagenesis ( QuikChange , Agilent ) and confirmed by sequencing . Standard neutralization assays with RVP were performed by incubating serial dilutions of antibody with DENV-1 RVP for 1 hour at 37°C , followed by addition of Raji-DC-SIGNR cells . Infection was carried out at 37°C and monitored by flow cytometry 48 hours later for GFP expression . To assess the role of temperature on MAb activity , neutralization assays were performed as above , and designated as “reference” neutralization profiles . Additional RVP-antibody complexes , following the initial 1 hour incubation at 37°C , were further incubated at 37°C or 40°C for incremental lengths of time , followed by infection of Raji-DC-SIGNR cells . Relative infectivity was determined after comparison to infectivity of DENV-1 RVP incubated at the same temperature in parallel in the absence of antibody . RVP were produced from HEK293T cells to represent various stages of maturation ( standard ( containing a heterogeneous mixture of partially mature and mature ) or mature ( produced in the presence of an over-expression of furin ) ) according to published protocols [29] . Standard neutralization assays with RVP were performed by incubating serial dilutions of antibody with DENV-1 RVP for 1 hour at 37°C , followed by addition of Raji-DC-SIGNR cells . Infection was carried out at 37°C and monitored by flow cytometry 48 hours later for GFP expression . To assess the role of temperature on MAb activity , neutralization assays were performed as above , and designated as “reference” neutralization profiles . ( a ) E protein dimer . Docking of the E111-DIII structure and the WNV E16 Fab-DIII ( PDB 1ZTX ) onto the pre-fusion dimer structure of DENV2 ( PDB 1OAN ) was based upon superimposition of DIII . ( b ) E protein trimer . The same procedure was used for docking of the E111 scFv onto the post-fusion DENV-1 trimer structure ( PDB 3G7T ) . ( c ) Virions . The coordinates for the full mature ( PDB 1KR4 ) , immature ( PDB 3C6D ) , and 1A1D-2-bound ( PDB 2R6P ) DENV-2 virus assemblies were downloaded from VIPERdb [65] ( http://viperdb . scripps . edu/ ) . The surface of the virus was clipped to reveal the interior of the virion models . All structural representations were colored and rendered using PyMOL ( The PyMOL Molecular Graphics System , Version 1 . 4–1 . 5 . 1 Schrödinger , LLC . , http://www . pymol . org ) .
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Within each Dengue virus ( DENV ) serotype , viruses are subdivided into genotypes based upon the protein sequence variation . Infection with a given serotype is believed to induce neutralizing antibodies that provide long-term immunity against secondary infection by a strain of the same serotype . However , recent studies suggest that some classes of neutralizing antibodies fail to inhibit infection equivalently for all genotypes within a DENV serotype . DENV1-E111 is an example of an antibody that differentially neutralizes infection of DENV-1 strains . We used structural and molecular approaches to determine that DENV1-E111 binds to an epitope in domain III of the envelope protein . Although the epitope sequence varied between DENV-1 genotypes , inhibitory activity of the antibody remained unequal when we exchanged the amino acids within the epitope among genotypes . Docking of our structures onto DENV virion models revealed that the DENV1-E111 epitope was inaccessible , suggesting that the antibody recognizes an uncharacterized virus conformation . Our studies suggest that DENV virion structures differ in a genotype-dependent manner , which can impact the inhibitory activity of antibodies that recognize cryptic epitopes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"virology",
"biology",
"microbiology",
"viral",
"structure",
"viral",
"diseases"
] |
2012
|
Structural Basis of Differential Neutralization of DENV-1 Genotypes by an Antibody that Recognizes a Cryptic Epitope
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Polycomb group proteins are transcriptional repressors that play a central role in the establishment and maintenance of gene expression patterns during development . Using mice with an N-ethyl-N-nitrosourea ( ENU ) -induced mutation in Suppressor of Zeste 12 ( Suz12 ) , a core component of Polycomb Repressive Complex 2 ( PRC2 ) , we show here that loss of Suz12 function enhances hematopoietic stem cell ( HSC ) activity . In addition to these effects on a wild-type genetic background , mutations in Suz12 are sufficient to ameliorate the stem cell defect and thrombocytopenia present in mice that lack the thrombopoietin receptor ( c-Mpl ) . To investigate the molecular targets of the PRC2 complex in the HSC compartment , we examined changes in global patterns of gene expression in cells deficient in Suz12 . We identified a distinct set of genes that are regulated by Suz12 in hematopoietic cells , including eight genes that appear to be highly responsive to PRC2 function within this compartment . These data suggest that PRC2 is required to maintain a specific gene expression pattern in hematopoiesis that is indispensable to normal stem cell function .
Suppressor of Zeste 12 ( Suz12 ) was identified in a genetic screen performed in Drosophila to define transcriptional repressors [1] . Biochemical studies subsequently identified Suz12 as a component of a multimeric protein complex , termed Polycomb Repressive Complex 2 ( PRC2 ) , which is responsible for di- and tri-methylation of histone 3 at lysine 27 ( H3K27 ) [2–5] . The core components of PRC2 are conserved between fly and vertebrates; in addition to Suz12 , they include the methyl-transferase Enhancer of Zeste 2 ( Ezh2 ) and various forms of the Embryonic Ectoderm Development protein ( Eed ) ( reviewed in [6] ) . Genome-wide location analysis—performed in mouse , human , and fly—confirmed that PRC2 components and tri-methylated H3K27 ( H3K27-3Me ) are enriched within the promoters of transcriptionally repressed genes [7–14] . Disruption of the complex has profound consequences during development and in human disease , which illustrates the important role epigenetic marks play in maintaining appropriate patterns of gene expression . Many insights into the mechanism of PRC2 action have come from studies focused on its role in Homeobox gene repression in Drosophila . Work with the Ultrabithorax promoter demonstrated that repression mediated by PRC2 involves the recruitment of a distinct Polycomb complex , termed Polycomb Repressive Complex 1 ( PRC1 ) [2 , 5] . Later studies confirmed that the Drosophila protein Polycomb ( Pc ) , a core component of PRC1 , binds with high affinity to H3K27-3Me via its chromodomain [15 , 16] . Because H3K27 methylation is performed by PRC2 , these findings suggested a hierarchical relationship between the two complexes , in which PRC2 initiates silencing by targeting PRC1 to specific regions of chromatin ( reviewed in [6] ) . The relationship between PRC2 and PRC1 is further complicated in vertebrates where many Polycomb group homologs are present [17] . Indeed , the hierarchy amongst distinct Polycomb group complexes and their dynamic composition and interaction in physiological processes , such as X-inactivation , remain incompletely understood [18] . Newly identified links between PRC2 and other epigenetic regulators , such as DNA methyl-transferase and noncoding RNA molecules ( ncRNA ) , suggest that PRC2 coordinates a variety of processes that function in concert to initiate and maintain the repressive chromatin state [19 , 20] . Given that PRC2 regulates numerous target genes , it is perhaps not surprising that disruption of the complex has major implications during the early stages of development , where the correct temporal and spatial control of gene expression is critical . Murine models of PRC2 deficiency have demonstrated that each component is absolutely required for embryonic development [21–23] . The analysis of embryos that lack PRC2 components has revealed deficiencies at implantation and early post-implantation stages in development , which is consistent with the involvement of PRC2 in pathways that influence cellular proliferation [24–26] . Further investigation of PRC2 function in the adult mouse has been restricted to the use of conditional alleles , which have been generated for Ezh2 [27] , and viable hypomorphic alleles that include Eed1989 [21] . The study of Polycomb group proteins in vertebrate hematopoiesis has largely focused on PRC1 components , the best-characterised amongst these being Bmi-1 . Bmi-1 is a critical regulator of self-renewal in hematopoietic stem cells ( HSCs ) [28–30] that mediates its effect in part through control of the Ink4a-Arf locus [29 , 31 , 32] . In comparison , relatively little is known about the activity of PRC2 within the HSC compartment , although several observations suggest that perturbation of PRC2 influences HSC biology . Hypomorphic alleles of Eed have demonstrated a critical role for PRC2 in restricting the proliferation of early lymphoid and myeloid progenitors [33 , 34] . In this context , the function of PRC2 appears to oppose the activity of PRC1; however , the precise molecular targets that contribute to this phenotype remain unknown [33] . Proliferative defects have not been reported in mice that lack Ezh2 within the hematopoietic compartment , although this finding may be complicated by impaired B cell and T cell maturation [27 , 35] . Further evidence of a role for PRC2 in the stem cell compartment has come from the finding that forced expression of Ezh2 appears to prevent the exhaustion of HSCs during serial transplantation [36] . Thrombopoietin ( Tpo ) is the primary regulator of platelet production in vivo [37 , 38] . Deletion of the Tpo gene , or the Tpo receptor ( c-Mpl ) , in mice results in a severe reduction in platelet count [39–41] , and mutations that disrupt the Tpo/c-Mpl pathway are the major cause of the rare human disorder congenital amegakaryocytic thrombocytopenia [42 , 43] . Signalling through c-Mpl also supports the development of long-term HSCs [44] , and impaired signalling through the receptor is associated with functional deficiencies in HSCs and progenitor cells in both mice and humans [41 , 45–47] . We have performed a large-scale ENU mutagenesis screen to identify mutations that rescue platelet production and HSC function in c-Mpl–/– mice . This approach has previously identified mutations in c-Myb that result in supra-physiological platelet production [48] . Herein we describe the isolation of a mutation in Suz12 , identified in the platelet 8 ( PLT8 ) pedigree , which causes a global reduction in the abundance of PRC2 . Impairment of PRC2 in Suz12Plt8/+ mice caused changes in steady-state hematopoiesis that were associated with enhanced HSC and progenitor cell activity .
An ENU mutagenesis screen was performed with c-Mpl−/− mice to identify mutations that suppress thrombocytopenia and/or stem cell defects . The average platelet count in c-Mpl–/– mice is 112 ± 78 × 106/ml ( mean ± standard deviation; n = 179 ) . The founder of the PLT8 pedigree was identified among a population of G1 animals segregating ENU-induced mutations due to its unusually high platelet count ( 361 × 106/ml ) , more than three standard deviations above the mean . The phenotype was found to be heritable and was therefore likely to be the result of a germline ENU-induced mutation . The Plt8 mutation was generated on a C57BL/6 background and , for the purposes of mapping , was crossed to 129/Sv to generate C57BL/6:129/Sv F1 animals . F1 animals with an elevated platelet count ( the PLT8 phenotype ) were intercrossed to produce an F2 population for positional cloning . Initial results from the genome-wide scan localised the mutation to a 6 . 5-Mb interval on Chromosome 11 , between D11MIT245 and D11MIT120 . There was a reduced frequency of C57BL/6 homozygosity at this position—below the expected frequency of 25%—which suggested the presence of a mutation that was lethal when homozygous ( Figure 1A , at left ) . Analysis of the peripheral blood demonstrated that mice that carried C57BL/6 DNA at this position ( as heterozygotes ) had a higher mean platelet count when compared to mice that were 129/Sv homozygotes , or to a control F2 population , which suggested that this region harboured the Plt8 mutation ( Figure 1A , at right ) . Additional microsatellite markers were used to refine the Plt8 candidate interval . An additional 531 PLT8 F2 mice were genotyped , and the candidate interval was reduced to 1 . 4 Mb between D11CAR28 and D11CAR48 ( from base pair 79314902 to 80704402 ) ( Figure 1B ) . We sequenced the exons and splice sites of six genes within the candidate interval: Suppressor of Zeste 12 ( Suz12 ) , Cytokine receptor-like factor 3 , Ring finger protein 135 , Rhomboid veinlet-like protein 4 , Zinc finger protein 207 , and Cyclin-dependent kinase 5 activator 1 precursor . A single base pair deletion was identified in a splice acceptor site of the 16th exon of Suz12 ( Figure 1C ) . The Plt8 mutation was present in all affected PLT8 animals and was not identified in wild-type littermates , or other available mouse strains that included 129/Sv , C3H , and Balb/c . A complementation test was performed to verify that the Plt8 mutation impairs the functional activity of Suz12 . Mice heterozygous for the Plt8 mutation ( Suz12Plt8/+ ) were mated to mice that carried a loss-of-function genetrap allele ( Suz12502gt/+ ) to generate compound heterozygotes . The genetrap allele , herein referred to as Suz12502gt , has been characterised previously and shown to impair Suz12 function [23] . No compound heterozygotes ( Suz12Plt8/502gt ) were identified from 100 pups analysed ( Table S1 , chi squared p-value = 2 . 50 × 10−7 ) , which indicated that Suz12Plt8/502gt mice die prior to weaning . Pasini and colleagues previously demonstrated that Suz12502gt/502gt embryos die approximately 8 d after fertilization [23] . Similarly , embryos homozygous for the Plt8 mutation were reduced in number at embryonic day 8 . 5 ( E8 . 5 ) and were smaller than wild-type and heterozygous embryos ( unpublished data ) . These results strongly suggest that the Plt8 mutation impairs Suz12 function . Suz12 mRNA and protein were analysed to determine the mechanism by which the Plt8 mutation affects Suz12 function . Consistent with a deletion in the splice acceptor site of exon 16 , an analysis of Suz12 mRNA by reverse-transcriptase ( RT ) -PCR demonstrated aberrant splicing of the Suz12 transcript in mice that carry the Plt8 mutation ( Figure 2A ) . A longer Suz12 transcript was evident in cDNA from bone marrow and spleen of these mice , and DNA sequencing confirmed that the longer mRNA resulted from inappropriate inclusion of the 15th intron within the mature transcript . Inclusion of the 15th intron introduces a stop codon that is predicted to truncate 115 amino acids at the C terminus of the protein ( Suz12ΔC115 ) . Suz12 protein levels were reduced in lysates prepared from Suz12Plt8/+ embryos . There was no evidence of the predicted truncation product Suz12ΔC115 ( Figure 2B ) , despite the fact that the anti-Suz12 antibody could detect the truncated protein when it was expressed exogenously in transfected fibroblasts ( Figure S1 ) . Ezh2 protein levels were also lower in Suz12Plt8/+ embryos ( Figure 2B ) , a finding consistent with previous reports that Suz12 influences the stability of other PRC2 components [23] . Impaired production of Suz12 and Ezh2 had only a modest effect on the total amount of H3K27-3Me in mutant embryos . Similar changes were evident in embryos that were heterozygous for the genetrap allele ( Figure 2B ) , suggesting that the primary effects of both mutations are to reduce steady state levels of Suz12 protein . Analysis of peripheral blood confirmed that Suz12Plt8/+ c-Mpl–/– mice had a significantly increased platelet count ( 252 ± 54 × 106/ml ) when compared to Suz12+/+ c-Mpl–/– littermates ( 107 ± 54 × 106/ml ) ( Table 1 ) . Although mildly elevated , the increase in platelet count in Suz12Plt8/+ mice on a c-Mpl+/+ background was not statistically significant ( Table 1 ) . White blood cell numbers were also elevated in Suz12Plt8/+ mice , independent of the c-Mpl genotype , due to an increased number of lymphocytes . Platelet volume , red cell count , and hematocrit were not affected by the Plt8 mutation . Similar changes were evident in the peripheral blood of mice that carry the genetrap allele ( Suz12502gt ) , which confirmed that the elevation in platelet count was a result of impaired Suz12 function ( Table 1 ) . The mixed genetic background of Suz12502gt/+ mice is likely to account for variation in the magnitude of the changes . We also identified elevated platelet counts in mice that carried a null allele of Ezh2 ( 93 ± 14 × 106/ml in Ezh2+/+ c-Mpl–/– mice compared to 152 ± 14 × 106/ml in Ezh2+/- c-Mpl–/– littermates , p = 0 . 0095 ) [22] , which further suggests that impairment of PRC2 underlies the phenotypic changes evident in Suz12Plt8/+ mice . Consistent with the elevation in platelet count , megakaryocyte numbers were increased in the bone marrow of Suz12Plt8/+ c-Mpl–/– mice , and no significant increase was seen in Suz12Plt8/+ c-Mpl+/+ mice ( Figure 3A ) . Histological examination demonstrated normal megakaryocyte morphology ( unpublished data ) , and no differences in megakaryocyte DNA-ploidy were evident ( Figure 3B ) . To characterize the hematopoietic progenitor cell compartment , in vitro colony assays were performed . Numbers of progenitor cells responsive to several stimuli appeared normal in Suz12Plt8/+ mice ( Table 2 ) . Megakaryocyte progenitor numbers were slightly elevated in bone marrow and spleen cultures from Suz12Plt8/+ c-Mpl+/+ mice; however , this difference was not statistically significant and was not evident in mice on a c-Mpl–/– background ( Table 2 ) . Multipotential hematopoietic progenitor cells can be quantified using their propensity to form colonies in the spleen of lethally irradiated mice; these colonies are referred to as colony-forming units spleen ( CFU-S ) [49] . In agreement with previous studies that detailed a reduction in stem cell function in c-Mpl–/– mice , these animals show a dramatic reduction in CFU-S compared with c-Mpl+/+ mice ( Figure 4 ) [45] . c-Mpl–/– mice that carry the Plt8 mutation had a significantly increased number of CFU-S when compared to Suz12+/+ c-Mpl–/– littermates ( Figure 4 ) . This increase was not observed on a c-Mpl+/+ background . The number of immunophenotypic HSCs was quantified to determine whether the stem cell compartment was expanded in Suz12Plt8/+ mice . Consistent with previous reports of progenitor cell defects , the number of lineage marker negative ( Lin– ) , Sca-1+ , c-Kit+ ( LSK ) cells in the bone marrow was reduced in the absence of c-Mpl ( ∼2-fold , p < 0 . 001 ) ; however , there was no discernable difference in the total number of LSK cells between Suz12Plt8/+ and Suz12+/+ mice ( Figure 5 ) . The cell surface proteins CD34 and FMS-like tyrosine kinase 3 ( Flt3 ) were used to subdivide the LSK population into long-term ( LT ) HSCs , short-term ( ST ) HSCs , and lymphoid primed multi-potent progenitors ( LMPPs ) [50] . c-Mpl–/– mice exhibit a marked reduction in the frequency of CD34– Flt3– LSK cells , the population that contains LT-HSCs , which is similar to results obtained with Tpo–/– animals [44] . Rather than being increased , the proportion of CD34– Flt3– LSK cells was slightly lower in Suz12Plt8/+ mice , and this difference was more pronounced in c-Mpl+/+ mice ( p < 0 . 01 ) ( Figure 5B ) . The increased frequency of lymphoid-biased progenitors ( CD34+ Flt3+ ) may explain the elevated production of peripheral blood lymphocytes in Suz12Plt8/+ c-Mpl+/+ mice . The functional activity of long-term repopulating stem cells was measured in Suz12Plt8/+ mice by competitive and serial transplantation of bone marrow into lethally irradiated recipients ( see Materials and Methods ) . Both platelet and white blood cell counts were modestly elevated in recipients of Suz12Plt8/+ c-Mpl+/+ marrow relative to controls , which recapitulated results seen in unmanipulated Suz12Plt8/+ c-Mpl+/+ mice ( Table S2 ) and demonstrated that the phenotype was intrinsic to the bone marrow . Platelet count was similar in recipients of Suz12Plt8/+ c-Mpl–/– and Suz12+/+ c-Mpl–/– marrow , which is likely due to the low representation of c-Mpl–/– cells in these animals ( Table S2 ) . Suz12Plt8/+ bone marrow made a greater contribution to hematopoietic tissues than the wild-type competitor cells , irrespective of the c-Mpl genotype ( Figure 6A ) . This difference was most apparent on the c-Mpl–/– background , where the increase was statistically significant in the peripheral blood , spleen , and bone marrow . The low contribution of c-Mpl–/– bone marrow to hematopoiesis , even in the presence of a c-Mpl–/– competitor , is most likely due to compromised competition with residual host-derived wild-type marrow . This effect is compounded in recipients of secondary transplants , with Suz12Plt8/+ c-Mpl–/– cells contributing 18% of the bone marrow , whereas Suz12+/+ c-Mpl–/– cells represented just 3% . Although their contribution to hematopoiesis was elevated , Suz12Plt8/+ HSCs were not rapidly exhausted and continued to contribute effectively to hematopoiesis in tertiary recipients ( Figure 6B ) . Previous reports have highlighted a critical role for Ezh2 and the PRC2 complex during B cell maturation [27]; however , the representation of Suz12Plt8/+ cells was consistent across various cell lineages , which included B cells , T cells , granulocytes , and macrophages . This suggested that the Plt8 mutation does not impair differentiation ( unpublished data ) . The phenotype of Suz12Plt8/+ mice reflects the effect of partial ( heterozygous ) loss of Suz12 function . To gain further insight into the role of Suz12 in hematopoiesis , we used short hairpin RNA ( shRNA ) -mediated silencing to more profoundly impair Suz12 expression . Retroviral shRNA constructs were designed to target two core components of PRC2 ( Suz12 and Ezh2 ) or a nonspecific sequence ( Nons ) , and they were validated in the GATA1– megakaryocyte-erythroid ( G1ME ) cell line [51] . A 70% reduction in Suz12 mRNA was observed in cells that expressed shRNA-Suz12 , and a similar reduction in expression was obtained with the construct that targeted Ezh2 ( Figure S2A ) . Analysis of protein expression revealed a dramatic reduction in Suz12 protein levels in cells that expressed shRNA-Suz12 ( Figure S2B ) . Ezh2 expression and H3K27-3Me levels were also reduced in these cells , which confirmed that PRC2 function was greatly impaired . H3K27-3Me levels were similarly reduced in cells that expressed shRNA-Ezh2 but not in cells that express shRNA-Nons . We next determined the effect of shRNA-mediated depletion of Suz12 in HSCs . To perform this experiment , CD45Ly5 . 1 recipient mice were transplanted with CD45Ly5 . 2 bone marrow that had been infected with either the MSCV LTR-miR30-SV40 GFP ( LMS ) -Nons or the LMS-Suz12 retrovirus . The proportion of virally transduced cells ( Ly5 . 2+ GFP+ ) was determined prior to transplant and then monitored in primary and secondary recipients . Thymocytes and splenocytes isolated from primary recipients were used to verify the reduction in Suz12 expression in vivo . Within the thymus , Suz12 protein expression was specifically reduced in cells infected with the LMS-Suz12 virus ( Ly5 . 2+ GFP+ ) ( Figure 7A ) . Ezh2 protein levels were also reduced in these cells ( Figure 7A ) , which is consistent with results obtained in G1ME cells . The expression of Suz12 and Ezh2 was not altered in Ly5 . 2+ GFP+ cells isolated from recipients of marrow infected with the LMS-Nons construct . Similar results were obtained when the level of Suz12 mRNA was quantified in these cells ( Figure S3 ) . The contribution of cells infected with LMS-Suz12 to recipient hematopoiesis increased steadily over the course of the experiment; they represented 15 . 2% of the donor population at the time of transplantation , which increased to 39 . 8% in primary recipients and to 49 . 7% in secondary recipients ( Figure 7B ) . This increase was specifically associated with Suz12 deficiency , as cells transduced with the LMS-Nons vector were present at a gradually reducing frequency at infection , in primary recipients , and in secondary recipients ( 23 . 7% , 18 . 1% , and 9 . 8% , respectively ) , which is consistent with results using unmanipulated wild-type bone marrow ( Figure 7B ) . In an attempt to standardise the three experiments and account for differences in the absolute number of GFP+ cells in each donor , a ratio was calculated using paired donor and recipient data . These data demonstrate that the representation of cells infected with the LMS-Suz12 construct increased by approximately 2-fold upon transplantation into primary recipients , and a similar increase was observed upon transplantation into secondary recipients ( Figure 7C ) , whereas the representation of LMS-Nons–infected cells remained relatively constant . Changes within the progenitor compartment were also greater than those observed in Suz12Plt8/+ mice; as both blast colony formation and megakaryocyte progenitor number was elevated in recipients of marrow infected with the LMS-Suz12 construct ( Table 3 ) . We next analysed gene expression changes in hematopoietic progenitors isolated from Suz12Plt8/+ mice and recipients of LMS-Suz12–infected bone marrow . Global gene expression was examined in LSK cells from the bone marrow of Suz12Plt8/+ c-Mpl+/+ and Suz12+/+ c-Mpl+/+ mice . Expression differences between the two genotypes were modest , which was consistent with studies performed with Suz12-deficient embryonic stem ( ES ) cells [9 , 52] . We selected 100 genes with the most significant differences for further analysis ( LSK top 100 , Table S3 ) . In addition , we isolated Ly5 . 1– Lin– c-Kit+ ( LK ) cells from mice reconstituted with LMS-Suz12– or LMS-Nons–infected bone marrow . Sca-1 expression was negligible in the Lin– fraction of the bone marrow of secondary transplant recipients , despite the long-term repopulating capacity of these cells; therefore , the progenitor-enriched LK cell population was used for gene expression analysis . We selected the 100 genes that changed most significantly , and were not viral-encoded , for further analysis ( LK top 100 , Table S4 and Text S1 ) . We analysed the overlap between the LSK and LK top 100 datasets , and we found eight genes that were over-expressed in both Suz12Plt8/+ LSK cells and LK cells deficient in Suz12 compared with controls ( Figure 8A ) , far more than expected by chance ( p < 0 . 00001 ) . Real-time quantitative PCR ( Q-PCR ) was used to confirm results obtained in the microarray and to better quantify the magnitude of the changes in gene expression . Similar to results obtained with thymocytes and splenocytes , Suz12 expression was markedly reduced in LK cells that express shRNA-Suz12 , whereas the expression of Bex2 and Bex4 was elevated . It remains to be determined whether the genes deregulated in Suz12Plt8/+ HSCs are direct targets of PRC2 .
Using a forward genetics approach , we identified a loss-of-function allele of Suz12 that suppresses the thrombocytopenia evident in c-Mpl–/– mice . As well as having an increased platelet count , Suz12Plt8/+ c-Mpl–/– mice display alterations in the number and function of multipotent hematopoietic progenitors and stem cells . Aspects of the Suz12Plt8/+ phenotype were only apparent in the absence of thrombopoietin signalling , which confirmed that c-Mpl–/– mice provide a sensitised background to detect changes in the progenitor compartment and in the platelet lineage [48]; however , the repopulating activity of HSCs was elevated , irrespective of the c-Mpl genotype . The stem cell phenotype was exacerbated when Suz12 was inhibited by shRNA-mediated silencing , providing independent confirmation that the mutant phenotype is a direct result of impaired Suz12 expression . The identification of the Plt8 mutation has shown that Suz12 is sensitive to gene dosage within the HSC compartment , an observation that was not appreciated in an earlier loss-of-function study [23] , and this lead us to investigate the function of Suz12 and PRC2 during hematopoiesis . Whether the reduction in Suz12 protein evident in Suz12Plt8/+ mice would affect the stability of the PRC2 complex was unclear . Previous studies have demonstrated that Ezh2 , Suz12 , and Eed are interdependent , such that a reduction in any one of the PRC2 components negatively affects the stability of the others . This is evident in mice harbouring loss-of-function alleles of PRC2 components [23 , 53] and in cell lines in which one of the components has been impaired by RNA-mediated silencing [11] . Mice that are heterozygous for the Plt8 mutation also display decreased levels of Ezh2 , suggesting that the reduced expression of Suz12 limits the formation of the PRC2 complex . Similarly , gene dosage effects are evident in mice that carry hypomorphic alleles of Eed ( Eed3354 or Eed1989 ) [21 , 33 , 54 , 55] or a loss-of-function allele of Ezh2 ( this study ) . The discovery that Ezh2 , Eed , and Suz12 are all haploinsufficient demonstrates that the activity of the PRC2 complex is exquisitely sensitive to alterations in the expression of its components . This is further supported by the observation that the composition and activity of the complex becomes altered when the components are expressed at inappropriate levels [56] . One of our key findings was that Suz12Plt8/+ mice have enhanced HSC activity , which likely accounts for changes that are evident in the peripheral blood . A similar phenotype has been described in mice that carry a hypomorphic allele of Eed . Eed3354/+ mice show elevated numbers of multi-potent progenitors in long-term bone marrow cultures [33] , and this finding was used to suggest an important function for PRC2 in restricting the proliferation of myeloid and lymphoid progenitors . Our study extends this result and has identified a role for PRC2 in regulating the functional activity of HSCs . The proliferative defects evident in Eed3354/+ mice worsen with age and ultimately progress to leukaemia [33] . Although HSCs derived from Suz12Plt8/+ mice were clearly more competitive than wild-type cells , leukaemia was not observed in Suz12Plt8/+ mice or in recipients of Suz12Plt8/+ bone marrow . It is tempting to speculate that the phenotypic similarities between Eed3354/+ and Suz12Plt8/+ mice result from their common contribution to the PRC2 complex , and that progression to leukaemia in Eed3354/+ mice is a consequence of a greater impairment to PRC2 activity . Elevated platelet counts evident in Ezh2+/- c-Mpl–/– animals strongly suggest that PRC2 is central to the phenotypic changes in Suz12Plt8/+ mice; however , it remains possible that Suz12 has a functional role that is independent of the complex . A series of elegant studies have demonstrated that PRC2 functions to maintain the undifferentiated state in embryonic stem cells , and it is quite possible that the complex fulfils a similar role in HSCs . An increased rate of differentiation , in the context of PRC2 deficiency , could explain the elevated contribution of Suz12Plt8/+ HSCs to hematopoiesis and is consistent with the progressive nature of the hematopoietic defects evident in Eed3354/+ mice [33] . Kamminga and colleagues recently demonstrated that exogenous expression of Ezh2 preserves stem cell function during serial bone marrow transplantation and suggested that PRC2 prevents replicative senescence within the HSC compartment [36] . With this in mind , we performed experiments to assess the integrity of homeostatic mechanisms that regulate the HSC pool in Suz12Plt8/+ mice . Although alterations in the number and function of HSCs were detected , several observations suggest that these cells retain their ability to self-renew and do not become senescent prematurely: first , the hematopoietic system is stable in Suz12Plt8/+ mice as they age; second , Suz12Plt8/+ HSCs continue to contribute effectively to hematopoiesis , even after three rounds of transplantation; and third , mice that carry the Plt8 mutation respond normally to treatment with the cytotoxic agent 5-fluorouracil , which selectively targets cycling cells ( unpublished data ) . It is likely that additional resources , such as conditional targeted alleles , will be required to determine the precise role of PRC2 within the HSC compartment . To better understand the mechanism by which the Plt8 mutation influences hematopoiesis , we investigated changes in gene expression associated with PRC2 deficiency . Before this study , knowledge of PRC2 target genes within the hematopoietic compartment was limited . We used shRNA-mediated silencing to impair Suz12 expression in the erythro-megakaryocytic cell line G1ME and in primary hematopoietic progenitors and stem cells . Global analysis of gene expression by microarray identified several hundred transcripts that were differentially expressed in cells that lacked Suz12 . The vast majority of these genes showed elevated expression , which supports the prevailing view that PRC2 and H3K27-3Me are required for the maintenance of transcriptional repression ( reviewed in [6] ) . Previous studies have identified genes that are regulated by PRC2 in a variety of different cell types , including mouse and human ES cells , fibroblasts , and numerous cell lines derived from tumours . Our results suggest that PRC2 regulates a distinct set of genes in hematopoietic cells , because very few of the genes identified as mis-regulated in Suz12-deficient hematopoietic cells have previously been reported as PRC2 targets [9–11 , 52] . We identified eight genes that were similarly altered in Suz12Plt8/+ LSK cells and Suz12-deficient progenitors ( LK cells ) , demonstrating that some target genes are conserved between stem cells and progenitors . This included the uncharacterised transcript 2810025M15Rik , which was also up-regulated in Suz12-deficient G1ME cells ( unpublished data ) . Other genes were specifically altered within primary progenitor cells , which included a series of genes that are inappropriately expressed in cancer cells . Bex2 , Bex4 , and Fibulin have been implicated in the progression of various types of cancer—including breast cancer , glioma , and prostate cancer [57–60]—and work with cell lines and primary tumour samples has provided evidence that epigenetic mechanisms contribute to the regulation of these genes . For example , both Bex2 and Bex4 become activated when cancer cells are treated with agents that inhibit DNA methylation [58] . Our results suggest that Polycomb group proteins contribute to silencing these genes . A recent study identified a subset of breast cancers that express high levels of Bex2 , and it will be important to determine whether these tumours display impaired PRC2 function [61] . Tumours that express Bex2 are highly sensitive to treatment with tamoxifen , and the inhibition of PRC2 may represent a mechanism to promote Bex2 expression . Few studies have addressed the role of these genes in leukaemogenesis , yet it has been shown that Bex2 is highly expressed in acute myeloid leukaemia samples that carry activating translocations in the trithorax group gene , Mixed lineage leukaemia ( Mll ) [62 , 63] . Bex2 expression appears to be highly responsive to changes that disrupt the balance between Polycomb and trithorax complexes . PRC2 has also been implicated in the development of acute promyelocytic leukaemia via direct interaction with the oncogenic fusion protein PML-RAR [64] . Our results suggest that PRC2 may contribute to leukaemogenesis by directly silencing tumour suppressor genes . Modulation of PRC2 complex , either through inhibition or enhancement of complex activity , has distinct consequences for the behaviour of HSCs . Major impairment of the complex is associated with defective maturation in lymphoid cells and leukaemia , whereas the modest reduction in complex activity in Suz12Plt8/+ mice enhances blood cell production and the performance of HSCs during transplantation . Our data support an important role for PRC2 in regulating gene expression during hematopoiesis . A more detailed knowledge of PRC2 target genes within the HSC compartment , and their response to altered expression of PRC2 components , will enable a better understanding of the role of the complex during development and in disease .
c-Mpl–/– mice used in this study were maintained on an inbred C57BL/6 background [41] . Male c-Mpl–/– mice were injected with 200–400 mg/kg N-ethyl-N-nitrosourea ( Sigma ) , which was dissolved in ethanol and diluted in sodium citrate buffer ( 100 mM sodium dihydrogen phosphate , 50 mM sodium citrate ) . ENU-treated mice were mated to c-Mpl–/– females to produce first-generation ( G1 ) mice for analysis . At 7 wk of age , G1 mice were bled , and platelet counts were measured using an Advia 120 automated haematological analyser ( Bayer ) . G1 mice with elevated platelet counts ( >300 × 106/ml ) were bred with c-Mpl–/– mice to test for heritability . Using this approach , the PLT8 pedigree was established , in which approximately 50% of mice showed elevated platelet count consistent with a dominant mode of inheritance . Progeny-tested mice , with inferred genotype C57BL/6 Mpl–/– Plt8/+ , were crossed with 129/Sv Mpl–/– +/+ mice to produce an F1 population . F1 mice with high platelet counts ( >150 × 106/ml ) were intercrossed to generate F2 mice for mapping . DNA was isolated from 90 F2 mice and a genome-wide scan was performed with polymorphic microsatellite markers . A candidate interval for Plt8 was identified between D11MIT245 and D11MIT120 on Chromosome 11 ( from base pair 77 , 045 , 359 to 83 , 660 , 314 ) . Additional SSLP markers were designed using PRIMER3 software available through the Whitehead Institute for Biomedical Research ( http://frodo . wi . mit . edu/ ) . A further 531 F2 mice were genotyped , and the candidate interval was refined to 1 . 4 Mbp between CAR28 and CAR48 ( from base pair 79 , 314 , 902 to 80 , 704 , 402 ) . Mapping and sequencing primers are included as supplementary information . Genomic DNA was isolated from affected PLT8 mice and exons were amplified by PCR . PCR was carried out with Platinum Taq polymerase ( Invitrogen ) in buffer supplied by the manufacturer . Reactions contained approximately 20 ng of template DNA , 2 . 5 mM MgSO4 , 50 μM dNTPs , 2 units of polymerase , and 10 pmol of each primer . PCR products were treated with ExoSAP-IT ( USB Corporation ) according to the manufacturers instruction , and sequenced using the Big Dye Terminator V3 . 1 sequencing kit ( Applied Biosystems ) . Sequencing reactions were centrifuged through G-50 sephadex columns ( GE Healthcare ) to remove additional dye products , before processing on an ABI 3700 sequence analyser ( Applied Biosystems ) . Manual or automated counts were performed on blood collected from the retro-orbital plexus into sample tubes coated with EDTA ( Sarstedt , Germany ) . In vitro colony assays were used to characterise hematopoietic progenitors as described [41] . Bone marrow ( 2 . 5 × 104 cells ) or spleen cells ( 5 × 104 cells ) were cultured in 1 ml of 0 . 3% agar in DMEM supplemented with 20% ( v/v ) FCS and various recombinant cytokines as defined in the text . CFU-S were enumerated 12 d after transplantation of donor bone marrow ( 1 . 5 × 105 c-Mpl–/– cells or 7 . 5 × 104 c-Mpl+/+ cells ) into lethally irradiated recipients . Spleens were fixed in Carnoy's solution ( 60% ( v/v ) ethanol , 30% ( v/v ) chloroform and 10% ( v/v ) glacial acetic acid ) . Competitive transplantation studies were performed using CD45Ly5 . 2 donor animals and CD45Ly5 . 1 recipients . In each experiment , 1 × 106 test cells ( CD45Ly5 . 2 ) were transplanted into lethally irradiated CD45Ly5 . 1 recipients ( 5 per donor marrow ) , with an equal number of CD45Ly5 . 1 competitor cells . Competitor cells were matched by c-Mpl genotype , because c-Mpl–/– HSCs are rapidly out-competed by c-Mpl+/+ cells [45] . Peripheral blood was analysed at 28 d and at 56 d post transplant , and after 3 mo , bone marrow , spleen , thymus , and peripheral blood were analysed and serial transplantations were performed . In each case , the representation of test and competitor ( CD45Ly5 . 2 and CD45Ly5 . 1 ) was measured in B cells ( B220+ ) , T cells ( CD4+ , CD8+ ) , and in macrophages and neutrophils ( Gr1+/Mac1+ ) . Bone marrow from primary recipients was pooled within each donor group for use in secondary transplants , and the representation of CD45Ly5 . 2 cells was measured before transplantation . For secondary transplants , 2 × 106 test cells were injected into each CD45Ly5 . 1 recipient , and tertiary transplants were performed in the same manner . Bone marrow was isolated into CATCH buffer ( Phenol-red free , Ca2+-free Hank's balanced salt solution with 3% ( w/v ) BSA , 1 . 3 mM sodium citrate , 1 mM adenosine , 2 mM theophylline and 3% ( v/v ) FCS ) , and stained with FITC-conjugated anti-CD41 antibody ( BD Biosciences ) . Samples were then treated with concentrated propidium iodide ( 0 . 05 mg/ml in 3 . 4 mM sodium citrate ) for 1 h . Cells were washed in CATCH buffer , and aggregates were removed by passage through a 100-μm sieve . Samples were then treated with 50 μg/ml RNAse H ( Promega ) at room temperature , before analysis on a FACScan 2 flow cytometer ( BD Biosciences ) . Protein lysates were prepared from primary tissues , or cell lines , in RIPA buffer ( 1% ( v/v ) Nonidet P-40 , 0 . 1% ( w/v ) SDS , 0 . 5% ( w/v ) sodium deoxycholate , 150 mM NaCl , 50 mM Tris . HCl pH 7 . 5 ) supplemented with protease inhibitors ( Roche Diagnostics ) . 293T cells grown in DMEM with 10% ( v/v ) FCS were transfected with expression constructs using FuGENE-6 reagent ( Roche Diagnostics ) . Cells were lysed after 48 h and proteins were separated by SDS-PAGE . Protein was transferred to a PVDF membrane and blotted with antibodies to detect Suz12 , Ezh2 , Histone 3 , H3K27-3Me ( Upstate ) , Akt ( Cell Signaling ) or the FLAG-epitope ( M2 ) ( Sigma ) . Retroviral supernatants were prepared by transient transfection of 293T cells with plasmids that encode viral envelope proteins and a specific LMS/LMP knock-down vector . shRNAmir constructs ( pSM2 ) that target Suz12 ( A: V2MM_96046 or B: V2MM_196969 ) , Ezh2 ( A: V2MM_35988 or B: V2MM_25325 ) , and a nonspecific sequence ( Nons ) were obtained from Open Biosystems , and the hairpin sequence was subcloned into the LMS/LMP vectors [65 , 66] . The LMS/LMP vectors drive expression of a modified micro RNA ( mir30 backbone ) with selectable markers EGFP or EGFP/puromycin , respectively . 293T cells were transfected using the calcium phosphate precipitation method . 293T cells were treated with 25 μM chloroquine for 30 min prior to transfection in DMEM with 10% ( v/v ) FCS . The precipitated DNA was added dropwise to the cells , and the media was changed after an 8-h incubation . Media was replaced with Iscove's modified Dulbecco's medium ( IMDM ) with 10% ( v/v ) FCS after 24 h , and viral supernatants were harvested the following day . C57BL/6 ( CD45Ly5 . 2 ) mice were treated with a single dose of 150 mg/kg 5-fluorouracil ( 5-FU ) ( ONCO-TAIN , Mayne Pharmaceuticals ) by intra peritoneal injection . After 5 d , bone marrow was collected from femurs and tibias into PBS with 10% ( v/v ) FCS . Red blood cells and dead cells were removed by centrifugation through Ficoll-Paque ( GE Healthcare ) . Cells were washed once with PBS , and resuspended in IMDM supplemented with 10% ( v/v ) FCS and cytokines ( 10 ng/ml IL-6 , 5 ng/ml IL-3 , 50 ng/ml Flt3 ligand , and 50 ng/ml SCF ) . Cells were grown overnight at 37 °C in a humidified atmosphere with 10% CO2 in air . Retroviral supernatants were applied to culture dishes pre-treated with RetroNectin ( Takara Biosciences ) , and centrifuged at 4000g for 1 h at 4 °C . Bone marrow cells were co-cultured with the virus in the presence of polybrene ( 4 μg/ml ) for 24 h to allow for infection . Cells were washed out of polybrene-containing medium into fresh medium , and incubated for 24 h . Cells were removed from dish and washed twice in BSS 3% FCS before being counted . These cells were used to reconstitute lethally irradiated CD45Ly5 . 1 recipients; approximately 5–10 × 105 viable cells were injected into each recipient . Total RNA was extracted from tissues or cell lines using Trizol reagent ( Invitrogen ) , and reverse transcribed with an oligo-dT primer using Superscript-III Reverse Transcriptase according to the manufacturers instructions ( Invitrogen ) . For sorted cell populations , RNA was prepared using RNeasy mini purification columns ( Qiagen ) . Q-PCR reactions were set up to quantify expression of mouse Suz12 , Ezh2 , Eed , Hprt1 , Bex2 , Bex4 , and Hmbs using specific pre-designed Taqman gene expression assays ( Mm01304152_m1 , Mm00468449_m1 , Mm00469651_m1 , Mm00446968_m1 , Mm02528127_s1 , Mm02376173_g1 , and Mm00660262_g1 , respectively ) ( Applied Biosystems ) . Typically , PCR reactions were performed in 10 μl volume , and included the following: 1 μl of cDNA , 0 . 5 μl pre-designed assay mix ( primers and sequence specific probe ) , 3 . 5 μl H2O , and 5 μl of 2x Taqman Universal Master Mix ( Applied Biosystems ) . All Q-PCR reactions were performed on the ABI 7900 HT real-time PCR platform ( Applied Biosystems ) . Ct values were derived using SDS2 . 2 software ( Applied Biosystems ) , and relative gene expression was calculated using the 2−ΔΔCt method [67] . Fluorophore- or biotin- conjugated antibodies directed against mouse CD4 ( clone GK1 . 5 ) , CD43 ( clone 57 ) , CD8 ( clone 53–6 . 7 ) , c-Kit ( clone 2B8 ) , Flt3 ( CD135 ) ( clone A2F10 . 1 ) , IgD ( clone 11–26c . 2a ) , IgM ( clone II/41 ) , CD45Ly5 . 1 ( clone A20 ) , CD45 . 2Ly5 . 2 ( clone 104 ) , Sca-1 ( clone D7 ) , Ter119 ( clone TER-119 ) , Thy1 . 2 ( CD90 . 2 ) ( clone 53–2 . 1 ) , and Rat Ig ( clone MRK-1 ) were obtained from Pharmingen . Anti-CD34 ( clone RAM34 ) was obtained from eBioscience , and goat anti-rat IgG was obtained from Southern Biotech . Rat monoclonal antibodies against the mouse antigens CD3 ( clone KT3–1 . 1 ) , CD19 ( clone ID3 ) , B220 ( clone RA3-6B2 ) , CD11b ( clone M1/70 ) , Gr1 ( clone IA8 ) , CD2 ( clone RM2 . 1 ) , CD8 ( clone 53–6 . 7 ) , Ter119 ( clone TER-119 ) were prepared in our own laboratory . Bone marrow was harvested from 7–12-wk-old C57BL/6 mice , or secondary transplant recipients of LMS-infected bone marrow , 4–6 mo post-transplant . Live nucleated cells were purified by centrifugation in Nycodenz medium ( Axis-Shield ) with a density of 1 . 086 g/cm3 . These cells were incubated with a cocktail of monoclonal antibodies against the lineage markers CD3 , CD19 , B220 , CD11b , Gr1 , CD2 , CD8 , and Ter119 prepared in our own laboratories , then mixed with BioMag goat anti-rat IgG beads ( Qiagen ) . Lin+ cells were depleted using a Dynal MPC-L magnetic particle concentrator ( Invitrogen ) . Remaining cells were stained with fluorophore-conjugated anti-Rat Ig antibodies to allow residual Lin+ cells to be gated out , then with monoclonal antibodies to Sca-1 , c-Kit , CD34 , Flt3 , and CD45Ly5 . 1 ( where applicable ) . Cells were flow sorted on a FACSDiva , FACSAria ( BD Biosciences ) , or MoFlo ( Dako ) . RNA extracted from 50 , 000–500 , 000 LSK or LK cells ( Lin– , Ly5 . 1– , c-Kit+ ) was labeled , amplified , and hybridised to Illumina MouseWG-6 V1 . 1 Expression BeadChips according to Illumina standard protocols . Samples were processed at the Queensland Institute of Molecular Biology , Brisbane , Australia , and the Australian Genome Research Facility , Melbourne , Australia . Each sample was derived from bone marrow LSK or LK cells from at least six donor mice . A total of 12 LSK arrays were performed ( 9 Suz12+/+ and 3 Suz12Plt8/+ ) . Data were analyzed in R and subjected to variance stabilising transformation and quantile normalization . Linear modelling using an empirical Bayes approach , including a batch factor , was applied to the data [68] . Data was corrected for multiple testing using Benjamini and Hochberg correction . For the combinatorial comparison with LSK and LK datasets , all probesets were considered ( irrespective of expression level ) . Data were normalized and corrected for multiple testing as above . A more detailed description of microarray data treatment is provided ( Text S1 ) . Microarray data is available in MIAME-compliant form at Array Express ( www . ebi . ac . uk/arrayexpress/ ) under accession ( E-TABM-380 ) .
The Entrez Gene ID ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=gene ) for genes and gene products discussed in this paper are as follows: Bex2 ( GI:12069 ) , Bex4 ( GI:19716 ) , Bmi-1 ( GI:12151 ) , c-Mpl ( GI:17480 ) , c-Myb ( GI:17863 ) , Cyclin-dependent kinase 5 activator 1 precursor ( GI:12569 ) , Cytokine receptor-like factor 3 ( GI:54394 ) , Eed ( GI:13626 ) , Ezh2 ( GI:14056 ) , Fibulin ( GI:14114 ) , Mll ( GI:214162 ) Rhomboid veinlet-like protein 4 ( GI:246104 ) , Ring finger protein 135 ( GI:71956 ) , Suz12 ( GI:52615 ) , Tpo ( GI:21832 ) , and Zinc finger protein 207 ( GI:22680 ) .
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The chromatin environment that surrounds a gene heavily influences the gene's transcriptional activity . Specific modifications on histone tails serve as signposts for the basal transcriptional machinery , reflecting a cell's developmental history and identifying genes that should be actively transcribed and those that must be repressed . Polycomb group proteins are involved in large , multiprotein complexes that catalyse the post-translational modification of histones . The disruption of these complexes induces wholesale changes in gene expression , a scenario commonly seen in diseases such as cancer . We have investigated the role of Polycomb group proteins during blood cell formation: in stem cells , progenitor cells , and mature blood cells . Using a variety of functional assays , we demonstrate an important role for Polycomb group proteins in restricting the activity of hematopoietic stem cells . To define the molecular targets of the complex , we examined gene expression profiles in cells with impaired expression of Polycomb group proteins . This analysis identified a set of target genes within the hematopoietic compartment that was distinct from those defined in embryonic stem cells and fibroblasts . This study provides new insights into the role of these proteins during hematopoiesis , and suggests a novel mechanism by which they might contribute to leukaemia .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
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[
"molecular",
"biology",
"genetics",
"and",
"genomics",
"hematology"
] |
2008
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Polycomb Repressive Complex 2 (PRC2) Restricts Hematopoietic Stem Cell Activity
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Functional oocytes are produced through complex molecular and cellular processes . In particular , the contribution of post-transcriptional gene regulation mediated by RNA-binding proteins ( RBPs ) is crucial for controlling proper gene expression during this process . DAZL ( deleted in azoospermia-like ) is one of the RBPs required for the sexual differentiation of primordial germ cells and for the progression of meiosis in ovulated oocytes . However , the involvement of DAZL in the development of follicular oocytes is still unknown . Here , we show that Dazl is translationally suppressed in a 3′-UTR-dependent manner in follicular oocytes , and this suppression is required for normal pre-implantation development . We found that suppression of DAZL occurred in postnatal oocytes concomitant with the formation of primordial follicles , whereas Dazl mRNA was continuously expressed throughout oocyte development , raising the possibility that DAZL is dispensable for the survival and growth of follicular oocytes . Indeed , follicular oocyte-specific knockout of Dazl resulted in the production of normal number of pups . On the other hand , genetically modified female mice that overexpress DAZL produced fewer numbers of pups than the control due to defective pre-implantation development . Our data suggest that post-transcriptional suppression of DAZL in oocytes is an important mechanism controlling gene expression in the development of functional oocytes .
The production of functional oocytes is an essential process in the female ovary , by which genetic information is continuously passed to the next generations . For successful oocyte development , gene expression needs to be precisely regulated according to the developmental stages and environmental cues such as gonadotropic hormones . Oocytes that fail to regulate proper gene expression are degenerated during their development or are unable to proceed with embryonic cleavage even if they are fully developed [1] . Therefore , unveiling the mechanisms controlling the quality of oocytes is a crucial issue to understand the molecular basis of female reproduction . Post-transcriptional gene regulation mediated by RNA-binding proteins is an important molecular mechanism involved in this process . An evolutionarily conserved and well-documented post-transcriptional event is the translational suppression and storage of maternal mRNAs with shorter poly ( A ) tails [2] . Although the genome is actively transcribed and proteins are produced during oocyte growth , transcription become inactive in full-grown oocytes and maternal mRNAs are used for protein synthesis in early zygote development [3] . These processes are orchestrated by a battery of relevant RNA-binding proteins , including cytoplasmic polyadenylation element binding proteins ( CPEB ) , maskin , and other germ cell-specific RNA-binding proteins [4 , 5 , 6 , 7] . In addition to the maturation process , germ cell-specific RNA-binding proteins are also responsible for multiple processes in oogenesis . For instance , CPEB1 and Pumilio1 are involved the progression of meiotic prophase I in the embryonic ovary[8 , 9] , and MSY2 is involved in follicle development after birth in mice [10 , 11] , suggesting the significant contribution of post-transcriptional gene regulation throughout oogenesis . Deleted in azoospermia-like ( DAZL ) is a member of the evolutionarily conserved DAZ family of RNA-binding proteins that acts as a translational activator in mice [12] . Biochemical and structural analyses showed that DAZL binds to the U-rich region of its target’s 3′-UTR [13 , 14 , 15] , and genetic analyses revealed that DAZL is indispensable for gametogenesis in both males and females [16 , 17 , 18] . As DAZL is reportedly required for sexual differentiation of primordial germ cells , progression of meiotic prophase I in embryonic female germ cells [19] , and for the progression of meiosis in maturating oocytes [20] , it is believed that DAZL is involved in female germ cell development throughout oogenesis . However , the role of DAZL in follicular oocytes remains unknown because Dazl-deficient oocytes die due to the failure of meiotic progression in the embryonic ovary [16 , 21] . Moreover , although the previous immunohistochemical analysis demonstrated that DAZL was expressed in both embryonic and follicular oocytes in postnatal ovaries [16] , it was also noted that DAZL signals were not detectable by western blotting in ovaries 1 to 2 weeks after birth [22] . Therefore , further analysis is required to clarify this contradiction . In this study , we investigated DAZL expression in embryonic and postnatal ovaries , and found that DAZL was translationally suppressed in a 3′-UTR-dependent manner in follicular oocytes . Genetic analysis by knocking out the Dazl gene in a follicular oocyte-specific manner indicated that Dazl is dispensable for follicular growth , maturation , and fertilization . On the other hand , the 3′-UTR-dependent suppression of DAZL in follicular oocytes is required for the progression of normal pre-implantation development . Our data clarify the previously ambiguous expression pattern of DAZL in the postnatal ovary , and simultaneously demonstrate the significance of the post-transcriptional suppression of DAZL in follicular oocytes .
To examine Dazl/DAZL expression in detail , we performed quantitative reverse transcription-polymerase chain reaction ( RT-qPCR ) and western blotting analyses using ovaries from embryos until juvenile stages ( Fig 1A and 1B ) . RT-qPCR data showed that Dazl mRNA was constantly expressed and expression differences were less than 2-fold among all stages investigated ( Fig 1A ) . On the other hand , DAZL expression was markedly changed during oocyte development ( Fig 1B ) . Although it was abundant in embryonic ovaries , with the strongest expression at embryonic day ( E ) 15 . 5 , the expression declined in newborn ovaries . Afterwards , DAZL expression further declined and was hardly detectable in 1- and 2-week ovaries , which is consistent with previous descriptions [22] . As DAZL expression was significantly decreased in the newborn ovary and onward , we next asked whether this reduction in DAZL was correlated with the formation of primordial follicles . In the embryonic ovary , oocytes are connected with each other by intercellular bridges ( ICBs ) . Within a few days after birth , ICBs are broken and each oocyte is enclosed by pre-granulosa cells , resulting in the formation of primordial follicles [23] . Thus , we examined the expression changes of DAZL in perinatal ovaries by immunostaining DAZL together with a granulosa cell marker , forkhead box protein L2 ( FOXL2 ) , from E18 . 5 to 1W ovaries . Strong DAZL signals were observed in most oocytes until the day of birth ( P0 ) , when a large number of oocytes were still connected with each other . However , at one day after birth ( P1 ) , its expression began to decrease in some oocytes ( Fig 1C , open arrowheads ) . The expression of DAZL was further decreased at two days after birth ( P2 ) , at which point , many oocytes formed primordial follicles ( Fig 1C , yellow arrowheads ) and exhibited weaker expression than cystic-oocytes ( Fig 1C , white arrowheads ) . Thereafter , the weakened DAZL expression was observed in 1-week ovaries . These data suggest that DAZL is decreases in oocytes coinciding with the development of primordial follicles . Both immunostaining and western blotting analyses revealed that DAZL was decreased in oocytes shortly after birth , which raised the possibility that DAZL is dispensable for follicular development . To test this possibility , we used conditional Dazl knockout ( cKO ) mice ( Fig 2A ) . Dazlflox mice were crossed with a postnatal oocyte-specific Cre mouse line , Gdf9-iCre , which expresses improved Cre recombinase from P2 oocytes [25] . The Dazl gene was successfully disrupted by Gdf9-iCre , as evidenced by RT-qPCR and western blotting , in which both Dazl mRNA and DAZL protein were hardly detectable in Dazl cKO ovaries ( Fig 2B ) . We also confirmed that our Dazl KO ( Dazl 1lox/1lox ) mouse line recapitulated the phenotype of previous Dazl knockout females ( S1 Fig ) [16] . On histological analysis , Dazl cKO ovaries as well as control ovaries contained both primordial and growing follicles ( Fig 2C ) . Notably , cKO ovaries did not have any significant differences in the number of primordial or growing follicles ( Fig 2D ) . These data suggest that DAZL is dispensable for the survival and growth of follicular oocytes . In order to evaluate the reproductive capability of Dazl cKO oocytes , we next crossed Dazl cKO females with wild-type ( WT ) males . We found that Dazl cKO females were fertile and produced a normal number of pups ( Fig 2E ) . The average litter size delivered from Dazl cKO females ( 11 . 3±0 . 62 ) was almost identical with that from WT ( 12 . 0±2 . 83 ) . We also confirmed that all progeny delivered by Dazl cKO females were heterozygotes for the Dazl1lox allele ( n = 258 ) . These results were surprising because a previous report stated that Dazl knockdown in MII oocytes results in the defective progression of the oocyte to zygote transition [20] . However , MII oocytes derived from Dazl cKO females did not have abnormal spindle morphology ( S2 Fig ) . These data indicate that DAZL is not required for the maturation of oocytes or subsequent fertilization . In embryonic male germ cells , Dazl is post-transcriptionally suppressed in a 3′-UTR -dependent manner by a male-specific RNA-binding protein , NANOS2 [26] . As DAZL decreases in postnatal ovaries , it is possible that Dazl is also post-transcriptionally suppressed in a 3′-UTR -dependent manner by unidentified mechanisms in follicular oocytes . In order to test this possibility , we used our bacterial artificial chromosome ( BAC ) -carrying transgenic mouse line , in which the FLAG tag was inserted at the C-terminus of Dazl and the Dazl 3′-UTR was flanked with Frt sequences ( Dazl 3F , Fig 3A upper ) [26] . The significance of the Dazl 3′-UTR for its expression was assessed by crossing the BAC transgenic female with a Rosa-Flp male ( Dazl 3F;Flp , Fig 3A lower ) . RT-qPCR showed that the amount of Flag-Dazl mRNA was increased in Dazl 3F;Flp ovaries after birth ( Fig 3B ) . However , the effect of removing the 3′-UTR was not clear because the difference in Flag-Dazl mRNA expression levels between Dazl 3F and Dazl 3F;Flp was less than 2-fold , and the total Dazl expression level ( Flag-Dazl +endogenous Dazl ) was not changed between Dazl 3F and Dazl 3F;Flp except in the P0 ovary ( Fig 3B and 3C ) . In contrast to the small increase in the mRNA level , FLAG-DAZL expression was greatly increased after birth ( Fig 3D ) . Although FLAG-DAZL ( filled arrowheads ) decreased in Dazl 3F ovaries from P0 onward , which was consistent with the reduction in endogenous DAZL ( open arrowhead ) , its expression was continuously observed in P0 , 1W , and 2W ovaries when the 3′-UTR was removed . Quantification of FLAG-DAZL expression revealed that its expression increased 20-fold in Dazl 3F;Flp at P0 ( Fig 3E ) . The results of western blotting were also supported by immunostaining . Both total- and FLAG-DAZL expression was strongly observed in Dazl 3F;Flp ovaries ( Fig 3F and S3 Fig ) , whereas their expression levels in WT and Dazl 3F ovaries were comparable with those in Dazl cKO ovaries . Furthermore , strong DAZL expression was observed in all stages of follicular oocytes in Dazl 3F;Flp ovaries ( S3 Fig ) . These data indicate that DAZL is post-transcriptionally suppressed in a 3′-UTR-dependent manner in follicular oocytes . To investigate the role of 3′-UTR-dependent DAZL suppression in female reproduction , we crossed BAC transgenic females with WT males when female mice reached 6 weeks old . Each pair was kept in a breeding cage until female mice became 30 weeks old , and the number of pups delivered during this period was counted . We found that BAC transgenic females were fertile regardless of the presence or absence of the Dazl 3′-UTR ( Fig 4A ) . The number of total pups was slightly lower by in Dazl3F females ( 39 . 8±5 . 5 , n = 5 ) compared with control females ( 53 . 1±9 . 1 , n = 7 ) . Interestingly , Dazl 3F;Flp females produced less than half the normal number of pups ( 18 . 6±11 . 3 , n = 5 ) . As 3FLAG-DAZL protein rescued the germless phenotype in Dazl-/- mice [26] , it is unlikely that the observed litter size reduction was caused by the expression of 3FLAG-DAZL . Thus , these results suggest that DAZL overexpression results in litter size reduction . We next analyzed the number of deliveries and the number of pups in each delivery . The number of pups in each delivery was fewer by Dazl 3F and Dazl 3F;Flp mice ( Fig 4B ) , but the number of deliveries was not significantly different among genotypes ( Fig 4C ) . These results suggest that the reduced female fecundity was due to defects during follicular development , fertilization , or zygote development after fertilization , but not to the shortened reproductive lifespan . To determine the cause of the litter size reduction in the DAZL overexpressing females , we examined the development of oocytes , fertilization , and pre-implantation development . Histological analysis revealed that BAC transgenic ovaries did not have significantly different numbers of primordial , primary , secondary or antral follicles compared with WT ovaries ( Fig 5A and 5B ) . We next asked whether ovulation normally occurs by counting the number of one-cell embryos ovulated . However , the number was not significantly different among the genotypes ( Fig 5C ) . These data suggest that folliculogenesis and subsequent ovulation proceeds normally even in BAC transgenic females . Thus , to examine whether these ovulated eggs developed normally , we measured the proportion of blastocysts by flushing E3 . 5 embryos from oviducts . We cultured the collected embryos for a further two days and then counted the embryos because the different timing of sexual behavior in each mouse pair influences the progression of early embryonic development ( Fig 5D ) . We found that only 56 . 1% of embryos derived from Dazl 3F;Flp females developed into blastocysts , whereas more than 97 . 3 and 97 . 1% embryos derived from control and Dazl 3F females became blastocysts , respectively . The development of the remaining 43 . 9% of Dazl 3F;Flp embryos stopped at the 1-cell to morula stages . These observations were reproduced in 1-cell culture experiments , in which development was specifically disrupted in embryos from Dazl 3F;Flp females ( S4A Fig ) . Statistical analysis revealed that development was arrested during 1- to 4-cell and 8-cell to blastocyst stages in embryos from Dazl3F;Flp mother ( S4B Fig ) . Furthermore , the spindle morphology was normal in Dazl3F;Flp oocytes ( S2 Fig ) . These results indicate that the reduction of pups in Dazl 3F;Flp females was due to defective pre-implantation development . As strong DAZL expression was observed in Dazl 3F;Flp until the MII oocyte stage but decreased in 1-cell embryos and was no longer detectable in 2-cell embryos ( S5 Fig ) , it is likely that abnormal expression of DAZL in oocytes causes the defective pre-implantation development .
In this study , we demonstrated that DAZL expression is post-transcriptionally suppressed in a 3′-UTR-dependent manner in postnatal oocytes . Although DAZL has been thought to function in postnatal oocytes , our data suggest that DAZL is not required for postnatal oocyte development . Supporting this idea , analysis of conditional Dazl knockout mice revealed that DAZL is dispensable for postnatal oocyte development . Furthermore , excess DAZL expression results in litter size reduction . These data indicate that post-transcriptional regulation of Dazl plays a crucial role in normal female reproduction . It was previously reported that DAZL was expressed in growing oocytes [16] , but a later study stated that DAZL was not detectable in the postnatal ovary [22] . Therefore , it has been unclear whether DAZL plays a role in follicular oocytes . Our results answered this question; DAZL expression is suppressed in follicular oocytes and is dispensable for oogenesis after birth . Interestingly , this suppression coincides with the formation of primordial follicles . As Dazl mRNA was continuously expressed in oocytes regardless of developmental stage , it is likely that post-transcriptional gene regulatory mechanisms are altered between cystic oocytes and follicular oocytes . Importantly , DAZL suppression requires its 3′-UTR , suggesting the presence of some mechanisms regulating DAZL expression in postnatal oocytes . In general , post-transcriptional regulation is conducted by microRNAs and RNA-binding proteins [27] . However , it was reported that the function of microRNA is globally suppressed in oocytes and early embryonic development [28] . Thus , it is possible that Dazl expression is regulated by some RNA-binding proteins ( RBPs ) . One possible candidate RBP for DAZL suppression is CPEB1 , a mammalian ortholog of Xenopus CPEB . CPEB acts as both a translational activator and suppressor of its target mRNAs depending on its phosphorylation state [29 , 30] . CPEB1 is expressed in postnatal oocytes and promotes the translation of Dazl in MII oocytes[20] , thus it may suppress Dazl in follicular oocytes . Further expression and functional analyses , including the phosphorylation state , of CPEB1 are required to address this question . Our oocyte-specific Dazl KO females exhibited no ovarian developmental defect and the MII oocytes had no spindle abnormalities . Furthermore , the Dazl cKO females produced normal numbers of pups . These observations were inconsistent with Chen and colleague’s results that DAZL depletion in MII oocytes results in defective spindle formation in meiosis II [20] . One possible explanation for this contradiction is the method of gene depletion . We used the Cre-loxP system for Dazl cKO in vivo , whereas Chen et al . used morpholino knock-down in MII oocytes . A recent zebrafish report found that approximately 80% of phenotypes induced by morpholino do not correlate with mutant phenotypes induced by ZFN , TALEN or CRISPER/Cas9; therefore , the above-mentioned knock-down phenotype may have emerged due to indirect effects [31] . Alternatively , it is possible that some system that compensates for DAZL function works in Dazl-cKO MII oocytes because a previous study reported that the activation of a compensation system rescued deleterious mutations , which was not observed after translational or transcriptional knockdown [32] . Further analysis is required to evaluate the contribution of RNA-binding proteins for the progression of meiosis II . Although DAZL expression is suppressed after birth , introducing the BAC transgenic allele in the Dazl+/+ background reduced litter size even in the presence of the 3′-UTR ( Fig 4 ) . As a previous study reported that Dazl dosage in females influences their litter size , and Dazl+/− females produced more pups than Dazl+/+ females [22] , the slight reduction in litter size by our Dazl 3F mice may be attributed to the dosage effect . However , our histological and embryo culture experiments did not reveal any abnormalities in Dazl 3F mice . In addition , we were unable to observe obvious differences in resorption after implantation . One possible explanation is that insertion of the BAC transgene influences female reproduction . Our results suggest that the suppression of Dazl translation in follicular oocytes is required for producing the proper number of progeny . However , why excess DAZL expression causes defective pre-implantation development remains still unclear . DAZL has been implicated in the positive regulation of translation [14 , 33 , 34] , thus it is possible that the observed defect may be due to abnormal translational promotion . Alternatively , it is also possible that excess DAZL abnormally suppresses its target RNAs because DAZL works as a component of stress granules , cytoplasmic RNP granules involved in translational suppression or mRNA storage , in the testis [35 , 36] . Therefore , it is likely that suppression of DAZL expression in follicular oocytes is an important molecular mechanism for controlling proper gene expression .
All mouse experiments were approved by the Animal Experimentation Committee at the National Institute of Genetics ( approval number 30–5 ) and Yokohama National University ( approval number 2017–09 ) and conducted under the Regulations for Animal Experiments at the National Institute of Genetics , Research Organization of Information and Systems and the guideline at Yokohama National University . Mice were housed in a specific-pathogen-free animal care facility at the National Institute of Genetics ( NIG ) . All experiments were approved by the NIG Institutional Animal Care and Use Committee and the animal experimental committee at Yokohama National University . The genetic background of mice used in this study was C57BL/6N ( Clea Japan ) , except in the DAZL expression analysis and conditional Dazl knockout mice ( mixed genetic background of ICR and C57BL/6N ) . The BAC-carrying transgenic mouse line was generated in a previous study [26] . The BAC transgenic mice were backcrossed with C57BL/6N at least 3 times . Dazl flox mice were generated from an ES cell line produced by the Knock Out Mouse Project ( KOMP , Dazltm2a ( KOMP ) Wtsi ) . Total RNAs were isolated from whole gonads of wild-type and BAC transgenic mice at each stage by RNeasy Mini Kit ( Qiagen ) . One hundred ng ( 1W to 5W ) and 40 ng ( E12 . 5 to P0 ) of total RNA were used for cDNA synthesis using Prime Script RT Reagent Kits with gDNA Erase according to the manufacturer’s protocol ( Takara ) . Real time PCR was performed with KAPA SYBR FAST qPCR kits using a thermal cycle dice real time system ( Takara ) . The obtained data was normalized by Mvh . The following primers were used for PCR amplification: Dazl Forward: 5′−CACGCCTCAGTGACTCGGCGAC−3’ Reverse: 5′−CGAAGCATACAGACAGTGGTC−3’ Mvh Forward: 5′−GTTGAAGTATCTGGACATGATGCAC−3’ Reverse: 5′−CGAGTTGGTGCTACAATAATACACTC−3’ G3pdh Forward: 5′−ACCACAGTCCATGCCATCAC−3’ Reverse: 5′−TCCACCACCCTGTTGCTGTA−3’ FLAG tagged Dazl Forward: 5′−CACGCCTCAGTGACTCGGCGAC−3’ Reverse: 5′−CACCGTCATGGTCTTGTAGTC−3’ Dazl cKO Forward: 5′−GACTTACATGCAGCCTCCAACCATG−3’ Reverse: 5′−AACAGGCAGCTGATATCCAGTGATG−3’ Ovaries were lysed in RIPA buffer ( 50 mM Tris-HCl ( pH8 . 0 ) , 150 mM NaCl , 0 . 5% Sodium deoxycholate , 0 . 1% Sodium dodecyl sulfate , 1% NP-40 ) and sonicated . After removing the debris by centrifugation , lysates were dissolved in 2xSDS sample buffer , and heated . MII oocytes and 1-cell zygotes were lysed in 10μl 2xSDS sample buffer . Each sample was applied to gels for SDS-PAGE and transferred to nitrocellulose membranes . The membranes were blocked in 5% skim-milk in TBST ( 50mM Tris-HCl ( pH7 . 5 ) , 150mM NaCl , 0 . 1% Tween-20 ) for 1 hour at room temperature ( RT ) . Membranes were incubated with primary antibodies ( Abcam , anti-rabbit DAZL antibody , 1:2000 for ovarian sample or 1:500 for MII , 1-cell and 2-cell zygote / Abcam , anti-rabbit DDX4 antibody , 1:1000/Santa Cruz , anti-mouse βactin , 1:2000/Sigma , anti-FLAG antibody , 1:2000 ) diluted in 3% skim-milk in TBST or Can Get Signal immunoreaction Enhancer Solution ( TOYOBO ) overnight at 4°C . After washing the membranes with TBST , membranes were incubated with anti-rabbit HRP-conjugated secondary antibody ( Cell signaling , 1:5000 ) and anti-mouse HRP-conjugated secondary antibody ( Cell signaling , 1:5000 ) in TBST or Can Get Signal immunoreaction Enhancer Solution , respectively , at RT for 90 min . The signals were detected by SuperSignal West Femto Maximum Sensitivity Substrate ( Thermo Scientific ) and AE-9300H EZ-CAPTURE MG ( ATTO ) . Western blotting results were quantified by Gel Analysis with ImageJ software . Ovaries were fixed in 4% PFA ( paraformaldehyde ) at 4°C overnight and embedded in paraffin wax . Each sample was sliced at 6-μm thickness and placed on glass slides . After removing the paraffin wax and autoclaving in antigen unmasking solution/high pH ( Vector Laboratories ) , glass slides were washed in PBST ( PBS , 0 . 1%Tween-20 ) and pre-incubated in 3% skim milk in PBST blocking solution at RT for 1 hour . The slides were reacted with primary antibodies ( Anti-DAZL antibody , Abcam , 1:200 / Anti-FOXL2 antibody , Abcam , 1:200/ Anti-FLAG antibody , SIGMA , 1:10000 ) at 4°C overnight . Then , slides were washed with PBST and incubated with second antibodies ( Alexa 488 Donkey anti-Rabbit , Life technologies , 1:1000 /Alexa 594 Donkey anti-Mouse , Life technologies , 1:1000/Alexa 594 Donkey anti-Goat , Life technologies , 1:1000 / Cy5 Donkey anti-goat , Rockland , 1:1000 ) at RT for 60 min . DNA was counter-stained with DAPI , and fluorescent images were obtained using confocal microscopy FV1200 ( Olympus ) . Ovaries were fixed with 4% PFA ( paraformaldehyde ) at 4°C overnight , which was then graded to 30% sucrose , and ovaries were then embedded in O . C . T compound ( Sakura Fine tek ) . Each sample was sliced at 6-μm thickness . After removing the O . C . T compound , slides were incubated with 3% skim milk in PBST ( PBS , 0 . 1% Tween-20 ) for 1 hour . Primary antibody reactions were performed with the following dilutions ( Anti-DAZL antibody , Abcam , 1:200 / Anti-FOXL2 antibody , Abcam , 1:200 ) at 4°C overnight . After washing with PBST , secondary antibody reaction was performed with the following dilutions ( Alexa 488 Donkey anti-Rabbit , Life technologies , 1:400 /Alexa 594 Donkey anti-Goat , Life technologies , 1:400 ) at RT for 90 min . Slides then were counter-stained by DAPI at RT for 15 min . Fluorescent images were obtained by confocal microscopy FV1200 ( Olympus ) . MII oocytes were fixed with MeOH at -20°C for 3 minutes , washed with PBS-TX ( 0 . 1%TritonX , PBS ) , and were then incubated with blocking solution ( 3%BSA , 0 . 1%TritonX , PBS ) at 4 oC for 3 hours . Primary antibody reactions were performed with the following dilutions ( Anti-α-tubulin antibody , Sigma , 1:1000 ) at 4°C overnight . After washing with PBS-TX , secondary antibody reaction was performed with the following dilutions ( Alexa 488 Donkey anti-Rabbit , Life technologies , 1:1000 ) and DAPI at RT for 60 min . Then , oocytes were washed with PBS-TX . Fluorescent images were obtained using confocal microscopy FV1200 ( Olympus ) . Histological analysis was carried out by PAS ( Periodic acid-Schiff ) staining according to the standard protocol . Briefly , ovaries were fixed in Bouin solution , embedded in paraffin wax , and sliced at 6-μm thickness . The sections were submerged in xylene , 100% , 90% , 70% ethanol , and distilled water at RT , and stained with PAS solution . Ovarian images were obtained with an inverted microscope BX 51 and 61 ( Olympus ) . Follicle stages were counted on every 5 sections . Dazl+/+ , Dazl 3F , and Dazl 3F;Flp females at 6 weeks old were crossed with C57BL/6N males , and kept together until female mice reached 30 weeks old . The number of pups and deliveries was recorded . Pups were removed after counting the number and sex . Females that killed their pups were excluded from the analysis . To obtain MII , 1-cell and 2-cell oocytes for western blotting , female mice were injected with PMSG ( ASKA Pharmaceutical ) . Forty-eight hours after PMSG injection , mice were stimulated with hCG ( ASKA Pharmaceutical ) for 14 h and MII oocytes were collected . To obtain western blotting samples of 1-cell and 2-cell embryos , each female was crossed with a WT male after hCG injection . Eggs with obvious abnormalities were removed from experiments . One-cell embryos for investigation of ovulation number and pre-implantation development investigation were obtained from the ampulla of pregnant females at E0 . 5 . Blastocysts for examining progression of early embryonic development were obtained by flushing oviducts at E3 . 5 . Collected blastocysts were cultured for two days in KSOM medium ( Ark resource ) . Significance was assessed by the Student’s t-test for differences between two samples . For quantitative analyses among multiple samples , significance was assessed using one-way ANOVA followed by Tukey HSD ( Honest Significant Difference ) test . Asterisks in figures indicate significance: *P < 0 . 05 , **P < 0 . 005 .
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Evolutionarily conserved DAZ family genes are indispensably involved in germline development . Dazl ( deleted in azoospermia-like ) is a member of the mammalian DAZ family of genes , and plays crucial roles in the sexual differentiation of primordial germ cells and spermatogenesis , and is implicated in the progression of meiosis II in ovulated eggs . Despite its importance for multiple processes during germline development , its participation in follicular oocyte development is enigmatic . This study addressed this issue and found that DAZL is translationally suppressed in postnatal oocytes in a 3′-UTR-dependent manner . Furthermore , this suppression is required for normal pre-implantation development after fertilization , suggesting the presence of an unidentified mechanism controlling DAZL expression . Our data provide new insights for post-transcriptional gene regulation involved in oocyte development .
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2018
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Requirement of the 3′-UTR-dependent suppression of DAZL in oocytes for pre-implantation mouse development
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The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions . Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks . In this report , we combine resting state functional connectivity MRI ( rs-fcMRI ) , graph analysis , community detection , and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies . As we have previously reported , we find , across development , a trend toward ‘segregation’ ( a general decrease in correlation strength ) between regions close in anatomical space and ‘integration’ ( an increased correlation strength ) between selected regions distant in space . The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks . Communities in children are predominantly arranged by anatomical proximity , while communities in adults predominantly reflect functional relationships , as defined from adult fMRI studies . In sum , over development , the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults . We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition . Further , graph metrics ( e . g . , clustering coefficients and average path lengths ) are similar in child and adult graphs , with both showing “small-world”-like properties , while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults . These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways .
In previous work regarding task-level control in adults , we applied rs-fcMRI to a set of regions derived from an fMRI meta-analysis that included studies of control-demanding tasks . This analysis revealed that brain regions exhibiting different combinations of control signals across many tasks are grouped into distinct “fronto-parietal” and “cingulo-opercular” functional networks [21] , [36] ( see Table 1 and Figure 1 ) . Based on functional activation profiles of these regions characterized in the previous fMRI study , the fronto-parietal network appears to act on a shorter timescale , initiating and adjusting top-down control . In contrast , the cingulo-opercular network operates on a longer timescale providing “set-initiation” and stable “set-maintenance” for the duration of task blocks [37] . Along with these two task control networks [21] , [36] , a set of cerebellar regions showing error-related activity across tasks [36] formed a separate cerebellar network ( Figure 1 ) . In adults , the cerebellar network is functionally connected with both the fronto-parietal and cingulo-opercular networks [21] , [22] . These functional connections may represent the pathways involved in task level control that provide feedback information to both control networks [22] , [36] . Another functional network , and one of the most prominent sets of regions to be examined with rs-fcMRI , is the “default mode network” . The default mode network ( frequently described as being composed of the bilateral posterior cingulate/precuneus , inferior parietal cortex , and ventromedial prefrontal cortex ) was first characterized by a consistent decrease in activity during goal-directed tasks compared to baseline [38] , [39] . Resting-state fcMRI analyses have repeatedly shown that these regions , along with associated medial temporal regions , are correlated at rest in adults [15] , [16] , [32] , [40] . While the distinct function of the default mode network is often linked to internally directed mental activity [39] , this notion continues to be debated [25] , [32] , [41]–[44] . In two prior developmental studies , we used rs-fcMRI to examine the development of the task control and cerebellar functional networks [22] and , separately , the default mode network [32] . The first study , addressing functional connectivity changes within and between the two task control networks and the cerebellar network [22] , showed that the structure of these networks differed between children and adults in several ways ( see [22] ) . In general , many of the specific changes showed trends of decreases in short-range functional connections ( i . e . , correlations between regions close in space ) and increases in long-range functional connections ( i . e . , correlations between regions more distant in space ) . We suggested that these global developmental processes support the maturation of a dual-control system and its functional connections with the cerebellar network [22] . These results have now been replicated in a developmental resting connectivity study targeting sub-regions of the anterior cingulate [34] . The development of the default mode network was independently examined in a separate analysis [32] . In children , the default mode network was only sparsely functionally connected . Many regions were relatively isolated with few or no functional connections to other default mode regions . Over age , correlations within the default mode network increased and by adulthood it had matured into a fully integrated system . Interestingly , as opposed to the task-control and cerebellar networks , very few short-range functional connections involving the default mode network regions existed in children . Hence the numerous strong short-range functional connections that decreased with age when investigating the dual control networks were not seen within the default network . In fact , some connections such as the functional connection between the ventromedial prefrontal cortex ( vmPFC; −3 , 39 , −2 ) and anterior medial prefrontal cortex ( amPFC; 1 , 54 , 21 ) regions , which are fairly close in space ( i . e . , short-range at ∼2 . 7 cm ) , had a substantial increase in correlation strength over development [32] . The observation that different analyses suggested different developmental features suggests a need for a more nuanced and integrated characterization of the development of functional networks . The goal of this manuscript is to employ several different network analysis tools to provide such a characterization . Visualization techniques such as spring embedding , and quantitative measures , including ‘small world’ metrics and community detection algorithms , will be applied to these networks in an attempt to identify principles for the changes observed across development . Because of the overlapping and sometimes inconsistent use of terminology between neuroscience and the computational sciences , we will briefly define two terms for the purposes of this paper . The term “networks” will be used in the typical cognitive neuroscience formulation: a group of functionally related brain regions ( as described above ) . The overall collection of regions ( encompassing all four “networks” ) will be referred to as the “graph . ”
Graph theory analyses were applied to 210 subjects , aged 7–31 , to investigate the emergence of temporal correlations in spontaneous BOLD activity between regions of the default mode , cerebellar , and two task-control networks . For this initial analysis , average age-group matrices were created using a sliding boxcar grouping of subjects in age-order ( i . e . , group1: subjects 1–60 , group2: subjects 2–61 , group3: subjects 3–62 , etc . ) . This generated a series of groups with average ages ranging from 8 . 48 years to 25 . 58 years . Each of the groups' average correlation matrices was converted into a graph , with correlations between regions greater than or equal to 0 . 1 considered as functionally connected . In a first analysis , we used a visualization algorithm commonly used in graph theoretic analyses known as spring embedding that aids in the qualitative interpretation of graphs ( Figure 2 and Video S1 ) [45] . In spring embedding , the positions of the nodes ( i . e . , regions ) in a graph are based solely on the strength and pattern of functional connections instead of their anatomical locations . In this procedure , each functional connection between a pair of nodes is treated as a spring with a spring constant related to the strength of the specific correlation . The entire system of pair-wise regional functional connections is then iteratively allowed to relax to the lowest global energetic state , i . e . , groups of nodes that are strongly interconnected will be placed close together even if anatomically distant . By creating spring embedded graphs for each of the sliding boxcar groups in age-order , a movie representation can be made that shows the development of the network relationships ( from average age 8 . 48 to 25 . 48 years ) ( Video S1 ) . The panels in Figure 2 provide snapshots from child , adolescent , and adult average ages in this movie . In both Figure 2 and Video S1 , each node is color-coded in two ways: the outer border represents the general anatomical location ( i . e . , cerebral lobe ) of the node; the inner core color represents the coding by “function” as defined by a large number of fMRI studies . One of the primary observations from the movie relates to this anatomical-functional distinction . In children , regions appear to be largely arranged by anatomical proximity . This arrangement can be seen in Figure 2 and Video S1 where , in children , regions can be readily grouped by cerebral lobe ( outline colors of spheres in Figure 2 and Video S1 ) . Over age , as functional connections mature , the node arrangements change such that anatomically close regions are now largely distributed across the graph layout , in a pattern more aligned with the mature networks' functional properties ( core colors of spheres in Figure 2 ) [21] , [36]–[39] . Thus , across development , local clusters of regions “segregate” from one another and “integrate” into more distributed adult functional relationships with more distant regions . A group of regions in the frontal cortex provides a particularly salient example of segregation . Frontal cortex contains regions that , in adults , are members of each of the task-control networks ( e . g . , dlPFC , frontal , dACC/msFC ) and the default network ( e . g . , vmPFC , amPFC ) . As can be seen in Figure 2A ( and Video S1 ) , extensive correlations exist between most of these frontal regions in childhood ( see blue cloud Figure 2A ) . Over the developmental window afforded by the current dataset , some of these strong “frontal-frontal” correlations begin to weaken . With increasing age , regions in the frontal cluster segregate into 3 separate functional networks . Accompanying this segregation is strong integration within the functional networks . The default mode network provides the clearest example . As illustrated in Figure 2B ( and in Video S1 ) , correlations between regions of the default mode network are weak ( or absent ) in children ( red cloud , Figure 2B ) . Just as functional connections between the set of frontal regions are related to their anatomical proximity in children , the regions of the default mode network are each functionally connected to anatomical neighbors , and not to other members of the anatomically dispersed default mode network . Over age , however , the functional connections between default mode network regions mature and the network integrates into a highly correlated system in adults ( Figure 2B and Video S1 ) ( also see [32] ) . We note that these results were not specific to the 60-subject boxcar , and persist with smaller subject boxcars as well ( see Video S2 ) . The qualitative observations noted above can be quantified using community structure detection tools . Using such an approach is particularly important because of the bias inherent in relying on qualitative methods for deciding whether groups of regions that appear to be clustered are indeed clustered , and because of the a priori definitions of each network . As stated by Newman: Among the many methods used to detect communities in graphs , the modularity optimization algorithm of Newman is one of the most efficient and accurate to date [46] . This method uses modularity , a quantitative measure of the observed versus expected intra-community connections , as a means to guide assignments of nodes into communities . We applied the modularity optimization algorithm to the group connectivity matrices derived from the sliding boxcars described above . Measures of modularity ( Q ) were high , and did not show large changes across the age range ( Figure 3A and Figure S1 and Figure S2 ) . This result was not dependent on any particular threshold ( Figure S1 ) . Although comparable community structure was detected at all ages examined , the components of the communities varied by age . As per our qualitative approach described above , in children , region clusters were largely arranged by cerebral lobe; while in adults , regions were largely clustered by their adult functional properties ( Figure 4A ) . Again , this result was not unique to any particular threshold ( Figure 4B and 4C ) or size of boxcar ( Figure S3 ) . We do note , however , that limited data points ( i . e . , subjects ) are available between the ages of 16 and 19 years ( see Materials and Methods ) and that our estimate of the specific transitions within this period should be interpreted with care . As previously reported [22] , [34] , the segregation of closely apposed regions and the integration of distributed functional networks is associated with a general decrease in correlation strength between regions close in space and an increase in correlation strength between many regions distant in space . This trend is shown in Figure 5 and also Figure S4 . Long-range functional connections tend to be weak , but increase over time ( warm colors above the diagonal in Figure 5C and 5D and Figure S4C and S4D ) , integrating distant regions into functional networks . Short-range functional connections tend to be stronger ( i . e . , higher correlation strength ) in children , yet those regions that do change predominantly become weaker over age ( cool colors below the diagonal in Figure 5A and 5B and Figure S4A and S4B ) . However , there are some interesting nuances to this trend that deserve mention . For instance , not all short-range functional connections decrease in strength over age ( Figure 5A and 5B and Figure S4A and S4B ) . While few , some of the short-range functional connections , typically those in the same network , increase in strength over age ( Figure 5A and Figure S4A ) . Similarly , although many long-range functional connections increase in strength , many others do not statistically change across development ( Figure 5C and , 5D and Figure S4C and S4D , grey connections ) . In a seminal 1998 paper , Watts and Strogatz noted that the topology of many complex systems can be described as “small world” , a type of graph architecture that efficiently permits both local and distributed processing . Graphs with a regular , lattice-like structure have abundant short-range connections , but no long-range connections . Local interactions are thus efficient , but distributed processes involving distant nodes require the traversal of many intermediate connections . Conversely , completely randomly connected graphs are fairly efficient at transferring distant or long-range signals across a network , but they are poor at local , short-range information transfer . Watts and Strogatz , and others , often describe “small world” properties with two metrics: the average clustering coefficient and average path length of a graph . The clustering coefficient measures how well connected the neighbors of a node are to one another . The average path length measures the average minimum number of steps needed to go between any two nodes . Lattices , optimized for local processes , have high average clustering coefficients but long average path lengths . Conversely , random graphs , which have no preference for short-range connections , have low average clustering coefficients and short average path lengths , making them well suited for communication between distant nodes . One of Watts & Strogatz's key insights was that by randomly rewiring a relatively small number of connections in a lattice graph ( i . e . , introducing a few long-range connections ) , a graph could retain its high average clustering coefficient , but dramatically reduce its average path length , thereby enabling efficient short- and long-range processes . It is this hybrid graph topology ( i . e . , high clustering coefficients and short path lengths ) that matches the observed “small world” networks in many complex systems [47] . As previously reported [21] , [48] , [49] , relative to comparable lattice and completely random graphs , the adult graph architecture showed high clustering coefficients and short path lengths , consistent with the ‘small world’ architecture ( Figure 3B and 3C ) . Interestingly for these networks , in children ( i . e . , as early as age 8 ) , these metrics were quite similar to adults ( Figure 3B and 3C ) , and over age there was very little change in path lengths and clustering coefficients relative to comparable random and lattice graphs . It was originally anticipated that path lengths would decrease over age as long-range anatomical connections were added . Yet even at the youngest ages examined , path length was already quite short , near those of random graphs . Importantly , these results were not dependent on any particular threshold ( Figure S5 ) . We note that while the results shown here are largely descriptive , the error bars provided in Figure 3B and 3C constructed from random graphs underscores the difference between random configurations and the observed trends .
As early as 1875 spontaneous synchronized neural activity has been used to study various aspects of adult brain organization [50]–[53] . However , despite the passing of over 130 years since its initial use , there remains uncertainty as to the role of intrinsic spontaneous brain activity in brain function . In adults , spontaneous correlated activity has been suggested to be important for gating information flow [54] , building internal representations [43] , [44] , [54] , and maintaining mature network relationships [43] , [44] , [54] . Much less work has been done in regards to development , but there are suggestions that spontaneous activity is important for the establishment of early cortical patterns ( e . g . , ocular dominance columns ) [55]–[58] and may over time represent ( in a Hebbian sense ) a history of repeated co-activation between regions [21] , [22] , [27] , [32] , [34] , [59] , [60] . Within this framework , the changes in the correlation structure of spontaneous activity over development seen in this report may provide insight regarding the arrangement by which brain regions are communicating in children compared to adults . If we consider the previously mentioned postulates , our results suggest that , typically , the most efficient way for children to respond to processing demands is to utilize more “local” level interactions as compared to adulthood . That is , in childhood there is , relatively greater co-activation of anatomically proximal regions than for adults with similar processing demands . A clear example of this is seen in Brown et al . [3] , where identical task performance on lexical processing tests strongly activates a large set of visual regions in children , but strong visual activation is much more restricted in adults . These relationships may be reflected in correlated spontaneous activity measured via rs-fcMRI . The correlations in our youngest children would then represent the anatomical and spontaneous activity-defined initial regional relationships plus 7 years of experience-dependent Hebbian processes tuning these developing connections . The “local to distributed” organizing principle resonates with recent suggestions that perceptual and cognitive development involve the simultaneous segregation and integration of information processing streams [1] , [22] , [76] , [79] , [80] . For instance , the “interactive specialization” hypothesis advanced by Johnson and colleagues , is consistent with these findings [1] , [81]–[83] . Johnson points out that cortical regions and pathways have biased information processing properties at birth due to anatomic connectivity , yet they are much less selective than in adults ( i . e . , they are “broadly tuned” ) . Interactive specialization predicts that shortly after birth , large sets of regions and pathways will be partially active during specific task conditions , However , as these pathways interact and compete with each other throughout development , selected regions will come online , be maintained , or become selectively activated or “tuned” as particular pathways dominate for specific task demands . Thus , regional specialization relies on the evolving and continuous interactions with other brain regions over development . If one extends this framework to the network level , the increases , decreases , and maintenance of correlation strengths seen between regions may reflect “specialization” of specific neural pathways to form the functional networks seen in adults . The “local to distributed” developmental trajectory , discussed above , seems to be driven by an abundance of local , short range connections that generally decrease in strength over age as well as distant , long range connections that generally increase in strength over age . Given the more prevalent short-range connections in children , we expected a more lattice-like structure , with high clustering coefficients and relatively high path lengths . The results , however , clearly indicated that path lengths were near those of equivalent random graphs , and that the child functional networks are already organized as small world networks . This result can be explained in the context of the re-wiring procedure discussed by Watts and Strogatz [47] . Randomly rewiring a small percentage of local connections in a lattice has a mild linear effect on clustering coefficients , but a highly non-linear effect on path lengths . This is to say , that by rewiring a small fraction of a lattice's connections , substantial drops in path lengths can be seen , with almost no change in the clustering coefficient . In late childhood , as shown in Figure 5 and Figure S2 , there are already a significant number of long-range short cuts present . These long-range functional connections are likely responsible for the relatively short path lengths in the child group . We anticipate that if the developmental trajectory of short and long-range functional connections were extended to younger ages , fewer long-range ‘short-cut’ functional connections would be present , and more short-range functional connections would exist . Hence , the path lengths at these younger ages ( <7 years old ) would likely be longer . Nevertheless , by 8 years old , the networks already display ‘small world’ properties similar to those of adult networks , indicating that efficient graph structures are already in place for both local and distant processing , though they are organized differently than in later development . While we identified small world properties in both child and adult graphs , the size of the graph is relatively small with only 34 nodes . Therefore , it is possible that with an increased number of nodes the specific results identified here will change , a possibility that will be addressed in further studies . The regions used in the present analyses were all derived from adult imaging studies . It seems likely that additional regions may be included in one or more of these networks in childhood . In addition , individual differences with regards to the regions and networks chosen likely exist . Future work that includes regions derived from studies using a child population and obtaining the functional connections within subjects from individually defined functional areas may refine the networks and developmental timecourses presented here [84] . Of note , resting-state functional connectivity has been reported to be constrained by anatomical distance ( i . e . , correlations between regions decrease as a function of distance following an inverse square law ) [85] . Thus , if a shift in this general bias occurred with development , then it is feasible that some of the changes seen here could be related to such a shift . With this said , the specificity of the connection changes observed over age , the number of connections that run opposite to the general trends , and the similarity of the distance relationship in connectivity between children and adults when plotting all possible connections ( see Figure S6 ) , all suggest that the majority of changes observed here are not related to changes in this bias . In addition , while there are now reports suggesting that changes observed over development with blood oxygen level dependent ( BOLD ) fMRI are not the product of changes in hemodynamic response mechanisms over age [86] , [87] , differences in the hemodynamic response function between children and adults could conceivably affect our results [88] . A limitation of rs-fcMRI in general is the restricted frequency distribution that can be examined . rs-fcMRI is used to measure correlations in a very low frequency range , typically below 0 . 1 Hz . Dynamic changes in correlations in other frequency distributions could exist ( for example see [89] ) . It is also possible that there are undetected developmental changes in power across frequency bands orthogonal to the changes visualized here . The combination of other imaging and psychometric techniques with rs-fcMRI will likely help address these considerations . Characterizing additional networks and how these changes map onto behavior will also help further characterize functional brain development . Specifically , future work that demonstrates a direct relationship between behavior and the developmental trajectory seen here with rs-fcMRI , is presently needed to confirm ( or reject ) many of the theories presented here and elsewhere . Importantly , consideration of these issues need not be limited to developmental studies , but should be considered whenever investigators compare groups with rs-fcMRI . Nonetheless , the general results presented here represent a strong set of hypotheses to be tested in broader domains and larger-scale brain graphs . First , that by age 8 years , regional relationships , as defined by rs-fcMRI , are organized as small-world-like networks , which , relative to adults , emphasize local connections . Second , that for the same regions , adult networks show similar network metrics but with regional relationships that have a longer-range , more distributed structure reflecting adult functional histories . In other words , the modular structure of large-scale brain networks will change with age , but even school age children will show relatively efficient processing architecture .
Subjects were recruited from Washington University and the local community . Participants were screened with a questionnaire to ensure that they had no history of neurological/psychiatric diagnoses or drug abuse . Informed consent was obtained from all subjects in accordance with the guidelines and approval of the Washington University Human Studies Committee . fMRI data were acquired on a Siemens 1 . 5 Tesla MAGNETOM Vision system ( Erlangen , Germany ) . Structural images were obtained using a sagittal magnetization-prepared rapid gradient echo ( MP-RAGE ) three-dimensional T1-weighted sequence ( TE = 4 ms , TR = 9 . 7 ms , TI = 300 ms , flip angle = 12 deg , 128 slices with 1 . 25×1×1 mm voxels ) . Functional images were obtained using an asymmetric spin echo echo-planar sequence sensitive to blood oxygen level-dependent ( BOLD ) contrast ( volume TR = 2 . 5 sec , T2* evolution time = 50 ms , α = 90° , in-plane resolution 3 . 75×3 . 75 mm ) . Whole brain coverage was obtained with 16 contiguous interleaved 8 mm axial slices acquired parallel to the plane transecting the anterior and posterior commissure ( AC-PC plane ) . Steady state magnetization was assumed after 4 frames ( ∼10 s ) . Functional images were first processed to reduce artifacts [23] , [90] . These steps included: ( i ) removal of a central spike caused by MR signal offset , ( ii ) correction of odd vs . even slice intensity differences attributable to interleaved acquisition without gaps , ( iii ) correction for head movement within and across runs and ( iv ) within run intensity normalization to a whole brain mode value of 1000 . Atlas transformation of the functional data was computed for each individual via the MP-RAGE scan . Each run then was resampled in atlas space ( Talairach and Tournoux , 1988 ) on an isotropic 3 mm grid combining movement correction and atlas transformation in one interpolation [91] , [92] . All subsequent operations were performed on the atlas-transformed volumetric timeseries . For rs-fcMRI analyses as previously described [16] , [23] , several additional preprocessing steps were used to reduce spurious variance unlikely to reflect neuronal activity ( e . g . , heart rate and respiration ) . These steps included: ( 1 ) a temporal band-pass filter ( 0 . 009 Hz<f<0 . 08 Hz ) and spatial smoothing ( 6 mm full width at half maximum ) , ( 2 ) regression of six parameters obtained by rigid body head motion correction , ( 3 ) regression of the whole brain signal averaged over the whole brain , ( 4 ) regression of ventricular signal averaged from ventricular regions of interest ( ROIs ) , and ( 5 ) regression of white matter signal averaged from white matter ROIs . Regression of first order derivative terms for the whole brain , ventricular , and white matter signals were also included in the correlation preprocessing . These pre-processing steps likely decrease or remove developmental changes in correlations driven by changes in respiration and heart rate over age . Resting state ( fixation ) data from 210 subjects ( 66 aged 7–9; 53 aged 10–15; 91 aged 19–31 ) were included in the analyses . For each subject at least 555 seconds ( 9 . 25 minutes ) of resting state BOLD data were collected . 34 previously published regions comprising 4 functional networks ( i . e . , cingulo-opercular , fronto-parietal , cerebellar , and default networks; see Table 1 and Figure 1 ) were used in this analysis [16] , [21] , [22] , [37] . For each region , a resting state timeseries was extracted separately for each individual . For 10 adult subjects , resting data was continuous . For the remaining 200 subjects , resting periods were extracted from between task periods in blocked or mixed blocked/event-related design studies [22] . These concatenated-extracted rest periods were shown to be equivalent to continuous resting data in a recent study describing this method [23] . In addition , several previous findings using this technique [21] , [22] , [32] have now been replicated using continuous resting blocks [27] , [33] , [34] and other continuous resting data [89] . To examine the functional connections within and between the large set of regions used in this manuscript we chose to use graph theory . Graph theory is particularly well suited to study large-scale systems organization across development , but requires the data be organized into specific correlation matrices . To do this , for each of the 210 subjects , the resting state BOLD timeseries from each region was correlated with the timeseries from every other region , creating 210 square correlation matrices ( 34×34 ) . Average group matrices were then created using a sliding boxcar grouping of subjects in age-order ( i . e . , group1: subjects 1–60 , group2: subjects 2–61 , group3: subjects 3–62 , … group151: subjects 151–210 ) , thus generating a series of groups with average ages ranging from 8 . 48 years old to 25 . 48 years old with each group composed of 60 subjects . Average correlation coefficients ( r ) for each group were generated from the subjects' individual matrices using the Schmidt-Hunter method for meta-analyses of r-values [21] , [85] , [93] . In cases when the terms “child” or “adult” are used , the matrices or results referred to are the first and last of the sliding boxcar groups respectively , i . e . , the child group is the youngest 60 subjects , with an average age of 8 . 48 years old , and the adult group is the oldest 60 subjects , with an average age of 25 . 48 years old . To generate a dynamic representation of the functional connections between regions across development , each of the groups' correlation matrices was converted into a thresholded graph , such that correlations higher than r≥0 . 1 were considered connections , while correlations lower than the threshold were not connections . For our initial analyses [21] , [22] , [32] graphs in child and adult groups were presented in either a pseudo-anatomical fashion or in their actual 3D positions ( in Talairach space ) . Here we add another representation often used in graph theory - spring embedding . In this procedure , a spring constant is added to all of the connections in the network allowing for the pairwise regional connections to relax to their lowest energetic state . The algorithm applied in the present analysis is known as Kamada-Kawai [45] - one of the most commonly used strategies for displaying graph network data . In brief , each functional connection between a pair of nodes is treated as a spring with a spring constant related to the strength of the specific correlation . The nodes are then randomly placed in a plane , which places high strain on the “spring-loaded” connections . The algorithm then iteratively adjusts the positions of each node to reduce the total energy of the system to a minimum . As the pair-wise connections relax to their lowest energetic states the “natural” configuration of the network is revealed . By observing multiple “spring embedded” graphs across the subjects in age-order , approximately representing a 6 month temporal sliding box car ( i . e . , group1: subjects 1–60 , group2: subjects 2–61 , etc . ) , a movie representation can be made that shows the development of the full system ( see Video S1 ) . The interpolations , algorithm application , and movie production were performed using MATLAB ( The Mathworks , Natick , MA ) and SoNIA ( Social Network Image Animator ) [94] . Communities for our graph were detected with the modularity optimization method of Newman [46] . The modularity , or Q , of a graph is a quantitative measure of the number of edges found within communities versus the number predicted in a random graph with equivalent degree distribution . A positive Q indicates that the number of intra-community edges exceeds those predicted statistically . A wide range of Q may be found for a graph , depending on how nodes are assigned to communities . The set of node assignments that returns the highest Q is the optimal community structure sought by the modularity optimization algorithm , which follows a recursive two-step process . First , a modularity matrix similar to a Laplacian is constructed from the nodes in question , comparing observed versus expected edges . If this matrix has a positive eigenvalue , the eigenvector of the largest eigenvalue is used to split the nodes into two subgraphs , and Q is calculated . Second , nodes are swapped individually between the two subgraphs to see if an increase in Q can be found . Once a maximal Q is found from these swaps , the process is repeated on the subgraphs . At any point in this process , if the matrix has no positive eigenvalues , or if a proposed split does not increase Q , the subgraph is set aside , and defines a community . To detect communities in our networks over a range of ages , we used the sliding boxcar group average correlation matrices described above in “Generation of average group correlation matrices across development . ” With weights retained , the modularity optimization algorithm was applied to each matrix along the sliding boxcar . A range of thresholds was explored to define connections for these calculations ( see Figure 4 and Figure S1 ) . Any particular threshold did not change the conclusions presented in the main manuscript . A threshold of 0 . 10 was chosen to display in the main manuscript because it balances two principles: ( 1 ) eliminating a multitude of weak correlations , which may obscure more physiologically relevant correlations , and ( 2 ) retaining high graph connectedness , so that communities arise from partitioning and not thresholding . Graph connectedness captures the extent of nodes fragmented from the main graph due to increasing thresholds . It is defined for a graph of N nodes as the mean of an NxN matrix , where cell i , j is 1 if a path exists between node i and node j ( self-connections are allowed ) , and is 0 otherwise . A graph in which all nodes can reach each other has 100% graph connectedness , whereas a fragmented network in which some nodes cannot reach the rest has a lower connectedness . The modularity optimization analysis returned a set of community assignments for the nodes , as well as the Q of the graph with those assignments . The group assignments for the nodes were converted to colors and are displayed in Figure 4 . The robustness of the community assignments was also tested using a different information theoretic procedure implemented by Meila , [95] , which utilizes the measure ‘variation of information ( VOI ) ’ ( see Figure S7 and also [96] ) . All calculations were performed in MATLAB ( The Mathworks , Inc . , Natick , MA ) . To characterize the relationship between connection length and the change in correlation strength over development , we split all 561 possible connections into 4 groups based on vector distance . Since using vector distance as an approximate for connectional distance is much more inconsistent when comparing ROIs across the midline , only intrahemispheric connections or connections to midline structures ( i . e . , within 5 mm of the midline ) were examined . These connections were then sorted by connection length and plotted on a graph where the x-axis corresponds to the child correlation strengths and the y-axis corresponds to the adult correlation strengths ( Figure 5 and Figure S2 ) . On both the graphs ( Figure 5 ) and the cortical surfaces ( Figure S2 ) , the color of the lines denotes the strength of correlation . Significant differences seen in Figure 5 and Figure S2 were obtained via direct comparison between children ( the youngest 60 children out of 210 total subjects; age 7 . 01–9 . 67; average age 8 . 48 ) and adults ( the oldest 60 adults out of 210 total subjects; age 22 . 47–31 . 39; average age 25 . 48 ) . Two-sample two-tailed t-tests ( assuming unequal variance; p≤0 . 05 ) were performed on all potential connections that passed the above criteria . Fischer z transformation was applied to the correlation coefficients to improve normality for the random effects analysis . To account for multiple comparisons the Benjamini and Hochberg False Discovery Rate [97] was applied . Connections that were significantly different between groups , but r<0 . 1 in both groups , were not displayed . The small-world metrics were calculated according to descriptions by Watts and Strogatz [47] . In the main manuscript , calculations were performed on the group average correlation matrices thresholded at 0 . 10 and converted to binary matrices ( for analysis across varying thresholds see Figure S3 ) . For each matrix across age , the average clustering coefficient and average path lengths were compared to those values in lattices with equivalent N ( number of nodes ) and K ( number of connections ) . To ensure that our matrices also differed from random graphs , 100 random graphs with equivalent degree distributions were also created . From these graphs mean average path lengths and clustering coefficients were calculated . These metrics are presented in Figure 3 and Figure S3 . All calculations were performed in MATLAB ( The Mathworks , Natick , MA ) .
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The first two decades of life represent a period of extraordinary developmental change in sensory , motor , and cognitive abilities . One of the ultimate goals of developmental cognitive neuroscience is to link the complex behavioral milestones that occur throughout this time period with the equally intricate functional and structural changes of the underlying neural substrate . Achieving this goal would not only give us a deeper understanding of normal development but also a richer insight into the nature of developmental disorders . In this report , we use computational analyses , in combination with a recently developed MRI technique that measures spontaneous brain activity , to help us to understand the principles that guide the maturation of the human brain . We find that brain regions in children communicate with other regions more locally but that over age communication becomes more distributed . Interestingly , the efficiency of communication in children ( measured as a ‘small world’ network ) is comparable to that of the adult . We argue that these findings have important implications for understanding both the maturation and the function of neural systems in typical and atypical development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/neurodevelopment",
"neuroscience/cognitive",
"neuroscience"
] |
2009
|
Functional Brain Networks Develop from a “Local to Distributed” Organization
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Future HIV vaccines are expected to induce effective Th1 cell-mediated and Env-specific antibody responses that are necessary to offer protective immunity to HIV infection . However , HIV infections are highly prevalent in helminth endemic areas . Helminth infections induce polarised Th2 responses that may impair HIV vaccine-generated Th1 responses . In this study , we tested if Schistosoma mansoni ( Sm ) infection altered immune responses to SAAVI candidate HIV vaccines ( DNA and MVA ) and an HIV-1 gp140 Env protein vaccine ( gp140 ) and whether parasite elimination by chemotherapy or the presence of Sm eggs ( SmE ) in the absence of active infection influenced the immunogenicity of these vaccines . In addition , we evaluated helminth-associated pathology in DNA and MVA vaccination groups . Mice were chronically infected with Sm and vaccinated with DNA+MVA in a prime+boost combination or MVA+gp140 in concurrent combination regimens . Some Sm-infected mice were treated with praziquantel ( PZQ ) prior to vaccinations . Other mice were inoculated with SmE before receiving vaccinations . Unvaccinated mice without Sm infection or SmE inoculation served as controls . HIV responses were evaluated in the blood and spleen while Sm-associated pathology was evaluated in the livers . Sm-infected mice had significantly lower magnitudes of HIV-specific cellular responses after vaccination with DNA+MVA or MVA+gp140 compared to uninfected control mice . Similarly , gp140 Env-specific antibody responses were significantly lower in vaccinated Sm-infected mice compared to controls . Treatment with PZQ partially restored cellular but not humoral immune responses in vaccinated Sm-infected mice . Gp140 Env-specific antibody responses were attenuated in mice that were inoculated with SmE compared to controls . Lastly , Sm-infected mice that were vaccinated with DNA+MVA displayed exacerbated liver pathology as indicated by larger granulomas and increased hepatosplenomegaly when compared with unvaccinated Sm-infected mice . This study shows that chronic schistosomiasis attenuates both HIV-specific T-cell and antibody responses and parasite elimination by chemotherapy may partially restore cellular but not antibody immunity , with additional data suggesting that the presence of SmE retained in the tissues after antihelminthic therapy contributes to lack of full immune restoration . Our data further suggest that helminthiasis may compromise HIV vaccine safety . Overall , these findings suggested a potential negative impact on future HIV vaccinations by helminthiasis in endemic areas .
Human immunodeficiency virus ( HIV ) and parasitic helminthic worm infections are highly prevalent and geographically overlap each other in sub Saharan Africa ( SSA ) [1 , 2] . A majority of inhabitants harbor at least one or more species of parasitic helminth infection [3–6] and an estimated 50% of the chronically infected individuals living in high-risk rural communities are co-infected with HIV [7] . Furthermore , re-infections after successful treatments are also very common in endemic areas . Therefore , it is very likely that successful future HIV vaccines will be administered to people who already have ongoing helminthiasis or have been previously infected and treated . Current HIV-1 vaccine research suggests that a successful HIV vaccine will need to induce effective T cell and functional antibody responses , where a key component of immune protection would be conferred through a T helper 1 ( Th1 ) immune pathway [8 , 9] . Induction of potent T cell mediated immune responses has previously been demonstrated using heterologous prime-boost vaccination strategies that utilise DNA and viral vaccine vectors such as modified Vaccinia Ankara ( MVA ) [10–14] , while induction of durable antibody immune responses may require immunisation with HIV envelope protein-based vaccines [15–18] . It is widely accepted that an ideal HIV vaccine should induce both anti-HIV cellular responses and HIV Env-specific antibodies to destroy virus-infected cells and neutralize viruses at portals of entry respectively in order to clear the virus before dissemination into the tissues or block viral entry at the mucosal sites [8 , 9 , 19 , 20] . During chronic schistosomiasis , parasite eggs are lodged in the liver and intestinal tissue [21 , 22] resulting in predominantly T-helper 2 ( Th2 ) immune responses [23–27] and the induction of anti-inflammatory regulatory T-cells ( Treg ) which suppress the innate and adaptive T- and B-cell responses [24 , 28 , 29] . This has been shown to lead to general hyporesponsiveness which may adversely impact standard immunizations , by suppressing immune responses to Th1-type vaccine and impairing the expansion of pathogen-specific cytotoxic T lymphocyte ( CTL ) responses [30–37] . Parasitic helminth infections are currently treated with chemotherapeutic drugs such as praziquantel ( PZQ ) for schistosomiasis [38–40] and ivermectin or mebendazole for geohelminths [41–43] , which are cost-effective interventions . However , re-infection after effective treatment is common and frequent in populations in endemic areas [44] . Several animal and clinical studies have reported that helminth infections impair the outcome of a variety of vaccines , including Salmonella [45]; BCG [30 , 46–48] , tetanus [46 , 49–51] , diphtheria toxoid [52] , HBV [53] , pneumococcal [54] and live attenuated oral cholera vaccines [55] . However , elimination of helminth infection has also been shown to at least partially restore this abrogation [56] . Furthermore , individuals treated with antihelminthics show higher frequencies of BCG-specific IFN-γ and IL-12 producing cells than untreated helminth infected individuals [30] . Previous HIV vaccines studies reported reduced vaccine-induced immunity in schistosome-infected mice [57] and partial restoration after elimination of helminths [58 , 59] . However , it is not clear if antibody responses are attenuated as these studies evaluated only cellular responses to Gag as they were monovalent candidate vaccines . Current vaccine candidates and future successful vaccines will likely include multiple immunogens , including Env , in order to broaden the vaccine targets and the capacity to cross-neutralise the majority of transmitted viruses . [60 , 61] . It is well-accepted that helminth-induced Th2 responses play an important role in host protection [62 , 63] . Since Th1 and Th2 display reciprocal antagonist , it would be anticipated that HIV vaccine-generated Th1 responses may reduce host immunity against helminth-associated pathology , thereby compromising the safety of an otherwise effective HIV T-cell vaccine . Poxvirus-vectored HIV vaccines are promising candidates for induction of T cell responses and therefore this is a relevant safety issue which remains under-investigated in the helminthic infection background . We have previously described the development of two multigene candidate vaccines , the SAAVI DNA-C2 and SAAVI MVA-C , which express matched HIV-1 subtype C proteins ( Gag , RT , Tat , Nef and Env ) [11 , 64–66] . These vaccine candidates have been evaluated further in nonhuman primates [18 , 67] and Phase 1 clinical trials [15 , 16] . Also , we have evaluated these vaccines in combination with an HIV-1C gp140ΔV2 Env protein [11 , 15 , 16 , 18 , 64 , 65 , 67] . In the current study , we investigated the impact of chronic schistosomiasis on the immunogenicity of these vaccines in a mouse model and whether the elimination of worms by antihelminthic chemotherapy prior to immunization benefits vaccination outcome . We further investigated whether the S . mansoni eggs ( SmE ) in the absence of active infection , which mimics the state whereby SmE remain trapped in the tissues shortly after antihelminthic treatment , has an adverse effect on vaccine immunogenicity . Lastly , we evaluated helminth-induced pathology to predict HIV vaccine safety in helminth endemic areas . Our findings show that mice infected with S . mansoni displayed reduced magnitudes of vaccine-specific cellular and humoral responses and anthelminthic treatment with PZQ failed to restore levels of anti-gp140 antibodies while partially reversing the adverse impact on cellular responses . Unexpectedly , vaccination with a T-cell based vaccine regimen was observed to worsen helminth-associated pathology suggesting potential safety concerns in future mass HIV vaccination in helminth endemic areas .
To assess the impact of Sm-infection on systemic immune responses , we quantified systemic Th1 and Th2 immune responses in uninfected and Sm-infected mice following MVA+gp140 and DNA+MVA vaccination . Con A stimulation of splenocytes from Sm-infected mice resulted in a significantly reduced IFN-γ:IL-4 ratio in DNA+MVA ( p<0 . 05 ) and MVA+gp140 ( p<0 . 01 ) vaccine regimens compared to Sm-uninfected mice ( Fig 1A ) . Similarly , Con A-stimulated splenocytes from Sm-infected mice produced significantly lower ( p<0 . 05 ) levels of IFN-γ and IL-2 ELISpot responses compared to splenocytes from uninfected mice ( Fig 1B ) . Furthermore , Sm-infected mice had a reduced frequency ( p<0 . 05 ) of cytokine-producing CD4+ and CD8+ T cells after re-stimulation of splenocytes with PMA/Ionomycin compared to splenocytes from uninfected control mice ( Fig 1C ) . Moreover , vaccinated Sm-infected mice displayed an impaired type 1 antibody response , indicated by reduced amount of type 1 total antibody isotypes [ ( IgG2a ( p<0 . 001 ) , IgG2b ( p<0 . 001 ) ] and increased type 2 antibody isotype [ ( total IgG1 ( p<0 . 001 ) ( Type 2-associated antibodies ) , IgM ( p<0 . 001 ) ] compared to uninfected but vaccinated mice and uninfected control mice ( Fig 1D ) . Sm-infection was accompanied with increased levels of the regulatory cytokine IL-10 ( S1C and S1I Fig ) . To determine the effect of chronic Sm infection on the HIV-1 vaccine-specific T cell immunity , Sm-infected and uninfected mice were vaccinated with either DNA+MVA or MVA+gp140 vaccine regimens and vaccine-specific T cell responses were determined using ELISpot , CBA and flow cytometry . To determine if elimination of schistosome infection prior to vaccination could reverse the effect on those responses , groups of mice were treated with PZQ before vaccinations . Vaccination with DNA+MVA induced significantly higher cumulative HIV-1 specific IFN-γ ( 2014 ± 177 . 4 SFU/106 splenocytes ) and IL-2 ( 174 . 1 ± 71 . 13 SFU/106 splenocytes ) ELISpot responses in uninfected mice compared to Sm-infected mice ( IFN-γ: 1420 ± 61 . 54 SFU/106 splenocytes and IL-2: 0 SFU/106 splenocytes ) ( Fig 2A and 2B ) . Responses to the RT ( CD8 ) peptide induced the highest number of IFN-γ secreting CD8+ and CD4+ T cells ( 1019 ± 217 . 3 SFU/106 splenocytes ) compared to other peptides in uninfected mice vaccinated with DNA+MVA . However , IL-2 SFU/106 cells were similar among different peptides stimulations . Similarly , vaccination with MVA+gp140 induced significantly higher cumulative HIV-1 specific IFN-γ ( 1838 ± 173 . 3 SFU/106 splenocytes ) and IL-2 ( 197 . 7 ± 20 . 12 SFU/106 splenocytes ) ELISpot responses in uninfected mice compared to Sm-infected mice ( IFN-γ: 1166 ± 132 . 2 SFU/106 splenocytes ) and IL-2: 11 . 89 ± 5 . 951 SFU/106 splenocytes ) ( Fig 2A and 2B ) . Responses to the Env ( CD8 ) peptide induced the highest number of IFN-γ secreting CD8+ and CD4+ T cells ( 553 . 3 ± 55 . 86 SFU/106 splenocytes ) compared to other peptides in uninfected mice vaccinated with MVA+gp140 . However , IL-2 SFU/106 cells were similar among different peptides stimulations . Vaccination after PZQ treatment had varying effects on the magnitudes of ELISpot responses . For DNA+MVA vaccine regimen , the cumulative magnitude of IFN-γ but not IL-2 SFU/106 cells was still significantly lower in treated mice compared with uninfected mice indicating partial restoration of responses to normal magnitudes ( Fig 2A and 2B ) . In contrast , the cumulative magnitudes of both IFN-γ and IL-2 SFU/106 cells between PZQ-treated and vaccinated mice and uninfected mice were similar for MVA+gp140 vaccine regimen , indicating restoration to near normal SFU/106 cells ( Fig 2A and 2B ) . Th1 cytokine levels were significantly reduced in Sm-infected mice . As shown in Fig 2C–2E , significantly higher levels of net cumulative IFN-γ ( 6523 ± 282 . 0 pg/ml ) ; IL-2 ( 84 . 86 ± 0 . 3147 pg/ml ) and TNF-α ( 251 . 2 ± 30 . 33 pg/ml ) ( Fig 2C–2E ) were released by splenocytes from uninfected mice in the DNA+MVA vaccine regimen compared to lower levels of IFN-γ ( 1899 ± 244 . 6 pg/ml ) ; IL-2 ( 23 . 55 ± 4 . 094 pg/ml ) and TNF-α ( 122 . 9 ± 17 . 45 pg/ml ) released in Sm-infected vaccinated mice ( Fig 2C–2E ) . Similarly , for the MVA+gp140 vaccine regimen , significantly higher levels of net cumulative Th1 cytokines: IFN-γ ( 2416 pg/ml ) ; IL-2 ( 63 . 79 pg/ml ) and lower TNF-α ( 112 . 48 pg/ml ) were released from splenocytes of uninfected vaccinated mice compared to lower levels of IFN-γ ( 789 pg/ml ) ; IL-2 ( 4 . 0 pg/ml ) and higher TNF-α ( 123 . 87 pg/ml ) released in Sm-infected vaccinated mice ( Fig 2C–2E ) . After treatment with PZQ , the levels of IFN-γ and IL-2 were observed to be significantly higher compared to Sm-infected mice for both DNA+MVA and MVA+gp140 vaccine regimens but noticeably lower than those of uninfected vaccinated mice , indicating only partial restoration to normal magnitudes . Furthermore , the frequencies of vaccine-specific cytokine ( IFN-γ , IL-2 and TNF-α ) producing T cells as determined by flow cytometry showed a similar general trend whereby lower levels of HIV-specific T cells were detected in Sm-infected mice compared with uninfected animals ( Fig 2F and 2G ) . For both vaccine regimens , Pol- and Env- specific CD4+ T cells were undetectable in Sm-infected vaccinated mice whilst they were readily detected at similar levels in both uninfected and PZQ-treated vaccinated mice indicating restoration of cytokine responses by PZQ treatment ( Fig 2F ) . However , Pol- and Env- specific CD8+ T cells were detected in Sm-infected mice at similar levels as the uninfected and PZQ-treated vaccine group except in the MVA+gp140 vaccine regimen where a significantly higher percentage of cumulative cytokine-producing CD8+ T cells in response to the Pol and Env CD8 peptides stimulation was observed in uninfected vaccinated mice ( 1 . 79 ± 0 . 04% ) compared to Sm-infected vaccinated ( 1 . 49 ± 0 . 06% ) mice . ( Fig 2G ) . Most of the cytokine producing CD8+ and CD4+ T cells belonged to the effector memory phenotype ( Fig 2H ) and the profiles of the memory phenotypes were similar in both uninfected and PZQ-treated vaccinated groups . To determine the effect of helminth infection of the development of Env-specific antibody responses , mice were infected with Sm and vaccinated with MVA+gp140 and humoral responses were determined by ELISA . Uninfected and vaccinated mice produced higher amounts of gp140-specific IgG antibodies compared to Sm-infected vaccinated mice across all IgG isotypes ( IgG1 [1681 ± 373 . 9 vs 140 . 8 ± 29 . 42 AUs]; IgG2a [4746 ± 1154 vs 71 . 14 ± 15 . 98 AUs]; IgG2b [2247 ± 553 . 9 vs 45 . 23 ± 12 . 65 AUs] ) . Treatment of infected mice with PZQ did not restore vaccine-specific antibody responses in infected mice as indicate by significantly lower titers of gp140-specific IgG antibodies across all IgG isotypes ( IgG1 [287 . 0 ± 79 . 96 AUs] , IgG2a [866 . 1 ± 514 . 3 AUs] , IgG2b [126 . 3 ± 28 . 41 AUs] ) compared to uninfected vaccinated control mice ( Fig 3 ) . We next sought to investigate whether Sm eggs ( SmE ) alone are capable of attenuating HIV vaccine-specific responses in the absence of an active Sm infection . To achieve this , we sensitized mice with 2 500 SmE intraperitoneally , challenged them with 2 500 SmE intravenously 14 days later and vaccinated them with the MVA+gp140 vaccine regimen . Cumulative cellular responses to the HIV peptides were measured in the spleens using CBA and ELISpot ( Fig 4A–4E ) and gp140 Env-specific antibodies in the sera ( Fig 4F ) . Cumulative HIV-1 IFN-γ SFU/106 ( Fig 4A ) , and IL-2 SFU/106 ( Fig 4B ) in SmE-inoculated vaccinated mice were noticeably lower , but not significantly when compared to SmE-free vaccinated mice . Similarly , levels of cumulative IFN-γ; TNF-α and IL-2 secreted by splenocytes were noticeably lower in SmE-inoculated vaccinated mice compared to those secreted by splenocytes from SmE-free vaccinated mice ( Fig 4C–4E respectively ) . However , SmE-inoculated mice had significantly lower amounts of gp140-specific IgG1 ( 656 . 8 ± 177 . 1 versus 1203 ± 152 . 0 AUs ) , IgG2a ( 71 . 14 ± 15 . 98 versus 238 . 1 ± 34 . 33 AUs ) , and IgG2b antibodies ( 82 . 73 ± 18 . 20 versus 218 . 3 ± 41 . 86 AUs ) compared to SmE-free mice ( Fig 4F ) , indicating broad attenuation of gp140 Env-specific antibody responses . Furthermore , we confirmed that SmE alone , in the absence of active infection , is capable of skewing the Th1/Th2 profile towards a Th2 response . As shown in Fig 4G , at 9 weeks post inoculation , the IFN-γ:IL-4 ratio was significantly lower in vaccinated SmE-inoculated ( 367 . 0 ± 38 . 34 pg/ml ) compared to SmE-free ( 597 . 2 ± 72 . 93 pg/ml ) vaccinated mice after stimulation with Con A . Similarly , stimulation of splenocytes with SEA resulted in a trend towards reduced IFN-γ:IL-4 ratio for SmE-inoculated mice compared to uninfected but vaccinated mice ( S2 Fig ) . We investigated whether vaccination with DNA+MVA exacerbates helminth associated pathology in chronically infected mice by determining granuloma sizes and hydroxyproline content in mouse livers and assessing hepatosplenomegaly . We also investigated whether treatment with PZQ prior to vaccination with the DNA+MVA regimen ameliorates tissue pathology in infected mice . Sm-infected and vaccinated mice developed significantly larger ( 60 . 27 ± 2 . 37 mm2 ) granulomas when compared to all the other groups ( vaccinated Sm-infected-PZQ treated [41 . 29 ± 1 . 66 mm2]; Sm-infected alone [40 . 79 ± 2 . 38 mm2] and Sm-infected-PZQ treated vaccinated [42 . 36 ± 2 . 26 mm2] ) ( Fig 5A and 5B ) . Sm-infected mice that were either vaccinated or unvaccinated developed hepatosplenomegaly as indicated by larger spleens and livers compared to vaccinated infected mice , naïve mice and vaccinated Sm-infected-PZQ treated mice ( Fig 5C and 5D ) . No difference in hydroxyproline content was observed between unvaccinated Sm-infected and vaccinated Sm-infected mice ( Fig 5E ) . Surprisingly , high levels of hydroxyproline content were observed in unvaccinated Sm-infected-PZQ treated mice compared to Sm-infected ( Fig 5E ) . However , Sm-infected mice had significantly higher number of eggs per gram of liver compared to unvaccinated and vaccinated Sm-infected mice that were treated with PZQ ( Fig 5F ) .
The present study investigated the impact of chronic schistosomiasis on the induction of T cell-mediated and antibody responses to candidate HIV vaccines in a mouse model , whether attenuation of vaccine responses can be reversed by pre-vaccination anti-helminthic treatment , and if vaccination with a T cell-based candidate vaccine has an adverse effect on helminth-associated pathology . Firstly , we sought to establish that the mouse model of chronic schistosomiasis worked well as to be expected on our hands . As we expected , our data confirmed that prior to vaccination , Sm-infected mice elicited predominantly Th2 responses and a decreased Th1 cytokine profile ( Fig 1A and 1B; S1 Fig ) , had impaired Th1 cytokine-producing CD8+ and CD4+ T cells ( Fig 1C ) and an increase in Th2 total antibodies in serum ( Fig 1D ) as well as enlarged spleens and livers ( Fig 5C and 5D ) compared to uninfected mice . These findings are consistent with previous reports that demonstrate that Sm infection and a host of other helminths skews the host’s immune responses from a Th1 towards a Th2 type with egg deposition and increased production of IL-4 as key driving forces [23 , 26 , 27 , 63 , 68–72] . Our data also agrees with previous studies that have demonstrated that IL-10 is also responsible for down-regulation of Th1 responses that is observed in schistosome infections [73 , 74] . IL-10 has been shown to mediate this down-regulation via an activation-induced cell death process resulting in apoptosis of CD4+ and CD8+ T cells which is also linked to the onset of egg-laying by the helminth parasite and formation of granulomas [75–77] . Our data also suggested a correlation between the decrease of cytokine-producing CD8+ T cells and levels of IgG2a and IgG2b antibodies observed in unvaccinated Sm and vaccinated Sm-infected mice . Previous studies have reported that cytokines produced by CD4+ and CD8+ T cells play an important role in the regulation of the humoral immune response and isotype switching [78–80] . The immunological consequence of a predominant Th2 biasing demonstrated in Sm-infected mice agrees with the concept of reciprocal antagonism between Th1 and Th2 as previously suggested [62 , 81] . Secretion of IFN-γ and IL-2 by T cells has been associated with suppression of viral replication in HIV-infected individuals and better proliferation of HIV-1-specific CD4+ and CD8+ T cells , suggesting that production of these Th1 cytokines by candidate HIV vaccines is a good indicator of vaccine-mediated immune protection [82] and good indications of polyfunctional CD4+ T cell responses [83] . In this study , we observed that the presence of Sm infection prevented optimal generation of vaccine-specific T cell responses following immunization with SAAVI vaccine candidates ( Fig 2 ) . As shown in Fig 2C–2E , vaccine-specific Th1 cumulative cytokine levels ( IFN-γ , IL-2 and TNF-α ) in recall responses to HIV peptides were significantly reduced in vaccinated Sm-infected compared to Sm-free mice vaccinated with DNA+MVA and MVA+gp140 , with exception to TNF-α in MVA+gp140 vaccinated groups which were similar in both vaccinated Sm-infected and Sm-uninfected but vaccinated mice . Similarly , the magnitudes of vaccine-specific cumulative IFN-γ and IL-2 T cell responses measured by ELISpot were significantly lower in Sm-infected vaccinated mice compared to Sm-uninfected mice vaccinated with DNA+MVA and MVA+gp140 ( Fig 2A and 2B ) . A similar decline has been reported in Sm-infected mice compared to uninfected controls following immunization with a DNA-vectored HIV-1 vaccine [57] . Also , the frequencies of vaccine-specific cytokine-producing CD4+ and CD8+ T cells in Sm-infected mice were observed in the current study ( Fig 2F and 2G ) , which may translate to a decrease in antibody population [84] . However , it was noted that the cytokine-producing T cells were predominantly of the effector memory phenotype in both Sm-infected and uninfected vaccinated mice ( Fig 2H ) . Vaccine-induced effector memory T cells have been associated with protection against mucosal SIV challenge in vaccinated rhesus monkeys [85] . In this study , the downregulation of these vaccine-specific cellular responses demonstrates the ability of Sm infection to negatively affect protective potential of candidate HIV-1 vaccines as have suggested by others [57 , 59] . The antibody responses to the gp140-Env protein were significantly impaired in Sm-infected vaccinated mice ( Fig 3 ) . The mean concentration of anti-gp140 antibodies in Sm-infected mice vaccinated with MVA+gp140 was significantly lower than that in Sm-free vaccinated control for all IgG isotypes ( IgG1; IgG2 and IgG2b ) . Antibody responses to specific HIV antigens have been proposed to correlate with protection [82 , 86]; thus , this was an interesting finding with far-reaching implications for future vaccine development . Elimination of helminth parasites with an antihelminth drug prior to immunization was expected to restore normal vaccine T cell responsiveness as previously demonstrated [59 , 87 , 88] . Our results show that treating mice with PZQ reversed tissue pathology as indicated by reduced spleen and liver weights sizes of granulomas and SmE deposition in the liver tissue in PZQ-treated mice compared with untreated mice ( Fig 5B–5D and 5F ) is consistent with earlier reports [89 , 90] . Treatment with PZQ has been shown to eliminate adult worms with no direct impact on the SmE already trapped in the tissues other than preventing continued egg deposition in treated subjects [38–40] and potentially restoring normal T cell immune responsiveness . However , our immunological data showed that treating mice prior to vaccination only partially restored the hosts’ vaccine-specific T cells responses ( Fig 2 ) . Surprisingly , the partial recovery of these responses did not translate in reduction of the magnitudes of Th2 cytokine responses ( S1 Fig ) . Anti-inflammatory cytokines such as IL-10 remained elevated despite treatment with antihelminth ( S1C and S1I Fig ) whilst previous studies in which PZQ was used reported similar findings [58 , 59] . However , it was unclear if the antibody responses were affected . In our study , Th1-type gp140-Env-specific antibody responses in Sm-infected mice were significantly lower despite treatment with PZQ ( Fig 3 ) . To our knowledge , no study has evaluated the ability of antihelminthic treatment in the restoration of antibody responses to HIV vaccines . However , it is has been suggested that the duration of infection prior and post treatment is an important factor which determines subsequent restoration of normal responses to vaccination [53 , 89 , 90] . A study by Chen et al . , showed a recovery of immune balance 16 weeks post-treatment [53] . Also , findings from the studies conducted by Da’dara’s group and Shollenberger’s groups , demonstrated that normal immune responses can be achieved 2–10 weeks post-treatment [58 , 59] . In contrast to our study , only a 1 . 5-week post-treatment period was allowed prior to commencing the vaccinations . Future studies should investigate varying post-treatment periods including multiple vaccinations to establish the optimal recovery durations to start vaccinations after antihelminthic treatment . This is particularly relevant if there will arise a need to integrate future HIV vaccinations with helminthic worms control programmes to improve the vaccination outcomes in helminth-endemic areas . This study went further to demonstrate that Th1 cellular responses elicited by DNA- and MVA- vectored HIV-1 vaccines exacerbated helminth-induced pathology . Sm-infected mice vaccinated with a DNA+MVA regimen had significantly larger granulomas as well as enlarged spleens and livers compared to Sm-infected unvaccinated groups ( Fig 5B and 5C ) . Treatment significantly reduced the pathology; however , a considerable number of eggs were still present in the liver tissues of treated mice ( Fig 5F ) . Surprisingly , the amount of hydroxyproline content , which is a measure of collagen content , was significantly higher in PZQ-treated uninfected mice compared with unvaccinated Sm-infected groups ( Fig 5E ) , suggesting that PZQ treatment may contribute to increased fibrosis of the liver ( Fig 5E ) as an adverse side effect . A recent study showed that a novel experimental drug ( Paeoniflorin ) used for treating schistosomiasis managed to control sclerosis better than PZQ [91] , pointing to a possible future replacement of PZQ as the antihelminthic drug of choice . Nevertheless , PZQ treatment resulted in reduced number of eggs per gram of liver tissue when compared with Sm-infected untreated mice ( Fig 5F ) . These findings highlighted the scientific challenges in the development of HIV vaccines for SSA , where parasitic helminthiasis is endemic . As discussed above , this study found lack of restoration of vaccine-specific responses upon PZQ-treatment prior to vaccinations while a substantial level of SmE burden was observed in PZQ-treated mice several weeks post-treatment . We , therefore investigated if Sm eggs in the absence of a live infection could result in downregulation of HIV-specific responses . Following an established Sm-egg model [92] , mice were inoculated with S . mansoni eggs and then vaccinated with candidate HIV vaccines to evaluate how these eggs affect vaccination outcomes . The IFN-γ:IL-4 ratio for the SmE-sensitized vaccinated mice was significantly smaller than the unsensitized vaccinated mice ( Fig 4G ) , indicating a considerable elevation of Th2 cytokines and down regulation of Th1 in SmE-inoculated mice comparable to SmE-unsensitized mice . However , this polarized Th2 immune responses appear to have had only partial effects on the vaccine-specific T cell responses ( Fig 4A–4E ) . Although reduced , the decrease in vaccine-specific cellular responses observed in SmE-inoculated mice was not significant . Surprisingly as with Sm live infection , antibody responses to HIV Env-gp140 were significantly reduced in the presence of SmE ( Fig 4F ) . As suggested previously [72] , this finding confirms that SmE trapped in the tissues play a critical role in attenuating the host’s vaccine-specific responses in Sm-infected individual and may explain why both cellular and antibody responses are still suppressed despite treatment in PZQ-treated groups . Thus , the possible mechanism by which Sm infection suppresses these HIV-specific cellular and humoral responses appear to involve the deposition of SmE in the tissues , which stimulates increased production of IL-4 and IL-10 with concomitant polarization of Th2 immune responses . This in turn may promote activation-induced apoptosis of HIV-specific CD4+ and CD8+ T cells resulting in attenuated induction of Th1 immune responses which are key components of HIV vaccine-specific responses . This finding further highlights another challenge that even after antihelminth treatment with PZQ , generation of optimal vaccine responses may not be achieved as helminth eggs left trapped in the tissues could still attenuate HIV vaccine-induced immune responses . In light of these findings , this study suggests that , whilst elimination of worms can offer an affordable and a simple means of antihelminthic treatment , only partially restoration of immune responsiveness to T cell-based vaccines for HIV-1 and other infectious diseases in helminth endemic settings may be achieved . Thus , it would be important to evaluate vaccine delivery systems that can potentially overcome the negative impact of concurrent helminthiasis as previously suggested [93] . An alternative avenue would be the discovery of antihelminthic drugs which are effective in elimination of SmE from the host’s tissues in addition to the elimination of the parasitic worms . Although , this study gives further information on the impact of helminth infection on the immunogenicity of HIV vaccines , not all immunological aspects could be elucidated . Thus , this study justifies further investigations with use of a nonhuman primate model such as baboons ( immune system is highly similar to humans ) to obtain a better understanding of these immune responses . The present study demonstrated that chronic helminth infection is associated with Th2-driven attenuation of both T cell and antibody response to HIV vaccines , and elimination of worm by chemotherapy partially restored T cell responses but not necessarily antibody responses . This study further demonstrated that vaccinating helminth-infected individuals with HIV vaccines that induce strong cellular responses may increase the pathology induced by the parasites , rendering the vaccine unsafe in helminth endemic areas . Lastly , this study suggests that the often-suggested integration of antihelminthic treatment programme with a successful future HIV vaccine might not result in improved vaccination outcome unless alternative antihelminthic drugs with a capacity to eliminate schistosome eggs from tissues are developed . In addition , we recommend that HIV vaccine development programs should consider designing vaccines that can overcome the adverse effects of helminth-induced immunity .
Biomphalaria glabrata snails ( Strain NMRI , NR-21962 ) , infected with Schistosoma mansoni ( Strain NMRI ) were provided by the Schistosome Research Reagent Resource Center ( NIAID , NIH , USA ) and maintained in our laboratory for generation of live S . mansoni ( Sm ) cercariae that were used in this study . S . mansoni eggs ( SmE ) were purchased from the Theodor Bilharz Research Institute ( Schistosome Biological Supply Center , Egypt ) and stored at -80°C until use . The integrity and viability of the eggs were evaluated using a light microscope prior to use . Both DNA- and MVA-vectored HIV-1 vaccines have been shown to elicit strong T cell responses in mice [11] , nonhuman primates [18 , 67] and clinical trials [16] . Female BALB/c mice ( 6–8 weeks old ) were purchased from South African Vaccine Producers ( SAVP ) ( Johannesburg , South Africa ) , housed in an Animal Biosafety Level 2 facility at the University of Cape Town and maintained in accordance with the South African National Guidelines for Use of Animals for Scientific Purposes ( SANS Code 10386: 2008 ) which is also in line with EU Directive 2010/63/EU . Experimental protocols performed in this study were reviewed and approved by the Animal Ethics Committee of the University of Cape Town ( UCT AEC: protocol number: 014/026 ) and performed by qualified personnel in compliance with the South African Veterinary Council regulations . A mixture of ketamine hydrochloride and xylazine was used to anaesthesise mice for all procedures that involved intramuscular or intravenous injections , infection with live Schistosoma mansoni cercariae , collection of blood by cardiac puncture and preparation for euthanasia . Euthanasia was done by cervical dislocation while the animals were under anaesthesia ( induced with a mixture of ketamine and xylazine as describe above ) . The study comprised of three experiments . In Experiment 1 and 2 ( Table 1 ) , mice were randomly allocated to six groups ( 5–8 mice per group ) per experiment . Mice receiving exposure to Sm ( 4 groups ) were infected percutaneously via the abdomen with 35 live S . mansoni cercariae at the beginning of the experimentation . Those receiving antihelminthic treatment were given two doses of PZQ ( Sigma Aldrich , USA ) by oral gavage ( 500 mg/kg; diluted in water containing 2% Kolliphor EL [Sigma Aldrich , USA] ) three days apart , between 8 and 8 . 5 weeks post infection . Animals were vaccinated twice , 4 weeks apart , starting at 10 weeks post infection ( Table 1 ) . In Experiment 3 ( Table 2 ) , mice were allocated to 4 groups ( 5 mice per group ) . Two groups were inoculated twice with SmE ( 2500 eggs per mouse ) , 14 days apart , initially by intraperitoneal route , and subsequently by intravenous route . As in Experiments 1 and 2 , mice were vaccinated twice , 4 weeks apart , starting at 1 week after the second inoculation with SmE ( Table 2 ) . Vaccinations with DNA-vectored ( 100μg DNA per mouse ) and MVA-vectored ( 106 plaque forming units per mouse ) HIV-1 vaccines were given intramuscularly . Vaccination with HIV-1 gp140 Env ( 10μg protein per mouse formulated in Imject Alum adjuvant [Thermo Scientific , USA] ) was administered subcutaneously . DNA+MVA vaccine regimens were given as DNA prime and MVA boost vaccine regimens whilst MVA+gp140 Env were given concurrently . Twelve days following the last vaccination , blood was collected by cardiac punctured , mice were euthanised and spleens and livers were harvested for evaluation of HIV immune responses and helminth-induced pathology . Splenocytes were prepared using a standard protocol [95] and stimulated with HIV peptides or mitogen stimulant at 2μg/ml ( S1 Table ) . IFN-γ and IL-2 ELISpot assays were carried out as previously described [11] . Cytometric bead array ( CBA ) assays were carried out as previously described [96] . Intracellular cytokine staining ( ICS ) and flow cytometry analysis was performed as previously described [12 , 14] with minor modifications . Briefly , cells were stained with a viability dye , violet amine reactive dye ( ViViD; Invitrogen , USA ) , at a pre-determined optimal concentration before staining for cell surface molecules with the following fluorochrome-conjugated antibodies: anti-CD3-Alexa 700 , anti-CD4-PE-Cy7 , anti-αCD8-APC-Cy7 , anti-CD62L-APC , and anti-CD44-FITC diluted to 0 . 2μg in staining buffer ( BD Biosciences , USA ) . Further intracellular cytokine staining was done with pooled PE-conjugated anti-TNF ( 0 . 2μg ) anti-IL-2 ( 0 . 06μg ) and anti-IFN-γ ( 0 . 06μg ) antibodies diluted in Perm/Wash buffer ( BD Biosciences , USA ) . To measure the level of HIV gp140 Env-specific antibodies in mouse sera , a standardised ELISA assay was established as previously described [97] . Briefly , ELISA plates were coated with 0 . 5μg/ml of gp140 protein diluted in PBS and incubated overnight at 4°C . Test mouse sera ( diluted 1:1000 ) were tested in duplicates . Mouse sera obtained from unvaccinated mice was used as a negative control while a reference serum sample prepared from mice previously vaccinated with HIV gp140 protein was used in 12 two-fold dilutions , starting at 1:100 , to generate a standard curve . For detection , appropriate secondary anti-mouse antibodies conjugated with horseradish peroxidase were used including the three anti-mouse IgG isotypes ( IgG1; IgG2a and IgG2b; Southern Biotechnology ) . After colour development using tetramethyl-benzidine substrate ( TMB; KPL , USA ) , the optical density ( OD ) was measured at 450nm ( with a reference filter set at 540 nm ) using a microplate reader ( Molecular Devices Corporation , USA ) . Based on the constants of the standard curve generated from the serially diluted reference sample , the reciprocal dilution giving an OD value of 1 ( against gp140 ) was assigned a value of 1000 antibody units ( AUs ) . The negative control ( unvaccinated mouse serum ) was assigned a reciprocal dilution of 0 and zero AUs . A reference sample was used on each ELISA plate to generate a standard curve from which the assigned AUs were used to extrapolate for test samples at a fixed dilution of 1:1000 . A cut-off value for positive antibody responses was set at 2 x the OD value of the negative control serum ( unvaccinated ) and those below the cut of value were assigned an antibody unit of zero . Spleens and livers were weighed prior to processing for immunological evaluation in the laboratory to determine if HIV vaccination worsens helminth-associated pathology . Livers were then fixed in 4% ( v/v ) buffered formalin solution . The fixed samples were then embedded in wax and processed . Sections ( 5–7μm ) were stained with hematoxylin and eosin ( H&E ) ( Sigma Aldrich , USA ) to show aggregation of white blood cells around the Sm eggs and Chromotrop-aniline blue solution ( CAB ) ( Sigma Aldrich , USA ) and counterstained with Weigert's hematoxylin ( Sigma Aldrich , USA ) to stain for collagen . Micrographs of liver granuloma were captured using a Nikon 90i wide-field microscope using a 5 . 0 megapixel colour digital camera running Nikon’s NIS-Elements v . 4 . 30 software ( Nikon Instruments Inc . , USA ) . The area of each granuloma containing a single egg was measured with the ImageJ 1 . 34 software ( National Institutes of Health , USA ) . A total of 25–30 granulomas per slide per mouse were included in the analyses . Data was presented as a mean area of each granuloma containing a single egg . The number of eggs per gram of liver was determined by counting individual eggs from hydrolysed liver under a microscope . Hydroxyproline content , which is a direct measure of collagen content in liver was determined using a modified hydroxyproline protocol by Bergman and Loxley [98] . Briefly , liver samples were weighed , hydrolyzed and added to a 40mg Dowex/Norit mixture . The supernatants were neutralised with 1% phenolphthalein and titrated against 10 M NaOH . An aliquot was mixed with isopropanol and added to chloramine-T/citrate buffer solution ( pH 6 . 5 ) . Erlich’s reagent ( 95% ethanol containing dimethylaminobenzaldehyde ( DMAB ) and concentrated hydrochloric acid ) was added and absorbance was read at 570 nm . Hydroxyproline levels were calculated using 4-hydroxy-L-proline ( Sigma Aldrich , USA ) as a standard , and results were expressed as μmoles hydroxyproline per weight of tissue that contained 104 eggs . Statistical analysis was performed using Prism version 5 . 0 ( GraphPad Software , USA ) . The t-test for independent unpaired non-parametric comparisons was applied to assess the level of significance between means ±SEM . Three independent experiments were conducted and all tests were two-tailed . p values <0 . 05 were considered as significant . The false discovery rate ( FDR ) with Benjamini-Hochberg-adjusted p<0 . 05 was performed as previously described [99] .
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Chronic parasitic worm infections are thought to reduce the efficacy of vaccines . Given that HIV and worm infections are common in sub-Saharan Africa ( SSA ) and their geographical distribution vastly overlaps , it is likely that future HIV vaccines in SSA will be administered to a large proportion of people with chronic worm infections . This study examined the impact of S . mansoni worm infections on the immunogenicity of candidate HIV vaccines in a mouse model . S . mansoni worm-infected animals had lower magnitudes of HIV vaccine responses compared with uninfected animals and elimination of worms by praziquantel treatment prior to vaccination conferred only partial restoration of normal immune responses to vaccination . The presence of S . mansoni eggs trapped in the tissues in the absence of live infection was associated with poor vaccine responses . In addition , this study found that effective immunization with some HIV vaccine regimens could potentially worsen worm-associated pathology when given to infected individuals . These novel findings suggest further research in HIV vaccines and future vaccination policies regarding the current clinical vaccines and future HIV vaccination with respect to parasitic worm infections especially in SSA .
|
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"Methods"
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2018
|
Chronic schistosomiasis suppresses HIV-specific responses to DNA-MVA and MVA-gp140 Env vaccine regimens despite antihelminthic treatment and increases helminth-associated pathology in a mouse model
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As we move forward from the current generation of genome-wide association ( GWA ) studies , additional cohorts of different ancestries will be studied to increase power , fine map association signals , and generalize association results to additional populations . Knowledge of genetic ancestry as well as population substructure will become increasingly important for GWA studies in populations of unknown ancestry . Here we propose genotyping pooled DNA samples using genome-wide SNP arrays as a viable option to efficiently and inexpensively estimate admixture proportion and identify ancestry informative markers ( AIMs ) in populations of unknown origin . We constructed DNA pools from African American , Native Hawaiian , Latina , and Jamaican samples and genotyped them using the Affymetrix 6 . 0 array . Aided by individual genotype data from the African American cohort , we established quality control filters to remove poorly performing SNPs and estimated allele frequencies for the remaining SNPs in each panel . We then applied a regression-based method to estimate the proportion of admixture in each cohort using the allele frequencies estimated from pooling and populations from the International HapMap Consortium as reference panels , and identified AIMs unique to each population . In this study , we demonstrated that genotyping pooled DNA samples yields estimates of admixture proportion that are both consistent with our knowledge of population history and similar to those obtained by genotyping known AIMs . Furthermore , through validation by individual genotyping , we demonstrated that pooling is quite effective for identifying SNPs with large allele frequency differences ( i . e . , AIMs ) and that these AIMs are able to differentiate two closely related populations ( HapMap JPT and CHB ) .
Genetic ancestry , as studied through DNA sequence variation , has shed light on the history , migration patterns , and relationships among human populations [1] , [2] . In the context of medical population genetics , genetic ancestry forms the basis of admixture mapping [3] . Additionally , genetic ancestry is useful for proper matching of cases and controls and is also an important covariate to consider in association studies for complex human traits [4] , [5] as spurious associations around variants with large allele frequency differences between populations have long been recognized as potential confounders [6]–[9] . For admixed populations , having an estimated proportion of genetic ancestry attributable to each ancestral population ( i . e . , the admixture proportion ) would also allow the construction of weighted reference panels , which has been shown to enable a more efficient design of a panel of tag SNPs to capture untyped variations over a genomic region ( e . g . , a candidate gene region ) and possibly facilitate more efficient imputation of untyped SNPs genome-wide in admixed populations [10] . Moreover , as we move forward from hypothesis-generating genome-wide association ( GWA ) studies , the research focus will start to shift to fine mapping of associated signals and/or pathways identified through such studies and will also expand to include understudied diseases as well as studies in additional populations of unknown ancestry . For all of these studies , knowledge of genetic ancestry ( and thus potential population substructure ) will be necessary . Currently , two main approaches exist for inferring genetic ancestry . If the ancestral populations of the population being studied are known , ancestry informative markers ( AIMs ) numbering in the hundreds can be genotyped to infer global ancestry via principal components analysis ( PCA ) or a clustering-based algorithm ( for examples , see [8]–[16] ) . However , often the ancestral populations are not known with confidence , and many markers would need to be genotyped in the discovery phase to assemble a panel of AIMs . Moreover , AIMs identified in this manner will only be informative for the axis of ancestry they are selected to explain ( e . g . , a panel of AIMs selected to differentiate between Africans and Europeans will be less effective for differentiating northern Europeans from southern Europeans ) . The alternative approach is to apply PCA to individual-level genetic data for a large number of loci , typically obtained from GWA studies , to infer global ancestry . The limitation of this approach is the high cost of obtaining genome-wide genotype data from a sizable cohort , particularly when studying a less well-funded phenotype . Therefore , the need to efficiently ( both in terms of cost and time ) assess the biogeographical ancestry in the study population and to rapidly screen hundreds of thousands of genetic makers for AIMs will be valuable for future genetic association and demographic studies . This is particularly true for populations of relatively complicated admixture or of origins dissimilar to standard reference populations such as those catalogued by the International HapMap Consortium [17] . One possible method for rapidly and inexpensively estimating admixture proportion and identifying AIMs in a cohort is through genotyping of pooled DNA . Genotyping pools of DNA from multiple individuals rather than genotyping each individual separately has been proposed as a cost-effective alternative to GWA studies ( see [18] ) . One study estimated that a 20-fold reduction in cost could theoretically be achieved if pooled genotyping were employed [19] . This reduction in cost would allow preliminary GWA studies of numerous orphan diseases to be conducted . For this reason , several reports have investigated the feasibility of and have developed analysis tools for genotyping pooled DNA using SNP microarrays ( see [19]–[26] , among others ) . Despite the potential cost-savings of pooled genotyping , drawbacks of not directly measuring individual genotypes include loss of the ability to study additional or sub- phenotypes within the pooled cohort and loss of the ability to detect gene-gene interactions ( see [20] ) . It has also not been shown definitively that small allele frequency differences between cases and controls can be reliably detected given the additional imprecision in allele frequency estimates due to pooling . Indeed , reproducible associations have only been reported for variants with large effect sizes ( for example , [20] , [27]–[30] ) , whereas common variants known to be associated with common diseases such as type 2 diabetes and obesity typically have modest effect sizes with odds ratios ranging from about 1 . 1 to 1 . 3 [31] . Because pooled genotyping may reliably detect SNPs with large between-group allele frequency differences [20] , [27]–[30] , we hypothesized that this approach may represent a feasible method to identify AIMs , as these are , by definition , markers that display large allele frequency differences between two populations . To test this hypothesis , we constructed four pools from African American samples and genotyped both the pooled and individual DNA samples at ∼900 K markers using the Affymetrix 6 . 0 array . Taking advantage of the expected allele frequency estimates based on individual genotypes , we established a set of quality control ( QC ) filters to enrich for SNPs truly displaying allele frequency differences between two pools and applied QC filter to a Hawaiian cohort , a Latina cohort , and two Jamaican cohorts that had been similarly pooled and genotyped . Then , based on the estimated allele frequencies for post-QC SNPs , we were able to reliably estimate admixture proportions in these pooled cohorts from admixed populations , using HapMap reference panels as proxies for the populations ancestral to the admixed populations . Moreover , we were able to identify AIMs informative for ancestry beyond what can be modeled by the HapMap reference panels . Therefore , genome-wide genotyping of pooled DNA appears to be extremely efficient and informative for assessing the genetic ancestry of a population .
In total we constructed four DNA pools of 521 African American samples from Maywood , IL ( MAY ) ; two pools of 321 African American women ( MEC-AA ) , two pools of 252 Native Hawaiian women ( MEC-H ) , two pools of 332 Latina women ( MEC-L ) , and two pools of 202 Japanese American women ( MEC-J ) from Los Angeles , CA and Honolulu , HI; six pools of 688 Jamaican samples from Kingston , Jamaica ( GXE ) ; and four pools of 480 Jamaican samples from Spanishtown , Jamaica ( SPT ) ( see Text S1 for details ) . Each pool was genotyped in triplicate using the Affymetrix 6 . 0 array . Samples comprising the MAY panel were also genotyped individually as part of a separate GWA study of obesity ( C . W . K . C . , H . N . L . , R . S . C . , X . Z . , and J . N . H . , unpublished ) . For each pool , pooled allele frequencies ( AF ) were estimated as the proportion of angular distance observed for the pooled sample relative to that observed for the individual samples on the same plate , and averaged over all replicates ( see Methods for details ) . Quality control ( QC ) was performed in two stages . First , any pool replicate with excessively low intensity , low call rate , or high heterozygosity compared to the other replicates within the same pool was either re-genotyped or dropped from the study ( see Text S1 ) . Second , because of the availability of individual genotype data for the MAY panel , it was used as a training set to establish a set of four SNP QC filters to preferentially eliminate SNPs that genotyped poorly or inconsistently ( see Methods , Text S1 ) . ∼306 K SNPs in MEC-H , ∼359 K SNPs in MEC-L , ∼346 K SNPs in MEC-J , ∼477 K SNPs in MEC-AA , ∼353 K SNPs in GXE , and ∼307 K SNPs in SPT passed all four QC filters . When examining the correlation of the estimated allele frequencies of one of the MEC-H pools with those of the other MEC-H pool the SNP QC filters were effective in removing the vast majority of SNPs predicted to have large allele frequency differences , even though the difference in predicted AF between the two pools was not part of the QC filter ( Figure 1A and 1B ) . Similar results were observed for the pools from other panels ( data not shown ) . These removed SNPs are likely to be false positives , as very few SNPs with large AF differences between two samplings from the same underlying population are expected . The effectiveness of the filters in removing poorly genotyped SNPs is also evident when comparing the estimated allele frequency by pooling to the actual allele frequency by individual genotyping in the MAY panel ( Figure S1 ) . We attempted to identify putative AIMs only among the SNPs that passed the QC filters ( below ) . To both assess genetic ancestry and identify new AIMs particular to the admixed populations , we first used our QC-filtered pooled genotype data to estimate the relative contributions of different continental ancestries to each of our admixed populations ( MEC-H , MEC-L , MEC-AA , GXE , and SPT ) . Second , for each of our admixed panels we constructed a corresponding weighted reference panel ( pseudopopulation ) based on the estimated admixture proportion , and identified putative AIMs , i . e . , SNPs with pooled AF estimates significantly different from those predicted by the pseudopopulation . Finally , we validated putative AIMs by genotyping the individuals who comprised the pools . To estimate the proportion of ancestry relative to the HapMap reference panels ( i . e . , the admixture proportion ) , we applied a linear regression-based approach to the QC-filtered data , overcoming the uncertainty in pooled AF estimates with the high density of SNPs . For each SNP , we modeled the estimated allele frequency of the pooled sample as a linear combination of the known allele frequencies in the HapMap YRI ( West African ) , CEU ( European ) , and/or CHB/JPT ( East Asian ) reference panels . The associated regression coefficients can be thought of as estimates of the proportional contribution from each of the reference panels ( see Methods ) . We first tested the method in a population of known ancestry . For the MAY pool , regression estimates from pooling yielded an estimated overall admixture proportion of ∼82 . 4% YRI and ∼17 . 5% CEU ( Table 1 ) . This estimate is very similar to that obtained using allele frequencies based on individual genotyping on pre- or post- QC-filtered SNPs ( ∼81 . 2% YRI and ∼17 . 8% CEU pre-QC , ∼80 . 6% YRI and ∼17 . 6% CEU post-QC ) , showing that the method is robust to pooling-associated error in estimating allele frequencies . Additionally , this estimate is also very close to that obtained when we restricted the analysis to genotypes at 699 published ancestry informative markers ( AIMs ) found on the Affymetrix 6 . 0 array [32] and estimated ancestry using STRUCTURE [13] ( ∼83 . 3% YRI and ∼16 . 7% CEU , Table 1 ) , and previously published estimates ( ∼81 . 2% YRI and ∼18 . 8% CEU [33]; ∼80 . 5% YRI and ∼19 . 5% CEU [34] ) for this population . To extend this method to additional admixed populations , we applied our regression method to the MEC-H and MEC-L pools , using allele frequencies in all three HapMap populations as the predictor variables . We estimated the Native Hawaiians to be closest to ∼5 . 6% YRI , ∼31 . 9% CEU , and ∼59 . 9% CHB/JPT , and the Latinas to be closest to ∼8 . 0% YRI , ∼61 . 1% CEU , and ∼29 . 2% CHB/JPT ( Table 1 ) . These estimates are consistent with our knowledge of the population history for Native Hawaiians and Latinas ( as East Asians are useful , though imperfect , surrogates for the ancestral Native American and Polynesian populations due to their relatively recent divergence from East Asians [11] , [35] , [36] ) , and are again very close to STRUCTURE-generated estimates based on 69 published AIMs previously typed in the MEC-H and MEC-L populations ( ∼3 . 5% YRI , ∼32 . 8% CEU , ∼63 . 7% CHB/JPT for MEC-H; ∼5 . 2% YRI , ∼66 . 0% CEU , ∼28 . 8% CHB/JPT for MEC-L , Table 1 ) [15] . We further estimated the MEC-AA pools to most closely correspond to 71 . 3% YRI and 24 . 1% CEU , the GXE pools to correspond to ∼86 . 8% YRI and ∼12 . 2% CEU , and the SPT pools to correspond to ∼82 . 2% YRI and ∼10 . 1% CEU ( Table 1 ) . Qualitatively , these estimates are consistent with reported estimates based on populations of similar demographic history . Namely , the Jamaican samples are expected to have proportionally more African ancestry than African Americans from Illinois [33] , while African Americans from Los Angeles , CA , are expected to have proportionally more European ancestry [37] . Interestingly , the SPT panel appears to have a component of missing ancestry ( summed proportion of admixture = 92 . 3% , Table 1 , and not improved substantially when the JPT/CHB panel was included , data not shown ) , yet displays relatively low FST when compared to its pseudopopulation ( Table S1; also see Discussion ) . To identify additional components of ancestry beyond those already modeled by the HapMap reference panels , we first constructed a corresponding pseudopopulation using the estimated admixture proportions for each of the populations pooled in this study . We then sought to identify potential AIMs that showed large differences in AF when comparing the pooled estimates to those based on the pseudopopulation ( see Methods for details ) . To obtain an initial approximation of the number of AIMs expected , we examined the distribution of AF differences between the pooled population and its respective pseudopopulation among the top 200 AIMs ( Figure 2 , Figure S2 ) . The distribution from the MAY pools serves as a null distribution for which few true AIMs are expected , as the admixture in this population is known to be very well described by the HapMap populations ( FST = 0 . 0016 between the MAY pools and their pseudopopulation , Table S1 ) . Relative to the distribution observed in the MAY pools , the distribution of the MEC-H pool displayed the most dramatic shift , followed by that of the MEC-L pool ( Figure 2 ) . The rightward shifts observed in the MEC-H and MEC-L pools are unlikely to be due to systematic error because the distribution of the MEC-AA pool ( which was constructed and processed at the same time ) appears similar to that observed in the MAY pools ( Figure 2 ) . On the other hand , the distributions from the GXE and SPT pools were similar in shape to that of the MAY pools , with only a slight rightward shift observed with the SPT pools ( Figure S2 ) . The relative degrees of rightward shift of the AF difference distributions corresponded with the rank order of the FST between the pooled panel and its respective pseudopopulation in all cases ( Table S1 ) , suggesting that the AIMs identified here are representative of the overall differentiation of the pooled panel and its pseudopopulation rather than being a biased set of SNPs that happen to show large AF differences due to pooling error . Taken together , these results suggest that many more AIMs with large AF differences informative for ancestral components not captured by the three HapMap panels likely exist in the MEC-H and MEC-L pools than in the MEC-AA and the Jamaican pools and can be identified through pooling . To validate the putative AIMs identified by pooled genotyping , we successfully genotyped 25 , 28 and 26 of the top candidate AIMs in the individuals that comprised the MEC-L , GXE and SPT pools . For MEC-H , we examined 19 of the top 4000 AIMs ( prior to pruning by distance ) that had been already genotyped in the laboratory . Given the success of genotyping pooled DNA in identifying disease variants with large AF differences between cases and controls ( see Introduction ) , we expected that the majority of the AIMs identified in the MEC-H and MEC-L panels would display true large AF differences between the pooled individuals and their corresponding pseudopopulations . Indeed , our estimates of AF differences in the MEC-H and MEC-L pools were generally quite close to the actual AF differences ( Figure 3 ) . A list of 438 and 431 putative AIMs genome-wide identified from MEC-H and MEC-L pools , respectively , is provided in Table S2 . However , we tended to over-estimate the AF differences of the putative AIMs in the GXE and SPT pools ( Figure 3 ) , both of which have much lower FST values when compared to their respective pseudopopulations . To further demonstrate that the AIMs selected via pooling would be informative in differentiating closely related populations , we sought to identify AIMs informative for distinguishing the two East Asian HapMap panels often grouped together by investigators: JPT ( Japanese ) and CHB ( Han Chinese ) ( FST = 0 . 0067 ) . We first removed population outliers along any of the top 10 principal components by EIGENSTRAT [4] using genome-wide Affymetrix 6 . 0 genotypes from HapMap phase 3 for JPT , CHB , and CHD ( Chinese from Metropolitan Denver , Colorado ) . Using genome-wide data , JPT was clearly distinguishable from the two Chinese populations along the first axis of variation ( eigenvector 1 ) , with the second axis ( eigenvector 2 ) starting to separate CHB from CHD , possibly reflecting a north to south cline among the Chinese ( data not shown ) . We identified AIMs by comparing the MEC-J pools to CHD ( which are both composed of Asian American individuals ) , and tested whether the 420 putative AIMs would be able to distinguish JPT from CHB . Indeed , using the panel of 420 putative AIMs , JPT and CHB were clearly separated from each other along the top principal component ( Figure 4A ) . Based on this set of AIMs , the FST between JPT and CHB is 0 . 026 , with a correlation of 0 . 946 with the true axis of variation ( inferred by genome-wide data; discussed in [8] , [14] ) . A set of 420 random SNPs was not able to distinguish the two East Asian populations ( Figure 4B ) ; ∼3100 random SNPs were necessary to achieve the same level of correlation with the true axis of variation ( data not shown ) . Thus , AIMs identified via pooling should be informative for distinguishing even two relatively closely related populations ( e . g . , JPT and CHB ) , and will likely be effective in distinguishing populations from neighboring countries ( e . g . , divergent European populations , where FST is typically on the order of 0 . 01 [4] ) . Overall , these results support our hypothesis that pooled genotyping may be most effective for detecting variants with large AF differences and that more AIMs exist in our Native Hawaiian and Latina cohorts that remain to be discovered . Additionally , this also suggests that the HapMap populations model the true genetic ancestry for the Jamaican populations accurately enough such that few SNPs with large AF differences would be detected .
Genotyping of pooled DNA has previously been proposed to be useful for several purposes . First , it has been shown that GWA studies using pooled DNA can efficiently screen large cohorts for variants with large AF differences between cases and controls [20] , [27]–[30] . Second , it has been shown that the ability to resolve individuals contributing trace amounts of DNA to a pool holds great promise for forensic science [38] . Here we have proposed and demonstrated that genotyping of pooled DNA using genome-wide arrays is an efficient means to identify AIMs and to estimate global ancestry . As the first study evaluating the efficacy of genotyping pooled DNA on the Affymetrix 6 . 0 platform , we first established a set of four SNP QC filters and showed that together the filters eliminated the vast majority of SNPs falsely displaying large allele frequency differences between pools ( Figure 1 ) , although at the apparent cost of an increased false negative rate ( see Methods , and data not shown ) . Using SNPs that passed our stringent QC filters , we demonstrated that the estimated admixture proportions for our admixed panels were very similar to those obtained using current techniques and were robust to any remaining pooling-specific error ( Table 1 ) . Note that while we adopted a linear regression approach to estimate admixture proportions , variable transformations ( such as the logit-transformation ) or other forms of regression analysis for modeling rates and proportions could also be considered . For the MEC-H and MEC-L panels , whose genetic ancestries were not sufficiently modeled by HapMap reference panels , we identified hundreds of AIMs with large AF differences by comparing these panels to their respective pseudopopulations and validated the top AIMs by individual genotyping ( Figure 3 ) . As markers informative for ancestry are those displaying large AF differences between populations ( in this case , ∼20% difference in the MEC-H and MEC-L pools ) , our successful identification of AIMs is consistent with the reported identification of disease variants with large AF differences in case-control studies using pooled DNA [20] , [27]–[30] . For identifying markers with moderate AF differences ( in this case , ∼10% difference in the GXE and SPT pools ) , pooling tends to overestimate the differences ( which is expected due to the “winner's curse” ) but can still identify such SNPs ( Figure 3 ) . We also showed that AIMs identified via pooling are effective in differentiating the two East Asian HapMap populations ( CHB and JPT ) using principal components analysis ( Figure 4 ) . In contrast to the MEC-H and MEC-L panels , the Jamaican pools ( GXE and SPT ) appeared to be much better modeled using just the YRI and CEU reference panels when we compared the distribution of AF differences among the top putative AIMs ( Figure 2 ) and the estimated FST between the pooled sample and its pseudopopulation ( Table S1 ) , to those from the African American ( MAY ) pools . As a result , we anticipated and determined that most AIMs identified in the Jamaican pools have AF differences with moderate values from ∼8% to 15% . Moreover , we noted that the SPT pools appeared to have a missing component of ancestry unexplained by the HapMap YRI and CEU panels ( summed proportion of admixture = 92 . 3% , Table 1 , and not improved substantially when the JPT/CHB panel was included , data not shown ) . The AIMs identified by comparing SPT to its pseudopopulation should be indicative of the missing ancestry . Given the modest AF differences detected between SPT and its pseudopopulation , it appears that these AIMs are informative for a between-population difference less than that expected for a between-population difference across continents ( data not shown ) . Therefore , we suspect that the missing ancestry is from a population more similar to either the YRI or the CEU panel ( or that YRI and/or CEU are inappropriate populations to serve as the ancestral populations for SPT ) , rather than due to contributions from other continental populations . Although it may appear contradictory that many more AIMs with large AF differences were detected in the MEC-H and MEC-L pools , despite a much higher level of genetic ancestry explained ( summed proportion of admixture = 97%–98% using all three HapMap panels ) than the Jamaican pools , this likely reflects the fact that the HapMap East Asian panels are acceptable , but not perfect , proxies for Polynesian or Native American ancestries on average . Thus , at least a subset of the AIMs identified in MEC-H and MEC-L should be informative for the difference between East Asians and Polynesians or Native Americans ( e . g . , due to drift ) . Therefore , the extent of the summed proportion of admixture of a pooled panel will not necessarily correlate with the expected number of AIMs with large AF differences . In light of the results presented here , we envision that studies using pooled DNA have great potential utility for future association studies . Given the success of identifying variants with large effect sizes using pooled DNA [20] , [27]–[30] , one potential use of genotyping pooled DNA is to quickly screen for the presence of variants with large effect sizes , which can provide guidance to study design for additional GWA studies using individual DNA . Moreover , we have shown that studying pooled DNA can be used to evaluate genetic ancestry and potential population substructure in the context of association studies . As future association studies expand beyond populations of European ancestry , our approach should allow rapid assessment of global ancestry to identify AIMs . Once AIMs are validated and genotyped in the study population , individual level genetic ancestry as well as local ancestry can be estimated for use as covariates in association studies where genome-wide data are not available . As genome sequencing and SNP discovery projects for additional species are completed , pooling-based experiments may also be an efficient first step in assessing genetic structure in populations from other species . Lastly , a rigorous assessment of GWA studies using pooled DNA for identifying disease variants with small effect sizes is needed . Our African American and Jamaican samples here were initially pooled by thresholded BMI , and the MEC samples were pooled by age at menarche status ( see Text S1 ) . A preliminary attempt to identify variants associated with BMI or age at menarche showed enrichment of variants with nominal associations when genotyped individually ( C . W . K . C . , Z . K . Z . G . , J . N . H . , unpublished ) . However , our power to detect strongly associated variants may have been limited by the number of replicates genotyped to control for error due to pooling , limitations of the platform used , and the small sample size relative to the expected effect sizes , and thus was not a focus of this paper . Although we utilized the availability of individual genotypes in informing our QC filters , individual level genotypes are not required to establish filter parameters . Given a population of individuals randomly pooled into multiple pools in order to assess the genetic ancestry of the population , one can compare pools in a pair-wise case/control-like fashion where no associations would be expected . Then , by assessing changes in the genomic control inflation factor [5] when different QC filter cut-offs are applied , one can adjust the filter parameters to suit the goals of the study and to reflect varying levels of tolerance for false positives . Therefore , for the three of the four filters established here that do not depend on individual genotypes ( FLD-filter , r-filter , and maf-filter ) , data quality and the study population will dictate the number of SNPs filtered given a particular threshold . Finally , it should be noted that the recommendations for use of genotyping pooled DNA on a genome-wide array – to determine genetic ancestry , to screen for disease variants with large AF differences , and to study population demographics – are made based on the current state of the technology and methodology . Given our experience with the Affymetrix 6 . 0 platform , we have focused on applications that require the detection of moderate to large allele frequency differences . We anticipate that advances in the genotyping platform and improvements in sample handling may enhance the overall data quality and accuracy of allele frequency estimates , and that the same filter parameters may retain more SNPs for analysis than did the conservative approach taken here . Thus , given a sufficiently robust platform , it may be increasingly possible to efficiently search genome-wide for variants that have small allele frequency differences between samples using pooled DNA .
The cohorts used in this study consisted of 775 African American individuals from Maywood , IL ( MAY ) ; 1 , 039 and 1 , 467 Jamaican individuals from Kingston ( GXE ) and Spanishtown ( SPT ) , Jamaica , respectively; and women from the Hawai'i and Los Angeles Multi-Ethnic Cohort ( MEC ) [39]: 391 African Americans ( MEC-AA ) , 298 Native Hawaiians ( MEC-H ) , 363 Latin Americans ( MEC-L ) , and 202 Japanese Americans ( MEC-J ) . In total , we constructed 22 DNA pools from the individual DNA samples: four pools from 521 MAY individuals , six pools from 688 GXE individuals , four pools from 480 SPT individuals , and two pools each from 321 MEC-AA , 252 MEC-H , 332 MEC-L , and 202 MEC-J individuals . Pools were initially constructed in case/control fashion by dichotomized BMI and age at menarche status ( Text S1 ) . For the purpose of identifying AIMs in this study the pools differing in menarche or obesity status were treated as independent samples from their respective admixed populations . The Birdseed algorithm [40] was used to estimate AA , AB , and BB cluster means and covariances of probe intensities for individuals on the same plate as the pooled samples , as well as to call the genotypes for these samples . Pooled samples were processed in the same fashion as individual samples , with the exception of using only median normalization without quantile normalization . Informed by the covariance matrices of the three genotype classes of the individuals on the plate , we calculated the angle θAA measuring the degree of rotation of the AA genotype cluster with respect to the horizontal axis ( i . e . , the probe intensity space of allele A ) for each autosomal SNP as the following ( Figure S3 , Text S2 ) :where Cxy , Cxx , and Cyy are from the covariance matrix of the AA genotype cluster:θAB and θBB were calculated similarly , using the appropriate covariance matrices . The intersection of the two lines angled at θAA and θBB and intersecting the center of the AA and BB genotype cluster centroids , respectively , was defined as the origin ( O ) , with respect to which new axes x' and y' were established . We then defined θpool , the angle of the replicate pool intensity with respect to the x' axis as:x'pool and y'pool represent the x'- and y'-coordinates of the replicate pool intensity , and NF is the normalization factor to adjust for differential allelic signal intensities using the location of the AB genotype cluster , akin to the various forms of k-correction proposed ( for example , [41] ) , given by:where x'AB and y'AB represent the x'- and y'-coordinates of the center of the AB genotype cluster . To estimate the pooled allele frequency ( AF ) for the A allele for each replicate given θpool , we used the following conversion:AF estimates for all replicates from a given pool were averaged to obtain the final pooled AF estimate . Informed by the genotype data from the individuals comprising the MAY pool , we explored several possible filtering methods to identify those that most efficiently eliminated SNPs that genotyped inconsistently in pooled DNA . We first compared the distributions of the 200 worst and best performing SNPs with respect to parameters of various potential filters to determine both which filtering methods were most effective and to approximate values for filter cut-offs . The worst and best performing SNPs were defined as follows: for each SNP , we calculated the corrected χ2 test statistic ( [21] and described below ) by comparing the two case pools to the two control pools from the MAY panel ( Text S1 ) ( using both actual genotypes and pooled estimates of AF ) . The worst performing SNPs were defined as those with the greatest corrected χ2 difference between individual data and pooled data . The best performing SNPs were defined as those with the least χ2 difference among SNPs with the most significant χ2 test statistics . We then defined the proportion of false positives ( PFP ) as the proportion of SNPs with an expected ( based on individual genotyping ) P-value of >0 . 05 that were ranked among the top 0 . 05% SNPs by estimated pooling P-value . PFPs were calculated for the pre- and post-filtered list of SNPs at various filter cut-offs to establish the final values used for each filter . In the manner described above , we established three filters that were effective in eliminating SNPs that genotyped poorly or inconsistently: 1 ) separation of individual genotype clusters based on Fisher's linear discriminant , a measure of distance between two clusters ( FLD-filter ) , 2 ) radius of intensity of the signal from pooled DNA ( r-filter ) , and 3 ) population minor allele frequency estimated from pooled DNA ( MAF-filter ) ( see Text S1 and Figures S4 , S5 , and S6 for details ) . In all cases we strived for filter cut-offs that stringently eliminated poorly performing SNPs while retaining sufficient SNPs for broad coverage of the genome ( Figure S4A , S4B and Figure S6 ) . Applying these three filters left ∼382 K SNPs for association analysis within the MAY panel , comparing the case pools to the control pools . The QC filters lowered the PFP from 0 . 793 to 0 . 642 , and improved the genomic control ( GC ) inflation factor [5] from 1 . 52 to 1 . 38 . Among the 809 independent SNPs with a P-value of 0 . 001 or lower ( based on individual genotyping ) , 397 SNPs ( or at least one proxy with r2>0 . 8 ) passed the three QC filters in pooling , for a false negative rate of 0 . 509 due to QC filtering . ( Note that post-QC SNPs are still subject to pooling-specific error , which is not yet accounted for at this step in the process . ) The relatively elevated inflation factor after applying the three filters likely represents error in the pooled AF estimates we were unable to account for in our study design . As one is often searching for variants with small AF differences between case and control groups in a disease association , we also recommend fitting the distribution of the pooled AF estimates from the case and control pools to the overall pooled AF distribution to ensure a similar distribution of AF estimates between the case and control pools . In our experience this further lowers the inflation factor ( from 1 . 38 to 1 . 08 in our data ) and improves the PFP ( C . W . K . C . , unpublished ) . By taking advantage of the individual genotypes from the MAY pools , we also established a filter to measure the consistency of the AF estimates for each SNP . Over the four MAY pools , we calculated the difference in the AF estimates between the pooled sample and the individual samples , and the variance across the four pools was used as a measure of consistency of the AF estimates ( hist-filter , see Text S1 for details ) . The effectiveness of the cut-off values for this filter was established by the changes in the GC inflation factor of a presumed null distribution in the comparison of one of the MEC-AA pools to the other ( Figure S4C ) . All four filters were applied in the analysis of all pooled panels other than the MAY panel in this study . To estimate the proportion of admixture in the pooled populations , we employed a linear regression model where the estimated allele frequency for SNP i was modeled as follows:Pui is the estimated allele frequency from pooling in the population of unknown admixture for SNP i , and is regressed on independent variables Pji , which is the allele frequency in the ancestral ( reference ) population j for SNP i with respect to allele A according to the Affymetrix 6 . 0 array annotation ( http://www . affymetrix . com/support/technical/annotationfilesmain . affx , GenomeWideSNP_6_Annotations , na25 ) . βj is the regression coefficient and is an estimate of the proportion of contribution from population j , and c is the constant combining error and unexplained ancestry ( i . e . , the intercept ) . Because allele A assignment on Affymetrix 6 . 0 array is independent of the minor allele at the locus , E ( Pji ) = E ( Pui ) = 0 . 5 , which is necessary for the accurate estimation of βj using regression ( Text S2 ) . βj's and their standard errors were estimated by multivariate linear regression using the method of least squares in R version 2 . 4 . 0 ( Vienna , Austria; http://www . r-project . org/ ) , using all SNPs that passed our QC filters ( see above ) and had genotyping success rates >0 . 8 in all three HapMap populations ( YRI , Yoruba in Ibadan , Nigeria; CEU , Utah residents with ancestry from northern and western Europe; JPT/CHB , combined Japanese in Tokyo , Japan and Han Chinese in Beijing , China ) . We used the three HapMap populations genotyped on the Affymetrix 6 . 0 array as our reference ancestral populations [42] . For estimating admixture proportions in MAY and MEC-AA , YRI and CEU were used as the reference populations . For estimating admixture proportion in the remaining pools ( GXE , SPT , MEC-H , and MEC-L ) , YRI , CEU , and combined JPT/CHB were used as the reference populations . For each pooled population , a corresponding pseudopopulation was constructed , in which the allele frequency of each SNP was calculated using the allele frequency catalogued in each of the reference populations , weighted by the estimated proportion of admixture , and adding the constant c . One factor that influences the analysis of pooled but not individual genotype data is that when DNA pools are genotyped , an estimated rather than observed number of allele counts is obtained . The variance around the estimated allele frequency obtained from pooled genotyping includes variance that arises specifically due to pooling in addition to the sampling variance . If the additional variance is not taken into account , a standard χ2 statistic will have a greatly inflated value . Here we corrected for this χ2 statistic inflation using a method proposed by Visscher and Le Hellard [21] , [43] , where the corrected statistic , , is given by:where is the standard ( naïve ) χ2 statistic based on estimated allele counts derived from the estimated pooled allele frequency . ( Note that when calculating , the minor allele frequency in either the case or the control pools must be >0 , otherwise the χ2 statistic cannot be calculated . Thus while not a formal QC filter , any SNP in which the estimated minor allele frequency was <0 in either the case or the control pool , a situation that would arise for very rare SNPs or erroneous hybridization signals , was dropped from analysis . ) V is the sum of the sampling variance for the case and control pools , given by:where and are the estimated pooled AF for the case and control pools , respectively . Var ( epcase ) and var ( epcontrol ) are the squared standard errors among the pooled AF estimates from all of the replicates for the case and control pools , respectively . V , var ( epcase ) and var ( epcontrol ) were calculated for each SNP tested for association . When multiple case or control pools were available , the total pooled allele frequency used was the weighted average ( by number of individuals in the pool ) of the pooled allele frequencies estimated for each pool . The pooled variance , var ( ep_totcase ) , is then given by:for k case pools each with ni replicates . SEi is the standard error of the estimated AF for the ith pool . The pooled variance for the control pool was calculated similarly . When identifying AIMs informative for ancestry over and above that explained by available reference panels , all pools from the population being studied were designated the “case” pools , and the pseudopopulation was used as the “control” pool . In this case , the sampling variance for the pseudopopulation was based on a population size of either 120 individuals ( if only YRI and CEU were used ) or 210 individuals ( if YRI , CEU , and JPT/CHB were all used ) . Pooling specific variance for the pseudopopulation was assumed to be 0 . Ancestry informative markers were selected for the GXE , SPT , MEC-H , and MEC-L panels by comparing the estimated allele frequency in each population by pooling to its respective weighted reference panel ( pseudopopulation ) , or for the MEC-J panel by comparison to the AF from the HapMap phase 3 CHD ( Chinese from Metropolitan Denver , Colorado ) population ( http://www . hapmap . org ) . Pseudopopulations were constructed based on the estimates of admixture proportion using the HapMap populations as proxies for the ancestral populations . For the Jamaican pools , SNPs were divided into three categories , based on the P-values associated with the surrounding SNPs in linkage disequilibrium ( LD ) with the SNP of interest . Here , P-values measure the extent to which pooled allele frequencies differ from those expected using the pseudopopulation . LD was determined using the set of pre- and post-QC filtered sets of SNPs , based on the HapMap YRI population; SNPs within 20 Mb of the SNP of interest were considered to be in LD if they had r2>0 . 5 in HapMap YRI with the SNP of interest . “Encouraging” SNPs had at least one SNP in LD with a GC-corrected P-value<0 . 05 and had at least half of the surrounding SNPs ( those in LD ) with GC-corrected P-values<0 . 1 . “Discouraging” SNPs had none of the SNPs in LD with GC-corrected P-values<0 . 1 . The remaining SNPs were categorized as “inconclusive , ” a category also encompassing SNPs with no other SNPs in LD . Non-discouraging ( i . e . , encouraging or inconclusive ) AIMs were then further pruned to remove any AIMs within 4 Mb of each other to obtain a panel of independent AIMs . We chose a set of 50 candidate AIMs each in GXE and SPT to be validated by individual genotyping , using two complementary approaches . First , we selected the top 25 SNPs based on GC-corrected P-value , excluding any SNPs categorized as discouraging when either the filtered or unfiltered set of SNPs in LD was examined for categorization . Second , we selected an additional 25 SNPs with GC-corrected P-values <1×10−3 , at least 2 SNPs in LD with the SNP of interest from the unfiltered dataset , and a categorization of encouraging when using both the filtered and unfiltered datasets for a set of SNPs in LD . For this second list , we chose the SNPs with the largest number of SNPs that were in LD that also had P-values <0 . 05 . Identification of AIMs in the MEC-H , MEC-L , and MEC-J panels was performed similarly , with the exception that AIMs in MEC-H were not pruned by distance in order to allow validation using SNPs previously genotyped in those samples . The HapMap reference panel representing the major ancestry in each of the MEC pools was used as the reference panel for LD determination ( i . e . , JPT/CHB for MEC-H and MEC-J , and CEU for MEC-L ) . Predicted AIMs and obesity-associated SNPs were validated by individual genotyping in the individuals comprising the pools using the Sequenom MassArray system ( see Text S1 ) .
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Many association studies have been published looking for genetic variants contributing to a variety of human traits such as obesity , diabetes , and height . Because the frequency of genetic variants can differ across populations , it is important to have estimates of genetic ancestry in the individuals being studied . In this study , we were able to measure genetic ancestry in populations of mixed ancestry by genotyping pooled , rather than individual , DNA samples . This represents a rapid and inexpensive means for modeling genetic ancestry and thus could facilitate future association or population-genetic studies in populations of unknown ancestry for which whole-genome data do not already exist .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/medical",
"genetics",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2010
|
Rapid Assessment of Genetic Ancestry in Populations of Unknown Origin by Genome-Wide Genotyping of Pooled Samples
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Homologous recombination is essential for crossover ( CO ) formation and accurate chromosome segregation during meiosis . It is of considerable importance to work out how recombination intermediates are processed , leading to CO and non-crossover ( NCO ) outcome . Genetic analysis in budding yeast and Caenorhabditis elegans indicates that the processing of meiotic recombination intermediates involves a combination of nucleases and DNA repair enzymes . We previously reported that in C . elegans meiotic joint molecule resolution is mediated by two redundant pathways , conferred by the SLX-1 and MUS-81 nucleases , and by the HIM-6 Bloom helicase in conjunction with the XPF-1 endonuclease , respectively . Both pathways require the scaffold protein SLX-4 . However , in the absence of all these enzymes , residual processing of meiotic recombination intermediates still occurs and CO formation is reduced but not abolished . Here we show that the LEM-3 nuclease , mutation of which by itself does not have an overt meiotic phenotype , genetically interacts with slx-1 and mus-81 mutants , the respective double mutants displaying 100% embryonic lethality . The combined loss of LEM-3 and MUS-81 leads to altered processing of recombination intermediates , a delayed disassembly of foci associated with CO designated sites , and the formation of univalents linked by SPO-11 dependent chromatin bridges ( dissociated bivalents ) . However , LEM-3 foci do not colocalize with ZHP-3 , a marker that congresses into CO designated sites . In addition , neither CO frequency nor distribution is altered in lem-3 single mutants or in combination with mus-81 or slx-4 mutations . Finally , we found persistent chromatin bridges during meiotic divisions in lem-3; slx-4 double mutants . Supported by the localization of LEM-3 between dividing meiotic nuclei , this data suggest that LEM-3 is able to process erroneous recombination intermediates that persist into the second meiotic division .
Meiosis is comprised of two specialized cell divisions that elicit the reduction of the diploid genome to haploid gametes . Homologous recombination occurs in the first meiotic division and is required for meiotic crossover ( CO ) formation [1] . COs are needed to shuffle genetic information between maternal and paternal chromosomes and are thus required to ensure genetic diversity . COs become cytologically visible as chiasmata and also provide stable connections between maternal and paternal homologous chromosomes ( homologues ) . Chiasmata counter the spindle force and thereby facilitate the accurate segregation of homologues in the first meiotic division . Meiotic recombination is initiated by DNA double-strand breaks ( DSBs ) generated by the conserved meiosis-specific Spo11 protein [2] . The number of DSBs generated by Spo11 exceeds the number of COs , ranging from ~2:1 in Saccharomyces cerevisiae to ~20:1 in maize [3–6] . In Caenorhabditis elegans , each chromosome pair receives 4–7 DSBs over the course of prophase I and typically only one DSB per homologous pair will mature into a CO event [7 , 8] . It is thought that the excessive number of DSBs is required to ensure that at least one CO occurs on each homologue , a notion supported by checkpoint mechanisms that delay meiotic prophase progression when the number of DSBs is reduced [9–11] . It is unclear how the obligate CO is selected from the pool of DSBs . The CO selection ( or designation ) correlates with the congression of several pro-CO factors into six distinct foci , one on each paired chromosome starting from the mid-pachytene stage . These include the cyclin-related protein COSA-1/CNTD1 , MSH-4/MSH-5 components of the MutSγ complex , the predicted ubiquitin ligases ZHP-3/RNF212 and HEI10 , the Bloom ( BLM ) helicase HIM-6 as well as its regulatory subunit RMH-1 [12–17] . When the CO designation sites are associated with those pro-CO factors , processing of meiotic recombination intermediates is biased towards the CO outcome . One of the meiotic recombination intermediates is called a Holliday junction ( HJ ) , a cruciform DNA structure formed as a result of a reciprocal exchange of DNA strands between homologous chromosomes [18] . While in fission yeast single HJs appear to predominate , direct evidence for the occurrence of double HJs ( dHJs ) was obtained in budding yeast [19] . dHJs can be processed to result in CO or a non-crossover ( NCO ) outcome , depending on the directionality of the cut made by structure-specific endonucleases [20] . There is emerging evidence that a combination of nucleases is required for the processing of meiotic HJs to promote CO formation and to resolve joint DNA structures that might impede proper chromosome segregation [21] . Only in fission yeast , deletion of a single nuclease MUS81 , leads to a defect in meiotic CO formation [22] . In budding yeast , absence of the MUS81-MMS4 , SLX1-SLX4 or YEN1 nucleases exhibits a modest reduction of meiotic COs [23] . The Exo1 nuclease and the mismatch-repair MutLγ complex Mlh1-Mlh3 have also been shown to contribute to HJ resolution [23 , 24] . Mouse gen1 mutants have no meiotic phenotype , while mus81 animals only show minor defects [25] . In C . elegans HJ resolution and CO formation appear to be conferred by at least two redundant pathways [26–28] . One pathway is defined by the MUS-81 and SLX-1 nucleases . Consistent with in vitro nuclease assays , it appears that SLX-1 might confer a first nick on a HJ , the nicked HJ being the preferred substrate for MUS-81 [29] . The second pathway comprises the XPF-1 nuclease and the BLM helicase HIM-6 . It is possible that HIM-6 might be able to unwind a HJ , which would generate a substrate cleaved by XPF-1 . Both pathways require SLX-4 as a scaffold protein . When both pathways are compromised , the CO frequency is reduced by roughly one third [26] . Since only a small subset of DSBs are designated as CO sites , the majority of DSBs have to be processed to favour inter-homolog NCO and/ or inter-sister recombination [30] . In budding yeast recombination events leading to the majority of NCO events mature early , whereas the CO events mature later [31 , 32] . Several helicases are proposed to mediate the disassembly of early recombination intermediates such as D-loop structures in a pathway called synthesis-dependent strand annealing ( SDSA ) . In budding yeast , this is driven by the Srs2 helicase [33] . In animals , this activity is ascribed to the BLM and RTEL helicases . In C . elegans deletion of the RTEL-1 helicase leads to an elevated number of meiotic COs [34] . In contrast , deletion of him-6 , the C . elegans BLM homologue , leads to reduced meiotic CO formation [34] . The presence of HIM-6 at CO designation sites infers a late pro-CO function [35 , 36] . Once dHJs are formed , they can either be dissolved by the BLM helicase and Top3 topoisomerase in a NCO manner or resolved by nucleases to form CO or NCO [33] . In this study , we report on roles of the LEM-3/Ankle1 nuclease in processing meiotic recombination intermediates . LEM-3 is only conserved in animals and the mammalian ortholog is referred to as Ankle1 [37 , 38] . C . elegans lem-3 mutants are hypersensitive to ionizing irradiation , UV treatment and DNA cross-linking agents [37] . LEM-3/Ankle1 contains an N-terminal LEM domain , Ankyrin repeats and a GIY-YIG nuclease motif . The same nuclease motif can also be found in bacterial UvrC nucleotide excision repair proteins and in the distantly related SLX1 nuclease [39] . Our data show that LEM-3 and MUS-81 act in conjunction to process early recombination intermediates in meiosis . Loss of LEM-3 and MUS-81 leads to aberrant profiles of recombination markers , delayed processing of markers for CO designation , increased apoptotic cell death and the formation of dissociated bivalents . In addition , we found that a considerable pool of LEM-3 localizes between dividing meiotic nuclei and chromosome segregation is compromised due to persistent chromatin linkages in the absence of both LEM-3 and SLX-4 , indicating that LEM-3 is able to process erroneous recombination intermediates that persist into meiotic divisions .
We and other groups previously showed that there are at least two pathways needed for the resolution of meiotic recombination intermediates: one dependent on SLX-1—MUS-81 and the other relying on XPF-1 . SLX-4 acts as a scaffold component in both pathways [26–28] . Given that viability and CO recombination are reduced but not eliminated when both the MUS-81 and XPF-1 pathways are compromised , we considered that there might be at least one additional nuclease that had not been identified . We therefore searched for nucleases which are synthetic lethal with SLX-4 and focused on LEM-3 in this study . Out of the three previously reported LEM-3 alleles , we used the lem-3 ( mn155 ) and the lem-3 ( tm3468 ) , the former leads to a premature stop codon at amino acid 190 leaving the N-terminal Ankyrin Repeat domain intact , but eliminating the nuclease domain , thus representing a null allele [37] . lem-3 ( tm3468 ) bears an in-frame deletion of 110 amino acids between the Ankyrin Repeat and the LEM domain [37] . We found that the lethality of lem-3 ( tm3468 ) ; slx-4 was increased to 90% while lem-3 ( mn155 ) ; slx-4 worms were 100% embryonic lethal ( Fig 1 ) . We employed lem-3 ( mn155 ) for our genetic analysis since it is a null allele for lem-3 , and generated double mutants with slx-1 , mus-81 and xpf-1: 100% of eggs laid by the slx-1 lem-3 double mutants failed to develop . 100% embryonic lethality was also observed in broods laid by mus-81 lem-3 double mutants ( Fig 1 ) . In contrast , the lethality of lem-3; xpf-1 double mutants was not different from xpf-1 mutants ( Fig 1 ) . In summary , these genetic data support two potential roles for LEM-3 to maintain embryonic viability: one parallel to SLX-4 and another parallel to MUS-81 and SLX-1 . We next wanted to test if the synthetic phenotypes we observed were linked to defects in meiotic chromosome axis formation , which plays a central role in organization and dynamics of meiotic chromosomes . In C . elegans , meiotic prophase progression , which occurs in a gradient of differentiation , can be visualized using dissected germlines . At the distal end of the gonad germ cell mitotically divide , before entering the transition zone where meiotic chromosomes reorganise into arrays of chromatin loops anchored to the chromosome axis [40] . The chromosome axis establishes a platform for homologous chromosome paring , DSB induction , synaptonemal complex assembly , and CO formation [41] . Highlighting the axial element HTP-3 indicated that chromosome axis formation occurred normally in lem-3 and slx-4 single mutants and lem-3; slx-4 double mutants ( S1 Fig ) . Since there are no overt defects in chromosome axis formation , we assessed whether the synthetic lethality we observed was due to a defect in meiotic recombination . In C . elegans , unrepaired DSBs activate checkpoints that induce apoptosis of late pachytene stage germ cells [42] . Directly scoring for the number of apoptotic corpses by DIC ( differential interference contrast ) microscopy revealed that apoptosis was increased in mus-81 worms , further increased in slx-4 worms , and that the highest level of apoptosis occurred in both mus-81 lem-3 and lem-3; slx-4 double mutant worms ( Fig 2A ) . Apoptosis was reduced in mus-81 lem-3; spo-11 triple mutants ( Fig 2A ) . Careful examination of DAPI stained germ cell nuclei by fluorescence microscopy revealed that pyknotic cells , which have abnormally condensed nuclei , became apparent in mid/late pachytene in the mus-81 single mutant and to a larger extent in the mus-81 lem-3 and lem-3; slx-4 double as well as the mus-81 lem-3; spo-11 triple mutant ( Fig 2B ) . In mus-81 lem-3; spo-11 triple mutant , some pyknotic cells were already evident in the transition zone and early pachytene stages , where apoptotic cells are not apparent based on morphology under DIC microscopy [42 , 43] . Given that mutation of spo-11 does not fully bypass excessive apoptosis , some DNA lesions in mus-81 lem-3 double mutants might be independent of the meiotic DSBs . We next wished to analyse key recombination intermediates in lem-3 single and mus-81 lem-3 double mutant worms compared to wild type . During meiosis , an excess of meiotic DSBs are generated but most breaks are repaired without leading to a CO outcome and generally only one break for each chromosome pair matures into a CO-designated site in C . elegans [44] . RAD-51 foci mark early recombination intermediates engaged in strand invasion . RAD-51 foci accumulate in the transition zone where meiotic DSBs are initiated ( Fig 3A , zone 3 ) and peak in early pachytene ( Fig 3A , zone 4 ) [45] . We found that the number of RAD-51 foci was comparable to wild type in mus-81 lem-3 double mutant worms , despite an increase of RAD-51 foci in mus-81 and lem-3 single mutants ( Fig 3A ) . RMH-1 foci label both CO and NCO recombination intermediates and appear and disappear later than RAD-51 , consistent with RMH-1 marking recombination intermediates after strand invasion [17] . We found that both lem-3 and mus-81 single mutants showed a wild type level of RMH-1 foci in early pachytene ( with an average of 11 foci ) while the number of foci seen in mid-pachytene in lem-3 ( on average 14 . 8 foci ) and mus-81 ( 12 . 9 foci ) was higher than wild type ( 10 . 8 foci ) ( Fig 3B–3D ) , indicative of delayed recombination intermediate processing . Increased RMH-1 foci numbers were also observed in xpf-1 single mutants and mus-81; xpf-1 double mutants , in which , respectively , one or two redundant pathways needed to resolve meiotic HJ are compromised ( Fig 3B–3D ) [26–28] , In contrast , the number of RHM-1 foci in mus-81 lem-3 double mutants was significantly lower than wild type throughout the pachytene stage ( Fig 3B and 3C ) . The number of recombination foci reflects the number of meiotic DSBs and the kinetics of their processing . Thus , our data suggest that LEM-3 and MUS-81 act in a similar step of DSB repair to ensure the proper maturation and/or turnover of meiotic recombination intermediates . We also tested whether CO designation occurs normally using a strain expressing a functional GFP::COSA-1 fusion in mus-81 lem-3 and lem-3; slx-4 double mutant worms . COSA-1 foci mark CO designated sites in late pachytene [12] . As previously reported for wild type , slx-4 and mus-81 single mutants , also only 6 CO designated sites are apparent in the lem-3 single and lem-3; slx-4 double mutants in late pachytene ( Fig 4A and 4B ) , indicating that CO designation is not perturbed . We note that while the majority of nuclei display 6 COSA-1 foci , a small number of nuclei with 7 COSA-1 foci ( 5/131 nuclei , p = 0 . 503 compared with 3/127 in wild type , not significant ) and 8 COSA-1 foci ( 4/131 nuclei , p = 0 . 0477 compared with 0/127 in wild type ) can be observed in mus-81 lem-3 nuclei . In the wild type , COSA-1 protein develops as prominent foci at late pachytene and gradually dissociates from chromosome pairs in diplotene [12] . While COSA-1 foci fully disassemble in wild type and lem-3 single mutant before the -3 oocyte ( Fig 4C and 4E ) , COSA-1 foci could still be detected in 50% of -3 oocytes in mus-81 single mutants ( n = 12 ) . Strikingly , 45% of the -2 oocytes ( n = 22 ) in mus-81 lem-3 double mutants displayed COSA-1 foci , which eventually disappeared in -1 oocytes ( Fig 4C and 4E ) . Persistent COSA-1 foci were also observed up to the -1 oocytes in slx-4 single and lem-3; slx-4 double mutants ( Fig 4C and 4E ) . The COSA-1 foci localized to the chromosome linkages between dissociated bivalents , which could have been destined to become a CO ( Fig 4D ) . Taken together , these data suggest that compromised recombination intermediate processing in mus-81 lem-3 double mutant leads to a delay in the dismantling of COSA-1 foci . The morphology and number of diakinesis chromosomes can serve as readout for meiotic recombination defects . In diakinesis , homologous chromosome pairs restructure to form bivalents , which can be observed as 6 DAPI-stained bodies in wild type maturing oocytes [7] . Defects in meiotic recombination can result in a failure to stably connect homologous chromosomes , which becomes apparent as univalents at diakinesis ( 12 DAPI-stained bodies when physical linkages between all six homologue pairs fail to form ) . Our prior analysis of slx-4 , as well as mus-81; xpf-1 double mutants , revealed a distinct phenotype [26] . In contrast to spo-11 , pairs of ‘univalents’ were found associated with each other , being linked by SPO-11 dependent chromatin bridges . We termed these structures as ´dissociated bivalents´ . We interpreted these structures as chromosome pairs that engage in meiotic recombination but do not resolve recombination intermediates thus leading to the linkage of maternal and paternal chromosomes . As expected , wild type , lem-3 and mus-81 single mutants predominately had 6 bivalents ( Fig 5 ) . In contrast , mus-81 lem-3 double mutant showed elevated numbers of dissociated bivalents ( Fig 5 , red arrow ) and chromosome fragments ( Fig 5 , red arrowhead ) , which can be interpreted as unrepaired meiotic DSBs . The dissociated bivalents can also be detected in slx-1 lem-3 mutants ( S2 Fig ) . Importantly , the analysis of slx-1 lem-3; spo-11 and mus-81 lem-3; spo-11 triple mutants revealed 12 univalents ( S2 Fig ) , indicating that the chromosome linkages we observed are spo-11-dependent and thus represent meiotic recombination intermediates . The rate of CO recombination is reduced in slx-1; xpf-1 and mus-81; xpf-1 double mutants by approximately one third [26] . Given that slx-1 lem-3 , mus-81 lem-3 and lem-3; slx-4 double mutants only sire dead embryos , we wanted to examine whether CO recombination is abolished in those double mutants . CO frequency and distribution can be investigated by meiotic recombination mapping [46] . We generated the lem-3 single and double mutants with chromosome V being heterozygous for the Hawaiian and Bristol backgrounds . To determine the recombination frequency and distribution we employed five single nucleotide polymorphisms ( snip-SNPs ) , which together cover 92% of chromosome V [26] . Given the lethality of various lem-3 double mutants , embryos were used for recombination mapping to avoid biasing the analysis on the basis of viability . We found that CO recombination rates were comparable to wild type in lem-3 single mutants ( S3 Fig ) . Furthermore , when analysing the respective compound mutants , we found that lem-3 did not lead to a decreased CO rate in conjunction with mus-81 or slx-4 single mutants ( S3 Fig ) . Thus , despite the chromatin linkages that occur in various lem-3 double mutants , CO recombination is not reduced . We next investigated if LEM-3 was involved in inter-sister recombination repair pathway . Depletion of SYP-2 , a component of the synaptonemal complex , abolishes the inter-homolog recombination and leads to the formation of 12 univalents , since breaks are likely repaired by using the sister chromatids as repair template ( S4 Fig ) [47] . If LEM-3 was able to promote inter-sister repair , lem-3; syp-2 double mutants would be expected to have an increased number of DAPI-stained bodies at diakinesis; a phenotype indicative of chromosome fragmentation . However , we observed an average of 11 . 4 DAPI-stained bodies in lem-3; syp-2 double mutants ( S4 Fig ) . Furthermore , when we blocked the formation of the synaptonemal complex in mus-81 lem-3 double mutants by syp-2 RNAi , the average number of DAPI staining bodies did not increase ( 12 . 1 , n = 19 , p = 0 . 102 >0 . 05 compared to syp-2 mutant ) . This result indicates that LEM-3 and MUS-81 are not involved in inter-sister recombination , or that a role in the resolution of inter-sister recombination intermediates is masked by a redundant pathway . In C . elegans , COs trigger the restructuring of bivalents in CO distal and CO proximal domains revealed by the differential location of phospho-histone H3 ( pH3 ) and the synapsis protein SYP-1 to the short arm , and the axial proteins LAB-1 ( Long Arm of the Bivalent ) and HTP-1/2 to the long arm of the 6 bivalents [48] . The lack of CO recombination as is the case in spo-11 mutants , leads to the formation of 12 univalents without such bifurcation . In contrast , “peculiar univalents” display the same reciprocal localization as bivalents , suggesting that a CO site has been designated leading to enrichment of the above-mentioned markers to apposed chromosomal domains [17 , 26 , 36] . ‘Peculiar univalents’ were previously observed in rmh-1 , him-6 and xpf-1 single mutants [17 , 26 , 36] . We found an increased prevalence of univalents in rmh-1 mus-81 lem-3 triple mutants compared to rmh-1 single mutants , as revealed by an increased number of DAPI-stained bodies that display the features of “peculiar univalents” ( Fig 6A and 6B , Please note , most quantification of univalents was done by DAPI staining , due to limited amounts of reagents to determine domain organization ) . Univalents were not detected in the lem-3 or mus-81 single mutants as well as in mus-81 lem-3 double mutants ( Fig 6C ) as evidenced by the 6 DAPI stained bodies we observed . In contrast , rmh-1 mus-81 and rmh-1 lem-3 double mutants both showed an average of 8 DAPI stained bodies compared to the average of 10 observed in the rmh-1 mus-81 lem-3 triple mutant ( Fig 6C and S5 Fig , clearly discernible univalents are highlighted ) . Thus , the increased prevalence of univalents in rmh-1 mus-81 lem-3 triple mutant might be the result of compromising several parallel recombination pathways . We next set out to test if these univalents result from the mis-direction of recombination intermediates towards a NCO pathway such as inter-sister repair or SDSA . In C . elegans , the BRC-1 homologue of the mammalian BRCA1 recombination protein , which forms a heterodimer with BRD-1 , has been proposed to be important for inter-sister repair in the germline [49] . Indeed , blocking inter-sister repair by introducing a brd-1 mutation into rmh-1 mus-81 lem-3 triple mutants resulted in a reduced number of univalents as revealed by an average of 8 . 5 DAPI stained bodies ( Fig 6C and S5 Fig ) . The rmh-1 mus-81 lem-3; brd-1 quadruple mutants also displayed a reduction of RAD-51 compared to rmh-1 mus-81 lem-3 triple mutants , indicative of altered processing of meiotic recombination intermediates or reduced HR repair ( Fig 6D ) . Altogether , these results indicate that LEM-3 might act in conjunction with MUS-81 and RMH-1 to process early recombination intermediate and the simultaneous absence of LEM-3 , RMH-1 and MUS-81 could lead to illegitimate recombination intermediates impeding CO formation . We next investigated the localization of LEM-3 using a strain expressing a GFP::LEM-3 fusion . Consistent with previous reports [37 , 50] , we found that LEM-3 localized as dots outside of the nucleus in the mitotic germ cells of wild type worms ( Fig 7A ) . LEM-3 foci ( typically no more than one per cell ) were occasionally observed in pachytene , both in and outside of the nucleus ( Fig 7B ) . These LEM-3 foci did not co-localize with the ZHP-3 marker that congresses into CO designated sites ( Fig 7C ) [51] . Interestingly , careful examination of cells undergoing meiotic divisions revealed that LEM-3 localized between dividing nuclei in meiosis II ( Fig 7D and 7E ) . To analyse whether LEM-3 has a role in meiotic chromosome segregation , we performed live cell imaging of the first and second meiotic cell divisions by using an integrated Histone mCherry::H2B fusion . We reasoned that the chromosome segregation in meiosis I and II might be affected if a chromosome linkage remains present between two homologues , or sister chromatids , respectively . Chromosome segregation in the lem-3 single mutant was similar to wild type ( Fig 7F , S1 and S2 Movies ) . As we had previously reported for double mutants affecting both the SLX-1/MUS-81 and the HIM-6/XPF-1 pathway , chromosome linkages appeared during the first meiotic division in slx-4 mutants ( 9/14 embryos , Fig 7F and 7G ) , consistent with an important role for SLX-4 in resolving inter-homolog recombination intermediates ( Fig 7E and 7G , S3 Movie ) [26] [48] . While the chromosome linkage could only be detected in 28 . 6% of slx-4 mutant embryos ( 4/14 ) during the second meiotic division , all lem-3; slx-4 double mutant embryos ( 19/19 ) showed extensive chromosome linkage formation in meiosis II ( Fig 7F and 7G , S4 Movie ) . These data indicate that LEM-3 might have a role in processing recombination intermediates that persist into the second meiotic division .
In this study , we investigated the interplay between the C . elegans LEM-3/Ankle1 nuclease and nucleases previously implicated in meiotic CO resolution . We provide evidence for two roles of LEM-3 during meiosis . First , LEM-3 acts in conjunction with the MUS-81 and SLX-1-SLX-4 nucleases to process various recombination intermediates during meiotic prophase . Second , LEM-3 functions as a backup nuclease to deal with persistent DNA linkages during meiotic divisions . The synthetic lethal interaction between LEM-3 and SLX-4 in C . elegans led us to investigate whether LEM-3 might act in parallel to the two identified redundant pathways for HJ resolution . Indeed , the lack of both LEM-3 and MUS-81 causes an increased number of dissociated bivalents ( Fig 5 ) , which represent unresolved recombination intermediates [26] . In addition , the profiles for major recombination markers are altered and CO maturation is delayed in mus-81 lem-3 double mutants , as revealed by persistent COSA-1 foci at the CO designation sites in -2 oocytes ( Fig 4C and 4E ) , suggesting that LEM-3 could be involved in processing of CO intermediates in the absence of CO resolvases such as MUS-81 . In contrast , the lem-3; xpf-1 double mutant showed no synthetic lethality , indicating that LEM-3 is able to process specific aberrant recombination intermediates that arise in mus-81 mutants . However , the lem-3 mutation in combination with slx-4 or mus-81 did not lead to a further reduction of CO frequency , or to an altered CO distribution ( S3 Fig ) . Thus , synthetic lethality of the mus-81 lem-3 mutant is not a result of decreased CO formation . RMH-1 foci label both CO and NCO recombination intermediates . A previous study showed that RMH-1 does not colocalize with RAD-51 and appears and disappears later than RAD-51 , suggesting that RMH-1 might act after RAD-51 removal and mark late recombination intermediates [17] . We found that the number of RMH-1 and RAD-51 foci was increased in lem-3 and mus-81 single mutants compared to wild type ( Fig 3A and 3B ) , suggesting that both LEM-3 and MUS-81 play a role in the proper maturation/turnover of recombination intermediates . The increased number of RMH-1 and RAD-51 foci could be due to an increased number of processed DSBs , or due to a delay in DSB processing . Interestingly , the number of RAD-51 foci was comparable to wild type in the absence of both MUS-81 and LEM-3 ( Fig 3A ) , indicating that the increased RAD-51 foci in mus-81 single mutants is depended on the activity of LEM-3 and vice versa . In contrast , the number of RMH-1 foci was decreased in the mus-81 lem-3 double mutant in early and mid pachytene compared to wild type ( Fig 3B ) . These data suggest that MUS-81 and LEM-3 individually have a function in processing recombination intermediates and appear to act in conjunction after strand invasion but before the formation of RMH-1 foci . Irrespective , these alterations in the kinetics of RAD-51 or RMH-1 foci reflect the production and/or processing of early meiotic intermediates but do not overtly affect the number of CO designated sites . The number of COSA-1 foci that mark CO designated sites is roughly normal in the mus-81 lem-3 double mutant ( Fig 4A and 4B ) . Only in 3% of mus-81 lem-3 mutant worms ( 4/131 nuclei ) the number of COSA-1 foci is increased to 8 , a finding which may indicate that the mechanisms leading the restriction to one CO per chromosome might be occasionally overwhelmed by unusual recombination intermediates occurring in this double mutant . Alternatively , this slightly increased number of COSA-1 foci could be linked to cells that become pyknotic and have abnormally condensed chromosomes and a higher number of smaller COSA-1 foci . Overall , our data suggest that LEM-3 and MUS-81 can process early recombination intermediates and contribute to the formation of recombination intermediates that recruit RMH-1 . How can we explain that mus-81 lem-3 stains have dissociated bivalents but do not have a reduced rate of CO recombination ? We interpreted these structures as chromosome pairs that engage in meiotic recombination but do not resolve recombination intermediates thus leading to the linkage of maternal and paternal chromosomes [26] . These structures were previously observed in slx-4 single mutant worms , as well as in mus-81; xpf-1 double mutants , and the CO rates were reduced as we expected [26 , 48] . We favour two possible explanations for this discrepancy . DNA linkages might define CO intermediates that can still result in a NCO outcome . For instance , in the classical HJ resolution model , 4-way joint molecules that link maternal and paternal chromosomes can be resolved depending on the symmetry of the cleavage leading to CO or NCO outcome . Alternatively , it might as well be that chiasmata , the structures that hold maternal and paternal chromosomes together at the CO site , could be weakened in mus-81 lem-3 double mutants , resulting in their dissociation . This dissociation could equally lead to a ‘dissociated bivalent’ phenotype . If such dissociation occurs , one does not expect reduced CO recombination . Both hypotheses are consistent with the delayed dissolution of COSA-1 foci . How might the activity of LEM-3 be involved in processing meiotic recombination intermediates ? It has been reported that LEM-3 and its human homologue ANKLE-1 are able to cleave supercoiled plasmid into relaxed circular ( nicked ) and linear DNA [37 , 38] . In addition , LEM-3 can cleave a DNA substrate that is rich in secondary structures [37] , indicating that LEM-3 might be a structure-specific endonuclease . Therefore , it is possible that LEM-3 can either process early recombination intermediates , such as D-loops , or else act at a late stage of meiotic recombination for HJ resolution to generate NCO products . It will be interesting to investigate the DNA substrate preference of LEM-3 in the future . Chromosome segregation can be affected if unresolved recombination intermediates remain present during meiotic divisions . We previously observed that the extensive chromatin bridges generated by depleting both XPF-1 and MUS-81 pathways during the first meiotic division are eventually resolved in meiosis II , suggesting that backup activities function at or after anaphase I [26] . Here we found that LEM-3 localises between dividing nuclei during meiotic division ( Fig 7D ) . In addition , depletion of LEM-3 leads to accumulation of chromosome linkages in the slx-4 mutant , especially during meiosis II ( Fig 7F ) , indicating that LEM-3 might have a role in proper chromosome segregation , by directly processing DNA linkages caused by unresolved recombination intermediates . A similar function has also been reported for the HJ resolvase GEN1/YEN1 nuclease in S . cerevisiae [52] . The budding yeast yen1 mutant does not have obvious meiotic defects , whereas the mus81 yen1 double mutant fails to segregate its chromosomes due to unresolved DNA joint-molecules during meiotic anaphase I and II [53] . Furthermore , the enzymatic activity of YEN1 is kept at a very low level in prophase but is highly induced at the onset of meiosis II , suggesting that it provides a safeguard activity that processes DNA linkages that escape the attention of MUS81 during meiotic divisions [53] . Mutation of the YEN1/GEN1 nuclease shows phenotypic variation in different organisms [21] . In C . elegans , no meiotic phenotypes were observed in the gen-1 single mutant on its own or in combination with him-6 or various nuclease mutants [54] . Our data suggest that LEM-3 might provide a failsafe system in C . elegans instead of GEN-1 , to ensure that all recombination intermediates are resolved at the final stage of gamete formation . Consistent with this idea we recently provided evidence that LEM-3 might bind to chromatin bridges in the contractile ring in mitotic cells to process a large variety of DNA intermediates linked to recombination failure , DNA catenation , DNA decondensation failure , and to DNA underreplication [50] In summary , we provide evidence for a role of LEM-3 in meiotic recombination intermediate processing in prophase I and in resolving persistent chromatin bridges during meiotic divisions . It will be interesting to see whether the mammalian LEM-3 orthologue Ankle1 has a role in meiosis .
Strains were grown at 20°C followed standard protocols [55] . N2 Bristol was used as the wild type . CB4856 Hawaii was used to generate strains for CO recombination frequency analysis . Strains used in this study are listed in S1 Table . The cop859 [Plem-3::eGFP::STag::lem-3::3′UTRlem-3] eGFP insertion was generated by Knudra ( http://www . knudra . com/ ) following the procedures described by Dickinson et al [56] . Exact details are available upon request . Germline immunostaining was performed as described previously with slight modifications [35] . Primary and secondary antibodies were used at the indicated dilutions: rabbit anti-HTP-3 ( 1:500 ) ; guinea pig anti-ZHP-3 ( 1:250 ) ; rabbit anti-AIR-2 ( 1:200 ) ; rabbit anti-RAD-51 ( 1:1000 ) ; mouse anti-GFP ( 1:500 ) ; anti-rabbit Alexa488 ( 1:400 ) ( Invitrogen ) , and anti-mouse Alexa488 ( 1:500 ) ( Invitrogen ) and anti-rabbit Alexa Fluor 568 ( 1:750 ) ( Life technologies ) . For DAPI staining the final concentration used was 2 μg/mL . Meiotic divisions were recorded by in utero embryo live imaging [57] . Worms were picked into a solution containing 1 mM levamisole to paralyze worms . Worms were mounted on 2% agar pads covered with a coverslip . Images were captured every 10 seconds using spinning-disk confocal microscopy . Microscopy images acquired with a Delta Vision microscopy were deconvolved and analysed using softWoRx Suite and softWoRx Explorer software ( AppliedPrecision , Issaquah , WA , USA ) . Images acquired with a spinning-disk confocal microscopy were analyzed by ImageJ software . RNAi was performed by feeding worms with bacteria containing plasmid that express double-stranded RNA for syp-2 [58] . Worms were fed on NGM plates supplied with 100 mg/L ampicillin and 1mM IPTG . An empty L4440 plasmid was used as a control for RNAi experiment . Meiotic CO frequency and distribution were assayed essentially as described [26] with slight modifications . Five snip-SNPs on Chr . V that differ between N2 Bristol and CB4856 Hawaii were used to determine the crossover landscape in embryos . Single embryo was transferred into lysis buffer by mouth pipetting using a capillary and incubated at -80°C for at least 5 min to help crack the embryo before lysis .
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Meiotic recombination is required for genetic diversity and for proper chromosome segregation . Recombination intermediates , such as Holliday junctions ( HJs ) , are generated and eventually resolved to produce crossover ( CO ) and non-crossover ( NCO ) . While an excess of meiotic double-strand breaks is generated , most breaks are repaired without leading to a CO outcome and usually only one break for each chromosome pair matures into a CO-designated site in Caenorhabditis elegans . The resolution of meiotic recombination intermediates and CO formation have been reported to be highly regulated by several structure-specific endonucleases and the Bloom helicase . However , little is known about the enzymes involved in the NCO recombination intermediate resolution . In this study , we found that a conserved nuclease LEM-3/Ankle1 acts in parallel to the SLX-1/MUS-81 pathway to process meiotic recombination intermediates . Mutation of lem-3 has no effect on CO frequency and distribution . Interestingly , prominent accumulation of LEM-3 is found between dividing meiotic nuclei . We provide evidence that LEM-3 is also involved in processing remaining , erroneous recombination intermediates during meiotic divisions .
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2018
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The conserved LEM-3/Ankle1 nuclease is involved in the combinatorial regulation of meiotic recombination repair and chromosome segregation in Caenorhabditis elegans
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Human mesenchymal stem cell ( hMSC ) delivery has demonstrated promise in preclinical and clinical trials for myocardial infarction therapy; however , broad acceptance is hindered by limited understanding of hMSC-human cardiomyocyte ( hCM ) interactions . To better understand the electrophysiological consequences of direct heterocellular connections between hMSCs and hCMs , three original mathematical models were developed , representing an experimentally verified triad of hMSC families with distinct functional ion channel currents . The arrhythmogenic risk of such direct electrical interactions in the setting of healthy adult myocardium was predicted by coupling and fusing these hMSC models to the published ten Tusscher midcardial hCM model . Substantial variations in action potential waveform—such as decreased action potential duration ( APD ) and plateau height—were found when hCMs were coupled to the two hMSC models expressing functional delayed rectifier-like human ether à-go-go K+ channel 1 ( hEAG1 ) ; the effects were exacerbated for fused hMSC-hCM hybrid cells . The third family of hMSCs ( Type C ) , absent of hEAG1 activity , led to smaller single-cell action potential alterations during coupling and fusion , translating to longer tissue-level mean action potential wavelength . In a simulated 2-D monolayer of cardiac tissue , re-entry vulnerability with low ( 5% ) hMSC insertion was approximately eight-fold lower with Type C hMSCs compared to hEAG1-functional hMSCs . A 20% decrease in APD dispersion by Type C hMSCs compared to hEAG1-active hMSCs supports the claim of reduced arrhythmogenic potential of this cell type with low hMSC insertion . However , at moderate ( 15% ) and high ( 25% ) hMSC insertion , the vulnerable window increased independent of hMSC type . In summary , this study provides novel electrophysiological models of hMSCs , predicts possible arrhythmogenic effects of hMSCs when directly coupled to healthy hCMs , and proposes that isolating a subset of hMSCs absent of hEAG1 activity may offer increased safety as a cell delivery cardiotherapy at low levels of hMSC-hCM coupling .
Ischemic heart disease , which results from reduced coronary flow of oxygenated blood , is a leading cause of myocardial infarction and heart failure . This insufficient oxygenation results in the death of cardiomyocytes , which are normally incapable of substantial regeneration . Therefore , despite tremendous advancements in pharmacological and interventional therapeutic approaches , ischemic heart disease continues to be responsible for nearly 1 out of 6 deaths in the United States [1 , 2] . This has motivated novel cardiotherapeutic strategies to repair and regenerate heart muscle , including human mesenchymal stem cell ( hMSC ) therapy , the method of interest in this study . In clinical trials for treating myocardial infarction , the delivery of autologous bone marrow derived hMSCs has demonstrated improved ventricular ejection , enhanced angiogenesis , decreased fibrosis and scar size , and minimal immune response [3] . However , the benefits have often been modest and transient [4 , 5] , underscoring a need to better understand and exploit the underlying mechanisms by which hMSCs interact with human cardiomyocytes ( hCMs ) [6] . This limited mechanistic knowledge further makes it difficult to ensure long-term stability , with seamless structural and functional integration into the host tissue [7–9] . Therefore , deeper investigation into the mechanisms of how hMSCs impact cardiac function is necessary . Proposed hMSC-hCM interactions predominantly include: reprogramming of host hCMs , transdifferentiation of hMSCs into hCMs , paracrine signaling , electrophysiological coupling , and cellular fusion [6 , 10] . Indirect paracrine signaling through the release of largely unidentified soluble factors is thought to play an important role [6 , 11]; however , hMSCs have also exhibited functional direct electrical interactions with cardiomyocytes both in vitro and in vivo [10 , 12–17] , motivating ongoing investigations of the electrophysiological coupling and cellular fusion mechanisms . In particular , Valiunas et al . showed that hMSCs form connexin 43-mediated gap junctions between each other and with acutely isolated canine cardiomyocytes , suggesting the ability to form heterocellular electrical networks [15] . Later in vitro studies showed that such electrical connections can be functional and potentially arrhythmogenic , as co-culturing murine cardiomyocytes with greater than 10 percent of hMSCs decreased conduction velocity ( CV ) and predisposed re-entrant arrhythmias [16] . Pro-arrhythmic characteristics were also detected in vivo , where pigs receiving intravenous injections of mesenchymal stem cells had decreased effective refractory periods [17] . Moreover , Shadrin et al . recently reported a 25–40% incidence of hybrid cell formation of hMSCs and neonatal rat ventricular myocytes through cell fusion [10] . However , species-specific effects can limit the clinical relevance of such animal and in vitro studies , and similarly controlled experiments are difficult to perform in human patients . While hMSC therapy clinical trials are yet to report arrhythmogenicity [18] , such adverse effects remain a concern . Therefore , in this study , it was of interest to assess the electrophysiological safety of various levels of direct hMSC-hCM electrical interactions under healthy conditions [18] , and to predict methods of improving the safety of this therapy . Mathematical modeling is a powerful tool that can simulate direct intercellular electrical interactions between hMSCs and hCMs . Electrophysiological models have been established to describe hCMs [19–21] , as well as their interactions with other resident heart cells [22–25] , but never before with hMSCs . Therefore , in this study , the various types of currents experimentally characterized in hMSCs [26–29] were mathematically modeled to simulate an empirically classified triad of hMSC families distinguished by their respective functional ion channels: Type A ) delayed rectifier-like hEAG1 and calcium activated potassium currents; Type B ) delayed rectifier-like hEAG1 , calcium activated potassium , tetrodotoxin ( TTX ) -sensitive sodium , and L-type calcium currents; and Type C ) calcium activated potassium and transient outward currents [26 , 28] . The empirical distinction of these three hMSC families was originally reported by Li et al . [26] based on patch clamp measurements of bone marrow-derived hMSCs obtained commercially and maintained in monolayer culture . We then simulated the electrical activity of hMSCs coupled to healthy hCMs , and interpreted the model findings within the context of prior in vitro and in vivo experiments to identify possible opportunities to minimize arrhythmic potential in future hMSC-based cell delivery cardiotherapies .
The hMSC transmembrane voltage can be modeled as: d V d t = - 1 C m ( I stim + I tot , i ) ( 1 ) where V is voltage , t is time , Cm is the cell capacitance , Istim is a stimulus current , and Itot , i is the total transmembrane ionic current of Type i hMSCs ( where i = A , B , or C ) . The total transmembrane ionic current for Types A , B , and C hMSCs are given by Eqs 2 , 3 and 4 , respectively: I tot , A = I KCa + I dr + I L , A ( 2 ) I tot , B = I KCa + I dr + I LCa + I Na + I L , B ( 3 ) I tot , C = I KCa + I to + I L , C ( 4 ) where IKCa is the calcium activated potassium current , Idr is the delayed rectifier-like hEAG1 current , IL , i is the leakage current for hMSC Type i ( where i = A , B , or C ) , ILCa is the L-type calcium current , INa is the TTX-sensitive sodium current , and Ito is the transient outward current . To describe each type of hMSC ionic current , either Hodgkin-Huxley-like or Markovian-like approaches were taken . Parameters for these models were fit to published experimental hMSC data using numerical methods described in S1 Text . Parameters used in this study can be found in Tables A-G of S1 Text . To quantify the impact of each hMSC parameter on the hCM APD , an established multivariable regression analysis was performed [46 , 47] . In 300 trials for each hMSC model , we randomly varied hMSC maximum conductance parameters and time constant parameters by a log-normally distributed pseudorandom scale factor with a standard deviation of 10% , as described elsewhere [48] . hMSCs were coupled to midcardial hCMs in a 1:1 ratio in this analysis . From the changes in the model APD outputs ( Y ) and parameters ( X ) , a linear approximation can be made to find the normalized parameter sensitivity vector ( B ) , such that Y ≈ XB . Therefore , a positive or negative sign of B ( i . e . , an element of B ) indicates a positive or negative correlation between the parameter of interest and APD , respectively . Furthermore , the magnitude of B indicates the sensitivity of the APD to the parameter of interest . To better demonstrate the sensitivity of the APD output to each hMSC cell type , B was scaled by σAPD , the standard deviation of the APDs for each set of 300 trials for a respective hMSC cell type .
Three novel electrophysiological models were developed for the triad of hMSC families based on empirical data [26] . After successfully modeling each type of current expressed in hMSCs ( Fig 1 ) , it was necessary to validate the whole-cell models by simulating Itot , A , Itot , B , and Itot , C . Total current whole-cell voltage-clamp simulations of Types A , B , and C hMSCs are shown in Fig 3 , along with schematics of functional currents for each cell type [26] . Like experimental recordings [26] , our simulation had a conditioning potential of -80 mV , followed by 10 mV voltage steps for 300 ms between -60 mV and 60 mV , and a final holding potential of -30 mV . Overall , fitting individual currents ( Fig 1 ) allowed for ample reconstruction of representative whole cell electrical activity . The simulations for Types A and B hMSCs , both of which possess delayed rectifier-like channel activity , generally agree with the magnitude and behavior of experimental total currents for a wide range of voltage contours [26] . As demonstrated by Li et al . , Idr at a potential of 60 mV has a standard deviation of approximately 90 pA , and the activation time constant for Idr at a holding potential of -80 mV has a substantial standard deviation of approximately 25 ms [26] . Since these deviations affect the amplitude and activation kinetics of Types A and B hMSCs , we performed a sensitivity analysis to determine the impact of these parameters on hCM APD ( see Sensitivity Analysis below for details ) . Type C hMSCs , absent of functional hEAG1 expression , also reproduced the magnitude and form of the experimental voltage-clamp experiments characterizing this hMSC family’s electrophysiological behavior [26] . Therefore , we used each of these hMSC models to predict the direct electrical interactions between hMSCs and hCMs . The three models developed in this study were each coupled and fused to hCMs to better understand direct cell-cell electrical interactions during hMSC cardiotherapies . To understand the arrhythmogenic effects of direct hMSC-hCM coupling at the tissue level , a VW analysis was performed on a single layer , anisotropic 5 cm × 5 cm 2-D midcardial tissue with 0% hMSCs ( healthy control ) , 5% , 15% , or 25% randomly inserted hMSCs repeated for three different configurations per condition ( see Fig 9A and S1–S4 Videos for sample re-entry simulations at selected S1–S2 intervals ) . As shown in Fig 9B , VWs lengthened with increasing percent of inserted hMSCs . Interestingly , at low ( 5% ) insertion levels , VWs were dependent on the type of coupled hMSCs ( Fig 9B ) ; inserting hMSCs with delayed rectifier-like activity ( i . e . Types A , B , and mixed populations of hMSCs ) led to substantially larger VWs ( approximately 15 to 20 ms ) compared to Type C hMSCs ( VW = 2 . 0 ± 0 . 5 ms , n = 3 ) . At greater levels of hMSC insertion ( i . e . , 15% and 25% ) , VWs were nearly independent of the type of coupled hMSCs , and VWs exceeding 50 ms were observed . The S1–S2 intervals that led to re-entry for each hMSC type at low levels of insertion are shown in Fig 9C . As expected , the shifts in S1–S2 intervals leading to re-entry depended on the different mean tissue APDs ( S12 Fig ) . Various modeling studies have demonstrated APD dispersion may influence re-entry [45 , 49] , while APD restitution slope , the range of DIs for APD restitution slopes greater than 1 , and CV restitution slope are key factors in restitution-induced instability [20 , 50–53] . Fig 10 illustrates the effects of the percentage and types of hMSCs on each of these arrhythmogenic factors . As expected , APD dispersion ( ζ ) increased with greater levels of hMSC insertion for all hMSC types ( Fig 10A ) . However , APD dispersion was approximately 21% , 18% , and 17% lower for Type C hMSCs compared to hMSCs with delayed rectifier-like activity at 5% , 15% , and 25% hMSC insertion , respectively ( Fig 10A ) . S12 Fig shows APD maps for cardiac tissues with 5% hMSC insertion . APD restitution slopes , as well as the range of DIs for slopes greater than 1 , were slightly decreased following coupling with each hMSC type ( Fig 10B; for raw APD restitution curves , see S13 Fig ) . Even at 25% hMSC insertion , the shift in maximum APD restitution slope was less than 10% ( Fig 10B ) . CV restitution slopes markedly decreased following hMSC insertion , by as much as 71% at 25% hMSC insertion ( Fig 10C ) . The coupling effects on CV restitution slopes were predominately dependent on percentage of hMSC inserted , rather than the type of hMSC ( Fig 10C ) . Altogether , this dispersion of refractoriness and restitution analysis supports the claim that increased arrhythmogenic potential of inserted stem cells is minimized by Type C hMSCs at low levels of hMSC insertion , as VW and APD dispersion are lowest for this cell type , without adversely affecting APD and CV restitution slopes in comparison to delayed rectifier-like hMSCs .
The ability of our computational models to reproduce empirical electrical hMSC and hMSC-hCM co-culture findings supports the validity of our results . As previously described , fitting individual currents ( Fig 1 ) allowed reconstruction of representative whole cell voltage-clamp data by Li et al . ( Fig 3 ) [26] . This enabled us to simulate hMSC-hCM coupling to develop insight into direct electrical effects of co-culturing these two cell types . The complex hMSC-hCM interactome , which also includes paracrine signaling [6 , 11] , makes it empirically infeasible to isolate direct electrophysiological coupling effects on APD . For example , Askar et al . have previously shown that the hMSC secretome alone significantly increases neonatal rat cardiomyocyte APD and significantly decreases Cav1 . 2 and Kv4 . 3 levels [54] , while DeSantiago et al . demonstrated the hMSC paracrine factors stimulate the L-type calcium channel current and calcium transient activity in mouse ventricular myocytes [55] . Furthermore , Askar et al . found the paracrine effects on APD to be dose-dependent [54] . Several studies [13 , 16 , 54] demonstrate that hMSC co-culture does not lead to APD shortening in vitro , whereas our model studies suggest direct hMSC-hCM coupling alone would tend to shorten APD . Therefore , we hypothesize that in the experimental setting , hMSC-mediated paracrine effects may overshadow the model-predicted APD shortening effects of direct heterocellular coupling . Furthermore , the hMSC secretome reportedly alters atrial myocyte conduction [56] , but does not significantly affect the conduction of ventricular myocytes [54] , making it reasonable to compare our model results to empirical conduction and VW findings . Studies have shown that sufficient hMSC supplementation decreases CV and CV restitution slopes [16 , 54] , consistent with our simulations . Specifically , Chang et al . observed co-culturing cardiomyocytes with greater than 10 percent of hMSCs decreased CV and the CV restitution slope [16] . Moreover , sufficient hMSC supplementation increased inducibility of re-entry [16] , which was also shown in our simulations ( Fig 9 ) . Based on our direct coupling-only simulations reproducing empirical co-culture conduction and VW findings , we hypothesize that in the experimental setting , hMSC-mediated paracrine effects on hCM conduction do not counteract the effects of direct heterocellular coupling demonstrated in this study , emphasizing the importance of understanding and minimizing the potential sources of hMSC-related arrhythmogenicity . Despite their non-excitable nature , hMSCs express gap junction proteins [15] and are therefore capable of influencing hCM action potentials . Furthermore , these effects cannot be presumed to be simply passive , as shown in Figs 5 and 6A . In the case of a passive cell , there is a consistent increase in hCM APD . The relatively large capacitance of hMSCs ( approximately 60 pF [26] , compared to 6 . 3 pF for cardiac fibroblasts [22] ) makes this effect substantial , resulting in increases in APD of approximately 50 ms with a population of 80% passive hMSCs with midcardial hCMs . These passive effects were not duplicated once the cells expressed their respective ionic currents . Unlike passive hMSCs , Types A and B hMSCs decreased APD independent of hCM cell type . For example , the APDs of midcardial hCMs shortened by approximately 88 ms with a population of 80% Type A or B hMSCs . This effect was exacerbated in cellular fusion , where midcardial hCM APD was shortened by approximately 120 ms . During an hCM action potential , the peak hEAG1 current was two-fold greater than the maximum magnitude of Ito , and nearly twenty-fold greater than the maximum magnitude of IKCa . The larger outward current of Types A and B hMSCs resists hMSCs from approaching the transmembrane voltage of hCMs , resulting in an overall larger sinking effect that shortens phase 2 of the cardiac action potential , and initiates phases 3 and 4 of repolarization earlier . Such drastic changes in the action potential waveform could be possible in vivo if the delivered stem cells cluster in regions of the heart [57] , such that hMSCs outnumber hCMs locally . This would be of even greater concern if the high incidence of cell fusion reported in vitro [10] were also found to occur in vivo as suggested by recent animal studies [58] . The implications of action potential variations include pathological electrical and mechanical states . Overall , our simulations suggest hMSC-hCM coupling: 1 ) alters action potential waveform at the single-cell and tissue level; 2 ) increases dispersion of APDs at the tissue levels; and 3 ) substantially decreases CV . Shortening of APDs by Types A and B hMSCs could have notable electrophysiological implications in the heart . Studies have shown that shortening of APDs could induce ventricular tachycardias , suggesting Types A and B hMSCs may be capable of pro-arrhythmic electrical remodeling [17 , 59 , 60] . Furthermore , one signature of ischemic patients is a loss of epicardial action potential dome , resulting in ST-segment elevation [61] . hMSC direct coupling to hCMs could exaggerate these effects by clustering in the epicardium and acting as an electrical sink , thus becoming pro-arrhythmic . Substantial decreases in APD due to Types A and B hMSCs could also portend altered Ca+2 transients in the hCM , resulting in decreased inotropy [62–67] . Such alterations could directly impact left-ventricular pressure development [22] , which is of particular concern for myocardial infarction patients who already suffer decreases in ejection fraction , preload , stroke work , rate of pressure development , and overall mechanical efficiency [68] . The large variability in electrical activity of Types A and B hMSCs presents another potential source of arrhythmogenicity . hCM APD was negatively correlated and highly sensitive to Types A and B hMSC Gjunction ( Fig 8A and 8B ) . This gap conductance has been shown empirically to be highly variable with a coefficient of variation of 87% [15] . The potentially irregular actions of Types A and B hMSCs are further amplified by the fact that hEAG1 activation kinetics are also highly variable , with a coefficient of variation of approximately 35% [26] . Since there is a highly negative correlation between hCM APD and numerous Idr components ( e . g . , its activating parameters and Gdr ) , and a highly positive correlation with its inactivating parameters , hMSCs with delayed rectifier-like currents are likely to be unpredictable in their direct effects on hCM APD . This is exacerbated by the fact that hMSC insertion leads to increased APD dispersion in a dose-dependent manner , which could unfavorably alter VWs and electrical stability [45 , 49] . Decreased CV caused by hMSC supplementation ( Fig 10C ) is an established source of re-entrant loops [16] , making hMSC-hCM direct coupling potentially arrhythmogenic . Chang et al . showed the potential of re-entrant arrhythmias in vitro was dependent on hMSC supplementation [16] , which was confirmed in our simulations ( Fig 9B ) . The decrease in CV is more drastic with increased hMSC supplementation ( Fig 10C ) , which may occur if hMSCs cluster in a localized region , resulting in an increased probability for re-entry . Decreased CV also plays a significant role in ischemic patients . Specifically , ischemic patients also have signatures of transmural conduction slowing , resulting in ST-segment elevation and T-wave inversion [61] . These abnormalities may be exacerbated by the decreased CV effects of hMSC insertion . Current hMSC cardiotherapies involve implementation of electrically-unspecified hMSCs . As a result , Types A and B hMSCs , which reportedly account for a majority of hMSCs [26] , will tend to dominate the electrical interactions with hCMs . This was seen in Figs 4 and 5 , where the mixed population of hMSCs acted almost indistinguishably from Types A and B hMSCs . This model study suggests that the isolation of Type C hMSCs , absent of delayed rectifier-like currents , may offer superior effectiveness and safety as a cell-based cardiotherapy at low levels of hMSC insertion by minimizing VWs and action potential waveform perturbations compared to other hMSC types . Type C hMSCs exhibited unique electrical activity that was intermediate between the passive and delayed rectifier-functioning hMSCs , resulting in a favorable gap current . The equilibrating source-sink actions within the Type C hMSC gap currents resulted in smaller deviations in the APD ( Figs 4 and 5 ) , corresponding to longer action potential wavelengths at the tissue level following hMSC insertion ( Table H of S1 Text ) , which we hypothesize contributed to this cell type having the smallest VW at low levels of hMSC insertion ( Fig 9B and 9C ) . This suggests a decreased likelihood of the potential adverse electrical effects previously described . It is also important to note that overall at the tissue level , the VW increased at greater levels of hMSC-hCM direct coupling , and became independent of hMSC type at moderate and high levels of hMSC insertion . Previous findings suggest the hMSC-hCM interactome involves not only intrinsic , direct cell-cell coupling , but also indirect paracrine signaling through the release of largely unidentified soluble factors and exosome nanovesicles [6 , 11] . Harnessing and delivering the key components of the hMSC secretome while circumventing the potentially pro-arrhythmic effects of direct cell-cell coupling may offer a superior cardiac therapy in the future . We note several limitations of the hMSC models developed . As previously discussed , the activation time constant for Idr at a holding potential of -80 mV has a coefficient of variation of approximately 35% [26] . This variability affects the output APD , as suggested by the sensitivity analysis , demonstrating the necessity for further empirical investigation into the kinetics of hMSC Idr . We also assumed that only a triad of families of hMSCs exist , but there may be more; for instance , it has been reported that ion channel expression varies with cell cycle progression [69–71] , which may contribute to the variable electrical families and activities of hMSCs . However , the limited understanding of this behavior in the context of hMSCs motivated us to focus only on the three previously characterized hMSC families . We also assume constant ionic concentrations across the hMSC cell membrane . Currently , there is not enough experimental data to sufficiently model intracellular calcium levels in hMSCs . Our sensitivity analysis demonstrates that APD is not highly influenced by channels that are largely affected by these variations ( e . g . , IKCa ) , justifying this assumption . Collecting more electrophysiological data on carrier proteins within hMSCs [29] would encourage incorporating transient behavior of ionic concentrations into our models . A second limitation was that we assumed healthy hCMs in order to develop insight into the arrhythmogenic effects of hMSC insertion into healthy cardiac tissue , effectively performing an in silico Phase I clinical trial . However , we did not consider the effects of microfibrosis or random microscale obstacles [24 , 72–77] . Each of these effects was purposely not considered in this study , as hMSC paracrine effects are expected to have a major impact on these changes [78–81] . The simulations performed in this study provide a framework for future investigation into each of these factors . Therapeutic hMSCs can disperse to both healthy and ischemic regions of the heart , motivating investigation of the effects of hMSC coupling with ischemic hCMs . This healthy hCM-only assumption made it appropriate to model local cardiac behavior ( i . e . , 5 cm × 5 cm heterogeneous anisotropic tissue ) rather than whole heart behavior . Studying the effects of various spatial distributions of hMSCs using a fully three-dimensional anatomically detailed model of the heart could represent an area for future investigation building on the electrophysiology models developed herein . A fourth limitation was that we did not model other factors that may influence electrical instability , such as short-term cardiac memory and intracellular calcium dynamics [20 , 82–84] . Instead , we prioritized other established factors of instability ( e . g . APD dispersion , APD restitution slopes , CV restitution slopes ) , and found several advantages of Type C hMSCs compared to the other mesenchymal stem cell families . Finally , we assumed no interplay between paracrine signaling and electrophysiological coupling . However , it was recently shown that paracrine signaling can cause upregulation of Cx43 and increase intercellular conduction in atrial myocytes [56] , as well as alter ion channel activity in ventricular myocytes [54] . We neglected paracrine mechanisms in our models , so investigating this time-dependent interaction would require further study . Based on these limitations , areas for future work include: 1 ) improving the models based on advancements in empirical data on hMSC electrophysiology; 2 ) considering the effects of microfibrosis or random microscale obstacles in combination with hMSC anti-fibrotic paracrine effects; 3 ) examining the electrical and electromechanical effects of hMSC models coupled with ischemic hCM models [85]; 4 ) modeling the interplay between electrophysiological effects and paracrine signaling in the hMSC-hCM interactome; and 5 ) empirically confirming our simulations , demonstrating that Type C hMSCs minimize the impact on APD and reduce the VW at low levels of hMSC insertion , offering a potential strategy for improving the safety of cardiac cell therapies . In conclusion , our study provides novel electrophysiological models of hMSCs that reproduce key experimental measurements from patch clamp studies , identifies mechanisms underlying the arrhythmogenic effects of hMSCs coupled to hCMs via gap junctions , underscores the electrical effects associated with hMSC-hCM fusion , and establishes the possibility of isolating a specific sub-population of hMSCs absent of hEAG1 delayed rectifier-like channel activity for minimizing the arrhythmogenic risk of future hMSC-based cell delivery cardiotherapies using low levels of hMSC coupling .
|
Myocardial infarction—better known as a heart attack—strikes on average every 43 seconds in America . An emerging approach to treat myocardial infarction patients involves the delivery of human mesenchymal stem cells ( hMSCs ) to the damaged heart . While clinical trials of this therapeutic approach have yet to report adverse effects on heart electrical rhythm , such consequences have been implicated in simpler experimental systems and thus remain a concern . In this study , we utilized mathematical modeling to simulate electrical interactions arising from direct coupling between hMSCs and human heart cells to develop insight into the possible adverse effects of this therapeutic approach on human heart electrical activity , and to assess a novel strategy for reducing some potential risks of this therapy . We developed the first mathematical models of electrical activity of three families of hMSCs based on published experimental data , and integrated these with previously established mathematical models of human heart cell electrical activity . Our computer simulations demonstrated that one particular family of hMSCs minimized the disturbances in cardiac electrical activity both at the single-cell and tissue levels , suggesting that isolating this specific sub-population of hMSCs for myocardial delivery could potentially increase the safety of future hMSC-based heart therapies .
|
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2016
|
Modeling Electrophysiological Coupling and Fusion between Human Mesenchymal Stem Cells and Cardiomyocytes
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Soluble ICAM-1 ( sICAM-1 ) is an endothelium-derived inflammatory marker that has been associated with diverse conditions such as myocardial infarction , diabetes , stroke , and malaria . Despite evidence for a heritable component to sICAM-1 levels , few genetic loci have been identified so far . To comprehensively address this issue , we performed a genome-wide association analysis of sICAM-1 concentration in 22 , 435 apparently healthy women from the Women's Genome Health Study . While our results confirm the previously reported associations at the ABO and ICAM1 loci , four novel associations were identified in the vicinity of NFKBIK ( rs3136642 , P = 5 . 4×10−9 ) , PNPLA3 ( rs738409 , P = 5 . 8×10−9 ) , RELA ( rs1049728 , P = 2 . 7×10−16 ) , and SH2B3 ( rs3184504 , P = 2 . 9×10−17 ) . Two loci , NFKBIB and RELA , are involved in NFKB signaling pathway; PNPLA3 is known for its association with fatty liver disease; and SH3B2 has been associated with a multitude of traits and disease including myocardial infarction . These associations provide insights into the genetic regulation of sICAM-1 levels and implicate these loci in the regulation of endothelial function .
A member of the immunoglobulin superfamily of adhesion receptors , ICAM-1 is expressed on endothelial cells where it serves as a receptor for the leukocyte integrins LFA-1 and Mac-1 [1] . A soluble form of ICAM-1 ( sICAM-1 ) is present in plasma and is thought to arise from proteolytic cleavage of the extra-cellular domains of ICAM-1 . Although the physiologic function of soluble ICAM-1 remains to be fully defined , plasma concentration of sICAM-1 have a predictive value for the risk of myocardial infarction , ischemic stroke , peripheral arterial disease and noninsulin-dependent diabetes mellitus in epidemiological studies [2]–[4] . We recently described a genome-wide association study of sICAM-1 in 6 , 578 apparently healthy women from the Women's Genome Health Study ( WGHS ) , which confirmed a known association at the ICAM1 locus and identified a novel association at the ABO locus [5] . These results were subsequently replicated in large-scale genomics studies from Barbalic [6] et al . and Qi [7] et al . Nevertheless , the total variance explained by these associations remained low ( 8 . 4% ) as compared to the relatively high heritability estimates ( from 0 . 34 to 0 . 59 ) [8] , [9] for sICAM-1 . We therefore hypothesized that other , weaker , common genetic determinants of sICAM-1 remained to be discovered . To explore this issue , we performed a larger genome-wide association study ( GWAS ) , evaluating 334 , 295 SNPs in 22 , 435 apparently healthy women of European ancestry from the WGHS .
We found that 67 SNPs passed our pre-specified threshold of genome-wide significance of P<5×10−8 for association with sICAM-1 ( Table S1 and Figure 1A ) . These SNPs clustered within 5 loci in the vicinity of ABO ( 9q34 . 2 ) , RELA ( 11q13 . 1 ) , SH2B3 ( 12q24 . 12 ) , ICAM1 ( 19p13 . 2 ) and PNPLA3 ( 22q13 . 31 ) . The ICAM1 [10] , [11] and ABO [5] loci have previously been identified as contributing to sICAM-1 levels , but the SH2B3 , RELA and PNPLA3 loci were not previously shown to be associated with sICAM-1 . The genomic context of these three latter loci is illustrated in Figure 2A , 2B and 2C . In order to determine whether more than one non-redundant association signal could be detected at each of these five loci , we applied a model selection algorithm . The SNP with the lowest P-value for association was the only one retained at every locus with the exception of the ICAM1 locus , where 5 SNPs were selected by the model ( Table 1 ) . Interestingly , model selected SNPs at the ICAM1 locus showed lower P-value when they were all included in a single multivariate model than when considered separately . Three of the model selected SNPs at the ICAM1 locus ( rs281437 , rs1801714 and rs11575074 ) were not significant at a genome-wide level of significance in a univariate analysis . We performed two analyses to determine if the multiple SNPs selected at the ICAM1 locus were the result of an underlying association with a known but untyped variant . First , we tested all imputed SNPs ( using MACH ) within 1 . 5 Mb of rs1799969 ( the lead SNP at that locus ) for association with adjusted sICAM-1 levels . No imputed SNP was more significant than the directly genotyped rs1799969 . Second , we tested the same set of imputed SNPs after additional adjustment of sICAM-1 levels for the effect of model selected SNPs . No additional SNP was associated at genome-wide significance . The 5 SNPs at the ICAM1 locus selected by our algorithm were also used in haplotype analysis using WHAP [12] , as implemented in PLINK [13] ( Table 2 ) . The estimate of the proportion of variance attributable to haplotypes , as well as their regression coefficients , is consistent with the linear model of these same SNPs , reinforcing the adequacy of an additive model to explain the association . Next we tested whether any additional SNPs are associated with sICAM-1 levels after adjustment for the model selected SNPs ( see Figure 1B ) . A single SNP was associated with sICAM-1 at genome-wide significance ( P = 5 . 4×10−9; −4 . 1 ng/mL per minor allele ) in the vicinity of the NFKBIB locus at 19q13 . 2 ( Figure 2D ) . This SNP , rs3136642 , is intronic to NFKBIB and had a minor allele frequency of 0 . 38 . The model selection algorithm retained no other SNP at the NFKBIB locus . Further adjustment of sICAM-1 values for rs3136642 did not identify any additional SNP with genome-wide significant association with sICAM-1 . We also performed GWAS analysis using imputed genotypes ( using MACH ) . Because no new locus reached genome-wide significance after adjustment for model selected SNPs , only results of directly genotyped SNPs are presented . These results were essentially unchanged when the first 10 components of a principal component analysis were included as covariates to account for sub-Caucasian stratification . All 4 novel loci identified in WGHS were replicated ( one-sided P<0 . 05 ) in 9 , 813 individuals from the CHARGE consortium [14] ( Table 3 ) . Collectively , the 5 SNPs at the ICAM1 gene locus explained 6 . 5% of sICAM1 total variance , whereas the other loci explained from 0 . 1 to 1 . 4% of the variance . In comparison , clinical covariates explained 19 . 5% of the variance ( Table 4 ) . For 4 of the loci , there was no strong evidence for non-additive effects of the minor allele as judged by lack of significance for a likelihood ratio test comparing the additive regression model to an alternative genotype model with an additional degree of freedom . However , the non-additive component was significant for rs507666 ( P = 9 . 3×10−6 ) at the ABO locus with a tendency toward a dominant effect ( mean sICAM-1 of 362 . 1 , 342 . 4 and 335 . 4 ng/mL for 0 , 1 and 2 minor alleles , respectively ) . The PNPLA3 SNP rs738409 also showed evidence of non-additive association ( P = 4 . 6×10−5 ) with a tendency toward a recessive model ( mean sICAM-1 of 352 . 8 , 356 . 0 and 367 . 7 ng/mL for 0 , 1 and 2 minor alleles , respectively ) . In spite of these non-additive trends , no additional locus reached genome-wide significance when a genotypic test , which does not assume an additive model of association , was conducted . Model selected SNPs were tested for association with other available inflammation markers ( C-reactive protein and fibrinogen ) . No significant association was noted ( P>0 . 01 ) after adjusting for multiple hypothesis testing . Model selected SNPs were also tested for association with incident cardiovascular events ( myocardial infarction , coronary revascularization , stroke and total cardiovascular event ) over a mean follow-up period of 14 years . A Cox proportional hazard model was used adjusting for age at study entry . Only the SH2B3 SNP rs3184504 was associated with incident myocardial infarction ( 315 events ) , with each minor allele increasing the risk ( P = 0 . 011; OR 1 . 23 95% CI 1 . 05–1 . 43 ) . The association remained significant after further adjustment for sICAM-1 levels ( P = 0 . 028; OR 1 . 20 95% CI 1 . 02–1 . 41 ) . Given the known association of sICAM-1 with cardiovascular risk and the association of selected SNPs with sICAM-1 , we estimated the power to detect an association between the SH2B3 SNP rs3184504 and myocardial infarction to be 6% , for alpha = 0 . 05 . In comparison , power varied from 5% ( rs281437 ) to 11% ( rs5498 ) for other SNPs . The PNPLA3 SNP rs738409 was tested for association with triglyceride , LDL cholesterol , HDL cholesterol and BMI as this gene is known to be involved in lipid metabolism and association with BMI has been previously suggested [15] . No significant association was observed . Since smoking accounts for a large fraction of the variation in sICAM-1 levels , we tested associated SNPs for interaction with smoking . A significant interaction was observed for the ICAM1 SNP rs1799969 ( interaction P = 1 . 6×10−9 ) whereby current smokers had a stronger genetic association , as we previously reported [16] . A novel interaction was also observed with the ABO SNP rs507666 , again with a stronger genetic association in current smokers ( P = 0 . 0003 ) . When restricting the GWAS analysis to current smokers , an additional association was observed with rs8034191 ( P = 3 . 5×10−8 ) . This latter SNP is located on chromosome 15 near the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5 . This locus is known to be associated with smoking behavior [17] , [18] and rs8034191 has recently been associated with smoking quantity [19] . No novel association was observed when restricting the GWAS analysis to non-smokers after adjustment for the previously described loci . We also tested whether multiple variants of individually weak effect could contribute to sICAM-1 levels . In cross-validation procedures , no increase in variance explained was observed when using P-value cut-offs less significant than 10−8 for inclusion of SNPs in gene scores ( see Figure 3 ) . In other words , selection of SNPs on the basis of P-value alone was not able to identify more of the genetic variance than could be explained by the SNPs with association P-value <10−8 .
Six loci – ABO , ICAM1 , NFKBIK , PNPLA3 , RELA and SH2B3 – have been identified in this report for association with sICAM-1 . While the ABO [5] and ICAM1 [10] , [11] loci had been previously reported , we extended the number of non-redundantly associated variants at the ICAM1 locus by demonstrating association of rs11575074 and rs1801714 in multivariate analysis along with the known rs1799969 , rs5498 and rs281437 SNPs [5] . Neither rs1801714 nor rs11575074 are predicted eQTL ( http://eqtl . uchicago . edu/Home . html ) , but rs1801714 is a missense variant ( P352L ) and rs11575074 is located in a predicted binding site for several transcription factors including PPARG [20] . The NFKBIK , PNPLA3 , RELA and SH2B3 associations are novel . No strong contribution of weakly associated variants was observed in the polygene analysis whereby SNPs of varying statistical significance were included in gene scores . Nuclear factor kB ( NF-kB ) proteins are a family of transcription factors involved in a number of physiological processes that include cell survival , proliferation , and activation . The NF-kB proteins ( NFKB1 or NFKB2 ) are bound to REL , RELA , or RELB to form the NF-kB complex . These complexes are typically localized in the cytoplasm , where they are trapped by binding to IkB inhibitory proteins NFKBIA or NFKBIB . Upon inflammatory simulation , IkB kinase A and B phosphorylate IkB inhibitory proteins and mark them for degradation via the ubiquitination pathway , thereby allowing activation of the NF-kappa-B complex . Activated NF-kB complexes translocate into the nucleus and bind to NF-kB DNA binding motifs . NF-kB triggers transcription of various genes critical to inflammation , such as cytokines , chemokines and cell adhesion molecules including ICAM1 [21] , [22] . Remarkably , two of the novel associations involve genes physically interacting with NF-kB . No genetic interaction , however , was noted between these two SNPs ( data not shown ) . Taken together , these results emphasize the importance of the NFKB pathway in the regulation of sICAM-1 levels . PNPLA3 encodes a protein of unknown function that belongs to the patatin-like phospholipase family . Members of that family are believed to complement hormone sensitive lipase for adipocyte triacylglycerol lipase activity . The methionine allele of the missense PNPLA3 SNP rs738409 ( Ile148Met ) has recently been associated with increased hepatic fat levels , hepatic inflammation and plasma levels of liver enzymes ( traits linked to insulin resistance and obesity ) [23] , [24] . Nevertheless , rs738409 has been shown not to be associated with insulin resistance [25] although a previous study demonstrated an association with insulin secretion in response to oral glucose tolerance test [15] . Levels of the inflammatory marker sICAM-1 are known to be correlated with insulin resistance and obesity [4] . Consistent with rs738409 modulating the response to insulin resistance and associated phenotypes , the risk allele for fatty liver disease was associated with increased sICAM-1 levels . SH2B3 encodes Lnk , an adaptor protein that mediates the interaction between extra-cellular receptors , such as the T-cell receptor and the thrombopoietin receptor MPL , and intracellular signaling pathways . Cells from Lnk-deficient mice show an increased sensitivity to several cytokines and altered activation of the RAS/MAPK pathway in response to IL3 and stem cell factor [26] . The same SH2B3 SNP rs3184504 identified in our study has previously been associated with multiple other traits , including blood pressure [27] , [28] , blood eosinophil number [29] , myocardial infarction [29] , celiac disease [30] , type I diabetes [31] , LDL-cholesterol [32] , asthma [29] , blood platelet number [33] , hemoglobin concentration [34] and hematocrit [34] . Furthermore , rs3184504 is a non-synonymous SNP ( Arg262Trp ) whose derived allele ( Trp ) is part of a haplotype that has been suggested to have been introduced 3 , 400 years ago and selectively swept in European populations [33] . The derived allele is the risk allele for coronary artery disease and was the allele associated with higher sICAM-1 concentration . Association of rs3184504 with sICAM-1 further demonstrates the remarkable pleiotropy of that genetic variant by extending its effect to endothelial cell adhesion molecules . An interesting hypothesis is whether changes in sICAM-1 are mediated through increased sub-clinical atherosclerosis , but further studies will be needed to address this question . In this report , we demonstrate genetic association of sICAM-1 with the ABO , ICAM1 , NFKBIK , PNPLA3 , RELA and SH2B3 loci . These findings broaden our current knowledge of the genetic architecture of sICAM-1 with identification of four novel loci . The novel association at PNPLA3 reinforces the importance of insulin resistance-related processes in the regulation of sICAM-1 levels . The observed associations also provide evidence of functional genetic variation at two genes – NFKBIK and RELA – well known for their implication in the NF-kB pathway , therefore providing a basis for the study of these polymorphisms in other conditions where this same pathway is involved . The results also extend the effect of the SH2B3 SNP rs3184504 to endothelial function .
All analyses were performed with approval of the institutional review board of the Brigham and Women's Hospital . All members of the WGHS cohort were participants in the WHS who provided an adequate baseline blood sample for plasma and DNA analysis and who gave consent for blood-based analyses and long-term follow-up . All participants in this study were part of the Women's Genome Health Study ( WGHS ) [35] . Briefly , participants in the WGHS include North American women from the Women's Health Study ( WHS ) with no prior history of cardiovascular disease , diabetes , cancer , or other major chronic illness who also provided a baseline blood sample at the time of study enrollment . For all WGHS participants , EDTA anticoagulated plasma samples were collected at baseline and stored in vapor phase liquid nitrogen ( −170°C ) . Circulating plasma sICAM-1 concentrations were determined using a commercial ELISA assay ( R&D Systems , Minneapolis , Minn . ) ; the assay used is known not to recognize the K56M ( rs5491 ) variant of ICAM-1 [36] and the 82 Caucasian carriers of this mutation were therefore excluded from further analysis . The intra-assay coefficient of variation was 6 . 7% and the reported intra-individual coefficient of variation 7 . 6% [37] . This study has been approved by the institutional review board of the Brigham and Women's Hospital . Additional clinical characteristics of this sample are provided in Table S2 . Samples were genotyped with the Infinium II technology from Illumina . Either the HumanHap300 Duo-Plus chip or the combination of the HumanHap300 Duo and I-Select chips was used . In either case , the custom content was identical and consisted of candidate SNPs chosen without regard to allele frequency to increase coverage of genetic variation with impact on biological function including metabolism , inflammation or cardiovascular diseases . Genotyping at 318 , 237 HumanHap300 Duo SNPs and 45 , 571 custom content SNPs was attempted , for a total of 363 , 808 SNPs . Genetic context for all annotations are derived from human genome build 36 . 1 and dbSNP build 126 . SNPs with call rates <90% were excluded from further analysis . Likewise , all samples with percentage of missing genotypes higher than 2% were removed . Among retained samples , SNPs were further evaluated for deviation from Hardy-Weinberg equilibrium using an exact method [38] and were excluded when the P-value was lower than 10−6 . Samples were further validated by comparison of genotypes at 44 SNPs that had been previously ascertained using alternative technologies . SNPs with minor allele frequency >1% in Caucasians were used for analysis . After quality control , 334 , 295 SNPs were left for analysis . Because population stratification can result in inflated type I error in a GWAS , a principal component analysis using 1443 ancestry informative SNPs was performed using PLINK [13] to confirm self-reported ancestry . Briefly , these SNPs were chosen based on Fst >0 . 4 in HapMap populations ( YRB , CEU , CHB+JPT ) and inter-SNP distance at least 500 kb in order to minimize linkage disequilibrium . Different ethnic groups were clearly distinguished with the two first components . 31 self-identified Caucasian women were removed from analysis because they did not cluster with other Caucasians , leaving 22 , 435 non-diabetic participants with non-missing sICAM-1 information for analysis . To rule out the possibility that residual stratification within Caucasians was responsible for the associations observed , a principal component analysis [39] was performed in Caucasians ( only ) using 64 , 205 SNPs chosen to have pair-wise linkage disequilibrium lower than r2 = 0 . 2 . The first ten components were then used as covariates in the association analysis . As adjustment by these covariates did not change the conclusions , we present analysis among Caucasian participants without further correction for sub-Caucasian ancestry unless stated otherwise . Plasma concentrations of sICAM-1 were adjusted for age , smoking , menopause and body mass index using a linear regression model in R to reduce the impact of clinical covariates on sICAM-1 variance . The adjusted sICAM-1 values were then tested for association with SNP genotypes by linear regression in PLINK [13] , assuming an additive contribution of each minor allele . A conservative P-value cut-off of 5×10−8 was used to correct for the roughly 1 , 000 , 000 independent statistical tests thought to correspond to all the common genetic variation of the human genome [40] , [41] . To investigate whether more than one SNP in each locus is independently associated with sICAM-1 , a forward selection multiple linear regression model was used . For each locus with at least one genome-wide significant SNP ( i . e . P<5×10−8 ) , all genotyped SNPs within 1 . 5 Mb of the most significantly associated SNP and passing quality control requirements were selected for potential inclusion in our model . The forward selection algorithm then proceeded in two steps . In the first step , all SNPs not yet included in the multiple regression model were tested for association with sICAM-1 . In step two , the SNP with the smallest P-value was included in the model if its multiple regression P-value was less than 5×10−8 . We then repeated steps one and two , such that a single SNP was added to the multiple regression model at each iteration . The algorithm was stopped when no more SNP passed the P<5×10−8 requirement . To test whether multiple genetic variants of individually weak effect could explain a substantial fraction of sICAM-1 variance , we performed a “polygene” experiment as previously described [42] . Briefly , we randomly divided our dataset in 5 equal parts . We then tested SNPs for association with sICAM-1 using 4 out the 5 parts and performed linkage disequilibrium pruning as implemented in PLINK ( r2>0 . 05 and distance <1 Mb ) . We then derived a gene score with non-redundant associated SNPs using varying P-value thresholds and weighting each SNP for its beta coefficient . Finally , we tested the gene score for association with sICAM-1 in the remaining one fifth of the total sample and calculated the adjusted R2 . This experiment was repeated 5 times using each one of the five parts as the gene score validation group alternatively . We sought to replicate the 4 novel loci identified in 9 , 813 individuals from the Cohorts for Heart and Aging Research in Genome Epidemiology ( CHARGE ) consortium [14] for whom plasma sICAM-1 concentration and genotypes were available . The CHARGE sample consists of 4 meta-analyzed cohorts: the Framingham Heart Study , the Cardiovascular Health Study , the Atherosclerosis Risk in Communities study , and the Rotterdam Study . Complete information on each study is available as Text S1 . Association analyses were performed on imputed genotypes using an additive genetic model on age and sex adjusted log-transformed sICAM-1 values .
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Soluble Intercellular Adhesion Molecule 1 ( sICAM-1 ) is an inflammatory marker that has been associated with several common diseases such as diabetes , heart disease , stroke , and malaria . While it is known that blood concentrations of sICAM-1 are at least partially genetically determined , our current knowledge of which genes mediate this effect is limited . Taking advantage of technologies allowing us to interrogate genetic variation on a whole-genome basis , we found that variation in the NFKBIK , PNPLA3 , RELA , and SH2B3 genes are important determinant of sICAM-1 blood concentrations . The NFKBIB and RELA genes are involved in regulation of inflammation . These observations are significant because this is the first report of genetic association within these extensively studied inflammation genes . The PNPLA3 gene has previously been associated with liver disease , and the SH2B3 gene has been associated with a multitude of traits including cardiovascular disease . Extension of these associations to sICAM-1 adds to the intriguing diversity of effects of these genes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cardiovascular",
"disorders/coronary",
"artery",
"disease",
"immunology/genetics",
"of",
"the",
"immune",
"system",
"genetics",
"and",
"genomics/complex",
"traits"
] |
2011
|
Genome-Wide Association Analysis of Soluble ICAM-1 Concentration Reveals Novel Associations at the NFKBIK, PNPLA3, RELA, and SH2B3 Loci
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The current reference test for the detection of S . mansoni in endemic areas is stool microscopy based on one or more Kato-Katz stool smears . However , stool microscopy has several shortcomings that greatly affect the efficacy of current schistosomiasis control programs . A highly specific multiplex real-time polymerase chain reaction ( PCR ) targeting the Schistosoma internal transcriber-spacer-2 sequence ( ITS2 ) was developed by our group a few years ago , but so far this PCR has been applied mostly on urine samples . Here , we performed more in-depth evaluation of the ITS2 PCR as an alternative method to standard microscopy for the detection and quantification of Schistosoma spp . in stool samples . Microscopy and PCR were performed in a Senegalese community ( n = 197 ) in an area with high S . mansoni transmission and co-occurrence of S . haematobium , and in Kenyan schoolchildren ( n = 760 ) from an area with comparatively low S . mansoni transmission . Despite the differences in Schistosoma endemicity the PCR performed very similarly in both areas; 13–15% more infections were detected by PCR when comparing to microscopy of a single stool sample . Even when 2–3 stool samples were used for microscopy , PCR on one stool sample detected more infections , especially in people with light-intensity infections and in children from low-risk schools . The low prevalence of soil-transmitted helminthiasis in both populations was confirmed by an additional multiplex PCR . The ITS2-based PCR was more sensitive than standard microscopy in detecting Schistosoma spp . This would be particularly useful for S . mansoni detection in low transmission areas , and post-control settings , and as such improve schistosomiasis control programs , epidemiological research , and quality control of microscopy . Moreover , it can be complemented with other ( multiplex real-time ) PCRs to detect a wider range of helminths and thus enhance effectiveness of current integrated control and elimination strategies for neglected tropical diseases .
Schistosomiasis control strategies are currently based on mass drug administration ( MDA ) with praziquantel to populations at risk [1] . Disease mapping , MDA allocation , and post-MDA monitoring of infection are based on standard microscopy techniques: urine filtration for Schistosoma haematobium , and Kato-Katz on stool for the other Schistosoma spp . , including S . mansoni . However , these techniques are laborious and there are recognized deficiencies in their sensitivity , thereby limiting the accuracy of screening and monitoring results , and thus appropriate decision-making [2] . This impairs the efficiency of global efforts to control and eventually eliminate schistosomiasis . Better diagnostics have great potential to improve the quality of schistosomiasis control programs . For S . haematobium , a good alternative to standard microscopy is already available in the form of hematuria dipstick tests [3] . The diagnosis of S . mansoni however , still heavily relies on the Kato-Katz thick stool smear . Several other detection tools have been proposed , including the circumoval precipitin test on serum samples [4 , 5] , the FLOTAC technique on fecal samples [6] , and the point-of-care circulating cathodic antigen assay ( POC-CCA ) for detection of Schistosoma antigen in urine samples [7 , 8] . In addition , DNA-based methods , such as real-time polymerase chain reaction ( PCR ) -based techniques , are increasingly being used for the detection of Schistosoma spp . infections [9–18] . The advantage of microscopy over Schistosoma species-specific antigen tests is that they can detect multiple helminth species , and that they are quantitative . These features make them better apt for large-scale use in integrated neglected tropical disease ( NTD ) control programs than the single-pathogen tests . PCR , in a multiplex format , has the same above-mentioned advantages as microscopy but has greater flexibility . Indeed , a multiplex PCR can detect all ( Schistosoma and other helminth ) species at the same time , and at any moment after the stool has been collected . Moreover , PCR is a highly standardized diagnostic procedure and it can also be used to detect parasitic protozoa or other microorganisms that cannot be identified by Kato-Katz . The aim of the present study was to compare Kato-Katz with PCR for the detection of Schistosoma—and soil-transmitted helminth ( STH ) —infections in stools from persons living in S . mansoni-endemic areas . To this end , stool samples from ongoing studies in two countries with different endemicity were examined using both tests .
Informed and written consent was obtained from all participants prior to inclusion into the study . For minors , informed and written consent was obtained from their legal guardians and assent was obtained from the children . The Senegalese survey was part of a larger investigation on the epidemiology of schistosomiasis and innate immune responses ( SCHISTOINIR ) for which approval was obtained from the review board of the Institute of Tropical Medicine , the ethical committee of the Antwerp University Hospital and ‘Le Comité National d’Ethique de la Recherche en Santé’ of Senegal . All community members were offered praziquantel ( 40 mg/kg ) and mebendazole ( 500 mg ) treatment after the study according to WHO guidelines [19] . The Kenyan survey was performed within the framework of the Schistosomiasis Consortium for Operational Research and Evaluation ( SCORE ) . Ethical clearance from this study was obtained from the Scientific Steering Committee of the Kenya Medical Research Institute ( KEMRI-SSC no . 1768 ) , the Kenyan Ethical Review Committee , and the Institutional Review Board of the Centers for Disease Control and Prevention in the USA . All children who were positive for Schistosoma infection were treated with praziquantel ( 40 mg/kg ) , and those positive for STHs were treated with albendazole ( 400mg ) . Samples were derived from one community-wide study population from a S . mansoni and S . haematobium co-endemic area in northern Senegal with high S . mansoni transmission [20–22] , and from a population of schoolchildren living in a S . mansoni mono-endemic area with comparatively low transmission in western Kenya [23] . The Senegalese survey was conducted in Ndieumeul and Diokhor Tack , two neighboring communities on the Nouk Pomo peninsula in Lac de Guiers ( Guiers Lake ) . Details on this study area have been described elsewhere [20–22] . Stool and urine samples were collected between July and October 2009 and stool samples for PCR were stored for each participant . Stool samples from a subsample of 197 individuals with complete parasitological data were analyzed by PCR . The Kenyan survey was conducted in the Asembo division of the Rarieda district along the shores of Lake Victoria in western Kenya , within the framework of a larger study on the distribution of S . mansoni amongst school children . Eight to twelve-year-old children attending public primary schools within a 10km from the lake were included ( 12km wide transect ) . In this area , S . haematobium is virtually absent . Stool samples were collected between October 2010 and April 2011 , preferentially from the lower prevalence zones [23] , and PCR was performed in a subsample of 760 children from 40 schools with complete parasitological data ( see also S1 STARD Checklist ) . In Senegal , two stool and two urine samples were collected from each participant on consecutive days . From each stool sample , a duplicate 25 mg Kato-Katz slide was prepared for quantitative detection of Schistosoma spp . eggs and qualitative diagnosis of STHs Ascaris lumbricoides and Trichuris trichiura by microscopy [24–26] . Duplicate slides were examined by two different technicians >24h after preparation of the Kato-Katz smear , and for S . mansoni the average egg count was calculated . In addition , filtration of 10 ml of urine was performed using a 12 μm pore-size filter ( Isopore , USA ) according to standard procedures to detect S . haematobium eggs [25] . Urine filters were read by a single technician . In Kenya , three stool samples were collected on consecutive days , and from each sample , duplicate 42 mg Kato-Katz slides were prepared for microscopy . Schistosoma mansoni was diagnosed quantitatively at least 24h after slide preparation . STHs were diagnosed qualitatively: A . lumbricoides and T . trichiura at 24h after slide preparation , and hookworm within 1h of slide preparation . Each slide was examined by two independent microscopists and the average was recorded . Urine filtration was not performed in Kenya . In both countries , microscopy was performed blinded to previous results , and S . mansoni infection intensity was expressed as the number of eggs detected per gram of feces ( epg ) . Egg-based microscopy results were compared to DNA-based PCR results . Real-time PCR was performed blinded to previous results . During preparation of the first stool sample , an additional amount of fecal material ( ~0 . 7ml ) was sieved and diluted in 2ml of 96% ethanol [12] . Samples were frozen , transported to the Netherlands , and stored for weeks to months until PCR analysis was performed at the Leiden University Medical Center . Washing of samples , DNA isolation and the setup of the PCR were performed with a custom-made automated liquid handling station ( Hamilton , Bonaduz , Switzerland ) . For DNA isolation , 200μl of feces suspension was centrifuged and the pellet was washed twice with 1ml of phosphate-buffered saline . After centrifugation , the pellet was resuspended in 200μl of 2% polyvinylpolypyrolidone ( Sigma ) suspension and heated for 10 min at 100°C . After sodiumdodecyl sulfate–proteinase K treatment ( 2h at 55°C ) , DNA was isolated using QIAamp DNA-easy 96-well plates ( QIAgen , Limburg , the Netherlands ) . In each sample , 103 PFU/mL Phocin Herpes Virus 1 ( PhHV-1 ) was included within the isolation lysis buffer [27 , 28] . A Schistosoma multiplex real-time PCR ( Schisto-PCR ) was performed as described previously [29] , with some minor modifications [30] . This PCR targets the Schistosoma-specific internal transcriber-spacer-2 ( ITS2 ) sequence of S . mansoni , S . haematobium , and S . intercalatum , as well as PhHV-1 as an internal positive amplification control . The ITS2-based PCR has been validated extensively with a panel of well-defined DNA and stool sample controls and is virtually 100% specific [30] . Amplification was performed by heating samples for 15 minutes at 95°C , followed by 50 cycles , each of 15 seconds at 95°C and 60 seconds at 60°C . Another multiplex real-time PCR , the ANAS-PCR [31] , was performed for the detection of STHs Ascaris lumbricoides , Necator americanus , Ancylostoma duodenale and Strongyloides stercoralis . In contrast to the ANAS-PCR , the Schisto-PCR was not designed to differentiate between the different species tested . Amplification , detection and data analysis were performed with the CFX96 Real-Time System version 1 . 1 ( Bio-Rad , Hercules , CA ) [29] . Negative and positive control samples were included in each PCR run . The PCR output from this system consisted of a cycle-threshold ( Ct ) value , representing the amplification cycle in which the level of fluorescent signal exceeded the background fluorescence . Hence , low Ct values correspond to high parasite-specific DNA loads in the sample tested , and vice versa . The maximum Ct value was 50 , and indicated DNA-negative stool samples . The Ct values of the internal PhHV-1 control were within the expected range for all samples , indicating that there was no evidence of inhibition of amplification in any of these samples . IBM SPSS 22 . 0 ( SPSS , Inc . ) was used for statistical analyses ( see also S1 Dataset and S1 SPSS Syntax ) . Results were considered significant when the p-value was <0 . 05 . Kappa ( κ ) values were calculated as follows to obtain the level of agreement between microscopy and PCR results beyond that which may be obtained by chance: κ=observed test agreement−expected test agreement1−expected test agreement Standard cut-off values were used for egg-based infection categories [1]: Schistosoma mansoni infections with 1–99 epg were classified as light-intensity , those with 100–399 epg as moderate , and those with ≥400 epg as heavy-intensity infections . DNA loads as reflected by Ct-values were not normally distributed . Consequently , the Mann-Whitney U test was used to determine differences in DNA loads between S . mansoni egg-negative and S . mansoni egg-positive individuals , and the Kruskal-Wallis test to determine differences in DNA loads between the different egg-based infection categories . Spearman’s rank correlation coefficients were calculated to investigate the correlation between egg- and DNA-based infection intensities , which did not show a linear trend . In the Senegalese study subjects , we investigated whether PCR outcomes were influenced by S . haematobium infection status . The Pearson Chi² test ( with continuity correction ) was used to compare PCR positivity between those with and without S . haematobium infection . The Mann-Whitney U test was used to compare DNA loads in stool samples between individuals with and without S . haematobium eggs in urine , as well as between individuals with single S . mansoni and with mixed Schistosoma infections stratified according to S . mansoni infection intensity . For the analysis of the Kenyan data at the school level , only schools with data on ≥15 children were included ( i . e . 24/40 schools ) . Pearson’s correlation coefficients were calculated to investigate the correlation between egg- and DNA-based infection prevalences in the different schools . Schools were classified into three groups according to their distance from the shore of Lake Victoria: A ) the highest prevalence zone ≤1200m from the lake; B ) moderate prevalence zone 1200-3800m from the lake; and C ) lowest prevalence zone >3800m away .
When only the first stool sample was taken into account , microscopy detected S . mansoni infections in 57 . 4% of subjects in Senegal and in 19 . 2% of subjects in Kenya ( Table 1 ) whilst PCR detected Schistosoma DNA in 72 . 6% and 32 . 4% of subjects , respectively . Thus , in Senegal , the Schisto-PCR detected 15 . 2% ( ( 143–113 ) /197 ) more infections than microscopy , and in Kenya , 13 . 2% ( ( 246–146 ) /760 ) more infections than microscopy . When two stool samples were taken into account , 68 . 5% and 25 . 9% S . mansoni-positives were detected by microscopy in Senegal and Kenya , respectively . When three stool samples were taken into account in Kenya , 29 . 5% S . mansoni-positives were detected by microscopy . While the percentages of S . mansoni-positives detected by microscopy increased with an increasing number of stool samples , they were still lower than those detected by Schisto-PCR in a single stool sample , in both countries . When based on the first stool sample , egg- and DNA-based results corresponded in 76 . 6% ( κ = 0 . 500 ) and 81 . 8% ( κ = 0 . 536 ) of subjects in Senegal and Kenya , respectively ( Table 2 ) . When egg counts were based on all stool samples provided ( 2 samples in Senegal and 3 in Kenya ) , test agreement increased to 81 . 7% ( κ = 0 . 561 ) , and 86 . 3% ( κ = 0 . 680 ) , respectively . Differences in test agreement between countries were mainly due to the fact that in Senegal , egg-negatives were more often found positive in PCR than in Kenya ( >twofold difference ) . Fig 1 demonstrates that mainly low-intensity infections were missed when egg counts were based on only one stool sample . People that were classified as having heavy infections by microscopy were always PCR-positive . Percentages of PCR-positives varied from 97% to 83% in the moderate egg count group , and from 79% to 87% in the group with light intensity infections . Median DNA loads were very similar in both countries for the different Schistosoma infection categories ( Fig 1 ) . In both countries , Spearman’s rank correlations between egg- and DNA-based infection intensities were statistically significant ( p<0 . 001 ) with correlation coefficients ranging from -0 . 638 to -0 . 782 . These correlations became stronger with the number of stool samples that were taken into account ( ρ = -0 . 747 and ρ = -0 . 782 for 1 and 2 stool samples , respectively , in Senegal; ρ = -0 . 638 , ρ = -0 . 708 , and ρ = -0 . 738 for 1 , 2 and 3 stool samples , respectively , in Kenya ) . Based on standard microscopy on stool and urine , 80% ( 157/197 ) of the Senegalese subjects were infected with either Schistosoma spp . The majority of these infections ( 92/157 ) were mixed S . mansoni and S . haematobium infections . Single S . mansoni infections were found in 22% , and single S . haematobium infections in 11% of subjects ( Table 3 ) . Table 3 compares Schisto-PCR outcomes according to Schistosoma infection status ( by microscopy ) . DNA-based infection frequencies were highest in those individuals with single S . mansoni and mixed infections and lowest in persons with single S . haematobium infections and those without any schistosome infection . As by definition , no Schistosoma eggs were observed in stools from uninfected people . In people with single S . haematobium infections , one would expect a similar ( low ) percentage of PCR-positives as in uninfected individuals . However , 59% of the single S . haematobium group was PCR positive , compared to 23% of the microscopy negatives ( p = 0 . 009 ) . Ct-values were comparable . Percentages of PCR-positives were similar in the single S . mansoni and mixed Schistosoma infection groups , but the mixed infection group showed significantly lower Ct-values ( p = 0 . 003 ) , indicative of a higher intensity of infection . No effect of the presence of S . haematobium on Ct-values in mixed as compared to single S . mansoni infections was observed after stratification for egg-based S . mansoni infection intensity ( Table 4 ) . To explore the diagnostic value of PCR on stool samples in identifying high-risk schools and/or communities , Kenyan test results were analyzed at school level . Data for 24 schools with at least ≥15 children per school , representing 688 school children , were aggregated . The median sample size per school was 27 ( range 15–47 ) . Fig 2 indicates a strong , linear correlation between the percentage of microscopy- and PCR-positives per school ( p<0 . 001 ) . DNA-based infection frequencies were consistently higher than egg-based infection frequencies at the school level when both were based on the same stool samples , and PCR identified 25% ( 22/24 versus 16/24 ) more S . mansoni-positive schools than microscopy . When egg counts from all stool samples were taken into account , microscopy identified more S . mansoni-positive schools ( 20/24 ) , and also more high-risk schools ( infection frequencies ≥50% [1] ) , as compared to when only the first stool sample was taken into account ( Fig 2 ) . In those high-risk schools , egg-based infection frequencies calculated from three stool samples ( six slides ) were as high as , or higher than DNA-based infection frequencies . In low-risk schools on the other hand ( infection frequencies <10% ) , PCR detected more infections than microscopy on three stool samples , and it detected more positive schools . In addition to Schistosoma , we investigated the occurrence of STH infections by Kato-Katz and ANAS-PCR . In both study areas , microscopy indicated low prevalences of STH infections and this was confirmed by PCR ( Table 5 ) . The two techniques detected similar percentages of A . lumbricoides-positives in both countries . Hookworm was only present in Kenya , and the ANAS-PCR showed that these infections only involved N . americanus . Interestingly , PCR detected more than threefold the number of hookworm infections than microscopy .
There are only a handful of studies that compared PCR outcomes with the reference method that is routinely used in endemic areas , i . e . microscopy on Kato-Katz smears [9] . Moreover these studies used different PCR targets [9] . A real-time PCR targeting the cytochrome c oxidase subunit I ( cox1 ) of S . mansoni found similar percentages of S . mansoni-positives as standard microscopy in a Senegalese population [12] . The sensitivity of this PCR was found to be suboptimal because the cox1 region shows considerable genetic variation [32] . PCRs based on the 121-bp tandem-repeat sequence showed more promising results with 7 to 28% higher percentages of S . mansoni infections detected than standard microscopy [10 , 13 , 17 , 33–37] . In contrast to the ITS2-based real-time PCR used in the present study however [29] , this PCR cannot quantify DNA loads . The present study was the first to compare standard microscopy to an improved Schisto-PCR targeting the conserved ITS2 sequence . The ITS2-based PCR detected 13–15% more Schistosoma-positive individuals than microscopy when both tests were performed on the same stool sample . These trends were very similar in the north of Senegal where S . mansoni prevalences are high [20] , and in the west of Kenya using stools from schools that had considerably lower S . mansoni prevalences [23] . In Kenya , 25% more schools with S . mansoni-infected children were identified based on PCR as compared to microscopy . We observed that the number of egg-positive individuals increased as more stool samples were taken into account . It is indeed well-known that the sensitivity of microscopy increases as more consecutive stool samples are included in the analysis [38] . This is likely due to the variability of egg counts for an individual with a given worm load [39 , 40] . More S . mansoni egg-negatives tested positive in PCR in Senegal than in Kenya . This between-country difference may be due to methodological differences between the two studies , such as the amount of fecal material examined per stool sample . In Senegal , 2x25mg fecal material was examined per stool sample while in Kenya 2x42mg was examined per stool sample and this may have resulted in relatively more false negative microscopy results for S . mansoni in Senegal . In addition , the co-occurrence of S . haematobium in the Senegalese population may have resulted in ‘false-positive’ PCR results , as the PCR may pick up some occasional S . haematobium DNA present in the stools . Trends for S . mansoni infection intensities were very similar to those of infection frequencies . While both PCR and microscopy proved adequate to detect S . mansoni infections with higher egg loads and , consequently higher fecal DNA loads , light infections were more likely to be missed by microscopy . People with light infections often showed low Schistosoma DNA levels in stool , and were egg-negative when one stool sample was considered . When more stool samples were tested , these people tended to shift from the negative egg-based infection category towards the light-intensity infection group . Likewise , comparison of the two techniques in Kenya showed that S . mansoni infections in children from schools with low prevalence and intensity were more likely to be missed by microscopy than those from schools with higher prevalence and intensity . It is indeed known that the sensitivity of microscopy is especially low in light-intensity infections , and in low-transmission areas [40] . Apparently , PCR does not suffer ( as much ) from this problem and may therefore be particularly useful in such situations . The strong correlation between egg counts and DNA loads in Senegal and Kenya , as well as between egg- and DNA-based infection frequencies in Kenyan schools suggests that DNA loads and DNA-based prevalences can be linked with egg counts and egg-based prevalences , respectively . This implies that the cut-offs which are based on S . mansoni egg counts and that are currently used for the allocation of control interventions ( e . g . for MDA [1] ) , may be conveniently translated into cut-offs based on fecal Schistosoma DNA loads . More studies are needed to assess this into more detail and in more geographical areas [41] . We found the performance of the Schisto-PCR to be very similar in Senegal and in Kenya , despite differences in the level of Schistosoma transmission , geographic S . mansoni strains , co-infecting helminths , and demographic composition as well as genetic background of the study population . An additional advantage of PCR is that it is more objective and uniform than microscopy . It does not suffer from methodological variations ( e . g . number and volume of stool samples , calculation of average egg count , quality of microscopy ) , or inter-observer variation , and it is less error-prone . Moreover , stool samples can be stored for later analysis by PCR and if needed , in a central laboratory . Hence , the Schisto-PCR may be particularly useful as an epidemiological tool to reliably compare levels of infection between geographical areas and between studies [42] . In addition , PCR can be used as a reference standard to assess the quality of locally used ( reference ) methods , and to compare the accuracy of diagnostic procedures between different study sites [43] . Multiplex PCR allows the detection of multiple helminth species , and this spectrum can be further expanded by combining different multiplex PCRs such as the Schisto-PCR and ANAS-PCR . In the present study , the ANAS-PCR confirmed microscopy results showing relatively low levels of STH infections . While microscopy and PCR gave similar results for A . lumbricoides , PCR was more sensitive in the detection of N . americanus than microscopy . These results are in accordance with previous studies that suggested multiplex PCR to be more sensitive than , or as sensitive as , microscopic techniques for the detection of hookworm and A . lumbricoides in areas of low STH transmission [44–46] . Additional advantages of the ANAS-PCR are that it can also detect S . stercoralis and that it can differentiate between the two common hookworm species N . americanus and A . duodenale . Very recently , our group further extended the Schisto- and ANAS- multiplex PCRs to include T . trichiura . In the near future , it will thus be possible to detect not only Schistosoma spp . but also the other most important intestinal helminths–A . lumbricoides , N . americanus , A . duodenale , S . stercoralis , and T . trichiura [47]–in one single analysis . This is not possible by microscopy .
In this study , we extensively evaluated the ITS2-based Schisto-PCR on stool samples for the detection of S . mansoni and showed that it outperforms standard microscopy on Kato-Katz smears . The Schisto-PCR was more sensitive in detecting S . mansoni than standard microscopy , which makes it particularly useful in low transmission areas , and consequently , in post-control settings . As such , it can be used in the context of schistosomiasis control and elimination , but also for epidemiological research , and for quality control of microscopy . Moreover , it can be complemented with other PCRs such as the ANAS-PCR to detect a wider range of helminths . In this way , DNA-based diagnostic tools may aid in enhancing effectiveness of current integrated NTD control and elimination .
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In the developing world , over 207 million people are infected with parasitic Schistosoma worms . Schistosoma mansoni is one of the most widespread species , and its routine diagnosis is based on microscopic detection of parasite eggs in stool samples . This technique is , however , highly observer-dependent and has suboptimal sensitivity . We compared the performance of stool microscopy with the highly specific real-time polymerase chain reaction ( PCR ) we recently described for the detection and quantification of parasite–specific DNA . We tested stool samples collected at two different epidemiological settings: a Senegalese population ( n = 197 ) from a high transmission area where S . mansoni and S . haematobium are co-endemic and a Kenyan school population ( n = 760 ) selected from zones with comparatively low S . mansoni transmission . Microscopy mostly missed low intensity infections that PCR was able to detect . Consequently , the PCR may be very useful for the detection of S . mansoni in areas with low levels of infection . Furthermore , being a highly standardized diagnostic procedure , the PCR may improve schistosomiasis control programs , epidemiological research , and quality control of microscopy . Also it can be easily combined with other PCRs to detect a wider range of helminth infections in a single stool sample .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"Conclusion"
] |
[] |
2015
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Is PCR the Next Reference Standard for the Diagnosis of Schistosoma in Stool? A Comparison with Microscopy in Senegal and Kenya
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Epithelial stem cells reside in specific niches that regulate their self-renewal and differentiation , and are responsible for the continuous regeneration of tissues such as hair , skin , and gut . Although the regenerative potential of mammalian teeth is limited , mouse incisors grow continuously throughout life and contain stem cells at their proximal ends in the cervical loops . In the labial cervical loop , the epithelial stem cells proliferate and migrate along the labial surface , differentiating into enamel-forming ameloblasts . In contrast , the lingual cervical loop contains fewer proliferating stem cells , and the lingual incisor surface lacks ameloblasts and enamel . Here we have used a combination of mouse mutant analyses , organ culture experiments , and expression studies to identify the key signaling molecules that regulate stem cell proliferation in the rodent incisor stem cell niche , and to elucidate their role in the generation of the intrinsic asymmetry of the incisors . We show that epithelial stem cell proliferation in the cervical loops is controlled by an integrated gene regulatory network consisting of Activin , bone morphogenetic protein ( BMP ) , fibroblast growth factor ( FGF ) , and Follistatin within the incisor stem cell niche . Mesenchymal FGF3 stimulates epithelial stem cell proliferation , and BMP4 represses Fgf3 expression . In turn , Activin , which is strongly expressed in labial mesenchyme , inhibits the repressive effect of BMP4 and restricts Fgf3 expression to labial dental mesenchyme , resulting in increased stem cell proliferation and a large , labial stem cell niche . Follistatin limits the number of lingual stem cells , further contributing to the characteristic asymmetry of mouse incisors , and on the basis of our findings , we suggest a model in which Follistatin antagonizes the activity of Activin . These results show how the spatially restricted and balanced effects of specific components of a signaling network can regulate stem cell proliferation in the niche and account for asymmetric organogenesis . Subtle variations in this or related regulatory networks may explain the different regenerative capacities of various organs and animal species .
Stem cells reside in specific niches that regulate their self-renewal and differentiation . In general , these niches consist of various types of neighboring differentiated cells , which provide a milieu of extracellular matrix and signaling molecules that regulate the unique behavior of stem cells . Epithelial stem cells have been identified in several adult tissues that undergo continuous turnover , such as hair , skin , feather , and gut [1–6] . Although mammals have lost the capacity for recurrent tooth renewal , some mammals , such as rodents , have incisor teeth that grow continuously . On the basis of cell cycle kinetics , DiI tracing , and the location of cells that retain bromodeoxyuridine ( BrdU ) label long term , it is thought that the epithelial stem cells of rodent incisors reside in the stellate reticulum core of the cervical loops ( Figure 1A and 1B; [1 , 7] ) . An interesting feature of the mouse incisor is that the cervical loop on the labial side is much thicker than that on the lingual side . It contains abundant stellate reticulum cells in its core , whereas the lingual cervical loop is very thin and only contains a few stellate reticulum cells . However , the molecular mechanisms underlying the asymmetric growth and size of these cervical loops are unknown . Most dental epithelial stem cells give rise to enamel-secreting ameloblasts . The enamel layer covers the dentin layer produced by mesenchymal odontoblasts . Besides the asymmetric growth and morphology of the cervical loops , enamel also exhibits asymmetric distribution in the mouse incisors . Enamel-producing ameloblasts differentiate only along the labial aspect of the mouse incisor , so enamel covers only the labial surface of the tooth , whereas the lingual surface is enamel-free and covered only by dentin ( Figure 1A ) . This , together with the continuous growth and wear of the mouse incisor , maintains its characteristic sharpness , which is crucial to its function for gnawing . We showed previously that ameloblast differentiation on the labial side of the incisor is induced by bone morphogenetic protein 4 ( BMP4 ) from mesenchymal odontoblasts , and that Follistatin inhibits BMP function in lingual-side dental epithelium , preventing enamel formation there [8] . Consistent with this , ameloblasts differentiate on both sides of incisors in Follistatin−/− mice , whereas overexpression of Follistatin in the dental epithelium ( Keratin 14 [K14]-Follistatin mice ) inhibits ameloblast differentiation and enamel formation . Hence , the asymmetric expression of Follistatin in the dental epithelium contributes to the asymmetry of enamel formation in the mouse incisors [8] . However , although the basis for the selective differentiation of ameloblasts on the labial incisor surface is partly understood , the mechanisms that underlie the regulation of the stem cells that give rise to ameloblast progenitors is unknown . The dental mesenchyme has important functions in influencing the epithelial stem cell niche . Fibroblast growth factor ( FGF ) signals from the mesenchyme have been shown to regulate Notch signaling in the epithelium [1] , and FGF10 has been identified as a necessary signal for epithelial stem cell maintenance , based on the hypoplastic morphology of the cervical loop in Fgf10−/− mice [9] . Although FGF10 and presumably Notch1 are important in stem cell maintenance , they are expressed in similar patterns on both the labial and lingual sides of the mouse incisor [1 , 10] . So far , the earliest gene reported to be expressed asymmetrically in the developing mouse incisor is Fgf3 . From embryonic day 16 ( E16 ) onwards , Fgf3 expression is restricted to a small area of dental papilla mesenchyme on the labial side directly underneath the thick labial cervical loop [9] . The present study started from the observation that incisor growth was markedly slowed in K14-Follistatin mice , and that the cervical loops were severely hypoplastic , whereas in Follistatin−/− mutants , the lingual cervical loop was enlarged . This suggested the hypothesis that Follistatin might in some way inhibit the maintenance or proliferation of dental epithelial stem cells . We show that a complex , highly integrated , and spatially regulated signaling network underlies the effect . Fgf3 expression in the mesenchyme underlies the cervical loops and correlates with their size , and FGF3 cooperates with FGF10 as a mesenchymal signal that stimulates the proliferation of dental epithelial stem cells and transit amplifying ( TA ) cells . BMP4 represses Fgf3 expression , whereas Activin , which is preferentially expressed in labial mesenchyme , inhibits the repressive effect of BMP4 and restricts Fgf3 expression to the labial dental mesenchyme , resulting in the large , labial-side stem cell niche . Moreover , unlike its function in ameloblast differentiation , Follistatin may antagonize the function of Activin , rather than BMP in the cervical loop area , and thereby limit the proliferation of lingual stem cells . This results in a small , lingual stem cell niche and , collectively , accounts for the asymmetry of the incisor . Thus , the number of dental epithelial stem cells and their spatial disposition are regulated by a complex signaling network involving Activin , BMP , FGF , and Follistatin that mediates the communication between mesenchymal and epithelial cells in the stem cell niche . These results show how the spatial regulation of signals can account for asymmetric organogenesis
We observed that incisor growth was markedly slowed in K14-Follistatin transgenic mice that overexpress the transforming growth factor β ( TGF-β ) antagonist Follistatin in the dental epithelium ( Figures 2A , 2B , and S1 ) . Histological analysis showed that in contrast to wild type , the labial cervical loop of K14-Follistatin newborns was severely hypoplastic and resembled the lingual cervical loop in size ( Figure 2C and 2D ) . Conversely , in Follistatin−/− embryos , which die at birth [8 , 11] , the lingual cervical loops frequently showed marked overgrowth and resembled the labial cervical loop in size ( Figure 2E ) . These observations suggested that Follistatin influences epithelial stem cell proliferation in the incisor tooth germ . To test this hypothesis , we performed BrdU incorporation experiments to assay cell proliferation . In wild-type mice , proliferative activity was most obvious in the labial inner dental epithelium , representing actively dividing TA cells that undergo further division and gradually become pre-ameloblasts and ameloblasts . BrdU-labeled cells were less apparent in the central core of the cervical loop where putative slow-cycling stem cells are located , and were very sparse in lingual cervical loop epithelium ( Figure 2F ) . In contrast , in K14-Follistatin mice , the number of stellate reticulum cells , as well as proliferating epithelial cells , were markedly decreased in the labial cervical loop ( Figures 2G and S1 ) . Hence , Follistatin overexpression strongly inhibits dental epithelial cell proliferation and results in a compromised stem cell niche . Conversely , Follistatin−/− incisor germs contained numerous BrdU-positive cells in both labial and lingual cervical loops ( Figures 2F , 2H , and S1 ) . This switch from lingual to labial cervical loop morphology in Follistatin−/− embryos demonstrates that Follistatin functions in the generation of incisor asymmetry through inhibition of epithelial stem cell and TA cell proliferation . The maintenance and proliferation of stem cells in the cervical loop epithelium depends upon FGFs that are expressed in the subjacent dental mesenchyme [1 , 9] . Fgf3 expression partly overlaps that of Fgf10 , but has a more restricted pattern in that it underlies only labial-side TA cells . In vitro bead implantation assays showed that , similar to FGF10 [1] , FGF3 stimulated epithelial cell proliferation in E15 incisors ( Figure S1 ) . In K14-Follistatin incisors , Fgf3 expression was completely down-regulated ( Figure 2I and 2J ) . Moreover , in Follistatin−/− mice , Fgf3 was ectopically expressed in lingual mesenchyme adjacent to the TA cells of the enlarged cervical loop ( Figure 2K ) . In contrast , Fgf10 expression appeared unchanged from wild type ( unpublished data ) . These results indicate that Follistatin prevents the expression of Fgf3 in the lingual dental mesenchyme , whereas forced expression of Follistatin leads to down-regulation of Fgf3 in the labial epithelium . Next we analyzed incisor development in Fgf3−/− and Fgf3−/−; Fgf10+/− compound mutants . In Fgf3−/− mutants at postnatal day 1 ( P1 ) , labial cervical loop morphology and ameloblast differentiation appeared normal ( Figure 3A and 3B ) . Fgf10−/− mice die at birth with hypoplastic cervical loops , whereas Fgf10+/− mice exhibit no abnormalities in incisors or molars [9] . In Fgf3−/−; Fgf10+/− compound mutants at P1 , however , the labial cervical loops were smaller than wild type ( Figure 3C ) . Moreover , at 5 wk , both Fgf3−/− and Fgf3−/−; Fgf10+/− lower incisors were white and lacked characteristic yellow-brown pigment , and the compound mutant incisors were thin and frequently broken ( Figure 3D–3F ) . Ground sections of Fgf3−/− incisors showed that an enamel layer was present ( Figure 3G and 3H ) , but a defect in enamel structure was indicated by the premature wear of molar teeth ( Figure 3K ) . Ground sections of Fgf3−/−; Fgf10+/− incisors showed that the enamel layer was either very thin or missing ( Figure 3I ) . In addition , the molars of Fgf3−/− and Fgf3−/−; Fgf10+/− mice were smaller than wild-type teeth , and at P1 , the folding of Fgf3−/− molar epithelium was aberrant , the cusps were shallow , and this phenotype became more severe in Fgf3−/−; Fgf10+/− molars ( Figure 3K , 3L , 3N , and 3O ) . These data indicate that FGF3 and FGF10 function cooperatively during tooth development . This was further supported by the observation that in an Fgf3−/−; Fgf10−/− double null mutant embryo , molar development was arrested prior to the bud stage ( Figure 3Q ) , whereas in the single knockouts , the molars , albeit smaller than wild type , underwent complete development ( Figure 3K; [9] ) . It appears that although FGF3 and FGF10 are not required for the early differentiation of ameloblasts during embryonic crown morphogenesis [9] , they regulate the maintenance and proliferation of epithelial stem cells in the cervical loops later in the erupted incisors . Thus , during incisor development , Fgf3 and Fgf10 genetically interact to maintain the epithelial stem cell pool that provides a continuous supply of ameloblast progenitors . Moreover , although FGF3 is not necessary for early cervical loop morphogenesis , the asymmetric expression of FGF3 appears to be sufficient to generate the asymmetric labial and lingual stem cell niches in mouse incisors . Several lines of evidence support this view . First , FGF3 protein induces intense cell proliferation in incisor epithelium ( Figure S1 ) . Second , Fgf3 is expressed asymmetrically in incisor dental mesenchyme , in a spatial manner that correlates with differences in cervical loop sizes . Last , ectopic Fgf3 expression was detected in the lingual dental mesenchyme underlying the enlarged cervical loop in Follistatin−/− incisors . These data suggest a model whereby the lingual cervical loop receives only an FGF10 signal , which maintains the basal epithelial stem cell proliferation that would be required for the continuous incisor growth . In contrast , the labial aspect receives both FGF3 and FGF10 signals , resulting in a larger stem cell niche with an increased number of proliferating stem cells . Because the cervical loop defects in K14-Follistatin mice are more severe than those in Fgf3−/− mice , other molecules besides FGF3 may also be affected by Follistatin overexpression . To explore the mechanism by which Follistatin , an extracellular antagonist of Activin and BMP [12] , inhibited Fgf3 expression , we first compared the expression of Fgf3 , Activin βA , Bmp2 , Bmp4 , Bmp7 , and Follistatin during incisor development . In E14 dental mesenchyme , striking co-expression of Fgf3 , Activin βA , and Bmp4 was observed; Follistatin was also expressed in the mesenchyme , but at higher levels in adjacent dental epithelium ( Figure 4A ) . At E15 , mesenchymal Fgf3 and Bmp4 expression persisted whereas Activin βA became restricted to the labial mesenchyme directly underlying the cervical loop epithelium; much weaker expression was observed around the lingual cervical loop ( Figure 4A ) . At E16 , Fgf3 expression also became asymmetric , being undetectable lingually and overlapping with Activin βA labially; Bmp4 was intensely expressed around both lingual and labial cervical loops , as well as in mesenchymal preodontoblasts on both sides . Bmp2 and Bmp7 were expressed symmetrically in dental papilla mesenchyme at all the stages examined ( Figure S2 ) . Follistatin was expressed widely in E15 dental epithelium . However , at E16 , Follistatin was down-regulated in the labial inner dental epithelium , but persisted in the outer dental epithelium . Hence , there was an obvious gap between these Follistatin-expressing cells and the dental papilla mesenchyme in the cervical loop area . On the lingual side , the thin layers of dental epithelium continued to express Follistatin intensely ( Figure 4A ) . Activin receptor-like kinase 3 ( Alk3 ) , the BMP receptor type 1A , and Alk4 , the Activin receptor type 1B , were expressed intensely in the incisor epithelium ( Figure 4B ) . To determine the inter-relationships between the different components of this gene regulatory network , we used organ cultures of dissected E16 incisors . Because we had shown previously that Follistatin antagonizes the inductive effect of BMP in differentiating ameloblasts [8] , we first inserted beads soaked in recombinant BMP4 into the cervical loop region of incisor explants and examined the expression of Fgf3 and Fgf10 . Unexpectedly , after 1 d in culture , Fgf3 expression was markedly down-regulated by BMP4 in the dental mesenchyme , whereas BMP4 had no effect on Fgf10 expression ( Figure 5A–5C ) . We next inserted beads soaked in the BMP inhibitor Noggin into the incisor cervical loop region . Ectopic Fgf3 expression was strongly induced in the lingual dental mesenchyme directly underlying the dental epithelium ( Figure 5D , arrow ) , confirming that BMPs inhibit Fgf3 expression in incisor dental mesenchyme . Consistent with this conclusion , K14-Noggin transgenic mice that overexpress Noggin in dental epithelium exhibit markedly overgrown incisors with increased epithelial proliferation in both the labial and lingual cervical loops [13] . Similar to Follistatin−/− mice and Noggin bead experiments , we detected ectopic Fgf3 expression in the mesenchyme underlying the lingual cervical loops in K14-Noggin incisors ( Figure S3 ) . Collectively , these results demonstrate that BMPs repress Fgf3 expression in dental mesenchyme in vivo , thereby negatively regulating epithelial stem cell proliferation in the mouse incisor . The observation that Follistatin and BMPs have similar effects on the cervical loop , i . e . , they both inhibit Fgf3 expression and growth of the epithelium , indicates that Follistatin is unlikely to act as a BMP antagonist in the cervical loop . We therefore chose to investigate the activity of Activin A , another target of Follistatin , in this system . Remarkably , similar to Noggin beads , Activin A beads also induced ectopic Fgf3 expression in lingual incisor mesenchyme directly underlying the dental epithelium ( Figure 5E , arrow ) . Thus , Activin A and BMP4 have opposite effects on Fgf3 expression . When Activin A and BMP4 beads were placed together in incisor explants , the inhibition of Fgf3 by BMP4 was abrogated ( Figure 5F ) . Next , we studied the effect of Activin on the proliferation of the cervical loop epithelium in cultured explants . When present for 1 or 2 d in incisor explant culture medium , Activin A did not significantly affect cervical loop growth ( unpublished data ) . However , after 4 d , it had induced marked overgrowth in both labial and lingual cervical loops , with extra budding and increased cell proliferation ( Figures 5G , 5H , 5J , and 5K ) . Consistent with the bead experiments , ectopic Fgf3 was also induced in the lingual dental mesenchyme directly underneath the enlarged cervical loop ( Figure 5M and 5N ) . Because the Activin null mutant mice lack incisors and die shortly after birth [14] , we used the selective inhibitor of ALK receptors ( SB431542 ) to prevent endogenous Activin/TGF-β signaling in the cultured incisor explants [15] . The addition of SB431542 in culture medium had no detectable affect after 1 or 2 d of culture ( unpublished data ) , but after 4 d , the labial cervical loop was thinner than normal , with a reduced number of stellate reticulum cells , and epithelial cell proliferation was decreased in both labial and lingual cervical loops ( Figure 5I and 5L ) . Also Fgf3 expression was down-regulated in the dental mesenchyme ( Figure 5M and 5O ) . These findings supported the role of Activin as a positive regulator of epithelial stem cell proliferation . Several lines of evidence support the idea that the effects of Activin and BMP on Fgf3 expression are indirect and that they are likely mediated via the epithelium . First , Activin beads did not induce Fgf3 expression in dental mesenchyme when the dental epithelium was removed , indicating either an indirect effect or a co-requirement for additional factor ( s ) ( Figure 6G ) . Second , the effects of Activin and the Activin inhibitor on epithelial proliferation was delayed , being detectable after 4 d in culture ( Figure 5G , 5H , 5J , and 5K ) , but not after 1 d ( Figure S1 , and unpublished data ) . Third , the ectopic Fgf3 expression in the Activin bead experiments was localized directly underneath the dental epithelium , not around the Noggin or Activin beads ( Figure 5D and 5E ) . Fourth , the BMP and Activin receptors , Alk3 and Alk4 , respectively , were intensely expressed in the dental epithelium . These results suggest that the regulation of Fgf3 expression in the dental mesenchyme by Activin and BMP is indirect and that dental epithelium is an indispensable part of this regulatory network . Thus , Activin acts as a positive regulator , whereas BMP4 is a negative regulator of Fgf3 expression , and hence of epithelial stem cell proliferation in mouse incisors , and the balance between these two mesenchymally expressed TGF-β superfamily members dictates the rate of epithelial stem cell proliferation . The finding that the preferential expression of Activin in labial mesenchyme precedes that of Fgf3 by a day ( Figure 4A ) further supports the conclusion that Activin acts upstream of Fgf3 . Lastly , we addressed the regulation of Fgf3 and Activin expression in incisor mesenchyme . Beads soaked in Activin A did not induce Fgf3 expression and neither did FGF3 induce Activin in dental mesenchyme ( Figure 6G and 6H ) . Both dental epithelium and beads soaked in FGF9 efficiently induced Fgf3 and Activin mesenchymal expression in explants ( Figure 6C–6F ) . However , Fgf9 expression in cervical loop epithelium becomes labially restricted later than Activin and Fgf3 do in the adjacent labial mesenchyme ( Figures 4A and S3; [16] ) . Therefore , FGF9 alone cannot account for the preferential expression of either gene in labial mesenchyme . Activin is so far the earliest gene showing asymmetric expression in incisor dental mesenchyme . Collectively , these studies integrate FGF , BMP , Activin , and Follistatin into a signaling network whose balance regulates the proliferation of epithelial stem cells and TA cells in the continuously growing rodent incisor ( Figure 7A and 7B ) . In our model , mesenchymally expressed FGF3 and FGF10 are key effectors of epithelial cell proliferation . BMP4 , which is symmetrically expressed in both labial and lingual dental mesenchyme , actively represses Fgf3 expression , and therefore acts as an endogenous brake on epithelial stem cell and TA cell proliferation . As discussed above , this effect of BMP4 apparently is indirect and involves as yet unidentified signaling events from the epithelium to the mesenchyme regulating Fgf3 expression . Fgf10 expression was not affected by BMP . Thus , modulation of FGF-induced epithelial stem cell proliferation by BMP4 and Activin is presumably restricted to the FGF3-dependent component . We hypothesize that the labial cervical loop receives both FGF3 and FGF10 signals and thus becomes hyperplastic relative to the lingual cervical loop , which receives only FGF10 . FGF10 may help maintain the basal level of epithelial stem cell proliferation needed to support continuous growth of the tooth and dentinogenesis on the lingual surface [17] . A key feature of the model is that beginning at E15 , Activin is expressed asymmetrically . Activin is weakly expressed in lingual dental mesenchyme but strongly expressed in labial dental mesenchyme . On the basis of our findings , we suggest that Activin abrogates the repressive effect of BMP4 on labial Fgf3 expression and thereby allows FGF3 to promote epithelial stem cell and TA cell proliferation in the labial cervical loop . This would indicate that the balance between two TGF-β superfamily members controls Fgf3 expression and thereby modulates epithelial stem cell proliferation . The continued intense expression of Follistatin in lingual dental epithelium would be expected to antagonize residual Activin in lingual mesenchyme , preserving the repressive effect of BMP on lingual Fgf3; whereas on the labial side , there is a gap between Follistatin-expressing outer dental epithelium and the subjacent dental papilla mesenchyme . Therefore , Follistatin may not be able to antagonize the activity of Activin , allowing intense expression of Fgf3 labially . Thus , this regulatory network model explains the increased number of stem cells in the labial compared to the lingual cervical loop , and the asymmetry of the epithelial stem cell niche in the mouse incisor . Although the modulation of proliferation and differentiation in other epithelial stem cell niches has been associated with variations in growth and morphogenesis [1–6] , this study is the first to demonstrate how fine tuning of a signaling network within a stem cell niche may directly account for asymmetric organogenesis . The composition of this gene regulatory network has important implications for organ regeneration . The observation that Activin , Bmp2 , Bmp4 , Bmp7 , Fgf3 , and Fgf10 expression is confined to the mesenchyme underlines the importance of the dental papilla mesenchyme in regulation of the epithelial stem cell niche . Significantly , mesenchyme also has a key inductive role in maintaining the regenerative potential of other organs . For example , in hair regeneration , several of these signals were also among the dermal papilla mesenchyme signature genes identified in a recent microarray analysis [18] . The delineation of this network also has evolutionary implications . Although continuous tooth development is often regarded as intrinsic to the rodent incisor , other rodents such as the vole ( Microtus sp . ) possess continuously growing molars with comparable stem cell niches [10] . Moreover , the Madagascar lemur Aye-aye , Daubentonia madagascariensis , a primate , also possesses continuously growing incisors [19] . These observations suggest that the dental epithelial stem cell niche is both robust enough to be retained throughout evolution , yet flexible enough to account for differences in the sizes , shapes , growth rates , and regenerative capacities of teeth in different animals . The fine-tuning of stem cell proliferation by precise modulation of signaling networks may be a general mechanism that accounts for the different regenerative capacities of other organs as well .
Follistatin−/− mice on a C57BL/6/J/129S6/SvEv background and K14-Follistatin transgenic mice in a B6D2F1 × C57BL/6 mixed background were generated and genotyped as described [8 , 11 , 20] . Fgf3−/− mice , Fgf10+/− mice , and K14-Noggin transgenic mice have been described [13 , 21 , 22] . The day of vaginal plug appearance was taken as E0 . Staged embryonic heads were fixed in 4% paraformaldehyde ( PFA ) , embedded in paraffin , and serially sectioned at 7 μm . Postnatal mouse heads were decalcified , using 12 . 5% EDTA containing 2 . 5% PFA , for 10–14 d and embedded in paraffin . Undecalcified mandibles were dehydrated in a graded ethanol series and embedded in methylmethacrylate [8] . Ground sections ( 100 μm ) were cut sagittally from mandibular incisors . [35S]-UTP ( Amersham , http://www . amersham . com ) labeled radioactive in situ hybridization on paraffin sections were performed as described [8] . The following in situ probes were used: murine Activin βA [8] , Bmp2 , Bmp4 , Bmp7 [23] , Follistatin [20] , Fgf3 [1 , 16] , Alk3 [24] , Alk4 [25] , and rat Fgf10 [1] . Digitalized images were processed with Adobe Photoshop ( http://www . adobe . com ) . Dark-field images were inverted , false colored with red , and combined with bright-field images [23] . Incisor tooth germs from staged embryos were dissected and cultured on 0 . 1-μm pore size Nuclepore filters at 37 °C at the medium–gas interface using a Trowell-type culture system [26] . Bead implantation assays were performed as described previously [27] . Affi-Gel agarose beads ( BioRad , http://www . bio-rad . com ) were incubated in Activin A ( 100 ng/μl ) , BMP4 ( 100 ng/μl ) , FGF3 ( 50 ng/μl ) , FGF9 ( 50 ng/μl ) , and/or Noggin ( 500 ng/μl ) , all from R&D Systems ( http://www . rndsystems . com ) . Control beads were incubated in BSA ( Sigma , http://www . sigmaaldrich . com ) . Beads were soaked in recombinant proteins at 37 °C for 45 min and placed on top of explants or carefully inserted into the incisor explants using fine forceps . After culturing in vitro for 20–24 h , explants were fixed with 100% ice-cold methanol for 2 min , 4% PFA overnight , and processed for whole-mount RNA in situ hybridization as described previously [8] . Some explants were post-fixed in 4% PFA , embedded in gelatin , and sectioned at 50 μm on a vibratome ( Microm HM650 V; Microm , http://www . microm-online . com ) . Dissection and culture of the proximal part of P1 incisor explants was performed as described [1] . Activin A ( 1 . 25 ng/μl; kindly provided by M . Hyvönen; [28] ) and selective and potent inhibitor of Activin receptor-like kinase ( ALK ) receptors ( SB431542 , 0 . 66 ng/μl; Sigma-Aldrich ) were added to the culture medium and , after culture , the explants were fixed in 4% PFA , embedded in paraffin , and serially sectioned . E18 . 5 pregnant mice were injected intraperitoneally with 1 . 5 ml/100 g body weight of BrdU solution ( Zymed Laboratories/Invitrogen , http://www . invitrogen . com/antibodies ) and killed after 2 h . In in vitro experiments , 10 μM BrdU was added to culture medium for the last 2 h of culture . Embryonic heads and cultured tissues were fixed in 4% PFA , embedded in paraffin , and immunodetected using a BrdU detection kit ( Zymed Laboratories/Invitrogen ) . The cell proliferation indices were determined by counting the BrdU-positive and -negative cells in the cervical loop epithelium and mesenchyme in defined areas . The cells of the labial- and lingual-side mesenchyme were determined from a 200 μm × 400 μm–wide region parallel to epithelium . The initial secretory-stage odontoblasts were chosen for the border of counting cells in the labial and lingual dental epithelium , respectively . The mean values were calculated from the two independent observers' data pools . When the numbers of proliferating cells in K14-Follistatin and Follistatin−/− were compared to the wild type ( Student t-test ) , all the differences were highly significant .
|
Stem cells reside in specific niches that regulate their self-renewal and differentiation , and are responsible for the continuous regeneration of tissues . Although the regenerative potential of mammalian teeth is limited , mouse incisors grow continuously throughout life and contain stem cells at their proximal ends in the so-called cervical loops . We have used a combination of mouse mutant analyses , organ culture experiments , and gene expression studies to identify the key signaling molecules that regulate epithelial stem cell proliferation in the cervical loop stem cell niche . We show that signals from the adjacent mesenchymal tissue regulate epithelial stem cells and form a complex regulatory network with epithelial signals . Stem cell proliferation is stimulated by fibroblast growth factor 3 ( FGF3 ) , and bone morphogenetic protein 4 ( BMP4 ) represses Fgf3 expression . In turn , Activin inhibits the repressive effect of BMP4 and Follistatin antagonizes the activity of Activin . We also show that spatial differences in the levels of Activin and Follistatin expression contribute to the characteristic asymmetry of rodent incisors , which are covered by enamel only on their labial ( front ) side . We suggest that subtle variations in this or related regulatory networks may explain the different regenerative capacities and asymmetric development of various organs and animal species .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"mus",
"(mouse)"
] |
2007
|
An Integrated Gene Regulatory Network Controls Stem Cell Proliferation in Teeth
|
The study of the effect of large-scale drivers ( e . g . , climate ) of human diseases typically relies on aggregate disease data collected by the government surveillance network . The usual approach to analyze these data , however , often ignores a ) changes in the total number of individuals examined , b ) the bias towards symptomatic individuals in routine government surveillance , and; c ) the influence that observations can have on disease dynamics . Here , we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them , which is illustrated using simulations and real malaria data . Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence , due to the strong influence of concurrent changes in sampling effort . We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence . We propose a model that avoids these problems , being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular , a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models . Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004–2008 ( prevalence of 4% with 95% CI of 3–5% ) , with outbreaks ( prevalence up to 18% ) occurring during the dry season of the year . After this period , infection prevalence decreased substantially ( 0 . 9% with 95% CI of 0 . 8–1 . 1% ) , which is due to a large reduction in infection incidence ( i . e . , incidence in 2008–2010 was approximately one fifth of the incidence in 2004–2008 ) . We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases .
Current best practices regarding the collection of disease data consist in the unbiased sampling of individuals ( e . g . , through aggressive active case detection; [1] , [2] ) using the most sensitive pathogen detection method available ( e . g . , polymerase chain reaction ( PCR ) for malaria ) . This type of individual-level data has provided important information regarding infection and disease ( symptoms+infection ) prevalence and risk factors; however , these data are costly and thus tend to be spatially and temporally restricted , curtailing their ability to detect important disease drivers that vary over long temporal and large spatial scales . Studies that focus on large geographical and/or long temporal-scale disease drivers typically rely on government-based surveillance data ( e . g . , malaria [3]–[6] , cholera [7] , [8] , measles [9] , [10] , american cutaneous leishmaniasis [11] , pertussis [12] , meningitis [13] , and dengue [14] ) . While government-based surveillance data provide a wealth of information on disease , these data are often collected opportunistically , which may severely bias inference drawn from these data [e . g . ] , [ 15 , 16] . For instance , individuals routinely sampled by the government health facilities are often symptomatic [17] , [18] . As a result , if part of the population is infected but asymptomatic , infection prevalence for the overall population cannot be estimated as if these data came from a random sample ( i . e . , the number detected to be infected divided by number of tested individuals ) nor as if all infected individuals had been detected ( i . e . , the number detected to be infected divided by total population size ) . Similarly , the number of individuals that seek help at a particular health facility may fluctuate considerably with time regardless of concurrent changes in infection prevalence or incidence ( e . g . , due to increases in catchment area , or a shortage of personnel or supplies ) , directly affecting the number of observed disease cases . Unfortunately , past analyses have typically considered only the number of disease cases per unit time ( e . g . , weekly or monthly ) , ignoring the total number of individuals examined per unit time ( but see [19] ) . The standard approach to analyze time-series data from the government surveillance system is to search for trends [e . g . ] , [ regression analysis]; [ 3] , [4] , [11] , [20]–[23] or scales of variability [e . g . ] , [ wavelet analysis]; [ 10] , [24–26] that match those of the explanatory variables . Recent work , however , has increasingly employed sophisticated statistical models , typically within the state-space modeling framework , to fit mechanistic disease dynamics models [e . g . ] , [ 7] , [9] , [27]–[32] . An important assumption within these state-space models is that observations provide information about the states but do not affect the underlying process . In the particular context of disease dynamics , the assumption is that the number of individuals diagnosed with a particular disease provides information on infection incidence or prevalence but does not influence disease dynamics ( the underlying temporal process ) . This is a valid assumption if tested individuals are not informed about test results nor treated for the disease ( e . g . , data consist on the number of deaths due to a particular disease ) . However , this assumption is violated if individuals that have a positive diagnosis are subsequently treated for the disease because treatment decreases the pool of infected individuals and thus affects disease dynamics . Here we refine the state-space framework to overcome the shortcomings we have described . Our approach scales-up the results from a detailed individual-level study to allow unbiased inference on infection prevalence from government-based syndromic surveillance data over larger geographical and longer temporal scales than would be possible using solely the individual-level data . Our approach also properly accounts for changes in sampling effort and the number of individuals diagnosed/treated for the disease and makes use of several short time-series ( rather than one long time-series ) to infer changes in infection prevalence and the drivers of these changes . While some of our assumptions are tailored to malaria , the general approach we put forth should be adaptable to other human diseases . We start our article by describing our data and the model we are proposing . We then use a ten-fold cross-validation exercise to show that the proposed model has a better out-of-sample predictive performance than a more phenomenological statistical model and a more mechanistic disease dynamics model . Next , we employ simulated data to show how inference on disease incidence can be severely distorted if one does not take into account concurrent changes in sampling effort and that observations affect disease dynamics . Finally , we illustrate our model by applying it to real malaria data from the western Brazilian Amazon .
Malaria health posts are the only source of antimalarial medication in the Brazilian Amazon and this medication can only be obtained with a positive malaria exam result . As a result , data from these health posts provide considerable information regarding changes in malaria prevalence and incidence , being the basis of the malaria surveillance system in Brazil [33] . The malaria data we use arise from the Brazilian surveillance network in three counties ( Acrelandia – AC , Placido de Castro – PC , and Senador Guiomard – SG ) in Acre state , western Brazilian Amazon . These data are aggregated by week t and county l . Over the entire 2004–2010 period , there were approximately 160 , 000 malaria tests , from which ∼20 , 000 were positive ( Figure 1 ) . In this dataset , individuals are sampled and tested for malaria ( through microscopy ) either because they believed they had malaria and sought help at the local government health facility ( passive case detection ) or because they were symptomatic when health agents visited their houses ( active case detection ) . In either case , individuals tend to be predominantly symptomatic . Let and be parameter sets containing the process and observation parameters , respectively . To draw samples from the posterior distribution of our latent states and parameter sets and , we need to determine up to a proportionality constant . Our approach adopts a slightly different factorization than the one used in the standard state-space models because the disease dynamics process depends on the observations from the previous time step . Here is our factorization: The posterior distribution of the states and parameters is obtained by Gibbs sampling . We use Metropolis-within-Gibbs sampling steps for all states and parameters due to the lack of a closed form expression for the full conditional distributions . Convergence of our Monte Carlo Markov Chain ( MCMC ) algorithm was evaluated using trace-plots . All analyses and figures were created using R version 2 . 13 . 2 [55] . We compare the out-of-sample predictive ability of the proposed model ( eqns . 8 and 11 ) with that of two alternative models . The first model is a phenomenological state-space model , where the latent states follow an AR-1 temporal process , while the second model is a mechanistic Susceptible-Infectious-Susceptible ( SIS ) model . The goal here is to compare the proposed model to models that would typically be proposed by a statistician ( AR-1 process on latent states ) or by a mathematical biologist ( SIS disease dynamics model ) . Details regarding the AR-1 and the SIS models are given in Text S1 . To determine the out-of-sample predictive performance of these three models , we conduct a 10-fold cross-validation exercise . First , we randomly partition our dataset into 10 sets . Then , we exclude one of these sets and use our algorithms to predict it based on information from the nine remaining sets . We compare the performance of these models by determining their mean squared error ( MSE , a standard model comparison measure that takes into account both bias and variance of estimators ) , where lower MSE values are preferred .
Our ten-fold cross-validation exercise ( i . e . , prediction of 10% of the real malaria dataset using the other 90% of the data to train the model ) revealed that the proposed model had a consistently better out-of-sample predictive performance when compared to the phenomenological AR-1 state-space model and the mechanistic SIS disease model ( Table 3 ) . In particular , the SIS disease model had a substantially worse MSE when compared to the other two models , revealing the negative impact of not allowing for process uncertainty . Based on these cross-validation results , we just report on the results from the proposed model from here onwards . Using the out-of-sample results , we indeed find that the proposed model fitted well the weekly number of malaria cases ( Figure 4 ) . The 95% credible intervals tended to include most of the out-of-sample observations , both in terms of the total number of positive malaria exams ( left panels in Figure 5 ) and the proportion of positive exams ( right panels in Figure 5 ) , indicating that uncertainty was adequately represented . Simulated data using eqns . 8 and 11 show that trends in the number of malaria cases do not necessarily correspond to equivalent trends in infection prevalence or incidence . For instance , increasing number of malaria cases does not necessarily imply increases in infection prevalence ( left panels in Figure 6 ) . Similarly , decreasing number of malaria cases might just reflect decreases in the number of individuals examined , rather than decreases in infection prevalence ( middle panels in Figure 6 ) . Finally , trends in the number of malaria cases do not imply similar trend neither in infection prevalence nor in infection incidence ( right panels in Figure 6 ) . These simulation results are intuitive if we recognize that the expected number of disease cases depends both on infection prevalence and on the total number of sampled individuals ( i . e . , in eqn . 8 ) . As a consequence , inference on infection prevalence or incidence based solely on the number of positive exams ( i . e . , ignoring the number of individuals examined ) might lead to spurious conclusions . The importance of allowing observations to directly affect disease dynamics is also illustrated using simulated data . We created a mock dataset where the number of malaria cases , the number of individuals examined , and infection incidence all exhibit the same temporal pattern ( Panels A , B and D in Figure 7 , respectively ) . As a result of the cancelling effect of greater number of individuals being treated precisely when infection incidence is higher , infection prevalence remains relatively constant ( Panels C in Figure 7 ) . We then estimated infection prevalence and incidence using our original model ( eqns . 8 and 10 ) and compared the resulting inference to that of a similar model that ignores that the observations ( i . e . , number of treated individuals ) decreases infection prevalence . To implement this assumption , we modify equation 10 as ( 10a ) Assuming that the observation parameters are known , both the original model and this alternative model inferred well the underlying infection prevalence ( top six panels in Figure 8 ) but led to substantially different inference on infection incidence ( bottom two panels in Figure 8 ) . In particular , the original model correctly inferred infection incidence ( bottom right panel in Figure 8 ) while the alternative model inferred an infection incidence of approximately zero ( bottom left panel in Figure 8 ) . The intuition for these results is simple . If the number of individuals being treated is changing but the inferred infection prevalence remains constant , this has to imply that the number of individuals being treated is precisely off-setting infection incidence . On the other hand , since the alternative model does not take into account the fact that treated individuals decrease prevalence , an estimated constant infection prevalence implies zero incidence . These results highlight the problem of ignoring that individuals treated for the disease directly influence disease dynamics . The depiction of the real data in Figure 1 already illustrates that sampling effort exerts considerable influence on the number of positive test results . For instance , the correlation between the number of exams and the number of disease cases was equal to 0 . 71 in our malaria dataset . Furthermore , there is considerable variation through time in the number of individuals that are examined . Thus , the common assumption that sampling effort is constant is likely to be unrealistic , particularly given the length of many of the disease time-series typically employed , such as those used to detect the effect of climate change on disease . As a result , analyses that rely solely on trends in the number of positive exams may generate misleading conclusions regarding disease dynamics . Our estimates of infection prevalence reveal a relatively high initial infection prevalence ( mean infection prevalence from 2004 to 2008 was 4% , with 95% credible interval ( CI ) of 3%–5% ) with large seasonal outbreaks , which was then followed by a substantial decline in prevalence ( mean infection prevalence for 2008–2010 was equal to 0 . 9% with 95% CI of 0 . 8–1 . 1% ) ( red line and polygon in Figure 9 ) . A large increase in infection incidence seems to occur immediately after the rainy season , leading to subsequent peaks in infection prevalence ( which can be as high as 18% ) during the dry season , although there is considerable variability both geographically ( from county to county ) and temporally ( year to year ) . A quantitative measure of association between prevalence and rainfall can be obtained using a permutation test , akin to the ones described in [23] . In this test , we compare precipitation when infection prevalence was at its highest versus at its lowest , for each year and location , yielding 21 ( 7 years×3 locations ) observations for each level of infection prevalence . Our permutation test strongly suggests that the observed difference in mean precipitation is highly unlikely under the null hypothesis of no association ( p-value<0 . 01 ) , consistent with the results from a large-scale analysis of malaria data spanning 7 states of the Brazilian Amazon , which found a negative correlation between precipitation and number of malaria cases [23] . The declining trend in infection prevalence may be attributed to a sharp decrease in incidence after week 210 ( from 2007 to 2008 , Figure 10 ) ; incidence in 2008 to 2010 was approximately 1/5 of the incidence in 2004 to 2007 . This abrupt decrease in incidence does not seem to be associated neither with land use/land cover changes ( e . g . , fire , deforestation rate , and forest cover ) nor with climate ( e . g . , Southern Oscillation index or Oceanic Niño Index ) ( data not shown ) . This decrease may be attributable to enhanced vector control activities but we lack data on these activities to test this hypothesis . Posterior distributions for the remaining model parameters are given in Text S1 .
We have described a novel model that circumvents some of the shortcomings of earlier modeling approaches . For example , our model is able to estimate infection prevalence despite the biases associated with government surveillance data by up-scaling information from a detailed individual level study . This capability of our model is particularly important for public health , where estimates of infection prevalence ( rather than disease prevalence ) are vital for disease control and elimination strategies . The ability to build on individual-level data ( unbiased but geographically limited and costly ) to extract information from the government surveillance data ( geographically extensive but often biased ) is likely to be important for the modeling of data from several other diseases . In particular , it reveals the potential benefits of coordinating careful individual level data collection with the modeling of large-scale patterns using government data . However , for this strategy to work well , it is critical that the collection of individual level data is done so that the results are representative for the region and time-frame of interest . Disease dynamics model are typically more complex than the model we have presented here , including age structure of the host population , vector dynamics , multiple parasites and strains , and an exposed state . Models containing these additional complexities , however , are rarely fitted to data , with parameters often simply assumed to be known [e . g . , 32] or extracted from the literature [e . g . ] , [ 12] , [44 , 46] . Attempts to fit these models directly to data often reveal that several parameters are unidentifiable [28] , [32] , [42]–[44] , [46] or rely on equilibrium assumptions to estimate these parameters [e . g . , 56] . Furthermore , these attempts typically assume either just observation error or just process stochasticity , but not both as our model [50] . Finally , these disease dynamic models have numerous simplifying assumptions of their own , which may lead to substantially different conclusions [47] , [48] . For these reasons , we have chosen to employ a model that is not as phenomenological as a regression model or wavelet analysis ( i . e . , we employ a realistic observation model to infer the underlying infection prevalence and allow for prevalence to decrease with the treatment of individuals ) nor mechanistic as disease dynamics models ( e . g . , we do not account for infection incidence being influenced by current infection prevalence ) . Cross-validation results suggest that our model may outperform more phenomenological methods ( e . g . , AR-1 state-space model ) and more mechanistic disease models that do not account for process uncertainty ( e . g . , the deterministic SIS disease dynamics model ) ( Table 3 ) . The statistical literature has traditionally assumed that observations do not alter the phenomenon or object that is being measured or assessed . Yet , some types of time-series data can clearly violate this assumption . In our case , a high number of individuals diagnosed to have malaria has the dual-role of suggesting a high infection prevalence at a particular time and a substantial decrease in infection prevalence in the next time step , since these individuals are subsequently treated for the disease . A similar example refers to the use of the number of carcasses encountered or harvested animals as a proxy for animal abundance [57] , [58] . The model we propose explicitly accounts for the fact that observations ( i . e . , the number of individuals diagnosed and then treated for the disease ) influence the underlying temporal process ( i . e . , infection prevalence dynamics ) , thus modifying the usual state-space approach . Using simulated data , we show that this characteristic is critical when inferring infection incidence ( bottom two panels in Figure 8 ) . When applied to the real malaria data , this model characteristic has allowed the identification of pronounced seasonal and long-term trends on infection incidence and prevalence , which might be associated with rainfall . The importance of letting observations affect disease dynamics depends on the nature of the observations . For instance , we believe this is an important problem that has been overlooked in previous malaria models [28] , [29] . On the other hand , this feedback of observations on the disease dynamics might not be necessary if the observations consist on the reported number of deaths attributed to a particular disease [e . g . ] , [ 7 , 27] . In this case , observations can be modeled simply as a fraction of the true number of individuals that died and left the infected pool . The proposed model also accounts for sampling effort ( i . e . , number of individuals sampled ) , an important characteristic that is surprisingly absent from the disease modeling approaches we know of , mechanistic or not . For example , there has been considerable contention regarding the role of climate change on the increasing number of malaria cases in the African highlands [3] , [22] , [59]–[61] . Could an increasing trend in sampling effort be a simple explanation for the observed trend in number of malaria cases ? Simulated and real data suggest that the effect of sampling effort might be substantial ( e . g . , Figure 1 and Figure 6 ) , which may be particularly important given the long-term nature of most of the time-series used for disease dynamics modeling [30] . Similar examples highlighting how changes in detection probability and health treatment seeking behavior can distort inference on disease dynamics are also given by [46] , [50] . Finally , the lack of more long time-series has been blamed for the considerable uncertainty regarding how climate and other environmental drivers affect disease [29] , [30] , [62] , [63] . Instead of relying on long but rare disease time-series , our model utilized multiple short time-series to infer on the effect of climate on disease dynamics . In summary , we have focused on three aspects that have typically been ignored by earlier modeling approaches , namely: a ) changes in sampling effort ( i . e . , total number of individuals examined ) , b ) the fact that government surveillance data are often biased towards symptomatic individuals , and; c ) the fact that observations ( i . e . , individuals diagnosed and subsequently treated for the disease ) often directly influence disease dynamics by decreasing infection prevalence . We note that the relevance of these aspects fundamentally depends on the particular disease and data that are being analyzed; yet , we highlight them because they ( to the best of our knowledge ) are overlooked in the literature , either individually or jointly . Furthermore , we emphasize that these shortcomings are not restricted to state-space models; they may occur in other modeling approaches as well . We believe that some of these problems are a legacy from the biomathematical origins of these disease dynamics models . Researchers employing these models have traditionally focused on studying the long-term behavior of this complex non-linear system , thus relying on parameters from the literature or on rough parameter estimates [64] . However , as the focus shifts to parameter estimation and quantitative disease prediction , greater attention will be needed regarding how disease data arise and how to properly estimate parameters from it . Our modeling approach has five important limitations . First , the proposed model conditions on the total number of exams at each time and county . By doing so , we avoid having to worry about factors that influence the total number of individuals examined , such as the opening of new health facilities , temporary lack of personnel , or shortage of supplies . However , this feature of our model precludes future predictions of future infection prevalence . This limitation can potentially be avoided by creating an additional model to predict the total number of exams . Second , we rely on individual level data to correct for the biased nature of the government surveillance data but individual level data might not be available or might not be representative of the geographical or temporal scale of the aggregate data . In this case , data from the literature might be used in place of the individual level data to create informative priors on the observation model parameters . Third , our observation model assumes that a ) symptom status is binary whereas , in reality , there is often a whole spectrum of symptoms [53] , which may in turn influence the probability of sampling the individual and detecting the pathogen; and b ) that the probability of symptoms given infection does not change with time . These assumptions may or may not be reasonable for other diseases and we believe that changing our observation model to accommodate for alternative assumptions , without compromising the ability to fit the model , is an important topic for future research . Fourth , our process model does not take into account the nonlinearities in disease transmission that are the hallmark of disease dynamics models . As noted before , it remains an important challenge to estimate parameter for these biologically inspired disease dynamics models , particularly if one is willing to take into account process uncertainty and a more realistic observation model . Finally , our results suggest large and relatively abrupt changes in infection incidence ( Figure 10 ) , which may not be realistic . Future research could focus on developing methods to infer smooth changes in infection incidence . In this article , we have conceptualized and implemented a model that takes into account how data arise and affect prevalence dynamics . While the exact model formulation ( e . g . , eqns . 8 and 11 ) was tailored to the available data and current understanding regarding malaria , the main contribution of this article is to shed light on the importance of a few shortcomings of current disease modeling approaches and to suggest some general strategies to overcome them . We believe that these features have the potential to considerably improve inference on the drivers of disease dynamics when using government surveillance data .
|
Disease data collected by the government surveillance system are frequently used to understand the influence of large-scale phenomena ( e . g . , climate ) on human health because these data often have a large temporal and/or geographical span . The down side is that a ) these data are often biased towards individuals that come to the health facilities ( i . e . , symptomatic individuals ) ; and b ) the number of individuals examined can vary substantially regardless of concurrent changes in prevalence or incidence ( e . g . , due to shortage of personnel or supplies in health facilities ) , directly impacting the number of disease cases detected . Current modeling approaches typically ignore these peculiarities of the government data . Furthermore , current approaches do not take into account that observations directly influence disease dynamics since individuals with a positive diagnosis are often subsequently treated for the disease . In this article , we develop a novel model to circumvent these shortcomings and apply it to simulated data , highlighting how inference on infection incidence and prevalence might be misleading when some of the issues mentioned above are ignored . Finally , we illustrate this model using malaria data from the Brazilian Amazon , revealing the strong role of precipitation on infection prevalence seasonality and striking patterns in infection incidence .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Improving the Modeling of Disease Data from the Government Surveillance System: A Case Study on Malaria in the Brazilian Amazon
|
As fundamental processes in mitochondrial dynamics , mitochondrial fusion , fission and transport are regulated by several core components , including Miro . As an atypical Rho-like small GTPase with high molecular mass , the exchange of GDP/GTP in Miro may require assistance from a guanine nucleotide exchange factor ( GEF ) . However , the GEF for Miro has not been identified . While studying mitochondrial morphology in Drosophila , we incidentally observed that the loss of vimar , a gene encoding an atypical GEF , enhanced mitochondrial fission under normal physiological conditions . Because Vimar could co-immunoprecipitate with Miro in vitro , we speculated that Vimar might be the GEF of Miro . In support of this hypothesis , a loss-of-function ( LOF ) vimar mutant rescued mitochondrial enlargement induced by a gain-of-function ( GOF ) Miro transgene; whereas a GOF vimar transgene enhanced Miro function . In addition , vimar lost its effect under the expression of a constitutively GTP-bound or GDP-bound Miro mutant background . These results indicate a genetic dependence of vimar on Miro . Moreover , we found that mitochondrial fission played a functional role in high-calcium induced necrosis , and a LOF vimar mutant rescued the mitochondrial fission defect and cell death . This result can also be explained by vimar's function through Miro , because Miro’s effect on mitochondrial morphology is altered upon binding with calcium . In addition , a PINK1 mutant , which induced mitochondrial enlargement and had been considered as a Drosophila model of Parkinson’s disease ( PD ) , caused fly muscle defects , and the loss of vimar could rescue these defects . Furthermore , we found that the mammalian homolog of Vimar , RAP1GDS1 , played a similar role in regulating mitochondrial morphology , suggesting a functional conservation of this GEF member . The Miro/Vimar complex may be a promising drug target for diseases in which mitochondrial fission and fusion are dysfunctional .
Mitochondrial fission , fusion and transport play important roles for the function of this organelle [1 , 2] . The balance between fusion and fission controls mitochondrial morphology , which is mediated by series of large dynamin-related GTPases [3] . Among these GTPases , mitofusin1/mitofusin2 ( MFN1/MFN2 ) and optic atrophy protein1 ( OPA1 ) are the core components that are responsible for mitochondrial fusion [4–7] , whereas dynamin-related protein 1 ( Drp1 ) is the core component that is responsible for mitochondrial fission [8 , 9] . In addition to these GTPases in dynamin-related family , mitochondrial Rho ( Miro ) , an atypical member of the Rho small GTPase family , has a well-known function of transporting the mitochondria along microtubules [10 , 11] . Miro also regulates mitochondrial morphology via inhibition of fission under physiological Ca2+ conditions , although the mechanism is not that clear [12–16] . Large GTPases such as dynamin-like GTPase family members hydrolyze GTP and exchange GTP and GDP without the assistance from other regulators [17 , 18] . However , members of the small GTPase family often require other proteins to help release their tightly bound GDP or enhance their low GTPase activities . These proteins are referred to as guanine nucleotide exchange factors ( GEFs ) and GTPase activating proteins ( GAPs ) , respectively [19] . To date , most small GTPases require unique GEFs or GAPs [19] . An understanding of the regulation of mitochondrial dynamics may help us to address many human diseases . For instance , mutations in OPA1 or MFN2 result in dominant optic atrophy or Charcot-Marie-Tooth neuropathy type 2A [20 , 21] . Abnormal mitochondrial fission also promotes aging and cell death [22 , 23] . In necroptosis , the formation of the necrosome promotes mitochondrial fission through dephosphorylation of Drp1 [24] . In neuronal excitotoxicity , calcium ions are overloaded , resulting in reduced levels of the MFN2 protein , which enhances mitochondrial fission and leads to neuronal necrosis [25 , 26] . In addition , other components such as Miro may participate in this process [26] . Miro has two EF hand motifs that bind calcium; thus , Miro can couple calcium increase with reduced mitochondrial motility to meet the locally increased energy demands [16 , 27] . Interestingly , Miro also promotes fission in the presence of excess calcium , which is distinct from its inhibitory role in fission under normal calcium concentrations [16] . It is unclear whether Miro plays a functional role in neuronal necrosis [26] . The mitochondrial morphology represents a transient balance between mitochondrial fusion and fission [28] . Using a systematic genetic screen in yeast covering approximately 88% of genes , 117 genes that regulate mitochondrial morphology were identified [29] . Similarly , a screen of 719 genes that are predicted to encode mitochondrial proteins in worms demonstrated that more than 80% of these genes regulate mitochondrial morphology [30] . Although many genes may regulate mitochondrial morphology , their relationships to the core mitochondrial fusion and fission components are unclear . In studying mitochondrial morphology , we accidently discovered that the loss of vimar ( visceral mesodermal armadillo-repeats ) , which encodes an atypical GEF [31–33] , promoted mitochondrial fission in Drosophila flight muscle cells . Furthermore , we found that vimar was capable of interacting with Miro in vitro . Genetically , vimar required normal GDP- or GTP-bound activity of Miro to affect mitochondrial morphology , suggesting vimar is likely the Miro GEF . In addition , we found that the Miro/vimar complex suppressed mitochondrial fission during necrosis and mitochondrial fusion in PINK1 mutant model of Parkinson’s disease ( PD ) , making vimar a potential drug target .
To identify novel regulators of mitochondrial morphology , we studied the flight muscle in Drosophila adults , because they have a stereotypic distribution of mitochondria in the longitudinal myofibers [34] . To visualize the mitochondria in the muscle cells , a muscle-specific promoter , Mhc-Gal4 , was used to drive a mitochondria-targeted GFP ( UAS-mitoGFP ) ; the progenies were referred to as Mhc>mitoGFP . The mitochondrial morphology was clearly observed ( Fig 1Aa and 1Af ) . Using these flies , we accidently observed that mitochondrial fission was enhanced when a vimar ( visceral mesodermal armadillo-repeats ) RNAi was expressed ( Fig 1Ab and 1Af ) . To further confirm the loss-of-function ( LOF ) effect of vimar , we tested vimark16722 , a P-element mutant with the mobile element inserted into the 5’-UTR region of the vimar gene . Again , we observed the trend of enhanced mitochondrial fission in the heterozygous vimark16722 mutant ( Fig 1Ac and 1Af ) . Because the homozygous vimark16722 mutant was embryonic lethal , we selected a deficient mutant ( Df ( 2R ) ED1612 ) covering the vimar locus and generated a trans-heterozygous vimar ( vimark16722/Df ) mutant to further test the effect of vimar . In these flies , the mitochondria exhibited a stronger fission morphology compared to the heterozygous mutant ( Fig 1Ad and 1Af ) . These results indicate that vimar plays a dominant role in regulating mitochondrial morphology in a dosage-dependent manner . To confirm the mitochondrial defect was generated from loss of vimar , we tried to rescue vimark16722/Df by tubulin-Gal4/UAS-vimar ( tubulin-Gal4 is a ubiquitously expressed promoter ) . The result showed that the shortened mitochondria in the vimark16722/Df mutant were rescued by vimar overexpression ( S1A Fig ) ; while overexpression of vimar alone did not affect the mitochondrial morphology ( Fig 1Ae and 1Af ) , suggesting that the levels of the vimar protein may be saturated under normal physiological condition . Using a polyclonal antibody of vimar , we confirmed that the protein levels of vimar were reduced in vimark16722 and vimar RNAi , and increased in the vimar overexpression line ( S1B and S1C Fig ) . To examine mitochondrial distribution , we studied Drosophila larval oenocytes because of their stereotypical location and morphology . The wild type mitochondria , labeled with UAS-mitoGFP driven by an oenocytes-specific promoter , PromE ( 800 ) -Gal4 , were evenly distributed in the cytosol ( Fig 1Ba ) . As positive controls , we knocked down Khc ( kinesin heavy chain ) , Milton ( an adaptor protein to link Khc to mitochondria ) and Miro , which are the core components of mitochondrial transport machinery [35] . The results showed that mitochondrial spreading was greatly reduced in the cytosol , and resulted in accumulation in the perinuclear region ( Fig 1Bb–1Bd ) . Interestingly , knocking down vimar by RNAi showed a similar distribution pattern ( Fig 1Be ) . For mitochondrial morphology , loss of Khc , Milton , Miro and vimar resulted in mitochondrial shortening ( Fig 1Ba'–1Be' and 1Bf ) . Similarly , mitochondria in the eye disc of GMR>mitoGFP/vimar RNAi was also shortened ( Fig 1C ) . These results suggest that vimar regulates mitochondrial morphology in different cell types , such as muscle , oenocyte and eye disc . Transport of mitochondria along the axon can be quantified in Drosophila neurons in vivo [36] . As a positive control , the CCAP-Gal4>Miro RNAi line ( CCAP-Gal4 is a promoter labeling a single axon within a neuron bundle ) displayed reduced flux of mobile mitochondria in both anterograde ( soma to synapse ) and retrograde ( synapse to soma ) transport ( Fig 1D ) . The CCAP-Gal4>vimar RNAi line showed a similar result ( Fig 1D ) . RNAi of both vimar and Miro resulted in a similar reduction of mitochondrial transport as Miro RNAi alone ( Fig 1D ) , suggesting vimar and Miro may function in the same pathway . To test vimar subcellular localization , proteins from the thoraces of adult flies ( Mhc>MitoGFP ) were extracted and separated into cytosolic and mitochondrial fractions . The Western blot data showed that endogenous vimar was present in both cytosol and mitochondria ( Fig 1E ) . Apart from mitochondria , to test whether Vimar can distribute in other subcellular compartments , we fractionized organelles of ER , lysosome and Golgi apparatus , and found that Vimar was also enriched in the ER fraction , as well as in the cytosol ( S2A Fig ) . This result is consistent with reports suggesting that Miro protein is localized and function in the site of mitochondria-ER junction [37 , 38] . We asked whether vimar regulates mitochondrial morphology through controlling the GTP/GDP exchange of Miro , because Miro is a well-known small GTPase that regulates mitochondrial transport and morphology [10 , 14] . First , we evaluated their physical interactions . The Flag-tagged Vimar ( Vimar-Flag ) and HA-tagged Miro ( Miro-HA ) were ectopically expressed in the HEK293T cells . By co-immunoprecipitation ( co-IP ) assays with anti-HA and anti-Flag antibodies , Miro and Vimar could pull down with each other ( Fig 2A ) . This result suggests that Miro and Vimar can bind with each other , at least under the overexpression conditions . Next , we tested their genetic interactions . The wing posture defects underline dysfunctional flight muscles that control wing position and movement [39] . It has been reported that overexpression of Miro induces mitochondrial enlargement [13 , 15 , 16] . Consistently , we observed this mitochondrial change in the Miro overexpression condition . Meanwhile , the wing posture defects of Mho>Miro flies increased progressively after eclosion and reached the maximum to approximately 30% at the seventh day after eclosion ( Fig 2B and 2C ) . Interestingly , vimark16722 almost completely abolished the wing defect induced by the Miro overexpression; while vimar overexpression greatly enhanced the wing posture defect . As controls , vimar overexpression alone or vimar mutant ( vimark16722 ) had no wing posture defect ( Fig 2C ) . For the mitochondrial morphology in the Mhc>mitoGFP flies , Miro overexpression resulted in aberrant mitochondrial size enlargements ( Fig 2Da and 2Db ) , and these defects could be rescued by the heterozygous vimark16722 mutant ( Fig 2Dc ) . Moreover , vimar overexpression further enhanced mitochondrial size increase under the Miro overexpression background ( Fig 2Dd ) . These results suggest that vimar may genetically interact with Miro . We cannot test effect of GOF vimar under the Miro RNAi background , because Miro RNAi did not induce the wing posture defects in the flight muscles . To further test Miro/vimar interaction , we generated transgenes of constitutively GDP-bound or GTP-bound mutant of Miro . The rational is that GOF or LOF vimar should not affect these mutant phenotypes if vimar functions as a Miro GEF . Based on a previous report [40] , the amino acid substitutions of A20V ( Miro20V ) and T25N ( Miro25N ) should render Miro constitutively GTP-bound and GDP-bound , respectively . As expected , a Miro25N overexpression in the flight muscle ( Mhc>Miro25N ) did not affect the wing posture ( Fig 2E ) or mitochondria morphology ( Fig 2Fa and 2Fb ) . In contrast , a strong wing posture defect ( Fig 2E ) and enlarged mitochondria size ( Fig 2Fc ) were observed in the Miro20V overexpression line ( Mhc>Miro20V ) . Importantly , GOF or LOF vimar failed to affect the defects in the Miro20V overexpression line ( Fig 2E , 2Fd and 2Fe ) . We also examined vimar effect on mitochondrial transport in the GOF MiroWT , Miro20V and Miro25N background . However , we found that almost no mitochondria were distributed in the axons in the GOF MiroWT or Miro20V background . This data is consistent with previous reports indicating GOF Miro strongly increased mitochondrial length and reduced transportation [41 , 42] . We could not examine their mitochondrial transports . In contrast , mitochondrial transport was unaltered under GOF vimar background or combined with Miro25N expression ( S2B Fig ) . Together , these results suggest that vimar requires the normal GTP/GDP binding activity of Miro for its function . To test whether the vimar/Miro interaction depends on the GTPase activity of Miro , we co-transfected vimar-Flag with inactive ( Miro25N ) and active ( Miro20V ) form of Drosophila HA-Miro in the HEK293T cells . The co-IP results showed that the vimar/Miro interaction was unaffected by these Miro mutants ( S2C Fig ) . This result suggests that Miro/vimar interaction is not regulated by the GTPase activity of Miro . For mitochondrial distribution of vimar under the LOF Miro background ( Mhc>mitoGFP/Miro RNAi ) , we observed that the mitochondrial fraction of vimar was unaltered ( S3A Fig ) . This result indicates that vimar may attach with mitochondria by itself or with other partners . It has been reported that mitochondrial shortening caused by Miro loss required the function of Drp1 [16] . Therefore , we could expect that loss of Drp1 might rescue the mitochondrial shortening in the muscle of Miro RNAi background . Indeed , it is the case ( S3B Fig ) . Regarding the interaction between Drp1 and Vimar , our data showed that loss of Drp1 also rescued the mitochondrial shortening of the Vimar mutant ( S3B Fig ) . This result indicates that Miro/Vimar complex is likely to regulate mitochondrial fission through Drp1 . To study whether Miro/vimar affected the Drp1 recruitment to mitochondria under Miro RNAi or vimar RNAi backgrounds , we used a transgene with a 9 . 35 kb genomic DNA insertion , which contains an endogenous Drp1 gene labeled by a HA tag ( Flag-FlAsH-HA-Drp1 ) [43 , 44] . The result showed that the mitochondria fraction of Drp1 monomer was unaltered in these RNAi conditions ( S3C and S3D Fig ) . This result indicates that loss of Miro/vimar may not affect the recruitment of Drp1 to mitochondria , and how Miro/vimar affects Drp1 function is unclear . Miro plays distinct roles in regulating mitochondrial morphology under normal and high calcium conditions [16] . In normal conditions , Miro increases mitochondrial size through inhibition of Drp1 function [13 , 15 , 16]; however , it promotes mitochondrial fission in high calcium conditions by increasing Drp1 activity , such as in depolarized neurons [16 , 35] . If vimar functions through Miro , we expect that vimar may promote mitochondrial fission in high calcium conditions . To test Miro/vimar response at high calcium state , We had previously established a fly model to study the high calcium-induced cellular response , and accomplished calcium overload by expressing a leaky cation channel , the glutamate receptor 1 Lurcher mutant ( GluR1Lc ) [45 , 46] . This fly model ( simplified as the AG model ) contained Appl-Gal4 ( a neuron-specific promoter ) , UAS-GluR1Lc and tub-Gal80ts ( an inhibitor of Gal4 at 18°C , which lost its function at 30°C ) . Thus , the AG flies were normal at 18°C , and calcium overload was induced upon a shift to 30°C [45] . Following the time progression after the GluR1Lc induction , calcium accumulates and neuronal necrosis increases gradually in the AG flies [45] . It is well known that mitochondrial fragmentation occurs upon calcium overloaded [47] . To recapitulate this phenomenon and observe mitochondrial morphology by live cell imaging , we added UAS-mitoGFP to the AG flies ( simplified as the AGM model ) . After the AGM larval flies were raised at 30°C to induce calcium influx for 20 hours , mitochondrial fragmentation in the chordotonal neurons showed subtle fission compared to control; while at the 26 hour , the mitochondria in the AGM dendrites underwent dramatic fragmentation ( Fig 3A–3D ) . For the rescue effect of a given genetic manipulation , we showed the 26 hour time point ( to rescue the more severe defects ) ; and for the enhancer effect of a given genetic manipulation , we showed the 20 hour time point ( to enhance a less defective phenotype ) . As a positive control , a LOF mutant of Drp1 , drp11 , which possessed an A186V amino acid substitution at the Dynamin-GTPase domain [44] , strongly suppressed the mitochondrial fission defect ( Fig 3Ac and 3Ad ) . These results suggest that the AGM model can be adopted to study the mitochondrial morphology in high calcium conditions . Because Miro promotes mitochondrial fission in the high calcium conditions [16] , we expected that vimar might enhance Miro function under high calcium concentrations; and the LOF vimar might rescue the mitochondrial fission defect in the AGM flies . Indeed , GOF vimar enhanced mitochondrial fission ( Fig 3Bb and 3Bc ) ; and vimark16722 rescued mitochondrial fission in the AGM flies ( Fig 3Cb and 3Cc ) . In the high calcium state , the mitochondrial localization of vimar was unaltered ( S4 Fig ) , indicating recruitment of vimar on mitochondria is likely independent on calcium level . To further test the role of Miro/vimar complex , we examined effect of the GOF Miro transgene in the vimark16722 background . The result demonstrated that the GOF Miro enhanced mitochondrial fission ( Fig 3Db and 3Dc ) , whereas vimark16722 could not rescue the defect ( Fig 3Dd ) , indicating that basal function of Miro may be partially independent from vimar . Together , these results suggest that vimar functions through Miro to regulate mitochondrial morphology in high calcium conditions . Mitochondrial fission may enhance calcium overload-induced necrotic cell death in neuron cultures [47] . However , there is still insufficient genetic evidence to demonstrate that mitochondrial fission plays a causal role in neuronal necrosis [48] . To study this question , we previously showed that we could quantify necrosis in the AGG flies ( the AG flies containing UAS-GFP ) at single cell resolution [45] . The result showed that Drp11 could rescue necrosis in the chordotonal neurons ( Fig 4A ) . In addition , the function of these neurons could be assessed at the behavioral level by quantifying adult fly death [45]; Drp11 rescued the lethality of the AG flies ( Fig 4B ) . Strikingly , vimark16722 exhibited a rescue effect in the AGG flies at both the cellular and behavioral levels ( Fig 4C and 4D ) . In contrast , the GOF vimar transgene had the opposite effect ( Fig 4E and 4F ) . This result is consistent with the suppression of mitochondrial fission in this mutant . Furthermore , the GOF Miro transgene enhanced necrosis; however , vimark16722 did not rescue the GOF Miro phenotype ( Fig 4G and 4H ) , similar to its effect on mitochondria . These results indicate that Miro has the dominant role in the Miro/vimar complex and that the Miro/vimar complex plays a functional role in neuronal necrosis . In the PINK1 mutant of the Parkinson's disease ( PD ) Drosophila model , mitochondrial fusion is enhanced , and the LOF Miro mutant could suppress this mitochondrial defect [13] . Therefore , we speculate that the LOF vimar mutant might rescue the defective mitochondrial fusion in the PINK1 mutant . To test this hypothesis , we studied a PINK1 mutant , PINK15 [49] . In the PINK15 flies , the mitochondria are abnormally elongated and fused ( Fig 4I ) , and the fly wing posture is defective ( Fig 4J ) . Strikingly , vimark16722 could rescue both the mitochondrial morphology and wing posture defects ( Fig 4I and 4J ) . Together , these results indicate that LOF of the Miro/vimar complex suppressed both mitochondrial fragmentation during necrosis and PINK1 mutant of Drosophila PD model . Furthermore , we found that vimark16722 and UAS-vimar had no effect on classical apoptosis induced by Hid expression [50] ( S5 Fig ) , suggesting that vimar may specifically affect PD and necrosis , but does not regulate apoptosis . A protein sequence comparison showed that Drosophila vimar shares great similarity with the mammalian protein RAP1GDS1 ( S6 Fig ) ; however , it is not clear whether vimar is a functional homolog of RAP1GDS1 [51] . Here , we further investigated the role of RAP1GDS1 in mitochondrial morphology . First , we used a lentivirus to transfect a RAP1GDS1 shRNA into HEK293T cells and established a stable cell line . As expected , the protein level of the RAP1GDS1 was significantly reduced in the shRNA line ( S7A Fig ) . Then , this shRNA line was transiently transfected with a mitochondrial reporter , mitoDsRed . We found that the mitochondrial length showed a trend of reduction in the RAP1GDS1 shRNA cells ( Fig 5A ) . Next , we studied the effect of RAP1GDS1 on necrosis . Necrotic cell death was induced by a calcium ionophore ( A23187 ) , which causes calcium overloading and necrosis [52] . As expected , the calcium ionophore induced mitochondrial fragmentation , and the RAP1GDS1 shRNA rescued the mitochondrial defect ( Fig 5B ) . To quantify the cell death , we measured cellular ATP level and performed propidium iodide ( PI ) staining [53] . The result showed that RAP1GDS1 shRNA rescued necrosis in both assays ( Fig 5C and 5D ) . Moreover , we tested the RAP1GDS1 shRNA in another human cell line , the SH-SY5Y neuroblastoma cells . Similar to the HEK293T cells , the RAP1GDS1 shRNA protected the SH-SY5Y cells from calcium overload ( S7B and S7C Fig ) . In addition , we examined the effect of a Miro-1 siRNA on calcium ionophore induced necrosis . The result showed that it also rescued the cell death ( Fig 5E and 5F , and the Miro-1 siRNA effect is shown in S7D Fig ) . Furthermore , the HA-tagged Miro1 and the Flag-tagged RAP1GDS1 could co-immunoprecipitate in vitro ( Fig 5G ) . Together , these results indicate that the function of the Miro1/RAP1GDS1 complex in regulating mitochondrial morphology and necrosis is conserved with the Drosophila Miro/vimar complex .
Mitochondrial function can be assessed by the enzymatic activity of citrate synthase ( CS ) , the first enzyme in the Krebs cycle that converts acetyl-CoA and oxaloacetate to citrate [54] . In cultured Drosophila S2 cells , vimar knock down by RNAi resulted in reduced CS activity [54] , indicating that vimar may positively regulate mitochondrial function . Because mitochondrial fission has generally been associated with reduced mitochondrial respiration [55] , the decreased CS activity may be a result of mitochondrial fission . Consistent with this notion , our results demonstrated that the LOF of vimar promoted mitochondrial fission . In addition , a GOF vimar transgene had a minimal effect on mitochondrial morphology , indicating that vimar activity might be saturated under normal physiological conditions . Because Vimar has been predicted to be a GEF , we hypothesized that vimar may regulate mitochondrial morphology by affecting a small GTPase , which requires a GEF to help with the GTP/GDP exchange process [19] . Interestingly , Miro is one such small GTPase that is known to play important roles in mitochondrial fission and transport [10 , 14 , 16] . We propose that vimar and Miro may function as a complex . First , a fraction of the vimar protein was localized to the mitochondria , possibly indicating a functional role on mitochondria . Interestingly , the mitochondrial localization of vimar seems not dependent on Miro , because LOF Miro did not affect the mitochondrial fraction of vimar . This indicates that vimar may directly bind with mitochondria or through other scaffolding proteins . Second , vimar and Miro could physically interact with each other , at least in vitro . Their interaction seems not affected by the GTPase activity of Miro , because the constitutively GDP- or GTP-bound Miro mutants did not affect their interactions . Third , vimar genetically interacted with Miro . This included the result demonstrating that the LOF vimar mutant reduced the effect of Miro on mitochondrial fission inhibition and the GOF vimar transgene had the opposite effect . Moreover , in the constitutive GFP-bound or GDP-bound Miro mutants , the effect of the GOF or LOF vimar was abolished . Therefore , vimar requires the normal GDP/GTP binding activity of Miro to function . It is also known that Miro1 overexpression increase mitochondrial size partially by suppression of the Drp1 function [15 , 16] . Consistently , increased mitochondrial fission in the LOF of Miro or vimar was abolished by loss of Drp1 , suggesting the Miro/vimar complex depends on Drp1 to regulate mitochondrial morphology . Familial PD caused by mutations in PINK1 or Parkin results in a series of mitochondrial dysfunctions , particularly the failure to eliminate damaged mitochondria through mitophagy [56 , 57] . In these PINK1 or Parkin mutants , the key proteins involved in mitochondrial fusion and fission , such as Marf/Mitofusin and Miro , accumulate [13 , 58] . In the PINK1 mutant flies , the flight muscle is damaged , resulting in wing posture defects [59] . Similarly , we observed that Miro overexpression in the flight muscle resulted in a strong wing posture defect . This result may explain the wing posture defect in the PINK1 mutant , in which the levels of the Miro protein are increased [13] . Our result demonstrated that the LOF of vimar could rescue the wing defect in the PINK1 mutant , consistent with the hypothesis that vimar functions through Miro . When the intracellular calcium level is high , Miro switches from promoting mitochondrial fission inhibition to enhancing mitochondrial fission [16] . The mechanism for this switch is unclear , although alterations of Drp1 function could be one possibility [16] . Interestingly , Gem1 , the yeast homolog of Miro GTPase , has been reported to function as a negative regulator for ER-mitochondria contacts , where Drp1 aggregates and cleaves mitochondria into smaller units [37] . This may serve as the mechanism for Miro to regulate mitochondrial morphology via Drp1 . In addition to affect mitochondrial fission , Miro also regulates mitochondrial transport in a calcium dependent manner . For mitochondrial transport , Miro forms protein complexes with Milton , a kinesin adaptor , and with motor proteins , such as kinesin and dynein [35] . In high calcium conditions , Miro alters its binding patterns and results in reduced transport activity [27 , 60 , 61] . Based on these reports , we proposed that the Miro/vimar complex acted together to affect mitochondrial morphology: at normal condition , Miro/vimar inhibits fission via Drp1; at high calcium state , Ca2+ bound Miro switches its function to promote fission . Indeed , vimar responds to the calcium change in the same way as Miro ( Fig 6 ) . In addition , our data demonstrated that knocking down RAP1GDS1 and Miro1 increased mitochondrial fission and could rescue calcium overload induced necrosis , similar to the loss of vimar or Miro in Drosophila . These data support the hypothesis that RAP1GDS1 is the mammalian homolog of vimar , supporting a previous prediction [51] . Mitochondrial fission plays important role in apoptosis by promoting mitochondrial outer-membrane permeabilization ( MOMP ) to release cytochrome c from the mitochondria [62] . The use of the Drp1 inhibitor mdivi to block fission has been shown to be an effective treatment for stroke [47] , and the function of mitochondrial fission on necrotic cell death has been well documented [24 , 26 , 48] . The uncertainty lies in the lack of genetic evidence and downstream mechanism of mitochondrial fission in necrosis [48] . Our data demonstrated that mitochondrial fragmentation occurred in necrotic neurons , and the LOF Drp1 and vimar mutants both suppressed neuronal necrosis . Much evidence suggests that the mitochondrial fusion and fission defects are directly linked to many human diseases [22] , and strategies that target the Miro/vimar complex may affect a broad spectrum of diseases . For instance , mutations in the fragile X mental retardation 1 ( FMR1 ) gene , which result from expansion of trinucleotide repeat in the 5′ untranslated region , often cause enhanced mitochondrial fission and mental retardation syndrome [63] . Likewise , aberrant mitochondrial fusion was observed in a Drosophila Alzheimer's disease model induced by the ectopic expression of a human tau mutant ( tauR406W ) [43] . In this case , the tau mutant may promote excessive actin stabilization to decrease Drp1 recruitment to the mitochondria , which results in excessive mitochondrial fusion and neurodegeneration [43 , 64] . Due to the dual function of the Miro/vimar complex in high-Ca2+ induced necrosis and PINK1 mutant induced PD , a drug to target this complex may benefit both disease states . As a modulator , it may be safer to target vimar/ RAP1GDS1 .
|
Mitochondrial dynamics including fusion , fission and transport are essential for energy supply in eukaryotic cells; and defects in mitochondrial dynamics often result in premature aging and diseases such as Parkinson's disease ( PD ) . In mitochondrial transport machinery , the Miro/Milton complex loads mitochondria onto microtubule through kinesin motor proteins; and regulates mitochondrial fusion and fission through unknown mechanisms . As a small GTPase , the exchange of GTP/GDP in Miro requires a specific guanine nucleotide exchange factor ( GEF ) . However , the GEF for Miro has not been identified . In this study , we identified Vimar as a new regulator of mitochondrial dynamics in Drosophila . We found that loss of vimar promoted mitochondrial shortening; and this function was mediated through Miro . As a GEF , Vimar partially localized on mitochondria and could physically interact with Miro . In the pathophysiological conditions , including a Pink1 mutant to model PD and a calcium-overload induced stress to model neuronal necrosis in Drosophila , loss of vimar suppressed both aberrant mitochondrial fusion and fragmentation in PD and necrosis , respectively . As the mammalian homolog of Vimar , RAP1GDS1 function was similar to Vimar . Therefore , Vimar/ RAP1GDS1 may be a great drug target to deal with diseases caused by defective mitochondrial dynamics .
|
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2016
|
Vimar Is a Novel Regulator of Mitochondrial Fission through Miro
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Many viruses subvert the host cell's ability to mount and complete various DNA damage responses ( DDRs ) after infection . HCMV infection of permissive fibroblasts activates host DDRs at the time of viral deposition and during replication , but the DDRs remain uncompleted without arrest or apoptosis . We believe this was in part due to partitioning of the damage response and double strand break repair components . After extraction of soluble proteins , the localization of these components fell into three groups: specifically associated with the viral replication centers ( RCs ) , diffused throughout the nucleoplasm and excluded from the RCs . Others have shown that cells are incapable of processing exogenously introduced damage after infection . We hypothesized that the inability of the cells to process damage might be due to the differential association of repair components within the RCs and , in turn , potentially preferential repair of the viral genome and compromised repair of the host genome . To test this hypothesis we used multiple strategies to examine repair of UV-induced DNA damage in mock and virus-infected fibroblasts . Comet assays indicated that repair was initiated , but was not completed in infected cells . Quantitative analysis of immunofluorescent localization of cyclobutane pyrimidine dimers ( CPDs ) revealed that after 24 h of repair , CPDs were significantly reduced in viral DNA , but not significantly changed in the infected host DNA . To further quantitate CPD repair , we developed a novel dual-color Southern protocol allowing visualization of host and viral DNA simultaneously . Combining this Southern methodology with a CPD-specific T4 endonuclease V alkaline agarose assay to quantitate repair of adducts , we found efficient repair of CPDs from the viral DNA but not host cellular DNA . Our data confirm that NER functions in HCMV-infected cells and almost exclusively repairs the viral genome to the detriment of the host's genome .
Human Cytomegalovirus ( HCMV ) is among the leading causes of birth defects in the United States , affecting an estimated 8000 children per year [1] . Each year ∼1% of all newborns are congenitally infected with HCMV . Of these infants , 5–10% manifest signs of serious neurological defects at birth [2]–[5] , with an additional 10–15% subsequently suffering consequences by age five . Recent literature also points to HCMV as a contributing agent for the development of certain types of cancers ( for review see [6] , [7] ) . Studies of HCMV infection in non-permissive cells indicate that HCMV can also act as a mutagen [8]–[10] , inducing “hit and run” damage . There is significant evidence that non-specific chromosomal aberrations and damage to the mitotic apparatus can occur in cells infected with a variety of human DNA and RNA viruses ( see [11] for review ) . Yet , only two viruses , the oncogenic adenoviruses ( Ad ) and HCMV , have been found to cause site-specific chromosomal damage [11]–[13] . We have shown that HCMV is able to induce specific damage in chromosome 1 at two loci , 1q23 and 1q42 [12] , [13] , as early as 3 h post infection ( hpi ) . In contrast to Ad type 12 [14] , [15] , induction of specific breaks by HCMV does not require de novo viral protein expression . Viral entry into the cell is sufficient to cause the specific breaks . It is also clear from the literature that many viruses interact with their hosts' DNA damage response ( DDR ) signaling molecules and repair machinery , often triggering responses upon initial entry and deposition of the genome in the nucleus or through successive rounds of replication . Some viruses are reported to utilize this initial DDR response to optimize infection , while others have been found to thwart it ( as reviewed in [16] , [17] ) . Work from our lab and others [18]–[20] has shown that host DDRs are activated both at the point of viral deposition and during late phase replication of HCMV in permissive fibroblasts , although the importance of this activation for establishing a fully permissive infection remains unclear . During HCMV infection , DDRs are not finished , resulting in incomplete repair without arrest or apoptosis . We have shown this is due , at least in part , to a differential association of the repair machinery components into the viral replication centers ( RCs ) . After extraction of soluble proteins , we determined three categories of association: specifically associated within RCs , diffused throughout the nucleoplasm and excluded from the RCs [18] . These earlier studies demonstrated specific viral associations with key players in the DDR pathways . Other studies have examined the capability of infected cells ( or cells expressing specific viral proteins ) to repair different types of damage after infection and found both increases and decreases in the ability to repair induced damage [21]–[40] . However , these earlier studies looked at total cellular DNA and did not examine repair of the viral and host genomes separately within these cells . We hypothesized that after the RCs were established , association of components of the DNA repair machinery within the RCs of the virus could favor viral repair , but more importantly , be detrimental to repair of the cellular DNA . To the best of our knowledge , our experiments in HCMV-infected cells are the first to examine repair in the host and viral DNA separately and the possibility of preferential repair in the viral DNA . To test our hypothesis , exogenous DNA damage was introduced into cells ( UV dimers ) via UVC ( hereafter UV ) irradiation . Analysis of damage repair used comet assays , immunofluorescent localization ( IF ) of UV-induced cyclopyrimidine dimers ( CPDs ) and T4 endonuclease V alkaline agarose ( T4 ) assays . These studies found that HCMV-infected cells , although capable of mounting a damage response to UV irradiation , were unable to completely repair all of the exogenously introduced DNA damage . In situ localization of the CPDs clearly showed that the residual damage detected in these cells was found entirely within the cellular DNA . Moreover , dual-color T4 assays revealed proficient repair of CPDs from viral DNA but defective repair of host DNA within infected cells . Thus , there was selective repair of DNA damage in viral when compared to cellular DNA in permissively infected fibroblasts , indicating that association of the host's DNA repair machinery with HCMV RCs has detrimental consequences for the host .
Our previous work reported tight association of some , but not all , of the ATM-mediated double strand break ( DSB ) and ATR- mediated stalled replication fork response proteins with HCMV viral RCs within the nucleus of permissively infected cells by 48 hpi [18] . These studies were performed using “extraction first” procedures [41] , which differ from the more common “fix first” technique which uses formaldehyde to initially fix proteins in place , followed by permeabilization in detergent to allow access of antibodies ( Abs ) into the cell . This “fix first” methodology allows for visualization of the entire complement of a given protein within the cell , regardless of how tightly or loosely it is associated with a given compartment . By contrast , an “extraction first” protocol initially treats cells with detergent and then fixes them in formaldehyde [41] . Initial extraction removes proteins that are not attached to the chromatin or scaffolding substructure of the nucleus , providing a clearer view of proteins associated with a given compartment or structure . It is often used in the visualization of DSB repair foci in damaged cells , as only a fraction of the entire protein complement will relocalize to sites of damage . Performing these two fixation/extraction procedures provided valuable information regarding the nature of protein interactions in infected cells . First , we concluded that the majority of a protein was tightly associated with the RCs if it was distinctly localized within the RCs using only “fix first” protocols . Second , if we saw more distinct localization of a protein with the RCs after “extraction first” conditions we inferred that only a portion of the protein was tightly associated with these centers . It should be noted that the proportion not tightly associated with the RCs was also not tightly associated with the host DNA . Lastly , we concluded the protein was not specifically associated or excluded from these centers when no clear localization to the RCs occurred using either “fix first” or “extraction first” conditions . A similar pattern of selective association of nucleotide excision repair ( NER ) proteins was found in permissively infected HFFs as had been observed for the DSB repair proteins [18] . Figure 1a shows an example of tight association of the Cockaine's Syndrome B ( CSB ) protein with the viral RCs within the nucleus ( as evidenced by colocalization with the viral processivity factor , UL44 ) . Figure 1b shows two other NER proteins , illustrating either tight association with the RCs ( XPD ) and an example of diffused nuclear staining ( XPG ) . Figure 1 also illustrates the nuclear localization of these three repair proteins in mock-infected cells as a control . In addition , a summary of all the NER-associated proteins tested for localization after infection is given in Table 1 . Table 1 indicates the localization of NER proteins using “fix first” or “extraction first” conditions , as indicated ( note: it is often difficult to use rabbit primary Abs at 48 hpi using “fix first” conditions due to non-specific binding to the virus-encoded Fc receptor in the cytoplasm ) . The differential localization we observed with the proteins crucial to NER within the RCs led us to hypothesize that the repair of viral DNA could be favored , potentially to the detriment of cellular DNA . This hypothesis was tested using UV irradiation of HCMV infected cells . The first method used to test our hypothesis and visualize repair of UV-induced damage was the single cell gel electrophoresis or comet assay system [42]–[45] . The comet assay has been used historically in the literature to analyze repair of UVC-induced damage . Comet tails are visualized by staining with the fluorescent DNA intercalating agent SYBR Green , which binds to both ss- and dsDNA . Although comet tails could represent other alkali-labile forms of damage , the literature suggests that the very large proportion ( >90% ) of damage observed in cells irradiated with low doses of UVC irradiation ( similar to our studies ) are UV dimers ( [46]–[48] and references within ) . Tails at early timepoints are believed to be the result of initial incision events associated with NER processing of UV dimers . These incisions ( strand breaks ) allow uncoiling/relaxation of the chromatin to occur . Electrophoretically induced migration of the uncoiled/relaxed DNA is visualized as the formation of a comet tail ( as reviewed in [49] ) . Therefore , only cells capable of initiating repair will have comet tails following UV irradiation [46] . Over a timecourse , successful repair is demonstrated by a decrease in both the number of cells with tails and the % DNA in the tails . Importantly , it has been shown convincingly that cells deficient in NER proteins , e . g . XP proteins , do not form tails above background levels in comet assays after UVC irradiation due to lack of incision events ( [46] , [47] and references within ) . Two independent comet experiments were conducted on HFFs infected for 48 h and then irradiated with 50 J/m2 of UV and allowed to repair for 2 , 6 or 24 h . It should be noted that this UVC dosage was non-lethal to the cells , with no cell loss in any of the experiments . Cell counts with and without UVC were essentially identical over the entire timecourse . The graph in Figure 2B shows the data from one of these experiments; data from the other was comparable . Comets were scored using VisComet software . The average % tail DNA for the population of cells scored in a given set is represented in each bar ( error bars represent one SD ) . Four populations are represented in the graph and are shown in different shades of grey: mock infected cells plus or minus irradiation ( M+UV and M alone , respectively ) and virus-infected cells plus or minus irradiation ( V+UV and V alone , respectively ) . A representative image of each group is shown in Figure 2A . As expected , M alone cells ( white bars ) produced very limited comet tails , with an average of less than 10% tail DNA . The M+UV cells ( light grey bars ) had significantly increased % tail DNA at early times , indicating their ability to begin NER incision events was intact . Twenty-four h of repair returned the M+UV cells' % tail DNA toward the M baseline . Surprisingly , the V alone samples ( dark grey bars ) had elevated % tail DNA throughout the timecourse . This seemingly perplexing result was investigated further ( see below ) . The V+UV samples ( black bars ) had a substantially larger % tail DNA than the M+UV cells as early as 2 hp irradiation . In contrast to the M+UV samples , the % tail DNA in the V+UV samples did not decrease during the ensuing 24 h period . In fact , the % tail DNA remained high through 48 h of repair time ( data not shown ) . Throughout the timecourse , the V+UV samples had statistically significant increases in % tail DNA over their M+UV counterparts ( as measured by unpaired t-tests and indicated by asterisks in the graph ) . The persistence of comet tails in the V+UV samples was examined further in BrdU pulse/chase experiments below . The distribution of % tail DNA within each sample type was plotted to distinguish whether changes were occurring over the timecourse ( Figure 2C - four ranges of % tail DNA are represented in shades of grey ) . The distribution shown represents the experiment in Figure 2B . The distribution plot shows the increasing percentage of M+UV cells with less than 10% tail DNA over the timecourse of repair . In contrast virtually all cells in the V+UV samples have very high levels of tail DNA ( greater than 50% ) for the entire timecourse . As mentioned above , unexpectedly , the V alone samples increased in tail DNA percentage over the timecourse . The source of comet tails in V alone samples was a conundrum . Electrophoretically induced migration of uncoiled/relaxed DNA is measured by the comet assay as the formation of a tail ( as reviewed in [49] ) . The body of comet assay literature strongly suggests that the relaxation associated with open replication forks and Okazaki fragments connected with replicating genomes could appear as tail DNA in this assay ( [50] and references within ) . Several studies have shown that HFFs infected in G0 with HCMV arrest at or near the G1/S transition , resulting in the replication of viral , but not cellular , DNA within these cells [51]–[54] . To determine whether the increase in % tail DNA in V alone samples was possibly attributable to the previously observed specific DSBs induced in a subset of cells by the incoming virus inoculum [12] or , more likely , the increase in viral DNA replication over time , the comet assays were repeated exactly as previously described in two parallel sets of samples ( eight groups in total ) . Ganciclovir , a viral replication inhibitor , was added to one of the parallel sets of cells beginning at 24 hpi and throughout the remainder of the experiment . Addition of the drug at 24 hpi interrupted the infection at the pre-replication foci stage and any further development of these foci ( and associated viral replication ) in the treated samples was halted . All samples were irradiated at 48 hpi and harvested 24 h later . We reasoned that , if comet tails in the V alone samples were due primarily to viral replication , ganciclovir treatment should reduce comet tail levels toward M alone background levels . As can be seen in Figure 3 , inhibition of viral DNA replication in the V alone samples dramatically reduced % tail DNA back toward M alone baseline levels . This reduction was the only statistically significant difference observed with the addition of ganciclovir to the samples ( as measured by unpaired t-tests and indicated with an * ) . This result led us to conclude that the majority of the DNA in the V alone comet tails was not due to specific DSBs , but rather primarily due to viral replication . Interestingly , only nominal decreases in the % tail DNA were seen in the V+UV cells treated with ganciclovir . Our comet assay experiments with these cells were likely detecting the initiation of DNA repair in both host and viral DNA , not replicating viral genomes , however the ganciclovir experiments could not definitively distinguish if irradiation had inhibited viral replication during the repair cycle in the V+UV cells . Therefore , we assessed the extent of viral replication over time by BrdU-labeling the viral DNA before and at several points after UV-irradiation . As described previously , cells were infected for 48 h on coverslips . Coverslips were then divided into two groups . One group was not irradiated and the second group received 75 J/m2 UV . In addition , one coverslip from each group was pulse-labeled with BrdU to provide a baseline of incorporation ( and viral replication ) ( t = 0 h in Figure 4 ) . The level of active viral replication in the two groups at each timepoint post irradiation was assessed by pulse labeling with BrdU ( as described in Materials and Methods ) just prior to harvesting coverslips ( one each from the unirradiated and irradiated groups ) . As seen in Figure 4 , the unirradiated samples continued incorporating BrdU into the replicating viral DNA located in the RCs ( upper panels , RCs are marked with arrows ) . However , after irradiation , viral replication essentially ceased in the irradiated samples ( bottom panels , +6 and +11 h images show essentially no RC staining ) . A small amount of BrdU incorporation into the DNA in the RCs was seen in these V+UV cells after 24 h of recovery , but the amount of incorporation was nominal compared to their unirradiated counterparts . We also believe that the signal observable in the RCs at 24 hp irradiation in the V+UV samples is most probably due to BrdU incorporation into repair patches during unscheduled DNA synthesis ( UDS ) [55] . Small regions ( of ∼20 nucleotides ) must be resynthesized after removal of CPDs from the viral DNA . BrdU incorporation is commonly utilized in the DNA repair field to demonstrate repair has actually occurred in the nucleus of irradiated cells via UDS . These experiments demonstrated that viral replication was not responsible for the large % tail DNA in the V+UV comet assay samples . There appeared to be residual damage in the V+UV cells in comparison to M+UV cells . We therefore investigated whether there was specific localization of these dimers within the nuclei . An Ab developed by Dr . Toshio Mori [56] and specific for the most prevalent form of UV-induced damage , CPDs , was used to immunofluorescently visualize induced dimers in situ at 0 and 24 hp irradiation . Removal of CPDs at doses ranging from 30–75 J/m2 UV was examined . Confocal microscopy found both M+UV and V+UV nuclei ( Figure 5A ) stained for CPDs across the entire nucleus at time 0 h post irradiation . CPD adducts were formed in both cellular and viral DNA , with minor variations in intensity seen across an individual nucleus and from cell to cell . Cells were pulse-labeled with BrdU prior to irradiation to allow localization of viral DNA within the RCs for further quantitative analysis . Cells were stained with CPD- ( green ) and BrdU- ( red ) specific Abs simultaneously . M+UV and V+UV cells both had residual CPDs 24 hp irradiation . However , in the V+UV cells , dimers were localized specifically to the periphery of the nucleus and dimers were largely absent from the viral RCs ( as localized by BrdU staining ) . It has previously been shown that cellular DNA is marginalized to the edges of the nucleus at late times pi using histone localization [57] . We stained both M and V cells at 0 and 24 hp irradiation with an Ab to detect the localization of histone H3 and found , much like Monier and colleagues , that this cellular histone associated almost exclusively with DNA at the edge of the nucleus and outside of the RCs in infected cells ( as marked by UL44 ) , but across the entire nucleus in mock-infected samples ( Figure 5B ) . This confirmed that residual CPDs were located primarily within the cellular DNA . CPDs appeared to be specifically removed from the viral RCs at all three doses of irradiation tested ( Figure 5a shows only images of cells treated with 75 J/m2; identical images were obtained at lower doses , which are not shown ) . These results led us to believe that in permissively infected HFFs irradiated at 48 hpi , there was preferential removal of CPDs from the viral DNA . The removal of CPD signal from these infected cells was quantitated over the 24 h period of repair . Images of infected cells dually-labeled for viral DNA ( BrdU ) and CPDs were captured at 0 and 24 hp irradiation using confocal microscopy . All images were captured using exposure times below which any pixels were saturated , including the brightest areas at the 24 hp irradiation V+UV cells' peripheries . Data from three separate experiments were analyzed using Metamorph software as described in the Materials and Methods . Briefly , after finding the center plane of each image , the RC area and the total area of the nucleus were defined and the integrated intensities ( INTINT ) of both regions were recorded . An example of the regions created by MetaMorph are shown mapped onto the infected cells in Figure 5A to illustrate the process . The RC region is outlined in red and the entire nucleus in white . Subtracting the INTINT of the RC from that of the entire nucleus determined the INTINT of the cellular DNA for each cell . For example , the intensity data for the cells shown in Figure 5A was: for the 0 h cell , Nucleus Integrated Intensity ( NII ) - 21 . 4 Million counts ( M ) , Replication Center Integrated Intensity ( RCII ) - 5 . 4 M , Host Integrated Intensity ( HII ) - 16 M; for the 24 h cell , NII- 5 . 7 M , RCII- 1 . 2 M , HII- 4 . 5 M . Initial comparisons found differences in the total CPD INTINT signal within the nucleus at the two timepoints post irradiation ( 0 and 24 h ) . A mixed-effects ANOVA model was used to test these data for statistical significance . Using the different experimental dates as blocking factors to control for technical variation among the dates on which the experiments were performed , the results showed that the CPD signal for the entire nucleus was significantly greater at 0 h than at 24 hp irradiation ( F = 20 . 7; df = 1 , 121; p-value<0 . 0001 ) . The averages for the three separate experiments are plotted ( and represented by different symbols ) in Figure 5C . The grey bars represent an average of the three separate experiments for ease of interpretation . The statistically significant decrease observed in the total CPD signal prompted further analysis of the component viral and host DNA signals . Two post hoc tests were performed to determine if the decrease in the CPD signal found in the entire nucleus was independently attributable to CPD signal changes in either the host or viral DNA . The change in CPD signal in the host DNA and in the viral DNA were analyzed separately , again using a mixed effect ANOVA model to control for technical variation among dates while testing the effect of time on removal . After correcting for multiple statistical tests on the same data , the difference between 0 and 24 hp irradiation in the signal intensity within the host DNA was not significant ( F = 3 . 1; df = 1 , 123; p-value>0 . 16 ) , whereas the difference between 0 and 24 hp irradiation in the signal intensity in the virus DNA was highly significant ( F = 64 . 5; df = 1 , 109; p-value<0 . 0002 ) . Again , the averages for each experiment were plotted in Figure 5D , with the grey bar representing the average of the three experimental points . Although the individual experiments showed different average raw intensities , the downward trend for each experiment demonstrated a statistically significant removal of CPD signal from the viral DNA . These results clearly indicated that the decrease in the CPD signal in the nucleus following 24 h of repair was due to a decrease in the CPD signal in the virus DNA with no parallel decrease in signal within the host DNA . Others have reported that viral DNA could potentially be replicated , packaged and transported out of the nucleus within a 24 h period [58] . To determine if specific removal of CPDs from the viral RCs was due solely to normal egress of the virus during active infection , the actively replicating virus within the RCs of HCMV infected cells was pulse-labeled with BrdU at 48 hpi . One half of the coverslips were irradiated with 75 J/m2 UV . It should be noted that experiments at 50 J/m2 produced identical results and are therefore not shown . Cells from both irradiated and unirradiated groups were harvested at 0 and 24 hp irradiation . All planes of a confocal image were projected into a single plane to gain a view of the entire cytoplasm and nucleus of an infected cell at the different times post BrdU pulse . Using these projected images , we could observe some movement of pulse-labeled virus-containing virions out into the cytoplasm of the unirradiated cells by 24 h post irradiation ( visualized as individual spots of BrdU in Figure 6 , top right panel ) . It should be noted that a significant fraction of the labeled viral genomes still remained within the RCs at this point . Conversely , we detected negligible movement of pulse-labeled viral DNA out of the RCs in the irradiated samples over the 24 h period ( Figure 6 , bottom right panel ) . This indicated that the decrease in CPD signal from the RCs observed in these cells was not caused by virus egress , but rather was due to selective removal of CPDs from the viral DNA . Global genomic repair of CPDs can be estimated using T4 endonuclease V cleavage analysis [59] . T4 makes a highly specific single strand nick 5′ of UV-induced CPD adducts [60] . Samples are then separated via electrophoresis on an alkaline agarose gel . In these T4 gels , DNA that is either undigested or unirradiated is visible as a distinct high molecular weight ( HMW ) band at the top of the lane . In contrast , UV-irradiated DNA subsequently digested with T4 and electrophoresed yields a smear of lower molecular weight fragments down the lane , indicating nicking at CPD lesions . Over the course of repair , the smear returns to a HMW band indicative of repaired , full length DNA . Until now , visualization of the cleavage products has typically been via 32P labeled probes [59] , [61] , [62] or ethidium bromide [63] , [64] . In Figure 7A , we show an example of mock-infected samples irradiated at 50 J/m2 and then digested with T4 , run on an alkaline agarose gel and stained with SYBR Gold , a ss and dsDNA binding dye , to illustrate these gels . It can be clearly seen that the undigested samples remain as HMW bands and the digested samples run as a smear in the gel . As repair occurs the length of the smear decreases and the HMW band returns , indicating removal of CPDs and religation of the DNA . When quantitating the extent of DNA damage , the average fragment length of DNA within the lane is inversely proportional to the number of CPD lesions present within the sample , i . e . - the smaller the average fragment length the more T4 cleavage sites , and therefore CPD lesions , present within the sample . These techniques have been used extensively to study genomic [59] , [63] , [64] and gene-specific [61] , [62] CPD repair in a variety of organisms . However , determining repair of virus and host genomic DNA independently within HCMV-infected cells proved more challenging . A traditional approach would probe , strip and reprobe a single Southern blot for host and viral DNA; however stripping introduces the potential loss of signal . We developed a new method to visualize both virus and host genomic DNA simultaneously on a single blot using a Li-Cor Odyssey infrared imager . To develop the dual-color Southern technique , we ran DNA isolated from mock-infected HFFs , pelleted viral particles and infected HFFs on a native gel before blotting to nitrocellulose and probing with digoxigenin-labeled host probes and biotin-labeled viral probes ( Fig . 7B ) . Host DNA was visualized in the red channel ( 685 nm ) and viral DNA was visualized in the green channel ( 785 nm ) . In the merged image , the DNA from the infected cells appeared yellow , since infected cells contained both host and viral DNA , while the uninfected cellular DNA was red and the purified viral DNA was green . After validating this dual-color Southern technique's ability to distinguish viral from host DNA , it was used to probe experimental T4 blots ( Fig . 7C ) . The far left panels of Figure 7C illustrate unirradiated DNA digested with T4 on these gels . The visible bands are equivalent to the HMW bands in the irradiated , undigested samples shown in Figure 7A . The right-hand panels in Figure 7C show the irradiated samples run on alkaline agarose gels . An analysis of the single channel blots and the overlay in these right-hand panels display several readily discernible features . First , after 48 h of repair , there was substantially more HMW DNA in the mock +T4 lane when compared to the infected +T4 lane in the red channel indicating decreased repair in the host DNA of infected cells ( cellular DNA- top panel ) . Second , analysis of the viral DNA in the infected cells also showed a substantial return of a HMW band after 48 h of repair , indicating efficient repair of UV-induced DNA lesions ( +T4 lane , green channel , middle panel ) . Lastly , analysis of the 48 h infected cell +T4 lane in the overlay blot clearly showed a gradient of colors , with the HMW band being predominantly green ( viral DNA ) and substantially more red signal ( cellular DNA ) within the smaller molecular weight fragments of the smear ( bottom panel ) . It is important to realize that although the decrease in the DNA smear was subtle in the mock-infected and viral DNA lanes , the reappearance of a “full length” product/band at the top of the lanes as the timecourse progresses was more significant and indicated dimer removal and completed repair . This band reappears convincingly in the mock-infected and viral DNA lanes , but is nominal in the host DNA within the infected cells . In Figure 7D , the results from five biological replicate experiments are plotted using different symbols , with the average of these experiments represented by bars for ease of comparison . The data is represented as “percent repair” of the dimers in this graph using the quantitation protocol described in Bespalov et al [59] . There is considerable variability in the results for these five T4 experiments . The variability is on par with that found in both the comet assays and the CPD removal experiments . Use of a Stratalinker for UV irradiation may have contributed to this variability , as the data shows that the initial induction of CPDs was not entirely consistent across experiments . However , rather than confound our results , we found highly statistical differences between groups as detailed below . As depicted in Figure 7D , mock-infected HFFs repaired an average of ∼50% of CPD adducts by 48 hp irradiation ( Figure 7D , blue bars ) . In contrast , host CPD repair in HCMV-infected cells plateaued at an average of ∼10% by 24 h and remained constant through 48 h of repair ( red bars ) , while within the same cells , an average of ∼60% of CPDs were repaired from within the viral DNA ( green bars ) . Statistical analyses were performed for each time point comparing the mean repair of the host DNA versus the viral DNA ( or versus mock DNA ) using one-tailed paired t-tests . A paired t-test controls for variation between experiments as well as unequal variances between the two measures ( host versus viral or mock DNA ) . At each time point , the amount of viral DNA repair was statistically greater than the amount of host DNA repair ( p<0 . 001 , p<0 . 01 , p<0 . 01 , for timepoints 6 , 24 and 48 , respectively using one-tailed paired t tests ) . Statistically significant differences between the host and mock DNA repair were only observed at 48 hp irradiation ( p<0 . 26 , p<0 . 30 , p<0 . 02 , for timepoints 6 , 24 and 48 , respectively ) . These significant differences are indicated by asterisks in 7D . Therefore , repair of viral DNA was initiated more quickly and progressed more rapidly than repair of the host DNA within the same cells . The differential in repair of host and viral genomic DNA in infected cells confirmed our IF observation that CPDs were selectively removed from the viral DNA , but remained in the host genomic DNA .
The work reported here was based on the observed differential association of cellular repair proteins with viral RCs within the nucleus . We hypothesized that this association could favor viral repair and more importantly , be detrimental to repair of cellular DNA . To test this premise we UV-irradiated infected cells and then analyzed the removal of UV dimers by three methods; comet assays , IF localization of CPDs and dual-color T4 assays . Comet assays revealed that although infected cells were capable of mounting a repair response , they were unable to complete repair of all of the exogenously introduced damage . In situ localization of the CPDs showed that residual damage was confined to the cellular DNA . Lastly , dual-color T4 assays revealed faster and more significant repair of CPDs in the viral DNA than the host DNA within infected cells . Over the past decade a great deal of work has focused on interactions of viruses and their host's DNA damage signaling molecules and repair machinery . Many of these studies ( including our own [18] ) have examined the triggering of ATM- and ATR-mediated DDRs by both DNA viruses and retroviruses ( as reviewed in [16] ) . Certain viruses ( for example , Adenovirus ) actively thwart these damage responses , while other viruses ( like HIV ) require a DDR to replicate to full capacity . These studies have been informative and have discovered specific viral interactions with key players in these repair pathways; however they have not assessed the ramifications of infection upon the cell's subsequent ability to repair further insult to its DNA . A number of studies have analyzed a cell's repair capabilities following infection . These studies include the repair of exogenously introduced damage in the cellular DNA in the context of single viral protein expression [21]–[32] and the effects of a complete infection [33]–[40] , [65] . These papers have examined the capacity of the cell's homologous recombination , base excision , nucleotide excision and non-homologous endjoining repair machinery to function , with the very large majority of the investigations finding decreased capacity of the cell to repair damage after viral protein expression ( or full infection ) commenced . Only four of these studies have reported evidence of an increase in repair capacity of the cell after infection or viral protein overexpression [21] , [23] , [36] , [40] . Our results extend this analysis and separate the two genomes within an infected cell . We demonstrate that , at least in the context of HCMV-infected fibroblasts , there is increased repair of UV-induced CPDs in the viral DNA , without a corresponding increase in repair of the host DNA . In the next few paragraphs we will focus on the above studies most pertinent to our own results , emphasizing studies examining interactions with the NER machinery and/or with HCMV infection's influence on cellular damage repair . Several studies have utilized expression of single viral proteins in the analysis of UV damage . Expression of the Hepatitis B X protein ( HBX ) in different cell types [22] , [25] , [27] , [29] , [32] or expression of the Epstein Barr virus proteins EBNA3C or LMP1 in transfected cells [26] decreased repair efficiency of UV-induced damage in transfected cells . More pertinent to our study was that of Liang and colleagues [28] , which used a herpesviral protein ( γ herpesvirus 68 protein M2 ) and methodology similar to our own . Mouse 3T3 cells expressing M2 were assessed for the ability to repair exogenously induced UV damage . At low dosage ( 2 . 5 J/m2 ) M2-expressing cells' capacity to remove dimers was decreased , which was most pronounced at 24 hp irradiation . More dramatically , at 30 min post irradiation at very high dose ( 5000 J/m2 ) M2-expressing cells formed no comet tails , indicating they did not even initiate repair . Using a dimer-specific Ab they saw dramatically reduced dimer removal in the M2-expressing cells . Liang's results indicate that an M2-expressing cell had impaired ability to repair exogenous damage in host DNA via NER . We wonder if viral DNA would have been preferentially repaired if it had been present in these experiments ? An additional three studies have looked at NER repair in the context of full infection . Duong and colleagues [35] found reduced efficiency of Hepatitis C-infected cells to reactivate ( and therefore repair ) transfected UV-irradiated reporter plasmids ( compared to uninfected control cells ) . Similarly , Philpott and Buehring found that multiple HTLV- and bovine leukemia virus-transformed lines ( as well as cells transformed with just the HTLV Tax protein ) had a decreased ability to repair a reporter construct damaged by UV [39] . Bowman and colleagues [65] looked at the removal of CPDs from host DNA during SV40 infection using dimer-specific Abs in slot blot analysis and found a decreased removal of these adducts . As in our studies , they utilized T4 assays to examine removal of damage from both the transcribed ( transcription-coupled NER ) and the non-transcribed ( global genomic NER ) strands of a cellular gene , DHFR . Interestingly , they found that repair of only the non-transcribed strand of DHFR was affected by SV40 infection , indicating that repression of p53 by SV40 might be involved ( discussed further below ) . Once again , the question remains whether analysis of the SV40 DNA would have revealed increased and more rapid repair of the viral DNA in these cells . The last set of papers that should be addressed deal specifically with repair in HCMV-infected cells . The literature has revealed varying effects of damage , depending upon the system being examined . Ranneberg-Nilsen and colleagues examined the capability of HCMV-infected human embryonic lung fibroblasts ( infected under conditions similar to our study ) to carry out BER [40] , and found approximately twofold changes in repair , with different substrates being removed with greater or lesser efficiency . Studies from our own lab [36] , using the same fibroblasts and HCMV isolate ( Towne ) as used in the current study , found that homology directed repair ( HDR ) was more efficient after infection , regardless of whether the reporter construct was integrated into the host cell genome or expressed transiently . Thus , neither BER nor HDR was affected as significantly as we have found NER to be . Two additional works address the effects of HCMV infection on the introduction and frequency of DNA chromosome anomalies induced by subsequent exposure to genotoxic agents . The first [33] infected non-permissive peripheral blood lymphocytes ( PBLs ) with HCMV at low MOI in the presence of camptothecin and observed a synergistic increase in chromosome damage ( including chromosome breaks ) , even in the absence of viral gene expression . These findings support our supposition that the repair of multiple forms of damage is inhibited in HCMV-infected cells . A separate study by Deng and coworkers [34] used freshly stimulated PBLs infected with HCMV at a higher MOI of 4 . Their findings suggested that HCMV infection sensitized the chromosomes to drug-induced damage . Deng and coworkers' observation indicated that chromosome anomalies were present even without de novo viral gene expression in the non-permissive PBLs . This result is consistent with our earlier findings [12] that de novo viral protein expression was not required to induce site-specific chromosome damage . Our earlier results also indicated that certain virion-associated proteins cannot only induce damage , but may also interact specifically with the damage machinery to inhibit its operation . These last studies have suggested experiments we intend to pursue in the future . First , does the same decrease in repair of cellular DNA occur if there is no replication of viral DNA within cells ? This could be determined in non-permissively or semi-permissively infected cells by ascertaining whether a set of viral proteins and/or viral RC association of cellular proteins needs to occur for this effect to be observed . The results of the ganciclovir experiments shown in Figure 3 suggest that establishment of fully functioning replication centers may not be required for negative effects on cellular NER repair . Second , would the same decreases in repair capacity be seen in latently infected cells or cells with limited viral replication ( such as long-term infected neurons [66] ) ? Third , does the presence or absence of the p53 protein play a role in repair of different types of damage within infected cells ? Certainly the reports of others [29] , [32] , [65] mentioned above indicate that , at least in the context of repair of UV-induced damage , interactions of the virus with p53 might influence global genomic repair within the cellular DNA . Our earlier studies have shown clear interactions with , and the importance of , p53 to HCMV replication [67]–[69] , indicating p53 may play a role in the selective repair of viral over cellular DNA . Our study is not the first to look at the capacity of an infected cell to repair exogenously introduced DNA damage . However , utilizing novel techniques , our experiments assessed initiation of repair , removal of CPDs and repair of the DNA substrate in both the cellular and viral DNA separately . Comet assays indicated that infected cells were fully capable of initiating repair , but still retained residual damage 24 hp irradiation . Confocal images of infected cells with separately labeled viral DNA ( using BrdU pulse-labeling ) showed definitive removal of CPD signal from viral DNA in the RCs but no statistically significant removal from the host genome . Importantly , this indicated the residual comet tail damage observed in the V+UV samples was due to persistence of CPDs in the host DNA and not in the viral genome . Additionally , development of a dual-color Southern methodology has allowed utilization of the well-established T4 assay to analyze two separate DNA genomes simultaneously . These dual-color T4 assays demonstrated faster and more significant repair of CPDs from the viral DNA than the host cellular DNA within the same cell . It is our belief that the compromised capability of infected cells to repair damage may ultimately be manifested in the induction of CNS defects in the HCMV-infected neonate . Future studies will extend this avenue of investigation .
Primary human foreskin fibroblasts ( HFFs ) ( a gift from Steven Spector , UCSD ) were isolated from tissue and propagated in Earle's minimal essential media ( MEM ) supplemented with 10% heat inactivated fetal bovine serum ( FBS ) , L-glutamine ( 2 mM ) , penicillin ( 200 U/ml ) , streptomycin ( 200 µg/ml ) , and amphotericin B ( 1 . 5 µg/ml ) . Cells were grown in humidified incubators maintained at 37°C and 5% CO2 . G0 synchronized HFFs were trypsinized , counted , reseeded at a lower density and allowed to settle for approximately 2 h . Cells were infected at a multiplicity of infection ( MOI ) of 5 with the Towne strain of HCMV , obtained from ATCC ( #VR 977 ) . Two to four hpi , virus inoculum was removed and cells were refed with media and allowed to incubate as described below . The virus was propagated under standard procedures [70] . HFFs were mock- or virus-infected as described above . At 48 hpi , coverslips were harvested for colocalization of cellular NER proteins with the viral processivity factor , UL44 . Coverslips were treated in one of two ways . In the first method , cells were extracted-first in a CSK buffer solution ( 10 mM Pipes , 100 mM NaCl , 300 mM sucrose , and 3 mM MgCl2 ) containing 0 . 5% Triton X-100 [41] . Cells were then rinsed in CSK twice and fixed with 3% formaldehyde in PBS ( with 0 . 5 mM MgCl2 , and 0 . 5 mM 3 mM CaCl2 ) for 10 min . In the alternate method , coverslips were extracted using standard formaldehyde fixation and Triton X-100 extraction as described previously [69] . See the Results section for further discussion of “fix first” versus “extract first” conditions and the information that can be gleaned from use of these different methods . Incubation of coverslips with Abs and mounting for examination were as described previously [69] . Nuclei were counterstained with Hoechst dye . The images of NER protein localization were obtained using a Nikon Eclipse E800 fluorescence microscope equipped with a Nikon DXM camera and Metavue software . Primary antibodies ( Abs ) used in Figure 1 and Table 1: mouse monoclonal Abs to XPB and XPD were kind gifts of Jean Marc Egly [71] , [72]; mouse monoclonal Abs to XPA ( 2A4 ) , XPG ( 8H7 ) and ERCC1 ( 3H11 ) and rabbit polyclonal Abs to XPC ( RW028 ) and XPF ( RA1 ) were kind gifts of Rick Wood [73]–[76]; mouse monoclonal Ab to UL44 ( 1202S - Rumbaugh Goodwin Institute ) ; rabbit polyclonal Ab to CSB ( Santa Cruz Biotechnology ) . Secondary Abs used in Figure 1A were donkey anti-rabbit TRITC-coupled Ab ( Jackson Immunoresearch ) and goat anti-mouse IgG1 alexafluor 488-coupled Ab ( Molecular Probes ) . Secondary Ab used in Figure 1B was goat anti-mouse IgG FITC-coupled Ab ( Jackson Immunoresearch ) . HFFs were infected as described above . At 48 hpi , cells were washed in PBS and one set of mock and viral plates were irradiated in a Stratalinker 1800 at a dose of 50 J/m2 . A second set of plates was left unirradiated . Irradiated cells were rinsed again , re-fed with media and allowed to recover for different periods of time ( 2 , 6 and 24 hp irradiation ) . At the given timepoints , cells were washed once in cold PBS then scraped into cold PBS in microfuge tubes . Cell suspensions were adjusted to 1 . 5×105 cells/ml . 50 µl of suspension was added to 500 µl of low melting point agarose ( 1% in PBS ) and 75 µl of this suspension was placed in a thin layer on a coated glass slide ( Trevigen ) . The agarose was allowed to gel at 4°C for 15 min . Cells were then lysed for 30 min in situ in a high salt/detergent solution ( 2 . 5 M NaCl , 1% sodium lauryl sarcosinate , 1% Triton X-100 ) at room temperature . DNA was denatured by treatment in alkali solution ( pH>13 ) for 40 min . Prepared slides were placed in an electrophoresis tank filled with the above alkali solution . Very low current ( 280–290 mA ) was applied to the tank for 20 min . Slides were dehydrated in EtOH , stained with Sybr Green ( which binds to both ss and ds DNA ) and visualized/photographed using a Nikon E800 Eclipse microscope equipped with a Nikon DXM camera and Act One software . VisComet software was used to analyze 50–100 cells/sample set ( except where noted ) of mock ( M alone ) , viral ( V alone ) , mock+UV ( M+UV ) and viral+UV ( V+UV ) at the given timepoints post irradiation . Comets were analyzed for % DNA in the tail . Data in Figures 2 and 3 are represented as the average of % tail DNA for the given sample set . Error bars represent one SD from that average . Each experiment was performed twice , with the data from a representative experiment shown in the figures . Unpaired t-tests were performed to assess the statistical significance between sample sets using GraphPad statistical software as noted . To distinguish whether changes were occurring over the timecourse , the distribution of % tail DNA within each sample type ( M alone , V , alone , M+UV , V+UV ) at the three different timepoints was plotted . In this plot , the percentage of DNA in the tail for each comet analyzed in a sample set was assessed and assigned to one of four categories ( <10% tail DNA , 11–25% tail DNA , 26–50% tail DNA or >50% tail DNA ) . The number of comets in each category was converted to a fraction of 100% and plotted . Synchronized cells were reseeded into plates containing glass coverslips and infected as described above . At 48 hpi , cells were irradiated ( or unirradiated ) with 75 J/m2 UV and harvested at the indicated times post irradiation . Cells were also pulse-labeled with BrdU just prior to irradiation . BrdU labeling enabled viral RC visualization ( as described previously [68]- 30 min pulse followed by 30 min chase in fresh media ) . After harvesting , cells were treated according to the methods in [77] , which exposes both UV dimers and BrdU residues . Briefly , cells were fixed in ice cold MeOH: Acetic Acid ( 3∶1 ) for 20 min and subsequently washed in cold 100% EtOH . DNA was denatured for 3 min at room temperature ( RT ) using 70 mM NaOH dissolved in 70% EtOH . Finally , cells were washed extensively in PBS and stored at 4°C until staining . Incubation of coverslips with Abs and mounting for examination were as described previously [69] . Cells were counterstained with Hoechst dye to visualize the nuclei . Mouse monoclonal Ab specific for CPDs has been described previously [56] . BrdU residues incorporated into viral DNA were stained with anti-BrdU rat monoclonal Ab ( Harlan Sera-Lab ) . Cells stained for CPDs ( detected with goat anti-mouse IgG2A Alexafluor 488 from Molecular Probes ) and BrdU ( detected with donkey anti-rat TRITC secondary Ab from Jackson Immunoresearch ) were analyzed and photographed on an Olympus Fluoview 1000 confocal microscope using a 60× Plan Apo oil objective lens ( 1 . 42 NA ) . Care was taken to avoid the presence of saturated pixels within the images . Samples were excited using 405 nm ( for BrdU ) , 488 nm ( for CPD ) and 561 nm ( for Hoechst ) laser lines . Images showing unirradiated samples stained for CPDs and BrdU were captured using the Nikon E800 Eclipse and Metavue software mentioned above . In parallel , coverslips were harvested using “fix first” conditions as described above . These coverslips were stained with a polyclonal rabbit Ab to histone H3 ( Millipore #06-755 detected using donkey anti-rabbit TRITC-coupled secondary Ab ( Jackson Immunoresearch ) . They were also stained with the above-mentioned Ab to UL44 ( detected using goat anti-mouse IgG1 AlexaFluor 488 ( Molecular Probes ) ) to localize the RCs . These coverslips were blocked in 30% human IgG ( instead of FBS ) to inhibit non-specific binding of the rabbit Ab to the viral assembly complex within the cytoplasm of infected cells . Image preparation and data generation were performed using MetaMorph ( MM ) Software ( Universal Imaging ) . Stacked confocal images were captured as TIFF images on an Olympus Fluoview 1000 using 0 . 41 µm stepping . Twenty to thirty cells were analyzed per experiment , per time point as described below . Three separate experiments were analyzed . Using MM software , the center plane of each cell was identified from the stack of confocal images . The center plane was defined as the largest cross-sectional area of the virus RC . The image containing the center plane for each cell was color separated . The red ( BrdU ) and green ( CPD ) channels were saved as new images . This was performed for each individual cell , including all cells from images containing multiple cells . Thresholding of each color-separated image was used to define contiguous regions ( in MM defined as Object ( s ) ) for each nucleus and RC ( many cells contained multiple RCs ) . Regions of Interest ( ROIs ) were created/saved surrounding these Objects ( using the MM create ROI around Objects function ) . The CPD Integrated Intensity ( INTINT ) for each entire nucleus ROI was recorded . The ROI ( s ) of each RC ( s ) was mapped onto its corresponding CPD nucleus image . The associated CPD INTINT of each RC ( s ) region was recorded . A total RC CPD INTINT for cells containing multiples RCs was summed from that cell's multiple RC CPD INTINTs . The CPD total for the host cellular DNA was defined as all CPDs outside of the RC ( s ) ( e . g . entire Nucleus CPD ( - ) Virus RC CPD = Host CPD ) . This data was analyzed using a mixed-effects ANOVA model ( SAS , Cary , NC ) comparing total CPD INTINTs between the 0 and 24 h post irradiation time points as described in the text . Cells were treated as described above for comet analysis ( irradiation of 50 J/m2 at 48 hpi ) . However , sample sets ( M alone , M+UV , V alone , V+UV ) were performed in duplicate . One of the sets continuously received 45 µM ganciclovir ( after 24 hpi to inhibit viral DNA replication ) and the second set a vehicle control . Cells were harvested at 24 h post irradiation for comet analysis as described above . For these experiments , 25–50 comets were scored per sample set . The experiment was repeated twice , and a representative sample set is shown in Figure 3 . Two plates of HFFs on glass coverslips were infected at an MOI of 5 . After 48 h , one coverslip from each plate was removed and pulse-labeled with BrdU for 30 min and then chased for an additional 30 minutes in fresh media . These two coverslips served as time +0 h for the irradiated and unirradiated plates , respectively . After the chase period , one of the BrdU-labeled coverslips ( and the remaining coverslips from its plate of origin ) was irradiated at 75 J/m2 . The other BrdU-labeled coverslip ( and its partners ) were left unirradiated . Time +0 h coverslips were then harvested . One h before each subsequent timepoint ( at 5 , 10 and 23 hpi , respectively ) , an additional coverslip from each plate was removed and pulse-labeled with BrdU in preparation for harvesting at the appropriate timepoints ( 6 , 11 and 24 hp irradiation ) . Cells were fixed and stained for BrdU incorporation into viral RCs as described previously [68] . Images were captured using the Nikon E800 Eclipse and Metavue software mentioned above . HFFs were infected on coverslips as described above . After 47 hpi , one h prior to irradiation at 75 J/m2 , infected cells were pulse-labeled with BrdU ( and then chased for 30 min as described above ) to label viral DNA within the RCs . Half of the coverslips were then irradiated with 75 J/m2; the second half was not irradiated . Timepoints were taken at 0 and 24 h post irradiation ( or control treatment ) . Coverslips were fixed and processed for BrdU localization as described previously [68] . Cells were analyzed on the Olympus confocal microscope described above . Each Z-series was subsequently projected using the Olympus FSW software option of ‘Duplicate as displayed’ to create a single plane , 8-bit image for Figure 6 . Viral supernatants were centrifuged through a 25% sucrose ( in PBS ) cushion at 23 , 000 rpm for 70 min at 10°C to pellet viral particles . Genomic DNA was extracted from HFF cells and viral particles as described previously [78] . HindIII-digested viral and HFF DNA were labeled with biotin-16-dUTP ( Roche ) and digoxigenin-11-dUTP ( Roche ) , respectively , using the BioPrime Array CGH genomic labeling module ( Invitrogen ) . The Li-Cor Odyssey Southern protocol was modified as follows . DNA was separated on a 1% native agarose gel . The DNA was depurinated for 15 minutes in 0 . 25 N HCl then denatured in 0 . 5 M NaOH and 1 . 5 M NaCl prior to transferring by capillary action onto 0 . 45 µm Magnacharge nylon membrane ( GE water and process technologies ) in 20× SSC ( pH 7 . 0 ) . After UV crosslinking , the membrane was prehybridized in a solution containing 5× SSPE , 2% SDS , 10% dextran sulfate , 1× Denhardt's solution and 10 µg/ml sheared , denatured salmon sperm DNA for 2–4 hours at 65°C . Labeled probes were boiled for 5 min and then rapidly chilled on ice for 10 min before addition to the prehybridization buffer and hybridization for 16 h at 65°C . The membrane was washed twice for 5 min in 2× SSPE at RT , twice for 15 min in 2× SSPE with 1% SDS at 60°C , and twice for 15 min in 0 . 1× SSPE at 60°C . The blot was blocked in 0 . 6% cold water fish skin gelatin ( Sigma ) in TBS with 0 . 5% Tween-20 ( TBST ) and 1% SDS for 1 h at RT . Anti-digoxigenin Ab ( Sigma ) was diluted 1∶1000 in 0 . 6% cold water fish skin gelatin in TBST and the blot was probed for 1 h at RT . The blot was washed at RT for 5 min in TBST , 10 min in TBST with 1% SDS , and three times with TBST for 5 min . Anti-mouse IRdye700 and streptavidin IRdye800 ( Rockland ) were diluted 1∶4 , 000 and 1∶20 , 000 , respectively in 0 . 6% cold water fish skin gelatin in TBST with 0 . 02% SDS and the blot was incubated 45 min in the dark at RT . The blot was washed at RT for 5 min in TBST , 15 min in TBST with 1% SDS , three times with TBST for 5 min , and twice in TBS for 5 min . The blots were scanned using a Li-Cor Odyssey infrared imager ( Li-Cor Bioscience ) . HFFs were infected and irradiated at 50 J/m2 as described above . Cells were harvested at 0 , 6 , 24 , and 48 h post irradiation . DNA was extracted as described above . 150 ng of DNA was digested with T4- or mock-digested and the digestions were loaded on a 1% alkaline agarose gel and separated at 25 V for 18 h as described previously [59] . The gel was neutralized for 45 minutes in 0 . 5 M Tris HCl pH 7 . 5 and 1 . 5 M NaCl prior to depurination , denaturation and capillary transfer as described above . Analysis of T4 Southerns for CPD removal was performed as described previously [59] . SYBR Gold stained gels were performed in the same fashion with the following exceptions: one microgram of DNA was loaded in each lane and gels were stained with SYBR Gold after neutralization . For statistical analysis , one-tailed paired t-tests were performed for each time point comparing the mean repair of the host DNA versus the viral DNA ( or versus mock DNA ) as described in the text .
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Human cytomegalovirus ( HCMV ) is a leading cause of birth defects . This may be due in part to this virus' ability to inflict specific damage to its host's DNA , combined with the disruption of an infected cell's ability to repair damage . Earlier studies found that components of the cell's repair machinery were differentially associated with the HCMV viral replication centers in the nucleus . Experiments here extend this observation to include components of the machinery involved in UV lesion repair . We hypothesized that association of components of the DNA repair machinery within the viral replication centers could favor the repair of viral DNA , but more importantly , be detrimental to the repair of cellular DNA . Infected cells were irradiated and examined for repair by three different methods . In the course of this study , we developed a new technique allowing simultaneous evaluation of both the viral and host genomes in an infected cell . These experiments found rapid , selective removal of UV lesions from the viral and not the cellular DNA within infected cells . Our results indicate the differential association of certain cellular repair proteins with this virus may have far-reaching implications in the disease pathogenesis of HCMV infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"microbiology",
"molecular",
"cell",
"biology"
] |
2012
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HCMV-Infected Cells Maintain Efficient Nucleotide Excision Repair of the Viral Genome while Abrogating Repair of the Host Genome
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Placebo response in the clinical trial setting is poorly understood and alleged to be driven by statistical confounds , and its biological underpinnings are questioned . Here we identified and validated that clinical placebo response is predictable from resting-state functional magnetic-resonance-imaging ( fMRI ) brain connectivity . This also led to discovering a brain region predicting active drug response and demonstrating the adverse effect of active drug interfering with placebo analgesia . Chronic knee osteoarthritis ( OA ) pain patients ( n = 56 ) underwent pretreatment brain scans in two clinical trials . Study 1 ( n = 17 ) was a 2-wk single-blinded placebo pill trial . Study 2 ( n = 39 ) was a 3-mo double-blinded randomized trial comparing placebo pill to duloxetine . Study 3 , which was conducted in additional knee OA pain patients ( n = 42 ) , was observational . fMRI-derived brain connectivity maps in study 1 were contrasted between placebo responders and nonresponders and compared to healthy controls ( n = 20 ) . Study 2 validated the primary biomarker and identified a brain region predicting drug response . In both studies , approximately half of the participants exhibited analgesia with placebo treatment . In study 1 , right midfrontal gyrus connectivity best identified placebo responders . In study 2 , the same measure identified placebo responders ( 95% correct ) and predicted the magnitude of placebo’s effectiveness . By subtracting away linearly modeled placebo analgesia from duloxetine response , we uncovered in 6/19 participants a tendency of duloxetine enhancing predicted placebo response , while in another 6/19 , we uncovered a tendency for duloxetine to diminish it . Moreover , the approach led to discovering that right parahippocampus gyrus connectivity predicts drug analgesia after correcting for modeled placebo-related analgesia . Our evidence is consistent with clinical placebo response having biological underpinnings and shows that the method can also reveal that active treatment in some patients diminishes modeled placebo-related analgesia . Trial Registration ClinicalTrials . gov NCT02903238 ClinicalTrials . gov NCT01558700
Positive medical responses to placebo treatments are a well-recognized phenomenon observed in many pathologies , with a higher prevalence for neurological and painful conditions [1 , 2] . Placebo analgesia is observed ubiquitously in pain treatment trials , especially in chronic pain populations , in which it often exhibits sustained effectiveness rivaling in magnitude that of the active treatment [3–5] . Yet , interpretation of placebo response in clinical observations remains questionable because of experimental design weaknesses , as repeatedly pointed out in the past [6–8] . So far , brain markers for placebo pain relief have been mostly studied for acute pain in the healthy population [9–13] , in which individual subject responses seem highly variable and prone to a multiplicity of influences [12] . Accumulating evidence indicates that neuroimaging findings of certain brain measures ( neuromarkers or neural signatures ) can predict acute pain perception [14] , development of chronic pain [15] , future learning [16] , intelligence [17] , and responses to pharmacological or behavioral treatments [18] . Because placebo response is believed to be driven by central nervous system mechanisms involved in expectation and inference about previous experience [11 , 12 , 19–22] , it is reasonable to presume that specific brain measures may predispose individuals to a placebo response . Recent neuroimaging [13 , 21 , 23] and genetic polymorphism [24] studies show results consistent with this hypothesis . Given the enormous societal toll of chronic pain [25] , being able to predict placebo responders in a chronic pain population could both help the design of personalized medicine and enhance the success of clinical trials [6 , 26] . In patients with chronic knee osteoarthritis pain ( OA ) , we used resting-state functional magnetic resonance imaging ( rs-fMRI ) combined with a standard clinical trial design to derive an unbiased brain-based neurological marker to predict analgesia associated with placebo treatment . We hypothesized that brain regional network information sharing ( functional connectivity ) properties should identify the placebo response . We reasoned that if we could uncover a brain marker prior to the start of the trials that forecasts how individual subjects will perform during these trials , then we could conclude that clinical placebo response , at least for sugar pill ingestion , is a predetermined brain process controlling the placebo response and with biological underpinnings useful in clinical decision making . After identifying and validating a brain connectivity-based placebo predictor derived from rs-fMRI scans before the start of treatment , we linearly modeled a predicted placebo response and applied it to the active drug treatment portion of the study . With this approach , we identified a brain region where functional connectivity was predictive of drug treatment response presumed to be minimally dependent on the influence of placebo . This procedure in turn uncovered the adverse effect of active treatment interfering with predicted placebo response .
In study 1 , 2 wk of placebo treatment was associated with a significant decrease in knee OA pain , with both VAS and Western Ontario and McMaster Universities Osteoarthritis Index ( WOMAC ) scores , across all 17 subjects ( Fig 2A and 2E ) . At the end of the 2-wk placebo treatment period , 8/17 ( 47% ) of participants were classified as placebo responders ( based on individual knee pain decrease , VAS ≥ 20% analgesia ) , and the others as nonresponders ( Fig 2B ) . Knee OA pain and the OA-specific pain and disabilities score ( WOMAC questionnaire outcome ) remained unchanged for nonresponders , while responders showed a mean decrease of 54 . 3% ( 95% confidence interval [CI] 29 . 7–79 . 0 ) in knee VAS pain and a mean 38 . 6% ( 95% CI 18 . 0–59 . 2 ) decrease in WOMAC score ( Fig 2C and 2F ) ( note that throughout the study we use VAS scores for classification and WOMAC as an unbiased alternative outcome measure for knee pain ) . In comparison to the matched study 3 no-treatment observational group ( in which 4 of 20 would be classified as responders to no treatment , based on VAS ≥ 20% analgesia ) ( see S1 Fig and S2 Table ) , placebo responders showed a large decrease in knee VAS pain in 2 wk ( Fig 2D ) . After a 2-wk washout period ( withdrawal of placebo pills ) , only knee VAS pain was obtained via a follow-up phone call , and placebo responders showed reversal of analgesia ( analysis of variance [ANOVA]: interaction group x time F ( 2 , 30 ) = 23 . 37 , p < 0 . 001 ) . In study 1 , whole-brain degree count maps , collected before the start of treatment , were used to identify potential brain regional markers for placebo propensity . Group differences in whole-brain degree count maps between placebo responders and nonresponders identified four brain regions that differentiated placebo responders from nonresponders . The highest significant difference was seen for the right midfrontal gyrus ( r-MFG ) ( p < 0 . 001 ) , ( Fig 3A and 3B ) . Degree counts derived from r-MFG showed higher connectivity to the rest of the brain for responders across all densities , with most significant difference seen at 10% density ( Fig 3C ) . At this density , average per voxel degree count within r-MFG was twice as high in responders as in nonresponders ( 3 , 256 ± 237 SE versus 1 , 777 ± 157 SE; t15 = 5 . 3 , p < 0 . 001 ) . Similar density-dependent degree count group differences were also seen for the other three brain regions ( posterior cingulate cortex [PCC] , anterior cingulate cortex [ACC] , and the right secondary somatosensory and primary motor cortex [r-S2/M1] ) . The r-MFG counts explained placebo analgesia magnitude for all participants , based on VAS and on WOMAC changes from baseline ( Fig 3D and 3E ) . Furthermore , to diminish the possibility that the r-MFG counts are related to a regression to the mean phenomenon ( rather than a placebo pill response ) , we examined whether the r-MFG counts reflected symptom severity at time of entry into the study . We found that r-MFG counts were not correlated with VAS prior to treatment but became correlated with placebo treatment , and a similar pattern was also observed for WOMAC ( S3 Table ) . Therefore , the r-MFG fulfills all the requirements for potentially predicting clinical placebo response , which we sought to validate in study 2 . Even though results in study 1 suggest that clinical placebo response is predictable and reversible , the results are based on a relatively artificial setting and a single-blinded study , designed to explore predictability of clinical placebo response . Thus , it was deemed necessary to test whether these findings can be generalized to the more natural setting of the standard clinical double-blind placebo versus active drug comparison scenario . To this end , we performed study 2 , in which the primary objective was to test-validate the results obtained in study 1 . In study 2 , the extent of pain relief , as measured by VAS or WOMAC ( Fig 4A and 4D ) , was similar for placebo and duloxetine treatments . Also , the number of treatment responders ( based on individual knee pain decrease over the course of a 3-mo treatment , VAS score ≥ 20% analgesia ) did not differ between patients randomized to placebo ( 10/20 , 50% ) and patients randomized to duloxetine ( 8/19 , 42% ) ( Fisher’s exact test p > 0 . 75 ) . The magnitude of pain relief in treatment responders also did not differ between treatment groups ( Fig 4B and 4E ) on both VAS and WOMAC outcome scores . Importantly , in comparison to the matched study 3 no-treatment observational group ( in which 7 of 20 would be classified as responders to no treatment at 3 mo , based on VAS ≥ 20% analgesia ) ( Fig 4C , S1 Fig , and S2 Table ) , placebo and duloxetine groups showed a larger decrease in the magnitude of knee VAS pain in 3 mo . Thus , the subjective self-report pain-related outcomes did not differentiate between placebo and duloxetine treatments but did show better analgesia for placebo and duloxetine treatment in contrast to no treatment . To establish the generalizability of the primary brain marker for placebo response in study 1 , we tested whether placebo predictive properties of r-MFG could be captured in study 2 participants . To ensure that the measure remained unbiased , only r-MFG degree counts were extracted for all study 2 subjects , obtained from the functional rs-fMRI scans performed before any treatments were dispensed . In study 2 OA patients who received placebo treatment , r-MFG degree counts were significantly higher in placebo responders ( t18 = 4 . 9; p = 0 . 0001 ) ( Fig 5A , left ) and differentiated between placebo responders and nonresponders at 95% accuracy ( Fig 5A , right ) . Perhaps more importantly , empirical placebo analgesia could be predicted from r-MFG degree counts when the best-fit linear regression equations , identified in study 1 between r-MFG degree counts and future placebo analgesia , were applied to study 2 r-MFG degree counts , both for VAS ( p = 0 . 004 ) and WOMAC ( p = 0 . 12 ) scores ( Fig 5B , left and right ) . Therefore , the placebo response predictive properties of r-MFG degree counts were validated for the placebo treatment arm of study 2 . Once again , to discount that r-MFG counts are a reflection of regression to the mean , we correlated the r-MFG values from study 2 placebo group with VAS and WOMAC values before treatment start and 3 mo after treatment . For both outcome measures , r-MFG counts were only correlated to knee pain after a 3-mo exposure to placebo pill treatment ( S3 Table ) . We additionally explored , in multiple regression models , the contribution of demographics ( age , gender , and pain duration ) , Beck Depression Inventory ( BDI ) , Pain Catastrophizing Scale ( PCS ) , past use of medications , and the r-MFG degree counts to explain VAS-based analgesia . The r-MFG degree count was the main contributor , explaining 37 . 5% of the variance . Age and gender also significantly contributed to the model by explaining 18 . 8% and 9 . 7% of unique variance , respectively . Performing the same analysis for WOMAC-based analgesia did not identify any additional contributions besides the r-MFG degree counts to the analgesia outcome . Given that in study 2 there was no difference in pain outcomes—VAS or WOMAC—between placebo and duloxetine treatments , one would conclude that duloxetine is no better than placebo for pain relief , at least in the OA patients in study 2 . The conclusion in turn leads to the hypothesis that r-MFG degree counts in the duloxetine arm , when entered into the regression equations derived from study 1 , should just as accurately predict analgesic outcome as in the placebo treatment arm in study 2 . In fact , this hypothesis was proven false . Degree counts in r-MFG did not differentiate between duloxetine responders and nonresponders . A two-way ANOVA for r-MFG degree count as a function of treatment type ( placebo or duloxetine ) and response type ( responders and nonresponders ) indicated a significant interaction ( two-way ANOVA , F1 , 34 = 12 . 60 , p = 0 . 0012 ) ; a post hoc Tukey HSD test indicated response type was significant for placebo treatment ( HSD test = 5 . 18 with Studentized Range critical p = 0 . 001 threshold of 5 . 09 ) , but not for duloxetine treatment ( HSD test = 2 . 01 with 0 . 05 threshold of 2 . 87 ) ( Fig 5C , left panel ) . Comparison of the receiver operating characteristic ( ROC ) curves obtained for placebo treatment ( Fig 5A , right panel ) and for duloxetine treatment ( Fig 5C , right panel ) indicated that they are significantly different ( difference of 0 . 62 , 95% CI ( 0 . 578–0 . 662 ) , p < 0 . 0001 ) , and applying the regression equation from study 1 to r-MFG degree counts in study 2 did not predict empirical analgesia for duloxetine treatment , both for VAS and WOMAC scores ( Fig 5D ) . This result demonstrates that the duloxetine treatment and placebo treatment outcomes are differentiable at the brain circuitry level , although clinically they may be indistinguishable . The bedrock assumption of all randomized controlled clinical trials is that placebo and active treatment responses are linearly additive [27] ( more complex interactions may also exist , for example [28] ) , that is , Empirical analgesia = Placebo response + Drug response . This model is inherently assumed in all clinical trials , as the primary statistical test in randomized clinical trials is always a competition between the effect sizes of the two responses . The dissociation between our reported pain relief and r-MFG degree counts in the placebo- and duloxetine-treated subjects suggests that this linear additive relationship may not always be valid . We therefore pose the model as a formal hypothesis and examine its implications regarding ( 1 ) observed versus expected analgesia and ( 2 ) underlying brain information sharing properties . Given that the placebo arm of study 2 was fully explainable from study 1 results , r-MFG degree counts in the duloxetine treatment arm must also reflect the magnitude of placebo response in the patients randomized to active treatment . Thus , one can calculate a predicted placebo response in the duloxetine arm using the regression equation derived from study 1 and based on r-MFG degree counts in the duloxetine-treated patients . The above equation then becomes the following: Empirical analgesia=Predicted placebo response + Drug response + Error . The Error term would have contributions from all parameters of the equation , at least because of measurement errors , and remains unknown . On the other hand , the individual subject predicted placebo response compared to the empirical analgesia provides an estimate of individual participant drug response ( assuming that Error = 0 in above equation ) ( Fig 6A ) . We observe in Fig 6A that relative to the individual predicted placebo analgesia ( gray bars ) , ingestion of duloxetine appears to have increased observed analgesia in subjects 1–6 and had no visible additional effect in subjects 7 , 8 , 10 , and 14–16 , while in subjects 9 , 11 , 13 , and 17–19 , duloxetine actually diminished the modeled placebo analgesia . Our model thus unravels the extent of efficacy of the active drug after correcting for modeled placebo responses . Moreover , these results indicate that a purely additive model cannot hold for the current data because only in one subgroup did duloxetine treatment increase observed analgesia from predicted placebo analgesia , while in another subgroup , it interfered with and diminished expected placebo analgesia . Banking on the notion that r-MFG is reflecting predicted placebo response , in the duloxetine-treated subjects we can estimate the expected duloxetine-related analgesia from the difference between predicted placebo response and observed analgesia ( assuming Error = 0 in the linear equation ) . The difference between the red and grey bars in Fig 6A can then be considered the active drug treatment-related estimated analgesia after correcting for modeled placebo analgesia . Therefore , this difference provides the metric with which we test for the existence of a brain region predictive of future placebo-corrected response to duloxetine . Note the fact that the failure to differentiate behaviorally between placebo and drug response has no direct bearing on the hypothesis that a drug response prediction can be constructed by linearly modeling away the expected placebo response . Both duloxetine and placebo responses are mediated through central mechanisms; as a result , the interaction between and across identifiable brain regions may in turn explain their pain relief relationships . In two participants ( 4 and 6 ) , there was minimal predicted placebo response but above threshold empirical analgesia ( 20% analgesia dotted line ) , suggesting that these subjects were the drug responders with the least contribution from modeled placebo response . Therefore , a brain area with higher degree counts for these two subjects compared to the rest of the duloxetine responders ( subjects 1–3 , 5 , and 7–8 ) may identify a brain region specific to drug treatment propensity . This search resulted in pinpointing the right parahippocampal gyrus ( r-PHG ) where the degree count was higher in subjects 4 and 6 from the remaining six duloxetine responders ( Fig 6B ) . To assess the validity of this region , the r-PHG degree count was extracted for all duloxetine-treated subjects and correlated to the difference between empirical VAS analgesia and predicted placebo response ( i . e . , estimated placebo-corrected drug response ) . This correlation was significant ( p = 0 . 048 ) for the duloxetine-treated group ( Fig 6C left ) . When the same analysis was done for the unbiased WOMAC outcome measure ( based on the best-fit equation derived from study 1 for WOMAC ) , it too showed a significant correlation between r-PHG degree counts and the difference between empirical WOMAC analgesia and predicted placebo response ( p = 0 . 033 ) ( Fig 6C right ) . Both correlations reinforce the idea that the r-PHG degree counts reflect/predict future drug responses , after accounting for the modeled placebo response . In an exploratory multiple regression analysis , we examined the contribution of demographics , BDI , PCS , and past use of medications on the relationship between r-PHG and the difference between empirical analgesia and predicted placebo response , using VAS or WOMAC measures; no additional significant contributions were identified .
One weakness of the current study is the limited number of subjects used , counterbalanced by the reversibility of placebo response during washout and a robust validation and by showing the superiority of placebo analgesia relative to the no-treatment group both at 2 wk and at 3 mo , which altogether distinguish clinical pill placebo in OA from statistical confounds . An important design limitation of our study was the independent recruitment for study 3 . Ideally , the no-treatment-arm patient entry should have been randomized within study 1 and study 2 recruitments . We should also qualify that observed analgesia seen in placebo responders may still be considered ( although unlikely ) a reflection of natural recovery or symptom fluctuation rather than definitely caused by the placebo , because study 3 subjects were not randomized into study 1 and 2 , pain ratings were only collected at entry and end of treatment , and the washout period knee pain was collected over the phone ( thus , the rapid and complete reversal of analgesia may be due not just to cessation of pill administration but also to the change in environmental cues ) . Even though we used a single threshold ( 20% ) for defining placebo responders , the identified brain property—degree counts of r-MFG—identifying placebo responders could also predict the continuous measure of magnitude of placebo analgesia for VAS and for WOMAC in study 1 and study 2 , implying that the identified brain marker is not strictly dependent on the specific analgesia threshold chosen . The ubiquity or specificity of the brain marker uncovered for placebo response in OA remains to be identified across types of chronic pain and for various placebo-type manipulations . Moreover , the predictability of future drug response after modeling away the placebo response requires replication and trial designs in which the error term in the linear equation can be systematically estimated and the general applicability of the linear model tested in contrast to more complex model designs ( e . g . , equations incorporating multiplicative or higher order polynomial terms ) , as well as testing for drug-type specificity . The current study falls within the general effort of using neuroimaging technology to forecast the future health status of individuals , which is showing predictive value across many medical domains [18] . The opportunity presented with identification of placebo and placebo-corrected drug response predictive brain markers , specifically in chronic pain patients and for clinical trials using neutral instructions , presents both a concrete and humanitarian possibility of decreasing suffering with the recognition and identification of individual differences in brain function . If future similar studies can further expand and eventually provide a brain-based predictive best-therapy option for individual patients , it would dramatically decrease unnecessary exposure of patients to ineffective therapies and decrease the duration and magnitude of pain suffering . Moreover , if placebo response can be predictably removed/reduced in clinical trials , then , besides reducing the cost of clinical trials , the efficacy and neurobiology of therapies can be identified more accurately and at the level of the individual .
All participants gave written informed consent to procedures approved by the Northwestern University Institutional Review Board committee ( STU00039556 for study 1 and 2 , STU00059872 for study 3 ) . We recruited a convenience sample of 143 community-based people with knee osteoarthritis ( OA ) through public advertisement and Northwestern University-affiliated clinics . A total of 20 patients were recruited for study 1 , 70 for study 2 , and 53 for study 3 . From these , 45 either did not complete the studies they were enrolled in or their brain scans did not pass the quality assessment pipeline ( see Fig 1 , S1 and S2 Tables for demographics ) . Of the remaining 98 patients , 17 took part in study 1 , 39 in study 2 , and 42 in study 3 ( from which 20 were selected to match our other groups ) . We also recruited 20 age-matched healthy control subjects . Healthy subjects were matched to the mean age and gender distribution of all OA patients . All OA participants met the American College of Rheumatology criteria for OA ( confirmed by TJS ) and had pain of at least 1-y duration . A list of inclusion and exclusion ( mainly presence of other chronic pain conditions and major depression ) criteria was imposed , including a knee-pain intensity of at least 4/10 on the 11-point numerical rating scale ( NRS ) within 48 h of the screening visit . A detailed list of all inclusion and exclusion criteria as well as clinical trial registrations is presented in the Supporting Information file ( see S1 Text ) . Data from three different studies were used in the manuscript ( Fig 1 ) . Study 1 ( Clinicaltrials . gov accession number: NCT02903238; protocol details in S1 Study Protocol; relevant checklists in S1 and S2 Checklists ) constituted the discovery group and was used to identify and localize brain functional differences between placebo responders and nonresponders . All study 1 participants ingested placebo pills ( lactose ) once a day for 2 wk in a single-blind design . Prior to the experiment , participants were informed that they would have an equal chance of receiving a placebo pill or an active drug . The research staff knew that all patients were receiving placebo pills . Pain and behavioral parameters were collected in person before and after treatment , and knee pain VAS scores were additionally collected 2 wk after drug washout via a phone call . For all patients , brain scans were collected prior to treatment . Study 2 ( Clinicaltrials . gov accession number: NCT01558700; protocol details in S1 Study Protocol; relevant checklists in S1 and S2 Checklists ) was performed independently and after the end of study 1 . This study served as the validation group and involved a double-blinded trial in which patients received placebo or duloxetine for 3 mo . Study 2 participants ingested either placebo pills or duloxetine at a dose of 30 mg for the first week and escalated to 60 mg for the rest of the treatment period , except for the last week , when the dose was decreased back to 30 mg . Drug preparation was made by an independent clinical research assistant , and the research staff providing treatment to patients were kept blind at all times about subjects’ treatment . For this study , a parallel assignment intervention model was used , with a simple randomization using a 1:1 allocation ratio . Randomization codes were prepared by an independent clinical research assistant under TJS’s supervision , used for drug preparation , and then concealed until the end of the study . The research staff performing recruitment and collecting data were never in contact with the randomization codes until the end of the study . The main purpose of study 2 was to validate the placebo propensity marker found in study 1 , but in a real clinical trial environment to mimic what is normally performed ( e . g . , by pharmaceutical companies ) for drug efficacy assessment . Another aim of study 2 was to test the specificity of the brain biomarker to treatment type ( i . e . , whether the biomarker identified in study 1 reflects general response to treatment or placebo response specifically ) . Behavioral and clinical parameters were obtained before and after treatment . Brain scans were collected prior to treatment . For study 1 and 2 , patients were asked to discontinue their medications 2 wk prior to the beginning of the trial and were provided with acetaminophen as rescue medication . In this study , two duloxetine-treated and three placebo-treated patients reported worsening of knee pain; four duloxetine-treated and three placebo-treated patients reported dizziness and grogginess symptoms . No serious adverse events were reported . Study 3 ( protocol details in S2 Study Protocol ) was also run independently from study 1 and 2 . For this study , participants did not receive any medications and were asked to continue their regular treatment regimen . Patients from study 3 represent the natural progression of the OA condition within the same time frame as study 1 and 2 . The purpose of study 3 was therefore to account for regression to the mean or any other biases that could influence the outcome of the subjects’ report of OA pain in the clinical trial setting . Patients from all studies completed a general health questionnaire and a VAS ( on a 0 to 10 scale ) for their knee OA pain . Patients from study 1 and 2 also completed the WOMAC , the BDI , and the PCS . All questionnaires were administered on the day of brain scanning . Response categorization and brain regions of interest were identified using only the VAS measure and then tested for consistency using WOMAC . Thus , WOMAC provided an unbiased estimate of treatment response and of brain regional properties . Analgesic response was defined a priori on an individual basis as at least a 20% decrease in VAS pain from baseline to the end of treatment period; otherwise , subjects were classified as nonresponders . This threshold for analgesic response was chosen based on our earlier results [15] and also based on a recent meta-analysis estimate of the size of placebo analgesia [32] . In study 2 , to partially compensate for regression to the mean effects , VAS was measured 3 times over a 2-wk period prior to the start of treatment and after cessation of medication use , averaged , and used as the indicator of pain at entry ( designated as baseline in the figures ) . For all participants in studies 1 and 2 , MPRAGE type T1-anatomical brain images were acquired as described before [15] . Briefly , a 3T Siemens Trio whole-body scanner with echo-planar imaging ( EPI ) capability using the standard radio-frequency head coil with the following parameters: voxel size 1 × 1 × 1 mm; TR = 2 , 500 ms; TE = 3 . 36 ms; flip angle = 9°; in-plane matrix resolution = 256 × 256; slices = 160; and field of view = 256 mm . rs-fMRI images were acquired on the same day and scanner with the following parameters: multi-slice T2*-weighted echo-planar images with repetition time TR = 2 . 5 s , echo time TE = 30 ms , flip angle = 90° , number of slices = 40 , slice thickness = 3 mm , and in-plane resolution = 64 × 64; the number of volumes was 300 . The 40 slices covered the whole brain from the cerebellum to the vertex . All MRI data are available on openfmri . org . As we described before [15] , the preprocessing of each subject's time series of fMRI volumes was performed using the FMRIB Expert Analysis Tool ( FEAT [41] , www . fmrib . ox . ac . uk/fsl ) and encompassed the following: discarding the first five volumes to allow for magnetic field stabilization , skull extraction using BET , slice time correction , motion correction , spatial smoothing using a Gaussian kernel of FWHM 5 mm , and high-pass temporal filtering ( 150 s ) . Several sources of noise , which may contribute to non-neuronal fluctuations , were removed from the data through linear regression . These included the six parameters obtained by rigid body correction of head motion , the global BOLD signal averaged over all voxels of the brain , signal from a ventricular region of interest , and signal from a region centered in the white matter . All preprocessed fMRI data were registered into standard MNI space multiplied by a common gray matter mask generated from all subjects in the study ( this step was performed in order to limit all analyses to a common set of gray matter voxels ) and subsequently down-sampled to yield 29 , 015 regional cortical and subcortical nodes ( 4 x 4 x 4 mm isometric voxels ) . To construct the whole-brain voxel-wise connectivity networks for each subject , we first computed the Pearson correlation coefficient ( R ) for all possible pairs of the 29 , 015 cortical and subcortical voxel time series from the preprocessed rs-fMRI data . For each subject , the threshold was calculated to produce a fixed number of edges M to be able to compare the extracted graphs [42] . Therefore , the values of the threshold are subject dependent . Each of these extracted graphs comprised N = 29 , 015 nodes corresponding to the number of voxels and M undirected edges corresponding to the significant nonzero absolute values of correlation greater than the value of the threshold . Since the value of the chosen threshold is important [42 , 43] , we chose to test several values of threshold , from a conservative threshold corresponding to 2% connection density ( the percentage of edges with respect to the maximum number of possible edges [ ( N x N– 1 ) / 2] ) to a lenient threshold corresponding to 20% link density . Networks constructed at 2% link density are dubbed sparse networks , while those constructed at 20% are dense . In general , results are presented over a range of thresholds to give the reader a sense of the ( lack of ) dependence of a property upon thresholds , and no formal definition of threshold ranges is proposed since it is essentially arbitrary . To localize the nodes ( voxels ) that exhibited significant changes in number of connections ( degrees ) in responders and nonresponders in Study 1 , we performed a whole-brain analysis . First , for each subject and link density , we computed the number of edges ( links ) for each node from the brain graph , using the BCT toolbox [44] . The number of degrees was used to construct single brain volume in standard MNI space for each subject , in which the value assigned for each node corresponds to the degree of that given node . Differences in nodal degree between responders and nonresponders , at each link density , were carried out using Randomise in FSL [45] . This technique uses permutation-based inference to allow for rigorous comparisons of significance within the framework of the general linear model with p < 0 . 05 . Group differences were tested against 5 , 000 random permutations , using the threshold-free cluster enhancement ( TFCE ) method . The maps between responders and nonresponders were contrasted at all densities , and the final difference map represented the conjunction of significantly different voxels across at least eight out of the ten densities evaluated after TFCE correction . Finally , the conjunction map presented in Fig 2B was used to determine regions of interest that serve as brain biomarkers for placebo response . Pretreatment degree counts for the regions of interest identified in study 1 were determined for all study participants , and we tested whether these values can predict placebo analgesia and/or duloxetine analgesia in study 2 . Prediction accuracy was tested using a binary ( i . e . , prediction of group responders versus nonresponders ) or a continuous model ( prediction of response magnitude ) . The significance of the binary classification was determined by an ROC area-under-the-curve ( AUC ) analysis that identifies the sensitivity and specificity of predicting future treatment outcomes . The significance of the continuous prediction was determined using a regression analysis of predicted versus observed outcomes . The relationship between outcome and degree was determined from linear fitting ( y = ax + b ) for study 1 , where y = represents % analgesia response , a = fitted slope , and x = degree count of region of interest ( ROI ) . This equation was used to determine the outcome ( y2 ) in study 2 ( y2 = ax2 + b ) , where x2 represents the degree count of the ROI . The significance of the prediction was assessed by correlation analysis between the predicted outcome ( y2 ) and the observed response for patients in study 2 . For Figs 2 and 4 , a repeated-measures ANOVA compared between groups and time effects . Post hoc comparisons’ p-values ( after Bonferroni correction for multiple comparison ) are indicated on the figures . For Fig 5A and 5C , two-way ANOVA was used , and the post hoc p-value is indicated on the figure . For Fig 6B , a nonparametric test was used because the number of observations was small . Multiple regression analysis was performed with the linear regression tool in SPSS software . VAS analgesia was set as the dependent variable , and r-MFG count value , gender , age , pain duration , past medication use , and BDI and PCS scores were set as independent variables . The stepwise forward elimination method was then used to generate a multifactorial regression model , using a p < 0 . 05 criterion for adding variables and a p < 0 . 1 criterion for removing a variable and repeating the process until none improved the process . To compare between ROC curves in Fig 5 , we used the online tool provided from varrstats ( vassarstats . net/roc_comp . html ) , which is using the method described in [46] . This provided us with the difference and standard error . We then calculated the 95% CI using the following formula: CI = ± Zα/2 * σ / √ ( n ) , where Zα/2 represents the z-value at α/2 ( where α is the confidence level , here 95% ) , σ is the standard error , and n is the sample size .
|
Placebo response is extensively studied in healthy subjects and for experimental manipulations . However , in the clinical setting it has been primarily relegated to statistical confounds . Here , for the first time we examine the predictability of future placebo response in the clinical setting in patients with chronic osteoarthritis pain . We examine resting-state functional magnetic-resonance-imaging ( fMRI ) brain connectivity prior to the start of the clinical trial and in the setting of neutral instructions regarding treatment . Our results show that clinical placebo pill ingestion shows stronger analgesia than no treatment and is predictable from resting state blood-oxygen-level-dependent ( BOLD ) fMRI , and right midfrontal gyrus degree count ( extent of functional connectivity ) identifies placebo pill responders in one trial and can be validated ( 95% correct ) in the placebo group of a second trial , but not in the active drug treatment ( duloxetine ) group . By modeling the expected placebo response in subjects receiving active drug treatment , we uncover a placebo-corrected drug response predictive brain signal and show that in some subjects the active drug tends to enhance predicted placebo response , while in others it interferes with it . Together , these results provide some evidence for clinical placebo being predetermined by brain biology and show that brain imaging may also identify a placebo-corrected prediction of response to active treatment .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
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2016
|
Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials
|
Neglected Tropical Diseases ( NTDs ) are a group of several communicable diseases prevalent in the tropical and subtropical areas . The co-endemicity of these diseases , the similarity of the clinical signs , and need to maximize limited financial and human resources have necessitated implementation of integrated approach . Our study aims to share the lessons of this integrated approach in the fight against Buruli ulcer ( BU ) , leprosy and yaws in a rural district in Benin . It is a cross-sectional study using a single set of activities data conducted from May 2016 to December 2016 . Health workers and community health volunteers involved in this study were trained on integrated approach of the Buruli ulcer , leprosy and yaws . Village chiefs were briefed about the activity . The trained team visited the villages and schools in the district of Lalo in Benin . After the education and awareness raising sessions , all persons with a skin lesion who presented voluntarily to the team were carefully examined in a well-lit area which respected their privacy . Suspected cases were tested as needed . The socio-demographic information and the characteristics of the lesions were collected using a form . A descriptive analysis of the epidemiological , clinical and laboratory variables of the cases was made using Excel 2013 and SPSS version 22 . 00 . In the study period , 1106 people were examined . The median ( IQR ) age of those examined was 11 ( 8; 27 ) years . Of 34 ( 3 . 1% ) suspected BU cases , 15 ( 1 . 4% ) were confirmed by PCR . Only three cases of leprosy were confirmed . The 185 ( 16 . 7% ) suspected cases of yaws were all negative with the rapid test . The majority of cases were other skin conditions , including fungal infections , eczema and traumatic lesions . The integrated approach of skin NTD allows optimal use of resources and surveillance of these diseases . Sustaining this skin NTD integrated control will require the training of peripheral health workers not only on skin NTD but also on basic dermatology .
Neglected Tropical Diseases ( NTD ) are a diverse group of communicable diseases prevalent in tropical and subtropical areas . They are endemic in 149 countries . At least 100 of these countries have two or more endemic NTD . At least 30 of these countries have 6 or more endemic NTD [1 , 2] . They affect more than a billion people and cost developing economies billions of dollars every year . The socio-economic consequences are enormous [1] . The populations most affected by NTD are the poor [2 , 3] . Of the 20 NTD , most of them are endemic in Africa . Case management interventions are implemented for specific NTD by the World Health Organization Regional Office for Africa . Eight of those NTD have cutaneous manifestations: Buruli ulcer ( BU ) , leprosy , yaws , cutaneous leishmaniasis , lymphatic filariasis morbidity , scabies , ectoparasites and mycetoma . WHO has recently published an integrated training manual on skin NTDs to help countries move forward in this direction [4] . Worldwide , progress has been made in the fight against these diseases . So by 2020 , the objectives are respectively the eradication of yaws , the elimination of leprosy and the control of BU [5–7] . The co-endemicity of certain diseases; the similarity of the clinical signs as well as the scarcity of financial , human and temporal resources called for an integrated management of these diseases [8 , 9] . So at the May 2013 World Health Assembly and at the September 2013 WHO Regional Committee for Africa , two resolutions were adopted ( WHA 66 . 12 AFR / RC63 . R6 ) , both of which recommending the integration of NTD management programs [3 , 10] . BU is endemic in southern Benin; whereas leprosy is endemic in both southern and northern Benin . Yaws outbreaks were reported in Benin and were effectively treated in the 1980s [11 , 12] but surveillance was not maintained . Onchocerciasis , lymphatic filariasis , loa loa , schistosomiasis , soil-transmitted helminthiasis , African Human Trypanosomiasis , dracunculiasis , trachoma , leprosy and Buruli ulcer are relevant NTDs in Benin [13] . Taking into account the 2013 resolution of the World Health Assembly , we set out to develop the integrated screening of BU , leprosy and yaws in the district of Lalo in southern Benin . The goal of this study is to share the lessons learned during this experience of the integrated management of three skin NTD in a rural district of Benin .
This study using a single set of activities data , was approved and authorized by the institutional review board of the National Program against Leprosy and Buruli Ulcer of Benin ( nr013/PNLLUB/SA ) . It was also approved and authorized by the regional health authorities of Lalo ( nr20/16/BZ-KTL/SA ) as well as by the chiefs of the villages . Informed consent was obtained orally from all adult participants and from parents , caretakers , or legal representatives of participants aged ≤18 years after obtaining their assessment . For this study using a single set of activities data , verbal informed consent was necessitated given both high rates of illiteracy and the need to provide more details about the study in local languages; most participants spoke only local languages . Verbal informed consent was documented as “verbal informed consent given: yes or no” in a register . Data were processed with strict respect for confidentiality and anonymity . At the national level , the BU and leprosy management programs are already integrated since they are overseen by one national program known as the “Programme National de Lutte contre la lèpre et l’Ulcère de Buruli” ( PNLLUB ) . At the operational level , the leprosy and BU management programs are handled by different health centers . In Benin , four specialized facilities known as “Centre de Dépistage et de Traitement de l’Ulcère de Buruli” ( CDTUB ) , located in the southern part of the country ensure BU management and supervise the peripheral health centers where uncomplicated cases are managed . As for leprosy , screening and care are provided by eight specialized facilities known as “Centre de Traitement Anti Lèpre” ( CTAL ) , which are spread across the country . There is no yaws program and surveillance activities are done as part of the work of the CDTUB and CTAL , which act as sentinel sites . This cross-sectional study using a single set of activities data took place from May 1st 2016 to December 31st 2016 in the district of Lalo . It is located in the south-west of Benin , 150 km from Cotonou , the country’s economic capital . This district has an area of 432 km2 [14] . It is divided into 11 sub-districts with a total population estimated at 132 , 964 inhabitants in 2016 [15] . BU and leprosy are endemic in this district [16] . This district has 115 primary schools , a CDTUB and each of its sub-districts has a health center . Thirty community health volunteers are actively involved in skin NTDs control in this district . We trained the health workers ( nurses , midwives and laboratory technicians ) , teachers and community health volunteers in the integrated control and management of leprosy , BU and yaws . These actors had already demonstrated their skills in the control of BU [17] . The training of the health workers covered basic epidemiology , clinical diagnosis , differential diagnosis , complications , social consequences and treatment of these three diseases were discussed . The training for the community health volunteers and teachers was mainly focused on clinical diagnosis to increase their capacity to suspect cases . The training for the health workers was principally focused on clinical diagnosis , performing rapid diagnostic test for yaws and case management . Furthermore , the heads of sub-districts and village chiefs received briefing on these diseases in order to obtain their support and involvement . Social mobilization was carried out the day before as well as the day of the screening with the involvement of opinion leaders and village chiefs . A schedule for the screening was made and approved by the health authorities of the area . In accordance with this schedule , the trained health workers went to villages and primary schools . The activity started with an education and awareness raising session on BU , leprosy and yaws . Using the video projector , documentaries on BU and leprosy were shown to the participants . After that , the trained health workers and community health volunteers provided clear explanation on the disease by using the WHO posters on BU , leprosy and yaws . After the education and awareness raising session , verbal informed consent was obtained from all persons with a skin lesion who presented themselves voluntarily to the team . Then they were carefully examined in a well-lit area which respected their privacy . The socio-demographic information and the characteristics of the lesions were collected using a form . Only the patients with skin lesions were excluded in this study . BU lesions were classified according to the WHO categories: Category I ( a single lesion with a diameter ≤ 5 cm ) ; Category II ( a single lesion with a diameter between 5 and 15 cm ) ; Category III ( a single lesion with a diameter >15 cm; multiple lesions; osteomyelitis; a lesion located in a critical area such as the eyes , breasts or genitals ) [18] . Leprosy cases were classified as paucibacillary or multibacillary . Disabilities due to leprosy were classified according to WHO recommendations [19] . For suspected BU cases , swabs or fine needle aspirate samples were collected and sent to the laboratory “Laboratoire de Référence des Mycobactéries” in Cotonou for confirmation by Polymerase Chain Reaction ( PCR ) for IS2404 . Suspected yaws cases were confirmed using a rapid non-treponemal and treponemal test ( DPP Syphilis Screen and Confirm Assay , Chembio Diagnostic Systems , Medford , NY , USA ) . Suspected cases of leprosy were referred to the nearest CTAL for confirmation . Confirmed cases of BU were treated at CDTUB in Lalo in accordance with the WHO protocol [20] . Other cases of chronic ulcers were treated according to their etiology at the CDTUB in Lalo . Leprosy cases were referred to the nearest CTAL for care . Other dermatosis cases were treated as outpatients and the complicated cases were referred to the Departmental Hospital . The results of this study were given to the district health authorities and the “Programme National de Lutte contre la Lèpre et l’Ulcère de Buruli” ( PNLLUB ) for decision-making . The base maps were loaded on the DIVA-GIS program ( http://www . diva-gis . org/ ) and completed by base maps of the “Programme National de Lutte contre la Lèpre et l’Ulcère de Buruli” ( PNLLUB ) . The map was made using the software QGIS version 1 . 8 . 0 . The data was recorded in Excel 2013 and analyzed with SPSS version 22 . 00 . We conducted a descriptive analysis of the cases’ epidemiological , clinical and biological variables . To calculate the detection rate , the cases were compared to each sub-district’s population projection for 2016 made by the “Institut National de la Statistique et de l’Analyse Economique” of Benin [15] .
A total of 1394 people were screened . There were 288 ( 20 . 7% ) persons who did not have skin lesions , therefore , were excluded and 1106 ( 79 . 3% ) participants who had skin lesion were included ( Fig 1 ) . The general characteristics of the patients with skin lesions as well as the characteristics of the lesion were summarized in the Table 1 . The median age ( IQR ) of the patients with skin lesion was 11 ( 8; 27 ) years . Just over one-third ( 37 . 0% ) of those surveyed were female and 65 . 9% were children less than 15 years old . Of the 34 ( 3 . 1% ) suspected BU cases , 15 ( 1 . 4% ) were confirmed by PCR . Only 3 cases of leprosy from the same village were confirmed clinically . The rapid DPP test results for all 185 ( 16 . 7% ) suspected yaws cases were all negative . The majority of cases were other non-NTD skin conditions , including fungal infections ( Pityriasis versicolor , Tinea capitis , Tinea pedis ) , eczema and traumatic lesions . These conditions can be reliably diagnosed clinically by trained health workers . The detection rate for BU cases ranged from 0 to 34 cases per 100 , 000 inhabitants per sub-district . The median ( IQR ) age of BU patients was 21 years ( 10 . 5; 32 . 5 ) . Out of the 15 confirmed BU patients , 3 ( 20% ) had Category III lesions and only 1 patient had a limitation in movement of the joint ( Fig 2 ) . The mapping of confirmed BU and leprosy cases is presented in Fig 2 .
In this study , we share our experience of the integrated control and management of neglected tropical diseases in a rural district of Benin in accordance with the recommendation of resolutions WHA 66 . 12 AFR / RC63 . R6 of the 63rd World Health Assembly and the WHO Regional Committee for Africa [3 , 10] . To our knowledge , our study is one of the first attempts in West Africa to try to implement these resolutions and publish the results . In the implementation of this integrated management of NTD with cutaneous manifestations , we used the approach proposed by Mitja et al [21] . We identified co-endemic communities; we trained the team; carried out social mobilization; performed mobile medical consultations; conducted the screening and management of cases; mapping and held review meetings . During the mobile medical consultations , all persons with skin lesions were directed to a fixed site in their village , where they were examined and treated for all skin conditions and not just for NTD with cutaneous manifestations . Mobile medical consultations are better than active door-to-door searches that require a lot of resources and are no longer recommended by the WHO for leprosy screening [22] . In addition , mobile medical consultations focused on all skin diseases have the advantage of reducing the stigma and discrimination that would result from the search of a specific NTD with cutaneous manifestations [23–26] . In our study , we screened 1 , 106 people to detect only 15 BU cases ( 1 . 4% ) and 3 cases of leprosy ( 0 . 3% ) . In Malawi , Kelias et al reported 1% cases of leprosy among people with skin lesions examined in the community [27] . Our study seems to have the advantage of being efficient since , with the same resources , at least 3 types of NTDs were actively sought at the same time . Although the number of BU and leprosy cases which were confirmed and treated may seem low , these cases were detected early . Indeed , only 20% of the BU cases detected had Category III lesions and one out of the 3 leprosy cases had a Grade 2 disability . Early detection helps reduce long costly hospital stay , the functional limitations and the socio-economic consequences [28–32] . In this study , no case of yaws was confirmed . However , it is too early to state that there are no yaws in this rural district and screening should continue . Moreover , this integrated management allowed the community-based screening team to maintain continuous monitoring; to suspect cases of yaws; to detect other NTD ( lymphatic filariasis ) and other dermatological conditions . This contributed to the reduction of under-reporting of NTD with cutaneous manifestations . In addition , 4 out of 5 patients ( 79 . 6% ) had a dermatosis other than NTD with cutaneous manifestations . These other dermatoses were mostly fungal infections that had to be taken care of . Previous studies have shown that fungal skin infections are very common in the community [27 , 33] . Thus , this activity helped to solve an ethical problem as there were no exclusions: all cases were treated or referred to the dermatologist . Furthermore , a resolution adopted by the WHO Regional Committee for Africa also recommends that African countries promote leadership in order to establish and strengthen national integrated NTD programs and foster multi-sectoral collaboration [10] . The good planning and success of the implementation of the integration result therefore from the mobilization of health system stakeholders and community actors . The ownership and adherence of the health authorities to this approach was indicated by the active participation of the PNLLUB , the health authorities , the local elected officials and the community health volunteers . One of the limitations of this study is the absence of a dermatologist on the community screening team . In fact , for a population of more than 11 million inhabitants , Benin has less than fifteen dermatologists ( less than three dermatologists for about two million people ) . All of them are located in the big cities . As a result , it is impossible to have a dermatologist in rural areas where NTD with cutaneous manifestations are more endemic [34–36] . Another limitation of this study is the organization of community consultations . As a matter of fact , patients who have unsightly lesions may be ashamed to appear in public [23 , 25 , 37 , 38] . But since the screenings were preceded by education and awareness raising sessions , the relatives of these patients at least had the information and could then discreetly take them to the referral health center for their care . In this study , we did not evaluate the cost of these activities . Future studies might focus on financial aspects . At the end of this study , we can conclude that the integrated control and management of skin NTD allows optimal surveillance of these diseases . Sustaining this integrated management will require the training of peripheral health workers not only on skin NTD with but also on basic dermatology . We believe that our results will help other districts in Benin and other countries in the implementation of the integrated management of skin NTD in the world in general and in Africa in particular .
|
Neglected Tropical Diseases are a group of several communicable diseases prevalent in the tropical and subtropical areas . The co-endemicity of these diseases , the similarity of the clinical signs , need to maximize limited financial and human resources have necessitated the integrated approach . Our study aims to share the lessons learned from the integrated approach in the fight against Buruli ulcer , leprosy and yaws in a rural district in Benin . In this study , health workers , community health volunteers involved in this study were trained on integrated approach of the Buruli ulcer , leprosy and yaws . The trained team visited the villages and schools in the district of Lalo in Benin . After the education and awareness raising session , all persons with a skin disease who presented voluntarily to the team were carefully examined in a well-lit area which respected their privacy . Of 1106 people with skin diseases , 15 ( 1 . 4% ) were Buruli ulcer and three ( 0 . 3% ) were leprosy . The majority of cases were other skin conditions . Sustaining this skin Neglected Tropical Diseases integrated control will require the training of peripheral health workers not only on skin Neglected Tropical Diseases but also on basic dermatology to deal with other common skin diseases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"bacterial",
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2018
|
Integrated approach in the control and management of skin neglected tropical diseases in Lalo, Benin
|
In BALB/c mice , susceptibility to infection with the intracellular parasite Leishmania major is driven largely by the development of T helper 2 ( Th2 ) responses and the production of interleukin ( IL ) -4 and IL-13 , which share a common receptor subunit , the IL-4 receptor alpha chain ( IL-4Rα ) . While IL-4 is the main inducer of Th2 responses , paradoxically , it has been shown that exogenously administered IL-4 can promote dendritic cell ( DC ) IL-12 production and enhance Th1 development if given early during infection . To further investigate the relevance of biological quantities of IL-4 acting on DCs during in vivo infection , DC specific IL-4Rα deficient ( CD11ccreIL-4Rα-/lox ) BALB/c mice were generated by gene targeting and site-specific recombination using the cre/loxP system under control of the cd11c locus . DNA , protein , and functional characterization showed abrogated IL-4Rα expression on dendritic cells and alveolar macrophages in CD11ccreIL-4Rα-/lox mice . Following infection with L . major , CD11ccreIL-4Rα-/lox mice became hypersusceptible to disease , presenting earlier and increased footpad swelling , necrosis and parasite burdens , upregulated Th2 cytokine responses and increased type 2 antibody production as well as impaired classical activation of macrophages . Hypersusceptibility in CD11ccreIL-4Rα-/lox mice was accompanied by a striking increase in parasite burdens in peripheral organs such as the spleen , liver , and even the brain . DCs showed increased parasite loads in CD11ccreIL-4Rα-/lox mice and reduced iNOS production . IL-4Rα-deficient DCs produced reduced IL-12 but increased IL-10 due to impaired DC instruction , with increased mRNA expression of IL-23p19 and activin A , cytokines previously implicated in promoting Th2 responses . Together , these data demonstrate that abrogation of IL-4Rα signaling on DCs is severely detrimental to the host , leading to rapid disease progression , and increased survival of parasites in infected DCs due to reduced killing effector functions .
Leishmania spp . are protozoan parasites that are transmitted by Phlebotomus spp . sandflies and can cause several forms of disease in humans , ranging from localized cutaneous lesions to visceral Leishmaniasis , where parasites invade internal organs such as the spleen and liver . The incidence of disease is approximately 1 . 5 million per annum for cutaneous Leishmaniasis , and 500 000 per annum for visceral Leishmaniasis , which is usually fatal if left untreated [1] . Currently there is no vaccine . To identify correlates of immune protection , which may aid in vaccine design and therapeutic strategies , experimental models of cutaneous Leishmaniasis have been established in which disease is induced by infecting mice subcutaneously with L . major . Susceptible BALB/c mice show progressive lesion development with dissemination of parasites to visceral organs , while resistant C57BL/6 mice are able to control infection and heal lesions [2]–[4] . Lack of healing in BALB/c mice is associated with a T helper ( Th ) 2 response characterized by secretion of interleukin ( IL ) -4 , IL-5 , IL-9 and IL-13 [3] , [5]–[8] , high anti-Leishmania antibody titres [8] , [9] and alternative activation of macrophages [9] , [10] . In contrast , resistant C57BL/6 mice develop protective Th1 responses with production of IL-12 and IFN-γ , associated with classical activation of macrophages and killing of parasites by effector nitric oxide production [9] , [11]–[14] . IL-4 and IL-13 , both of which signal through a common receptor chain , the IL-4 receptor alpha ( IL-4Rα ) are known to be important susceptibility factors in L . major infection [3] , [6] , [8] , [15] , [16] . Both BALB/c and C57BL/6 mice secrete IL-4 early after infection however , production of IL-4 is sustained in susceptible BALB/c mice and transient in resistant C57BL/6 mice [17] , [18] . It appears that resistant mouse strains redirect the early Th2 response in an IL-12-dependent mechanism , while in susceptible mice the Th2 response persists and dominates the disease outcome by suppressing effector mechanisms needed for parasite killing [3] . While IL-4 is the primary inducer of Th2 responses [19] , paradoxically it has also been shown that IL-4 promotes IL-12 production by bone marrow-derived dendritic cells ( BMDCs ) stimulated with CpG or LPS [20]–[23] . Furthermore , administration of 1 µg of recombinant IL-4 at 0 and 8 hours after infection with L . major led to increased IL-12 mRNA expression by dendritic cells ( DCs ) in vivo , promoted Th1 responses and rendered mice resistant to infection [21] . It has also been shown that global abrogation of IL-4Rα renders mice resistant to L . major only in the acute phase of infection , with mice continuing to develop necrotic footpad lesions during the chronic phase [15] . However , specific abrogation of IL-4Rα on CD4+ T cells does lead to resistance , indicating a protective role for IL-4Rα signalling on non-CD4+ T cells [24] . A candidate for this protective role may therefore be DCs . These sentinels of the immune system are specialized antigen-presenting cells , proficient at uptake of antigen , migration to the lymph nodes ( LN ) and activation of lymphocytes . Consequently , they play a critical role in the initiation and differentiation of the adaptive immune response [25] , [26] . To investigate the role of IL-4Rα signaling on DCs in resistance to Leishmania , CD11ccreIL-4Rα-/lox mice , deficient in IL-4Rα signaling on DCs , were generated and infected with L . major LV39 and IL81 strains . CD11ccreIL-4Rα-/lox mice were hypersusceptible to both strains of L . major , with increased footpad swelling and necrosis and substantially increased parasite burdens in peripheral organs , including the brain . Hypersusceptibility in CD11ccreIL-4Rα-/lox mice was associated with an upregulation of Th2 responses , impairment in iNOS production by macrophages and inflammatory DCs and increased parasite loads in LN and spleen DCs . Therefore , it is clear that IL-4Rα signaling has important effects on DC phenotype during cutaneous L . major infection , and is necessary to avoid rapid disease progression in the host . This study therefore expands our knowledge on the role of dendritic cells during cutaneous Leishmaniasis and on the effects of IL-4Rα signaling on dendritic cells .
Mice expressing cyclization recombinase ( Cre ) under control of the cd11c locus [27] were backcrossed to BALB/c for 9 generations , then intercrossed with global IL-4Rα ( IL-4Rα-/- ) [15] BALB/c mice to generate CD11ccreIL-4Rα-/- BALB/c mice . These mice were subsequently intercrossed with floxed IL-4Rα ( IL-4Rαlox/lox ) BALB/c mice ( exon 6 to 8 flanked by loxP ) [28] to generate CD11ccreIL-4Rα-/lox BALB/c mice ( Figure 1A ) . CD11ccreIL-4Rα-/lox mice were identified by PCR genotyping ( Figure 1B ) . Analysis of IL-4Rα surface expression on different cell types by flow cytometry demonstrated that IL-4Rα was efficiently depleted in DCs of the lymph nodes , spleen , skin and lungs , when compared with IL-4Rα-/lox littermate controls and IL-4Rα-/- mice ( Figure 1C ) . As expected CD11c+ alveolar macrophages also had abrogated IL-4Rα surface expression . Other cell types such as T cells , B cells and macrophages had comparable IL-4Rα expression to IL-4Rα-/lox littermate controls . Cre-mediated IL-4Rα deletion in DCs was confirmed at the genomic level by performing PCR for IL-4Rα exon 8 ( absent in IL-4Rα-deficient cells ) normalized to IL-4Rα exon 5 ( present in all cells ) using DNA from CD11c+MHCII+ DCs sorted from the spleens of naïve mice ( Figure 1D ) . To assess functional impairment of DCs in CD11ccreIL-4Rα-/lox mice , we generated bone marrow-derived dendritic cells and stimulated them with LPS in the presence or absence of IL-4 or IL-13 . IL-4 is known to enhance DC production of IL-12 in an IL-4Rα dependent manner , so called “IL-4 DC instruction” [21]–[23] . As expected , BMDCs derived from IL-4Rα-/lox mice and BALB/c wildtype controls had significantly increased IL-12 production after the addition of IL-4 ( Figure 1E ) . In contrast , LPS/IL-4 stimulated BMDCs derived from CD11ccreIL-4Rα-/lox mice or from global IL-4Rα-/- mice showed similar levels of IL-12 to those stimulated with LPS alone , with IL-4 having no effect . This demonstrates functional impairment of IL-4Rα signaling on DCs from CD11ccreIL-4Rα-/lox mice . In fact , after the addition of LPS alone , BMDCs with a functional IL-4Rα already showed a trend towards increased IL-12p40 levels , suggesting that endogenous levels of IL-4 found in the culture could influence these BMDCs . IL-13 did not increase levels of IL-12 , confirming previous DC stimulation studies [22] . As previously reported [29] , IL-4 and IL-13 had no significant effect on BMDC maturation , as shown by similar expression of MHCII , CD86 , CD80 , CD83 and CD40 ( data not shown ) . Total yield of BMDCs per precursor cell seeded was similar in CD11ccreIL-4Rα-/lox mice and littermate controls and survival after maturation was not significantly different ( data not shown ) . In order to investigate the role of IL-4Rα signaling on DCs during cutaneous Leishmaniasis , CD11ccreIL-4Rα-/lox mice were infected subcutaneously with 2×106 stationary phase metacyclic promastigotes of L . major LV39 ( MRHO/SV/59/P; Figure 2A , 2B and 2C ) or with a more virulent GFP-expressing L . major IL81 ( MHOM/IL/81/FEBNI; Figure 2D , 2E and 2F ) strains into the hind footpad . As previously shown [15] , [24] , C57BL/6 mice and IL-4Rα-/- deficient BALB/c mice controlled lesion development during acute infection with both L . major strains ( Figure 2A and 2D ) , which correlated with low parasite numbers in infected footpads ( Figure 2B and 2E ) and draining lymph nodes ( Figure 2C and 2F ) . Susceptible WT BALB/c and IL-4Rα-/lox littermate control mice developed progressive footpad swelling after infection with both strains ( Figure 2A and 2D ) , with increased parasite burdens in the infected footpads ( Figure 2B and 2E ) and draining LN ( Figure 2C and 2F ) . Hemizygous ( IL-4Rα-/lox mice ) had slightly reduced footpad swelling compared to BALB/c mice in IL81 infection . The greater virulence of IL81 is reflected in more rapid disease progression , with footpad swelling and parasite burden reaching similar levels by 4 weeks to those obtained with LV39 in 8 weeks . Of importance , CD11ccreIL-4Rα-/lox mice were hypersusceptible to acute L . major infection compared to heterozygous littermate controls and BALB/c mice , showing considerably worsened disease progression when infected with either strain ( Figure 2A and 2D ) , with earlier and dramatically larger footpad lesions , and development of early necrosis ( Figure 2A and 2D ) . Increased disease progression was accompanied by significantly higher parasite numbers in the footpads ( Figure 2B and 2E ) and LN ( Figure 2C and 2F ) of infected animals . In addition , infection with a 10-fold lower dose of L . major LV39 also resulted in a hypersusceptible phenotype in CD11ccreIL-4Rα-/lox mice ( Supplementary Figure S1 A–C ) . Histopathological analysis of CD11ccreIL-4Rα-/lox footpads at week 4 after infection with the virulent IL81 revealed severe destruction of epidermis , connective tissue and bone as a result of footpad necrosis , accompanied by increased inflammatory infiltrates and a high load of extracellular L . major amastigotes ( Figure 2G ) . In contrast , infected footpads of IL-4Rα-/lox revealed moderate dermal inflammatory infiltrates with mostly intact epidermis , connective tissue and bone . Together , these data reveal that IL-4Rα signaling on DCs play an important role in host protection against acute L . major infection . Th1/Th2-type responses were investigated in CD11ccreIL-4Rα-/lox mice and controls during acute cutaneous leishmaniasis ( IL81 ) . Antigen-specific restimulation of CD4+ T cells sorted from the LN of infected mice and co-cultured with fixed antigen-presenting cells and soluble Leishmania antigen ( SLA ) revealed a significantly reduced IFN-γ response in CD11ccreIL-4Rα-/lox mice in comparison to the resistant C57BL/6 or IL-4Rα-/- strains as well as to the susceptible IL-4Rα-/lox littermate controls ( Figure 3A ) . Conversely , the levels of IL-4 , IL-13 and IL-10 were significantly higher in CD11ccreIL-4Rα-/lox mice compared to IL-4Rα-/lox , IL-4Rα-/- and C57BL/6 mice ( Figure 3B , 3C and 3D ) . The observed shift in cytokine responses was confirmed in LN cells , stimulated with anti-CD3 or SLA ( data not shown ) and systemically in the quality of Leishmania-specific antibody immune responses . Sera of week 4 infected mice revealed a predominant type 1 antibody response in IL-4Rα-/- mice , as shown by elevated levels of Leishmania-specific IgG2a ( Figure 3E ) . In contrast , CD11ccreIL-4Rα-/lox mice displayed a predominant type 2 antibody response shown by marked production of IgG1 and total IgE , which was significantly higher than that observed in littermate IL-4Rα-/lox mice ( Figure 3F and 3G ) . A shift towards Th2-type responses also occurred in CD11ccreIL-4Rα-/lox mice in a 10-fold lower dose L . major LV39 infection ( Supplementary Figure S1 D–H ) . As IFN-γ-induced nitric oxide synthase ( iNOS ) production by classically activated macrophages ( caMphs ) is a key control mechanism in L . major infection [14] , the activation state of macrophages was determined in the infected footpad at week 4 after infection . Inflammatory macrophages ( CD11b+MHCII+CD11c− ) from CD11ccreIL-4Rα-/lox mice had significantly reduced iNOS expression compared to those of littermate IL-4Rα-/lox control mice ( Figure 3H ) . Conversely , expression of arginase 1 , a marker of alternatively activated macrophages ( aaMphs ) , was higher in macrophages of CD11ccreIL-4Rα-/lox mice ( Figure 3I ) . This altered phenotype was confirmed in iNOS and arginase activity assays performed on total footpad cells stimulated with LPS ( Figure 3J and 3K ) . Together , these results demonstrate a shift towards Type 2 responses and reduced macrophage effector functions in CD11ccreIL-4Rα-/lox mice . In L . major LV39 infection , parasites were present only in footpads and the draining lymph nodes at week 3 , whereas by week 8 parasites had disseminated to the spleen and liver in both CD11ccreIL-4Rα-/lox mice and littermate controls ( Figure 4A and 4B ) . Parasite burdens were much higher in the organs of infected CD11ccreIL-4Rα-/lox mice , compared to littermate control mice . Moreover , in some CD11ccreIL-4Rα-/lox mice , but not in control mice , L . major parasites had disseminated as far as the brain by week 8 after infection ( Figure 4B ) . Similar disease progression was observed after infection with L . major IL81 ( Figure 4C ) , where CD11ccreIL-4Rα-/lox mice already displayed noticeable splenomegaly at 4 weeks post infection ( data not shown ) , and had strikingly increased parasite burdens in all organs analyzed , including the brain ( Figure 4C ) . Histological analysis confirmed the increased presence of disseminated parasites in the spleen and liver of CD11ccreIL-4Rα-/lox mice ( IL81 , week 4 ) , as shown by the high load of extracellular L . major amastigotes ( spleen ) and the prevalence of inflammatory foci and leishmanial bodies in mononuclear cells ( liver ) ( Figure 4D ) . The presence of parasites in brains of perfused CD11ccreIL-4Rα-/lox mice ( IL81 , week 4 ) was also confirmed by confocal microscopy ( Figure 4E ) . Parasites were not visible in the brains of littermate controls ( data not shown ) . These results demonstrate a drastic increase in numbers of disseminated parasites in peripheral organs of infected CD11ccreIL-4Rα-/lox mice . Although it has been reported that dissemination could occur within hours after high-dose parasite inoculation [30] , infection with GFP+ L . major IL81 and analysis by flow cytometry demonstrated that GFP+ parasites was not detectable in the spleen at 1 or 3 days post infection , whereas at week 4 there was an increase in GFP+ cells compared to day 0 ( Supplementary Figure S2 ) . In order to determine if dendritic cells could harbor L . major parasites , GFP-expressing L . major parasites ( IL81 ) were used to track infected cell populations in different organs by flow cytometry at different time-points ( day 3 , day 7 and week 4 ) after infection . Parasite replication occurred in GFP+ cell populations that were sorted and plated out for limiting dilution assays , indicating that GFP positivity was a good marker for viable parasites associated with cells ( Supplementary Figure S3 ) . At day 3 after GFP-L . major IL81 infection , plasmacytoid DCs ( pDCs ) , macrophages and neutrophils had infiltrated the infected footpad ( Figure 5A ) . By 4 weeks post infection , numbers of infiltrating cells had increased substantially , with conventional DCs ( cDCs ) also now present in high numbers ( Figure 5B ) . The number of infiltrating cells was significantly higher in CD11ccreIL-4Rα-/lox mice compared to IL-4Rα-/lox mice ( Figure 5B ) . At the early time point in FP , macrophages were infected with GFP+ Leishmania , with similar numbers in CD11ccreIL-4Rα-/lox mice and littermate controls ( Figure 5C ) . This was in contrast to the draining lymph node , where conventional and plasmacytoid DCs were infected , with higher numbers of DCs infected in CD11ccreIL-4Rα-/lox mice compared to controls ( Figure 5D ) . Similar results were obtained at day 7 post-infection ( data not shown ) . At week 4 post infection , the footpad harbored a pool of infected cells , namely macrophages , cDCs and neutrophils ( Figure 5E ) , while in the draining lymph node , the cDCs were still infected compared to the other cell types ( Figure 5F ) . Again the number of infected DCs was significantly higher in CD11ccreIL-4Rα-/lox mice ( Figure 5E and 5F ) compared to littermate controls . However , overall numbers of DCs infiltrating the LN at week 4 after L . major IL81 infection were similar in both CD11ccreIL-4Rα-/lox mice and littermate control mice ( data not shown ) , suggesting that differences in parasite killing and not DC migration were responsible for the increased number of infected DCs in CD11ccreIL-4Rα-/lox mice . Infected DCs were also found in the spleen , with significantly increased numbers of infected cells in CD11ccreIL-4Rα-/lox mice compared to controls ( Figure 6A ) . However , overall numbers of DCs infiltrating the spleen were also increased to a similar degree in both CD11ccreIL-4Rα-/lox mice and littermate controls at week 4 ( data not shown ) , again suggesting that differences in parasite killing and not DC migration were responsible for the increased parasite loads in CD11ccreIL-4Rα-/lox mice . Although it is well known that iNOS-mediated NO production in classically-activated macrophages drives intracellular killing of L . major parasites , a recent study has now implicated a population of iNOS+ – producing inflammatory DCs in controlling Leishmania infection [31] . We therefore examined iNOS production by DCs in CD11ccreIL-4Rα-/lox and littermate control mice using intracellular FACS . In hypersusceptible CD11ccreIL-4Rα-/lox mice , a significantly reduced percentage of CD11chighMHCIIhigh DCs produced iNOS compared to DCs from IL-4Rα-/lox littermate control mice ( Figure 6B ) . This was confirmed at the level of intracellular NO expression , which was also reduced in DCs from CD11ccreIL-4Rα-/lox mice ( Figure 6C ) . Together , these data demonstrate that DCs from CD11ccreIL-4Rα-/lox mice have reduced NO killing effector functions , further explaining the increased parasite burdens in the DCs of these mice . Previous studies using BMDCs found that IL-4-mediated instruction results in reduced IL-10 production that is responsible for increased IL-12p40 production by DCs upon stimulation with IL-4 plus CpG or LPS [21] , [23] . To test whether endogenous amounts of IL-4 could mediate DC instruction in vivo , CD11ccreIL-4Rα-/lox mice and controls were infected with L . major IL81 . At 4 weeks post infection , total LN cells were restimulated with SLA and cytokines were measured in the supernatant . Lymph node cells from infected CD11ccreIL-4Rα-/lox mice produced significantly reduced IL-12p40 but increased IL-10 compared to littermate IL-4Rα-/lox mice ( Figure 7A ) . Moreover , intracellular cytokine staining revealed that DCs from CD11ccreIL-4Rα-/lox mice produced less IL-12p40 and more IL-10 than those from littermate IL-4Rα-/lox controls ( Figure 7B and Figure S4 ) . Quantification of mRNA found decreased expression of the Th1-promoting cytokine genes for IL-12p40 ( Figure 7C ) and IL-18 ( Figure 7D ) in sorted LN DCs from CD11ccreIL-4Rα-/lox mice compared to controls . In contrast , there was a trend towards increased mRNA expression of IL-10 as well as significantly increased mRNA expression of IL-23 and activin A , cytokines which are involved in inducing Th2 responses by promoting Th17 and alternative activation of macrophages , respectively [32] , [33] ( Figure 7E–G ) . In addition , differences in IL-12p70 production were detected in vitro . L . major/IL-4 stimulated BMDCs derived from IL-4Rα-/lox mice showed increased IL-12p70 production , whereas IL-4 had no additive effect on IL-12p70 production in BMDCs from CD11ccreIL-4Rα-/lox mice ( Figure 7H ) . IL-13 did not increase IL-12p70 production , as previously shown [22] .
Understanding mechanisms of immune control in cutaneous Leishmaniasis is critical for the design of effective therapeutics and vaccines . Although several studies have clearly established that IL-4 is a key cytokine in the development of non-healing disease in BALB/c mice [8] , [19] , [34] , [35] , apparently contradictory evidence also suggests that IL-4 has the ability to instruct protective Th1 responses [21] , [36]–[41] . The term “instruction theory” was coined when IL-4 was found to promote increased production of IL-12 by BMDCs [20]–[22] . IL-4 , but not IL-13 , enhances the production of IL-12 induced by pathogen products via signalling through the type 1 IL-4 receptor [21] , [22] . The mechanism behind instruction was found to be inhibition of IL-10 by IL-4 , leading to higher levels of IL-12 and increased protective Th1 responses [23] . Several studies also indicate that IL-4 and IL-13 may play a role in promoting DC maturation [22] , [42] . However , most in vitro and in vivo studies on the effects of IL-4 and IL-13 on DCs have been conducted with exogenously administered IL-4 or IL-13 , and thus the relevance of biological quantities of IL-4 signalling through IL-4Rα on DCs during disease in vivo has not been demonstrated . To address these issues , dendritic cell-specific ( CD11ccreIL-4Rα-/lox ) BALB/c mice were generated using the cre/loxP recombinase system under control of the cd11c locus . These mice were found to have abrogated IL-4Rα expression on DCs and alveolar macrophages , with other cell types maintaining IL-4Rα expression and functioning . Infection of CD11ccreIL-4Rα-/lox mice with L . major LV39 and IL81 revealed IL-4Rα signaling on DCs to be highly important in protection against cutaneous Leishmaniasis . Compared to IL-4Rα-/lox littermate controls , CD11ccreIL-4Rα-/lox mice showed dramatically worsened disease progression , with increased footpad swelling and necrosis , and significantly higher parasite burdens both locally and in visceral organs such as the spleen and liver . As expected , genetically resistant C57BL/6 mice effectively controlled infection , as did global IL-4Rα-/- mice , which have been shown to be resistant during the acute phase of L . major infection , with disease progression in the chronic phase only [15] , [24] . Progressive disease during L . major infection in BALB/c mice has been attributed to the predominance of Th2 cytokines and type 2 antibody immune responses [8] , [9] , [11] , with a previous study by our laboratory showing that CD4+ T cell specific IL-4Rα deficient mice were highly resistant to L . major infection [24] . Analysis of CD4+ T cell cytokine responses in CD11ccreIL-4Rα-/lox mice revealed a decrease in IFN-γ accompanied by a marked increase in IL-4 , IL-13 and IL-10 , while increased secretion of IgG1 and IgE by B cells confirmed a shift towards a Th2-type immune phenotype . Aside from its role in instruction , IL-10 is known to be a susceptibility factor for L . major infection , being produced at higher levels in susceptible BALB/c mice and capable of suppressing Th1-mediated effector functions [3] , [43] . In humans , IL-10 is strongly associated with persistent infection [44] . IFN-γ plays an important role in mediating protective immunity during L . major infection by classically-activating macrophages to induce nitric oxide synthase-mediated NO production for intracellular killing of parasites [9] , [14] , [45] , [46] . Latent Leishmaniasis is reactivated in chronically infected healthy C57BL/6 mice by inhibition of endogenous NOS-2 , indicating that iNOS expression is crucial for the sustained control of L . major infection [9] , [31] , [47] . Induction of iNOS-mediated NO production is counter-regulated by IL-4/IL-13 and IL-4Rα , which promote the development of alternatively activated macrophages and arginase 1 production through depletion of L-arginine as a substrate for iNOS . Interestingly , IL-10 has also been shown to suppress intracellular killing of pathogens in macrophages by suppressing IFN-γ responses [48]–[50] and can induce an alternatively activated macrophage type phenotype in the absence of IL-4 and IL-13 [51] . Parasites such as Leishmania can utilize polyamines generated by arginase 1 activity for their own growth , making alternatively activated macrophages a favorable environment for their survival [52]–[54] . Accumulating reports have demonstrated a role for alternative macrophage activation and arginase 1 expression in influencing susceptibility to L . major infection [7] , [9] , [55] , [56] . LysMcreIL-4Rα-/lox mice which lack IL-4/IL-13 induced alternative activation of macrophages were found to have increased resistance to infection [9] , while neutralization of endogenous arginase 1 with N-hydroxy-nor-L-arginine leads to complete healing in BALB/c mice [55] . Macrophages from the footpads of CD11ccreIL-4Rα-/lox mice were found to have reduced iNOS expression and increased arginase 1 expression compared to those from littermate control IL-4Rα-/lox mice , demonstrating a shift in macrophage effector function most likely as a consequence of increased IL-4 , IL-13 and IL-10 . Recently it has been shown that DCs can also become alternatively activated by upregulating markers such as Ym-1 and RELM-α after administration of IL-4 [29] . In our study , the data suggest that IL-4Rα-independent alternative activation of DCs is also possible , as DCs from CD11ccreIL-4Rα-/lox had decreased iNOS expression , possibly a consequence of reduced IFN-γ and/or increased IL-10 and activin A , and had higher parasite loads than those from littermate controls . Previous studies have revealed that iNOS-producing DCs constitute a major Th1-regulated effector cell population and contribute to resistance to infection by L . major [31] , L . monocytogenes [57] and Brucella spp . [58] . The reduced ability of both macrophages and DCs to initiate NO-mediated killing of L . major in CD11ccreIL-4Rα-/lox mice is therefore likely to play a role in the uncontrolled parasite replication observed both in the footpad and at peripheral sites . In susceptible BALB/c mice , L . major parasites can disseminate within 24 hours from the site of infection in the footpad to the popliteal lymph nodes , spleen , liver , lungs and bone marrow [30] , [59] . However , L . major parasites were not detected at early time points during IL81 infection ( day 1 and day 3 ) but were detected at week 4 , and were also detected at week 8 but not at week 3 during LV39 infection , suggesting that parasite dissemination may have occurred at a later stage of infection . Dissemination is inhibited by the administration of recombinant IL-12 and resistant mouse strains restrict the spread of the parasites [30] . While several susceptible mouse strains have been reported to show some increase in dissemination [60]–[62] , disseminated parasite loads in CD11ccreIL-4Rα-/lox mice were unusually dramatic , with relatively higher parasite burdens in the spleens and footpads compared to other susceptible strains . Unexpectedly , parasites were even identified within the brain of some of the CD11ccreIL-4Rα-/lox mice . This suggests that the L . major parasites managed to cross the immunological blood-brain barrier , which has only rarely been reported for this cutaneous strain with very low levels of parasites detected [63] . However , dissemination of parasites to the central nervous system ( CNS ) has been frequently observed in visceral Leishmaniasis in both humans and dogs [64]–[67] . It has been suggested that parasites arrive in the CNS via infected leukocytes [65] and/or disruption to the blood brain barrier caused by inflammation [67] . Studying the mechanisms by which other pathogens , such as bacteria , invade the CNS may lend insights into Leishmania dissemination . Many intracellular organisms such as Mycobacterium tuberculosis , Listeria monocytogenes , Brucella spp . and Salmonella spp . appear to make use of the “Trojan-horse” mechanism , using phagocyte facilitated invasion for entry into the CNS [68] . After infection with intracellular pathogens , phagocytes undergo phenotypical changes , such as increased migratory activity and increased expression of adhesion molecules and proinflammatory cytokines , all of which could aid in dissemination and crossing of the blood-brain barrier [68] , [69] . Whether infected phagocytes are recruited to the CNS by specific or non-specific means is unknown [69] . In order to determine which cells were infected by L . major , mice were infected with GFP-IL81 parasites and cell populations containing parasites were identified by flow cytometry . At day 3 and 7 after infection , macrophages harbored L . major in the footpad , while pDCs and cDCs were found to be infected in the lymph node . Similar to other reports , this indicates that DCs were responsible for transporting parasites to the lymph node [70] . At week 4 , L . major parasites were still detected in macrophages in the footpads , as well as in DCs and neutrophils , but in the LN they were primarily found in DCs . The number of infected DCs in both footpad and LN was significantly higher in CD11ccreIL-4Rα-/lox mice . A previous study also reported that DCs were the primary infected cell population in the draining LN of L . major infected mice [70] . DCs were also infected with L . major parasites in the spleen , with CD11ccreIL-4Rα-/lox mice again showing a greater number of infected DCs . Numbers of DCs infiltrating the LN and spleen were equivalent in both CD11ccreIL-4Rα-/lox mice and littermate controls during infection . This suggests that the increased survival and/or growth of parasites in DCs , as a consequence of significantly reduced DC iNOS production , was responsible for the increase in infected cell numbers in CD11ccreIL-4Rα-/lox mice . Interestingly , a recent study found that infected DCs , which are monocyte-derived CD11b+ inflammatory DCs expressing Ly6C , F480 , Ly6G and iNOS , showed a unique ability to disseminate to peripheral sites in M . tuberculosis infection [71] . Furthermore , CD11b+Ly6C+ cells were found to be the principal phagocytic cells harboring L . monocytogenes in circulation [69] , [72] . We hypothesize that dendritic cells may therefore play a role in disseminating L . major parasites to peripheral sites and that their killing effector responses could be important in controlling disease . The reduced Th1 and increased Th2 responses in CD11ccreIL-4Rα-/lox mice suggests that instruction theory is relevant in vivo , and more importantly , that biological quantities of IL-4 acting through DCs can promote resistance to Leishmania infection . DCs from lymph nodes of CD11ccreIL-4Rα-/lox mice produced more IL-10 and less IL-12 than those from IL-4Rα-/lox mice . Quantification of mRNA expression also revealed interesting differences in DCs from CD11ccreIL-4Rα-/lox mice . Expression of the Th1-promoting genes for IL-12p40 and IL-18 was decreased compared to DCs from littermate control mice , while expression of the Th2-promoting genes for IL-23p19 and activin A were significantly increased . IL-23 production by DCs has been shown to promote Th17 [32] , leading to increased neutrophils that enhance susceptibility to L . major by acting as Trojan horses [73] . Activin A is a pleiotropic cytokine belonging to the TGF-beta superfamily , and has previously been found to promote alternative activation of macrophages by inducing Arginase 1 and decreasing IFN-γ-induced expression of iNOS [33] . The absence of IL-4Rα signalling on DCs therefore appears to have a more complex influence on the dendritic cells than just affecting IL-12 production during cutaneous Leishmaniasis in vivo . Dendritic cell instruction may not be restricted to Leishmaniasis , since other disease models have also demonstrated a protective role for IL-4 . Experimental infections with Candida albicans in IL-4 deficient mice led to impaired development of Th1 responses [38] , and a Th1 promoting effect of IL-4 has also been observed in autoimmunity [36] , [40] , [74] , tumor immunity [39] , [75] , [76] and contact sensitivity reactions [41] , [77] . There is also evidence to suggest that IL-4 may promote Th1 development in humans , since both human and mouse DCs produce increased levels of bioactive IL-12 after stimulation with IL-4 [20] . A similar effect was observed in human peripheral blood mononuclear cells ( PBMCs ) treated with IL-4 plus lipopolysaccharide or Staphylococcus aureus [78] . Incorporating exogenous IL-4 as an adjuvant for enhancing strong Th1 responses could therefore be utilised to boost vaccine efficiency against cutaneous Leishmaniasis . Accordingly , parallel studies examining the efficacy of IL-4 as an adjuvant during BMDC-mediated vaccination against L . major , found that IL-4 instruction of DCs was critical in eliciting protective immune responses [79] . The role of IL-4Rα signalling on DCs in eliciting immunity to other intracellular pathogens is therefore of interest to vaccination strategies , and an exciting avenue to be explored .
CD11ccre mice [27] were crossed with IL-4Rαlox/lox BALB/c mice [28] and complete IL-4Rα-/- BALB/c mice [15] to generate hemizygous CD11ccreIL-4Rα-/lox mice . Mice were backcrossed to a BALB/c background for 9 generations to generate CD11ccreIL-4Rα-/lox BALB/c mice . Hemizygous littermate controls ( IL-4Rα-/lox ) were used as controls in all experiments . Mice were genotyped as described previously [28] . All mice were housed in specific-pathogen free barrier conditions in individually ventilated cages . Experimental mice were age and sex matched and used between 8–12 weeks of age . This study was performed in strict accordance with the recommendations of the South African national guidelines and University of Cape Town of practice for laboratory animal procedures . All mouse experiments were performed according to protocols approved by the Animal Research Ethics Committee of the Health Sciences Faculty , University of Cape Town ( Permit Number: 009/042 ) . All efforts were made to minimize suffering of the animals . Genomic DNA was isolated from spleen DCs ( CD11c+MHCII+ ) sorted using a FACS Vantage flow cytometer ( BD Immunocytometry systems ) . Purity was determined by flow cytometry and checked by cytospin and staining with the Rapidiff Stain set ( Clinical Diagnostics CC , Southdale , South Africa ) and was at least 99% . A standard curve was prepared from serial 10-fold DNA dilutions of cloned IL-4Rα exon 5 and exon 8 DNA and RT-PCR was performed using the following primers; exon 5: forward 5′ AACCTGGGAAGTTGTG 3′ and reverse 5′ CACAGTTCCATCTGGTAT 3′ , exon 8: forward 5′ GTACAGCGCACATTGTTTTT 3′ and reverse 5′ CTCGGCGCA CTGACCCATCT 3′ . The following antibodies were used for flow cytometry: SiglecF-PE , CD11c-APC , MHCII-APC , F480-PE , CD11b-FITC , CD3-FITC , CD19-PE , PDCA-APC , SiglecH-PE , CD11b-PE , CD11c-PE , CD4-PerCP , CD8-PE , GR-1-PE , CD3-PerCP , anti-CD124-PE , rat anti-mouse IgG2a-PE , CD11c-biotin , CD103-biotin , CD124-biotin and rat-anti-mouse IgG2a biotin with streptavidin-APC ( all BD Bioscience , Erembodegem , Belgium ) and MHCII-biotin with PerCP streptavidin ( BD Bioscience ) . For intracellular cytokine staining , popliteal lymph node cells from L . major infected mice were seeded at 2×106 cells/well and stimulated at 37°C for 4 hours with phorbal myristate acetate ( Sigma-Aldrich ) ( 50 ng/ml ) , ionomycin ( Sigma-Aldrich ) ( 250 ng/ml ) and monensin ( Sigma-Aldrich ) ( 200 µM ) in DMEM/10% FCS . Dendritic cells were stained with CD11c-PE-Cy7 ( BD Bioscience ) and MHCII-APC , fixed and permeabilized , and intracellular cytokines were stained with anti-IL-10 , anti-IL-12 and isotype controls ( BD Bioscience ) ( all PE-labelled ) . Cells were acquired on a FACS Calibur machine ( BD Immunocytometry systems , San Jose , CA , USA ) and data were analyzed using Flowjo software ( Treestar , Ashland , OR , USA ) . BMDCs were generated from bone-marrow progenitors of CD11ccreIL-4Rα-/lox and littermate control mice using 200 U/ml recombinant mouse granulocyte-macrophage colony-stimulating factor ( GM-CSF ) ( Sigma-Aldrich ) as previously described [80] . On Day 10 , non-adherent cells were harvested and 5×105 BMDCs were stimulated with LPS ( Sigma-Aldrich; 1 µg/ml ) or Leishmania major IL-81 promastigotes ( MOI: 10 parasites/cell ) in the presence or absence of 1000 U/ml recombinant mouse IL-4 or IL-13 ( rIL-4/rIL-13 , BD Biosciences ) for 48 h . Following incubation , levels of IL-12p40 , IL-12p70 and IL-10 were measured in culture supernatants by ELISA as previously described [15] . Cytokines in cell supernatants were measured by sandwich ELISA as previously described [15] . For antibody ELISAs , blood was collected in serum separator tubes ( BD Bioscience , San Diego , CA ) . Antigen-specific IgG1 , IgG2a and IgG2b were quantified by ELISA , as previously described [15] . Detection limits were 5 ng/ml for IgG1 and IgG2b and 0 . 1 ng/ml for IgG2a and IgG3 . Total IgE was determined as described [15] . The detection limits was 8 ng/ml for total IgE . L . major LV39 ( MRHO/SV/59/P ) and GFP-expressing L . major IL81 ( MHOM/IL/81/FEBNI ) ( kind gift from Prof . Heidrun Moll , University of Würzburg , Germany ) strains were maintained by continuous passage in BALB/c mice and prepared for infection as described previously [15] . Anaesthetised mice were inoculated subcutaneously with 2×106 or 2×105 stationary phase metacyclic promastigotes into the left hind footpad in a volume of 50 µl of HBSS ( Invitrogen ) . Swelling of infected footpads was monitored weekly using a Mitutoyo micrometer calliper ( Brütsch , Zürich , Switzerland ) . Footpads , spleens and livers were fixed in 4% formaldehyde in phosphate buffered saline and embedded in wax . Tissue sections were stained with either haemotoxylin and eosin or Giemsa . Following infection of mice with GFP-L . major IL81 parasites for 4 weeks , isolated brain tissue was immediately embedded in OCT ( Tissue-Tek; Sakura , Zoeterwoude , Netherlands ) medium . Pre-fixing of tissues was avoided to minimize background staining from the fixative . OCT-embedded brain tissue were cut into 10 µm frozen sections and mounted on 3-aminopropyltriethoxysilane-coated slides . Following acetone fixation of tissue , sections were stained with nuclear stain Hoechst . Coverslips were then mounted on sections using Mowiol 4–88 mounting medium ( Calbiochem ) with anti-fade ( Sigma ) . Images were acquired and analyzed by Ziess LSM 510 confocal microscope ( Jena , Germany ) . Infected organ and tissue cell suspensions were cultured in Schneider's culture medium ( Sigma ) . Prior to removal of mouse brain tissue for detection of parasite burden , animals were perfused with 20 ml sterile saline solution . Detection of viable parasite burden was estimated by two-fold limiting dilution assay as previously described [15] . CD4+ T cells were positively selected using anti-CD4 MACS beads ( Miltenyi Biotec ) according to the manufacturer's instructions ( purity >95% ) . Thy1 . 2-labeled splenocytes were T cell depleted by complement-mediated lysis to enrich antigen presenting cells ( APCs ) . APCs were fixed with mitomycin C ( 50 µg/ml , 20 min at 37°C ) and washed extensively in complete IMDM . A total of 2×105 purified CD4+ T cells and 1×105 APCs were cultured with SLA ( 50 µg/ml ) . After 72 h incubation at 37°C , supernatants were collected and cytokine production analysed as previously described [28] . Muscle tissue was separated from infected footpads and digested in DMEM medium supplemented with Collagenase IV ( Sigman-Aldrich; 1 mg/ml ) and DNase I ( Sigma-Aldrich; 1 mg/ml ) at 37°C for 60 min . Following incubation , single cell suspensions were isolated by straining through 40 µM cell-strainers . Spleen cells were isolated by pressing through 70 µM cell-strainers , red blood cell lysis was performed and white blood cells were washed and resuspended in 10% DMEM ( Gibco ) . Total lymph node or footpad cells were labeled with specific mAbs and populations isolated by cell sorting on a FACS Vantage machine . Macrophages from the footpad were gated as CD11bhighMHCIIhighCD11c− cells and DCs , macrophages , neutrophils and B cells from the lymph node were gated as CD11chighMHCIIhigh , CD11bhighMHCIIhighCD11c− , GR-1highSSChighFSChighCD11c− and CD19+CD3−CD11c− cells , respectively . Cells were >98% pure and used for further analysis . Dendritic cells were stained with specific mAb and sorted from the LN of infected mice . Total RNA was extracted from dendritic cells using Tri reagent ( Applied Biosystems , Carlsbad , Calif ) and mini-elute columns ( Qiagen ) according to the manufacturer's protocol . cDNA was synthesized with Transcriptor First Strand cDNA synthesis kit ( Roche ) , and real-time PCR was performed by using Lightcycler FastStart DNA Master PLUS SYBR Green I reaction mix ( Roche ) on a Lightcycler 480 II ( Roche ) . Primers for IL-12p40: forward 5′ CTGGCCAGTACACCTGCCAC 3′ and reverse 5′ GTGCTTCCAACGCCAGTTC 3′ , IL-18: forward 5′ TGGTTCCATGCTTTCTGG 3′ and reverse 5′ TCCGTATTACTGCGGTTGT 3′ , IL-10: forward 5′ AGCCGGGAAGACAATAACTG 3′ and reverse 5′ CATTTCCGATAAGGCTTGG 3′ , IL-23p19: forward 5′ CAGCTTAAGGATGCCCAGGTT 3′ and reverse 5′ TCTCACAGTTTCTCGATGCCA 3′ and βA subunit ( Activin A ) : 5′ GAGAGGAGTGAACTGTTGCT 3′ and reverse 5′ TACAGCATGGACATGGGTCT 3′ . Values were normalized according to the expression of the housekeeping genes HPRT or rS12 . Lymph node and footpad cells collected at week 4 after infection were restimulated with LPS ( Sigma-Aldrich; 10 ng/ml ) . Supernatants were collected at 48 hours for quantification of nitric oxide [81] while arginase activity was measured in cell lysates [81] . Expression of intracellular iNOS and arginase was analyzed in CD11bhighMHCIIhighCD11c− macrophages and CD11chighMHCIIhigh DCs by flow cytometry using rabbit anti-mouse iNOS ( Abcam ) with goat anti-rabbit PE ( Abcam ) and goat anti-mouse arginase ( Santa Cruz Biotechnology ) with donkey anti-goat PE ( Abcam ) . Purified goat IgG and rabbit IgG were used as controls . Data is given as mean ± SEM . Statistical analysis was performed using the unpaired Student's t test or 1-way Anova with Bonferroni's post test , defining differences to IL-4Rα-/lox mice as significant ( * , p≤0 . 05; ** , p≤0 . 01; *** , p≤0 . 001 ) unless otherwise stated . ( Prism software: http://www . prism-software . com ) .
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Leishmaniasis is a parasitic infection caused by protozoan parasites of Leishmania species and is transmitted by the sandfly . Disease in humans ranges from localized cutaneous lesions to disseminated visceral Leishmaniasis . Mouse models of Leishmania major infection have demonstrated that a “healing” response in C57BL/6 mice requires the secretion of protective T helper ( Th ) 1 cytokines , including IFN-γ , which mediates parasite killing by inducing nitric oxide production . Conversely , “non-healer” BALB/c mice are unable to control infection and develop a Th2 immune response characterized by the production of IL-4 and IL-13 cytokines . Although IL-4 is the main inducer of Th2 responses , it has been shown that IL-4 can instruct dendritic cell ( DC ) -derived IL-12 production and Th1 development if administered during DC activation . To further investigate the role of DCs , a DC specific IL-4Rα-deficient mouse model was established . L . major studies demonstrated hypersusceptibility to infection and strikingly increased parasite loads in peripheral organs of mice lacking IL-4Rα on DCs . Moreover , increased parasite burdens were observed in host cells , including DCs , which showed reduced killing effector functions . In summary , this study demonstrates that IL-4Rα-mediated instruction of DCs occurs in vivo and is necessary to avoid rapid progression of disease in the host .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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Deletion of IL-4 Receptor Alpha on Dendritic Cells Renders BALB/c Mice Hypersusceptible to Leishmania major Infection
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Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management . For integrative genomic studies , it is critical to identify and correct these errors . Different types of genetic and genomic data are inter-connected by cis-regulations . On that basis , we developed a computational approach , Multi-Omics Data Matcher ( MODMatcher ) , to identify and correct sample labeling errors in multiple types of molecular data , which can be used in further integrative analysis . Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step . Applied to a large lung genomic study , MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold . In a simulation study , MODMatcher provided more robust results by using three types of omics data than two types of omics data . We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data , such as The Cancer Genome Atlas ( TCGA ) data sets .
Cells employ multiple levels of regulation that enable them to respond to genetic , epigenetic , genomic , and environmental perturbations . With advances in high-throughput technologies , comprehensive data sets have been generated to measure multiple aspects of biological regulation , such as genetics , transcriptomics , metabolomics , glycomics , and proteomics . To elucidate the complexity of cell regulation , diverse types of data from these different technologies must be integrated . Sample errors , including sample swapping , mis-labeling , and improper data entry are inevitable during large-scale data generation . Some of these errors can be detected during quality control ( QC ) on each type of data; however , others are more elusive and may affect integrative data analysis , depending on the integration methods used . In some integrative analyses , signature sets are first defined by each data type individually , for example signatures for gene expression , methylation , or copy number variation ( CNV ) . Then , the signatures are overlapped to identify high-confidence changes [1] . In such analyses , potential sample inconsistencies may have a limited effect on results . For example , assume that samples A and B are swapped in gene expression data . If both samples are involved in the same subgroup ( e . g . , normal control or disease ) , the derived signatures will not be affected by the sample mis-labeling error . In other integrative analyses , such as the genetic gene expression studies [2] , [3] , in which the aim is to discover how DNA variations or single nucleotide polymorphisms ( SNPs ) regulate gene expression changes , sample errors could have a larger effect . In one study , mis-matching of 20% of samples between genotype and gene expression data decreased the number of cis-eSNPs by 70% [4] . To fully understand biological systems , it is necessary to elucidate how genetic and epigenetic perturbations lead to transcriptomic and proteomic changes , which in turn contribute to the disease phenotype . Simultaneously considering different types of biological data can result a better understanding of biological systems [2] , [5]–[8] . With recent advances in high-throughput technologies , multiple layers of molecular phenotypes have been measured in the same sample for comprehensive survey of biological systems . To maximally utilize these data , it is necessary to properly match different types of data pertaining to the same sample or individual before integrative analyses . Here we present a sample mapping procedure called Multi-Omics Data matcher ( MODMatcher ) , which not only identifies mis-matched omics profile pairs , but also properly assigns them to the correct samples based on other omics data . We applied MODMatcher to two large-scale public multi-omics datasets: one from the Lung Genomic Research Consortium ( LGRC ) and one from The Cancer Genome Atlas ( TCGA ) . In both cases , adjustment for mis-matched samples improved data consistency and increased statistic power to identify biological regulations . All software programs and scripts are available at http://research . mssm . edu/integrative-network-biology/Software . html .
The LGRC is a consortium for studying chronic lung diseases including chronic obstructive pulmonary disease ( COPD ) . Clinical information and gene expression and methylation profiling data were obtained from the LGRC data portal ( http://www . lung-genomics . org ) . Genotype data was provided by the LGRC consortium . The data set consists of gene expression profiles of lung tissues from 219 patients with COPD and 108 non-disease controls ( CTRL ) , and methylation profiles of lung tissues from 173 COPD patients and 76 controls . First , the gender of each sample was inferred based on three types of data and compared to the gender annotated in clinical data . There was no ambiguity in gender prediction based on each individual type of data; the molecular profiles of different genders were clearly separated ( Figures 1–3 ) . However , we identified several mismatches between the predicted genders based on omics data and the clinically annotated genders . Among genders predicted by X-chromosome heterozygosity , we detected 4 mismatches in CTRL and 5 in COPD samples , corresponding to a mismatch error rate of 3 . 5% ( 9/256 ) for SNP genotype profiles ( Figure 1 ) . While there was no gender mismatch in CTRL samples , as judged by the expression level of Y-chromosome specific gene RPS4Y1 , we detected 5 gender mismatches in COPD , corresponding to a mismatch error rate of 1 . 5% ( 5/327 ) for gene expression profiles ( Figure 2 ) . Among genders predicted from the intensity of the Y-chromosome specific methyl probe close to FAM197Y2P ( see Methods ) , we found 1 gender inconsistency in CTRL samples and 15 in COPD samples , corresponding to a mismatch error rate of 6 . 4% ( 16/249 ) for methylation profiles ( Figure 3 ) . Overall , for 21 unique individuals ( Table S1 ) , the gender information inferred from different sources did not match either with one another or with clinical annotation , indicating sample alignment problems . According to the error rate of gender mismatches , gene expression profiling data was least likely to be mis-labeled , and methylation profiling data was most likely to be mis-labeled in the LRGC data set . Next , we iteratively matched SNP , gene expression , and methylation profiles using multi-omics identity similarity scores ( Figure 4 ) . We started with three sets of profile pairs with consistent inferred gender information: 179 pairs ( 50 CTRL and 129 COPD ) for genotype and gene expression data , 182 pairs ( 51 CTRL 131 COPD ) for genotype and methylation data , and 209 pairs ( 61 CTRL and 148 COPD ) for methylation and gene expression profiling data . Cis regulation pairs ( i . e . cis-eSNPs , cis-mSNPs , and cis methy-mRNA probes ) were identified separately for CTRL and COPD samples . Sample identity similarity scores , , and based on identified cis regulation pairs were calculated for all possible profile pairs . and were calculated from the distance between predicted and measured SNP genotypes . was measured by correlation of rank-transformed methylation and gene expression levels in samples ( Figure 5 , see Methods ) . The similarity scores for matched profiles were 3 . 8 , 3 . 2 , and 1 . 8 standard deviations better than the mean similarity scores for , , and , respectively ( Figure 6A–C ) . Thus , SNP-mRNA sample matches were more reliable than SNP-methylation or methylation-mRNA sample matches , perhaps because methylation data tends to be noisy due to intrinsic technical design [9] , [10] . Based on the gender-matching results , methylation profiles have a higher mis-label rate than other profile data , also contributing to the uncertainties of sample matching of methylation profiles . Next , we determined whether mis-aligned samples could be matched with other unmatched samples by reciprocal best matching , based on one type of identity similarity score . In other words , we tested whether mis-aligned genotype profile Gi had the highest similarity with an unmatched mRNA profile Ej among all mRNA profiles , and the unmatched mRNA profile Ej had the highest similarity with Gi among all genotype profiles as well . For the sample pair with a reciprocal best match , sample labels can be updated by comparison with mapping results based on other identity similarities . When all three types of data are available , the source of any sample labeling errors can be identified . It is also possible to remove or identify additional matched profiles that may be ambiguous as judged from , , or alone . Since cis-eSNPs pairs provided the best alignment signal , we started with sample matching by cis-eQTL . Then , samples were further matched by cis-mQTL and mRNA-methylation . For the SNP-mRNA profile match , we tested whether there was a methylation profile that matches well with both SNP and mRNA profiles in the matched pair . After each round of sample matching , the quality of sample alignment was assessed by counting the number cis pairs identified . For all pairs among these three data types , sample mapping correction significantly increased numbers of cis pairs identified ( Figure 7 ) . The number of cis-eSNPs stabilized within the first 5 rounds ( Figure 7A ) . However , the number of cis-mSNP pairs stabilized in much later rounds ( about 15–17 ) , as expected because of the higher mis-label error rate and greater noise in the methylation data . Nonetheless , the numbers of cis-pairs involving methylation profiles increased substantially with the improved sample matching ( Figure 7B and 7C ) . In COPD samples , the number of cis-eSNPs increased by ∼100% and the number of cis mRNA-methylation pairs increased by ∼200% . Consistently , fewer cis pairs were identified in the CTRL data set than in the COPD data set . This difference likely reflects disease biology . Although there were fewer CTRL than COPD samples and thus less statistical power , the trend of difference was the same when we sampled equal numbers of COPD samples to CTRL samples ( Figure S1 ) . Using a series of simulated data sets , we demonstrated that trio alignment ( considering three types of data simultaneously ) resulted in better alignment than duo alignments ( considering two types of data at a time ) combined . From the sample alignment of the LGRC data as describe above , we identified 76 COPD samples with aligned genotype , gene expression and methylation profiles . Among these 76 samples , only 65 could be correctly matched when individual similarity scores such as cis mRNA-methylation pairs were used . For a fair comparison of trio and duo alignment , we simulated sample labeling errors by randomly assigning sample labels using only these 65 COPD samples . As in the empirical data , we kept low error rates in SNP and gene expression profiling data . We increased the number of mis-labeled methylation profiles from 0 to 24 ( corresponding error rate 0% to 37% ) . At each error rate , we simulated 5 independent data sets and used the average for comparison . In both of duo and trio alignment , all three data types were used but in different ways . In duo alignment , we identified the sample pairs from each pair of data types independently and summarized them to have final pairs . For an example , a methylation profile can be matched with an mRNA profile directly based on the identity similarity score or through a chain of matches , in which the methylation profile is matched to an SNP profile which matches the mRNA profile . In trio alignment , there is an additional three-way identity similarity score that considers all three data types simultaneously ( as described in Methods ) . Both trio and duo alignment identified mis-matches and improved data quality . However , trio alignment was more robust and superior , especially when mis-labeling error rates were high ( Figure 8 ) . Trio alignment recovered more samples pairs and predicted sample pairs more accurately than alignments considering similarity scores independently . In trio alignment , the additional data type provided more bridging information for matching mis-aligned samples pairs . Thus , at the same mis-labeling error rate , trio alignment yielded a higher true positive rate and better coverage ( Figure 8 ) . As error rates increased , the benefit of using trio alignment became clearer . Thus , in correcting sample mis-matches , sample alignment considering three types of data simultaneously in sample alignment may have advantages over combining three independent duo-alignments . These simulation results confirm that sample alignment using multi-omics data is a critical QC step . Alignment that considers three types of omics data simultaneously is strongly recommended if applicable . Nevertheless , duo alignment is still useful for identifying and correcting mis-aligned pairs .
In large-scale genetic and genomic studies , errors in sample annotation or labeling are common and difficult to avoid completely . Identifying and correcting these errors is critical for statistical analysis , especially for integrative analysis . In this study , we introduce an iterative computational procedure , MODMatcher , that uses multiple types of molecular data ( e . g . , genotype , CNV , gene expression , and methylation profiles ) for sample alignment by using cis regulation pairs of each pair of data types to calculate sample identity similarity scores . When applied to two large public data sets , LGRC and TCGA , MODMatcher not only identified mis-aligned profile pairs but also corrected and rescued mis-labeled samples . In a simulation study of COPD samples in the LGRC set , sample alignment with three types of data ( trio matching ) performed better than alignment with two types of data ( duo matching ) . When applied to the GBM data set in TCGA , trio matching unambiguously identified the source of sample labeling errors . Thus , MODMatcher can rescue mis-aligned or mis-labeled samples to maximize statistical power in integrative analysis in large-scale genetic and genomic studies . Indeed , correction of mis-aligned samples increased both the number of cis pairs identified and the statistical significance . Sample labeling errors are not unique to a few data sets , but are inevitable for any large data sets , despite intensive efforts in QCing each type of data individually . Our methods based on methylation profiles for gender inference and alignment with other omics profiles are novel and have not been included in standard data QC procedures . We applied our methylation-based gender inference method to more TCGA data sets and demonstrated that gender can be unambiguously inferred from methylation profiles ( Figure S3 ) . We identified 1 , 4 , 1 , 2 gender mis-match errors in methylation profiles in data sets for colon adenocarcinoma ( COAD ) , kidney renal papillary carcinoma ( KIRC ) , acute myeloid leukemia ( LAML ) , and lung adenocarcinoma ( LUAD ) , respectively ( Table S3 ) . We also applied our methylation-gene expression profile matching method to additional TCGA data sets , COAD and lung squamous cell carcinoma ( LUSC ) , and identified multiple mis-label errors ( examples shown in Figure S4 ) . Thus , checking sample alignment is a critical and necessary QC step before integrative analysis . It is worth to note that the sample identity similarity scores , , , and , are calculated by using cis regulation pairs . Therefore , like the method of Westra et al . [4] , MODMatcher depends on initial sample alignments to generate cis regulation pairs . However , MODMatcher is more robust and can tolerate extra noise , as shown in the simulation study . If the error rate of initial alignment is too high ( e . g . , >30% mis-alignment ) , we may not be able to identify enough cis-regulation pairs to accurately align samples on the basis of a single identity score . But based on three-way similarity , more accurate matching pairs can still be identified . MODMatcher has several features not found in existing sample alignment methods such as MixupMapper [4] . First , we proposed novel methods for methylation profile based gender inference and sample alignment , and MODMatcher can be applied to diverse types of data , including genotype , gene expression , methylation , and CNV . MixupMapper can only be applied to genotype and gene expression data . Second , by using more than two types of omics profiles , MODMatcher can not only identify potential mis-labeled omics profile pairs , but also pinpoint which profiles in the pairs are mis-labeled ( Figures 9 and 10 ) , and do so more robustly than when only two types of omics profiles are used ( Figure 8 ) Even though MODMatcher is not designed for matching two types of omics profiles , it can be applied to data sets consisting of only two types of omics profiles . MixupMapper and MODMatcher can only be compared for their ability to match genotype and mRNA profiles . We applied MODMatcher to 8 data sets examined by MixupMapper ( downloaded from http://genenetwork . nl/wordpress/mixupmapper/#additional ) and compared alignment results based on the two methods ( Table S4 ) . MODMatcher results completely agreed with MixupMapper results in 6 of 8 data sets . For the two datasets in which the MODMatcher and MixupMapper results are different , we further assessed sample alignment quality by counting cis-eQLs identified based on the final matching results . We input final matching pairs identified by each method and their corresponding profiles to the same program , MatrixEQTL [11] , to identify cis-eQTLs . In both cases , more cis-eQTLs were identified with MODMatcher results than with MixupMapper results ( Table S5 ) . After labeling errors in omics profiles are identified and corrected by leveraging information from multiple omics profiles , the corrected profiles can be compared with clinical information to answer many biological questions , such as what genes' expression levels correlate with blood lipid level , and what genes' methylation levels correlate with survival of cancer patients . To accomplish these tasks , we assume that all clinical data are correct , which may not always be true . There could be errors in clinical data files , such as missing data , and row or column shifts . It is more challenging to identify and correct errors in clinical data files than it is to identify labeling errors in omics profiles . More research efforts are warranted for checking potential errors in the links between clinical data and omics profiles .
Gender information is generally included in clinical data . We also inferred gender information from genotype , gene expression , and methylation profiling data . The gender of samples can be predicted from X-chromosome heterozygosity rates determined with PLINK [15] . An individual is predicted to be male if the estimated inbreeding coefficient F is >0 . 8 and female if F<0 . 2 [16] . There were inconsistencies between gender inferred from genotype data and gender provided in clinical data for the LGRC samples ( Figure 1 ) . Gene expression levels of Y-chromosome specific genes can also be used to reliably predict gender information . RPS4Y1 ( ribosomal protein S4 , Y-linked 1 ) is highly expressed in male [17] . Its expression level can robustly classify samples into male and female [6] . Figure 2 shows gender mismatches between clinical and gene expression data in the LGRC data set . Raw intensity data in methylation profiling was used to determine whether probes mapped to Y-chromosome DNA fragments can be used to classify samples into male and female . Raw intensities of probes representing the Y-chromosome specific genes FAM197Y2P , TTTY15 , and TBL1Y were significantly associated with genders in the LGRC data set ( t-test p-values = 3 . 25×10−28 , 1 . 79×10−27 , and 8 . 71×10−26 , respectively ) . A methyl probe , “chrY:9994006” , representing FAM197Y2P is the best methyl probe for gender prediction and was used to classify samples in the LGRC data set into male and female . Figure 3 shows that a higher mismatch rate between clinical and methylation profiling data than other pairs of data matching in the LGRC data set ( Table S1 ) . Multiple omics data surveying different molecular traits pertaining to the same set of samples were mapped according to the flow diagram in Figure 4 . SNP genotype , gene expression , and methylation data are used for illustration purposes . Other types of data can be used as well . For example , CNV data was used instead of SNP data in the TCGA data sets . First , significant cis regulation ( cis-eSNPs , cis-mSNPs , and cis methyl-mRNA ) pairs were identified , and sample identity similarities were calculated based on these cis pairs as outlined above . Then , matches and mismatches between omics data were identified in the following steps ( ordered by confidence of each test ) : After label mis-matches between different types of omics data are identified and sample labeling errors are corrected by comparing multiple identity similarity measurements , the quality of sample alignment is re-assessed by counting the numbers of cis regulation pairs according to the updated data annotation . We iterate this process until data annotations are stable .
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Many human diseases are complex with multiple genetic and environmental causal factors interacting together to give rise to disease phenotypes . Such factors affect biological systems through many layers of regulations , including transcriptional and epigenetic regulation , and protein changes . To fully understand their molecular mechanisms , complex diseases are often studied in diverse dimensions including genetics ( genotype variations by single nucleotide polymorphism ( SNP ) arrays or whole exome sequencing ) , transcriptomics , epigenetics , and proteomics . However , errors in sample annotation or labeling often occur in large-scale genetic and genomic studies and are difficult to avoid completely during data generation and management . Identifying and correcting these errors are critical for integrative genomic studies . In this study , we developed a computational approach , Multi-Omics Data Matcher ( MODMatcher ) , to identify and correct sample labeling errors based on multiple types of molecular data before further integrative analysis . Our results indicate that signals increased more than 100% after correction of sample labeling errors in a large lung genomic study . Our method can be broadly applied to large genomic data sets with multiple types of omics data , such as TCGA ( The Cancer Genome Atlas ) data sets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"computational",
"biology"
] |
2014
|
MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis
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The asparagine hydroxylase , factor inhibiting HIF ( FIH ) , confers oxygen-dependence upon the hypoxia-inducible factor ( HIF ) , a master regulator of the cellular adaptive response to hypoxia . Studies investigating whether asparagine hydroxylation is a general regulatory oxygen-dependent modification have identified multiple non-HIF targets for FIH . However , the functional consequences of this outside of the HIF pathway remain unclear . Here , we demonstrate that the deubiquitinase ovarian tumor domain containing ubiquitin aldehyde binding protein 1 ( OTUB1 ) is a substrate for hydroxylation by FIH on N22 . Mutation of N22 leads to a profound change in the interaction of OTUB1 with proteins important in cellular metabolism . Furthermore , in cultured cells , overexpression of N22A mutant OTUB1 impairs cellular metabolic processes when compared to wild type . Based on these data , we hypothesize that OTUB1 is a target for functional hydroxylation by FIH . Additionally , we propose that our results provide new insight into the regulation of cellular energy metabolism during hypoxic stress and the potential for targeting hydroxylases for therapeutic benefit .
Hypoxia is a common feature of the microenvironment in a number of pathophysiologic conditions and represents a significant threat to cellular metabolic homeostasis [1] . Eukaryotic cells have evolved the capacity to rapidly sense changes in intracellular oxygen levels through a family of hydroxylases that confer oxygen-dependence upon the key transcriptional regulator of the adaptive response to hypoxia , termed the hypoxia-inducible factor ( HIF ) [2 , 3] . Hydroxylases were first identified as oxygen sensors in the HIF pathway and belong to the Fe ( II ) - and 2-oxoglutarate-dependent dioxygenase superfamily [4] . These enzymes catalyze the hydroxylation of proteins , in a manner that is dependent on the availability of molecular oxygen ( O2 ) . Therefore , hydroxylase activity is decreased when O2 is low [5] . Four discrete HIF-hydroxylase isoforms have been identified to date , three of which are prolyl-4-hydroxylases ( PHD1–3 ) ( Uniprot accession numbers: Q96KS0 , Q9GZT9 , Q9H6Z9 ) , which regulate the stability of HIFα subunits . In normoxia , the PHDs hydroxylate specific proline residues ( P402 and P564 on HIF-1α ) , which promotes binding of the von Hippel-Lindau ( VHL ) ( Uniprot accession number: P40337 ) protein followed by formation of an E3 ubiquitin ligase complex , HIFα ubiquitination and subsequent degradation [2] . In parallel with this , a second oxygen-dependent repression of HIF transcriptional activity is regulated by asparaginyl hydroxylation . The asparagine hydroxylase , termed factor inhibiting HIF ( FIH ) ( Uniprot accession number: Q9NWT6 ) , hydroxylates an asparagine residue within HIFα subunits ( N803 on HIF-1α ) , resulting in steric inhibition of its interaction with the transcriptional co-activator p300/CBP , thereby inhibiting HIF-dependent transcription [2] . In hypoxia , when HIFα hydroxylation is reduced , HIFα subunits escape degradation , translocate into the nucleus , bind to the subunit HIF-1β , form a transcriptional complex with p300/CBP and activate gene expression [6] . A number of proteins other than HIF are also sensitive to regulation by hypoxia . However , the mechanism governing their oxygen-sensitivity is less clear [7] . A key question that remains is whether functional hydroxylation is specific for the regulation of HIFα or if other oxygen-sensitive proteins are also regulated by this post-translational modification . While the number of proteins identified as being targets for proline hydroxylation is low [8–10] , FIH-dependent asparagine hydroxylation has been demonstrated for a larger group of non-HIF substrates , including ankyrin-repeat domain ( ARD ) -containing proteins such as tankyrase , notch-1 , ASPP2 , and IκBα [11–14] . However , the functional consequences of asparagine hydroxylation in general remain less clear [15 , 16] . Notably , a functional hydroxylation of the ion channel TRPV3 by FIH has recently been reported [17] . FIH homozygous knockout mice demonstrate a metabolic phenotype leading to the proposal that FIH is a key regulator of cellular metabolism [18] . However , a change of HIF activity alone due to FIH knockout could not explain all of the observed metabolic changes [18] , indicating HIF-independent mechanisms . Because of this , a key question remaining is whether other FIH target proteins are involved in the observed metabolic phenotype in FIH-deficient mice . In a previous study , we identified a number of putative hydroxylation substrates in the IL-1β signaling pathway that could account for the inhibition of IL-1β-induced inflammation observed in cells treated with pharmacologic hydroxylase inhibitors [19] . One identified candidate for asparagine hydroxylation was the deubiquitinase ( DUB ) ovarian tumor domain containing ubiquitin aldehyde binding protein 1 ( OTUB1 ) ( Uniprot accession number: Q96FW1 ) [19] . Interestingly , in a global proteomic analysis , OTUB1 was identified to interact with metabolic regulators [20] . Furthermore , mice deficient in OTUB1 demonstrate a lean body mass phenotype that is reflective of altered metabolic function ( http://www . mousephenotype . org/data/genes/MGI:2147616 ) . In the current study , we provide evidence that N22 of OTUB1 is a bona fide target for enzymatic hydroxylation by FIH as demonstrated by a combination of mass spectrometric techniques and in vitro peptide hydroxylation . Furthermore , site-directed point mutagenesis of N22 enhanced the OTUB1 interactome , particularly with respect to proteins involved in metabolism . Finally , overexpression of mutant OTUB1 resulted in cellular bio-energetic stress ( as reflected by enhanced AMP kinase activation ) when compared to wild-type ( WT ) OTUB1 , thus indicating a functional role for N22 hydroxylation in terms of regulating cellular metabolism . These data provide a mechanistic link between FIH-dependent hydroxylation of OTUB1 and alterations in cellular metabolism and contribute a further level of understanding to the vital link between cellular oxygen-sensing mechanisms and the control of cellular metabolism .
Previous studies have linked homozygous FIH deficiency in mice with a phenotype of disrupted energy metabolism . This is in part reflected by altered phosphorylation of AMPKα , which reflects a change in the cellular AMP:ATP ratio and therefore can be used as a surrogate marker of cellular metabolic stress . The molecular mechanisms underpinning this alteration in AMPKα phosphorylation remain unknown , but appear to be independent of the prototypic FIH substrate HIF [18] . In order to test whether FIH regulates energy metabolism at the cellular level , HEK293 cells were transfected with FIH-V5 for 24 h and cell extracts were generated either prior to the addition of fresh medium or 8–24 h later . Overexpression of FIH led to an increase in phosphorylation of AMPKα compared to cells transfected with an empty vector ( Fig 1A and 1B ) . This effect was most prominent prior to the addition of fresh medium . Overexpression of the catalytically dead mutant FIH H199A did not change AMPKα phosphorylation ( S1A and S1B Fig ) . These data support previous work linking FIH hydroxylase activity to the regulation of metabolic processes in cells . In a previous global screen , we identified the DUB OTUB1 as a putative new substrate for asparagine hydroxylation by FIH [19] . Furthermore , mice deficient in OTUB1 demonstrate a phenotype that is consistent with altered metabolism ( http://www . mousephenotype . org/data/genes/MGI:2147616 ) . Also , OTUB1 was shown to interact with proteins involved in the regulation of cellular metabolism [20] . In support of this , we found that overexpression of OTUB1 in HEK293 cells was associated with an increase in phosphorylation of AMPKα 24 h following the addition of fresh media ( Figs 1C , 1D and S2A ) . This indicates that the cells were experiencing metabolic stress as reflected by an imbalance in the cellular AMP:ATP ratio ( and subsequent AMPK activation ) when OTUB1 activity is increased . Furthermore , overexpression of FIH together with simultaneous knockdown of OTUB1 prevented the FIH-dependent increase in AMPKα phosphorylation ( S1C , S1D and S2B Figs ) . Overall , these data led us to hypothesize that OTUB1 hydroxylation by FIH may be a link in the association of FIH with an alteration in cellular metabolism . In order to determine whether OTUB1 is a bona fide FIH substrate , initially we used mass-spectrometry–based approaches . First , we tested whether OTUB1 is enzymatically hydroxylated ( as opposed to this being a spurious chemical oxidation event ) . To do this , in one set of cells we maximized the asparagine hydroxylation capacity by overexpressing FIH along with OTUB1 . A second set of cells also overexpressing OTUB1 ( but not FIH ) were treated with the pan-hydroxylase inhibitor DMOG to minimize hydroxylation capacity . Successful overexpression of OTUB1 is demonstrated by immunoprecipitation and quantitative mass spectrometric analysis ( Fig 2A ) . We next investigated the hydroxylation status of immunoprecipitated OTUB1 . Fig 2B shows an extracted ion chromatogram of an OTUB1 peptide containing the N22 residue . Mass spectrometric analysis revealed that the peptides with the shorter retention time corresponded with the hydroxylated form of this peptide . Decreased retention time has previously been demonstrated to be associated with FIH-dependent hydroxylation of peptides [11] . Tandem mass spectrometry ( MS/MS ) analysis of immunoprecipitated OTUB1 peptide demonstrated N22 hydroxylation in the sample where FIH was also overexpressed but not in the cells treated with DMOG ( Figs 2B , 2C and S3 ) . Importantly , the oxidation/hydroxylation of M31 ( reflecting a nearby spurious oxidation event ) in the same samples was independent of enzymatic hydroxylase activity ( Fig 2D ) . Methionine residues are highly susceptible to spurious oxidation [21] . In order to investigate if the observed OTUB1 N22 hydroxylation was regulated by physiologically relevant changes in the cellular microenvironment , we incubated HEK293 cells overexpressing both OTUB1 and FIH in 0 . 2% oxygen for 8 h with and without subsequent re-oxygenation at 21% oxygen for one additional hour . The analysis of the OTUB1 N22 hydroxylation levels by mass spectrometry showed a significant reduction of OTUB1 N22 hydroxylation in hypoxia which was significantly reversed by re-oxygenation ( Fig 2E and 2F ) . DMOG-dependent inhibition of hydroxylases led to a similarly reduced OTUB1 N22 hydroxylation level as hypoxia ( Fig 2F ) . Of note , the DMOG-dependent inhibition of OTUB1 hydroxylation was partly reversed by FIH overexpression when compared to the effect of DMOG without FIH overexpression ( Fig 2C and 2F ) . Nutrient starvation for 8 h with and without re-introduction of nutrients following for one additional hour also down-regulated OTUB1 N22 hydroxylation , although to a lesser degree than hypoxia ( S4A Fig ) . Overall , these data strongly support the contention that N22 of OTUB1 is a bona fide substrate for enzymatic hydroxylation by FIH and that this is regulated by changes in the cellular microenvironment such as hypoxia . We next investigated whether OTUB1 hydroxylation on N22 is FIH-dependent . Alignment of the amino acid sequence around N22 of OTUB1 with known FIH substrates and a recently published consensus sequence for FIH target proteins revealed that N22 of OTUB1 lies within a motif highly similar to the consensus sequence ( Fig 3A ) [22] . Protein sequence alignments indicated that the OTUB1 consensus sequence is evolutionary conserved within mammals ( S4B Fig ) . We next overexpressed FLAG-HA-OTUB1 in HEK293 cells and either treated these cells with non-targeting siRNA ( siNT ) or siRNA targeting endogenous FIH ( siFIH ) ( Figs 3B and S2C ) . OTUB1 was immunoprecipitated and the hydroxylation status of N22 was analyzed by quantitative mass spectrometry . The hydroxylation of N22 was decreased in cells treated with FIH siRNA supporting the concept that N22 of OTUB1 is a target for endogenous enzymatic FIH-dependent hydroxylation ( Fig 3C ) . Furthermore , to demonstrate that N22 is directly hydroxylated by FIH , FIH activity was measured in an in vitro CO2 capture assay using purified FIH and either wild-type OTUB1 peptides containing N22 or OTUB1 peptides in which N22 was replaced by an alanine residue ( N22A ) . Using this assay , which measures the turnover of 2-oxoglutarate into succinate and CO2 by FIH , we found a significant increase in FIH activity in the presence of wild-type but not mutant OTUB1 peptide ( Fig 3D ) . Taken together , these data demonstrate that N22 of OTUB1 is a bona fide substrate for enzymatic hydroxylation by FIH . We next investigated possible functional consequences of OTUB1 hydroxylation on N22 . To do this , we used the N22A mutant in order to prevent FIH-dependent hydroxylation at this site . HEK293 cells were co-transfected with either FLAG-OTUB1 WT or FLAG-OTUB1 N22A , along with FIH ( in order to maximize hydroxylation capacity; Fig 4A ) . Following immunoprecipitation of OTUB1 ( demonstrated in Fig 4B ) we identified the OTUB1 WT and the OTUB1 N22A interactomes by mass spectrometry . Initially , we confirmed the interaction of both OTUB1 WT and OTUB1 N22A with six previously described OTUB1 interacting proteins ( S5 Fig ) [20 , 23–27] . We next used the interaction of FIH with OTUB1 as additional positive control [19] and confirmed this interaction in the case of OTUB1 WT in the interactome experiment and also subsequently by western blot analysis ( Figs 4C and S5G ) . Interestingly , this interaction was greatly reduced with OTUB1 N22A , indicating a key role for this residue in the interaction between FIH and OTUB1 ( Figs 4C and S5G ) . Qualitative analysis revealed that 147 proteins were associated with OTUB1 WT , while 318 proteins were associated with OTUB1 N22A ( Fig 4D ) . Of the OTUB1 interacting proteins , 127 were associated with both OTUB1 WT and OTUB1 N22A , indicating that the core interactome is not affected by mutation of N22 . However , when we compared the OTUB1 WT and the OTUB1 N22A interactomes , we found that while just 13 proteins were enriched in their association with OTUB1 WT over OTUB1 N22A , 147 proteins were enriched in their association with OTUB1 N22A over OTUB1 WT ( Fig 4D ) . There were 86 proteins that had equivalent levels of interaction with both OTUB1 WT and N22A . This indicates that loss of hydroxylation on N22 leads to more than a doubling of the number of proteins in the OTUB1 interactome through the recruitment of new binding partners . Of note , the N22A point mutation of OTUB1 did not change its deubiquitinase activity , which indicates that this mutation does not significantly alter protein structure ( as catalytic activity is retained ) ( S6 Fig ) . Based on these data , we hypothesize that hydroxylation of N22 on OTUB1 profoundly alters its interaction with other proteins and is therefore likely of functional consequence . Ontological analysis of the proteins differentially associated with OTUB1 WT and OTUB1 N22A using the Panther database ( www . pantherdb . org ) revealed proteins associated with multiple biological processes ( S7A and S7B Fig ) . However , metabolism-associated proteins were most highly represented , which is in agreement with previously published data for wild-type OTUB1 by Sowa et al . , demonstrating that OTUB1 interacts with metabolic regulators [20] . Furthermore , OTUB1 N22A had increased numbers of metabolism-associated proteins when compared to OTUB1 WT ( Fig 4E and S1 Table ) indicating that loss of N22 hydroxylation may impact upon interaction between OTUB1 and multiple proteins important in the regulation of metabolism . In summary , we demonstrate that N22A mutation of OTUB1 profoundly alters its physical interactome . Of note , proteins associated with metabolic processes are heavily represented in this cohort . We next investigated the impact of N22A mutation of OTUB1 on FIH-dependent regulation of cellular metabolism under conditions of energy starvation in cultured cells . Simultaneous glucose , glutamine , and pyruvate deprivation caused an increase in the phosphorylation of AMPKα likely as a result of ATP depletion ( Fig 5A and 5B ) . Cells overexpressing both wild type OTUB1 and FIH ( to maximize OTUB1 hydroxylation ) showed similar levels of AMPKα activity as control cells , however , cells overexpressing both FIH and N22A mutated OTUB1 ( to minimize OTUB1 hydroxylation ) demonstrated robustly increased phosphorylation of AMPKα . These data are consistent with our hypothesis that FIH-dependent N22 hydroxylation of OTUB1 contributes to the regulation of cellular metabolism by FIH . We next investigated the impact of the hydroxylation of N22 on the OTUB1 protein . We considered a potential change of OTUB1 protein levels and its half-life due to the hydroxylation of N22 similar to the described regulation of the HIF-1α protein by prolyl hydroxylation . We therefore established HEK293 cells stably overexpressing FLAG-OTUB1 WT or N22A , which , at the same time , also carried a stably integrated shRNA targeting the 3′UTR of OTUB1 to diminish endogenous OTUB1 protein levels ( S8 Fig ) . We transiently transfected these cells with FIH-V5 to maximize FLAG-OTUB1 WT hydroxylation and analyzed OTUB1 WT and N22A protein levels for up to 48 h by western blot . No significant change between the protein levels of OTUB1 WT and OTUB1 N22A was observed ( Fig 6A and 6B ) . We next investigated if endogenous OTUB1 protein levels change in response to an alteration of N22 hydroxylation levels . We transiently transfected HEK293 cells with either empty vector ( control ) or FIH-V5 and treated the control cells with DMOG and the FIH overexpressing cells with DMSO . This experimental set up was similar to the experiment performed in Fig 2A–2D , which lead to maximally hydroxylated N22 of OTUB1 in the FIH overexpressing sample and to diminished hydroxylation of N22 in the DMOG-treated sample . We then analyzed endogenous OTUB1 protein levels in a time course for up to 48 h by western blot . No difference was observed between maximally hydroxylated OTUB1 and minimally hydroxylated OTUB1 protein levels ( Fig 6C and 6D ) . In order to investigate the half-life of OTUB1 depending on its hydroxylation status , HEK293 cells were incubated with DMOG for 16 h prior to the treatment with cycloheximide ( CHX ) to inhibit protein synthesis . Within a time frame of 6 h CHX treatment , in which HIF-1α protein levels significantly decreased , no change in OTUB1 protein levels were observed ( Fig 6E and 6F ) . Overall , these data demonstrate that the hydroxylation of OTUB1 at N22 does not impact on OTUB1 protein stability .
Hypoxia is a common feature of a number of diseases in which metabolism is significantly altered , including chronic inflammation and cancer . The mechanisms by which hypoxia-dependent alterations in metabolism occur in such disease states have important implications for disease development and potential targets for future therapeutic intervention . In this study , we provide new insight into the regulation of metabolism by hypoxia , which is mediated through the DUB OTUB1 . The identification of HIF as a ubiquitous master regulator of the cellular adaptive response to hypoxia and its oxygen-dependent regulation by 2-oxoglutarate-dependent hydroxylases were key discoveries in our developing understanding of the oxygen-sensing mechanisms which operate in eukaryotic cells [21 , 28–33] . Because several pathways apart from HIF also demonstrate sensitivity to hypoxia , it was initially anticipated that post-translational hydroxylation would be a common modification resulting in the conferral of oxygen sensitivity on multiple targets . However , while it appears that hydroxylation is indeed a common protein modification , understanding the functional role of this outside of the HIF pathway has remained elusive . Functional proline-hydroxylation of non-HIF proteins by the HIF prolyl hydroxylases has been proposed for a limited number of proteins , including FOXO3a , CyclinD1 , ATF-4 , and IKKβ [8–10 , 34 , 35] . However , asparagine hydroxylation by FIH appears to be a more commonly observed modification and has been clearly demonstrated for multiple non-HIF proteins , including several ARD-containing proteins such as tankyrase , notch-1 , and IκBα [11–13] . However , the functional impact of this on cellular signaling pathways ( if any ) remains unclear . Therefore , the identification of new functional hydroxylation events is of key importance in developing our understanding of this oxygen-sensitive , post-translational modification . It has recently been demonstrated that mice that are homozygously deficient in FIH demonstrate a metabolic phenotype characterized by ( for example ) reduced body weight , elevated metabolic rate , and hyperventilation [18] . In these studies , the cellular bioenergetic status was assessed by measurement of the activation of AMPK , a key gauge of cellular metabolic stress which becomes activated when ATP is depleted and the AMP:ATP ratio increases . Because these mice do not display a phenotype consistent with activated HIF , it appears that the mechanisms underpinning the metabolic phenotype are at least in part independent of the HIF pathway and depend upon other FIH-dependent pathways [18] . In this study , we identified OTUB1 to be a new FIH substrate that may be important in the regulation of cell metabolism ( as also reflected by altered AMPK activation ) and , as such , may provide mechanistic insight into the metabolic phenotype observed in the FIH knockout mouse . Of note , wild-type OTUB1 has been reported to interact with metabolic regulators [20] . Furthermore , while homozygous deletion of OTUB1 results in early lethality , mice heterozygously deficient in OTUB1 were reported to display a metabolic phenotype characterized by decreased lean body mass ( http://www . mousephenotype . org/data/genes/MGI:2147616 ) [36–40] . Taken together , these data suggest the possibility that OTUB1 hydroxylation may at least in part provide a molecular explanation for some aspects of the observed phenotype in FIH-deficient mice . Previous work has demonstrated a profoundly anti-inflammatory effect of pan-hydroxylase inhibitors ( which have inhibitory activity against both PHDs and FIH ) in multiple models of intestinal inflammation [41]; however , the full mechanism underpinning this remains unclear . Altered metabolism has recently been demonstrated to be a key regulator of inflammation [42] . Therefore , a possible contributory mechanism for the anti-inflammatory activity of hydroxylase inhibitors is through altered FIH-dependent hydroxylation of OTUB1 , leading to differential metabolism at inflamed sites . In our study , we found that N22 , the site of OTUB1 hydroxylation by FIH , is located in a region of the protein that may be key to determining its activity . OTUB1 is unusual in that it hydrolyzes specifically K48 ubiquitin bonds but also inhibits the formation of K63 and K48 ubiquitin chains via a non-canonical , non-catalytic function through inhibition of E2 ubiquitin ligase activity [43–45] . In the OTUB1 apoenzyme , the residues N-terminal to the OTU catalytic domain are disordered ( approximately amino acids 1 to 45 ) , whereas upon binding of both distal ubiquitin and an E2 ubiquitin-conjugating enzyme , for example UBCH5B , the folding of a significant portion of the tail becomes stabilized as a structured alpha helix ( amino acids 23 to 44 ) ( Fig 7 ) [26 , 27 , 43] . N22 is located at the junction of the α-helix and the remaining unstructured region ( amino acids 1 to 22 ) . This is similar to the C-terminal transactivation domain ( CAD ) of HIF-1α , which is disordered when it is unbound but forms three distinct α-helices upon binding to CBP/p300 , of which one helix includes N803 , the HIF-1α asparagine residue targeted for hydroxylation by FIH [46 , 47] . In an attempt to investigate the implications of the hydroxylation of OTUB1 at this key hinge region , we found no impact of OTUB1 hydroxylation on OTUB1 protein levels or half-life ( Fig 6 ) . Also , OTUB1 enzymatic activity was unaffected by the N22A point mutation ( S6 Fig ) . Therefore , a direct regulation of the OTUB1 interactome by the N22 hydroxylation resulting in differential substrate targeting seems likely . Consistent with this concept , protein:protein interactions have previously been demonstrated to be directly modified by asparagine hydroxylation in the HIF pathway . FIH-dependent hydroxylation of N803 disrupts HIF-1α interaction with the transcriptional co-activators p300/CBP regulating HIF-1α-dependent trans-activation of gene expression . Ongoing studies are investigating whether hydroxylation of wild-type OTUB1 at N22 impacts on its ( non- ) canonical activity . In summary , in this study we provide evidence that OTUB1 is a target for functional hydroxylation by FIH . We propose that this modification may have important implications for the regulation of cellular metabolism by changing OTUB1 substrate targeting under conditions of hypoxia , such as those that occur during ischemia , chronic inflammation , and tumor growth .
Human embryonic kidney cells ( HEK293 ) were cultivated under standard conditions and used for all experiments presented . Standard cell culture media was DMEM media containing 4 . 5 g/l glucose , sodium pyruvate and L-glutamine . As media for nutrient starvation experiments DMEM media without glucose , sodium pyruvate or L-glutamine was used . For the transient transfection of both siRNAs and plasmids Lipofectamine 2000 reagent ( Invitrogen ) was used according to the manufacturer’s description . The plasmid encoding FIH-V5 was a kind gift of Dr . Eric Metzen ( University of Duisburg-Essen , Essen , Germany ) , whereas the wild-type FLAG-OTUB1 coding plasmid was generously provided by Dr . Mu-Shui Dai ( Oregon Health and Science University , Portland , Oregon , United States ) [25] . The FLAG-HA-OTUB1 plasmid was a gift from Dr . Wade Harper ( Harvard Medical School , Boston , Massachusetts , US ) ( Addgene plasmid # 22551 ) [20] . The plasmid encoding FIH H199A has previously been described [32] . Nontargeting siRNA ( siNT ) was purchased from Dharmacon ( GE Healthcare ) ( ON-TARGETplus SMARTpool ) . The siRNA targeting FIH ( siFIH ) was produced by Eurofins Genomics according to a previously reported sequence [11] ( sequence F1 ) . siRNA targeting the 3′UTR of OTUB1 was produced by Eurofins Genomics according to a previously reported sequence [25] ( siRNA-4 ) . The FLAG-OTUB1 N22A mutant was generated with the Quikchange II XL Site-Directed Mutagenesis kit ( Agilent technologies ) according to the manufacturer’s description , using the plasmid encoding FLAG-OTUB1 wild-type as template . Successful mutation was confirmed by sequencing of the targeted site in the obtained plasmid . Protein concentrations of cell lysates for western blot analysis were determined using the Bio-Rad DC protein assay . Equal amounts of protein were separated by SDS PAGE , transferred to nitrocellulose membranes and detected using anti-OTUB1 antibody ( Cell Signaling ) , anti-β-actin antibody ( Sigma ) , anti-AMPKα antibody ( Cell Signaling ) , anti-phospho-AMPKα ( Thr172 ) antibody ( Cell Signaling ) , anti-FIH antibody ( Abcam ) , anti-α-tubulin antibody ( Santa Cruz ) , anti-FLAG antibody ( Sigma ) , anti-V5 antibody ( Invitrogen ) , or anti-HA antibody ( Roche ) . Peptide hydroxylation was assayed by the hydroxylation-coupled decarboxylation of [1-14C]-2-oxoglutarate by human FIH ( hFIH ) as described previously [50] . Each 40 μl reaction contained 3 . 5 μM MBP-hFIH , 625 μM peptide substrate , 300 μM FeSO4 , 40 μM 2-oxo[1-14C]glutarate ( 40 , 000 dpm ) , 4 mM ascorbate , 500 μM dithiothreitol , 0 . 4 mg bovine serum albumin , and 50 mM Tris-HCl ( pH 7 . 0 ) , and was incubated at 37°C for 60 min . Filters were dried , UltimaGold XR scintillatant added , and counted on a MicroBeta 2450 ( Perkin Elmer ) . Immunoprecipitation was carried out as previously described [19] . Briefly , cell lysates were incubated with the antibody-coupled beads anti-FLAG M2 affinity gel ( Sigma ) at 4°C for 1 h . Subsequently , the agarose beads were washed twice with lysis buffer ( 1% Triton X-100 , 20 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM MgCl2 ) and twice with washing buffer ( 20 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM MgCl2 ) . This was followed by sample preparation for mass spectrometric analysis as previously described [51] . The samples were analyzed by a Q-Exactive mass spectrometer ( Thermo Scientific ) and searched with MaxQuant . MS/MS spectra were searched against the human UniProt database ( www . uniprot . org ) . Variable modifications included ( MYWNDEPK ) hydroxylation/oxidation . For a more detailed description , see Scholz et al . 2013 [19] . The mass spectrometry proteomics data of the OTUB1 hydroxylation experiments have been deposited to the ProteomeXchange Consortium [52] via the PRIDE partner repository with the dataset identifier PXD002103 . The datasets of OTUB1 WT and OTUB1 N22A mutant co-precipitated proteins obtained by mass spectrometric analysis were first filtered for significant enrichment of proteins over control . Proteins were only considered as part of the interactome when the average of the six datasets obtained ( three biological and two technical replicates ) was at least 2-fold different to the negative control and , additionally , when this difference was statistically significant ( Student’s t test was applied ) . In addition , the obtained lists were analyzed for differences between the wild-type and mutant interactome . Proteins were only considered to be changed between these two groups when the difference was at least 2-fold and when this difference was statistically significant . These analyses were carried out with Excel ( Microsoft ) . The PANTHER database ( www . pantherdb . org ) was used for functional annotation of the obtained lists of proteins and to cluster the proteins according to the assigned gene ontology terms [53 , 54] . The protein interactions from this publication have been submitted to the IMEx ( http://www . imexconsortium . org ) consortium through IntAct [55] and assigned the identifier IM-23897 . For some identified peptides in this experiment , it was not possible to assign the sequence to one specific protein . These results will not be shown in the IntAct database but are available in S2 Data . 1 . 5 x 108 HEK293 cells stably expressing empty vector ( control ) , FLAG-OTUB1 WT or N22A were harvested by trypsinisation , washed 1x in cold PBS and lysed in 1 ml 1x TBS plus 1% NP40 , 5 mM MgCl2 , 1 mM PMSF and 1x Roche cOmplete EDTA-Free Protease Inhibitor Cocktail per sample on ice for 30 min . Lysates were clarified by centrifugation at 14 , 000 rpm at 4°C for 10 min and pre-cleared by incubating with 12 . 5 μl Sepharose 6 fast flow resin and 12 . 5 μl Protein G Dynabeads ( prepared according to manufacturer’s instructions ) for 30 min at 4°C with rotation . Immunoprecipitation was performed on pre-cleared lysates by adding 25 μl Protein G Dynabeads and 10 μl ANTI-FLAG M2 antibody per sample and incubating for 2 . 5 h at 4°C with rotation . Beads were then washed 3x in cold TBS + 0 . 2% NP40 with a final wash in cold TBS before resuspending in 60 μl TBS . Immunoprecipitated FLAG-OTUB1 WT and N22A ( bound to Protein G Dynabeads , equivalent to approximately 3 x 107 cells ) was incubated with 600 nM K48-tetraubiquitin ( K48-Ub4 ) at 37°C . Samples were harvested at 0 , 30 , and 60 min . Empty vector IP beads were used as a negative control and 26S proteasomes as a positive control for DUB activity . Samples were mixed with SDS loading buffer , heated at 90°C for 5 min and separated on 4%–12% bis-tris gels . Proteins were transferred to PVDF membranes and blocked in PBS/0 . 2% Tween-20 plus 3% BSA for 1 h at room temperature . Proteins were detected with HRP-conjugated P4D1 anti-ubiquitin antibody and . ANTI-FLAG M2 antibody . OTUB1 WT and OTUB1 N22A were amplified by polymerase-based chain reaction ( PCR ) using pFLAG-OTUB1 WT or N22A as template . The sequences were cloned into the entry vector pENTR4 ( Invitrogen ) using the restriction enzymes NcoI ( Thermo Scientific ) and XhoI ( Thermo Scientific ) . Subsequently , pDEST15-OTUB1 WT and N22A ( vector carrying the GST-tag ) were generated using the gateway system ( Invitrogen ) with the LR clonase II ( Invitrogen ) according to the manufacturer’s description . Escherichia coli BL21-AI ( Invitrogen ) were transformed with pDEST15-OTUB1 WT or N22A , respectively , and protein expression was induced by adding 0 . 2% L-Arabinose for 3h at 37°C . Bacteria were lysed using a Cell Disruptor ( TS Series Bench Top , Constant Systems Ltd . ) at 35 kpsi in two cycles . Lysates were cleared by ultracentrifugation at 162 , 000 xg and 4°C for 1 h ( Sorvall WX100 Ultracentrifuge ) and subsequently affinity purified with Glutathione Sepharose Fast Flow Columns ( GSTrap FF , GE Healthcare ) in the duo flow system ( Bio-Rad ) . Successful protein expression and purification was verified by Coomassie staining and western blot against OTUB1 . Purified GST-tagged OTUB1 WT and N22A were dialysed in 20 mM Tris , 150 mM NaCl , 1 mM DTT to remove GSH . Samples were loaded into SnakeSkin dialysis tubing , sealed and incubated in dialysis buffer at 4°C overnight with gentle stirring . Samples were transferred to fresh buffer for a further 1 h at 4°C . Final protein concentration was assayed using a Nanodrop 2000 spectrophotometer and checked by running equal concentrations of GST-OTUB1 WT and N22A on a 10% SDS PAGE gel and staining with Coomassie brilliant blue . Purified GST-OTUB1 WT and N22A were incubated with 600 nM K48-Ub4 at 37°C . Samples were harvested at 0 , 30 , and 60 min . K48-Ub4 alone was used as a negative control . Samples were mixed with SDS loading buffer , heated at 90°C for 5 min and run on 4%–12% bis-tris gels . Proteins were transferred to PVDF membranes and blocked in PBS/0 . 2% Tween-20 plus 3% BSA for 1 h at room temperature . Membranes were probed with HRP-conjugated P4D1 anti-ubiquitin antibody and anti-GST antibody . For the functional analysis of metabolic proteins enriched in the OTUB1 N22A interactome over OTUB1 WT interactome the online tool DAVID Bioinformatics Resources 6 . 7 ( http://david . abcc . ncifcrf . gov/ ) was used [56 , 57] . Functional annotation clustering was performed using default settings . Control plasmids and plasmids encoding FLAG-OTUB1 WT or FLAG-OTUB1 N22A were linearized by digestion with PvuI ( Thermo Scientific ) and transiently transfected into HEK293 WT cells . Cells were selected with 1 mg/ml G418 for 4 wk . An expression vector encoding a short hairpin RNA ( shRNA ) sequence targeting the 3′UTR of human OTUB1 ( Sigma , TRCN0000273238 ) and a non-targeting shRNA ( shControl ) ( Sigma , SHC016 ) were purchased from Sigma . Lentiviral particles for shOTUB1 and shControl were produced in HEK293T cells using the Vira-Power lentiviral expression vector system according to the manufacturer’s instructions ( Invitrogen ) . HEK293 cells overexpressing FLAG-OTUB WT or N22A or control cells were infected with shOTUB1 or shControl lentiviral particles followed by selection with 2 . 5 μg/ml puromycin for 4 wk . Primers designed for the site-directed mutagenesis of OTUB1 were as follows: Forward primer 5′-AGGCCAGACAGGCAACACCTTCGGAGTCGCTGC-3′ Reverse primer 5′-GCAGCGACTCCGAAGGTGTTGCCTGTCTGGCCT-3′ Primers designed for the cloning of OTUB1 WT and N22A into pENTR4: Forward primer 5′-ACGTCCATGGCGGCGGAGGAACCTCAGCA-3′ Reverse primer 5′-ACGTCTCGAGCTATTTGTAGAGGATATCGT-3′ Peptide sequences of peptides used in the in vitro hydroxylation assay: Human HIF1 N803: DESGLPQLTSYDCEVNAPI Murine Notch N2012: VEGMLEDLINSHADVNAVDD Human OTUB1 N22: QQQKQEPLGSDSEGVNCLAYDEAIMAQQDRIQQE Human OTUB1 N22A: QQQKQEPLGSDSEGVACLAYDEAIMAQQDRIQQE For the analysis of statistical significance one-way ANOVA followed by Tukey test was applied for comparisons of more than two different datasets . For the comparison of two different datasets , unpaired Student’s t test was applied . P-values < 0 . 05 were considered statistically significant .
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Hypoxia is a commonly encountered physiologic and pathophysiologic stress to which mammalian cells have evolved an effective adaptive response . This response is governed by a transcription factor termed the hypoxia-inducible factor ( HIF ) . The mechanisms linking the cellular sensing of oxygen levels to HIF activation have been elucidated and involve oxygen-dependent hydroxylation of HIF on proline and asparagine residues by a family of hydroxylases . A key question that remains unclear is the extent to which oxygen-dependent hydroxylation occurs as a functional post-translational modification outside of the HIF pathway . This is key to developing our understanding of whether hydroxylation is a general regulatory modification or one which has specifically evolved for the regulation of HIF . Here , we demonstrate that the deubiquitinase ovarian tumor domain containing ubiquitin aldehyde binding protein 1 ( OTUB1 ) is a target for functional hydroxylation by the FIH hydroxylase . Hydroxylation of OTUB1 by FIH on asparagine residue N22 results in a restriction in its interactome , leading us to hypothesize a possible role for hydroxylation in substrate targeting . Of interest , interactions of OTUB1 with a number of proteins involved in metabolism are altered upon removal of the hydroxylation site—implicating OTUB1 as a possible link between oxygen sensing and the regulation of metabolism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2016
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FIH Regulates Cellular Metabolism through Hydroxylation of the Deubiquitinase OTUB1
|
Previous work in Arabidopsis showed that after an ancient tetraploidy event , genes were preferentially removed from one of the two homeologs , a process known as fractionation . The mechanism of fractionation is unknown . We sought to determine whether such preferential , or biased , fractionation exists in maize and , if so , whether a specific mechanism could be implicated in this process . We studied the process of fractionation using two recently sequenced grass species: sorghum and maize . The maize lineage has experienced a tetraploidy since its divergence from sorghum approximately 12 million years ago , and fragments of many knocked-out genes retain enough sequence similarity to be easily identifiable . Using sorghum exons as the query sequence , we studied the fate of both orthologous genes in maize following the maize tetraploidy . We show that genes are predominantly lost , not relocated , and that single-gene loss by deletion is the rule . Based on comparisons with orthologous sorghum and rice genes , we also infer that the sequences present before the deletion events were flanked by short direct repeats , a signature of intra-chromosomal recombination . Evidence of this deletion mechanism is found 2 . 3 times more frequently on one of the maize homeologs , consistent with earlier observations of biased fractionation . The over-fractionated homeolog is also a greater than 3-fold better target for transposon removal , but does not have an observably higher synonymous base substitution rate , nor could we find differentially placed methylation domains . We conclude that fractionation is indeed biased in maize and that intra-chromosomal or possibly a similar illegitimate recombination is the primary mechanism by which fractionation occurs . The mechanism of intra-chromosomal recombination explains the observed bias in both gene and transposon loss in the maize lineage . The existence of fractionation bias demonstrates that the frequency of deletion is modulated . Among the evolutionary benefits of this deletion/fractionation mechanism is bulk DNA removal and the generation of novel combinations of regulatory sequences and coding regions .
Decades ago it was proposed that whole-genome duplication provides raw material for evolutionary innovation , as reviewed [1] . The angiosperm phylogenetic tree of organisms with complete genome sequence has provided evidence for repeated ancient tetraploidies in all lineages ( Figure 1 ) . However , tetraploidies occurring before approximately 150 million years ago ( MYA ) in plants and 500 MYA in animals are difficult to detect [2] . Genomes that have experienced tetraploidy events tend to reduce their genome structure toward their ancestral chromosome number and gene content , though not gene order . The mutational process accomplishing this reduction in gene content is called fractionation , and its mechanism is unknown . Theoretically , the expected fate of the average gene following tetraploidy is loss from one or the other , but not both , homeologous chromosomes [3] , [4] , . Previous studies on fractionation of the most recent tetraploidy in the Arabidopsis lineage ( known as the alpha tetraploidy event ) found significantly more gene loss on one homeolog than the other [7] . However , some genes are retained as homeologous pairs . This same study found that genes retained as pairs were significantly clustered and that any mechanism of fractionation causes clustering of retained genes , especially on the over-fractionated homeolog , as retained genes will inevitably be physically closer to each other once the intervening genes have been removed . Figure 2 illustrates expectations of biased and unbiased fractionation and shows how fractionation by any mechanism tends to cluster retained genes . Any of the following gene loss mechanisms could contribute to fractionation after a tetraploidy event: ( 1 ) single gene loss via inactivation and sequence randomization ( i . e . the pseudogene pathway ) as observed in mammals , including primates [8]; ( 2 ) single gene “loss” from orthologous sites by gene transposition , as was observed in the Brassicales [9]; ( 3 ) single gene loss by a short deletion mechanism; ( 4 ) multiple gene loss events of any type , like long ( multi-gene ) deletions or segmental transpositions . Lai and coworkers [10] compared five orthologous panels of bacterial artificial chromosome sequence for rice , sorghum , maize homeolog 1 , and maize homeolog 2 . Each set of panels was anchored on a gene shared by all four genomes . They found examples of genes that moved out of the syntenic position in maize but were conserved syntenically between rice and sorghum . Another likely mechanism for fractionation is short deletions via illegitimate , or intra-chromosomal , recombination , as introduced in point 3 above . Devos and coworkers [11] implicated recombination , both homologous and illegitimate , as the mechanism used by plants , including maize , to remove retrotransposons . This suggestion was based on finding short direct repeats from 2–13 bp , sometimes imperfect , flanking small deletions in the inferred target chromosome . This was the same conclusion derived previously from data implicating short deletions in nonfunctional transposons in Drosophila [12] . Citing bacterial illegitimate recombination studies , these researchers implicated recombination mechanisms as the transposon loss mechanism . Using sorghum as our primary outgroup and rice as a secondary outgroup , we examine in detail the gene and chromosome fragments identifiable at the current stage of fractionation in the maize inbred B73 , a genome sequenced recently [13] . We also examine such fragments in the recently sequenced soybean genome that result from a tetraploidy estimated to have occurred approximately 13 MYA [14] . We conclude that the most likely mechanism of fractionation is single gene loss by short deletions , predominantly in sizes ranging from 5 bp to 178 bp , with deletions being found 2 . 3 times more often on one homeolog than the other; infrequent longer deletions are possible . The fractionation mechanism , like the mechanism of transposon removal , is likely to be intra-chromosomal recombination , and this has general implications for bulk DNA removal and the wholesale generation of new sequence combinations .
To study the post-tetraploidy fractionation process in detail , both a sequenced genome that is undergoing fractionation and an outgroup with a sequenced genome that diverged before the tetraploidy event is required . For the most recent tetraploidy in maize , which happened from 5 to 12 MYA [15] , [16] , sorghum is such an outgroup . Sorghum diverged from the maize lineage just before the tetraploidy event ( Figure 1 ) [17] . Sorghum has been sequenced [16] , and the first assembly of a maize genome has recently been published [13] . In addition to its phylogenetic position , the maize lineage tetraploidy possesses another characteristic that recommends it for the study of fractionation: it is relatively recent . The alpha tetraploidy in Arabidopsis , at 23–50 MYA , is older than the most recent ( alpha ) tetraploidy in maize . Even so , the maize alpha tetraploidy is known to be highly fractionated [10] , [18] . Our primary research aim was to detail what happened to those orthologous ( syntenic ) genes shared by sorghum and maize following the maize tetraploidy . Using the procedures described in Methods , we identified 37 orthologous regions between sorghum and the corresponding maize homeologs retained after the maize alpha tetraploidy event . From these regions , we found that of the 2 , 943 sorghum-maize ( Sb-Zm ) syntenic shared genes that we studied , 43% of them were retained as homeologous pairs in maize . Note that we count as present any significant fragment of gene . If the maize tetraploidy behaved as other known tetraploidies in plants and microbes , retained genes should be enriched in those encoding transcription factors , as reviewed [19] . Indeed , the frequency of genes encoding transcription factors was 4 . 3 times greater among the retained genes as compared to the fractionated genes . Figure 3A is a cartoon of a GEvo output screenshot of a 13-gene segment of one of the 37 orthologous regions ( region Sb2 ) between sorghum and its two maize homeologs , as described in Methods . The GEvo comparative sequence alignment tool output generated the original blastn output detailed in Methods . Supplemental Information 1 ( Dataset S1 ) gives our primary data as inferred from analyses like that shown in Figure 3A . Dataset S2 shows how any one sorghum chromosomal region is orthologous to two maize regions , generating information essential to construct our sorghum-maize1-maize2 regions . One way to measure fractionation bias is to first assume that gene loss involves one gene independently from any other gene , and then count the number of gene losses ( deletions ) on one homeolog as compared to the number of gene losses on the other homeolog . If fractionation were unbiased , this ratio is expected to be 1∶1 . Other measures of fractionation include total number of genes or base pairs in an orthologous stretch , but counting deletions of shared genes is most direct , so we present this first . Figure 3B shows two representative diagrams of our data for shorter regions ( Dataset S3 contains all 37 such diagrams with bias statistics ) and indicates that fractionation has been significantly biased in 68% of our regions . Using data from nine representative longer sorghum regions ( Table 1 ) , we conclude that the over-fractionated chromosomes have 2 . 3 times as many deleted genes as do the under-fractionated chromosomes . We next asked , what was the average extent of gene loss ? Most importantly , are deletion events longer than one gene ? Figure 4A , B , C , and D monitors runs , or the sequential series of deleted genes , and Figure 4E monitors runs of retained genes . The experimental runs data plotted in Figure 4A , B , C , and E were compared to the 95% confidence interval around the median of Monte Carlo simulated data ( Methods ) based on the assumption that one gene is deleted at a time , and the chromosome was ligated before the next deletion , as such a mechanism would be predicted to work in nature . The distributions of Figures 4A , B , and C are all very similar: they differ only as to whether or not the over-fractionated or under-fractionated chromosome is evaluated or as to whether or not gene losses of 10 genes or greater were included . The most frequent run length in distributions Figure 4A , B , or C is one gene , followed by two genes , and so forth . If we recalculate expectations for distribution of Figure 4C using an evolutionary method that permits varying percentages of deletions of genes , the best fit is one-gene deletions 80% , two-gene deletions 15% , and three-gene deletions 5% ( Figure 4D ) . The possibility existed that longer deletion runs were not authentic deletions but were segmental translocations . Deletion runs consisting of 12 genes or more were found somewhere else in the genome . Deletions of between 11 and 6 genes were found elsewhere about 10% of the time , but identification was made more difficult because fractionation is expected to remove genes from any position in the genome , and sometimes is expected to leave behind fewer than the three syntenic genes needed for a positive identification . That is why deletions of 10 or more genes were removed from all distributions of Figure 4 except Figure 4A . There is a possibility , a possibility we evaluate , that the smaller deletions are also undetectable segmental translocations and not authentic deletions , but removal is essentially one gene at a time . We next asked if it were possible that , rather than being deleted , single genes observed as lost between orthologous maize and sorghum regions were instead transposed or translocated elsewhere in the genome , as we had observed for longer runs of genes . Large-scale single gene transposition has been documented in the eudicot order Brassicales [9] , and cases have also been reported in the maize lineage [11] . To address the possibility that the majority of the fractionating gene loss we were observing was actually a result of whole-gene transposition , we attempted to identify potentially orthologous maize genes in a position-independent manner . Sorghum genes with known orthologs in rice were blasted against sorghum , rice , and maize genomes , as described in Methods . From the resulting data we found that genes identified as retained had a mean of 1 . 57 copies in the genome , with a median of 2 genes . It is expected that this number would be less than 2 , as the manual annotators considered a gene to be retained if a significant fragment of it was still present , which included genes in the process of being removed by small deletions ( as will be discussed ) . Genes identified as being fractionated in Dataset S1 were present at a mean of 1 . 17 copies in the genome , with a median of 1 gene . A few of these extra copies are likely maize-specific duplications , but others no doubt represent apparently deleted homeologs that have transposed to other locations in the genome . Nevertheless , these data provide strong evidence that while some apparently fractionated genes may have been lost via translocation ( transposition to a new site ) , translocation is not the prevailing mechanism explaining our fractionation data . This conclusion does not imply that fragments of genes are not transposed around the genome , as is known to occur frequently via transposon-mediated gene capture [20] . Indeed , when we re-calibrated our search to find shorter stretches of high-identity sequence , we found many pieces of genes present at higher copy numbers elsewhere in the genome . Examination of a sample of these hits identified gene fragments , but no intact genes were found . As shown in Figure 2 , fractionation itself clusters retained genes . Figure 4E identifies runs of retained genes ( Bs ) and distributes them by run length and compares this to expectations based on deletions one gene at a time . Is this distribution more highly clustered than expected from fractionation alone ? The mode is clearly one retained pair , as expected . Expectation intervals were generated assuming that deletions occurred one gene at a time . Although clustering of retained genes is not dramatic , runs of retained genes greater than 9 gene pairs are not expected at all; in total , there are 62 genes ( out of 1 , 203 , or 5% ) in such longer runs ranging from 9 through 12 genes in length . When expectations are changed to be 80% single-gene deletions , 15% two-gene deletions , and 5% three-gene deletions ( Figure 4F ) , the actual and expected are similar . Now there are only four unexpected runs greater than 9 genes in length . With the exception of these few longer runs , genes are retained approximately as expected based on 80%/15%/5% 1/2/3 gene deletion predictions . Table 1 focuses on nine longer representative homeologous regions of maize representing different sorghum chromosomes . The under-fractionated and over-fractionated homeologs in maize are identified in this table ( Column 2 ) . This over/under designation derives from the deletion bias data quantified in Table 1 and evaluated for significance in Column C . Table 2 shows these data for each of the nine representative regions individually ( Column H ) . In this case , the numbers are less than 1 because the ratio is under-fractionated/over-fractionated , and the under-fractionated homeolog has fewer deletions . The homeolog with the fewest deletions contains the most genes , so another measure of fractionation is the number of genes on the under-fractionated/over-fractionated , where bias will now be indicated by ratios greater than 1 . The fractionation bias ratios , using total gene data , for each of the nine representative regions are listed in Column L of Table 1 . To extrapolate bias in our manually annotated regions to more of the genome , we used the slope of syntenic lines in Zm-Zm dot plots ( Dataset S4 ) . A slope of 1 implies unbiased fractionation . A significant difference in the number of genes or base pairs between the two homeologous maize chromosomes alters that slope from 1 , and this is what we observed . If the unit of Zm-under/Zm-over measurement is total number of genes annotated by maizegnome . org , the average slope value corresponds to a mean fractionation bias value of 1 . 5 ( Table 1 , Column M ) . If the units are in total base-pairs , the fractionation bias is 2 . 3 ( Table 1 , Column N ) . Again , the under/over direction of fractionation in both cases remains greater than 1 , as expected , but the dot-plot analysis made it possible to examine considerably longer regions of paired homeologs , each anchored on the indicated sorghum region . Most important here is that the three measures of bias based on gene number ( Columns L and M ) or base pair length ( N ) are concordant with expectations based on the rigorous deletion bias data generated manually for our representative regions . Based on the concordance between bias in orthologous gene loss and base pair length , and given that 85% of the maize genome is composed of transposable elements [13] , we conclude that homeologous regions that preferentially lose genes also lose intergenic , primarily transposon , DNA more frequently . Table 1 , Column O , reports our measured ratio of Ks [Zm-under/Sb] to Ks [Zm-over/Sb] for a total of 1 , 772 Sb-Zm-Zm gene units in the five sorghum homeologous regions for which highly significant under/over-fractionation expectations existed ( Table 1 ) . We removed 16% of pairs with the most extreme Ks ratios , many of which represent misalignments or alignments to pseudogenes . Using the remaining data , we found no difference between the Ks values between sorghum and either of the two maize homeologous regions . We conclude that mutation by base substitution and mutation by short deletion are mechanically distinct and are targeted differently . Three of our representative regions are within pairs of homeologous chromosomal arms: Sb1 = Zm1S/9L ( sorghum chromosome 1 = maize chromosome 1S and maize chromosome 9L ) , Sb3 = Zm3L/8L , and Sb6 = Zm2S/10L . The under-fractionated ( longer ) homeolog is the numerator . These are the only arm-arm exact homeologies in the maize genome; examination of syntenic Sb-Zm dot plots ( like that in Dataset S2 ) made clear that segments of these arms are not present syntenically on any other chromosomes . The total map unit's length of these maize chromosomal arms is known , making it possible to directly compare the degree of fractionation within any given arm to the overall recombination frequency within that arm . Mapping data for maize inbred T232×x inbred CM37 generated the following data [21]: the proportion of map units for under-fractionated arm/over-fractionated arms are Zm1S/9L = 0 . 9 , Zm3L/8L = 1 . 1 , and Zm2S/10L = 1 . 9 ( Table 1 , Column N ) . Note that although Zm2S/10L has the largest difference in recombination frequency , it has the lowest fractionation bias of these three paired arms ( Table 1 , Column C ) . We conclude that there is no obvious correlation between biased fractionation and overall frequencies of reciprocal recombination during meiosis . Even before BAC sequencing was complete , one group [22] identified methylation domains of maize chromosome in shoot or root nuclei using McrBC restriction endonuclease , a treatment that degrades DNA between methylated half sites of the form m5C-N40–500-m5C . McrBC is non-specific for different types of methylation patterns . Using this crude measure of methylated regions ( BAC start-stop ) in maize shoot nuclei , we overlifted ( translated the start-stop nucleotide designations ) the data from BACs to pseudomolecules and found no correlation at all between the over-fractionated and under-fractionated homeologs ( Table 1 , Column M ) . Two representative regions were concordant , two were not concordant , and one region was vastly over-methylated on the under-fractionated homeolog . Our methods for deciding whether or not a maize gene was retained ( “B” ) did not require that the entire coding sequence be present , but only a significant fragment . Because of this , our calculation of the number of whole sorghum-maize genes retained post-tetraploidy , about 40% , is surely an overestimate . If we were to assume that the process of fractionation is ongoing , we reasoned that some of our retained genes might have internal deletions whose flanking sequences might give us a clue as to the mechanism behind gene fractionation . By visual examination , we identified cases where a maize gene seemed to have a gap within an exon . To verify each fully flanked deletion , we extracted the sorghum exon sequence and used it as query for a blastn to rice , a grass that diverged from sorghum about 50 MYA [23] , to sorghum itself , and to the two homeologous maize regions . We then studied each Os-Sb-Zm1-Zm2 blastn result using GEvo , our synteny visualization platform ( in CoGe , Methods ) . We verified that eight genes , containing a total of 16 deletions , were fully flanked by conserved , known sequence . Table 2 gives the data for these fully flanked deletions . Figure 5A and B shows an exemplary GEvo graphic and the pertinent orthologous sequences of rice , sorghum , and the two maize homeologs . In two cases ( Sb01g039030 and Sb09g023840 , Table 2 ) , the apparent gap was actually several short gaps within the homeologous flanking sequence . The gap size within these 16 deleted regions ranged from 5 bp to 178 bp , with a mean gap size of 25 . 9 bp . Bias for gaps is consistent with the fractionation bias found locally: in other words , when a gap is present , it is in the maize homeologous gene located on over-fractionated chromosome 93% ( 15/16 ) of the time ( Table 2 ) . As mentioned in the Introduction , deletions due to illegitimate recombination are often flanked by a short stretch of sequence that , before the deletion , had been a direct repeat [11] . In theory , such repeats facilitate ectopic , intra-chromosomal , reciprocal recombination ( as drawn in Figure 5C ) generating a circle and a solo copy of the original repeat sequence in place of the sequence deleted ( the circle ) . Using ClustalW , we found such direct repeats flanking 10 of the 16 gaps in our study ( Table 2 ) . These repeats were between 3 and 24 bp in length; an example is given in Figure 5B . Notice how the repeats surrounding the gap in the fractionated homeolog are truncated in comparison to the repeat sequence within the whole homeolog: this is a typical footprint of intra-chromosomal recombination [11] . In an attempt to generalize our results from monocots ( e . g . grasses ) to eudicots ( e . g . legumes ) , we found several such small deletions where the inferred precursor sequence was flanked by direct repeats , within retained duplicate genes of Glycine max ( Gm: soybean , unpublished data ) from the more recent of the two easily observable tetraploid events in the sorghum genome . Soybean has had two recent genome duplication events , the most recent one ( alpha ) having taken place between 14 and 3 MYA [14] . The close relative , Medicago trunculata , was used as the outgroup in order to infer the precursor gene sequence before deletion . We conclude that small deletions are involved in the fractionation of genes following ancient ( successful ) plant tetraploidies .
Comparison of the sorghum outgroup to the newly released maize sequence permitted a detailed description of the consequences of tetraploidy and the ensuing fractionation process on grass genes shared orthologously between sorghum and maize . We used graphic displays of blast results , both as pairwise dot-plots ( SynMap ) and multiple ortholog line drawings ( GEvo ) , to facilitate large-scale genome analyses at the level where 100 bp deletions from genes were observed visually . The maize tetraploidy is much more recent than the previously studied alpha tetraploidy of Arabidopsis . Combining the power of the sorghum outgroup and the recent and potentially ongoing fractionation of the maize genome permitted a definitive description of the sequences left after fractionation . We observed: ( 1 ) If we define a gene stringently , then it appears that fractionation generally involves gene deletion , not gene repositioning . However , if we define a gene as a 150 bp fragment of exon , significantly more transposition/duplication is evident . Any transposon-capture [20] or fragment transposition mechanism could help explain these results . ( 2 ) If the unit of deletion is “genes , ” then the deletion mechanism of fractionation most frequently removes one gene ( Figure 4 ) . Indeed , our best-fit evolutionary model for predicting the actual gene loss on the over-fractionated chromosome was the loss of one gene 80% of the time , the loss of two genes 15% of the time , and the loss of three genes 5% of the time . The genes that resist fractionation are naturally clustered by fractionation , as predicted , though a few runs of retained genes are unexpectedly long ( Figure 4E and F ) . ( 3 ) The lower limit of gene loss was estimated from those infrequent deletions that were completely contained within an exon; these ranged from 5–178 bp in length . We think it likely that these intra-exon deletions are the consequence of a single event rather than the summation of an ongoing series of events . Because single genes were found with deletions in more than one exon , it is clear that smaller deletions ( less than 200 bp ) are common , but larger deletions also sometimes happen . We also found evidence that illegitimate recombination acts in soybean as it does in maize ( unpublished data ) , so this mechanism is not maize-specific . ( 4 ) By adding the orthologous rice genes to the Sb-Zm-Zm panel , we inferred the sequence of the maize ancestral chromosome before the small deletions described above took place . The ancestral to-be-deleted sequence was flanked by a direct repeat of between 3 and 24 bp in length . Such flanking repeats have been interpreted as signatures of illegitimate recombination . One such mechanism is intra-chromosomal recombination , which pairs on the direct repeat and generates a circle and a deletion [12] . ( 5 ) Overall , one homeolog is , on average , 2 . 3 times more likely to have a gene removed by deletion than the other homeolog , demonstrating biased fractionation . Biased fractionation was also seen by the team of researchers who collaborated to first describe the maize genome [13] . That the DNA between genes on the over-fractionated chromosome are even more over-fractionated than the genes themselves—DNA composed primarily or entirely of transposons thought to be without function—makes it unlikely that fractionation bias is the result of any sort of selection bias . ( 6 ) We found no correlation between Sb-Zm Ks values with over/under-fractionation . Divergence by point mutation and fractionation by short deletion are independent and independently regulated . ( 7 ) Preliminary identification of methylation domains in maize [22] permitted an attempt to correlate the number of such domains with over- or under-fractionation . We found no such correlation , but this does not rule out other types of epigenetic marks ( e . g . histone modification ) as possible tags for biased fractionation . ( 8 ) Although we implicate some sort of recombination mechanism to facilitate short deletion , there is no correlation between maize chromosome arms that are over/under-fractionated and the number of total map units ( % reciprocal recombination ) in those arms . Our detailed analysis evaluates one outcome of the maize alpha tetraploid fractionation , based on the B73 inbred line . Since gene fragments remain , we have no reason to believe that fractionation is complete , and if not complete , then it is probable that different accessions of the species Zea mays , and perhaps different inbred lines of the Zea mays mays subspecies , have different fractionation outcomes . We do not know how many individual deletions , on average , it takes to completely remove a gene . However , the observation that 93% of the deletions we found within exons were on the over-fractionated homeolog probably reflects the general scenario: one of the two homeologs is inactivated by deletion , at which point deletions of the other homeolog are selected against ( since this second deletion would result in the loss of the function encoded by the gene pair ) . Additional deletions would then accumulate only on the homeologous gene that suffered the original loss as fractionation of this now-inactivated gene progressed . Even so , it took little effort to find a case in soybean where a flanking repeat signature implied that an entire gene was removed in one deletion event ( Dataset S5 ) from a region where there were few exon deletions . We do not know unequivocally the relative frequency of this sort of larger deletion compared to genes being deleted away in smaller increments . Perhaps the nature and distribution of direct repeats , the length of the circle to be deleted , and the epigenetic receptivity of the target chromosome all contribute to the details of fractionation . Sometimes genes that resist fractionation , the retained genes , are significantly clustered ( [24] and Figure 2 ) beyond expectations derived from any mechanism of gene deletion . One explanation for this could be that genes that would be otherwise fractionated are protected by their position next to a fractionation-resistant gene . Alternatively , fractionation-resistant genes might exist as clusters in the pre-tetraploid ancestor . There are two occurrences of particularly large genomic consequence that happened along the maize lineage only after the divergence of maize and sorghum . First was the maize alpha tetraploidy event that is thought to have occurred roughly 12 MYA . Second , and later , was a massive bloom of transposable element activity , resulting in a modern maize genome 3 . 4 times as large as that of sorghum . About 85% of maize's 2 , 300 Mb genome is thought to be composed of transposons [13] , many of which inserted within the last 3 million years [25] . Illegitimate recombination has been proposed as a mechanism for genome-size reduction—transposon removal—independently in maize and Drosophila [11] , [12] . On a similar theme , some indels within genes in Arabidopsis appear to be due to illegitimate recombination [26] . Our evidence for ancestral flanking direct repeats , and our evocation of intra-chromosomal recombination , are therefore consistent with these previous studies . Unlike previous work , we have focused on typical genes that are targets of fractionation in order to address the mechanism of gene loss following tetraploidy . We now propose that illegitimate recombination is the primary means by which excess DNA in the form of redundant genes and transposons are removed from genomes . Intra-chromosomal recombination is one way to envision this sort of recombination , but any chromosomal complex that deletes between tandem repeat sequences would fit our data . This mechanism is a check against what has been called a “one-way ticket to genomic obesity” [11] . That is not to say that this mechanism evolved in any sort of purpose-oriented ( teleological ) way . The sort of purifying selection via deletion we observe in maize is very different from that described for primates , where genes are removed via the pseudogene pathway . For instance , the components of a pheromone signal transduction pathway lost in old-world monkeys , including humans , are still present in the form of identifiable pseudogenes [27] , and recent work indicates that 100% of human-specific gene losses among the primates studied are present in the genome as pseudogenes without deletions [8] . It is possible that mammals and plants evolved different mechanisms for genome purification , adapted to fit differences in their capacities to cope with high frequencies of individuals carrying DNA deletions without going extinct . Unlike transposons , coding regions , such as exons , do not have built-in long direct repeats and do not present obvious targets for illegitimate recombination . Nevertheless they do have randomly situated , shorter direct repeats , and we now know that some of these short repeats facilitate small deletions . An accumulation of such deletions could eventually lead to the disappearance of entire genes . Additionally , deletions in the cis-acting regulatory regions near genes could hypothetically give rise to a new regulatory binding activity . The same can be said for cis-acting regulatory sequences that affect a local chromosomal region rather than a single gene . Following deletion of intervening genes on fractionated chromosomes , new clusters of genes would be expected to respond in new ways to their local regulatory environment . Thus , in large and small ways , the fractionation mechanism we describe has the potential to create huge regulatory variation around genes as a by-product ( or “spandrel” ) of purifying selection . Whether or not the fractionation mechanism is induced by “excess” is not yet known . This discussion is not complete without considering the origin and utility of fractionation bias itself . The alpha-syntenic genome of maize is actually two genomes , the over-fractionated and the under-fractionated , and the total DNA and gene count differences between them are diagnostic for any longer stretch of chromosome . We show that Ks data neither support nor refute allotetraploidy . Allotetraploidy—for example , a tetraploidy following a very wide cross—could explain the origin of over- and under-fractionated genomes , where one of the genomes acquired an “invader” epigenetic tag in the new polyploid . Alternatively , the tetraploidy might have been autotetraploidy , and the mode of sexual transmission generated a genome-wide epigenetic tag . Either way , logic alone dictates that some sort of heritable genomic mark precedes the bias in fractionation since biased fractionation is ongoing . One immediate benefit of having such a tag could be to prevent homeologous pairing and consequent dysfunctional pollen and eggs . We do not have any direct data at the level of DNA or histone modification . We also do not know anything about the relationship between chromosome pairing/mispairing and the inferred epigenetic mark . In summary , we suggest that direct repeats throughout the genome facilitate frequent and continuous sequence deletion via illegitimate recombination . Repeats abound , so targets are not limiting . Among the evolutionary benefits of this selectively neutral deletion/fractionation mechanism is bulk DNA removal and the wholesale generation of new combinations of regulatory and coding sequences . Both tetraploidy and transposon blooms confront the genome with a great deal of potentially dispensable DNA , and both cases of genomic excess probably share the same purification mechanism: intra-chromosomal recombination . Fractionation bias demonstrates that the frequency of this mechanism can be modulated . The inducibility , target specificity , and rate modulation of purifying selection via illegitimate chromosomal recombination is a particularly important subject for further research .
The sorghum sequence was Sbi1 assembly and Sbi1 . 4 annotation ( Paterson et al . 2009 [23] ) downloaded from Phytosome V4 . 0 http://www . phytozome . net/sorghum , last modified 3-25-08 . The B73 maize genome sequence was obtained in the form of pseudomolecules in 3-09 ( ftp://ftp . genome . arizona . edu/pub/fpc/maize/ ) and stored in our CoGe platform as database 8082: http://synteny . cnr . berkeley . edu/CoGe/OrganismView . pl ? dsgid=8082; and with draft models annotations in 10-09 from maizegenome . org ( http://ftp . maizesequence . org/release-4a . 53/ ) . The sequence of these two releases is identical . The draft annotated maize sequence will be called “4a . 53” in the few instances where we use the official CDS models . The TIGR 5 Nipponbare rice assembly and annotation was downloaded onto our CoGe platform ( http://synteny . cnr . berkeley . edu/CoGe/ ) before the MSU6 update file://localhost/ ( http://rice . plantbiology . msu . edu/data_download . shtml ) , and was used in 2008 to generate the sorghum gene list used here; the differences between rice TIGR5 and MSU6 annotations are of no consequence to this project . The soybean and Medicago trunculata genomes were downloaded from Phytosome 4 . 0 http://www . phytozome . net/soybean . php and http://www . phytozome . net/medicago . php , respectively , in early 2009 . Sorghum genes were Sbi1 . 4 to which we added many genes on the basis of orthology to rice Nipponbare , TIGR 5; the added genes included many with corresponding RNAs since these are absent in Sbi1 . 4 . Dataset S1 uses the format Sbxgxxxxxx for Sbi1 . 4 genes and sorghum_chrmosomex_startx_stopx for genes we added based on Sb-Os orthology . The detailed syntenic alignment of the entire genomes of sorghum and rice was automated and frozen in September 2009 as the rice-sorghum CNS discovery Pipeline 1 . 0 . Dataset S6 diagrams this pipeline and details each step . What is most important is that any sorghum gene we use in this analysis is shared syntenically between sorghum and least one of the two possible homeologous maize positions . Some of our added sorghum genes are shared with maize as orthologs; those that are not shared with maize were not studied . After adding 10 , 585 new putative genes to the 34 , 003 official JGI ( Joint Genome Institute ) sorghum genes , the augmented sorghum genome was masked for any sequence repeated over 50 times , and then everything but exons or RNA-encoding sequence was additionally masked . This heavily masked sorghum genome was then used to query the maize genome . We found a total of 37 orthologous regions between sorghum and the corresponding maize homeologs retained after the maize alpha tetraploidy event ( Sb-Zm1-Zm2 ) in two ways , and both ways used applications available online in the CoGe comparative genomics platform ( http://synteny . cnr . berkeley . edu/CoGe/ ) . Central to our success was our ability to clearly visualize the locations of the many translocations and inversions that happened in both the sorghum and maize lineages . Knowing all breakpoints makes it clear that any single sorghum chromosome is orthologous to exactly two maize chromosome regions , even though many smaller segments are often involved ( Dataset S2 ) . To this end all of the 37 regions begin and end with at least one gene retained by both maize homeologs . In this way ( Methods ) , a total of 4 , 461 sorghum genes ( 10% of the sorghum genome ) were set up for manual evaluation . In order to define those genes that had an ortholog in maize , we condensed all members of locally duplicated arrays into one gene and discarded those 492 duplicates ( 11% ) , leaving one parent gene for each array . We also invalidated 74 genes that had annotation incongruencies , and then disregarded another 953 genes for which we failed to find maize orthologs . Each sorghum gene is given an evaluation code of “1” ( has an ortholog in the first Zm homeologous region ) , “2” ( has an ortholog in the second Zm homeologous region ) , or “B” ( has an ortholog in both Zm homeologous regions ) . The designations “O , ” “N , ” and “D”: D = local duplicate; N = invalid data; O = no ortholog in Zm . In each of the 37 regions within Dataset S1 , every sorghum gene has been annotated with one of these six symbols . A link ( tinyurl . com , a URL abbreviation service , or genomevolution . org ) is provided for each Sb-Zm1-Zm2 panel to facilitate the repetition of our research in the GEvo alignment graphic tool we used for research ( Dataset S1 ) . We were left with 2 , 943 orthologous Sb-Zm1-Zm2 genes spread over 37 Sb-Zm1-Zm2 regions . CoGe is an integrated collection of maintained databases , algorithms and applications useful to compare complete genomes on demand [28] , [29] and without which it would be difficult to perform our analyses in a reasonable amount of time . SynMap , within CoGe , is a dot plot application that implements the DAGchainer algorithm [30] to identify syntenic lines in two-dimensional arrays of blastn hits between two identical ( to find homeologies ) or different ( to find orthologies ) genomes . Each “dot” is a gene pair . The color of this dot can be portrayed to reflect Ks ( synonymous base-pair substitution frequency ) , so syntenic lines of different ages have different colors ( see Dataset S2 ) . Clicking on any dot in SynMap anchors the GEvo sequence comparison tool and automatically generates a blastn alignment output . Each output ( like a BLAST or LAGAN output ) includes a graph , a link , and can be repeated on demand with different settings . CoGeBlast takes sequence from any other CoGe applications or text as query to any number of genomes; the blastn or tblastx results may be downloaded into GEvo panels . GEvo panels may be combined via links to create experiments . Ks values may be calculated for each data point in SynMap if the genomes being compared are repeat-masked and have annotated CDS sequences . The 4a . 53 maize sequence was used . Several genomic comparisons in SynMap have Ks values pre-calculated , including sorghum/maize . Syntenic gene pairs were identified by using blastn with SynMaps's default settings [−W ( word size ) = 11 , −G ( gap open ) = −1 , −E ( gap extend ) = −1 , −q ( mismatch ) = −3 , −r ( match ) = 1] and an e-value cutoff 0 . 05 . These pairs were used to identify any putative homeologs between coding sequences using DAGchainer to identify collinear sets of putative genes with the following parameters: −D = 20 , −g = 10 , −A = 5 . Ks values for syntenic gene pairs were calculated by first performing a global alignment of virtual protein sequences using the Needleman-Wunsch algorithm [31] implemented in python ( http://python . org/pypi/nwalign/ ) . The BLOSOM62 scoring matrix was used for the alignments [32] . From these protein alignments , the codon DNA alignment was generated through back-translation . Ks values were calculated using codeml of the PAML software package [33] on the codon alignment with the following parameters: outfile = mlc , aaDist = 0 , verbose = 0 , noisy = 0 , RateAncestor = 1 , kappa = 2 , model = 0 , ndata = 1 , aaRatefile = wag . dat , Small_Diff = . 5e-6 , CodonFreq = 2 , runmode = −2 , alpha = 0 , omega = 0 . 4 , fix_kappa = 0 , Mgene = 0 , method = 0 , fix_omega = 0 , getSE = 0 , NSsites = 0 , seqtype = 1 , cleandata = 0 , icode = 0 , fix_alpha = 1 , clock = 0 , ncatG = 1 , Malpha = 0 , fix_blength = 0 . This pipeline is part of the SynMap application in the CoGe suite of comparative genomics software , and its dotplot visualization tool was used to generate the Ks color-coded lines of Dataset S2 , and its text output was used to supply the Ks values for the “sorghum/maize Ks differences” Methods section to follow . Since this Ks pipeline will calculate Ks values for erroneously aligned pairs , values far off from an expected normal distribution for any experiment were discarded . The entire sorghum genome was subjected to a 50× repeat mask , where every base pair that was covered more than 50 times by a blast hit from a whole-genome self-self blast was masked , using parameters of blastn at word size 16 and e-value cutoff of 0 . 001 . Repeats over 50× genome-wide were masked by changing their sequence to “x . ” We needed to use a step-wise approach to accomplish the same 50× mask for maize because a direct self-blast was too memory-intensive for our computers . First we self-blasted pseudomolecules 1–3 as if they were the whole maize genome and masked their 50× repeats . Then we added these 3 larger masked chromosomes to the other 7 unmasked and performed self-BLAST—as with sorghum above . Repeated sequences are color-coded pink in Figure 3 , panels B and C . The sorghum 50× masked genome was further masked for every sequence that is not either an Sbi1 . 4 exon or other sequence shared orthologously with rice , as derived from the rice/sorghum Pipeline 1 . 0 . The non-exon , non-conserved sorghum sequences masked by this method are colored orange in our GEvo graphics ( e . g . Figure 3 , panel A ) . The nine Sb-Zm1-Zm2 regions were derived from SynMap blastn [34] dotplots using the DAGchainer settings −g = 10 genes , −D = 20 genes , −A = 5 genes , and a Ks color code that clearly distinguishes syntenic lines reflecting sorghum/maize orthologs to lines reflecting more ancient syntenies . When a single stretch of sorghum clearly hits two longer stretches of maize , the center of the overlapped region was used as an anchor to create Sb-Zm1 and Sb-Zm2 GEvo panels , which are then combined into a single view . The sorghum/maize Ks-colorized dotplot can be seen in Dataset S2 , where the identification of Sb1 is illustrated . It is possible to regenerate a near-identical graphic in CoGe by visiting http://tinyurl . com/ygx2apu . The 28 additional Sb-Zm1-Zm2 regions were discovered by choosing as query exons from sorghum genes that encode transcription factors . Each query found , using CoGeBlast , two orthologs in maize about one-third of the time . From CoGeBlast output , it is easy to create Sb-Zm1-Zm2 GEvo panels . Lengths of these three chromosomes were adjusted so that a chosen segment of sorghum begins and ends with a retained gene , was entirely represented syntenically within the two maize segments , and syntenic coverage did not improve by adding 500 kb on both sides of the maize chromosomes . Inversions do not cloud our analyses because all inversions we include begin and end within each region . Our primary data of Dataset S1 required that every gene on the sorghum gene list receive one among several possible annotations . Genes in local arrays were marked as parent , duplicate ( D or DUP ) , or interrupter ( a gene located within a tandem repeat ) using published methods [7] and duplicates were marked and ignored subsequently; up to three interrupter genes were permitted . If a remaining gene occurred syntenically ( blastn bitscore >50 ) on a maize homeolog , then it was coded “1” or “2” if it occurred on only one of the homeologs or “B” if it occurred on both . A few genes were invalidated for technical reasons , and some genes were not found in the syntenic position in either maize homeolog ( encoded as “0” ) . Genes represented by fragments were counted as “present” even though they were almost certainly in the process of removal . In this manner , each of our 37 Sb-Zm1-Zm2 regions were reduced to a code of shared genes , like B1122BBB12BB2B21BBB11B21B , and trimmed to begin and end with a B ( present in both maize homeologs ) where the terminal Bs were not within inversions . For the diagrams of Figure 3 B , C , and D and Dataset S2 , and for all analyses of runs , as discussed in the text we removed runs of 1's or 2's that extended beyond nine genes . This is because our analyses suggested that a run of 10 or more 1's indicates that the 10 genes that would be the corresponding 2's had jumped elsewhere in the genome . The unmodified data are in Dataset S1 . At this time , accurate fractionation annotations would be difficult or impossible to achieve automatically largely because of biological complications involving inversions and also by contig misalignments during sequence assembly . The binomial test was used to evaluate the probability that the ratio of deletions on the maize homeologs could occur by chance given an expectation that a single deletion is equally likely to occur on either homeolog . The distribution of all observed deletion lengths is plotted in Figure 4 as the blue bars for the over- and under-fractionated homeologs . Using the initial hypothesis of a deletion mechanism that independently eliminates one gene at a time , a simulation of gene loss was carried out . Starting with a length equal to all genes , both deleted and still present , genes were deleted at random until the simulated number of deletions was equal to the true observed number . The distribution of apparent deletion lengths for the run was then saved and the preceding steps were repeated 1 , 000 times . This gives a distribution of frequencies of all deletion lengths . The median number of apparent deletion runs from these simulations is shown by the white circles in the grey lines of Figure 5 , with grey line itself marking the values between which the results from 95% of the simulations fall . For Figure 5E , which plots runs of genes conserved on both maize homeologs , the above model was modified by generating two lengths each equal to the total number of sorghum genes within the dataset , and then deleting genes from either one or the other sequence ( with an bias for deleting genes from one or the other dataset equal to that observed in the overall fractionation dataset ) until the number of retrain genes ( Bs ) was equal to the true number observed , with the constraint that once a simulated gene was deleted from one dataset , the orthologous gene in the other dataset would never be deleted . As the simulated distribution did not perfectly match the observed results , a genetic algorithm using 20 ( genetic ) character states , each representing a 5% ( 1/20 ) chance that a deletion would be some length between 1 and 5 genes long was used to determine , given the region length and the distribution of observed deletion lengths , the ratio between different deletion lengths to use in the simulation described above to achieve the best match between simulated and observed data . The fitness of solutions in the evolutionary algorithm were scored using the Monte Carlo method described in the proceeding paragraph ( with the modification that rather than fixing the deletion length at 1 gene , deletion lengths were selected using the weighted averages generated by the evolutionary algorithm ) with the most fit solutions being those where the median simulated number of deletion runs was least different from the observed number of runs . The genetic algorithm was allowed to run for 100 , 000 generations . These new weighted average deletion lengths can then be used to generate new sets of expectations for data , as seen in Figure 4D . The script used to run the genetic algorithm is available at http://code . google . com/p/bpbio/source/browse/trunk/scripts/fractionation/fractionation_ga . py and in Dataset S7 . Sorghum genes with known orthologs in rice were blasted against the sorghum ( JGI 1 . 4 gene models ) , rice ( TIGR 6 . 0 ) , and maize ( 4a . 53 filtered gene set; maizegenome . org ) datasets . We used the score of the best sorghum-rice alignment as a cut-off to avoid hits from genes that diverged before the rice sorghum split , and removed genes with more than one hit above that threshold in the sorghum-sorghum blast to avoid the inclusion of genes that duplicated in the sorghum-maize lineage since the divergence from rice . These criteria left us with a set of approximately 10 , 000 genes with a single hit in sorghum that had a greater bit score than any hit in rice , and one or more hits satisfying the same conditions in maize . 406 genes from this dataset overlapped with genes identified as retained ( noted as “B” in Dataset S1 ) by manual annotators , and 771 overlapped with genes identified as fractionated . Stretches of 10 or more genes deleted from the same chromosome were identified on Dataset S1 and the missing region was identified by a discontinuity in the appropriate sorghum/maize dot plot . We built a string of exons that identified each gene in the deleted region and used it as query to the subject maize genome . The maize genome was 50× repeat-masked , as described , and blastn used settings of word size 7 , and e-value <0 . 001 . Hits were achieved in CoGeBlast and evaluated in GEvo . Any three of the expected genes , arranged syntenically , in unexpected regions of genome were taken as evidence for a segmental translocation even though a gene might have been represented by a fragment rather than an entire gene . The coding sequences of the subset of genes from the JGI sorghum 1 . 4 gene set that had been identified as orthologous to a single rice gene were blasted against the MSU6 rice gene set and the maize 4 . a53 filtered gene sets as well as against the same sorghum gene set . For each sorghum gene , the bit score of the highest-scoring alignment against a rice gene was used as a cutoff to exclude hits from genes that had diverged from the gene being tested before the rice/sorghum split . Sorghum genes that hit one or more additional sorghum genes with bit scores higher than that cutoff were excluded from the analysis to exclude genes duplicated in the maize/sorghum lineage since the rice/sorghum split . The number of hits to genes in the maize filtered gene set for the remaining sorghum genes ( with scores higher than the best hit in rice ) was recorded . After the accuracy of a sample of the results were manually checked using CoGe , the final data were generated by looking at the average number of maize genes found using this process for genes assigned to the fractionated and unfractionated categories by manual annotation . Ks values for shared open reading frames in sorghum and maize ( 4 . a53 ) were precalculated and loaded into SynMap , in CoGe as described previously in METHODS . The sorghum/maize orthologs that also fell into the 37 regions that were hand-annotated for the primary fractionation data ( Dataset S1 ) were identified . Next , sorghum genes that hit to genes in both maize homeologs ( encoded “B” ) were paired and their Ks values compared . Data were reported in the format Sb-Zm1-under-fractionated/Sb-Zm2-over-fractionated . Visual examination of the Ks data showed a minority portion of very extreme ratios , likely the result of misalignments , alignments to pseudogenes , or alignments to non-orthologous genes . Such misalignments were expected due to the fragmented nature of many B73 genes and contig assembly error . The 16% of pairs with the most extreme ratios as compared to the median were removed from the dataset and not used to calculate results . We overlaid McrBC methylation data from [22] onto the annotated maize pseudomolecule sequence ( dataset Zm 4a . 53 ) and uploaded the modified database into the genome viewer we use with CoGe: GenomeView . We were able to visualize on GenomeView the locations of methylated sites on maize chromosome regions . After anchoring both maize homeologs to their orthologous sorghum sequence with the stop-start sites used in our fractionation analyses , we manually counted the number of methylation peaks in each maize homeologous region in question . Using GEvo , with parameters set for blastn with a spike-length of 15 bp , we visually scanned all retained maize genes from our Sb/Zm1/Zm2 dataset to look for gaps within exons of one or the other maize homeolog . This level of resolution did not permit us to identify single gaps less than approximately 15 bp long . However , we did not intend to be exhaustive . Once a gap was identified , we extracted the sorghum exon sequence and used it as query in a blastn comparison to rice; this use of the rice as a secondary outgroup often confirmed the sorghum full-length exon annotation , and when it did , we re-blastn'd this sequence against the multiple subjects rice ( Oryza sativa v5 masked repeats 50×X ) , sorghum ( vSbi1 . 4 exons , 50×X mask+syntenic thread with Os ) , and maize v4a . 53 to produce GEvo images like that shown in Figure 5A . We then took the corresponding exon sequence data from rice , sorghum , and both maize homeologs and used ClustalW ( http://www . ch . embnet . org/software/ClustalW . html ) to visualize the sequence alignment surrounding the gap , as well as the sequence on the homeolog without the deletion ( as in Figure 5B ) .
|
All genomes can accumulate dispensable DNA in the form of duplications of individual genes or even partial or whole genome duplications . Genomes also can accumulate selfish DNA elements . Duplication events specifically are often followed by extensive gene loss . The maize genome is particularly extreme , having become tetraploid 10 million years ago and played host to massive transposon amplifications . We compared the genome of sorghum ( which is homologous to the pre-tetraploid maize genome ) with the two identifiable parental genomes retained in maize . The two maize genomes differ greatly: one of the parental genomes has lost 2 . 3 times more genes than the other , and the selfish DNA regions between genes were even more frequently lost , suggesting maize can distinguish between the parental genomes present in the original tetraploid . We show that genes are actually lost , not simply relocated . Deletions were rarely longer than a single gene , and occurred between repeated DNA sequences , suggesting mis-recombination as a mechanism of gene removal . We hypothesize an epigenetic mechanism of genome distinction to account for the selective loss . To the extent that the rate of base substitutions tracks time , we neither support nor refute claims of maize allotetraploidy . Finally , we explain why it makes sense that purifying selection in mammals does not operate at all like the gene and genome deletion program we describe here .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/plant",
"genomes",
"and",
"evolution",
"genetics",
"and",
"genomics",
"genetics",
"and",
"genomics/comparative",
"genomics"
] |
2010
|
Following Tetraploidy in Maize, a Short Deletion Mechanism Removed Genes Preferentially from One of the Two Homeologs
|
Evolutionary conflict permeates biological systems . In sexually reproducing organisms , sex-specific optima mean that the same allele can have sexually antagonistic expression , i . e . beneficial in one sex and detrimental in the other , a phenomenon known as intralocus sexual conflict . Intralocus sexual conflict is emerging as a potentially fundamental factor for the genetic architecture of fitness , with important consequences for evolutionary processes . However , no study to date has directly experimentally tested the evolutionary fate of a sexually antagonistic allele . Using genetic constructs to manipulate female fecundity and male mating success , we engineered a novel sexually antagonistic allele ( SAA ) in Drosophila melanogaster . The SAA is nearly twice as costly to females as it is beneficial to males , but the harmful effects to females are recessive and X-linked , and thus are rarely expressed when SAA occurs at low frequency . We experimentally show how the evolutionary dynamics of the novel SAA are qualitatively consistent with the predictions of population genetic models: SAA frequency decreases when common , but increases when rare , converging toward an equilibrium frequency of ∼8% . Furthermore , we show that persistence of the SAA requires the mating advantage it provides to males: the SAA frequency declines towards extinction when the male advantage is experimentally abolished . Our results empirically demonstrate the dynamics underlying the evolutionary fate of a sexually antagonistic allele , validating a central assumption of intralocus sexual conflict theory: that variation in fitness-related traits within populations can be maintained via sex-linked sexually antagonistic loci .
Understanding the mechanisms that promote variation in fitness-related traits within populations presents an enduring challenge in evolutionary biology [1] , [2]: intralocus sexual conflict is predicted to be one such mechanism [3]–[6] . Intralocus conflict occurs when the same allele at a single locus provides net fitness benefits when expressed in one sex but net fitness costs when expressed in the other [7] . Although this conflict can potentially be resolved by the evolution of sexual dimorphism [8] , a growing body of studies provide evidence that substantial sexually antagonistic variation occurs in both natural [9] , [10] and laboratory-adapted populations [11]–[18] . To date , the main approaches used to identify the presence of intralocus sexual conflict have been the detection of negative genetic correlations for fitness between males and females [9]–[17] and experimental evolution using sex-limited selection [14] , [19] . These studies have highlighted the extent to which sexually antagonistic selection affects fitness-related traits , and have identified candidate sexually antagonistic genes . However , no previous empirical studies have characterized the evolutionary dynamics of a specific sexually antagonistic allele . We aimed to validate predictions made by intra-locus sexually antagonistic theory by experimentally engineering a novel sexually antagonistic X-linked allele . We empirically explored a fundamental principle of intralocus sexual conflict theory: that a recessive allele that benefits the heterogametic sex but harms the homogametic sex can invade a population , even when the cost exceeds the benefit , if the locus is located on the homogametic sex-chromosome [6] . This prediction arises because at low population frequency the costly effects of the allele for the homogametic sex are limited to homozygotes , which are rare , whereas the benefits are always expressed in the hemizygous sex . Consequently , such an allele could theoretically invade and reach an equilibrium frequency [6] . This makes the X-chromosome a potential hot spot for such sexually antagonistic genetic variation [20] and thus an ideal target for intralocus sexual conflict research . We first used genetic manipulations to generate a putative sexually antagonistic allele on the X-chromosome of Drosophila melanogaster . We then tested: a ) the magnitude of the cost to females ( in terms of offspring production ) and benefits to males ( in terms of mating success ) , b ) whether the allele could invade and persist in a population and how the invasion dynamics compared to predictions derived from theoretical models , and c ) whether the evolutionary persistence of the allele was dependent upon the benefit provided to males .
To create a novel sexually antagonistic allele on the D . melanogaster X chromosome , we used two genetic constructs: 1 ) Df ( 1 ) Exel6234 , a genetic deficiency which covers the sex-peptide receptor gene and 4 other genes of unknown function [21] and 2 ) w1118 , a loss of function allele for the white gene which determines eye color [22] . Both Df ( 1 ) Exel6234 and w1118 are located on the X-chromosome . Homozygous Df ( 1 ) Exel6234 females fail to react to the male seminal protein , sex peptide [23] , and show reduced levels of sex-peptide-induced post-mating responses . For example , Df ( 1 ) Exel6234 females lay significantly fewer eggs after mating than wild-type females [21] . Flies lacking white have white eyes , and white-eyed males suffer from impaired vision and reduced mating success compared to wild-type males ( which have red eyes ) in photophase ( i . e . , the light ) [24] , but not in the scotophase ( i . e . , the dark ) [25] . In contrast , females lacking white suffer no obvious reduction in adult fitness ( i . e . , lifespan , fecundity or fertility ) under standard laboratory conditions [26] . The Df ( 1 ) Exel6234 deficiency carries a white+ transgene [27] , which provides a partial rescue of white mutations ( i . e . , red eyes and improved vision ) . Tight linkage between the Df ( 1 ) Exel6234 deficiency and the white+ transgene ensures that recombination between them is negligible . Thus , in a w1118 background , male hemizygote and female homozygote carriers of Df ( 1 ) Exel6234 possess red eyes , whilst heterozygote females possess orange eyes ( Figure 1 ) . We confirmed that red-eyed Df ( 1 ) Exel6234 bearing males have increased competitive mating success relative to w1118 white-eyed males in photophase , presumably due to improved vision . In direct , one-on-one , male-male competition , Df ( 1 ) Exel6234 bearing males were significantly more likely to achieve the first mating with a single virgin female in photophase ( 26/28 trials , binomial test , p<0 . 0001 ) but not in scotophase ( winning 14/28 trials , binomial test , p = 0 . 57 ) . We also tested whether the SAA has an effect on male post-copulatory competitive ability . Female D . melanogaster mate multiply [28] resulting in sperm competition [29] , [30] , and variation in sperm competitive ability can potentially have major impacts on male fitness [31] , [32] . However , we found no significant differences in the sperm defense ( P1 ) or sperm offense ( P2 ) abilities of SAA and control males ( P1 assay , Z = 1 . 145 , P = 0 . 252; P2 assay , Z = 0 . 247 , P = 0 . 805; Figure S1A and S1B ) . As expected , homozygous Df ( 1 ) Exel6234 females suffer significant reproductive costs compared to heterozygote and control females ( Figure 2a , Table S1 ) . Thus , in a w1118 background population , Df ( 1 ) Exel6234 fits the conditions required for an X-linked sexually antagonistic allele: it benefits one sex but harms the other . Moreover , the costs of Df ( 1 ) Exel6234 to females are recessive: we detected no significant fecundity cost to heterozygote females ( Figure 2a , Tables S1 , S2 ) . We hereafter refer to individuals carrying the deficiency Df ( 1 ) Exel6234 as the SAA ( sexually antagonistic allele ) flies and non-carriers as controls ( Figure 1 ) . All experimental flies carry w1118 . We predicted that selection favouring the SAA males should drive the SAA allele to higher frequency in populations when it is rare , whilst selection against the SAA homozygote females should drive the SAA frequency down when it is common . To test the evolutionary fate of the male-beneficial , female-detrimental SAA , we simultaneously set up four replicate experimental populations ( P1–P4 ) containing a mixture of SAA and control individuals . We initiated the populations with a SAA frequency of 3% and tracked the frequency of SAA for 16 generations in P1–P4 , and a further 7 generations in two of these populations that we randomly selected ( P1 and P2 ) . Populations were maintained on a 12∶12 light dark cycle , and thus for 50% of the time ( during the photophase ) , SAA males were predicted to possess a mating advantage ( D . melanogaster mating activity occurs slightly more frequently in the dark [33] , [34] when the mating advantage of SAA males is absent ) . We observed matings in P1–P4 during photophase over multiple generations , allowing us to estimate the relative mating fitness of SAA- versus control males in the population cage environment . We found that , as expected , SAA-males possessed a significant mating advantage in P1–P4 during photophase ( Figure 2b ) . Using these male mating frequency estimates ( and assuming equal mating success between SAA and control males during scotophase ) , together with the expected mating rates during light vs dark phases [33] , [34] and the genotype-specific frequencies of offspring produced from each type of cross ( Table S1 ) , we generated quantitative predictions for the spread and equilibrium of the SAA based on Rice's population genetic model [6] . Parameterizing the model with these data leads to the prediction that , over evolutionary time , the SAA should reach an equilibrium frequency at which the fitness cost to homozygote SAA females will exceed the fitness benefits to SAA-males ( Figure 2c ) . As predicted , average SAA frequency in P1–P4 significantly increased from the 3% starting frequency and appeared to reach a plateau at an equilibrium frequency . Initially , the frequency increased more rapidly than predicted by the model but thereafter stabilized around 8% ( Figure 3a ) , which broadly agrees with the model predictions over the first 23 generations ( Figure 3b ) . The model predicts an ultimate equilibrium of 12 . 6% ( 0 . 05–0 . 20 95% CI ) after 700 generations , suggesting that over the 23 generations we measured , the SAA may not have reached its final equilibrium frequency . To test the prediction that , due to the harmful effects on female fecundity , the SAA frequency should decline if the SAA is common , we set up a further 4 populations ( P5–P8 ) with a range of higher initial SAA frequencies ( 31% to 85% ) and measured SAA frequency over 3 subsequent generations . As expected , SAA frequency significantly declined in P5–P8 . Moreover , the steepness of the decline was significantly greater in populations with higher initial frequencies ( Figure 3c ) , confirming that SAA cannot be maintained at high frequencies , and suggesting that – regardless of the original frequency – SAA tends to converge towards a single stable equilibrium . A central assumption of our hypothesis is that the SAA invades , and is maintained in the population , as a result of the mating advantage it provides males during photophase . Without this advantage , we expect a decline in the SAA and eventual extinction due to the costs imposed upon SAA females . To test this prediction we set up replicate populations of P1 and P2 at generation 16 ( in which the SAA frequencies were 0 . 073 and 0 . 033 , respectively ) and maintained adults in these populations in permanent dark ( P1 dark , P2 dark ) conditions , under which SAA males should posses no mating advantage . To control for the disruption to circadian rhythm we set up replicate control populations maintained in permanent light ( P1 light , P2 light ) . We measured SAA frequency over 6 subsequent generations in the dark and light populations . As expected , within each replicate SAA frequency significantly decreased in the dark population relative to the light population ( Figure 4a and 4b ) indicating that the SAA male mating advantage in photophase is essential for the maintenance of SAA . Surprisingly , SAA did not increase in light populations , suggesting that additional hours of light did not provide significant additional fitness benefits to SAA males over the standard 12∶12 light∶dark conditions . Male Drosophila require scotophases to initiate courtship efficiently [35] , therefore courtship and mating in SAA males might have been negatively affected by permanent light . Additionally , there may be constraints on male courtship rates , mating rates or ejaculate production that set an upper limit to SAA male reproductive capacity . Nevertheless , the results provide support for the hypothesis that SAA persists in populations as a result of the mating advantage it provides males during photophase . Our experimental data indicate that 1 ) SAA frequency declines when it is common , because there is a large negative impact on the fecundity of homozygous females 2 ) SAA persists in populations because of the mating benefit it provides males in photophase , and SAA frequency declines towards extinction if the mating advantage of SAA males is abolished and 3 ) SAA has a single equilibrium frequency that is of broadly similar magnitude to that predicted by models based on intra-locus sexual conflict theory . Quantitative discrepancies between the model and our empirical data – for example , the surprisingly rapid increase in SAA frequency in the P1–4 lines – may derive from a range of factors . For example , any potential subtle effects of the Df ( 1 ) Exel6234 deficiency that have not been characterized – on development time , ejaculate depletion rates or other traits that might impact male or female fitness – might contribute to differences between model predictions and our observed SAA frequencies . Nevertheless , our results provide robust qualitative support for sexually antagonistic evolution . Previous empirical evidence for intralocus sexual conflict derives from studies that demonstrate negative intersexual correlations for fitness , sexually antagonistic selection on phenotypes , or changes in sexually dimorphic traits under sex-limited evolution ( reviewed in reference [4] ) . Here we provide direct experimental support for the idea that that sexually antagonistic alleles can invade and persist in populations . Thus , our work provides a novel demonstration that – as predicted by theory – evolution can maintain fitness variation within populations via sex chromosome-linked sexually antagonistic alleles .
The control , white-eyed whiteDahomey , stock [36] was generated by repeatedly backcrossing w1118 into the Dahomey wild-type background ( >7 generations ) . Df ( 1 ) Exel6234 [21] was backcrossed for 5 generations into whiteDahomey to generate SAA flies . Thus , all flies were in the same genetic background before experiments began . All stocks and experimental flies were maintained in plastic vials or bottles on sugar-yeast-molasses medium with ad libitum live yeast granules at 25°C on a 12∶12 hr light dark cycle ( except where specified ) . We used a standard density method to rear flies . First instar larvae were picked from petri dishes containing an agar-grape-juice laying medium and placed in batches of 150 into plastic bottles containing 50 mL of food . We measured male mating success by introducing a single virgin wild-type female ( N = 28 ) into a vial containing a virgin control male and a virgin SAA male of matched age . Experiments were conducted in light or in dark under red-light ( D . melanogaster cannot see red light ) . We recorded which male mated first . To assay the post-copulatory competitive ability of SAA and control males , we conducted tests of sperm defense ( P1 , the paternity share of the first male to mate with a female ) and sperm offense ( P2 , the paternity share of the second male to mate with a female ) . The competitor males and the females were homozygous for the sparklingpoliert ( spapol ) mutation [37] . spapol homozygotes posses a distinct eye phenotype which allows for easy visual determination of paternity . All flies were 3–5 days post-eclosion at the time of first mating . To assay P1 , single virgin spapol females were first mated to either a SAA or control male , and then mated to a single spapol male 24 hours after this initial mating . Females were then allowed to oviposit individually in vials for 24 hours . Offspring from these vials were assayed for paternity ( SAA , N vials = 23; control , N vials = 27 ) . The P2 assay was identical except that the matings were reversed: the first mating was conducted with spapol males , and the second mating with either a SAA or control male ( SAA , N = 21; control , N = 16 ) . To measure offspring production of females we placed 5 3-day old virgin SAA , heterozygote or control females in vials with 5 virgin SAA or control males of the same age ( i . e . , 6 cross combinations ) . Flies were transferred to fresh vials every 2 or 3 days until day 10 when they were separated into pairs of 1 male and 1 female and transferred to fresh vials for 24 hrs . Eggs oviposited over the 24 hrs were counted . 14 days later the eclosed offspring were counted and scored for eye colour . Flies for the 1st generation P1–P8 populations were virgins generated from crosses between heterozygote females and SAA and control males . P1–P4 initially contained 9 SAA and 81 control males , and 100 control females ( i . e . , 3% SAA bearing X-chromosomes , 97% control X-chromosomes ) . Initial numbers of SAA and control males , and SAA , heterozygote and control females were , respectively , P5 ) 44 , 56 , 4 , 42 , 54 ( i . e . , 31% SAA X-chromosomes ) ; P6 ) 65 , 35 , 12 , 56 , 31 ( i . e . , 48% SAA ) ; P7 ) 81 , 19 , 29 , 57 , 14 ( i . e . , 65% SAA ) ; P8 ) 94 , 6 , 64 , 33 , 2 ( i . e . , 85% SAA ) . These proportions were calculated based on selection at Hardy-Weinberg equilibrium using rudimentary fitness estimates ( calculated when P5–P8 were set up ) for each genotype ( 1 for SAA and 0 . 55 for control males , 0 . 388 for SAA females , 0 . 9 for heterozygote females , and 1 for control females ) . Adult flies were placed in a 4 . 5 L plastic cage containing a food bottle , which was replaced every 2 or 3 days . After 8 days eggs were collected for propagation of the subsequent generation . 13 days later ( i . e . , typically 2–3 days after the majority of flies had eclosed , allowing ample time for development ) , offspring were counted and eye colour recorded to determine genotypes . The proportions of genotypes were calculated and the next generation of 100 males and 100 females was established for each population based on these proportions , rounded to the nearest integer . During photophase we made a total of 62 spot-check mating observations on P1–P4 – over generations 1 , 3–7 , 9 , 11 , 12 and 15 – to estimate the relative mating success of SAA and control males in the population cage environment . We modeled the spread and maintenance of the SAA using a standard population genetic approach . We consider a population of SAA and control genotypes . At each generation the number of matings between males and females of each genotype combination was calculated based on the frequency of each male and female genotype in the population and the empirically-derived advantage for the SAA allele in males . This SAA male advantage was calculated by taking the mean mating success of males during light phases in the experimental environment ( Figure 2b ) , and adjusting it for the hours of light in the light-cycle ( e . g . 12∶12 ) and the proportion of matings expected to occur in light vs dark ( 0 . 402∶0 . 598 , light∶dark , calculated from references [33] , [34] ) . The frequencies of each male and female genotype for the following generation were then calculated based on the mean number of surviving offspring of each genotype produced by each type of mating ( i . e . , male-female genotype combination ) observed in our experiments ( Table S1 ) . We set the initial genotype frequencies at generation 1 to be the initial frequencies used in the experiment and determined the equilibrium SAA frequency after 1000 generations . To generate confidence intervals around the predicted equilibria , we introduced the random selection of 300 offspring genotypes from all those generated to make up the next generation . This step mirrors the experimental procedure , in which 300 larvae were taken each generation from all those available . The total number of offspring generated ( from which 300 were selected ) varied with each generation and with the parameter values used , and was typically 2500–5400 . Each run of this simulation model generated new frequencies of the SAA at each generation . We performed 100 runs of the model with each set of parameter values and then calculated at each generation the mean , standard deviation , and 95% confidence interval for SAA frequency . Data were analysed using R and JMP v9 . SAA male mating advantage was calculated using chi square tests on the total number of observed SAA-male and control-male mating opportunities taken as a proportion of the total number of potential mating opportunities ( i . e . , a product of the frequency of SAA in each generation and the total number of mating observations each generation ) . P1 and P2 data for the sperm competitive ability assays could not be satisfactorily normalized and so were analyzed using Wilcoxon signed ranks tests . Analyses using parametric methods ( i . e . , t-tests on data that was Box-Cox transformed ) produced qualitatively similar ( i . e . , non-significant ) results . Female fitness costs of bearing the SAA were analyzed using a generalized linear model ( GLM ) with Poisson error distribution on the total number of offspring resulting from each of the six combinations of parental crosses . Father ( 2 level factor ) , mother ( 3 level factor ) and their interaction were specified as fixed effects . SAA frequency data in P1–P8 and in the light/dark lines were analyzed with generalized linear mixed-effects ( GLMM ) models . To account for replicate lines and for repeated measures across generations , line within generation was specified as a random effect in all GLMM models . Generation and , where appropriate , ln generation , light manipulation or initial SAA frequency were specified as fixed effects . To analyze the change in SAA frequency in P1–4 in more detail we conducted a segmented regression . We partitioned the data based on the observation that the change in SAA frequency appeared to follows 3 distinct phases of increase , decrease , and plateau . Thus , we tested for changes in SAA frequency between generations 1–6 , 6–10 , and 10–16 .
|
Males and females are markedly different in many features , meaning that a trait that is beneficial for one sex may be detrimental for the other . Recent studies show that this type of sexual antagonism is abundant in natural populations; however , no study has tested the evolutionary fate of a sexually antagonistic allele . Using genetic manipulations to alter female fecundity and male mating success , we generated a novel sexually antagonistic allele in Drosophila melanogaster , allowing us to study whether such an allele can persist in populations . We show that the sexually antagonistic allele causes more harm to females than it provides benefits to males but—as predicted by theory—it is able to persist in the population . This is because the harmful effects to females are both recessive ( it is only harmful when two copies of the allele are present ) and linked to the X-chromosome , so females are rarely harmed when the allele is at low frequency . These results show how a sexually antagonistic allele can be maintained in populations and contribute to maintain variation in male and female reproductive success .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"sexual",
"selection",
"sexual",
"conflict",
"population",
"genetics",
"biology",
"evolutionary",
"biology",
"evolutionary",
"processes",
"evolutionary",
"genetics",
"evolutionary",
"theory"
] |
2012
|
Experimental Evolution of a Novel Sexually Antagonistic Allele
|
FBW7 is a crucial component of an SCF-type E3 ubiquitin ligase , which mediates degradation of an array of different target proteins . The Fbw7 locus comprises three different isoforms , each with its own promoter and each suspected to have a distinct set of substrates . Most FBW7 targets have important functions in developmental processes and oncogenesis , including Notch proteins , which are functionally important substrates of SCF ( Fbw7 ) . Notch signalling controls a plethora of cell differentiation decisions in a wide range of species . A prominent role of this signalling pathway is that of mediating lateral inhibition , a process where exchange of signals that repress Notch ligand production amplifies initial differences in Notch activation levels between neighbouring cells , resulting in unequal cell differentiation decisions . Here we show that the downstream Notch signalling effector HES5 directly represses transcription of the E3 ligase Fbw7β , thereby directly bearing on the process of lateral inhibition . Fbw7Δ/+ heterozygous mice showed haploinsufficiency for Notch degradation causing impaired intestinal progenitor cell and neural stem cell differentiation . Notably , concomitant inactivation of Hes5 rescued both phenotypes and restored normal stem cell differentiation potential . In silico modelling suggests that the NICD/HES5/FBW7β positive feedback loop underlies Fbw7 haploinsufficiency . Thus repression of Fbw7β transcription by Notch signalling is an essential mechanism that is coupled to and required for the correct specification of cell fates induced by lateral inhibition .
FBW7 belongs to the family of SCF ( Skp1 , Cul1 , F-box ) -E3 ligases , which degrades several oncoproteins that function in cellular growth and division pathways , including c-MYC , CYCLIN-E , c-JUN , and Notch proteins . Three FBW7 isoforms have been identified ( FBW7α , FBW7β , FBW7γ ) , each with an isoform-specific first exon , linked to 10 shared exons . Each isoform is expressed from its own promoter allowing isoform-specific transcriptional regulation and tissue-specific expression . Whether FBW7 isoforms show preferential degradation of substrates is still controversial , although studies have shown that c-MYC , CYCLIN-E , and PIN1 are degraded specifically by FBW7α [1]–[3] . FBW7β , however , has remained more enigmatic , partly due to its lower absolute mRNA abundance in several cell lines and tissues , when compared to Fbw7α [2] , [4] . A further level of complexity of FBW7 function is added by the fact that different substrates are regulated in a tissue-specific manner by FBW7 [4]–[6] . Intestinal stem cells are located in the crypt base where they produce rapidly proliferating daughter cells , transit amplifying ( TA ) cells , which fill the crypts and gradually lose their progenitor identity to differentiate into the two main epithelial lineages upon reaching the crypt-villus junction . The absorptive lineage comprises all enterocytes , while the secretory lineage is composed of goblet cells ( secreting protective mucins ) , enteroendocrine cells ( secreting hormones like serotonin or secretin ) , and Paneth cells ( secreting bactericidal proteins , and restricted to the bottom of the crypt in the small intestine [7] ) . TA cells inevitably encounter a binary decision point that will determine whether they differentiate along an absorptive or a secretory pathway [8] , [9] . The Notch pathway is a key regulator of this choice . RBP-Jκ conditional knockout mice or treatment of mice with a γ-secretase inhibitor results in secretory cell expansion [10] . Conversely , in transgenic mice expressing the activated form of Notch1 ( NICD1 ) , goblet cells are absent and the proliferative compartment is expanded [11] . FBW7 has proven to be a critical regulator of intestinal stem cell differentiation , as its deletion in the gut significantly increased NICD1 protein levels and reduced goblet cell numbers [5] . Another example demonstrating the importance of FBW7 in Notch biology and function is that of neural stem cells ( NSCs ) . At the beginning of neurogenesis , neuroepithelial stem cells give rise to radial glial stem cells ( RGCs ) , which represent the major population of NSCs at later stages of embryonic cortex development [12] . Notch activity is very high in RGCs , and needs to be downregulated for neuronal differentiation to occur [13] . Overexpression of NICD1 has been shown to be sufficient to promote radial glial identity during embryogenesis , while abrogation of Notch signalling leads to depletion of RGCs [14] , [15] . In line with these observations , we have shown that absence of Fbw7 in NSCs causes severely impaired RGC stem cell differentiation , accompanied by accumulation of the FBW7 substrate NICD1 [4] . The Notch signalling pathway is a highly conserved pathway that is not only involved in the development and stem cell biology of the mammalian intestine and brain , but controls cell differentiation decisions in a wide range of metazoan species , in a broad range of cell types within a single organism , and at different steps during cell lineage progression . Mammals have 4 Notch receptors ( Notch1–4 ) , 3 Delta-like ligands ( Dll1 , 3 , 4 ) , and 2 Serrate-like ligands termed Jagged ( Jagged1 and 2 ) . Ligand binding triggers a complex proteolytic cascade involving ADAM proteases and an intramembranous enzyme complex called γ-secretase , which results in the release of the cytoplasmic domain of Notch proteins from the plasma membrane . The Notch intracellular domain ( NICD ) shuttles all the way from the cell membrane to the nucleus , where it binds to RBP-Jκ and other proteins , and establishes an activator complex , leading to the expression of target genes . In mammals , the best-characterized Notch target genes belong to the Hes ( Hairy Enhancer of Split ) and Herp/Hey ( Hes-related repressor proteins with Y-box ) family of basic helix-loop-helix ( bHLH ) transcriptional repressors [16] , [17] . An important function of the Notch pathway is in lateral inhibition—an interaction between equal adjacent cells that serves to drive them towards different final states . The basic principle of lateral inhibition is that activation of Notch represses production of the Notch ligand . Consequently , the cell with lower Notch activity produces more ligand , and this activates Notch signalling in the neighbouring cell , which results in reduced ligand production . This in turn enables the cell with lower Notch activity to increase its ligand production even further , because it receives a weakened inhibitory signal back from its neighbours . The effect of this feedback loop is that any initial difference in Notch activity between them , whether stochastic or genetically controlled , is amplified to drive the neighbouring cells into opposite Notch-level status and hence into different developmental pathways [18] . In this manuscript we describe the identification of a novel intracellular positive feedback loop that connects Fbw7 and Notch: FBW7 not only downregulates stability of NICD protein , as previously established , but is also itself transcriptionally downregulated by NICD ( via the action of NICD on Hes5 ) . We demonstrate that FBW7 is haploinsufficient for Notch-dependent physiological functions , as Fbw7Δ/+ heterozygous mice show impaired differentiation of intestinal goblet cells and NSCs . This haploinsufficiency is greatly dependent on the newly identified negative transcriptional regulation of the Fbw7β promoter by Hes5 protein . We can further show for the first time a pronounced isoform-specific function of FBW7β in driving Notch1 intracellular domain ( NICD1 ) degradation . Genetic rescue experiments and computer modelling of Notch signalling suggest that the FBW7β/NICD/HES5 feedback loop modulates Notch-dependent cell fate decisions and underlies Fbw7 haploinsufficiency .
We have previously used conditional gut-specific knock-out mice allowing for deletion of Fbw7 specifically in the intestinal tissue to investigate Fbw7 function in gut biology and tumourigenesis . Mice harbouring an Fbw7 allele in which exon5 was flanked by two loxP sites were crossed to villin-cre transgenic mice , previously shown to provide efficient gut-specific Cre activity [19] . Deletion of exon 5 , which encodes most of the F-box , an essential domain of FBW7 , disrupts the Fbw7 open reading frame and prevents production of detectable FBW7 protein [20] . Mono-allelic FBW7 mutations are frequently observed in human colorectal cancer ( CRC ) and we described that also in the mouse Fbw7 heterozygosity greatly increased intestinal tumour number in the APCMin/+ mouse model [5] , indicating that FBW7 haploinsufficiency in intestinal tumour formation is conserved between mouse and human . Fbw7f/+; villin-cre heterozygous ( Fbw7ΔG/+ ) mice showed a significant decrease in goblet cell differentiation , suggesting that FBW7 is a haploinsufficient regulator of goblet cell fate decisions in the gut ( Figure 1a , b ) . FBW7 controls the stability of several proteins with well-documented functions in the intestine such as NICD [21] , N-terminally phosphorylated c-JUN [22] , c-MYC [1] , and CYCLIN-E [23] . We next determined to what extent protein levels of these substrates were deregulated by heterozygous Fbw7 inactivation . Western blot analysis revealed an increase in NICD1 , but the protein levels of N-terminally phosphorylated c-JUN , c-MYC , and CYCLIN-E were less affected in Fbw7ΔG/+ mice ( Figure 1c , Figure S1a ) . To have a more quantitative measure for NOTCH and c-JUN activity , we performed q-PCR analysis of classical target genes of both transcription factors ( Figure 1d , Figure S1d–e ) . In agreement with the western blot analysis , c-Jun and c-Myc mRNA levels were unaffected in Fbw7ΔG/+ intestines , while Hes5 mRNA was significantly increased ( Figure 1d ) . Thus only NICD1 , but none of the other substrates tested , was increased in Fbw7 heterozygous mice . To further investigate FBW7 haploinsufficiency in a second tissue , we analysed NSCs from FBW7 wild-type and heterozygous animals . We generated conditional brain-specific knock-out mice allowing the deletion of Fbw7 specifically in the brain . The aforementioned Fbw7 f/+ mice were crossed to Nestin:Cre transgenic mice previously shown to provide efficient brain-specific Cre activity [4] . NSCs were prepared from E13 . 5 embryos and maintained as an adherent monolayer culture . These cultures were induced to differentiate by withdrawing growth factors , and 3 d after the induction of differentiation , the percentage of remaining Nestin-positive NSCs was determined . A significantly higher number of Fbw7ΔN/+ NSCs retained Nestin expression as compared to the wild-type controls ( Figure 1e , 1f ) , which coincided with elevated NICD1 protein levels in Fbw7ΔN/+ NSCs ( Figure 1g , Figure S1b ) . Consequently , mRNA levels of Hes5 , but not c-Jun or c-Myc , were significantly elevated in Fbw7 ΔN/+ NSCs ( Figure 1h , Figure S1c ) . Thus Fbw7 is haploinsufficient for Notch degradation during both goblet cell and NSC differentiation . To understand the haploinsufficiency of FBW7 function , we explored the possibility of feedback regulation and investigated the expression of Fbw7 in Fbw7f/+ control and Fbw7ΔG/+ heterozygous intestine and Fbw7ΔN/+ heterozygous NSCs . The Fbw7 locus encodes three different Fbw7 isoforms ( Fbw7α , Fbw7β , Fbw7γ ) that are not generated by alternative splicing; rather , each isoform has its unique 5′UTR and is transcribed from an isoform-specific promoter ( Figure 2a ) [24] . We have previously shown that the α and β Fbw7 isoforms are expressed in the intestine and the brain , whereas the γ isoform was undetectable [4] , [5] . Using quantitative qPCR analysis we show that the Fbw7α isoform is 170- and 10-fold more abundant than the Fbw7β isoform in the intestine and NSCs , respectively ( Figure S2 ) . To circumvent a potential alteration in mRNA stability of the Fbw7Δ allele , we used Q-PCR primers located in exon5 , which is missing in the Fbw7Δ allele ( Figure 2a ) . Thus using this approach exactly 50% of the normal amount of Fbw7 mRNA is expected in Fbw7Δ/+ heterozygous cells . However , Fbw7 mRNA levels in Fbw7ΔG/+ intestine and Fbw7 ΔN/+ NSC were only 30% of controls , a reduction of about 40% from the expected expression of the intact allele ( Figure 2b ) . Q-PCR analysis using isoform-specific primers , which detect both the wild-type and the ΔFbw7 alleles ( Figure 2a ) , showed that Fbw7α mRNA levels were only slightly reduced in control Fbw7f/+ and Fbw7ΔG/+ intestines as well as in Fbw7ΔN/+ NSCs . In contrast , expression of Fbw7β mRNA was greatly reduced in Fbw7ΔG/+ intestines and Fbw7ΔN/+ NSCs ( Figure 2b ) . Mono-allelic ( i . e . , heterozygous ) FBW7 mutations are frequently observed in human CRC , and Fbw7 heterozygosity greatly increases intestinal tumour number in the APCMin/+ mouse model [5] . Similarly , a reduction in Fbw7β mRNA was observed in tumours from APCMin/+; Fbw7ΔG/+ mice compared to APCMin/+; Fbw7f/+ tumours ( Figure 2c ) . To gain insights into the mechanism of Fbw7 transcriptional regulation , we performed an in silico transcription factor binding site analysis of the genomic Fbw7 locus . This revealed the presence of putative N-box sites , the consensus binding element recognized by HES transcription factors , in the promoters of both Fbw7α and Fbw7β ( Figure 2d ) . Our attempts to perform Chromatin immunoprecipitation ( ChIP ) analysis on endogenous HES5 failed as we were unable to identity a suitable Hes5-specific antibody ( Figure S3 ) . For this reason Flag-HES5 was overexpressed in HCT116 colon cancer cells and ChIP performed using Flag antibody . This revealed binding of HES5 protein to the N-box in the neurogenin3 promoter ( NGN3 ) , a known HES target gene [25] , which served as a positive control . However , we also observed some unspecific DNA binding of FLAG-HES5 relative to control vector transfected cells at the GAPDH , β-ACTIN , and CYCLIND1 promoters , which all served as negative controls . HES5 bound to predicted N-box elements present in the FBW7α and FBW7β promoters to a similar extent to NGN3 , but did not bind significantly to a putative N-box in exon1 of FBW7β ( Figure 2e ) . When inserted into a luciferase reporter construct , the FBW7β promoter fragment including the functional ( N1 ) Hes5 binding site ( FBW7β N1-luc ) was repressed by HES5 overexpression , whereas an FBW7β fragment covering exon1 ( FBW7β–N2-luc ) and lacking the N1 site was unaffected . Mutation of the N1 N-box ( FBW7β N1-mut-luc ) rendered the FBW7β promoter fragment unresponsive to HES5 ( Figure 2f , g ) . Together these data point very strongly to a specific role for HES5 in regulating FBW7 transcription . To further validate FBW7 as a direct transcriptional target of HES5 , NICD1 was ectopically expressed in HCT116 colon cancer cells . NICD1 expression resulted in increased HES5 mRNA levels , but had no effect on HES1 . Moreover , FBW7β and to a lesser extent FBW7α mRNA levels were strongly repressed ( Figure 3a ) . shRNA-mediated knock-down of HES5 reversed the repression of FBW7α and FBW7β expression ( Figure 3b ) . Similar results were obtained in NSCs ( Figure S4a , b ) . The direct repression of FBW7β expression , and to a lesser extent , of FBW7α , by HES5 implies that NICD1 , HES5 , and FBW7 are connected through a feedback loop . This leads to the unexpected prediction that overexpression of HES5 should result in a cell-autonomous increase in NICD1 protein levels ( Figure 3c ) , but that this increase should be impaired in cells deleted for the E3 ligase ( that is , FBW7 ) regulating NICD turnover . To test this hypothesis , we used a set of human colon cancer HCT116 cell lines that have homozygous isoform-specific FBW7-null mutations [2] . GFP-tagged-HES5 or GFP alone was overexpressed in HCT116 FBW7–wt , FBW7α-null , and FBW7β-null cells followed by intracellular NICD staining and FACS analysis . FACS analysis on GFP+ gated cells revealed that NICD1 protein levels in HCT116 are not uniform , rather that there are two distinct subpopulations with different NICD1 levels . This resembles the bi-stability observed when lateral inhibition operates , and thus should be affected by the intracellular NICD—>Hes5 —| Fbw7 —| NICD positive feedback loop . In line with this , the majority of FBW7-wt cells were in the low-NICD state , while a greater proportion of FBW7β-null cells were in the high-NICD state ( Figures S5a–c and S6a ) . Expression of HES5-GFP shifted these proportions , leading to a marked cell-autonomous increase in the percentage of cells in the high-NICD1 state in FBW7–wt and FBW7α-null cells , but this increase was drastically impaired in FBW7β-null cells ( Figure 3c and Figure S5 ) . Conversely , silencing HES5 ( sh-HES5-GFP ) led to a cell-autonomous reduction in the percentage of cells in a high-NICD state in FBW7-wt and FBW7α-null cells , which was compromised in FBW7β-null cells ( Figure 3d and Figure S5 ) . These data imply that FBW7β is the predominant isoform involved in the NICD1/HES5/FBW7 feedback loop . To formally show that FBW7β regulates NICD degradation , we performed cycloheximide chase experiments for NICD turnover in FBW7-wt , FBW7α-null , and FBW7β-null cells . We found that NICD turnover was reduced in FBW7β-null cells by comparison with FBW7-wt and FBW7α-null cells ( Figure 3e , 3f ) . Accordingly , we observed less ubiquitylation of NICD in FBW7β-null cells ( Figure S8c ) . Q-PCR analysis performed in the same set of Fbw7-mutant cell lines confirmed that only loss of FBW7β resulted in increased HES5 mRNA levels ( Figure S6b ) . Together , these data demonstrate a crucial role of FBW7β in regulation of NICD turnover . To further investigate HES5 function in our proposed loop , we characterized the phenotype of Hes5-deficent mice in the intestine and NSCs . Hes5−/− mice are viable , but mutant phenotypes in various organ systems such as the eye , inner ear , and nervous system have been described [26]–[28] . However , the function of HES5 in the intestine and in NSCs has not been analysed . The absence of HES5 led to a significant increase in intestinal goblet cell number by approximately 50% ( Figure 4a , b ) . Q-PCR analysis revealed increased Fbw7β expression , and also the mRNA levels of the HES target gene Dll1 and the goblet cell marker Muc2 were augmented while Fbw7α transcript levels remained unchanged ( Figure 4c ) . Loss of HES5 in the brain caused no obvious phenotypic abnormalities , consistent with previous observations [29] . However , NSCs cultured from Hes5−/− animals showed significant premature differentiation of Nestin-positive cells with a concomitant mild increase of Map2 positive neurons ( Figure 4d , e ) . Deletion of Hes5 in NSCs also led to a significant increase in Fbw7β and Dll1 expression ( Figure 4f ) . We next tested whether the NICD1/HES5/FBW7β feedback loop might underlie the functional haploinsufficiency of FBW7 . We generated compound mutant mice heterozygous for Fbw7 in a Hes5−/− background ( Fbw7ΔG/+; Hes5−/− , Fbw7ΔN/+; Hes5−/− mice ) . Strikingly , goblet cell numbers were restored to wild-type levels in Fbw7ΔG/+; Hes5−/− mutant mice ( Figure 5a , b ) , as were the numbers of Nestin-positive and Map2-positive cells in NSC differentiation cultures ( Figure 5d , e ) . Importantly , the repression of Fbw7β transcription in heterozygotes was rescued in Fbw7ΔG/+; Hes5−/− , and Fbw7ΔN/+; Hes5−/− mutant mice ( Figure 5c , f ) . Thus , HES5 deficiency and FBW7 heterozygosity rescue each other , providing strong evidence that the two proteins are connected by a feedback loop . Our experiments imply that , overlaid on the standard gene regulatory circuit of Delta-Notch-mediated lateral inhibition , there is an intracellular feedback loop involving Fbw7: NICD stimulates expression of Hes5; Hes5 represses Fbw7β; and Fbw7β drives degradation of NICD . The net action of this NICD—>Hes5 —| Fbw7 —| NICD feedback loop is positive: it tends to amplify the effect of any change in any of the three components . This can explain why Fbw7 is haploinsufficient , in the sense that loss of just one allele of the gene is enough to cause a marked shift in the ratio of secretory ( low NICD ) to absorptive ( high NICD ) cells in the gut , or of neurons to progenitors in the brain . Intuitive arguments are , however , untrustworthy when applied to systems with feedback . We have therefore investigated a mathematical model of the Delta-Notch lateral inhibition circuitry incorporating the intracellular Fbw7 feedback loop , to see whether it can indeed give rise to the observed phenomena . In Figure 6 , we compare the predicted multicellular patterns of differentiation under four conditions , corresponding to the genotypes Fbw7+/+;Hes5+/+ ( b ) , Fbw7+/−;Hes+/+ ( c ) , Fbw7+/+;Hes5−/− ( d ) , and Fbw7+/−;Hes5−/− ( e ) . In ( d ) , where Hes5 is absent , the proportion of secretory cells is increased; in ( c ) , where Hes5 is present but one of the two Fbw7 gene copies is defective , we see the opposite effect , reflecting haploinsufficiency of Fbw7; and in ( e ) , where both types of mutation are present , their effects cancel out , restoring the normal ratio of secretory to absorptive cells . These results depend , of course , on the values assumed for the parameters in the model , for many of which we can only make rough guesses . The results of the modelling should therefore be viewed not so much as quantitative predictions , but rather as a demonstration that the experimental observations ( Figures 1a , 4a , and 5a ) are indeed consistent with a mechanism of the type proposed . Intuitively , it seems that the Fbw7 loop superimposed on the standard lateral-inhibition circuitry should tend to amplify the differences between neighbouring cells and perhaps speed up the creation of a salt-and-pepper pattern . Moreover , as we have argued , it could explain why loss of a single Fbw7 gene copy has an unexpectedly large effect on the ratio of differentiated cell types in this final pattern .
Notch signalling is a key pathway that controls differentiation decisions in a vast number of cell types . SCF ( Fbw7 ) is an important negative regulator of NICD function [30] , [31] , and many , though not all , of the phenotypes observed in Fbw7 mutant animals can be attributed to deregulation of Notch activity [4] , [5] , [21] , [32]–[34] . In this study we show that FBW7β is the isoform responsible for NICD degradation and also reveal that the functional relationship between FBW7β and Notch is not uni-directional , but that FBW7β and NICD are connected through a double-negative , i . e . positive , feedback loop . We propose that the NICD/HES5/FBW7β feedback loop functions to refine the classical lateral inhibition mechanism ( Figure 6a ) . Notch signalling represses transcription of Notch ligands , which leads to unequal levels of Notch signalling in neighbouring cells . We propose here that increasing levels of Notch activity results in reduced expression of Fbw7β , which in turn will lead to a further increase in NICD1 protein levels . Similarly , attenuation of Notch signalling will decrease NICD1 levels , as Fbw7β will be more highly expressed . Whereas NICD/Notch ligand regulation operates non-cell-autonomously , the NICD/HES5/FBW7β loop results in a cell-autonomous amplification of inequalities in Notch activity . This mechanism will help the cell to stably attain a Notch-high or Notch-low state , thereby solidifying cell fate decisions . NICD1 stands out among all the SCF ( Fbw7 ) substrates as it is the only substrate that is noticeably increased in Fbw7Δ/+ heterozygous cells . Mechanistically , this is explained by the positive feedback causing repression of the wild-type Fbw7 allele in Fbw7Δ/+ heterozygous cells . Thus , instead of being reduced to just 50% of normal , Fbw7 mRNA levels are reduced even further . Absolute quantification of Fbw7 mRNA abundance in the intestine and NSCs has shown that the reduction in total Fbw7 mRNA in heterozygous animals cannot be accounted for solely by the reduction in levels of the Fbw7β isoform . We believe that the small but consistent reduction of the more abundant Fbw7α mRNA ( reflecting its moderate regulation by Hes5; see Figure 3a , b ) contributes to the overall regulation of total Fbw7 mRNA levels in heterozygous cells . Previous reports have generated Fbw7β–specific knockout mice , which are viable , but comprehensive analyses of Notch-mediated phenotypes in brain or gut were not performed [35] . The same holds true for Hes5−/− mice , which had not been reported to have abnormalities in intestinal or NSC differentiation . In our analysis we have clearly shown that decreased levels of Fbw7β or loss of Hes5 have a profound effect on patterns of differentiation in the intestine and in NSCs . There are various reports regarding the localisation of FBW7β and its contribution to substrate turnover . Some accounts report that FBW7β localises to the cytosol [1] , [36] , whereas others have found it in the ER and Golgi [35] . Also in the cells we studied FBW7β localised predominantly in the cytoplasm but some nuclear localisation could also be observed , especially in response to proteasome inhibitor treatment ( Figure S7a–c ) . Conversely FBW7β is able to interact with both endogenous NICD and overexpressed NICD ( Figure S8a , b ) and the ubiquitylation of overexpressed NICD is severely impaired in HCT116-FBW7β-null cells ( Figure S8c ) . The cytoplasmic presence of FBW7β might even explain why the observed haploinsufficiency of Fbw7Δ/+ animals is restricted to NICD1 . Many Fbw7 substrates are predominantly nuclear , whereas NICD shuttles from the cytoplasm into the nucleus , and is thus present in both subcellular compartments . On a similar note , Ye et al . have shown that FBW7β is the predominant isoform responsible for CYCLIN-E turnover , which is primarily nuclear , but shuttles between cytoplasm and nucleus , like NICD [37] . A recent study , using homozygous isoform-specific FBW7-null mutations in human colon cancer HCT116 cells , has shown that FBW7α is the major isoform contributing to c-MYC and SREBP degradation [2] . We have used those cells to show that FBW7β is the isoform regulating NICD degradation . While our data suggest that Fbw7β is the major isoform regulating NICD degradation , Fbw7α possibly also contributes to the proposed feedback loop . Further , we can confirm previous studies showing that c-MYC is primarily degraded by FBW7α ( Figure S6c ) and [2] . The difference in substrate specificity and absolute abundance of the Fbw7 isoforms , together with their heterogeneous tissue distribution , could also possibly explain the varying penetrance of Fbw7 deletion in different organs . FBW7 is frequently mutated in a large variety of human tumours [24] . In particular , loss-of-function mutations in FBW7 are very commonly found in human CRC [38] . Interestingly , about 70% of FBW7 mutations are mono-allelic , and only about 30% of the colorectal tumours with FBW7 mutations show loss-of-heterozygosity ( LOH ) [39] , [40] . FBW7 mRNA levels are significantly lower in human CRC tumour tissues than in normal intestinal tissue , and low FBW7 expression correlates with poor prognosis [41] . In a mouse model for human CRC , it was clearly shown that Hes5 expression was upregulated in tumours carrying FBW7 heterozygous mutations when compared to tumours wild-type for FBW7 [5] . Thus FBW7 heterozygosity results in increased Hes5 expression both in human colorectal tumours and in the APCmin;Fbw7ΔG/+ mouse model , suggesting that the NICD/HES5/FBW7β positive feedback loop is the molecular mechanism that underlies FBW7 haploinsufficiency in tumour suppression . Thus the feedback loop created through repression of Fbw7β by NICD plays a crucial part in Notch-regulated cell fate decisions , not only in normal tissues but also in the evolution of a large class of cancers .
Fbw7flox , Villin-cre , Nestin-cre; APCmin/+ and Hes5−/− mice have been described before [19] , [20] , [42]–[44] . HCT116-wt , HCT116-Fbw7-α-null , and Fbw7-β-null cells have been described previously [2] . Cells were cultured in DMEM and 10% FBS . Cells were plated at subconfluence and transfected with Lipofectamine 2000 ( Invitrogen ) . NSCs were isolated as spheres from E13 . 5 fore- and midbrains of Fbw7f/f , Fbw7ΔN/+ , Fbw7ΔN/+; Hes5−/− and Hes5−/− mouse embryos . Cells were initially cultured as spheres under self-renewal conditions , as previously described [4] . Adherent NSC cultures were derived as previously described [45] with minor modifications . Briefly , primary spheres were plated in Neurobasal Medium ( Invitrogen ) supplemented with 1% Penicillin/Streptomycin ( 10 , 000 U/ml; Invitrogen ) , 1% L-glutamine ( 200 mM; Invitrogen ) , 2% B27 supplement ( Invitrogen ) , 1% N-2 supplement ( Invitrogen ) , 20 ng/ml EGF ( PeproTech ) , 20 ng/ml FGF-basic ( PeproTech ) , and 1 µg/ml laminin ( Sigma ) . All experiments were performed using adherent NSCs . For differentiation , growth factors were withdrawn from the medium and 10% NeuroCult Differentiation Supplement ( StemCell Technologies ) was added . Under differentiation conditions , cells were plated on poly-L-ornithine ( 0 . 01% solution; Sigma; diluted 1∶10 in 150 mM disodium tetraborate; Sigma ) coated cover slips . For transfection , NSCs were plated at subconfluence and transfected with Lipofectamine 2000 according to the manufacturer's protocol ( Invitrogen ) . Cycloheximide was used at a final concentration of 100 µg/ml ( Sigma ) . The Notch expression vector ( Notch-IC-ΔOP ) was a gift from Anna Bigas [46] . p-Super-sh-control , p-Super-sh-Hes5-1 , and p-Super-sh-Hes5-2 were generated by cloning short hairpin containing oligos into pSuper vector following the manufacturer's instructions ( Oligoengine ) . Silencing oligo sequences were sh-Hes5-1 ( cagcctgcaccaggactac ) ; sh-Hes5-2 ( ggaagccggtggtggagaa ) . pCMV6-Hes5-gfp was purchased from Origene . pDest-flag was purchased from Invitrogen . pDest-Hes5-flag was generated by Gateway cloning of PCR-amplified Hes5 into pDest-flag . The oligonucleotide sequences used to amplify the DNA fragments for luciferase constructs are: pGL3-Fbw7β-cd fwd: 5′ TTTGACAGGGCATAGTCTCCTC 3′; pGL3-Fbw7β-cd rev: 5′ GCTCACAGTCTTTCCGTTATTATTTGC 3′; pGL3–Fbw7β-ef fwd: 5′ ATTGTCCCTGAAGGTAGTTGTG 3′; pGL3–Fbw7β-ef rev: 5′ TTTGGAGCCGACAGCATTTG 3′; pGL3–Fbw7β-cdmut: N box motif was modified via Geneart; pGL3–Fbw7β-efmut: N box motif was modified via Geneart . HCT116 cells were transfected with the indicated plasmids with Lipofectamine 2000 ( Invitrogen ) . Transient transfections of the experimental samples and controls of Firefly and Renilla luciferase reporters was performed and measured using the Dual-Luciferase Reporter Assay System ( Promega ) , 36 h posttransfection . Data are expressed as fold induction after being normalised using tk-renilla luciferase ( mean ± SD; n = 3 ) . ChIP analysis was performed as described previously [47] . Cells were transfected with Lipofectamine 2000 ( Invitrogen ) with empty-flag or Hes5-flag prior to collection . Immunoprecipitations were carried out with anti-Flag antibody directly conjugated to agarose beads . The oligonucleotide sequences used to amplify the DNA fragments by q-PCR are: Fbw7α ( ab ) -fw: 5′-TGAATATCATGAAAAGATGCTGTATCAG-3′; Fbw7α ( ab ) -rev: 5′-TCAAGCATGTTTGCCTTTATGTTT-3′; Fbw7β ( cd ) -fw: 5′-TGGGCTTTTCTAGCTCAAGGAAT-3′; Fbw7β ( cd ) -rv: 5′-TTCATCTTGCAACTTCCTTCACA-3′; Fbw7β ( ef ) -fw: 5′-TCCCGAGAAGCGGTTTGAT-3′; Fbw7β ( ef ) -rv: 5′-GCAGAACCGGCAACAAAACT-3′; Ngn3-fw: 5′-CCCCTCCAGGACAGATGCT-3′; Ngn3-rv: 5′-CTGGTCAGGCCACCTCAGA -3′; Gapdh-fw: 5′-TGAGCAGTCCGGTGTCACTA-3′; Gapdh-rv: 5′-AAGAAGATGCGGCTGACTGT-3′; Actin-fw: 5′-GGATGCAGAAGGAGATCACTG-3′; Actin-rv: 5′-CGATCCACACGGAGTACTTG-3′; Cyclind1-fw: 5′-CGCCCCACCCCTCCAG-3′; Cyclind1-rv: 5′-CCGCCCAGACCCTCAGACT-3′ . Quantitative real-time PCR was accomplished with SYBR Green incorporation ( Platinum Quantitative PCR SuperMix-UDG w/ROX , Invitrogen ) using an ABI7900HT ( Applied Bioscience ) , and the data were analyzed using the SDS 2 . 3 software . For qRT-PCR analysis , total mRNA was isolated from ileum fraction obtained as described before [5] . Total RNA was used from adherent NSC cultures . Results , normalized to β-actin , were presented as fold induction over control mice . The list of primers that were used for Q-PCR analysis of mouse tissues were: F-c-Jun: 5′-TGAAAGCTGTGTCCCCTGTC-3′; R-c-Jun: 5′-ATCACAGCACATGCCACTTC-3′; F-Fbw7α: 5′-CTGACCAGCTCTCCTCTCCATT-3′; R-Fbw7α: 5′-GCTGAACATGGTACAAGGCCA-3′; F-Fbw7β: 5′-TTGTCAGAGACTGCCAAGCAG-3′; R-Fbw7β: 5′-GACTTTGCATGGTTTCTTTCCC-3′; F-Fbw7 ( exon5 ) : 5′-TTCATTCCTGGAACCCAAAGA-3′; R-Fbw7 ( exon5 ) : 5′-TCCTCAGCCAAAATTCTCCAGTA-3′; F-Actin: 5′-TCTTTGCAGCTCCTTCGTTG-3′; R-Actin: 5′-ACGATGGAGGGGAATACAGC-3′; F-Hes1: 5′-TCAGCGAGTGCATGAACGA-3′; R-Hes1: 5′-TGCGCACCTCGGTGTTAAC-3′; F-Hes5: 5′-TGCAGGAGGCGGTACAGTTC-3′; R-Hes5: 5′-GCTGGAAGTGGTAAAGCAGCTT-3′; F-Dll1: 5′-CATGAACAACCTAGCCAATTGC-3′; R-Dll1: 5′-GCCCCAATGATGCTAACAGAA-3′; F-Muc2: 5′-TGTGGGACTTTTGCCATGTACT-3′; R-Muc2: 5′-GCAAGAGCACCTGTGATCCA-3′; F-c-Myc: 5′-CCTAGTGCTGCATGAGGAGA-3′; R-c-Myc: 5′-TCTTCCTCATCTTCTTGCTCTTC-3′ . Immunoblots were carried out as previously described [4] , [5] . Antibodies to c-JUN ( BD biosciences ) , p-c-JUNser73 ( Cell Signalling ) , active-NOTCH-1 ( Abcam ) , p-c-MYC ( Cell Signaling ) , c-MYC ( Santa Cruz ) , CYCLIN-E ( Santa Cruz ) , and β-ACTIN ( Sigma ) were used . Mice were euthanized by cervical dislocation and the small intestines prepared for histology as described before [48] . Sections were cut at 4 µm for Haematoxylin & Eosin staining and PAS/AB staining . To quantify goblet cells , AB/PAS+ cells were quantified from at least 100 villi from comparable intestinal regions from at least 5 mice from each genotype and the data represented as the mean ± SEM . HCT116 cells transfected with the indicated plasmids were fixed for 10 min in 1% PFA , permeabilized in PBS+0 . 5% Triton for 10 min at RT , and blocked in PBS+2% FCS for 30 min . After blocking , cells were incubated with anti-NICD antibody ( 1∶500 dilution in PBS+2% FCS ) for 30 min . Cells were washed in PBS+2% FCS and incubated with donkey-anti-rabbit-Alexa647 secondary antibody ( 1∶1000 in PBS+2% FCS ) for 30 min in the dark at RT . Cells were analysed in an LSRII cytometer . Overlay Histograms ( Hes-GFP or sh-Hes5-GFP versus their controls ) were represented as NICD-Alexa-647 versus cell numbers on GFP+ gated cells . The number of GFP+ cells quantified for each individual sample , the single histograms , and the percentage of cells in high-NICD and low-NICD state are indicated in Figure S5 . Cells from differentiation cultures were fixed for 20 min in 4% paraformaldehyde and permeabilized in ice-cold Methanol for 20 min . For immunocytochemistry , antibodies against NESTIN ( BD ( monoclonal ) ) and MAP2 ( Sigma ( monoclonal ) ) were used . DNA was counterstained with 4′-6-Diamidino-2-phenylindole ( DAPI; Sigma ) . To describe the Delta-Notch-Fbw7-Hes gene regulatory circuit , we adapted a standard simple Delta-Notch lateral-inhibition model , adding the Fbw7 feedback loop as in Figure 6a . We represented the dynamics by a set of differential equations , which we solved numerically using Mathematica to determine the final state of a two-dimensional array of cells . The model assumes that there are two relevant Hes genes , HesX and Hes5 , where HesX stands for one ( or more ) of the many other members of the Hes/Hey family that are expressed in gut and CNS . HesX ( by itself ) represses Delta , while HesX and Hes5 act in parallel to repress Fbw7 . Loss of functional Hes5 thus leads roughly to a doubling of Fbw7 expression and can be compensated by a halving of the Fbw7 gene dosage . With our chosen model parameters , the Fbw7 positive feedback loop gives rise to bistability , allowing a cell exposed to a given level of Delta signalling from its neighbours ( above a certain low Delta threshold ) to exist in either a low- or a high-NICD state ( as suggested by the data; see Figures 3c , d and S5a ) . This biases the outcome of Delta-Notch-mediated lateral inhibition . In the version of the model used to compute Figure 6 , we postulate molecular lifetimes such that the dynamics of the Fbw7 loop are fast compared with the dynamics of the Delta-Notch loop . Each cell then moves rapidly to a low- or high-NICD state , with a relative probability dependent on the starting conditions and genotype , creating an initial random multicellular pattern that is subsequently adjusted by lateral inhibition . The adjustments follow a simple rule: thanks to bistability , low-NICD cells can persist regardless of the states of their neighbours , but any high-NICD cell that is entirely surrounded by other high-NICD cells is eventually converted to a low-NICD state . This is because high NICD entails a near-zero level of Delta production , and the high-NICD state becomes unstable when levels of Delta signalling from neighbours fall very low . The model assumes that cells all start in an approximately similar state but with some small random variation from cell to cell , reflecting genetic noise , whose consequences are amplified through the Fbw7 and Delta-Notch feedback loops to give a final pepper-and-salt pattern . Results of the computation are shown for a 10×10 hexagonal array of cells , with cyclic boundary conditions . Mathematical details of the model and values of the parameters are given in Data S1 . The Mathematica program is available on request from julian . lewis@cancer . org . uk . Statistical evaluation was performed by Student's unpaired t test . Data are presented as mean ± SEM . *p≤0 . 05 was considered statistically significant . **p≤0 . 01 was considered highly statistically significant . ***p≤0 . 001 was considered very highly statistically significant .
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The Notch signalling pathway is a highly conserved system that controls cell differentiation decisions in a wide range of animal species and cell types , and at different steps during cell lineage progression . An important function of the Notch pathway is in lateral inhibition—an interaction between equal adjacent cells that drives them towards different final states . The basic principle of lateral inhibition is that activation of the Notch cell surface receptor represses production of the Notch ligand ( also borne on the cell surface ) . Consequently , cells expressing less Notch produce more Notch ligand that can activate the Notch pathway in neighboring cells and thereby amplify the differences between these cells . However , the additional regulatory circuits required to fine-tune this delicate process have so far remained elusive . Here we describe the identification of a novel intracellular positive feedback loop that connects Fbw7 ( the ubiquitin ligase responsible for targeting Notch for degradation ) and Notch itself . We show that Fbw7 reduces the stability of Notch intracellular domain ( NICD ) protein , as previously established , but also that the fbw7 gene is itself transcriptionally downregulated by the Notch effector Hes5 . Thus we conclude that increased Notch activity causes NICD stabilisation . Further , we demonstrate that perturbation of this regulatory loop is responsible for the Fbw7 haploinsufficiency observed for Notch-dependent functions in intestine and brain stem cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"cell",
"fate",
"determination",
"neural",
"stem",
"cells",
"stem",
"cells",
"signaling",
"molecular",
"development",
"gene",
"expression",
"biology",
"molecular",
"cell",
"biology",
"cell",
"differentiation",
"dna",
"transcription",
"adult",
"stem",
"cells"
] |
2013
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Fbw7 Repression by Hes5 Creates a Feedback Loop That Modulates Notch-Mediated Intestinal and Neural Stem Cell Fate Decisions
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Lipids are constantly shuttled through the body to redistribute energy and metabolites between sites of absorption , storage , and catabolism in a complex homeostatic equilibrium . In Drosophila , lipids are transported through the hemolymph in the form of lipoprotein particles , known as lipophorins . The mechanisms by which cells interact with circulating lipophorins and acquire their lipidic cargo are poorly understood . We have found that lipophorin receptor 1 and 2 ( lpr1 and lpr2 ) , two partially redundant genes belonging to the Low Density Lipoprotein Receptor ( LDLR ) family , are essential for the efficient uptake and accumulation of neutral lipids by oocytes and cells of the imaginal discs . Females lacking the lpr2 gene lay eggs with low lipid content and have reduced fertility , revealing a central role for lpr2 in mediating Drosophila vitellogenesis . lpr1 and lpr2 are transcribed into multiple isoforms . Interestingly , only a subset of these isoforms containing a particular LDLR type A module mediate neutral lipid uptake . Expression of these isoforms induces the extracellular stabilization of lipophorins . Furthermore , our data indicate that endocytosis of the lipophorin receptors is not required to mediate the uptake of neutral lipids . These findings suggest a model where lipophorin receptors promote the extracellular lipolysis of lipophorins . This model is reminiscent of the lipolytic processing of triglyceride-rich lipoproteins that occurs at the mammalian capillary endothelium , suggesting an ancient role for LDLR–like proteins in this process .
Organisms need to tightly regulate the balance between energy intake , usage and storage . Imbalances in these processes are at the heart of several major human health problems such as obesity , cardiovascular disease and diabetes [1] . In recent years , the use of Drosophila and other genetically tractable model organisms has provided novel approaches and insights into the study of the mechanisms controlling energy balance . In particular , genetic screens have shown their potential for the identification of novel genes and regulatory mechanisms involved in the maintenance of lipid homeostasis . Importantly , despite the evolutionary distance separating humans from flies , many of the central pathways controlling metabolism are conserved ( for reviews , see [2]–[6] ) . Despite these advances , several basic aspects of Drosophila lipid metabolism are still unknown . Here we focus on the mechanisms controlling the cellular uptake of neutral lipids . Most metazoans accumulate triacylglycerol ( TAG ) , a strongly hydrophobic molecule with a high energy content , as the main substrate for energy storage . Large amounts of TAG are stored in fat body cells , the Drosophila equivalent of adipocytes , but most other cell types also accumulate limited amounts of it as intracellular lipid droplets . Because of their hydrophobicity , the extracellular transport of lipids requires dedicated mechanisms to increase their solubility in extracellular fluids . In mammals , lipids are packed into several types of lipoprotein particles which contain a hydrophobic core of neutral lipids ( mostly TAG and esterified cholesterol ) surrounded by a monolayer of phospholipids . In addition , apolipoproteins stabilize and regulate these particles [7] . Similar lipoproteins , named lipophorins , are also found in insects [8] , [9] . They share the same basic structure and play similar functions as mammalian lipoproteins . In Drosophila , apolipophorins are exclusively synthesized in the fat body [10] , [11] , where they are partially lipidated and released into the hemolymph . It has been suggested that lipophorins act as a reusable shuttle in lipid transport . Lipids , primarily diacylglycerol ( DAG ) , derived from the digestion of food in the gut or from the mobilization of lipids in the fat body , are loaded onto pre-formed , circulating lipophorins , then transported through the body via the hemolymph and unloaded upon reaching peripheral tissues for use as a source of energy and phospholipids . During this cycling process , negligible degradation of apolipophorin occurs [8] , [12] . In mammals , the Low Density Lipoprotein Receptor ( LDLR ) and other related proteins mediate endocytosis and the clearance of lipoproteins from plasma [13] . Similar proteins belonging to the LDLR family , known as lipophorin receptors , were subsequently identified in insects . They can bind to lipophorins and mediate their endocytosis both in cell culture systems and in vivo [14]–[16] . Because of these properties , it has been suggested that lipophorin receptors may play an important role in insect lipid metabolism [17] . Here , we examine the function of Drosophila lipophorin receptors in the uptake of neutral lipids . We show that this organism has two lipophorin receptor genes , the lipophorin receptor 1 ( lpr1 ) and lpr2 , which are translated into multiple , functionally diverse isoforms . lpr1 and lpr2 are required for neutral lipid uptake in imaginal disc cells and oocytes . Our results suggest a model where lpr1 and lpr2 promote the extracellular hydrolysis of neutral lipids contained in lipoprotein particles .
A defining characteristic of the LDLR family of transmembrane receptors is the presence of an ectodomain containing specific combinations of three types of protein modules: LDL receptor type A modules ( LA ) , also called complement-type repeats , EGF modules and YWTD β-propellers [18] . This structural definition was used to identify LDLR family members in the Drosophila genome . Of the seven genes identified ( Figure S1 ) , two are arranged in tandem and show a high degree of homology to insect lipophorin receptors . On this basis , we named then lipophorin receptor 1 ( lpr1 ) and lpr2 ( Figure 1A ) . The analysis of cDNA clones generated during large scale transcriptomic studies [19] suggested the existance of multiple isoforms for each gene . To further characterize the range of isoforms derived from lpr1 and lpr2 , we obtained additional cDNAs from whole adult fly RNA , as well as from RNA purified from specific tissues . These cDNAs were genotyped by PCR and sequenced , leading to the identification of a total of 6 isoforms for lpr1 and 5 for lpr2 ( Figure 1B ) . The different isoforms result from alternative splicing and from the use of two alternative promoters for each gene , that we named proximal and distal promoters , referring to their chromosomal position with respect to the centromere . The number and organization of exons are remarkably similar between the two genes , with lrp1 and lpr2 exons in equivalent positions coding for equivalent protein domains . Isoforms differ in three main characteristics: ( 1 ) the number and identity of LA modules; ( 2 ) the presence of an extended putative O-glycosylation region rich in serine and threonine next to the transmembrane domain and ( 3 ) the presence of an N-terminal domain with no homology to other proteins ( non-conserved N-terminal domain , NCN ) ( Figure 1B ) . It is noteworthy that isoforms transcribed from the distal promoters are predicted , using PrediSi software [20] , to have unusually long putative signal peptides: 68 and 88 amino acids for lpr1 and lpr2 , respectively . Isoforms transcribed from the proximal promoters are predicted to have more typical signal peptides of 20 ( lpr1 ) and 24 ( lpr2 ) amino acids . To examine the role of the lipophorin receptors in lipid metabolism , we first generated three small deletions in the lpr1-lpr2 genomic region ( Figure 1A ) . Df ( 3R ) lpr1 completely deletes lpr1; Df ( 3R ) lpr2 deletes exons 1 to 8 of lpr2 , including the promoters and translation initiation codons , while Df ( 3R ) lpr1/2 affects both genes . The breakpoints for each deficiency were confirmed by PCR analysis . Even though several lpr2 exons are still present in Df ( 3R ) lpr2 and Df ( 3R ) lpr1/2 chromosomes , we did not detect Lpr2 protein expression in either deficiency using an antibody which recognizes Lpr2 intracellular domain , which is common to all Lpr2 isoforms ( Figure S2C–S2F and not shown ) . This data strongly suggests that Df ( 3R ) lpr2 is a null allele for lpr2 and Df ( 3R ) lpr1/2 is a null allele for both lpr1 and lpr2 . Flies with mutations in individual lipophorin receptor genes as well as the double mutant Df ( 3R ) lpr1/2 were homozygous viable and displayed fertility phenotypes: Df ( 3R ) lpr1 females laid eggs which hatched at rates similar to wild-type females ( 85 . 5% hatching rate for Df ( 3R ) lpr1 compared to 87 . 5% for the wild-type stock Oregon R , n = 200 ) . Df ( 3R ) lpr2 females laid eggs but most of them failed to hatch ( 0 . 5% hatching rate; n = 200 ) . Df ( 3R ) lpr1/2 females were completely sterile , where the few eggs laid by young flies failed to hatch . These results indicate that lpr2 , and to a lesser extent lpr1 , are required for normal oogenesis . The Drosophila ovarian follicle is composed of a 16-cell germ-line cyst with one oocyte and 15 nurse cells , which is surrounded by somatic follicle cells . During vitellogenesis , the oocyte and nurse cells increase in volume and accumulate large amounts of yolk proteins and neutral lipids ( Figure 2F–2I ) that are captured from the surrounding hemolymph [21] . The Yolkless ( Yl ) receptor mediates the endocytic uptake of yolk proteins [22] , [23] . However , no receptor involved in lipid uptake has been reported . To analyze whether lpr1 or lpr2 mediate lipid uptake during vitellogenesis , we first examined the lipid content of ovaries from wild-type and Df ( 3R ) lpr2 females with the lipophilic nile red dye . Accumulation of neutral lipids was first visible in stage 9 wild-type egg chambers and reached a maximum by the end of vitellogenesis at stage 11 ( Figure 2F–2I ) . A marked decrease in lipid droplets was observed in Df ( 3R ) lpr2 egg chambers ( Figure 2J , compare to Figure 2G . Figure S3A ) or when this deficiency was combined with the double mutant Df ( 3R ) lpr1/2 ( not shown ) . Most of the embryos originating from Df ( 3R ) lpr2 mutant oocytes could not complete embryogenesis and died at various stages of development , showing generalized apoptosis and pleiotropic phenotypes such as muscle detachment and nervous system malformations ( not shown ) . A small number of Df ( 3R ) lpr2 egg chambers exhibited higher lipid levels ( Figure S3A ) . It seems likely that the few embryos that successfully hatched from Df ( 3R ) lpr2 females ( 0 . 5% ) were derived from these egg chambers . We used a Lpr2-specific antibody to examine the distribution of Lpr2 in wild-type egg chambers , detecting Lpr2 protein at the membranes of nurse cells and oocytes in vitellogenic egg chambers ( Figure 2C–2E ) . This expression was low at the beginning of vitellogenesis ( stage 8 , Figure 2C asterisk ) and increased as the egg chamber matured , being maximal at stage 11 ( Figure 2E ) . In situ hybridization detected a similar expression pattern for lpr2 transcripts ( Figure 2B ) . Together , these results demonstrate that Lpr2 is the major receptor involved in the uptake of neutral lipids by nurse cells and oocytes . In contrast , our results indicate that lpr1 is not essential for this process since egg chambers from Df ( 3R ) lpr1 females had normal amounts of neutral lipids ( Figure 2L , Figure S3A ) . However , lpr1 appears to play some role in oogenesis since Df ( 3R ) lpr1/2 females , which lack both receptors , exhibited stronger and qualitatively distinct phenotypes compared to Df ( 3R ) lpr2 mutants . Ovaries from Df ( 3R ) lpr1/2 females were severely reduced in size and contained abundant cellular debris . DAPI staining revealed condensation and fragmentation of the nurse cell nuclei at stages 9–10 , indicating cell degeneration ( Figure 2O , compare to Figure 2N . Figure S3B ) , a phenotype not observed in Df ( 3R ) lpr2 females , explaining the egg laying phenotype described for these mutants earlier . Starvation and other adverse stimuli are known to activate an oogenesis checkpoint that results in the apoptosis of egg chambers at mid-oogenesis [24] . Thus , the degeneration of Df ( 3R ) lpr1/2 ovaries probably results from activation of this checkpoint . As expected , the few non-degenerating stage 10 egg chambers that can be found in young Df ( 3R ) lpr1/2 females had extremely low lipid content ( Figure 2K ) . These results indicate that lpr1 has a partially redundant function during oogenesis , which was only revealed in the absence of lpr2 . Accordingly , we detected lpr1 expression in nurse cells and follicle cells by in situ hybridization and RT-PCR ( Figure 2A , Figure 5G ) but not by antibody staining ( not shown ) , suggesting that Lrp1 protein levels might be low . Oogenesis is a complex process regulated by hormonal signals that relay information about the nutritional status of the female and other stimuli [25] . Thus , it is conceivable that mutations in lpr1 and lpr2 could affect oogenesis , at least in part , by altering the hormonal or nutritional status of the female . To address this point , we eliminated lpr1 and lpr2 exclusively in the oocyte and nurse cells by generating Df ( 3R ) lpr1/2 germ-line clones . The resulting females were sterile but laid abundant non-viable eggs , the egg chambers had low lipid content ( Figure 2M ) and no signs of degeneration were observed at mid oogenesis ( not shown ) . Taken together , these experiments indicated that lpr2 and to a minor extent lpr1 , are autonomously required in the oocyte and nurse cells to mediate lipid uptake during vitellogenesis . In addition , they suggest that somatic expression of the lipophorin receptors contributes to the regulation of the mid-oogenesis checkpoint . To examine whether lpr1 or lpr2 are involved in lipid uptake in other tissues , we analyzed larval imaginal discs , an epithelial tissue known to accumulate abundant intracellular lipid droplets [26] . Wild-type wing imaginal discs displayed strong neutral lipid accumulation in the wing pouch region ( Figure 3A ) . In contrast , Df ( 3R ) lpr1/2 wing imaginal discs exhibited severely reduced levels of intracellular lipids droplets ( Figure 3B ) , suggesting that lipophorin receptors might mediate neutral lipids uptake in imaginal disc cells in a similar way to the oocyte . Significantly , animals with single mutations in lpr1 or lpr2 did not exhibit this strong phenotype: Df ( 3R ) lpr1 discs were undistinguishable from wild-type discs ( Figure 3C ) while Df ( 3R ) lpr2 discs showed a mild reduction in lipid droplet content ( Figure 3D ) , indicating that the functions of lpr1 and lpr2 are mostly redundant in this tissue . Consistent with a redundant function , we detected transcripts of both lpr1 and lpr2 in the wing pouch region by in situ hybridization ( Figure 3E , 3F and [27] ) . High levels of Lpr2 protein were also detected in the wing pouch by immunostaining , with two stripes of lower expression along the antero-posterior and dorso-ventral compartment borders ( Figure 3G , 3H ) . To examine whether Drosophila lipophorin receptors had an impact on total lipid content , we measured total TAG content in Df ( 3R ) lpr1 , Df ( 3R ) lpr2 and Df ( 3R ) lpr1/2 male flies under a normal diet as well as under starvation conditions . No differences were observed between the three genotypes and the wild-type control ( Figure S4A–S4F and Text S1 ) . The fat body stores most of the fly's TAG reserves and in agreement with our previous results , we did not observe differences in the number or size of lipid droplets in the fat body of Df ( 3R ) lpr1/2 and control animals ( Figure S4G–S4J ) , suggesting that TAG storage in the fat body is independent of the lipophorin receptors . Finally , since impaired neutral lipid uptake by peripheral tissues could have an impact on circulating lipid levels , we measured TAG content in the hemolymph of control , Df ( 3R ) lpr1 , Df ( 3R ) lpr2 and the double mutant animals . We did not observe statistically significant differences between the four genotypes ( Figure S4K and Text S1 ) . lpr1 and lpr2 are transcribed as multiple isoforms ( Figure 1B ) , raising the issue of whether they share similar properties regarding lipid uptake . To answer this question , we first compared the isoforms transcribed from the corresponding distal versus proximal promoters . We generated two HA-tagged transgenes that allowed controlled expression of Lpr2F isoform ( UAS-lpr2F ) and Lpr2E isoform ( UAS-lpr2E ) and examined their ability to rescue lipid uptake in Df ( 3R ) lpr1/2 animals . Expression of UAS-lpr2E in the posterior compartment of the wing imaginal disc driven by en-gal4 completely rescued lipid accumulation in that compartment ( Figure 4A ) . In contrast , expression of UAS-lpr2F did not rescue lipid uptake ( Figure 4B ) . In a similar assay , we found that whereas Lpr1H isoform rescued lipid uptake , expression of Lpr1D did not ( Figure 4C , 4D ) . Equivalent results were obtained when we examined the role of these isoforms during vitellogenesis . Germ-line expression of UASp-lpr1J or UASp-lpr2E , driven byV32-gal4 , rescued oogenesis and fertility of Df ( 3R ) lpr1/2 females . The amount of lipid droplets accumulated in nurse cells was similar to the wild-type ( Figure 5C , 5E , compare to wild-type in Figure 2G ) and the number of degenerating egg chambers was dramatically reduced ( Figure 5D , 5F and Figure S3B ) . In contrast , expression of UASp-lpr2F did not rescue fertility , lipid uptake nor egg chamber degeneration ( Figure 5A , 5B ) . The surprising finding that only a subset of lpr1 and lpr2 isoforms tested were able to mediate lipid uptake prompted us to examine the essential protein domains required for this function . Lipid uptake-promoting isoforms Lpr1H and Lpr2E are both transcribed from the corresponding alternative distal promoters and contain a non-conserved N-terminal domain ( NCN ) adjacent to the LA module 1 ( LA-1 ) , encoded by exons 2 and 3 respectively . In contrast , these domains are not present in the lipid uptake-defective Lpr2F and Lpr1D isoforms ( Figure 1B ) . Thus , a sequence critical for the uptake of neutral lipids may lie in this N-terminal region . However , given that Lpr1H and Lpr2E both contain eight LA modules whereas Lpr2F and Lpr1D have seven , it is also possible that the total number of LA modules is the key factor in defining lipid uptake function . To test the role of the NCN and LA-1 , we made a chimeric protein identical to Lpr2F except for its signal peptide , which was replaced with the NCN and LA-1 domains from Lpr2E , generating UAS-lpr2F+LA1+NCN . Overexpression of this transgene in the posterior compartment of Df ( 3R ) lpr1/2 wing imaginal discs completely rescued the accumulation of lipid droplets in this compartment ( Figure 4F ) , demonstrating that this N-terminal segment was able to confer lipid uptake activity . To analyze whether NCN , LA-1 or both domains were required , we prepared two more transgenes . In UAS-lpr2F+LA1 , the LA-1 domain from Lpr2E was inserted in between the signal peptide and LA-2 of Lpr2F . In UAS-lpr2F+NCN , the lpr2F signal peptide was replaced by the lpr2E NCN domain ( Figure 4G , 4H ) . Using our rescue assay , only UAS-lpr2F+LA1 was able to mediate lipid uptake ( Figure 4G , 4H ) . Thus , the NCN domain , even though present in all tested lipid uptake-promoting isoforms , was not essential for this function . These results suggested that the LA-1 module was critical for the uptake of neutral lipids . However , we still could not exclude the possibility that it is the number rather than the identity of LA modules that determines lipid uptake capacity . To analyze this possibility , we examined the Lpr1J isoform which is identical to the lipid uptake-promoting Lpr1H isoform except that it has seven LA modules due to the absence of LA-4 . In our rescue assay , Lpr1J did mediate lipid uptake ( Figure 4E ) , indicating that it is the presence of LA-1 and not the number of LA modules that defines the ability to mediate lipid uptake . Moreover , in an additional transgene we modified Lpr2F by introducing a tandem duplication of LA-2 , so that the new protein ( Lpr2F+LA2 ) had eight LA modules but still lacked LA-1 . Lpr2+LA2 did not mediate lipid uptake , confirming our previous conclusion ( Figure 4I ) . Taken together , these experiments indicate that the key property that distinguishes lipid uptake-promoting from lipid uptake-defective isoforms is the presence of the LA-1 domain . It should be noted that all isoforms containing LA-1 are transcribed from the distal promoters . Consistent with the lipid uptake phenotypes of Df ( 3R ) lpr1/2 mutants in wing imaginal discs and ovaries , RT-PCR experiments showed that the predominant lpr1 and lpr2 isoforms expressed in these tissues were transcribed from the distal promoters and thus contained the LA-1 domain ( Figure 5G ) . During the rescue experiments described in the previous sections , we realized that expression of UAS-lpr2E in the posterior compartment driven by en-gal4 not only autonomously rescued lipid uptake in that compartment but also promoted the formation of lipid droplets in a one to two cells wide region of anterior tissue abutting the anterior-posterior compartment border ( Figure 6B , arrow ) . Analysis of confocal sections spanning the thickness of the imaginal disc confirmed that cells not expressing UAS-lpr2E but adjacent to expressing cells , accumulated higher levels of lipid droplets than more anteriorly located cells ( Figure 6A , arrow ) . Similar results were obtained using a different Gal4 driver line to express UAS-lpr2E in the dorsal wing disc compartment ( Figure S5C , S5D ) . To analyze whether non-autonomous lipid uptake could be detected in other tissues , we returned to the egg chamber because of its particular morphology . In the Drosophila egg chamber , the oocyte and nurse cells are surrounded by a closely associated somatic follicular epithelium . This allowed us to examine whether expression of the lipophorin receptors in the germ-line could non-autonomously direct lipid uptake in the follicular epithelium . Follicle cells of ovaries dissected from Df ( 3R ) lpr1/2 females had very few lipid droplets ( Figure 6C ) . However , after V32-gal4-mediated expression of UASp-lpr2E exclusively in the germ-line , we observed a remarkable increase in the number and size of lipid droplets in the follicular epithelium ( Figure 6D ) . In fact , the rescue was similar to the one obtained by the expression of the lipid uptake-promoting UAS-lpr1J isoform directly in the follicle cells using the follicle cell driver CY2-gal4 ( Figure 6E ) . These results indicated that Lpr2E can non-autonomously promote neutral lipid uptake in adjacent cells . All members of the LDLR family are known to mediate endocytosis of ligands in clathrin coated vesicles [28] . In Drosophila , Lpr1 and Lpr2 have been shown to endocytose lipophorins when overexpressed in imaginal discs [11] , [27] . Thus , a possible hypothesis for lipophorin receptor-mediated lipid uptake is that the receptors induce the endocytosis of lipophorin particles resulting in the catabolic hydrolysis of lipids in lysosomes . However , our finding that lipophorin receptors have non-autonomous activity appears to be in conflict with this simple hypothesis , prompting us to test the requirement of endocytosis for lipophorin receptor-mediated lipid uptake . We took advantage of the fact that the Drosophila oocyte is a well characterized model of endocytosis , and importantly , that the endocytic pathway can be blocked in the oocyte and nurse cells without affecting their survival [29] , [30] . We generated germ-line clones of a null rab5 mutation , an essential mediator of endocytic vesicle formation and maturation [31] . As previously reported , rab52 mutant oocytes were unable to accumulate yolk proteins , demonstrating that the endocytic pathway was blocked [29] , [30] ( Figure 6G' , compare to 6F' ) . More importantly , rab52 mutant egg chambers showed a normal accumulation of intracellular lipid droplets , indicating that the endocytosis of lipophorins is not required for neutral lipid uptake during vitellogenesis ( Figure 6G , compare to 6F ) . Similarly , the AP-2 adaptor complex is not required for neutral lipid uptake ( Figure S6 and Text S1 ) . Together , these results suggest that in Drosophila , the neutral lipid uptake promoting activity of lipophorin receptors occurs extracellularly and that the targeting of lipophorins to lysosomes for their catabolic degradation is not required . In this scenario , a possible mechanism for lipophorin receptors to promote the uptake of neutral lipids would be by stabilizing lipophorins at the extracellular matrix . We tested this hypothesis by examining the extracellular distribution of lipophorins in Df ( 3R ) lpr1/2 imaginal discs that overexpressed UAS-lpr2E in the posterior compartment . We observed increased accumulation of extracellular lipophorins at the basolateral membranes of cells expressing Lpr2E ( Figure 7A , 7B , arrows ) . In contrast , similar expression of the lipid uptake-defective Lpr2F isoform had no visible effect on the extracellular distribution of lipophorin ( Figure 7C , 7D ) . Similar results were obtained overexpressing UAS-lpr2E and UAS-lpr2F with additional gal4 drivers ( Figure S7 ) .
During vitellogenesis , the nurse cells and the oocyte grow rapidly accumulating large amounts of yolk proteins and lipids from the hemolymph over approximately 18 hours [21] , [54] . Work from the Mahowald lab has shown that Yolkless , an LDLR family protein , mediates the endocytic uptake of yolk proteins in Drosophila [22] , [23] . Here we demonstrate that a different receptor type , the lipophorin receptor , is essential for the uptake of neutral lipids during vitellogenesis ( Figure 2 ) . This is clearly shown in Df ( 3R ) lpr2 females and in double mutant lpr1− , lpr2−germ-line clones . In both cases , the mutant egg chambers accumulate low levels of neutral lipids ( Figure 2J , 2M and Figure S3A ) . In addition to impaired lipid uptake during vitellogenesis , we observed a second phenotype in Df ( 3R ) lpr1/2 double mutant females , where most of the egg chambers degenerated at mid-oogenesis ( Figure 2O and Figure S3B ) . A simple explanation for this phenotype would be that degeneration was triggered by the low lipid content of Df ( 3R ) lpr1/2 egg chambers . In fact , it is known that multiple challenges like starvation , extreme temperatures or chemical treatments , trigger a mid-oogenesis checkpoint and induce apoptosis at this stage [24] . Significantly , flies with a mutation in the gene midway , which encodes an acyl coenzyme A: diacylglycerol acyltransferase required for the synthesis of TAG , were described to have severely reduced levels of neutral lipids in the germ-line and displayed apoptosis at mid oogenesis , thus paralleling the Df ( 3R ) lpr1/2 phenotype [55] . However , it was difficult to fully attribute degeneration to low lipid levels as we observed some experimental conditions which resulted in egg chambers with very low levels of neutral lipids but that did not undergo degeneration . In particular , in Df ( 3R ) lpr1/2 germ-line clones degeneration was absent even though the neutral lipid content of the egg chambers was low ( Figure 2M ) . Similarly , expression of UAS-lpr1J exclusively in the follicle cells of Df ( 3R ) lpr1/2 females abolished egg chamber degeneration even though neutral lipid accumulation in the nurse cells and oocytes was low ( Figure 6E and not shown ) . These experiments suggest that the lipophorin receptors might have an additional function in the follicle cells which is necessary to avoid egg chamber degeneration . Accordingly , we detected lpr1 expression in the follicular epithelium ( Figure 2A , inset ) . In this direction , it has recently been described that blocking the nutrient sensing TOR pathway in follicle cells induced apoptosis at mid oogenesis [56] . Thus , Lpr1 could be required to maintain elevated levels of TOR activity in follicle cells . In interpreting these results , we should also consider the non-autonomous effects of lipophorin receptors . We have shown that expression of UAS-lpr2E exclusively in the oocyte and nurse cells increases lipid uptake in the follicle cells ( Figure 6D ) , which could potentially impact on their nutritional status and restore their putative anti-apoptotic activity . Conversely , expression of the transgene in the follicle cells might slightly increase lipid uptake by the oocyte and nurse cells , even though we have been unable to detect this effect ( Figure 6E ) , and provide enough lipids to bypass the mid-oogenesis checkpoint . More studies will be required to assess the role of the lipophorin receptors in the follicular epithelium . Drosophila Lpr1 and Lpr2 are bona fide members of the LDLR family , sharing a similar organization of proteins domains with the human LDLR , ApoER2 and VLDLR . The human LDLR is the archetypical endocytic receptor . It is expressed in the liver where it mediates the endocytosis of cholesterol-rich LDL , regulating LDL concentration in serum . Endocytosis of LDL results in the catabolic processing of both , the lipidic and proteic moieties of LDL in lysosomes [13] . Other members of the LDLR family are also well known endocytic receptors with a broad variety of ligands [28] , [57] . Drosophila lipophorin receptors can also mediate endocytosis of their ligands . It has recently been reported that Lpr1 is expressed in garland cells and pericardial athrocytes where it is critical for the endocytic clearance of serpin/protease complexes from the hemolymph , thus regulating the innate immune response [45] . Overexpression of Lpr1 and Lpr2 in imaginal discs also induced the endocytosis of lipophorins , which colocalized with endocytic markers [11] , [27] . Similarly , the locust lipophorin receptor mediated lipophorin endocytosis in the fat body and in cell culture [14]–[16] . Despite this well documented endocytic activity of LDLRs , our data demonstrates that neutral lipid uptake mediated by Drosophila lipophorin receptors does not require the endocytosis of lipophorin particles . Three lines of evidence support this conclusion: ( 1 ) Blocking endocytosis did not affect lipid uptake in the egg chambers ( Figure 6G and Figure S6 ) ; ( 2 ) overexpression of Lpr2E in groups of imaginal disc cells induced lipid uptake both in cells expressing the receptor and in a 1–2 cell diameter region of adjacent cells ( Figure 6A , 6B , and Figure S5C , S5D ) and ( 3 ) expression of Lpr2E in the oocyte and nurse cells promoted lipid uptake in the adjacent , somatic follicular epithelium ( Figure 6D ) . Our results also indicate that Lpr2E is able to locally increase the concentration of lipophorins in the extracellular space ( Figure 7A , 7B and Figure S7B , S7D , S7F ) . Taking into account this data , we propose the following model for lipophorin receptor-mediated neutral lipid uptake: lipophorin receptors interact with lipophorins at the cell surface and promote the extracellular hydrolysis of their DAG core by facilitating the activity of an as-yet-unidentified lipase , associated with the extracellular matrix . The free fatty acids generated during DAG hydrolysis could diffuse a few cell diameters away before being captured by cells , explaining why lipophorin receptors can promote lipid uptake non-autonomously . Significantly , physiological data obtained from studies of flight muscles and oocytes in insects indicated that lipid uptake mostly occurs without the concomitant degradation of the apolipophorin ( for reviews , see [54] , [58] ) , which is consistent with our hypothesis . Moreover , a lipophorin-specific lipase activity associated with muscle and oocyte cell membranes was detected [54] , [58] . Our model offers a possible explanation to understand why only a subset of lpr1 and lpr2 isoforms mediates lipid uptake , whereby only the lipid-uptake promoting isoforms can stabilize lipophorins in the extracellular matrix ( Figure 7 and Figure S7 ) . Alternatively , if lipophorin receptors must interact with both , a lipophorin particle and a lipase to generate a ternary complex and facilitate lipolysis , then the lipid uptake-defective isoforms might lack the ability to interact with the lipase . Identification of such putative lipase ( s ) will be necessary to test this hypothesis . The proposed model displays a number of resemblances to the lipolytic processing of triglyceride-rich lipoproteins in the microvascular endothelium of adipose tissue , heart and striated muscles in mammals . Circulating triglyceride-rich lipoproteins , chylomicrons from the intestine and VLDL synthesized by the liver , reach the capillary endothelium where they interact with lipoprotein lipase at the luminal surface . Lipoprotein lipase is essential for the lipolytic processing of chylomicrons and VLDL , generating non-esterified fatty acids from the TAG fraction of lipoproteins . The free fatty acids are then transported to the underlying adipocytes and myocytes by specific transporters such as CD-36 . Once inside these cells they are re-esterified into newly synthesized TAG stores or enter the β-oxidation cycle ( for a recent review , see [59] ) . Recent data indicated that the extracellular lipolysis of TAG-rich lipoproteins is strongly potentiated by the endothelial protein GPIHBP1 . This protein is essential for the transcytosis of lipoprotein lipase from the basolateral to the apical capillary endothelial surface [60] . In addition , it has been suggested that it may facilitate lipolysis by simultaneously interacting with lipoprotein lipase and chylomicrons in the luminal surface of capillaries , providing a molecular platform for lipolysis to occur [61] . In agreement with this essential functions , Gpihbp1-deficient mice manifested severe hyperchylomicronemia [61] . The VLDLR , which is also expressed at the capillary endothelium , seems to participate in the lipolytic processing of TAG-rich lipoproteins in similar ways . The VLDLR can mediate the transcytosis of lipoprotein lipase across cultured endothelial cells [62] and interacts with both , lipoprotein lipase and ApoE containing TAG-rich lipoproteins , potentially tethering them to the endothelium surface and thus promoting the action of lipoprotein lipase ( for a review , see [63] ) . These potential functions were supported by the phenotype of vldlr− mice , which showed delayed clearance of TAG-rich lipoproteins after a meal [64] and increased plasma TAG levels under a high fat diet [65] but normal lipoprotein profiles under regular feeding conditions [66] . Unfortunately , these weak phenotypes have hampered the elucidation of the precise roles that VLDLR plays during the processing of TAG-rich lipoproteins in vivo . We propose that in Drosophila , lipophorin receptors have an activity similar to the bridging role proposed for GPIHBP1 and VLDLR in mammals , bringing lipophorins and a putative lipophorin-specific lipase into close contact on the cell surface and promoting in this way the lipolysis of lipophorins . We speculate that during evolution , a protein related to VLDLR had a critical role in promoting the extracellular hydrolysis of lipoproteins . In insects , this function is carried out by the lipophorin receptors whereas in mammals , GPIHBP1 appears to have taken most of this function , with VLDLR retaining a minor role . Our data supports an ancient function for the LDLR family in promoting the extracellular lipolytic processing of lipoproteins .
Generation of deficiencies in the lpr1-lpr2 genomic region: The three deficiencies used in this work were created by flipase-mediated interchromosomal recombination between the following pairs of FRT-containing transposon insertions: Df ( 3R ) lpr1: P{XP}d03066 and P{XP}d10508 to generate a 19 . 7 Kb deletion; Df ( 3R ) lpr2: PBac{RB}e00374 and PBac{WH}f03030 to generate a 29 . 7 Kb deletion; and Df ( 3R ) lpr1/2: PBac{RB}e00374 and P{XP}d10508 to generate a 49 , 7 Kb deletion [67] , [68] . The deficiency breakpoints were checked by PCR using appropriate primers flanking the predicted breakpoints . To generate Df ( 3R ) lpr1/2 clones in the female germ-line , Df ( 3R ) lpr1/2 was recombined with FRT82B [69] using standard genetic techniques . Females of the genotype: y , w , hs-flp; FRT82B , Df ( 3R ) lpr1/2/FRT82B , ovoD1 were heat-shocked for one hour at 37°C several times during larval stages . The resulting adult females were fed yeast over two days before their ovaries were dissected and processed for immunostaining . To generate rab5 germ-line clones , females of the genotype y , w , hs-flp; rab5 , FRT40A/ovoD1 , FRT40A were similarly treated . The rab5 , FRT40A/CyO stock was obtained from Antoine Guichet [30] . FRT82B , ovoD1 and ovoD1 , FRT40A chromosomes were described in [70] . To quantify egg hatching rates , less than a week old adults of the following genotypes: wild-type Oregon R , w;Df ( 3R ) lpr1/TM6 and w;Df ( 3R ) lpr2/TM6 were kept on abundant yeast paste for two days . Homozygous females were selected and allowed to lay eggs in apple plates for periods of 4 hours . About 200 eggs were individually placed in agar plates and incubated at 25°C for 48 hours . The number of empty egg shells and non-eclosed eggs was quantified . Some of the cDNAs corresponding to lpr1 and lpr2 isoforms used in this work were isolated and sequenced by the Berkeley Drosophila Genome Project [19] . In particular , we used the following full length cDNAs: Lpr1D ( RE14223 ) , Lpr1H ( LD21010 ) , Lpr1J ( RE40649 ) , Lpr2F ( GH26833 ) and Lpr2E ( LD11117 ) . To identify additional isoforms , total RNA was isolated from whole adult male flies and specific tissues -wing imaginal discs , ovaries , adult brain and adult fat body- using Trizol reagent ( Invitrogen ) . 1 µg RNA for each sample was retrotranscribed using random primer and Transcriptor Reverse Transcriptase ( Roche ) . The obtained cDNA libraries were PCR amplified using four oligo pairs which specifically amplified the complete coding regions corresponding to lpr1 and lpr2 genes transcribed from the proximal and distal promoters . The PCR products were directly cloned into pGEM-T ( Promega ) or alternatively , used for a second nested PCR reaction before purification and cloning of the products . 33 isolated cDNAs were genotyped by PCR and selected clones were also verified by sequencing to unambiguously define their specific combination of exons . Signal peptides were predicted using the SignalP 3 . 0 Server [71] and Predisi [20] . RT-PCR was used to examine lpr1 and lpr2 transcription in whole adult flies , larval fat body , wing imaginal discs , ovaries , adult brains and adult fat body . For adult fat body preparation , abdomen carcasses were used which primarily contained fat body . However , contaminating tissues including oenocytes , dorsal vessel , epidermis and muscle were also present in small quantities . Two oligo pairs were used for each gene , which specifically detected exons 2–3 ( distal promoter isoforms; lpr1: attcggcaaatgctgcactgc and tgtgatccttgcagtccgcatc , lpr2: accacccagtcagagttaacaac and tgtggtccgggcaatccgagga ) and exons 4–5 ( proximal promoter isoforms; lpr1: cgaacctctcaaccaaacggat and gccagaacgcgaaaactttgg , lpr2: aagaaacggacgtgtgtgctc and ccaatccgacgactctggag ) . In addition , oligo pairs for the ribosomal protein gene rp49 were used as control ( gaccatccgcccagcatacaggc and gagaacgcaggcgaccgttgg ) . The oligos were designed to amplify a region encompassing an intron to distinguish cDNA products from genomic DNA products . A rough estimate of relative expression levels in the different tissues was obtained by comparing the PCR products at 20 and 30 cycles to avoid reaching an amplification plateau . To generate α-Lpr1 and α-Lpr2 antibodies , DNA fragments containing the complete Lpr1 or Lpr2 intracellular domains were amplified by PCR from lpr1 cDNA RE38584 and lpr2 cDNA GH26833 . The fidelity of the amplification was checked by sequencing and the fragments were cloned in-frame with the 6xHis tag present in the bacterial expression vector pET14b . Protein expression was induced in E . coli . The Lpr1 fragment was purified under denaturing conditions by immobilized metal affinity chromatography ( IMAC ) and dialyzed to remove urea . The protein precipitated during dialysis and the precipitate was used to immunize guinea pigs . Lpr2 fragment was purified from the soluble fraction by IMAC . Antibodies were raised in guinea pigs by Cocalico biologicals , Inc . Other antibodies used were: rabbit anti-apolipophorin 1∶500 [10]; rat anti-HA 1∶500 ( Roche ) ; and the dye DAPI to label nuclei . For immunostaining , imaginal discs and ovaries were dissected and fixed for 20 minutes with 4% formaldehyde dissolved in PBS ( PP ) at room temperature , followed by a second fixation in PP plus 0 . 1% triton X-100 . Tissues were extensively washed for 1 hour in PBS containing 0 . 3% triton X-100 ( PBT ) and blocked in 1% BSA dissolved in PBT for another hour . After incubation with the primary antibodies overnight at 4°C , tissues were extensively washed three times for a total of one hour in PBT and incubated with the fluorescent secondary antibodies ( alexa-fluor conjugates from Invitrogen ) for two hours ( imaginal discs ) or overnight ( ovaries ) . After washing , tissues were mounted in vectashield media . To detect extracellular lipophorins in imaginal discs , we used an extracellular staining protocol modified from [72] . Imaginal discs were dissected and accumulated in Sf-900 cell culture media ( Invitrogen ) on ice . They were incubated with α-Lipophorin antibody diluted 1∶100 in cell culture media for 30 minutes with constant rocking and washed four times for a total of 30 minutes with PBS . Incubations and washes were done at 4°C to inhibit antibody endocytosis . Imaginal discs were then fixed with PP and from this point on processed following the standard immunostaining procedure . Neutral lipids in imaginal discs and ovaries were visualized by nile red staining . Fixed tissues were incubated with 0 . 002% nile red diluted in PBT for 60 minutes and washed for 10 minutes in the same buffer without the dye . To prepare embryos for microscopy , we avoided the use of organic solvents that would otherwise extract neutral lipids . Thus , after removing the chorion with bleach , we fixed embryos with a heat treatment of 5 seconds at 90°C and devitelinized them by hand using a sharp needle . The embryos were then incubated with the nile red solution as above . We analyzed the tissue distribution of lpr1 and lpr2 transcription by in situ hybridization using DIG-labeled antisense RNA probes . Some probes were specific to particular isoforms whereas others recognized all isoforms derived from lpr1 or lpr2 . They were prepared from the following DNA fragments: ( 1 ) All lpr1 isoforms: a 594 base pair DNA fragment including 64 base pairs from the C-terminal coding region and part of the 3′UTR of cDNA RE14223 . ( 2 ) All lpr2 isoforms: a 581 base pair fragment including 91 base pairs from the C-terminal coding region of cDNA GH26833 and part of its 3′UTR . ( 3 ) lpr1 isoforms transcribed from the distal promoter: a 413 base pair fragment derived from exons 1 and 2 of cDNA LD21010 . ( 4 ) lpr2 isoforms transcribed from the distal promoter: a 500 base pair fragment derived from exon 2 of cDNA LD11117 . Since the two lpr genes are highly homologous , probes were designed from the most divergent regions to avoid cross-reactivity . Probes 1 and 2 were used in Figure 3E , 3F and probes 3 and 4 in Figure 2A , 2B . The in situ hybridization protocol was carried out according to [73] . To illustrate immunostaining and in situ data , representative images were selected from each experiment after analyzing a minimum of 10 individual imaginal discs or egg chambers from at least two independent experiments . A UAS-lpr1D transgene was made by cloning the complete coding sequence of lpr1 cDNA RE14223 [74] into the pUAST plasmid [75] . UAS-lpr1B , UAS-lpr1J , UAS-lpr2F and UAS-lpr2E transgenes were obtained by first amplifying the complete corresponding coding regions by PCR using the following templates: LD21010 , RE40649 , GH26833 and LD11117 [74] respectively . The resulting fragments were fused to a C-terminal 3xHA tag and transferred to pUAST attB [76] to obtain transgenic flies by the integrase phiC31 based system [76] . This method allows for the integration of the transgenes into the same chromosomal location , minimizing positional effects on transcription . All pUAST attB transgenes used were inserted into the 22A landing site [76] . The pUAST or pUAST attB plasmids do not allow expression in the germ-line . Thus , for expression in oocytes and nurse cells we generated UASp-lpr1J , UASp-lpr2F and UASp-lpr2E using the plasmid pUASP [77] . The inserts are identical to UAS-lpr1J , UAS-lpr2F and UAS-lpr2E including the C-terminal 3xHA tag . To generate the lpr2F-lpr2E chimeras , a NotI site was first inserted after Leu30 , located between the signal peptide and the LA-2 domain of lpr2F ( GH26833 ) by directed mutagenesis , generating pAC-lpr2F-NotI . NotI flanked fragments containing LA1 ( from Ser186 to Thr232 ) and LA2 ( from Glu233 to Cys272 ) were generated by PCR using lpr2E cDNA LD11117 as template , subsequently cloned into the NotI site of pAC-lpr2F-NotI and transferred to pUAST attB to generate UAS-lpr2F+LA1 and UAS-lpr2F+LA2 respectively . UAS-lpr2F+LA1+NCN and UAS-lpr2F+NCN were similarly generated by replacing the Asp718 ( located at the transcription start site ) -NotI region of pAC-lpr2F-NotI vector by fragments containing LA1+NCN ( from Met1 to Thr232 ) and NCN ( from Met1 to Ile185 ) regions of lpr2E cDNA LD11117 obtained by PCR and flanked by Asp718 and NotI sites . In all cases , the limits of the protein domains coincided with exon boundaries . All chimeras also contain a C-terminal 3xHA tag . En-Gal4 , obtained from A . Martínez-Arias , was used to drive expression at the posterior compartment of wing imaginal discs , V32-Gal4 , a gift from Daniel St Johnston , to drive expression at the germ-line , and CY2-gal4 [78] to direct expression at the ovarian follicle cells .
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Understanding the complex mechanisms that regulate the storage of caloric surpluses in the form of fat is critical in view of the public health problems caused by the continuous rise of obesity and diabetes . Important advances in the field have been obtained from studies using simple animal models like worms or flies . Here we focus on the molecular mechanisms involved in how cells capture neutral lipids from the extracellular milieu , using the fruit fly Drosophila melanogaster as a model organism . Lipids are transported through the blood or the insect hemolymph as small particles known as lipoproteins . We show that two Drosophila proteins related to the mammalian Low Density Lipoprotein Receptor , Lipophorin Receptor 1 and 2 , are essential for the cellular acquisition of neutral lipids from extracellular lipoproteins . We have found that the endocytic uptake of the lipoprotein particles was not required for this process . Instead , we propose that lipophorin receptors favor the extracellular hydrolysis of lipids contained in lipoproteins , followed by uptake of the released free fatty acids . This process is similar to the extracellular processing of lipoproteins that takes place in the capillaries of mammals , suggesting an ancient role for LDLR–related proteins in the extracellular processing of lipoproteins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"developmental",
"biology/cell",
"differentiation",
"genetics",
"and",
"genomics/gene",
"function",
"cell",
"biology"
] |
2011
|
Drosophila Lipophorin Receptors Mediate the Uptake of Neutral Lipids in Oocytes and Imaginal Disc Cells by an Endocytosis-Independent Mechanism
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Chagas disease caused by Trypanosoma cruzi is a neglected disease that affects about 7 million people in Latin America , recently emerging on other continents due to migration . As infection in mice is characterized by depletion of plasma L-arginine , the effect on infection outcome was tested in mice with or without L-arginine supplementation and treatment with 1400W , a specific inhibitor of inducible nitric oxide synthase ( iNOS ) . We found that levels of L-arginine and citrulline were reduced in the heart and plasma of infected mice , whereas levels of asymmetric dimethylarginine , an endogenous iNOS inhibitor , were higher . Moreover , L-arginine supplementation decreased parasitemia and heart parasite burden , improving clinical score and survival . Nitric oxide production in heart tissue and plasma was increased by L-arginine supplementation , while pharmacological inhibition of iNOS yielded an increase in parasitemia and worse clinical score . Interestingly , electrocardiograms improved in mice supplemented with L-arginine , suggesting that it modulates infection and heart function and is thus a potential biomarker of pathology . More importantly , L-arginine may be useful for treating T . cruzi infection , either alone or in combination with other antiparasitic drugs .
Chagas disease ( or American Trypanosomiasis ) is caused by Trypanosoma cruzi , a protozoan parasite of the Kinetoplastidae family [1] . About 7 million people are affected , with 100 million at risk of infection in 21 Latin American countries . Currently , Chagas disease is considered by the WHO as a neglected tropical disease [2] , though several cases of Chagas disease have been reported outside of Latin America , in countries such as Spain [3] and the USA [4] due to migration . Treatment with antiparasitic drugs is effective during the acute phase , but not in the chronic phase , where it presents many undesirable secondary effects . Chagasic cardiomyopathy is the most common cause of disability in chronically infected patients , and unfortunately , treatment with benznidazole in chronic patients has shown low effectiveness [5]; thus , there is a need to progress towards new therapies and biomarkers of pathology . We have previously described that during T . cruzi infection , there is infiltration by monocytic myeloid-derived suppressor cells ( M-MDSCs ) in cardiac tissue . M-MDSCs are characterized by their expression of arginase 1 ( Arg-1 ) and inducible nitric oxide synthase ( iNOS ) and their ability to suppress T cell proliferation . Remarkably , high levels of Arg-1 expression have been correlated with L-arginine depletion [6] , in agreement with other reports that arginase activity is the main cause of the low availability of L-arginine for nitric oxide ( NO ) production by iNOS [7] . Thus , administration of dietary L-arginine may be beneficial for the host during T . cruzi infection . L-arginine is considered semi-essential in mammals , as dietary supplementation is needed during stressful conditions such as pregnancy , trauma or infection , during which the requirements exceed the production capacity of the organism [8] . L-arginine metabolism is a complex biological process , as it serves as the substrate of several enzymes . Arg-1 catalyzes conversion of L-arginine to L-ornithine which subsequently converts into L-proline , responsible for collagen and polyamine synthesis necessary for cell proliferation . L-arginine is also metabolized by iNOS for the production of citrulline and NO , [9 , 10] which is capable of killing the parasite [11] . In fact , L-arginine enhanced the NO-dependent killing of intracellular T . cruzi in murine peritoneal macrophages [12] . Regulation of iNOS activity is a similarly complex process . Asymmetric dimethylarginine ( ADMA ) is generated by catabolism of proteins with methylated arginine residues [13] , and is an endogenous inhibitor of iNOS [14] . Low levels of L-arginine causes down-regulation of iNOS expression [15] and reduced NO production by substrate competition [16] . Thus , there is cross-regulation of enzymatic activity by the different products of L-arginine metabolism [17] . We previously described that L-arginine supplementation during T . cruzi infection decreases parasite burden in mice [6] . Here we analyzed L-arginine-related metabolites from infected mice , observing increased ADMA and a reduction in citrulline and arginine levels in plasma and heart tissue , which reflects reduced iNOS activity , pointing to all of them as potential biomarkers of pathology . More importantly , L-arginine supplementation was found to increase survival and improve cardiac performance ( assessed by electrocardiography ) in infected mice , suggesting that it may be useful as a treatment , either alone or in combination with antiparasitic drugs .
BALB/c mice ( 6–8 week-old ) were purchased from Harlan-Interfauna Iberica and Charles River Laboratories España , and maintained at the animal facility of the Centro de Biología Molecular Severo Ochoa ( CBMSO , CSIC-UAM , Madrid , Spain ) animal facility . For some experiments , mice were maintained at the Animal Resource Facility of the Centro de Investigaciones en Bioquímica e Inmunología ( CIBICI-CONICET , Córdoba , Argentina ) and at the Instituto Venezolano de Investigaciones Científicas ( IVIC , Caracas , Venezuela ) . In vivo infections were performed with strain Y of T . cruzi as described previously [6 , 18] . Groups of 6 mice were infected by intraperitoneal ( IP ) injection with 2 , 000 blood trypomastigotes per mouse , except when otherwise indicated . Evolution of such an infection is characterized by high parasitemia ( usually with two peaks observed during the second and third weeks post-infection ) , with 100% mortality in BALB/c mice by 30 days post-infection ( d . p . i . ) when 2 , 000 parasites are inoculated per mouse . With a lower inoculum ( 50 parasites/mouse ) , parasitemia is lower and the survival rate is around 60% . Groups of 3–6 non-infected control mice were included in each experiment . Survival was monitored daily and parasitemia levels were checked every 2–3 days by the Brener method [19] . Clinical disease scores were determined by visual evaluation of parameters such as stooped posture , bristly back hair , presence of ventral urine stains and lack of activity , assigning a numerical value from 0 ( minimal symptoms ) to 4 ( maximum symptoms ) . Blood samples were collected periodically and tissue samples were collected after animals were euthanized at the end of the experimental period . When indicated , drinking water was supplemented with fresh L-arginine mono-hydrochloride ( Sigma-Aldrich ) every other day to a final concentration of 3 . 75 mg/ml , and 20 mg/kg of the iNOS-specific inhibitor 1400W ( Sigma-Aldrich ) [20] was administrated daily by IP injection . This study was carried out in strict accordance with the European Commission legislation for the protection of animals used for scientific purposes ( directives 86/609/EEC and 2010/63/EU ) . Mice were maintained under specific pathogen-free conditions at the CBMSO ( CSIC-UAM ) animal facility . The protocol for the treatment of the animals was approved by the “Comité de Ética de Investigación” of the Universidad Autónoma of Madrid , Spain ( permits CEI-14-283 and CEI-47-899 ) . Experiments performed in Argentina followed the recommendations in “The Guide for the Care and Use of Experimental Animals” ( Canadian Council on Animal Care ) . Animal handling and experimental procedures were approved by the Institutional Experimentation Animal Committee of The National University of Córdoba ( permit 388/11 ) , and animals were maintained at the Animal Resource Facility of the CIBICI-CONICET ( NIH-USA assurance number A5802–01 ) . Experiments performed in Venezuela were in strict accordance with “Bioethics and Biosafety Norms” ( 3rd edition ) approved by Fondo Nacional de Ciencia y Tecnología de Venezuela ( FONACIT ) , Ministerio de Ciencia y Tecnología of Venezuela ( 2011 ) , the Asociación Venezolana para la Ciencia de los Animales de Laboratorio , and “International Ethical Standards for Research Biomedical in Animals of the WHO” ( 1982 ) ; animal handling and experimental procedures were approved by the Comité de Bioética Institucional ( permit DIR-0031/1582/2017 ) . Animals had unlimited access to food and water , and at the conclusion of the studies were euthanized in a CO2 chamber with every effort made to minimize their suffering . Concentration of nitrites ( NO2Na ) , indicative of NO production , was measured in plasma and cell culture supernatants using the Griess reagent following the directions of the manufacturer ( Sigma-Aldrich ) . When indicated , ornithine , urea , proline , putrescine , citrulline , ADMA and L-arginine levels were determined in mouse tissue extracts by Metabolon Inc . , and expressed as ScaledImpData as previously described [21] . L-arginine level in plasma was determined after centrifugation at 20 , 800 g to remove protein precipitates , and 5 μl was analyzed using an HPLC chromatograph coupled to a triple quadrupole mass spectrometer ( Varian 1200L; Agilent Technologies ) as previously described [6] . ADMA concentration was determined using the mouse ADMA ELISA kit following the directions of the manufacturer ( Cusabio ) . Protein extracts were prepared from heart tissue perfused with PBS containing 1 IU/ml of heparin , cut into small pieces using a sterile scalpel blade followed by mechanical disruption using a PT1300D homogenizer ( Kinematica Polytron , Fisher Scientific ) in Triton X-100-based protein lysis buffer as previously described [18] . For western blot , 15 or 50 μg of tissue extract was fractionated by SDS polyacrylamide gel electrophoresis and transferred to a nitrocellulose membrane ( Hybond-ECL , Amersham Biosciences ) , stained with Ponceau ( Pierce ) , photographed and blocked in 5% fat-free milk or 5% BSA in 0 . 1% Tween-20 Tris-buffered saline . Membranes were incubated overnight at 4–8°C with a 1:1 , 000 dilution of rabbit anti-iNOS ( sc-50 ) or rabbit anti-Arg-1 ( sc-18354 ) from Santa Cruz Biotechnology . Then , membranes were incubated with horseradish peroxidase-conjugated anti-rabbit IgG ( Thermo Scientific ) , and detection was carried out with Supersignal detection reagent ( Pierce ) followed by photographic film exposure . Pelleted RAW 264 . 7 macrophages were resuspended in complete RPMI 1640 with 5% FBS , with or without 100 μM L-arginine supplement , and infection was done with 10 trypomastigotes per macrophage ( ratio 10:1 ) . After 72 h post-infection ( h . p . i . ) , nitrite levels were determined in culture supernatants as described above , and parasites were quantified by microscopic observation of culture supernatants in a Neubauer chamber at 7 d . p . i . As previously described [22] , hearts were collected from mice at 14 d . p . i . and placed in 10% neutral buffered formalin for at least 4 h at room temperature , followed by incubation in 70% ethanol overnight . Samples were then embedded in paraffin ( Tissue Embedding Station Leica EG1160 ) , and 5 μm tissue sections were prepared ( Microtome Leica RM2155 ) , dewaxed and rehydrated , stained with H&E staining and mounted permanently in Eukitt´s quick-hardening mounting medium ( Biochemika , Fluka analytical ) . Sections were analyzed on a Leica microscope using the 20-40x magnification objectives . Alternatively , hearts from mice were collected and fixed in 4% paraformaldehyde in PBS for 2 h at room temperature , followed by incubation in a 30% sucrose solution at 4°C overnight as described [18] . Tissues were then embedded in Tissue-Tek OCT in Cryomolds ( Sakura ) , frozen in dry ice , stored at -80°C , and 10 μm sections were cut using a cryostat Leica CM1900 . Slides were fixed in acetone for 10 min at room temperature and incubated 10 min with NH4Cl to reduce autofluorescence; nuclei were stained using 1 μg/ml DAPI ( 268298 , Merck ) . Prolong Gold Antifade Reagent ( Invitrogen ) was used to mount the slides that were kept at 4°C until observation . Stained slides were observed with an LSM710 confocal laser scanning microscope , coupled to an AxioimagerM2 microscope ( Zeiss ) . Micrographs were processed using ZEN ( Zeiss ) or Fiji software [23] . In both cases , inflammatory infiltration was estimated in binary images using the Fiji plugin for particle analysis and quantification . Mice were previously anesthetized with a single IP bolus of 25 mg/kg pentobarbital and 25 mg/kg ketamine . Electrocardiography ( ECG ) was performed using a bipolar system in which the electrodes were placed subcutaneously at the xiphoid cartilage ( positive electrode ) , right shoulder ( negative ) , and left shoulder as previously described [24] . Electrodes were connected to a Bioamp amplifier ( AD Instruments , Bella Vista , Australia ) and were digitalized through a PowerLab 8sp A/D converter ( AD Instruments ) . Digital recordings were analyzed with Chart software for Windows v7 . 3 . 1 ( AD Instruments ) , with events registered to 1 K/s and filtered to 60 Hz . Continuous ECG recordings were obtained for determining basal heart rate , defined as the point where there was no variation above 5% . At that point , 5mg/kg nitroglycerin ( NG ) was administered via IP to a group of mice , and subsequently 1 . 1 mg/kg isoproterenol , a non-selective beta adrenergic with positive chronotropic effects , was added IP to control mice and mice supplemented with fresh L-arginine mono-hydrochloride ( 3 . 75 mg/ml; Sigma-Aldrich ) drinking water every other day . The register was followed until the end of the Iso effect evidenced by a decrease of the heart rate . Variation of heart rate and T/S waves with respect to pre-isoproterenol values were determined , and wave morphology was recorded . R and T axis measurements were based on the fact that cardiac depolarization and repolarization spreading constitute a vectorial magnitude . Depending on electrode placement , it is reflected in the ECG as positive or negative deflection or waves; R wave represents the principal vector of left ventricle depolarization and T wave is the electrical reflection of cardiac endocardial to epicardial repolarization vector . Based on that , it is possible to estimate the mean vector between two perpendicular bipolar and unipolar ECG leads ( I vs aVF; II vs aVL; III vs aVR ) , graphing the R and T electrical values and determining the angle relative to the cardiac electrical center . Together , these measurements allow estimation of heart orientation . For in vivo experiments , data are shown as means ± SEM . Significance was evaluated by Student’s t-test when two groups were compared , by One-way ANOVA followed by the Tukey post-test for the analysis of parasitemia , and the Long-Rank ( Mantel-Cox ) test for survival using GraphPad Prism 5 . 00 software ( La Jolla , CA , USA ) .
Infection of BALB/c mice with a lethal dose ( 2 , 000/mouse ) of T . cruzi produced high levels of parasitemia ( Fig 1A ) and increased expression of iNOS and Arg-1 in heart tissue ( Fig 1B ) , consistent with our previous reports [6 , 18] . Urea cycle metabolites such as proline , ornithine , and urea are shown to illustrate collagen and polyamine synthesis during T . cruzi infection , and citrulline as an indirect measure of iNOS activity . At 21 d . p . i . there was a significant decrease in citrulline levels in the plasma and a significant increase in ADMA ( Fig 1C ) , which is known to inhibit iNOS activity [25] . In addition , there was significant plasma L-arginine depletion and a decrease in the L-arginine/ADMA ratio ( Fig 1C ) . Plasma levels of ornithine , urea and proline did not significantly change during infection . Similarly , a significant decrease and increase in citrulline and ADMA , respectively , were seen in heart tissue at 21 d . p . i . ( Fig 1D ) . However , levels of ornithine , urea , proline and putrescine levels also increased ( Fig 1D ) . L-arginine and the L-arginine/ADMA ratio were also decreased in heart tissue upon infection ( Fig 1D ) . To investigate the possible effects of reduced L-arginine levels on parasite replication , RAW 264 . 7 macrophages were infected with T . cruzi , and NO and parasite load were measured with or without L-arginine supplementation in the growth medium . The results showed that extracellular L-arginine is required for both NO production ( Fig 2A ) and intracellular parasite killing ( Fig 2B ) . The above results lead us to investigate the effects of dietary L-arginine supplementation during T . cruzi infection in mice . For this , drinking water was supplemented with L-arginine , and parasitemia , survival , and clinical score were determined periodically through the end of the study at 21 d . p . i . L-arginine supplementation in infected mice caused a significant decrease in parasitemia ( Fig 3A ) , and more strikingly a significant increase in survival ( Fig 3B ) . In accordance with that , clinical scores during the peak of parasitemia ( from 17–27 d . p . i . ) were significantly lower in L-arginine-fed animals ( Fig 3C ) , with a decrease in parasite load in the heart at 21 d . p . i . ( Fig 3D ) . In addition , L-arginine supplementation increased heart inflammation and decreased parasite burden , suggesting an enhanced immune response ( S1 Fig ) . Moreover , in infected mice L-arginine supplementation restored 61 . 8% of basal plasma L-arginine levels compared to 18 . 9% in non-treated controls at 21 d . p . i . ( Fig 4A ) , without affecting the infection-induced increase in plasma ADMA levels ( Fig 4B ) . Furthermore , plasma nitrites ( mean of 0 , 14 and 21 d . p . i . ) were significantly higher in mice supplemented with L-arginine compared to non-treated controls ( Fig 4C ) . To investigate whether L-arginine supplementation had any effect on iNOS activity , we infected mice and treated them with L-arginine , alone or in combination with 1400W , a specific inhibitor of iNOS . This experiment was performed with a sublethal inoculum ( 50 parasites/mouse ) to allow analysis of the beneficial and/or detrimental effects of the various compounds when combined . Despite the low initial dose , parasitemia reached a maximum of about 450 , 000 parasites per ml of blood ( Fig 5A ) . Peak parasitemia ( mean of 9 , 11 , 13 , 15 , 17 and 19 d . p . i . ) and clinical scores ( mean of 18 , 19 and 21 d . p . i . ) were higher in mice treated with 1400W than in other groups ( Fig 5B and 5C , respectively ) . In addition , L-arginine supplementation was able to partially prevent the detrimental effect of iNOS inhibition by 1400W ( Fig 5B and 5C ) . Finally , plasma nitrites were significantly higher at 21 d . p . i . in L-arginine-treated mice , an effect prevented by addition of 1400W ( Fig 5D ) . As cardiac disturbances are a hallmark of Chagas disease [1] , the effects of L-arginine on cardiac function under metabolic stress conditions were analyzed . At 14 d . p . i . , ECGs of infected mice were measured before ( Pre-Iso ) and after administration of isoproterenol ( Iso ) , which increases the cardiac rate by binding to β1 adrenergic receptors that induce metabolic stress by reduction of coronary flux [26] . In addition , immediately before to Iso administration , we supplemented with a single bolus of NG , an NO donor . ECG measurements showed that the amplitude of the S wave in infected mice after Iso treatment changed dramatically , but not in those continuously supplemented with L-arginine ( Fig 6A and S1 Video ) . Fig 6B shows the plot of mean ΔS amplitude ( Iso S wave amplitude minus pre-Iso wave amplitude ) of an ST segment selected five minutes after Iso treatment in infected control and L-arginine-treated mice . Fig 6C shows plots of ΔHeart rate [beats per minute ( bpm ) in Iso minus bpm in Pre-Iso] versus ΔS amplitude in six experimental groups ( Control , NG and L-arginine both in non-infected and infected mice ) . Greater ΔS amplitude variations were observed in infected mice compared to the non-infected ones ( Fig 6C , left panels ) . Moreover , in infected mice , the decrease in ΔS amplitude caused by Iso administration was partially reversed by NG , though it increased ΔHeart rate ( Fig 6C , middle panels ) , a symptom of hypotension [27] . In contrast , infected mice , but not non-infected ones , showed decreased ΔS amplitude in response to L-arginine supplementation , as well as decreased ΔHeart rate ( Fig 6C , right panels ) . Infection induced lateralization ( changes in heart orientation ) at 14 d . p . i . , reflected by increased R and T axis angles ( Fig 7A and 7C ) , which was prevented after L-arginine supplementation . In addition , heart perimeter and septum thickness were significantly decreased after L-arginine supplementation ( Fig 7B and 7C ) .
Many physiologic and pathophysiologic processes are modulated by arginine availability [17] . We previously found elevated levels of Arg-1 in acute T . cruzi infection , associated with plasma L-arginine depletion [18] . Thus , it was hypothesized that L-arginine supplementation could be beneficial for infected mice . The results shown here indicate that there was an improvement in the disease outcome , evidenced by decreased parasite burden , higher survival , lower clinical scores and improved cardiac performance . Moreover , they suggest that L-arginine supplementation may be useful , either alone or in combination with other drugs for antiparasitic therapy . The observed beneficial effects of L-arginine supplementation may explain previous reports that it prevented vertical transmission in a rat model [28] . Although our study was done with a unique strain of the parasite , it is highly virulent and with cardiac tropism , characteristics that give further strength to our results . In addition , we very recently described the existence of common pathophysiologic patterns linked to clinical outcome of Chagas disease , conserved among the genetically diverse infecting strains , which suggests that our approach could be valid [29] . Levels of iNOS expression have been associated with parasite control , since NO is toxic for parasites , both extracellular and inside macrophages [11] . However , data from iNOS-deficient mice is controversial in the context of T . cruzi infection [30 , 31] . Increased levels of iNOS expression were observed in the heart tissue of infected mice compared to non-infected mice , which correlates with a slight increase of NO in the plasma . Our results showed L-arginine depletion and high levels of ADMA after T . cruzi infection , pointing to them as potential biomarkers of pathology . In agreement , we found lower levels of citrulline ( suggesting lower iNOS activity ) during infection both in plasma and heart tissue . Lower L-arginine and higher ADMA may constrain iNOS activity , leading to insufficient NO production which is required for the control of parasite replication [11] . Thus , iNOS inhibition by metabolites such as ADMA could partially explain the conflicting results in the field , since iNOS expression might not always directly correlate with NO production , especially in highly virulent infections such as in this study . Moreover , the heart levels of L-arginine were decreased and associated with an increase in ornithine , proline , putrescine and urea , likely indicating an activation of the polyamine pathway that may lead to pathological fibrosis and cardiac remodeling [32] . Altogether , our results suggest that NO levels in infected control mice did not increase enough to control parasite replication . Moreover , the L-arginine/ADMA ratio was greatly reduced during infection , likely contributing to pathology . Interestingly , a low L-arginine/ADMA ratio is also an indicator of vascular and cardiac alteration , and has been described as a predictor of NO bioavailability and mortality in dilated cardiomyopathy [33] , a disease with some similarities with Chagasic cardiomyopathy . One possible mechanism by which infection modulates intracellular and plasma ADMA levels may be increased protein degradation by parasite proteases . This would increase intracellular ADMA levels that could reach the extracellular milieu through cationic amino acid transporters ( CATs ) , which are differentially expressed in tissues . CATs can also allow entry of ADMA into distant cells and tissues ( reviewed in [34] ) . In T . cruzi infection , we have reported increased expression of CAT-1 and CAT-2b in heart tissue , likely expressed by infiltrating cells , in particular MDSCs [18] . CAT-1 and CAT-2b should allow cellular entry and exit of L-arginine and ADMA . In the endothelium , vasodilation can be stimulated by arginine ( arginine paradox ) , which may be explained if eNOS ( endothelial nitric oxide synthase isoform ) activity is inhibited by ADMA , and relieved when the L-arg/ADMA ratio increases [35] . Thus , it is likely plasma ADMA rather than intracellular ADMA that determines NOS inhibition . Macrophages are thought to be the most important effector cells in eliminating T . cruzi parasites , via a NO-mediated killing process [11] . Our results show that NO production in infected macrophages is strongly dependent on extracellular L-arginine , as described in other infections [36] . More interestingly , NO produced by enzymatic conversion of L-arginine by iNOS was needed for the elimination of parasites , indicating a crucial role of extracellular L-arginine for parasite killing , as previously described [12] . We also showed that dietary L-arginine supplementation significantly decreased parasitemia in infected mice , but more importantly reduced clinical scores by more than 80% while preventing death in 80% of the infected mice . This effect was associated with recovery of basal levels of L-arginine in the plasma compared to unsupplemented mice , and to an increase of NO production that in turn allows more efficient parasite killing . Additionally , there was increased heart inflammation after L-arginine supplementation that likely contributed to lower parasite burden . This indicates a notable high beneficial effect of L-arginine supplementation in the outcome of the infection . Since extracellular L-arginine levels clearly impacted iNOS-derived NO , we treated mice with the specific iNOS inhibitor 1400W , finding that iNOS inhibition significantly increased parasitemia and clinical score with respect to L-arginine supplemented mice . Despite this , there were no significant changes in NO production after 1400W treatment , though the combination of L-arginine slightly increased NO production compared to treatment with 1400W alone . This suggests NO production contributes to the beneficial effect mediated by L-arginine supplementation during acute T . cruzi infection . Cardiac disturbances are a hallmark of Chagas disease [1] , and the severe alterations of heart function evidenced by ECG records that are associated with the risk of sudden death [37] are very frequent in Chagas disease . ECG findings suggest that L-arginine supplementation also exerts a cardioprotective effect during T . cruzi infection . It has been described that pre-treatment with L-arginine can attenuate cardiac hypertrophy through regulation of key enzymes of the polyamine and NO production pathways [38] , which were found to be altered in T . cruzi infection . Of note , L-arginine treatment has also been shown to improve isoproterenol-impaired basal left ventricular systolic function , likely mediated by NO production [39] . Also , morphological heart parameters significantly normalized after L-arginine supplementation in infected mice , leading to prevention of heart lateralization . There was also lower ΔS amplitude in infected compared to non-infected mice , and was dramatically decreased in mice infected and supplemented with L-arginine , suggesting an improvement in heart perfusion . T . cruzi may well be affected by L-arginine supplementation , though unlike other kinetoplastids it is unable to utilize L-arginine for proliferation , and is insensitive to ornithine decarboxylase ( ODC ) inhibitors such as DFMO [40 , 41] because it lacks ODC [42] and therefore cannot synthesize putrescine for proliferation . Instead , T . cruzi is dependent on polyamine uptake for growth and survival [42 , 43]; thus , it stands to reason that L-arginine supplementation could enhance polyamine synthesis , which could be taken up by the parasite , increasing its proliferation . However , this is not the case as there is inhibition of parasite burden , indicating that L-arginine supplementation is primarily used by iNOS for NO production . In summary , our results suggest that decreased levels of L-arginine and the presence of ADMA in plasma and tissues of infected hosts may be indicative of the severity of acute T . cruzi infection , and therefore are putative candidates for biomarkers of pathology . More importantly , our findings suggest that dietary supplementation with L-arginine in infected hosts , either alone or in combination with other antiparasitic drugs , may be useful for fighting infection , partially overcoming iNOS inhibition and allowing more efficient parasite killing by NO , while improving cardiac output , leading to increased survival and better clinical outcomes .
|
Trypanosoma cruzi is the causative agent of the neglected Chagas disease in humans . During infection in mice , depletion of plasma L-arginine is correlated with mortality . L-arginine is a semi-essential amino acid needed for cell proliferation , and is the substrate of arginase 1 ( Arg-1 ) and inducible nitric oxide synthase ( iNOS ) , which is involved in the immune response against infections . Observed L-arginine depletion is likely caused by increased Arg-1 activity , but the effect on immune response are still unknown . Our hypothesis is that L-arginine depletion may block nitric oxide ( NO ) production by iNOS , which is needed for parasite killing . To test this hypothesis , mice were supplemented with and without L-arginine , and the differential effect of treatment with an iNOS inhibitor was determined . L-arginine supplement was beneficial to the mice , lowering mortality and improving disease outcome and heart function . The beneficial effect was associated with increased levels of NO , thus low levels of L-arginine and NO are considered candidate markers of pathology . Finally , as L-arginine is a common dietary supplement , it may be useful for treatment of Chagas patients , either alone or in combination with antiparasitic drugs .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2018
|
L-arginine supplementation reduces mortality and improves disease outcome in mice infected with Trypanosoma cruzi
|
One of the simplest organisms to divide asymmetrically is the bacterium Caulobacter crescentus . The DivL pseudo-histidine kinase , positioned at one cell pole , regulates cell-fate by controlling the activation of the global transcription factor CtrA via an interaction with the response regulator ( RR ) DivK . DivL uniquely contains a tyrosine at the histidine phosphorylation site , and can achieve these regulatory functions in vivo without kinase activity . Determination of the DivL crystal structure and biochemical analysis of wild-type and site-specific DivL mutants revealed that the DivL PAS domains regulate binding specificity for DivK∼P over DivK , which is modulated by an allosteric intramolecular interaction between adjacent domains . We discovered that DivL's catalytic domains have been repurposed as a phosphospecific RR input sensor , thereby reversing the flow of information observed in conventional histidine kinase ( HK ) -RR systems and coupling a complex network of signaling proteins for cell-fate regulation .
Cell fate decisions driven by the spatial organization of signaling proteins are a hallmark of asymmetric divisions in systems as diverse as stem cells and bacteria . Caulobacter , a model system for localization-dependent signaling network studies , divides asymmetrically yielding two distinct daughter cells: a motile swarmer cell and a replication-competent stalked cell ( Figure 1 ) . Cell division in Caulobacter is a stepwise process that includes a cytoplasmic compartmentalization stage [1] during which the inner membrane closes to form a barrier between the incipient swarmer and stalked cell compartments ( Figure 1A ) [2] , [3] . This event segregates crucial signaling proteins [2] , [4] , [5] and culminates in the swarmer cell-specific activation of the global transcriptional regulator CtrA ( Figure 1B ) [6] , which controls the expression of more than 100 genes involved in cell division and asymmetric polar organelle development [7] . The two daughter cells are distinguished by a compartment-sensing module composed of the response regulator ( RR ) DivK and two histidine kinases ( HKs ) , DivJ and PleC , which differentially regulate the CtrA signaling pathway upon compartmentalization . DivJ and PleC respectively occupy the old and new cell poles ( Figure 1A ) [4] , [5] . Prior to cell division , the cytoplasm contains a homogeneous mixture of phosphorylated ( DivK∼P ) and unphosphorylated DivK [8] . In pre-divisional cells , however , PleC ( positioned at the new pole ) functions as a DivK phosphatase , while DivJ ( positioned at the old pole ) functions as a DivK kinase [4] , [5] , [9] . In this way , compartmentalization promotes accumulation of unphosphorylated DivK in the incipient swarmer cell and DivK∼P in the incipient stalked cell [10] , [11] , providing unique cell-type markers . In Caulobacter , the DivK phosphorylation state provides the basis for a regulatory switch that underlies divergent cell-fates upon cell division [12]–[15] . This switch is achieved through the activity of the pseudokinase DivL , which activates the CckA-ChpT-CtrA phosphorelay pathway in the uncomplexed state and inhibits this phosphorelay when bound to DivK∼P ( Figure 1B ) [13] . Previous in vitro work has shown that DivL has a binding preference for DivK∼P over unphosphorylated DivK , and genetic evidence suggests that the DivL-DivK∼P interaction leads to inhibition of the hybrid-HK CckA activity [13] . Additionally , DivL appears to play a role in the regulation of CckA polar localization and autokinase activity [13] , [16] . Thus , DivL functions as a critical link that couples two distinct signaling pathways ( Figure 1B ) . DivL has a predicted domain architecture that is typical of many HKs , consisting of multiple PAS domains fused to a C-terminal HK module that contains dimerization and histidine phosphotransfer ( DHp ) and catalytic and ATP-binding ( CA ) domains ( Figure 1C ) . In contrast to typical HKs , a conserved tyrosine ( Tyr550 ) appears in place of the predicted phosphorylatable histidine throughout DivL homologs in α-proteobacteria [17] , with the exception of the Magnetospirillum magneticum DivL that contains a non-phosphorylatable alanine ( Figure 1C ) . Furthermore , DivL's essential functions in vivo do not require a phosphorylated DHp domain [12] , [13] , [16] , and the CA domain is dispensable for activity [12] , [18] . Divergent α-proteobacteria that lack a DivK ortholog also frequently lack the catalytic domains ( DHp-CA ) of their DivL orthologs ( Figure 1C ) [19] . The co-evolution of DivK with the DHp-CA domains of DivL suggests that DivL and DivK may work together as a regulatory module within the cell-cycle [19] . Despite the apparent connection between DivK and DivL in cell fate signaling , the molecular nature of the interaction between this unconventional HK-RR pair is poorly understood . To gain insight into this signaling system , we solved the crystal structure of the HK region of DivL at 2 . 5 Å resolution and interrogated the interaction between DivL and both DivK and DivK∼P . Our results show that Caulobacter has repurposed a conserved HK as a highly specific sensor module for a phosphorylated RR∼P .
Previous experiments [13] , [14] , and our results obtained from in vivo co-immunoprecipitation analysis ( Figure S1 ) all support an interaction between DivL and DivK . We employed a fluorescence polarization assay [20] to measure the binding affinities between DivL and DivK , as well as between DivK and other HKs in the compartment sensing signaling modules ( Figure 1B , PleC and DivJ ) and the differentiation module ( Figures 1B , CckA and the ChpT histidine phosphotransfer protein , and 2 ) . We fluorescently labeled DivK with a BODIPY dye and measured binding by mixing 10 µM of HK with 250 nM of labeled DivK or DivK∼P ( see Materials and Methods ) . As shown in Figures 2 and S1 , DivJ bound with similar affinity to both phosphorylated ( Kd = 16±3 . 2 µM ) and unphosphorylated ( Kd = 8±2 . 1 µM ) forms of DivK , whereas PleC preferentially bound to DivK∼P ( Kd = 2 . 4±0 . 9 µM ) over DivK ( Kd = 26±30 µM ) . DivL ( 152–769 ) bound exclusively to DivK∼P with an apparent Kd of 11±4 . 2 µM , and very limited binding to unphosphorylated DivK ( Figure S1 ) . No binding of DivK to CckA or ChpT was observed regardless of the DivK phosphorylation state , even when the RR was present at concentrations as high as 100 µM . Thus , DivK or DivK∼P binds specifically to DivJ , PleC , and DivL with affinities within an order of magnitude of values reported for canonical HK-RR pairs ( ∼1 µM ) , while non-cognate pairs have binding affinities greater than ∼75 µM [21] . Thus , DivK impacts the CckA-ChpT-CtrA pathway without directly interacting with this set of signaling proteins . To define the structural basis for the phosphorylation-dependent binding of DivK to DivL , we determined the crystal structure of DivL's HK region ( residues 523–769 , Figures 1C and 3 ) by the multiple-wavelength anomalous diffraction ( MAD ) method using a gold derivative . The final model ( PDB code 4q20 ) was refined to an Rcryst of 20 . 2% and an Rfree of 23 . 2% using native data up to 2 . 5 Å resolution ( Table S2 ) . The model of DivL displays good geometry with an overall quality score that ranks in the 100th percentile compared to other structures with similar resolution , as calculated by MolProbity [22] . The asymmetric unit contains a DivL dimer ( Figure 3A ) , which is consistent with size exclusion chromatography showing dimeric DivL in solution ( observed MW = 50±15 kDa , predicted dimer MW = 47 . 3 kDa ) . Overall , the DivL structure resembles other characterized HKs , such as the Thermotoga maritima HKs: HK853 [23] , [24] and ThkA [25] , although the DivL dimer is asymmetric . Asymmetric dimers were also observed in several recent HK structures ( additional discussion included in Text S1 ) [26]–[29] . The dimer interface buries ∼3 , 900 Å2 total surface area . Each DivL monomer ( DivL-A and DivL-B ) consists of a DHp domain with two helices , α1 and α2 ( residue range 526–608; since the α1–α2 loop is disordered in the crystal structure we assigned the connectivity of the two DHp helices in each monomer as in other class I HKs [23] ) , and a C-terminal CA domain with a highly conserved fold ( residue range 609–758 ) . The functionally important residue Tyr550 ( colored in yellow in Figure 3A and 3B ) is located at the middle of the first DHp helix ( α1 ) . Except for the substitution of a normally phosphorylatable histidine with tyrosine , DivL appears to retain all of the characteristic HK motifs , such as H , N , G1 , F , and G2 boxes ( Figure S2 ) . A highly conserved patch of residues located near Tyr550 was revealed by mapping the degree of sequence conservation of DivL orthologs onto the DivL structure ( Figure S2 ) . We note with interest that this surface is typically involved in the RR recognition [23] , [30] . The CA domains of the DivL dimer are almost identical ( RMSD of 0 . 64 Å for 144 Cα atoms ) . However , the DHp domains display significant structural differences ( RMSD of 3 . 4 Å for 80 Cα atoms ) , indicating that the DivL dimer is asymmetric . The conformational differences between two DivL monomers are best described as rigid-body movements of substructures ( Figure 3B–3D ) . Three rigid substructures can be identified in each monomer ( Figure 3B ) , with each substructure representing a functional module . The largest substructure ( residue range 591–758 ) consists of the CA domain and the “output helix , ” which contains the C-terminal portion of α2 ( α2C ) . The interface between α2C and CA is stabilized by clusters of highly conserved hydrophobic residues ( Figure S3 ) . A highly conserved CA domain and a very similar arrangement between α2C and CA can be identified in DivL , HK853 , ThkA , and QseC ( Figure S3 ) , suggesting that this structural arrangement is a conserved HK feature . The second substructure ( “RR docking module” ) consists of the C-terminal portion of α1 ( α1C ) and the N-terminal portion of α2 ( α2N ) ( Figure 3B ) ; the equivalent region in HK853 is involved in its cognate RR recognition . [23] . The third substructure ( “input helix” ) consists of the first portion of α1 ( α1N ) , which is connected to the N-terminal PAS sensor domains . When two DivL monomers are superimposed about the RR docking module , the relative movements between the other two substructures become apparent ( Figure 3C and 3D ) . The input helix in DivL-B is kinked more significantly around Tyr550 compared to DivL-A , resulting in a conformational change with respect to the Tyr550 side chain . This bending of the DHp helices is a common feature in many HKs [23] , [26] , [31] , [32] , due to the conserved residue Pro555 [32] . The “output helix+CA” module interacts and moves in tandem with the input helix , with a pivot point at the ATP binding site ( proximal to Leu722 ) . As a result , the bending of the input helix repositions the output+CA module with respect to the RR docking module , which may reflect HK conformational changes crucial for propagating long-range signals from the sensor domain to the catalytic domain or vise visa ( Movies S1 and S2 illustrates the “morphing” between the two DivL conformations ) and illustrated as a cartoon in Figure 3D . The structural movements produce an additional contact interface between DHp and CA in DivL-A , resulting in an increase in the buried surface area ( 1 , 730 Å2 total ) compared to that of DivL-B ( 1 , 400 Å2 total ) ( Figure S3 ) . The asymmetry of the DivL dimer likely reflects some inherent conformational flexibility of DivL in solution , even though it could be induced by the crystal packing . The temperature factor ( i . e . , B-values ) distribution supports the assertion that the DivL structure is highly flexible , save for the central four-helix bundle near Tyr550 where there is less conformational freedom ( Figure S3 ) . To build a computational model of the DivL-DivK∼P interaction , we compared the primary sequence of these proteins to the T . maritima HK-RR co-crystal structure of HK853-RR468 [23] . DivL's HK region shares 27% sequence identity with HK853 , while DivK shares 36% sequence identity with RR468 , the cognate RR for HK853 . Strikingly , the surface of HK853 that directly interacts with RR468 is highly conserved in DivL , suggesting that the interaction between DivL and DivK is comparable to that observed between HK853 and RR468 . Hence , we modeled a DivL-DivK∼P complex based on the HK853-RR468 co-complex ( Figures 4A and S4 ) . The resulting model places complementary surfaces of DivL and DivK∼P together with favorable interactions , supporting the model's feasibility . In this model , Leu565 and Gly561of DivL interact with a hydrophobic patch on the DivK∼P surface formed by Leu17 , Leu21 , Pro106 , and Ile107 . Furthermore , Ala582 of DivL makes an additional hydrophobic contact with Leu13 of DivK∼P . Potential hydrogen bonds between DivL and DivK are also predicted , such as Tyr562OH with Leu13O and Leu17N , Arg553N1 and Ala84O , and His579Nε2 with Asp20Oδ2 . To interrogate the predicted interface , we introduced targeted mutations ( R553A , T557N , Y562A , H579E , and Y550H ) within DivL at the putative DivK∼P docking site ( Figure 4A , highlighted in green ) and measured the effect of each mutation on the DivL-DivK∼P interaction . Each mutant was selected based upon introducing a significant side-chain perturbation , with the exception of T557N , which was selected based upon the presence of Asn ( N ) at this position in the DivJ kinase sequence . Each of these mutant proteins was purified and subjected to gel-filtration , which indicated that each variant formed dimers in solution . Four mutations ( R553A , T557N , Y562A , and H579E ) resulted in significant loss of DivK∼P binding ( Figure 4B ) , with apparent dissociation constants measured to be 8–15-fold less than observed for wild-type DivL ( Figure S5 ) , highlighting the importance of these residues for binding of DivK∼P to DivL and supporting the validity of the DivL-DivK∼P computational model . The Y550H substitution did not reduce the affinity of DivL for DivK∼P . Intriguingly , T557N did not improve DivL's binding affinity for unphosphorylated DivK and instead diminished affinity for both forms of DivK . This indicates that DivL may bind DivK in a slightly different manner than DivJ . We also did not discover any mutations within this binding surface that switched binding specificity from DivL-DivK∼P to DivL-DivK . Due to the asymmetric nature of the DivL dimer , Tyr550 of the DivL-A subunit ( green in Figure 4A ) is situated above the canonical phosphotransfer site ( where the hydroxyl group may form hydrogen bond[s] with the phosphoryl group on Asp53 of DivK∼P ) , while in DivL-B ( cyan in Figure 4A ) , both Tyr550 and the CA domain are displaced from the DivK active site ( see Figure S4 ) . This observation implies that conformational changes in DivL may impact affinity for DivK∼P . As a result , the asymmetric DivL dimer presents two different RR binding sites that share a common core surface but vary in the surface aspect involving Tyr550 and the ATP lid . DivL exhibits extensive structural homology with the PAS-linked HK ThkA [25] , and comparative analysis suggests that a conserved surface-exposed patch on the DivL CA domain ( Figure S2 ) could participate in a similar PAS domain interaction as observed in the ThkA structure . Because a PAS-CA interaction potentially restricts CA domain movement , we analyzed binding of DivK to DivL in the presence and absence of one or more DivL PAS domains to determine whether they influence the interaction between DivK and DivL . For these studies , we constructed DivL mutants containing 3 , 2 , 1 , or 0 PAS domains and measured DivL-DivK or DivL-DivK∼P binding for each construct as a function of DivL concentration ( 0–120 µM ) . To our surprise , removal of all DivL PAS domains almost completely eliminated specificity for DivK∼P: DivL containing three PAS domains ( residues 152–769 ) was highly selective for DivK∼P ( Kd = 11 µM for DivK∼P and >150 µM for DivK ) , whereas a DivL variant with no PAS domains ( i . e . , crystallization construct ) bound phosphorylated and unphosphorylated DivK with similar affinity ( Kd = 15 µM for DivK and 7 µM for DivK∼P ) ( Figure 5A ) . DivL constructs containing three , and two PAS domains bind specifically to DivK∼P , while the 1-PAS domain DivL construct did not bind DivK∼P and may be less stable than other DivL constructs ( Figure 5B ) . These results indicate that DivL's PAS sensor domains play a critical role in DivK∼P selectivity . Previously , a DivL mutant ( A601L ) was shown to disrupt the DivL-DivK∼P interaction and thereby leading to the upregulation of CckA kinase activity in vivo [13] . Residue Ala601 lies at the interface between the input and output helices and away from the DivK∼P binding site ( Figure 5C ) , indicating that the A601L mutation may impact DivL-DivK∼P binding through an allosteric effect rather than by modifying the docking site . Critically , this interface includes the C-terminal end of the DHp domain that has been shown to alter its structure by helical unwinding or bending to activate sensor kinases upon receiving a signal in the Bacillus subtillus HK KinA [33] . To determine whether the disruption of DivK∼P binding is due to perturbation of the RR module itself or of the arrangement of the PAS and CA domains with respect to the RR docking module , we evaluated the impact of A601L and a bona fide RR docking surface mutation ( R553A ) on DivK∼P binding to DivL that either contains or lacks PAS domains . We found that the R553A substitution disrupts DivK∼P binding regardless of whether the PAS domains are present . The DivL ( A601L ) variant binds DivK∼P and weakly to unphosphorylated DivK , but only when the PAS domains are deleted ( Figure 5D ) . The observed PAS-domain-sensitive DivK∼P binding to DivL ( A601L ) suggests that interdomain interactions influence the binding of DivK∼P to DivL and explains in part the requirement for the PAS domains in DivK∼P selectivity . Specifically , in the DivL-A conformation the output helix forms a three-helix bundle in association with the two input helices ( Figure 5C ) , whereas the output helix is isolated from the input domain in the DivL-B conformation ( Figure 5C ) . Therefore , modulations of interactions at the input-output helix interface likely alters the PAS and CA inter-domain arrangement with respect to the DivK∼P binding site . Consistent with in vivo studies that indicate DivL's kinase activity is not essential for viability [12] , [13] , [16] , we observed in vitro that DivL does not exhibit autokinase activity and that a Y550H mutation could not rescue activity ( Figure S6 ) . Loss of autokinase activity may be rooted in poor ATP binding as we found that DivL binds weakly to the ATP analog TNP-ATP with an apparent binding constant of 57±8 µM ( Figure S7 ) [34] . DivL binds to TNP-ATP greater than 19× weaker than most prototypical HKs that exhibit binding constants in the range of 0 . 5–3 µM [34]–[37] , with the exception of one study that reported a 294 µM Kd for the catalytic domains of PhoQ [38] . We also crystallized DivL in presence of various nucleotide analogs , but no nucleotide or magnesium ion could be identified in the density map ( Figure S7 ) . Structural analysis of the crystallized DivL conformation shows that both nucleotide-binding sites are either inaccessible or too small to accommodate ATP ( Figure S7 ) . Compared to other HKs , the DivL ATP lid mobility is more restricted since it is sandwiched between Tyr550 and the remainder of the CA domain . These results suggest that DivL has little intrinsic kinase activity compared to functional HK controls , which is consistent with our structural and nucleotide binding data . We also asked if DivL functions as a DivK phosphatase in a manner similar to PleC ( Figure 6A and 6B ) . DivK∼P was generated by addition of His-DivJ , which was then purified by metal affinity chromatography . The remaining ATP in the DivK∼P reaction mixture was converted to ADP by incubation with hexokinase and glucose . DivK∼P phopho-aspartate stability was then compared over a 4 hour time frame in the presence of either PleC or DivL . We found that PleC reduced the DivK∼P half-life from 126 min to 8 min . However , the DivK∼P stability was increased in the presence of DivL ( half-life >700 min ) . Furthermore , restoration of a phosphorylatable histidine on DivL ( DivL Y550H ) has no impact on DivK∼P phosphostability . Therefore , we conclude that DivL does not function as a DivK∼P phosphatase . It has been established that DivK allosterically activates the kinase activity of DivJ and PleC [9] . In light of these findings , we questioned whether any autokinase activity of DivL could be stimulated by DivK using a coupled-enzyme assay which assays ATPase activity ( see Materials and Methods ) . Consistent with previous reports , we observed stimulation of DivJ autokinase activity in the presence of DivK . In contrast , no DivL autophosphorylation was observed upon addition of DivK or DivK∼P ( Figure 6C ) . However , we did observe phosphate accumulation on DivL Y550H in the presence of DivK∼P and ATP ( Figure 6D ) . In the absence of ATP no significant accumulation of phosphate signal was observed upon DivL Y550H ( Figure S8 ) , indicating that DivK∼P likely stimulate DivL autokinase activity in the presence of a canonical phosphate acceptor ( i . e . , His550 ) . We do note it is formally possible DivK∼P has the ability to back transfer to DivL Y550H , but only in an ATP-dependent manner . Taken together , our results suggest that DivL does not act on DivK by modifying the RR phosphorylation state . Instead , based on previous genetic studies [13] , [16] , it appears the consequence of the DivL-DivK∼P interaction is to allosterically modulate DivL's regulatory functions towards CckA ( Figure 1B ) . Previous genetic experiments have indicated that excess CtrA∼P can impact cell growth , motility , replication , cell division [7] , and DivL localization ( Figure 7A ) [39] . Since the DivL-DivK∼P interaction is known to result in inhibition of the CckA phosphorelay [13] , we predicted that the DivL mutations characterized here , which impact binding of DivK∼P to DivL , should consequently reduce cell fitness through dysregulation of CtrA signaling . Therefore , we generated translational fusions of each divL mutant allele described above to the coding region for eYFP and introduced the fusion constructs into the Caulobacter genome at the xylX locus under control of the xylose inducible PxylX promoter [40] . The strain background employed in each experiment contained a previously constructed divL depletion construct in which the wild-type divL ORF was placed under the control of the vanillate-inducible PvanA promoter at the chromosomal vanA locus [16] . In the resulting merodiploid strains , wild-type and mutant divL alleles could be conditionally expressed through the addition of vanillate or xylose , respectively , to the growth medium . We expressed multiple divL-eyfp mutant alleles ( Y550H , R553A , and A601L ) grown in peptone yeast extract ( PYE ) media supplemented with xylose . Cell lengths were significantly longer in cells expressing Y550H , R553A , and A601L , while Y562A exhibited no obvious cell morphology defects ( Figure 7B ) . Plating efficiency assays indicated growth defects ranging from very mild ( Y562A ) or moderate ( Y550H ) to severe in A601L cells ( Figure 7C ) . Interestingly , the R553A mutant was similar to Y550H on PYE plates with no xylose , but additional expression of the mutant by addition of xylose further inhibited the growth of the R553A mutant specifically , suggesting a dominant negative effect ( compare Figures 7C with S9 ) . Monitoring growth in liquid culture by optical density also revealed additional xylose induced expression of R553A was toxic ( Figure S9 ) . In a mixed population of cells grown in minimal media , we observe that 42% of cells expressing DivL-eYFP exhibit an observable monopolar DivL population ( Figure S9 ) , whereas only 20% of cells containing DivL-DivK∼P binding mutations ( Y550H , R553A , and A601L ) had an observable monopolar focus and the Y562A DivL mutant had a mild reduction ( 32% ) in the proportion of cells with monopolar DivL ( Figure S9 ) . Overall , these results demonstrate that two DivL-DivK∼P binding mutants , A601L and R553A , have severe impacts on cell fitness , while the Y562A mutation has a relatively mild effect even though DivK∼P binding is similarly affected by that substitution in vitro . Intriguingly , the strain containing the Y550H mutation exhibits clear growth and morphology defects despite the fact that DivK∼P binding to DivL is not disrupted in that background , suggesting that Y550H perturbs the activity of DivL in a distinct manner .
Our structural and biochemical analysis showed that DivL lacks both kinase and phosphatase activity but still retains its ability to dimerize and bind DivK∼P specifically . The DivL mutant Y550H supports viability but impacts cell morphology , doubling time , and the subcellular localization of DivL . These defects suggest that the replacement of histidine by tyrosine is critical for optimal DivL function . We observed that DivL Y550H bound to DivK∼P with similar affinity as the wild type , indicating that the tyrosine substitution neither enhances nor diminishes DivK∼P binding . The Y550H mutation did not rescue autonomous autokinase activity in vitro , however , DivL ( Y550H ) autokinase activity can be stimulated by DivK∼P in the presence of ATP ( Figure 6D ) . Thus , the H to Y substitution in DivL does not perturb HK-RR binding but abrogates kinase activity . From these studies , we propose that the DivL pseudokinase has repurposed its catalytic HK fold to function primarily as a sensory module for a phosphorylated RR . Loss of catalytic function ensures orthogonal sensor function while preventing unwanted signal modification ( i . e . , inter-conversion of DivK and DivK∼P ) that would compromise asymmetric cell division . Previous studies of two-component systems have highlighted the critical role of the RR docking module in determining HK-RR phosphotransfer specificity [21] , [41]–[45] . Consistent with these studies , we identified four point mutations within the RR docking module that disrupt DivK∼P binding to DivL ( Figure 4 ) . Thus , our results support the notion that DivL binds DivK∼P in a conserved manner similar to a canonical HK-RR pair [23] , [41]–[44] . However , a key question remains: how does DivL specifically recognize DivK∼P ? The tyrosine or histidine side chain at position 550 could contribute to the specific recognition of the phosphoryl group on DivK directly , by forming a hydrogen bond . However , we do note that Y550F has been previously shown to specifically bind phosphorylated DivK over unphosphorylated DivK [13] . Furthermore , the DivL RR docking surface , which includes Tyr550 , may be tuned to specifically recognize structural changes in DivK that are induced by phosphorylation . Here , we observed that the PAS sensor domain plays an unexpected role in phosphospecific discrimination of DivK∼P over DivK ( Figure 5 ) . One possible role of the PAS sensor domain could be to directly sterically block unphosphorylated DivK binding while permitting DivK∼P binding , or perhaps more likely the DivL PAS domains could indirectly reconfigure the DivK binding site to promote phosphospecific binding . The PAS domain's role in phosphospecific RR binding is consistent with the finding that optimal WalK [46] and ThkA [25] phosphatase activity requires a PAS module . More generally , it appears that PAS sensor domains may regulate kinase/phosphatase activity by influencing the phosphospecific binding preference of an HK for its cognate RR . Several recent biochemical studies of HKs have revealed that HKs transmit signaling information over a long-range through large-scale conformational changes in order to regulate activity . DHp conformational rearrangements , such as “twisting” ( cogwheeling or rotation ) and “bending” ( kinking ) , can produce unique functional states that are crucial for HK regulation [24] , [26]–[28] , [32] , [47]–[50] . Our rigid-body structural analysis of DivL suggests that interhelical bending could alter the relative position of domains that regulate DivL function . Indeed , our binding studies indicate that a mutation at the interface between two rigid bodies ( A601L ) , disrupts DivK∼P binding in a PAS-domain-dependent manner ( Figure 5C ) . Furthermore , the A601L mutation causes a more severe phenotype than does the direct RR docking mutation Y562A . This suggests that A601L may be pleiotropic , perhaps simultaneously affecting upstream ( i . e . , DivK∼P binding ) and downstream events ( i . e . , CckA inhibition ) . Residue Arg553 may also serve a switch-like role triggering conformational changes upon DivK∼P binding by interacting with DivK∼P in the complexed state , and in the uncomplexed state interacting with the output-helix residue Asp595 ( Figure 5 ) . Taken together , this model may explain why A601L and R553A have stronger impacts on cell fitness than the Y562A variant . Additionally , DivK∼P-dependent rescue of DivL autokinase activity in the presence of Y550H suggests that DivK∼P binding triggers conformational changes in DivL that are necessary for ATP binding and autokinase activity . Thus , we propose a model in which the interaction between Tyr550 and the phosphorylation site of DivK∼P induces bending in the α1 helix of DHp , which is then propagated to the rest of the structure via rigid-body movements . As a result of the conformational change , the CA and PAS domains of DivL may re-position to allow formation of additional contacts with bound DivK∼P . In the absence of catalytic functions , we suggest that DivL exploits the underlying large-scale conformational re-arrangements of the HK-fold to function as a DivK∼P-modulated two-state molecular switch ( Figure 8 ) . Asymmetric dimers have been proposed to represent important activation intermediates [26]–[29] , [51] , and it is conceivable that DivL undergoes a symmetric-asymmetric conformational switch upon DivK∼P binding . However , the mechanism by which DivK∼P-dependent conformational changes in DivL impact the CckA-ChpT-CtrA phosphorelay [12] , [13] , [15] , [39] , [52] remains unclear . It is plausible that DivL interacts directly or indirectly with CckA in the multi-protein polar complex , thus allowing the structural changes in DivL induced by DivK∼P to propagate to changes in CckA activity . Future experiments will focus on dissecting DivL's essential regulation of the CckA-ChpT-CtrA pathway . A common bacterial signaling strategy is to wire two-component systems as simple orthogonal linear signaling arrays to limit cross-talk and maintain the integrity of signaling information [43] , [44] . Phosphorelay pathways offer a way to branch signaling pathways in order to enable multiple signaling inputs [30] or outputs [45] , [53] . Signaling directionality in the two-component systems from a kinase to a response regulator apparently limits bacterial signal processing capacity relative to more complex eukaryotic systems . Here , we demonstrated an exception to this paradigm in which loss of HK catalytic activity repurposes an output module as a phosphospecific RR input sensor , thereby reversing the flow of information observed in conventional HK-RR systems . This reversal allows DivL to couple two distinct signaling pathways by serving as an “output” for a compartment sensing pathway and an “input” for the differentiation phosphorelay ( Figure 8 ) . The extent to which nature utilizes this reversed signaling warrants further investigation . For example , it could allow transmission cytosolic stress signals to the periplasm via membrane-bound HKs . The presence of pseudokinases in two-component signaling systems therefore offers a general mechanism for coupling signaling pathways , reminiscent of complex eukaryotic signaling networks .
DNA primers , plasmids , plasmid construction methods , and bacteria strains used in this study are listed in Tables S3 , Table S4 , Table S5 , and Table S6 , and detailed cloning methods are included in Text S2 . The domain architecture of DivL was annotated using the HHpred structure prediction algorithm [54] . For the DHp-CA construct we retained a significant portion of the N-terminal input helix that connects the sensor domains to the DHp domain to generate DivL ( 523–769 ) . Constructs with N-terminal start codons at: 411 containing one PAS domain , 281 containing two PAS domains , and 152 containing three PAS domains . DivL residues 1–53 , which were predicted to form a transmembrane helix followed by a coiled-coil region , were not included to improve in vitro solubility . Protein expression of all DivL variants , DivJ , PleC , and DivK followed the same protocol and is described in detail below for the crystallization construct DivL ( 523–769 ) . Protein expression of ChpT , CckA , and CckA H322A were performed as described previously [45] . Plasmid pWSC8 was then transformed into chemically competent Rosetta ( DE3 ) pLysS cells , and plated onto selective LB media ( 30 µg/ml chloramphenicol and 50 µg/ml kanamycin plates ) , and grown overnight at 37°C . From a single colony , an overnight 25 ml LB ( chloramphenicol: 20 µg/ml; kanamycin: 30 µg/ml ) culture was inoculated and grown to saturation overnight . From this saturated culture a 2L LB culture was inoculated and grown to mid-log phase ( ∼0 . 6 OD ) . Expression of DivL ( 523–769 ) was induced with 333 µM isopropyl-β-D-thiogalactopyranoside ( IPTG ) for 4 hours at 25°C . The cells were harvested in an ultracentrifuge at 4°C , 30 minutes , 5 , 353 g . The resulting pellet was resuspended in 40 ml 50 mM HEPES pH 7 . 9 , 0 . 5 M NaCl and centrifuged at 3 , 700 g at 4°C for 20 to yield a cell pellet stored at −80°C . Cells were thawed on ice and resuspended in 40 ml of lysis buffer ( 50 mM HEPES pH 8 . 0 , 0 . 5 M KCl , 1 mM DTT , 25 mM imidazole , and 200 U of benzonase nuclease ) supplemented with SIGMAFAST™ protease inhibitor tablets ( Sigma ) . The cell suspension was lysed with three passes through a French Press at 20 , 000 psi . Insoluble cell debris was pelleted via centrifugation ( 27 , 216 g , 45 min at 4°C ) . The resulting supernatant was incubated with 2 ml of a 50% slurry of Ni-NTA agarose at 4°C for 1 hour . The Ni-NTA agarose was pelleted and washed with 150 ml of Ni-NTA wash buffer ( 50 mM HEPES [pH 7 . 9] , 0 . 5 M KCl , 1 mM DTT , and 25 mM imidazole ) . Then His6-DivL ( 523–769 ) was eluted from the agarose with Ni-NTA elution buffer ( 50 mM HEPES [pH 8 . 0] , 0 . 5 M KCl , 1 mM DTT , and 250 mM imidazole ) and concentrated to a 2 ml volume using Amicon Centrifugal Filter Units ( 10 kDa cutoff ) . This concentrated elution was loaded onto a Hi-Prep 16/60 Sephadex S-200 gel filtration column ( GE Healthcare ) , eluted in kinase buffer ( 50 mM HEPESáKOH [pH 8 . 0] , 200 mM KCl , 0 . 1 mM EDTA , 10% ( v/v ) glycerol , and 1 mM DTT ) . The eluted His6-DivL ( 523–769 ) was then concentrated to ∼15 mg/ml using amicon centrifugal filter units , aliquoted and frozen in liquid nitrogen for crystallization trials . The concentration of His6-DivL ( 523–769 ) was determined using ε280 of 8 , 605 M−1cm−1 . MBP-DivK expression and purification followed the same protocol , however after elution from the Ni-NTA agarose TEV protease was used to cleave off the His-MBP-TEV site tag of DivK . Briefly , His6-MBP-DivK was diluted to 2 mg/ml and mixed with His6-TEV protease and the solution was dialyzed overnight at 4°C in two 12 ml Slide-A-Lyzer 10 k MWCO dialysis cassettes against 2L dialysis buffer ( 20 mM HEPESáKOH [pH 8 . 0] , 100 mM KCl , 0 . 5 mM EDTA , 10% glycerol , 1 mM DTT ) . The following day , the His6-tagged unreacted protein impurities were removed by subtractive Ni-NTA affinity purification with 6 ml of 50% slurry of Ni-NTA agarose affinity resin ( 5Prime ) equilibrated in dialysis buffer; the eluate containing cleaved DivK was collected . The initial crystallization conditions for DivL were obtained using the sparse matrix screening method ( Hampton Research ) . The conditions were subsequently optimized manually to improve the quality of the crystals . The crystals used for structure solution were obtained using the hanging-drop vapor diffusion method at 22 . 5°C . The reservoir well contained 500 µl 0 . 1 M HEPES ( pH 7 . 5 ) and 18% PEG 10000 , while the drop contained 1 . 2 µl of DivL ( concentration 5 . 8 mg/ml ) mixed with 1 . 2 µl of the reservoir solution . For cryoprotection , the crystals were gradually transferred to a series of reservoir solutions containing incrementally higher concentration of PEG 4000 , up to a final concentration of 30% prior to flash freezing in liquid nitrogen . The data were indexed and processed in the trigonal space group P3121 with unit cell dimensions of a = 69 . 5 Å and c = 194 . 4 Å . To obtain heavy atom derivatives , crystals were soaked in cryo solution containing 10 mM KAu ( CN ) 2 for 10 min . For co-crystallization with nucleotides , MgCl2 and each nucleotide ( ATP , ADP , AMP-PNP , AMP-PSP , and TNP-ATP ) were added in ∼10 molar excess to the protein before crystallization . In addition , attempts were made to soak the native crystals in the presence of nucleotides and magnesium . Native data and multiwavelength anomalous diffraction ( MAD ) data for the gold derivative , and data for crystals obtained in the presence of different nucleotides were collected at the SSRL Beamline 12-2 . The datasets were collected at 100 K using a Pilatus 6M pixel array detector ( Dectris ) . Each dataset was integrated using XDS and then scaled with the program XSCALE [55] . Gold sites were located with SHELXD [56] using the data corresponding to the peak wavelength ( 1 . 0397 Å ) of the Au-MAD experiment . Phase refinement ( FOM = 0 . 32 for three Au sites ) and automatic model building were performed using autoSHARP [57] and BUCCANEER [58] . Model completion and refinement were performed with COOT [59] and BUSTER [60] . Additional CCP4 programs [61] were used to for data conversion and other calculations . Data reduction and refinement statistics are summarized in Table S2 . Atomic coordinates and experimental structure factors for DivL at 2 . 5 Å resolution have been deposited in the PDB ( http://www . rcsb . org ) under accession code 4q20 . A gel filtration standard ( Bio-rad ) containing thyroglobulin ( bovine ) , γ-globulin ( bovine ) , ovalbumin ( chicken ) , myoglobulin ( horse ) , and vitamin B12 were used to generate a molecular weight standard plot using a Superdex 200 10/300 GL column ( GE Healthcare ) . A 2 mg/ml sample of His6-DivL ( 523–769 ) was loaded onto the column and eluted after 14 . 2 ml , corresponding to a molecular weight of 50±15 kDa ( dimer = 57 . 4 kDa ) . Error bars for molecular weight estimated as the full width at half maximum ( FWHM ) for the eluting peak . Immunoprecipitations were performed as previously described [62] . A 500 ml culture of wild type Caulobacter cells ( strain LS101 ) and a strain carrying divL-m2 ( LS4468 ) [16] were grown in PYE media at 28°C to mid-log phase . The cells were harvested in a centrifuge at 4°C , 15 min , and 7 , 800 rpm . Cells were then washed with CO-IP buffer ( 20 mM HEPES pH 7 . 5 , 100 mM NaCl , 20% glycerol ) , and pelleted in a centrifuge at 4°C , 15 min , and 9 , 000 rpm . Cells were resuspended in 29 . 2 ml PBS ( pH 6 . 8 ) containing 1% formaldehyde and allowed to cross-link at room temperature for 30 minutes . Cells were pelleted in a centrifuge at 4°C , 15 min , 9 , 000 rpm and then resuspended in 30 ml of CO-IP buffer supplemented with protease inhibitors . The pellet was snap frozen with liquid nitrogen and stored at −80°C . The cell suspension was thawed and lysed with three passes through a French Press at 20 , 000 psi . After lysis , 2 mM EDTA and 1% Triton X-100 were added and allowed to incubate on ice for 60 min . Insoluble cell debris was pelleted via centrifugation ( 9 , 000 rpm , 10 min at 4°C ) . Lysate was incubated with 50 µl of FLAG-M2 agarose ( FLAGIPT-1 kit; Sigma ) overnight on a nutator at 4°C . Beads were washed twice with co-IP buffer supplemented with 0 . 05% NP-40 , then washed five times with wash buffer ( 50 mM Tris-HCl , 150 mM NaCl , protease inhibitors , 0 . 05% NP-40 ) and proteins were eluted by incubating with 3× FLAG peptides . Western blots were performed as previously described [62] . Antibody dilutions were as follows: α-FLAG ( Sigma ) 1∶1 , 000 , ) and α-DivK ( 1∶5 , 000 ) . Films were scanned and processed with Adobe Photoshop . DivK was labeled at Cys-99 using thiol-reactive BODIPY FL N-aminoethyl malemide ( Invitrogen ) . DivK was mixed together with 10-fold excess BODIPY FL N-aminoethyl malemide and allowed to react for 2 hours at room temperature , and unreacted dye was quenched with mercaptoethanol . BODIPY-DivK was purified via dialysis to remove unreacted fluorescent dye . BODIPY-DivK∼P was generated by mixing 250 nM DivJ with 1 mM ATP and 5 mM MgCl2 and incubated for 40 min . Mock binding assays were done in the presence of [γ-32P]ATP , and under these reaction conditions scintillation counting combined with Bradford assay estimate >75% of DivK was in the phosphorylated state . Upon phosphorylation of BODIPY-DivK by 250 nM DivJ no change in fluorescence polarization was observed . This finding indicates that BODIPY-DivK∼P was likely a monomer under reducing buffer conditions ( 1 mM DTT ) and that no significant DivJ was bound to DivK . For binding assays using unphosphorylated DivK , 1 mM non-hydrolyzable AMP-PNP and 5 mM MgCl2 were included in the buffer . BODIPY-DivK∼P or BODIPY-DivK was then incubated with varying kinase concentrations for 45 minutes to reach binding equilibrium . Fluorescent DivK was excited at 470 nm and emission polarization was measured at 530 nm in a Molecular Devices SpectraMax M5 plate reader . Fluorescent polarization measurements were performed in triplicates , and three independent trials were averaged with error bars representing the standard deviation . We extensively screened buffer conditions to optimize DivL-DivK binding by exploring KCl concentration , % glycerol , and pH . Of these parameters , DivL-DivK binding was most sensitive to KCl concentration and low KCl ( 50 mM ) promoted the DivL-DivK interaction ( Figure S1 ) . Interestingly , ATP appeared to promote the interaction between DivL-DivK and DivL-DivK∼P ( Figure S1 ) . ATPase activity of DivL constructs was measured using a coupled-enzyme assay [63] , [64] . Proteins were mixed in kinase buffer supplemented with 1 mM ATP , 10 mM MgCl2 , 1 mM phosphoenolpyruvate , 0 . 2 mM NADH , 2 units of pyruvate kinase , and 6 . 6 units of lactate dehydrogenase . Reactions were performed in triplicate in 200 µl volumes and loaded into a clear , polystyrene 96-well plate . Reactions were initiated by the addition of protein , and absorbance at 340 nm was recorded every 10 seconds for a 30 minute period . The slope of a stable linear absorbance decay was measured to calculate ATP hydrolysis rates , using a NADH Kpath value of 3 , 248 OD M−1 [63] . Background rates of ATP hydrolysis and NADH oxidation were measured and subtracted from observed ATP hydrolysis rates of all DivL constructs . CckA ( 70–691 ) and DivL ( 152–769 ) constructs at 5 µM were incubated for one hour at room temperature in kinase buffer supplemented with 0 . 5 mM ATP , 0 . 167 µCi/µl [γ-32P]ATP , and 5 mM MgCl2 in a total reaction volume of 50 µl . Reactions were stopped by the addition of 2× Laemmli sample buffer , then loaded onto 12% Tris-HCl gels for electrophoresis . The radioactivity in wet gels was recorded on phosphor storage plate for 3 h , and then imaged on a Typhoon fluorescence imager ( Molecular Dynamics ) . Quantitation of band intensities was measured using ImageJ and band intensities for three individual experiments were averaged . The mean intensity and standard deviation were plotted using Prism 6 software ( GraphPad ) . For DivK∼P phosphatase assays , DivK was incubated with 250 nM His-DivJ , 1 mM ATP , and 5 mM MgCl2 on Ni-NTA agarose resin for 40 min . DivK∼P was eluted and purified away from His-DivJ , followed by a second round of Ni-NTA purification . Purified DivK∼P was then incubated with excess hexokinase and glucose for 10 minutes to convert remaining ATP into ADP . Purified DivK∼P was then incubated with either PleC , DivL ( 152–769 ) , or DivL ( 152–769 ) Y550H for varying amounts of time . Reactions were quenched with 4× SDS-PAGE sample buffer . Gels were processed as described above in the autophosphorylation section . The mean intensity and standard deviation from three independent experiments were plotted using Prism 6 software ( GraphPad ) . A solution of 1 µM TNP-ATP [34] was compared with a solution of 2 µM TNP-ATP with 10 µM DivL ( 523–769 ) in 50 mM HEPES×KOH ( pH 8 . 0 ) , 200 mM KCl , 0 . 1 mM EDTA , 10% ( v/v ) glycerol . Solutions were allowed to reach equilibrium for 30 min at room temperature , prior to fluorescence measurements . An emission profile was collected over a wavelength range of 475–750 nm . Presented curves in Figure S7 are average values of three replicates . Filter binding assays were designed to capture transiently bound [γ-32P] ATP [65] , [66] . Solutions of DivL ( 523–769 ) , DivL ( 523–769 ) Y550A , CckA ( 70–691 ) H322A , ChpT , and BSA at 10 µM protein concentration was mixed with 27 . 5 fmol of [γ-32P] ATP in kinase buffer at room temperature . Empirically , we found a 30-minute period allowed adequate time for equilibration , while minimizing hydrolysis and release of ATP . Solutions were passed over a nitrocellulose membrane filter and washed three times with 1 ml of kinase buffer . Bradford assays indicated >95% of protein remained bound to the nitrocellulose . Subsequently , nitrocellulose filters were submerged into scintillation fluid and scintillation counted to quantify bound [γ-32P] ATP . [γ-32P] ATP standard solutions were used to determine the specific activity and convert scintillation counts in fmol bound to the nitrocellulose . [γ-32P] ATP only solutions were passed through nitrocellulose membranes to quantify non-specific binding . Each of the pXYFPC-divL mutant strains were transformed into Caulobacter NA1000 strain AA871 that is ΔdivL vanA::divL . In these strains the impact of divL mutant expression was evaluated by removal of vanillate from media to deplete wild-type divL from cells , and addition of xylose to express divL-yfp mutants . Mutant divL genes were examined in a strain where the native copy of divL was deleted , the WT divL placed under vanillate induction at the vanA locus , and a mutant divL-yfp was placed at the xylose-inducible xylX locus of the chromosome . Cells were grown for 24 h in PYE containing no vanillate and no xylose . Cells were then diluted in fresh , xylose-containing media to optical density at 600 nm ( OD-600 ) of 0 . 02 ( diluted to approximately 0 . 06 for efficiency of plating assays ) . The cells were then allowed to grow for several hours in the xylose until either being plated or imaged . For efficiency of plating assays , cells grew for either 0 or 6 hours after addition of xylose . Cells were spotted on PYE-agar plates in 3 µl volumes . The first , leftmost spot was spotted from the 0 . 06 OD-600 culture , and subsequent dilutions were made serially in 5-fold steps . The xylose content of the plates was varied from 0 to 0 . 3% w/v , and cells were plated from liquid media containing an equal amount of xylose . The plates were then grown at 28°C for 2 days and subsequently imaged . For liquid culture growth curves , we measured the OD-600 every 1–2 hours for 10 hours or until the culture reached saturation . ΔdivL vanA::divL xylX::divL-yfp WT , Y550H , R553A , Y562A , and A601L strains ( WSC399 , WSC308 , WSC310 , WSC312 , and WSC314 ) were cultured overnight in M2G supplemented with 5 µM vanillate and sub-cultured into fresh media containing 0 . 03% xylose for 6 hours or until they reached an OD600 of 0 . 4 for divL localization assays . Cell suspensions were then dried onto agarose pads ( 1 . 5% agarose in M2G ) and imaged on a Leica DM 6000 B microscope with a HCX PL APO 100°—/1 . 40 Oil PH3 CS objective , Hamamatsu EM-CCD 15 C9100 camera and Metamorph ( Molecular Devices ) . Both phase-contrast and fluorescence images were recorded . Images were processed in Adobe Photoshop , and quantitative analysis of images was performed using ImageJ's cell-counter . For morphology assays , the cells were grown overnight in PYE and switched into fresh PYE with xylose . After 6 hours of xylose induction , cells were placed on a PYE-agar pad for imaging . Images were processed in Adobe Photoshop .
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Across all kingdoms of life the generation of cell-type diversity is the consequence of asymmetry at the point of cell division . The bacterium Caulobacter crescentus divides asymmetrically to produce daughter cells that have distinct morphology and behavior . As in eukaryotes , an unequal distribution of signaling proteins in daughter Caulobacter cells triggers the differential read-out of identical genomes . A critical interaction between two protein molecules – a protein kinase ( DivL ) and a response regulator ( DivK ) – is known to occur exclusively in one daughter cell and to thereby regulate differentiation . However , mapping the observed signaling interconnections that drive asymmetric division has been difficult to reconcile with traditional models of bacterial signaling . Here we determine how DivL detects and processes this DivK signal . Although DivL has an architecture that is typical of histidine kinases , which normally act by regulating the phosphorylation state of the appropriate response regulator , DivL's essential functions do not require kinase activity and DivL does not add or remove phosphate from DivK . Instead we find that DivL has converted its output kinase domain into an input sensor domain that specifically detects phosphorylated DivK , and we identify key features of DivL that underlie this specificity . This novel reassignment of sensory functions reverses the conventional kinase-to-response-regulator signaling flow and logically couples linear signaling pathways into complex eukaryote-like networks to regulate cell development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
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2014
|
Cell Fate Regulation Governed by a Repurposed Bacterial Histidine Kinase
|
Understanding the etiology of metastasis is very important in clinical perspective , since it is estimated that metastasis accounts for 90% of cancer patient mortality . Metastasis results from a sequence of multiple steps including invasion and migration . The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore , they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur . To elucidate the role of individual mutations or their combinations affecting the metastatic development , a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes , and predict the effect of not yet experimentally assessed mutations . The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines . A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose . In addition , we have systematically predicted alleviating ( masking ) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype . We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability . Among them , the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects , which is in agreement with a recent published experiment in a mouse model of gut cancer . The mathematical model can recapitulate experimental mutations in both cell line and mouse models . Furthermore , the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology .
Understanding the etiology of metastasis is very important in clinical perspective . Despite the progress with treatment of the primary tumours , the chances of survival for a patient decrease tremendously once metastases have developed [1] . It is estimated that metastasis accounts for 90% of cancer patient mortality [2] . It is now understood that the metastatic process follows a sequence of multiple steps , each being characterised by a small probability of success: 1 ) infiltration of tumour cells into the adjacent tissue , 2 ) migration of tumour cells towards vessels , 3 ) intravasation of tumour cells by breaching through the endothelial monolayer , 4 ) travelling in the circulatory or in the lymphoid system , 5 ) extravasation when circulating tumour cells re-enter a distant tissue , and 6 ) colonisation and proliferation in distant organs [3] . The early stages of invasion and migration are tightly controlled in normal cells and can be drastically affected by malignant mutations . It has been shown indeed that primary and secondary tumours have a common gene signature [4] that mediates the initial stages of metastasis while extravasation and colony formation by a ( tumour ) cell does not require malignant gene alterations [5] , supporting the idea that the later stages of metastasis are affected by the anatomical architecture of the vascular system [6] . Here , we focus on the ability of cancer cells to infiltrate and migrate into the surrounding tissue . The first step towards the formation of secondary tumours is acquiring the ability to migrate . In order to gain motile capacity , epithelial cells need to change their morphology through Epithelial to Mesenchymal Transition ( EMT ) , a process which occurs during development ( EMT type 1 ) , wound healing ( EMT type 2 ) and under pathological conditions such as cancer ( EMT type 3 ) [7 , 8] . EMT type 3 is characterised by both loss of E-cadherin ( cdh1 ) and invasive properties at the invasive front of the tumour [9] . Gene expression of E-cadherin is inhibited by the transcription factors Snai1/2 , Zeb1/2 and Twist1 , while gene expression of N-cadherin ( cdh2 ) is induced by the same transcription factors [8 , 10 , 11] . These transcription factors activate other genes that initiate EMT [11–13] and are induced by several signalling pathways including TGF-β , NF-κB , Wnt and Notch pathways [8 , 14 , 15] . On the contrary , the transcription factor p53 has been shown to inhibit EMT via degradation of Snai2 [16]; however , a p53 loss of function ( LoF ) alone is not sufficient to induce EMT [17] . After the switch of E-cadherin to N-cadherin expression , the cell-cell contacts are weakened [18 , 19] and cancer cells can pass the basal membrane to infiltrate the surrounding tissue [20] . The process of local invasion can be active since tumour cells can secrete Matrix Metalloproteinases ( MMPs ) that dissolve the lamina propria [21] . MMPs are also able to digest other components of the extracellular matrix ( ECM ) and thereby to release growth factors and cytokines that are attached to the ECM [21 , 22] which in turn activate the tumour cell’s ability to propagate the dissolvement of the lamina propria . Finally , after dissolving the lamina propria and invading the ( local ) stroma , cancer cells can migrate to distant sites by intravasation and extravasation of the vascular system [2] . To gain insight in the regulation of the metastatic process , several groups have developed mathematical models of various aspects of it [23–29] ( S1 Text ) . Our aim is to understand the role of gene alterations in the development of metastasis . In many ( experimental or in silico ) models , EMT is described as a very important step in acquiring metastasis and even considered to be synonymous to appearance of metastasis [30–32] . Due to EMT role in metastasis , much research has been performed to elucidate its regulation . The regulation of EMT is known to be complex and simple intuition is not sufficient to comprehend how genetic alterations ( mutations and copy number variations ) affect it . Logical modelling can give qualitative insight on how they could affect EMT and subsequently metastasis . Previously , we have constructed a detailed map of molecular interactions involved in EMT regulation which is freely available at [33] , and based on its structural analysis , we hypothesized a simple qualitative mechanism of EMT induction through upregulation of Notch and simultaneously deletion of p53 . This prediction has been experimentally validated in a mouse model of colon carcinoma [31] . In the present study , we significantly extend the biological context and provide a mathematical framework for the description of the necessary conditions for having metastasis , going beyond the regulation of EMT only . We take into consideration the gained motility and ability to invade as determinants of the metastatic process . For this purpose we largely extended and re-designed the signalling network including more molecular players and phenotypes , and translated the network into a formal mathematical model , allowing prediction of the metastasis probability and the systematic analysis of mutant properties . Therefore , this work represents a significant progress with respect to the previous results , allowing reconsideration of the qualitative hypothesis suggested before using a formal mathematical modelling approach . First , we introduce a logical model that recapitulates the molecular biology of the early steps in metastasis . The construction of the influence network and the choice of the logical rules are both based on knowledge derived from scientific articles . The final readouts of the model are the phenotype variables CellCycleArrest , Apoptosis and the aggregated phenotype Metastasis that combines the phenotypes EMT , Invasion and Migration . We have chosen those final read-outs , as we believe that a metastatic phenotype depends on the occurrence of EMT , invasion and migration . In addition , apoptosis is of importance to the system as during healthy conditions , the cells undergo apoptosis when the cells detach from the basal membrane [34] . Suppressing apoptosis during migration is a required key feature . Our interest in cell cycle arrest is due to results of the mouse model [31] that show decreased proliferation . We try to model this feature in our logical model by looking at the regulation of cell cycle arrest . We did not focus on other phenotypes ( or cancer hallmarks ) such as proliferation explicitly , senescence , or angiogenesis . These are often considered in cancer studies but they were out of the scope of this work , which focused on depicting early invasion modes and not specifically on how tumour growth is regulated . The model inputs have been selected to represent external signals necessary for the metastasis initiation pathways . The Boolean model that we show here describes a possible regulation of the metastatic potential of a single tumour cell and not of multiple cells or a tissue . We provide a simplified version of the model where some genes are grouped into modules ( or pathways ) allowing an analysis based on pathways rather than individual genes . Both versions of the models are validated by reproducing the phenotypic read-outs of published experimental mouse and cell line models . We then analyse the two models and formulate several types of predictions: at the level of individual genes , e . g . exploring the individual role of each EMT inducer in metastasis; and at the level of pathways , e . g . investigating the functional role of each pathway in triggering metastasis . The logical models can suggest a systematic mechanistic explanation for the majority of experimentally validated mutations on the local invasion and migration processes . Moreover , we were able to establish a link between the solutions of the mathematical model and the gene expression data from cell lines in which EMT was transiently induced . In addition , we have applied this method to the analysis of transcriptomes of tumour biopsies . Lastly , we investigate how genetic interactions between different gene mutations can affect the probability of reaching a metastatic outcome . Our analysis predicts the effect of single mutations and the genetic interactions between two single mutations with respect to several cellular phenotypes . Our model proves an exceptionally efficient synergetic effect of increased activity of Notch in combination with a decreased activity of p53 on metastasis in accordance with our previous work [31] .
The construction of the influence network is based on scientific articles that describe the interactions between nodes of the model . We first selected the main genes or proteins that may contribute to activating EMT , regulating early invasion and triggering metastasis . We then searched for experimentally proven physical interactions that would link all these players , and simplified the detailed mechanisms into an influence network . For example , it has been shown experimentally that AKT protein phosphorylates and stabilises MDM2 , which in turn inhibits p53 by forming a complex , leading to protein degradation of p53 . We simplified the biochemical reactions by a negative influence from AKT to p53 . The influence network is then translated into a mathematical model using Boolean formalism ( see below for details ) . We verified the coherence of the network by comparing the outcome of the perturbed model to the observed phenotypes of mutants found in the literature . The final model is the result of several iterations that led to the accurate description of most of the published mutants related to the genes included in our model . Once the model was able to reproduce most of the published mutant experiments , we simulated mutants and conditions not yet assessed experimentally and predicted the outcome . From the influence network recapitulating known facts about the processes , we develop a mathematical model based on the Boolean formalism . To do so , we associate to a node of the influence network a corresponding Boolean variable . The variables can take two values: 0 for absent or inactive ( OFF ) , and 1 for present or active ( ON ) . The variables change their value according to a logical rule assigned to them . The state of a variable will thus depend on its logical rule , which is based on logical statements , i . e . , on a function of the node regulators linked with logical connectors AND ( also denoted as & ) , OR ( | ) and NOT ( ! ) . A state of the model corresponds to a vector of all variable states . All possible model states are connected into a transition graph where the nodes are model states and the edges correspond to possible transitions from one model state to another . The transition graph is based on asynchronous update , i . e . , each variable in the model state is updated one at a time as opposed to all together in the synchronous update strategy . Attractors of the model refer to long-term asymptotic behaviours of the system . Two types of attractors are identified: stable states , when the system has reached a model state whose successor in the transition graph is itself; and cyclic attractors , when trajectories in the transition graph lead to a group of model states that are cycling . In this model , no cyclic attractors were found for the wild type case . However , we do not guarantee the non-existence of cyclic attractors in some of the perturbed cases , as perturbations of the model may create new dynamics . A logical rule is written for each variable of the model , corresponding to a node in the influence network , in order to define how its status evolves ( ON or OFF ) . In this rule , the variables of the input nodes are linked by logical connectors according to what is known about their combined activities . There are several cases to consider: ( 1 ) The simplest logical rule that can be assigned is when a variable depends on the activity of a single input: for instance , the transcription factor Twist induces the transcription of the cdh2 gene ( see Table 1 ) ; ( 2 ) In the case of an input that has a negative effect on the activity of its target , the Boolean operator “NOT” or “ ! ” is used: EMT is , for example , activated by CDH2 but inactivated by CDH1 , thus for EMT to be activate , CDH1 should be OFF and CDH2 should be ON . The complete logical rule for the activation of EMT will be EMT = 1 ( ON ) if CDH2 & ! CDH1 ( see Table 1 ) ; ( 3 ) In some cases , a gene can be activated by two independent genes reflecting two different conditions and thus inputs are linked by an OR , e . g . , DKK can be activated either by CTNNB1 or by NICD , independently of each other; ( 4 ) In some other cases , two activators are linked by an AND connector , e . g . , ZEB2 whose activity depends on several inputs including TWIST1 & SNAI2 which are needed simultaneously: it has been observed experimentally that both transcription factors Twist1 and Snai2 are required to induce gene expression of zeb2 . All models are available in GINsim format in S1 File . MaBoSS is a C++ software for simulating continuous/discrete time Markov processes , defined on the state transition graph describing the dynamics of a Boolean network . The rates up ( change from OFF to ON ) and down ( from ON to OFF ) for each node are explicitly provided in the MaBoSS configuration file together with logical functions , which allows working with physical time explicitly . All rates are set to be 1 in this model since it is difficult to estimate them from available experimental facts . Probabilities to reach a phenotype are computed as the probability for the phenotype variable to have the value ON , by simulating random walks on the probabilistic state transition graph . The probabilities for the selected outputs are reported for each mutant based on predefined initial conditions ( which can be all random ) . Since a state in the state transition graph can combine the activation of several phenotype variables , not all phenotype probabilities appear to be mutually exclusive . For example , Apoptosis phenotype variable activation is always accompanied by activation of CellCycleArrest phenotype variable ( because p53 is a transcription factor of p21 , responsible for cell cycle arrest , and the miRNAs , activated by the p53 and its family members , lead to a cell cycle arrest ) , and activation of the Metastasis phenotype variable is only possible when all three EMT , Invasion and Migration phenotype variables are activated . With MaBoSS , we can predict an increase or decrease of a phenotype probability when the model variables are altered , which may correspond to the effect of particular mutants or drug treatments . If mutation A increases the Apoptosis probability when compared to the Apoptosis probability in wild type , we conclude that mutant A is advantageous for apoptosis . All models are available in MaBoSS format in S1 File . The pathway activity ( synonymously , module activity ) score in a tumour sample is defined as the contribution of this sample into the first principal component computed for all samples on the set of the module target genes , as it was done in [35] . This way , we test target gene sets selected from MSigDB [36] and KEGG [37] databases together with few tens of gene sets assembled by us from external sources . The gene lists for each module is provided in S5 Table . Differential activity score of each module was estimated by t-test between metastatic and non-metastatic groups and significantly active/inactive modules were selected according to p-value <0 . 05 condition . We conducted our study on the publicly available data of human colon cancer from TCGA described in [38] . By excluding rectal cancers from the original dataset , the remaining 105 tumour samples were included in our analysis , classified into two groups ( ‘metastatic’ M1 = 17 tumours and ‘non-metastatic’ M0 = 88 tumours ) according to clinical information about metastasis appearance during cancer progression . We used gene expression data generated from A549 lung adenocarcinoma cell line that was treated with TGF-β1 ligand at eight different time points [39] . In short , gene expression was measured for three replicates at each time point using Affymetrix Human Genome U133 Plus 2 . 0 Array . For more information about treatment and growth protocols see [40] . We followed the following six steps to link transcriptome data to the stable states of the model ( described in detail in S2 Text ) : ( 1 ) We first matched the genes of the model with their HUGO names . For phenotypes such as Apoptosis , Migration or Invasion , the genes coding for CASP9 , CDC42 , and MMP2 were used as biomarkers , respectively . These readouts were selected as the most representative of the process; they were chosen based on the changes of the expression of a list of candidate genes we explored throughout the experiments . ( 2 ) We averaged the genes over the 3 replicates for time point T0 ( initiation of experiment with no TGF-β ) , for T8 ( identified as the beginning of EMT ) , for T24 ( EMT in process ) and for T72 ( last point ) . ( 3 ) Using several methods ( binarization algorithms available in [41] ) , we identified a threshold of expression and binarized the data accordingly . Among our list of genes , only 11 of them have significant expression dynamics along the experiment: cdh1 , cdh2 , ctnnb1 , egfr , mapk1 , mmp2 , smad3 , snai2 , tgfb1 , vim and zeb1 . The other genes were either always ON or always OFF throughout the 72 hours of experiments because the expression is either above or below the threshold we set . ( 4 ) We associated a label ( phenotype ) to the 9 stable states of the logical model based on the activity status of the phenotype variable . ( 5 ) The similarity matrix was computed according to the following rule: for each stable state and for each time point , if a gene is ON ( = 1 ) or OFF ( = 0 ) in both the vector of discretized expression data and the vector of the stable state , we set the entry in the similarity matrix to 1 , otherwise , it is set to 0 . ( 6 ) For each time point and each stable state , we then summed up the corresponding similarity matrix row , and set an expression-based phenotypic ( EBP ) score for each stable state . The highest EBP score for each time point corresponds to the phenotype that is the closest to the studied sample and is representative of the status of the cells . The non-linear principal manifold was constructed for the distribution of all single and double mutants of the model in the space of computed model phenotype probabilities , using elastic maps method and ViDaExpert software [42–44] . We preferred using a non-linear version of principal component analysis ( PCA ) for data visualisation in this case , because it is known to better preserve the local neighbourhood distance relations and allows more informative visual estimation of clusters compared to the linear PCA of the same dimension [42] . For data analysis , only those “mixed” phenotypes were selected whose probability expectation over the whole set of single and double mutants was more than 1% . It resulted in a set of 1059 single and double mutants embedded into 6-dimensional space of phenotype probabilities for which the principal manifold was computed . The results of double mutants were used to quantify the level of epistasis between two model gene defects ( resulting either from gain-of-function mutation of a gene or from its knock-out or loss-of-function mutation ) with respect to metastatic phenotype . The level of epistasis was quantified using the simplest multiplicative null model applied for the event of not having metastasis: ε = ( 1-p12 ) - ( 1-p1 ) ( 1-p2 ) , where p1 and p2 are the probabilities of having metastasis in single mutants , and p12 was the probability of having metastasis in the double mutant . Therefore , negative values of the epistasis score E correspond to synergistic interactions when two gene defects amplify each other’s effect stronger than expected in the multiplicative model . On the contrary , positive values correspond to alleviating effect , when the effect of one gene defect could be masked ( sometimes , even reduced to zero ) by the second mutation . For genetic network visualisation , we kept the most significant interactions with ε<-0 . 2 or ε>0 . 3 values . These thresholds were chosen because at these levels we observed gaps in the distribution of ε values . The complete list of interactions together with p1 , p2 , p12 and ε values can be found as a Cytoscape 3 session ( S2 File ) .
Mesenchymal cells are characterised by their increased motility , loss of cdh1 ( coding for E-cadherin ) expression , increased expression of cdh2 ( coding for N-cadherin ) , and presence of vimentin ( Vim ) [7 , 10 , 45] . The EMT program can be initiated by the transcription factors snai1 , snai2 , zeb1 , zeb2 and twist1 . They are considered to be the core regulators of EMT as each has been shown to down-regulate cdh1 [46–50] . In turn , the genes coding for these core EMT-regulators are subjected to regulation by other signalling pathways . The TGF-β pathway has been reported to be able to induce EMT [7 , 51] , but other pathways are also involved in EMT including Wnt , Notch and PI3K-AKT pathways [52–56] . Furthermore , microRNAs regulate the Snai and Zeb family members . For example , miR200 targets snai2 , zeb1 and zeb2 mRNA [57–59] whereas miR203 targets snai1 and zeb2 mRNA [59] , and miR34 targets snai1 mRNA [60] . The transcription of these microRNAs is under the control of p53 [61–64] . The miR200 expression can also be induced by p63 and p73 proteins , while miR34 is only induced by p73 but is down-regulated by p63 [65–67] . The microRNAs can be down-regulated by the EMT-inducers Snai1/2 , and Zeb1/2 [59 , 60 , 68] . Note that the proteins p63 and p73 have been identified as members of the p53 protein family since their amino acid sequences share high similarity with that of p53 [69] . They are able to bind to the promoters of the majority of the p53-target genes and therefore have overlapping functions in cell cycle arrest and apoptosis [70 , 71] . The p53-family members are involved in cross-talks with Notch and AKT pathways: p63 protein is inhibited by the Notch pathway , p53 by AKT1 and AKT2 [69 , 72–76] while p73 is down-regulated by p53 ( itself negatively regulated by p73 ) , AKT1 , AKT2 , and Zeb1 [69 , 72 , 77] . The PI3K-AKT pathway has been considered to be important in evading apoptosis and cell cycle arrest by modulating the TRAIL pathway , down-regulating pro-apoptotic genes and phosphorylating p21 [78–80] . More recently , AKT has been assigned additional but important roles in the development of metastasis . AKT1 suppresses apoptosis upon cell detachment ( anoikis ) of the ECM [34] . The different isoforms of AKT seem to have opposing roles in the regulation of microRNAs: AKT1 inhibits miR34 and activates miR200 while AKT2 inhibits miR200 and activates miR34 [81] . Another opposing role for both AKT isoforms has been found in migration . AKT1 inhibits migration by phosphorylating the protein Palladin; phosphorylated Palladin forms actin bundles that inhibit migration . AKT2 increases the protein Palladin stability and upregulates β1-integrins stimulating migration [82 , 83] or by inhibiting TSC2 that , in turn , activates RHO [84] . Furthermore , AKT1 inhibits cell cycle arrest while AKT2 activates it [85 , 86] ( all these effects are shown implicitly in Fig 1A ) . Extracellular stimuli are also included in the logical model . Growth factors ( GF ) are soluble ligands that can be excreted locally or from longer distances and are able to activate the PI3K-AKT , and MAPK pathways [87 , 88] . Another extracellular stimulus might be the extracellular microenvironment ( ECMicroenv ) with components that are not soluble including the extracellular matrix . The ligands for the TGF-β pathway can be imbedded in the extracellular matrix [89–91] and the ligands for the Notch pathway are transmembrane proteins from adjacent neighbouring cells [92 , 93] . These mechanisms are depicted in an influence network ( Fig 1A ) . The network is composed of nodes and edges , where some nodes represent biochemical species ( proteins , miRNAs , processes , etc . ) and others represent phenotypes , and edges represent activating ( green ) or inhibitory ( red ) influences of one node onto other node . Each edge is annotated and supported by experimental papers ( see S1 Table ) . Throughout the article , we will use the general term “phenotypes” to refer to “phenotype variables” , which correspond to the four outputs: CellCycleArrest , Apoptosis , Metastasis ( depending on EMT , Migration and Invasion ) , and Homeostatic State ( HS ) as presented below . To assess the importance of each pathway on metastasis , apoptosis and cell cycle arrest , we simulated a gain of function or a loss of function , in the reduced model , for each module and for all combinations of inputs . These simulations mean that when an important entity in a pathway is altered , it affects the whole pathway activity . The model shows that mutations leading to either GoF or LoF of each pathway have opposing results in the occurrence of migration and for the occurrence of metastasis ( S2 Table ) . The Notch_pthw is an exception in this: both a GoF and LoF of the Notch pathway can lead to a stable state solution with metastasis ON . This might indicate that Notch ( pathway ) activity must be in a certain range in order to have a non-pathological effect or that Notch is important for the functioning of some dynamic feedback controls preventing metastasis ( so fixing it at a particular value would destroy these feedbacks ) . In addition , GoF of the Notch_pthw , TGFb_pthw , ERK_pthw , EMT_reg or AKT2 shows their inhibitory role in the apoptotic process as it has been demonstrated before [113–117] . For the p53 , TGF-β , EMT_reg and miRNA pathways , mutations leading to activation or inhibition have opposing results in regulating invasion when either the pathway is activated or inhibited . This effect on invasion is a direct result of having an activating or inhibiting role on EMT except for the TGF-β pathway . The role of TGF-β pathway has been investigated . The activation of TGF-β pathway might be dependent on the micro-environment as its ligands can be found in the extracellular matrix [89–91] . The triple mutant: Notch_pthw GoF , p53 LoF and TGFb_pthw LoF leads to one stable state in which the EMT_reg is ON but no metastasis , migration , invasion or apoptosis are reachable ( S2 Table ) indicating that activation of TGF-β pathway ( e . g . , by the peripheral tumour cells more exposed to the micro-environment ) is required to have metastasis in the double mutant by activating invasion [118 , 119] . To identify for each EMT regulator ( Snai1 , Snai2 , Zeb1 , Zeb2 , Twist1 ) their specific role in the different cell fates considered in our model , we simulated LoF and GoF mutants and observed that all GoF , except for that of Snai2 , led to the loss of apoptosis ( S3 Table ) . Metastasis can be reached for all GoF mutants but other phenotypes can still be reached depending on the combinations of inputs . The single deletions of each EMT regulator show that Zeb2 and Twist1 are required for metastasis . Zeb2 controls migration mainly through VIM but has no direct impact on invasion . Twist1 LoF , on the contrary , modulates negatively the possibility to reach not only the metastatic phenotype but also EMT , migration and invasion . Twist1 controls EMT through Cdh2 that controls migration and EMT . Other factors , such as CTNNB1 ( β-catenin ) or TGF-β , play a role in triggering the metastatic process by modulating invasion or migration , but our model suggests that the main EMT regulators are Zeb2 , Twist1 or Snai2 , either as loss of function for Zeb2 and Twist1 , or gain of function for Snai2 . Note that by definition , Cdh2 is absolutely required for metastasis to occur because of its direct role in controlling EMT and migration . In our model , Cdh1 inhibits EMT ( directly ) and migration ( through CTNNB1 and VIM ) but not invasion . Since all three phenotypes are required for metastasis , the process is thus impaired when Cdh1 is over-expressed [121 , 122] . The probability of achieving the metastatic phenotype for all possible single and double mutants was systematically computed using MaBoSS [123] . Each single and double mutant is characterised by the distribution of phenotype probabilities . A non-linear PCA analysis was performed as described in Methods , which allowed to group together single and double mutants having similar effect on the model phenotypes ( Fig 2A ) . In this plot , one can distinguish six major clusters ( a to f ) which can be tentatively annotated as “almost wild-type” ( no significant changes in the phenotype probabilities compared to the wild-type model ) , “risk of metastasis” ( elevated probability of having metastasis though not equal to 1 ) , “apoptotic” ( for these mutants Apopotosis and CellCycleArrest phenotypes are activated ) , “EMT without migration” ( for these mutants , presented as two clusters , the formation of metastases cannot be accomplished because the cells did not acquire ability to migrate ) , “cell cycle arrest only” ( these mutants are found arrested without starting EMT or invasion or apoptotic programs ) . The direction of increased metastasis probability is shown by dashed line in Fig 2A , which ends at NICD GoF/p53LoF double mutant for which the probability of having metastasis equals to 1 , according to the model ( whereas single p53 LoF mutation belongs to “almost wild type” and single NICD GoF mutation belongs to “risk of metastasis” clusters respectively ) .
In this study , we propose a logical model focusing on the specific conditions that could allow the occurrence of metastasis . Our model of the metastatic process represents its early steps: EMT , invasion and migration . A cell acquires the capability to migrate when both EMT and invasion abilities have been acquired . These steps are regulated by several signalling pathways , where genetic aberrations could influence the efficiency of metastatic process . Both the influence network and the assignment of logical rules for each node of this network have been derived from what has been published from experimental works as of today . With this model , we were able to explore known conditions ( and predict new ones ) required for the occurrence of metastasis . Our influence network describes the regulation of EMT , invasion , migration , cell cycle arrest and apoptosis known from the literature . In this regulatory network , cell cycle arrest and apoptosis are mechanisms or phenotypes that maintain homeostasis of organs [127] or ways to evade metastasis . Cell migration depends on pathways involving AKT , ERK , Vimentin , miR200 and p63 but also on the acquisition of EMT and invasive abilities such as producing MMPs to dissolve the laminae propria enabling migration to distant sites . Cells that have only invasive properties are not able to move as they are still well attached to their surrounding neighbouring cells resulting in absence of cell migration . Only when those two requirements are met and the other pathways allow migration , can metastasis occur . The role of each EMT regulator , for acquiring invasive properties , has been investigated and the model shows that each individual EMT regulator is sufficient to induce EMT when over-expressed and with the appropriate initial conditions . The model also predicts that a LoF mutation of the EMT regulators does not affect metastasis except for ZEB2 and TWIST1: ZEB2 inhibition leads to abrogation of migration , while a TWIST1 LoF leads to inhibition of EMT , since TWIST1 is the only transcription factor that can induce transcription of cdh2 gene which is required to have EMT . These regulators are interesting targets for therapy since both are more downstream in the metastasis’ cascade knowing that most activating mutations occur relatively more upstream e . g . KRAS and EGFR mutations . The model has been validated using experimental data by matching the transcriptomic data with stable state solutions of the logical model . The direct comparison between stable states and gene expression of tumour samples shows no conclusive results . This may be due to that only at the front of tumours , cells undergo EMT and this signal is obscured by the bulk of the tumour [30 , 128] . On the other hand , the model matches well the transcriptomic data from a time course experiment of lung carcinoma cell lines in which EMT was induced by increasing concentration of TGF-β . Qualitative simulations of the model using MaBoSS confirmed that single mutations are not sufficient to enable metastasis . Therefore , we systematically computed the level of epistasis of each two-gene mutation with respect to reaching the metastatic phenotype . We determined which double mutations are the most efficient for inducing metastasis with NICD GoF/p53 LoF mutations being the most efficient combination of gene knock-out and over-dosage , as this double mutant leads in silico to 100% probability of having metastasis . In our previous work , this specific double mutation NICD GoF/p53 LoF has been carried out experimentally in a mouse model , by crossing the villin-CreERT2 mice [129] ( in this study referred as p53 LoF ) and RosaN1ic mice [130] ( in this study referred as NICD GoF ) with the isogenic C57BL/6 animals to generate the NICD GoF/p53 LoF compound mice . These compound mice develop intestinal tumours with metastatic tumours to distal organs [31] . Our logical model successfully reproduces experimental observations of the compound mouse and proposes mechanisms explaining the metastatic phenotype with high penetrance in mice . In addition , we have investigated the role of TGF-β pathway in metastasis and showed its crucial role in the metastatic phenotype in the double mutant . Suppressing the TGF-β pathway might be an interesting target therapy to control metastasis , however future studies are required . We also explored the activity of the Wnt pathway in the double mutant . Increased activity of the Wnt pathway due to mutations in the apc and ctnnb1 genes leads to tumourigenesis of many cancers [131–133] and subsequently to metastasis [134 , 135] . Our mathematical model predicts phenotypes that correspond to adenocarcinomas as a result of linear progression of acquired mutations during sporadic colorectal cancer ( CRC ) suggested by the “Vogelstein sequence” [136] but no metastasis is reached with the model . Indeed , when we simulate APC LoF , KRAS GoF and p53 LoF ( the Vogelstein sequence ) , the model predicts stable states of cells that are not arrested in the cell cycle , can undergo EMT and can invade ( see S4 Table ) . Thus our logical model supports the hypothesis that the Wnt pathway contributes to tumour initiation [137] . However , there is still a debate if the Wnt pathway is actively involved in metastasis . For example , a negative correlation has been demonstrated between the presence of β-catenin and metastasis in breast cancer [138] , in lung cancer [139–141] , and in CRC [142–144] . It has been also demonstrated that the canonical Wnt pathway ( β-catenin-dependent pathway ) is suppressed at the leading edge of the tumour [145] and this might happen without affecting the β-catenin protein levels [146 , 147] . In the mouse model with Notch GoF /p53 LoF double mutation , in some tumours samples , mutations in apc and ctnnb1 have been found but also tumours without those mutations have been shown to acquire metastasis . Both truncated APC and mutations in β-catenin correspond in our mathematical model to full activation of CTNNB1 and this will induce activation of AKT1 . In our model , activation of AKT1 will inhibit migration and therefore inhibit metastasis . Appearance of metastasis in the mouse model with activated Wnt pathway might be putatively explained if one looks at the length of the truncated APC isoform for tumours with apc mutation . The APC mutation found in the Notch GoF /p53 LoF mouse model results in a relatively large truncated APC isoform that might still have inhibitory effect on β-catenin [148] . More details about the APC isoforms and its effect on β-catenin can be found in S3 Text . Another explanation for having metastasis in tumours with active Wnt pathway might be the involvement of another mutation that affects the akt1 or the akt2 gene . According to our model , the Wnt pathway inhibits metastasis by up-regulation of AKT1 . There are tumours in CRC patients ( TCGA data from http://cbioportal . org , [31] ) that can have an akt2 gene amplification or a homozygous deletion or missense mutation of akt1 . AKT2 induces migration while AKT1 inhibits migration thus the ratio AKT1 to AKT2 might be an important determinant for acquiring metastasis in the colon . Indeed studies have shown that AKT2 is predominant in sporadic colon cancer [149] and have a critical role in metastasis in CRC [150] . A Boolean model of EMT induction has been recently published , where the theoretical prediction that the Wnt pathway can be activated upon TGF-β administration was validated experimentally by measuring increased gene expression of the Wnt target gene axin2 in Huh7 and PLC/PRF/5 cell lines [151] . Those cell lines are derived from hepatocellular carcinomas [152 , 153] and both can harbour known mutations [154] and unconfirmed mutations ( http://tinyurl . com/l6mjd8y ) that affect the signalling pathways: the Wnt pathway has constitutive activity in the Huh7 cell line [137 , 155] . An alternative explanation could be that our model is more specific for epithelial cancers as the model depicts many reactions observed in epithelial cells; it has been shown that different types of cancer have different protein ( or isoforms ) abundance [112 , 149] . Therefore , our model might be less adequate in predicting the activity for certain nodes for hepatocellular carcinoma and lung adenocarcinoma . EMT is considered to be the first step and is very often modelled as an equivalent of having metastasis once it is activated . We provide here a logical model that proposes the involvement of three independent processes in order to have metastasis: EMT , invasion and migration . These phenotypes are controlled by an intricate network and only when EMT , invasion and migration do occur , can metastasis happen . The logical model explores the mechanisms and interplays between pathways that are involved in the processes , identifies the main players in these mechanisms and gives insight on how these pathways could be altered in a therapeutic perspective . Note that other mechanisms involving other alterations in the pathways that we model , or in other pathways might also take place , and we do not claim that our approach cover all possibilities of inducing metastasis . Still , our approach provides candidate intervention points for designing innovative anti-metastatic strategies .
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We provide here a logical model that proposes gene/pathway candidates that could abrogate metastasis . The model explores the mechanisms and interplays between pathways that are involved in the process , identifies the main players in these mechanisms and gives some insight on how the pathways could be altered . The model reproduces phenotypes of published experimental results such as the double mutant Notch+/+/p53-/- . We have also developed two methods: ( 1 ) to predict genetic interactions and ( 2 ) to match transcriptomics data to the attractors of a logical model and validate the model on cell line experiments .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration
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An increasing number of small RNAs ( sRNAs ) have been shown to regulate critical pathways in prokaryotes and eukaryotes . In bacteria , regulation by trans-encoded sRNAs is predominantly found in the coordination of intricate stress responses . The mechanisms by which sRNAs modulate expression of its targets are diverse . In common to most is the possibility that interference with the translation of mRNA targets may also alter the abundance of functional sRNAs . Aiming to understand the unique role played by sRNAs in gene regulation , we studied examples from two distinct classes of bacterial sRNAs in Escherichia coli using a quantitative approach combining experiment and theory . Our results demonstrate that sRNA provides a novel mode of gene regulation , with characteristics distinct from those of protein-mediated gene regulation . These include a threshold-linear response with a tunable threshold , a robust noise resistance characteristic , and a built-in capability for hierarchical cross-talk . Knowledge of these special features of sRNA-mediated regulation may be crucial toward understanding the subtle functions that sRNAs can play in coordinating various stress-relief pathways . Our results may also help guide the design of synthetic genetic circuits that have properties difficult to attain with protein regulators alone .
Small noncoding RNAs ( sRNAs ) have been demonstrated in recent years to play central regulatory roles in prokaryotes and eukaryotes [1–4] . Organisms that use sRNAs in post-transcriptional regulation range from bacteria to mammals . Interestingly , sRNAs are predominantly implicated in regulating critical pathways , such as stress responses in bacteria [5–15] , or developmental timing and cell differentiation in plants and metazoans [16 , 17] . Despite the recent surge of interest in sRNAs , their regulatory role in bacteria has actually been a subject of research for the last several decades . Early on , sRNAs were mainly recognized for their specialized roles in controlling the transposition of insertion elements [18 , 19] , in regulating plasmid copy number during plasmid replication [20–23] , and in mediating plasmid maintenance through the toxin-antidote system [24] . Those sRNAs studied are encoded on the antisense strand and in cis with their targets [23 , 25] , to which they bind through perfect base-pairing . This class of sRNAs will be referred to hereafter as antisense RNAs . In accord with their biological functions [25] , some of these antisense RNAs are metabolically stable ( e . g . , the ones controlling transposition [26] ) , whereas others are very unstable ( such as the ones controlling plasmid copy number [27 , 28] ) . For the latter , it has been demonstrated that the strength of inhibition is strongly related to the binding rate , rather than the binding affinity , of the antisense RNA and its target [29 , 30] . Until recently , only a few cases involving regulation by trans-encoded sRNA were known [31 , 32] . The advent of large-scale experimental techniques [33–36] and bioinformatic methods [35 , 37–39] has led to the identification and the subsequent verification of numerous such sRNAs in a variety of bacterial species in the past five years . Currently , there are over 70 such sRNAs identified in Escherichia coli [6 , 8 , 40] . Like regulatory proteins , these sRNAs can regulate the expression of multiple target genes , and are themselves regulated by one or more transcription factors . They have been implicated in the regulation of important pathways including oxidative response [15] , osmotic response [13 , 32] , acid response [9 , 10] , quorum sensing [7] , SOS response to DNA damage [11] , glucose-phosphate stress[14] , and more [5 , 6 , 8] . The mechanisms by which trans-acting sRNAs exert their effect are diverse . Most act by binding to the 5′ untranslated region ( UTR ) of a target mRNA [2 , 3 , 6] , with specificity achieved through ( often imperfect ) base-pairing between the two RNA molecules . Upon binding , these sRNAs can reduce the efficiency of translation initiation—e . g . , by interfering with ribosomal binding—or the stability of the target mRNA . Among these sRNAs that down-regulate their targets are RyhB ( regulator of iron metabolism ) [41–44] , OxyS ( oxidative stress ) [15] , and MicC and MicF ( osmotic stress ) [13 , 32] . In contrast , RprA and DsrA promote translation of their target , rpoS ( encoding the stationary phase sigma factor σs ) [5] , whereas GadY— the only sRNA in E . coli known to bind the 3′-UTR of its target— stabilizes its target [10] . A large class of trans-acting sRNAs bind tightly to the RNA chaperone Hfq , a highly abundant protein that also binds the target mRNA in a number of cases studied [15 , 45–48] . Binding to Hfq may protect these sRNA molecules from degradation in the absence of their mRNA targets [42 , 49–51] . Hfq has also been shown to facilitate the pairing of an sRNA with its target mRNA [43 , 52] , leading to the inhibition of translational initiation . In turn , pairing of the sRNA and mRNA exposes both molecules to rapid degradation [42 , 43 , 49 , 53] . Importantly , the interaction between the sRNA and its target is noncatalytic in nature , since a given sRNA molecule may be degraded along with its target , instead of being used to regulate other targets [42] . Some antisense RNAs can also interact with their targets in a noncatalytic fashion . For example , the antisense RNA RNA-OUT forms a highly stable complex with its target RNA-IN , encoding the IS10 transposase [54] . With a half-life of over 2 h for this complex [55 , 56] , the active antisense RNA may be regarded as irreversibly “consumed” by its target once the two bind . A similar stability is shown by CopA and its mRNA target [57] , which codes for the R1 plasmid replication initiation protein RepA [28] . Although the extended base-pairing between the antisense RNA and its target eventually exposes the sRNA–mRNA complex to degradation by RNase III , this coupled degradation has little effect on repression itself [56 , 58] . Thus , for this class of sRNA regulators , repression is implemented by the irreversible sRNA–target complex formation , which is also noncatalytic . The noncatalytic nature of sRNA–target interaction is qualitatively different from the catalytic effect of many protein regulators on the expression of their targets ( e . g . , protein regulators are not consumed upon regulating their targets ) . It is then interesting to ask whether sRNA-mediated regulation has special features distinct from protein-mediated regulation . Here we address this question using a combination of experimental and theoretical approaches . First , we describe the results of theoretical analysis that predicts a number of novel features for noncatalytic gene regulation by sRNAs . These features include a tunable threshold-linear expression pattern , a robust noise resistance characteristic , and a built-in capability for hierarchical cross-talk . These predictions are validated by a series of detailed experiments that quantified the regulatory effects exerted by the trans-acting sRNA , RyhB , on several targets in E . coli . We further extended the experiments to characterize the regulatory effect of the antisense RNA , RNA-OUT , to test the prediction that the novel features described above depended only on the noncatalytic nature of gene regulation and not necessarily on the degradation of the regulators themselves .
The noncatalytic nature of sRNA-mediated gene regulation suggests a novel threshold-linear mode of action , by which the expression of a target gene is silenced below a threshold , and is gradually activated above it ( Figure 1 ) . Consider first a case where sRNA and mRNA are co-degraded in a one-to-one fashion . In this case , if the transcription rate for the target mRNA ( αm ) is below that for the sRNA ( αs ) ( Figure 1A ) , then most of the targets are expected to pair with the sRNAs and be rapidly degraded , as suggested recently by Lenz et al . [7] . Conversely , if the transcription rate of the mRNA exceeds that of the sRNA ( Figure 1B ) , then most of the sRNAs are expected to turnover , whereas the unconsumed mRNAs are free to be translated into proteins . In the latter regime , the expressed protein level would reflect the difference between the two transcription rates . This scenario is summarized by the blue line in Figure 1C , where the steady state mRNA level of the target gene ( m ) is plotted against its transcription rate ( αm ) . Messenger RNAs are expected to accumulate only if the target transcription rate exceeds the threshold , which is given by the transcription rate of the sRNA αs ( vertical dashed line ) . The above qualitative prediction can be formulated quantitatively via a simple kinetic model for sRNA-mediated gene silencing . The model is cast in terms of two mass-action equations [7 , 59] for the cellular concentrations of the sRNA ( s ) and its target mRNA ( m ) In this model , transcription of mRNAs and sRNAs are characterized by the rates αm and αs , and their turnover by rates βm and βs respectively . The coupled degradation between sRNA and its target is described through a second-order kinetic constant k . The levels of Hfq and any endoribonuclease involved are assumed to be at saturation and are not tracked explicitly . The predicted pattern of gene expression is obtained by solving Equation 1 in the steady state , with the steady state mRNA level expressed in terms of the two control variables , αm and αs , and an effective parameter λ = βmβs/k . The latter , being the ratio of the spontaneous and mutual turnover rates , characterizes the rate of mRNA turnover that is not due to the sRNA , and is referred to below as the leakage rate; it is a ( inverse ) measure of the strength of sRNA–mRNA interaction . For strong , rapid sRNA–mRNA interactions , the leakage rate is small and the solution ( Equation 2 ) is given approximately by In the absence of leakage ( i . e . , λ = 0 ) , Equation 3 is just the threshold-linear function depicted by the blue line of Figure 1C . For small but finite λ , the mRNA level is somewhat larger , especially near the threshold ( where the denominators of the λ terms become small ) . Thus , leakage makes the transition smoother , as illustrated by the red line of Figure 1C , but does not change the qualitative feature of the threshold-linear form . We note that the value of the threshold ( αs ) is set by the sRNA transcription rate and is hence a dynamic variable that is controllable by the genetic circuit ( rather than a fixed quantity such as the binding affinity encoded by the genomic sequence . ) In particular , the threshold value is not affected by the strength of the interaction parameter k ( as long as the leakage λ is reasonably small to preserve the threshold-linear form ) . More generally , it is possible that degradation of the mRNA in the complex does not always lead to the degradation of the sRNA . Suppose that a fraction p < 1 of the sRNA is co-degraded with the mRNA . By repeating the above analysis , we find the same results , except that αs and λ in Equations 2 and 3 are replaced by αs/p and λ/p ( Text S1 ) . Thus , partial co-degradation of the sRNA would effectively increase the threshold target transcription rate and also increase the leakage . However , it is not expected to change the form of the threshold-linear response . Alternatively , the effect of p < 1 could be accounted for by rescaling both axis of Figure 1C ( i . e . , m and αm ) by a factor p . In a typical experiment , only the relative magnitudes of m and αm are determined ( see , e . g . , Materials and Methods ) . Therefore , the value of p does not make a difference when confronting the predictions of the model with experimental data , and we will use the steady-state solution ( Equation 2 or 3 ) below , regardless of the value of p . The kinetic model ( Equation 1 ) provides quantitative predictions given the knowledge of the different kinetic parameters . Below , we will apply the model to the sRNA RyhB [41–43 , 60] , which is one of the best characterized trans-acting sRNAs and for which ample kinetic data exist for us to infer realistic values for all of the essential model parameters ( see Materials and Methods , with the results summarized in Table 1 ) . From these parameter values , we estimate a leakage rate λ ≈ 0 . 1 nM/min for RyhB . This is small but non-negligible compared with the relevant range of the transcription rates , αm and αs . In fact , the smoothened threshold-linear expression pattern plotted in red in Figure 1C is the steady-state solution ( Equation 2 ) , with the RyhB parameters listed in the 3rd column of Table 1 . To validate the kinetic model ( Equation 1 ) , we tested experimentally its direct prediction , namely a smoothened threshold-linear response function as depicted in Figure 1C ( red line ) . To this end , we characterized quantitatively the response of a target gene regulated by the sRNA RyhB . RyhB is involved in regulation of iron homeostasis in E . coli , and is expressed at low cellular iron levels . Its targets include iron-storage and oxidative response genes [41 , 60] whose expressions are needed to combat problems associated with elevated iron levels , but not when iron is deficient [61] . To circumvent the complex regulation of the endogenous system , we constructed a synthetic target gene , consisting of the 5′ control region and the first 11 codons of sodB ( which is the strongest known natural target of RyhB [42 , 60] ) , translationally fused to the coding sequence of the reporter gfp . The target gene , crsodB-gfp , was driven by an inducible lac promoter , PLlac-O1 [62] , and placed on the multi-copy plasmid pZE12S ( see Materials and Methods ) . This construct allowed us to control the transcription rate of the target , αm , by changing the concentration of the inducer isopropyl β-d-1-thiogalactopyranoside ( IPTG ) in the medium . To quantify the relation between IPTG concentration and target transcription rate , we first characterized the bare target expression by transforming the pZE12S plasmid into a ryhB− strain of E . coli BW-RI . Expression of the target was assayed by measuring green fluorescent protein ( GFP ) fluorescence in the resulting cells grown in minimal M63 glucose medium with various amounts ( 0–0 . 5 mM ) of IPTG ( Figure S2 ) . For each concentration of IPTG , we use the slope of the fluorescence versus optical density at 600 nm ( OD600 ) plot ( for OD600 < 0 . 2 ) to define the promoter activity ( see Materials and Methods for a detailed description ) . We then repeated these measurements in cells harboring pZE12S and either chromosomal or plasmid-encoded ryhB . The expression rate of RyhB , αs , was controlled by a variety of means as detailed below . The GFP expressions at each level of RyhB expression ( extracted from plots similar to Figure S2 for each strain ) were then plotted against the above-defined promoter activity at the corresponding IPTG levels ( Figure 2A ) . As a calibration , we first observed that GFP expressions in wild-type ( ryhB+ ) cells ( strain ZZS22 ) grown in media with 100 μM FeSO4 ( red circles ) are indistinguishable from those of the ryhB− cells ( strain ZZS21 ) ( dashed black line ) , indicating the complete repression of RyhB activity at such a high iron level as expected . For the same wild-type ( ryhB+ ) cells grown in media with no added iron ( red crosses in Figure 2A ) , GFP expressions were moderately reduced across all IPTG levels , and more so for a strain carrying a multi-copy plasmid that harbors ryhB driven by its native promoter ( green crosses , strain ZZS24 ) . These results are qualitatively consistent with the expected increase of RyhB expression upon reducing the iron level and upon adding multi-copy plasmid-borne sources . To see the effect of even higher RyhB expression , we characterized GFP expression for a strain which was deleted of the chromosomal ryhB gene , but carried another plasmid harboring the ryhB structure gene driven by the strong synthetic PLtet-O1 promoter [62] inducible by anhydrotetracycline ( aTc ) ( strain ZZS23 ) . In the absence of aTc in the growth medium ( blue circles ) , GFP expression was essentially indistinguishable from that of the RyhB-less strain ( ZZS21 , dashed black line ) . The addition of small amounts of aTc ( blue crosses , squares , and asterisks ) drastically reduced GFP expression ( up to 30-fold reduction compared with that of the RyhB-less strain ) . Altogether , using combination of chromosome and plasmid sources for RyhB , we present in Figure 2A the response of the target gene crsodB-gfp to varying promoter activities in the presence of six different levels of RyhB expression . To verify that RyhB regulation was indeed achieved primarily through changes in the target mRNA level , we quantified the levels of the crsodB-gfp mRNA directly for strain ZZS23 ( harboring plasmid-borne PLtet-O1:ryhB ) at two distinct levels of RyhB expression ( corresponding to 0 and 5 ng/ml aTc added to the growth medium ) and a variety of transcription levels for the crsodB-gfp target ( 0 . 1 , 0 . 25 , 0 . 5 mM IPTG in the growth medium ) using quantitative real-time PCR ( RT-PCR ) ( Figure 2B ) . We find that reduction in mRNA level is consistent with the corresponding reduction in GFP fluorescence; compare the solid and striped bars . The interaction between RyhB and its endogenous targets depends on the RNA chaperone Hfq [41 , 43 , 49] . To demonstrate that the interaction between RyhB and the synthetic target , crsodB-gfp , shares this property , we repeated our measurements in strains deleted of hfq . In Figure 2C ( middle group ) , we show the ratio between GFP fluorescence levels in a hfq− strain expressing RyhB from the PLtet-O1 promoter ( ZZS23q ) and a RyhB-less hfq− strain ( ZZS21q ) for various levels of the inducers IPTG and aTc . We found that in the absence of hfq , the aTc dependence of the isogenic hfq+ strain ( Figure 2C , left group ) was completely abolished , and GFP expressions all became the same as those of the RyhB-less strains ( all of the bars of the middle group take on values ∼1 ) . The results indicate that hfq is required for the repression effect observed here . This behavior is expected for a RyhB target , since RyhB accumulation and RyhB-target interaction requires Hfq [41–43 , 53 , 63] . As a different control , we characterized the GFP expression for hfq+ strains in which the plasmid pZE12S was replaced by pZE12G , harboring the same PLlac-O1:gfp reporter , except that the 5′-UTR of the gfp gene was a short 27-base segment containing a strong ribosomal binding site instead of the sodB control region ( see Materials and Methods ) . In Figure 2C ( right group ) , we show the ratio between GFP fluorescence in a strain expressing RyhB from the PLtet-O1 promoter ( strain ZZS13 ) to that in the RyhB-less strain ( ZZS11 ) . We find that different degrees of RyhB expression have no effect on the observed GFP activity for all the inducer levels tested , indicating that the sodB control region is required for interaction . The data of Figure 2A reveal a spectrum of gene expression patterns: with RyhB expression strongly repressed ( blue and red circle , red crosses ) , expression of the target gene was mainly controlled by the activity of its own promoter ( controlled by IPTG ) , whereas for high RyhB expression ( blue asterisks ) , target expression was greatly reduced regardless of the promoter activity . This qualitative behavior is what would be expected based on the model depicted in Figure 1 . To make quantitative comparison with the predictions of the kinetic model ( Equation 1 ) , the data in Figure 2A were fitted to the steady-state solution of the model , Equation 2 . This fit requires a single global parameter ( associated with the leakage rate λ ) and one additional free parameter ( corresponding to the activity of the promoter expressing the sRNA , αs ) per curve . The latter characterizes the position of the softened threshold , and is listed in Table S1 for each RyhB source studied . The corresponding best-fit curves are shown as the colored lines in Figure 2A . The data of Figure 2A are fitted very well by softened threshold-linear form predicted by the model: a prominent feature of the predicted behavior—that target gene expressions all have the same linear dependence on its promoter activity at high expression levels much beyond the threshold , i . e . , —is clearly reflected by the red , green , and the top blue curves for which the thresholds are much below the maximal promoter activity probed . The predicted threshold-linear response is best seen for intermediate RyhB expressions ( green and blue crosses , blue squares ) ; target expression was strongly repressed at low transcription levels , but turned up sharply for increasing activities of the target promoter . Another way to present or view the threshold-linear response is that the fold-repression exhibited at a given RyhB transcription rate should decrease as the rate of target transcription increases . This is shown for the data of Figure 2A in Figure S4 . We performed similar characterization for strains harboring a synthetic chromosomal target ( PLlac-O1:crsodB-gfp inserted at the attP site ) ; see caption of Figure 2D for details . While quantitative characterization of GFP expression becomes much more difficult for this chromosomally encoded target due to the low expression level , we see qualitatively from Figure 2D that the same trend is obtained . As motivated in the theoretical study , we expect the threshold-linear response to be a generic feature of noncatalytic mode of gene regulation , not necessarily limited to sRNA-target pairs that undergo coupled degradation . For a number of the antisense RNA-target pairs , e . g . , CopA/RepA of the R1 plasmid [57] and RNA-OUT/RNA-IN of the transposon IS10 [55 , 56] , the pairing of the antisense RNAs with their respective targets was found to be irreversible but stable for hours . From the theoretical perspective , as long as the duplex does not dissociate back into the two active RNA components at relevant time scales , the system can still be described by the kinetic model ( Equation 1 ) if we identify m and s as the free mRNA and sRNA concentrations . We thus expect the same smoothened threshold-linear response as described above . We tested this prediction using RNA-OUT , the antisense sRNA that regulates the transposition of the IS10 insertion element in E . coli [18 , 19] . In IS10 , the transposase gene ( referred to here as is10in ) is driven by the pIN promoter . Located only 35 bases downstream on the opposite strand is the pOUT promoter , which drives the transcription of the gene is10out encoding RNA-OUT . The prefect base pairing between the two RNA molecules at the 5′-UTR of is10in leads to a strong irreversible binding [55 , 56] , which represses the translation of is10in [54] with only mild effect on its stability [56] . Quantitative data from previous experiments [26 , 56 , 64–68] in which RNA-OUT was expressed in both cis and trans allowed us to estimate key parameters for this sRNA-target pair ( Text S1 and Table 1 , column 4 ) . Most of these parameters take on values similar to those we estimated for RyhB and its sodB target ( Table 1 , column 3 ) . However , the degradation rate of RNA-IN , βm , is larger than the corresponding rate of typical RyhB targets [64] , making the leakage rate λ larger . We therefore expect is10in to exhibit a somewhat smoother threshold-linear expression pattern . In the native IS10 system , however , the sRNA and its target are expressed in cis . This is likely to increase the sRNA-target binding rate ( k ) substantially , hence reducing the leakage λ and making the transition sharper . To measure the effect of repression by RNA-OUT , we constructed a synthetic target consisting of a modified is10in control region translationally fused to gfp . The control region we use differs from that of the native is10in in two nucleotide positions , making its ribosome binding site ( RBS ) stronger ( see [64] and Materials and Methods ) . The target gene , referred to as cris10-gfp , was inserted immediately downstream of the PLlac-O1 promoter in plasmid pZE12IS . Promoter activities at eight levels of IPTG ( 0–0 . 75 mM ) were established as described before ( Figure S2 ) , by measuring GFP fluorescence in a strain ( ZZS31 ) which carries pZE12IS but no RNA-OUT . As a controlled source of RNA-OUT , we used the pZA31O plasmid , which harbors the is10out gene driven by the strong synthetic PLtet-O1 promoter [62] . We measured the response function at four different expression levels of RNA-OUT using different concentrations of the inducer aTc ( 0 , 2 , 6 , and 10 ng/ml ) . The data obtained ( symbols in Figure 3A ) were fitted to the steady-state solution ( Equation 2 ) as described above; best-fit parameters are given in Table S2 . The fitted curves are presented as the solid lines in Figure 3A . In the absence of aTc , cris10-gfp expression coincides with that of the corresponding strain with no RNA-OUT source ( dashed black line ) . At higher levels of aTc , the threshold-linear response is recovered , displaying a smooth transition as expected . To verify that RNA-OUT repressed the translation of cris10-gfp mRNA without significantly altering its accumulation , we quantified the mRNA concentration of cris10-gfp using RT-PCR . The result is shown in Figure 3B . Whereas the GFP expression is repressed by more than 4-fold upon the addition of 10ng/ml aTc ( solid blue and red bars ) , the mRNA levels were hardly affected by aTc addition ( striped blue and red bars ) . Together , the results of Figure 3A and 3B validate the prediction that coupled degradation is not necessary for the threshold-linear form if the coupling between the sRNA and its target is irreversible . Some trans-acting sRNAs ( including RyhB ) have been shown to regulate multiple targets [6 , 8 , 60] whose expressions are independently regulated [69] . Because any one of the targets can reduce the level of sRNA , it is plausible for sRNA to mediate indirect interaction ( cross-talk ) between its different targets . In Figure 4A , we compare the expression of our reporter target , crsodB-gfp , in cells with and without the sodB structure gene ( strains ZZS22 and ZZS22s respectively ) , both containing the chromosomal ryhB gene . Figure 4A shows that ( i ) the expression of the crsodB-gfp reporter is affected by the existence of the chromosomal sodB gene , with up to 4-fold higher expression in the sodB− mutant ( strain ZZS22s ) , and ( ii ) the degree of enhanced reporter expression in this strain is dependent on the transcriptional activity of the reporter ( the x-axis , controlled by IPTG ) . In comparison , no significant difference in expression was observed between sodB+ and sodB− in ryhB− mutants ( unpublished data ) . The results of Figure 4A suggest that the expression of the chromosomal sodB indeed interfered with the repression of crsodB-gfp by RyhB as anticipated . It is straightforward to extend the kinetic model ( Equation 1 ) to the case of multiple targets and account for the indirect interaction between them ( Text S1 ) . Assuming similar degradation rates for the two targets in the absence of the sRNA , the expressions for the mRNA level take the same functional form in the presence or absence of additional targets . To address the data of Figure 4A , we performed independent fits of the data of the sodB+ strain ( red line in Figure 2A ) and the data of the sodB− strain . The ratio of the two is given by the black curve in Figure 4A . The shape of this curve shows that the effect of the sodB gene on the expression of the GFP reporter was peaked at a level of its promoter activity that corresponded to the expression threshold of the sodB+ strain ( ZZS22 ) ( see the position of the kink of the red line of Figure 2A ) . This is a manifestation of the general prediction of the theory that target expression is most sensitive to changes in sRNA levels at the threshold , where the transcription of the sRNA and its target just balances . Further evidences for cross-talk between different targets of RyhB are given in Figure 4B . The mRNA levels of two chromosomal RyhB targets ( sodB and fumA [41 , 60] ) , as quantified by RT-PCR , are shown for different expression levels of the synthetic target gene ( crsodB-gfp ) driven by the PLlac-O1 promoter carried on the pZE12S plasmid . Open bars correspond to the control strain with no RyhB source ( ZZS21 ) , and colored bars correspond to different degrees of RyhB expression corresponding to different levels of aTc ( in strain ZZS23 ) . The x-axis indicates different levels of target expression , induced by IPTG . At basal expression level ( no IPTG added ) , expression of the chromosomal targets is repressed by RyhB up to 10-fold ( compare the blue and red bars for [IPTG] = 0 ) . High expression of the plasmid target effectively rescues the chromosomal targets from repression ( [IPTG] = 0 . 5 mM ) . These data suggest that the cross-talk between different targets may allow for one target to relieve sRNA-mediated repression of another target . To explore this possibility , we used our model ( Equation 5 in Text S1 ) to calculate the expression level of a reporter target ( geneR ) that is regulated by the same sRNA as another target gene ( geneT ) . We denote the transcription rates of the two genes by αR and αT , respectively , and their binding rates to the sRNA by kR and kT . The predicted dependence of geneR mRNA level on the ratio between transcription rates of the two genes ( αT/αR ) , and the ratio between the two binding constants ( kT/kR ) , is displayed in Figure 4C ( where geneR mRNA level is measured in units of its level in the absence of the sRNA ) . In this figure , transcription rate of geneR is chosen to be 5 times smaller than that of the sRNA . Therefore , in the absence of geneT ( αT = 0 ) , expression of geneR is strongly suppressed by the sRNA . Figure 4C portrays a hierarchical cross-talk effect: the expression of a weakly interacting target ( e . g . , geneR , with a small kR ) is highly affected by another target that is more strongly interacting ( e . g . , geneT , with kT > kR ) ; see the large kT/kR region of Figure 4C , where the expression of geneT ( increasing αT/αR ) indirectly activates geneR by relieving the sRNA repression . Conversely , a strongly interacting target ( e . g . , geneR , with a large kR ) is expected to be much less affected by a weakly interacting one ( e . g . , geneT , with kT < kR ) . Thus , in the small kT/kR region of Figure 4C , the expression of geneR remains suppressed even when geneT is highly expressed . Interestingly , our calculation predicts that for large kT/kR , the response of geneR to changes in the transcription rate of geneT may be very sharp . For example , the data of Figure 4C allow for an effective Hill coefficient ∼10 for kT/kR ≈ 2 . Thus , the sensitivity of the sRNA-mediated repression may be translated into sensitivity in the indirect interaction between its targets .
Analysis of a simple model of protein-mediated gene regulation ( Text S1 ) predicts that regardless of whether a protein regulator acts as a transcriptional repressor or as a catalyst of mRNA degradation , target expression always increases linearly with the promoter activity . The ratio between expression levels at different concentrations of the regulator is independent of the target activity ( Figure 5B ) . Thus , one can safely talk about the strength of repression in term of the fold-change in gene expression in the presence and absence of the repressor without referring to the rate of target transcription . This is , however , not the case for the threshold-linear mode that characterizes sRNA-mediated regulation . Here the fold-change depends not only on the presence of the repressor , but also on the transcription of the target ( Figure 5A , arrows ) . For the same degree of repressor transcription ( e . g . , compare the red and blue lines ) , the fold repression could be small ( e . g . , 2-fold ) above the threshold and large ( e . g . , 25-fold ) below the threshold . This property may have functional consequences: sRNA may serve to tightly shutdown a gene that is repressed by other means . However , at circumstances that allow for high expression of the target , sRNA expression may exert virtually no effect . Moreover , in the threshold-linear mode of sRNA-mediated gene regulation , the onset of repression is set by comparison of transcription rates between sRNA and its target . As a result , the threshold value is dynamically tunable through controlling the rate of sRNA transcription . In contrast , protein–operator binding affinity , which controls the onset of repression in protein-mediated regulation , is fixed genetically by the operator sequence . Dynamic control of the latter would require other cofactor ( s ) and auxiliary binding sites and become more elaborate to implement . Of course , the more complex mode of control described here for sRNA can , in principle , be realized through more complex promoters involving more complex protein–protein interactions [74] . Also , features of sRNA-mediated regulation discussed here may also be realized by proteins that regulate the proteolysis of their targets in noncatalytic ways . In the latter case however , the steady co-degradation of protein regulators may pose a substantial metabolic load . In a number of cases studied , a sRNA serves as a node in a regulatory cascade . Expression of the sRNA may be controlled by protein regulator that senses ( directly or indirectly ) an environmental signal . For example , the Ferric Uptake Regulator ( Fur ) is activated by free Fe2+ ions and negatively regulates transcription of RyhB , which in turn regulates targets whose expressions are required when Fe2+ is abundant in the cytoplasm [41 , 60] . Our results suggest that sRNA regulators may be more than a simple “inverter” of such a protein regulator . sRNA regulators could act , for example , as a “stress-relief valve . ” In the iron example , whereas Fur senses levels of Fe2+ continuously ( through rapid equilibration between Fur and Fur–Fe2+ ) , we predict that targets of RyhB will only be expressed when the Fe2+ level crosses some threshold . This threshold can be set dynamically for each target by regulators controlling its transcription . Recently it has been suggested that targets of microRNA regulation in eukaryotes may be classified as “switch , ” “tuning , ” and “neutral” targets , depending on their response to microRNA level [75 , 76] . In the framework presented here , these classes correspond to targets whose transcription rate is well below , near , or well above that of the RNA regulator . We emphasize , however , that the threshold-linear picture we draw is only applicable if the level of the free RNA regulator is affected by its interaction with its targets , i . e . , for regulators that operate in the noncatalytic mode . This is yet to be established for microRNAs in eukaryotes . Our model predicts that deep in the repressed state , the sRNAs strongly repress variations in protein expression . The effect of noise on gene expression is a subject of extensive current research [77–80] . We studied this effect theoretically by generalizing the model ( Equation 1 ) to incorporate stochastic fluctuations ( Text S1 ) . In Figure 5C , we compare results of stochastic simulations for two genes with the same low mean protein expression: geneA is silenced by a sRNA , and geneP is repressed transcriptionally by a protein regulator . In general , we predict a much-reduced variance in protein level for sRNA-mediated regulation ( Text S1 ) . This can be understood by inspecting the time courses of protein expression ( Figure 5C ) . With the protein regulator ( red curve ) , any leakage in transcription is amplified through translation , resulting in large bursts of protein expression , as was recently observed experimentally [81 , 82] . With the sRNA ( blue curve ) , gene expression is expected to be much smoother , because mRNA molecules are rarely translated . This difference in the noise properties may be very important in situations where a large burst of proteins will switch a cell from one stable state to another . In cases such as stress responses where unintentional entry into the alternative state may be harmful and spontaneous switching is to be avoided , sRNA-mediated regulation might possess a distinct advantage . Attenuation of noise by decreased burst size may also be accomplished by eukaryotic microRNAs [76] , through a decrease in mRNA stability or inhibition of translation . sRNA-mediated regulation was predicted to be ultrasensitive to small changes in sRNA expression near the threshold [7] . A common measure for the abruptness of a transition , referred to as the “sensitivity , ” is the maximal slope of the response curve , m ( αs ) , in a double-log plot . From the solution ( Equation 2 ) , we find this sensitivity to be given by , which quantifies our statement that lower leakage makes a sharper transition , and also predicts a sharper transition for highly expressed targets . For sRNA regulators described by the parameters of Table 1 , we find the sensitivity to be given approximately by 2 . 5 for αm = 1 nM/min , and 4 . 3 for αm = 3 nM/min . In comparison , the sensitivity of a protein repressor is bounded by the Hill coefficient , which is typically ≤2 , although higher sensitivity ( 3∼4 ) can also be accomplished via , e . g . , DNA looping [73] . On the other hand , much higher sensitivity can be achieved by processes such as those with zeroth-order kinetics [83] . Our data demonstrate how the activity of a strong target of RyhB may influence the expression of another target . In particular , we show that over-expression of a plasmid-borne target relieves completely the strong sRNA repression of its chromosomal target . Generalizing our kinetic model offers a simple intuitive picture ( Figure S1 ) . A weak sRNA target ( geneR ) is completely repressed by the sRNA when another , stronger target ( geneT , with kT ≫ kR ) is not expressed ( Figure S1A ) . Expression of the latter captures a significant portion of the sRNAs , thus allowing some mRNA molecules of geneR to be translated into proteins ( Figure S1B ) . On the other hand , expression of another target weaker than geneR may not attract enough sRNA to affect the expression of geneR ( unpublished data ) . In the context of a single target , our model predicts that the strength of the sRNA–target interaction influences only the smoothness of the transition , but not the threshold value of the threshold-linear expression pattern . However , when multiple targets are expressed simultaneously , the different mRNA species are expected to compete for association with the same pool of sRNA , and the relative interaction strength becomes a key determinant of the complex interactions that ensue . The interaction strength of the different targets sets their relative position in the cross-talk hierarchy , where targets of a given binding strength affect—but are not affected by—targets of lower binding strength . Through quantitative characterization of gene regulation for two distinct classes of sRNA regulators , we have shown that sRNA-mediated regulation has many functional properties that are fundamentally different from the classical , protein-mediated mode of gene regulations . Analysis of our model suggests that sRNAs may offer tight regulation below the threshold ( repressing the average expression and reducing fluctuations ) accompanied by derepression away from the threshold . Taken together , this suggests that sRNAs working in the threshold-linear mode may be particularly suitable for a “stress-relief” mechanism , where no action is elicited until a tolerance threshold is exceeded . Knowledge of these properties is essential to an integrated understanding of gene regulatory systems , and may inspire the design and synthesis of novel genetic circuits [84] with properties difficult to obtain by using regulatory proteins alone .
All experiments were performed with BW-RI cells derived from E . coli K-12 BW25113 [85] , with the transfer of the spr-lacI-tetR cassette from DH5α-ZI cells [62] by phage P1 transduction . This cassette provides the constitutive expression of lacI and tetR genes [62] . For some experiments , ryhB and/or sodB were deleted from BW-RI [85] . These strains were then transformed by the following target and source plasmids . All strains and plasmids used are summarized in Table 2 . pZE12-luc , whose copy number has been estimated at 50–70 copies [62] , was used to make the target plasmid pZE12S . Using site-directed mutagenesis , an EcoRI site was created by adding GAAT immediately downstream of +1 of the PLlac-O1 promoter . The region between the newly created EcoRI site and the resident EcoRI site 6 bp upstream of RBS was then deleted by EcoRI digestion and subsequent religation , yielding pZE12-lucM . The KpnI-XbaI flanking luc gene in pZE12-lucM was replaced by the gfpmut3b structure gene [86] . This yields pZE12G , which harbors the PLlac-O1:gfpmut3b construct with a 5′-UTR defined by an EcoRI site immediately downstream of +1 and a KpnI site immediately upstream of the translation start of gfpmut3b . The 15-base sequence sandwiched by the EcoRI and KpnI sites , ATTAAAGAGGAGAAA , contains an RBS indicated by the underlined bases . The 5′-UTR from the control region of sodB ( crsodB , from −1 to +88 relative to the transcriptional start site of sodB and including the first 11 codons ) was cloned into the EcoRI and KpnI sites of pZE12G , yielding pZE12S . pZE12S therefore contains the ColE1 ori , the PLlac-O1 promoter [62] , and crsodB fused to the coding sequence of the gfpmut3b gene . Similarly , the control region of is10in ( from +1 to +36 ) was substituted for crsodB in pZE12S , yielding pZE12IS . To improve the expression level , the RBS in the is10in control region was modified by changing TC ( +16 to +17 ) to GG . Three sRNA-source plasmids ( pZA30R , pZA31R , and pZA31O ) , were derived from the pZA31-luc plasmid , which has been estimated to maintain at 20–30 copies per cell [62] . Each contains the p15A replication ori and is marked by chloramphenicol resistance . First , a NdeI site was added immediately downstream the +1 of the luc gene by inserting ATG between +2 and +3 , and a BamHI site was added downstream of luc by inserting ATC between the 1 , 772th and 1 , 773th nucleotides , yielding pZA31-lucNB . For pZA31R , the ryhB gene ( from +1 to +96 cloned directly from E . coli K-12 ) was ligated into the NdeI/BamHI sites of pZA31-lucNB , replacing the luc gene . For pZA30R , the PLtet-O1 promoter and the luc gene of pZA31-lucNB were replaced by PryhB:ryhB ( from −62 to +96 cloned directly from E . coli K-12 MG1655 ) , which contains the ryhB gene and its native promoter . Finally , for pZA31O , the is10out gene ( from +1 to +103 ) was substituted for the luc gene in pZA31-lucNB . In addition , we transferred the target crsodB-gfp to the attP site of strain ZZS00 ( ryhB− ) chromosome using the method of Diederich et al . [87] . Briefly , a SalI/BamHI-flanked PLlac-O1: crsodB-gfpmut3b containing the downstream terminator was cloned into the same sites of pLDR10 containing the attachment site attP and encoding the chloramphenicol ( Cm ) resistance . The recombinant plasmid was digested with NotI and the larger portions of the plasmids containing the fragment of interest but not the ori were religated . The circular DNA molecules were transformed into ZZS00 cells expressing the int gene contained in pLDR8 , a helper plasmid bearing a temperature-sensitive ori and encoding the kanamycin ( Km ) resistance . The transformations were applied on LB+Ap plates that were incubated at 42 °C . The transformants were tested for sensitivity to Cm and Km . The ampicillin ( Ap ) -resistant but Cm- and Km-sensitive transformants were identified as the clones that carry the DNA fragment of interest at the attP site of E . coli chromosome . BW-RI strains each containing the target and/or source plasmids were grown in M63 minimal media with 0 . 5% glucose , and standard concentrations of the appropriate antibiotics . The overnight cultures were diluted into fresh M63 media ( OD600 ≈ 0 . 002 ) containing the appropriate antibiotics as well as varying amounts of the inducers ( aTc , IPTG , FeSO4 ) in the wells of 48-well plates . The plates were incubated with shaking at 37 °C and taken for OD600 and fluorescence measurements every hour for up to 12 h ( until a final OD600 of 0 . 2–0 . 3 ) using a TECAN Genios-Pro plate reader ( http://www . tecan . com ) . Each measurement was repeated 3–6 times and the data were analyzed as discussed below . For RT-PCR measurements , overnight cultures were used to inoculate M63 medium with 0 . 5% glucose , standard concentrations of the appropriate antibiotics , and various concentrations of inducers to an initial OD600 of 0 . 001 and grown in 48-well plates in a 37 °C incubator . OD600 and GFP fluorescence were monitored periodically ( if applicable ) . When OD600 of these cultures reached 0 . 3–0 . 5 , approximately 109 cells of each culture were harvested in a microcentrifuge at 4 °C , treated with 10 mg/ml lysozyme in TE buffer ( pH = 8 . 0 ) and total RNA was collected using an Absolutely RNA miniprep kit ( Stratagene; http://www . stratagene . com ) . The prepared samples were then treated with Turbo DNA-free DNase ( Ambion; http://www . ambion . com ) , and PCR controls were performed on each sample to verify the absence of contaminating DNA . cDNA was prepared with 1 μg of RNA from each sample using Superscript III First Strand Synthesis system ( Invitrogen; http://www . invitrogen . com ) . Dilutions of the resulting samples were then used as the template in PCR reactions using iQ SYBR Green Supermix ( Bio-Rad; http://www . bio-rad . com ) in a Smart Cycler thermal cycler ( Cepheid; http://www . cepheid . com ) . To measure expression from a chromosomal target , cells were grown overnight in minimal media with antibiotics . Cultures were then diluted to OD600 = 0 . 001 , and grown in a 12-well plate with 3 ml of culture in each well , with appropriate antibiotics and inducers . To determine the growth rate , OD600 was measured every 60 min . Cultures were grown at 37 °C with constant shaking until they reach OD600 = 0 . 3 , at which time 1 . 7 ml of each culture was spun down and resuspended in 1 ml phosphate buffer solution ( PBS ) . GFP fluorescence was measured using a Becton-Dickinson FACSCalibur flow cytometer with a 488-nm argon excitation laser and a 515- to 545-nm emission filter ( FL1 ) at a low flow rate . Photomultiplier tube ( PMT ) voltage was set to 950 V , and a linear amplifier was set at 9 . 5× . Forward scatter and fluorescence values were collected for 50 , 000 cells . To obtain gene expression patterns for the different strains , we averaged ( for each time point ) the data obtained from the different repeats for each combination of strain and inducers . First , the cell doubling rate ( μ ) was obtained as the slope of a linear fit of log2 ( OD600 ) versus time for each strain and condition; this yielded a doubling time of ∼2 h for most strains . Next , for all of the time points concerning each strain and condition , we plotted the average fluorescence versus average OD600 on linear-linear plot and extracted the slope ( f ) . In Figure S2 we show , for example , GFP fluorescence against OD600 for the ryhB− strain ( ZZS21 ) , together with the fitted slopes . Each slope gave the average fluorescence per growing cell ( in unit of relative fluorescence units ( RFU ) /OD ) for that strain and the corresponding growth condition . The raw fluorescence production rate per cell was computed as fμ ( 1 + μτ ) [88] , upon taking into account of the maturation kinetics of GFPmut3 ( maturation half-life τ of ∼30 min ) [86] . We then subtracted away from this raw rate the background fluorescence production rate , obtained in the same way from data collected from our negative control strain BW-NULL . This yielded the rate of GFP production synthesis from PLlac-O1 , and is referred to as the GFP expression . The results were plotted in Figure S3 at each IPTG level for different levels of RyhB expression , via the PLtet-O1 promoter controlled by the amount of aTc in the growth medium . To fit the experimental data with the steady-state solution ( Equation 2 ) , we assume that the GFP expression defined above is proportional to m , the steady-state mRNA level , i . e . , GFP expression = bm , where b reflects the rate of GFP translation and maturation . Then , Equation 2 can be written in the following way , where is the GFP expression in the absence of the sRNA , referred to as the promoter activity and set by the IPTG concentration , is proportional to the transcription rate of the sRNA ( and therefore takes different values for different experiments ) ; and is proportional to the leakage parameter ( defined in Results ) . The latter is independent of the sRNA activity , and should be chosen once for all experiments . We fitted the data to f ( a , aa , aλ ) using a standard Levenberg-Marquardt algorithm implemented in MATLAB ( MathWorks; http://www . mathworks . com ) , with the least-square error defined as The values of the best-fit parameters obtained are given in Table S1 in terms of 0 . 5 confidence intervals . The values of the model parameters can be estimated from various experiments . Consider first RyhB and its targets [41 , 42] . In the absence of its targets , the Hfq-bound sRNA RyhB is very stable , with a half-life of ∼30 min [42 , 49] , yielding βs ∼ 1/50 min−1 . Similarly , from the half-life of ∼6 min for sodB mRNA [42] in the absence of RhyB , we have βm ∼ 1/10 min−1 . Moreover , DNA microarray experiments [69 , 89] indicated approximately 10–20 copies/cell for the sdhCDAB and sodB mRNA in rich medium ( where iron is abundant and RyhB is expected to be repressed ) . This suggests a target transcription rate ( αm ) of ∼ 1 nM/min in the state where mRNA is expressed . In general , αm is controlled by various cellular signals ( e . g . , sdhCDAB by Crp-cAMP ) and can typically vary ∼10-fold . ( The DNA microarray study of Zhang et al . [69] showed approximately 5-fold change in sdhCDAB and sodB mRNA levels under various physiological conditions . ) On the other hand , the activity of the RyhB promoter has a broad range , since it is strongly regulated by Fur-Fe2+ whose concentration can vary over 1000-fold [61] . We model the latter by allowing αs to take on the range from 0 . 1/min to 10/min . Finally the coupled degradation rate k can also be deduced from the results of Masse et al . [42] ( assuming p of order 1 ) . Because RyhB is shown to disappear in the presence of its targets within 3 min , then by using an estimated target mRNA concentration of 20 nM , we find 1/50 ( nM min ) −1 , which is close to the diffusion-limited association rate for typical small proteins [90 , 91] and is similar to what has been observed directly for the sRNA OxyS and its target fhlA [92] , as well as for the antisense hok/sok pair [93] . Finally , we consider RNA-OUT and its target , the mRNA of is10in . RNA-OUT itself is extremely stable , with a half-life dictated by dilution due to growth βs ∼ 0 . 02 min−1 [26] , while the half-life of is10in mRNA is typical to bacterial mRNA ( 2–3 min , βm ∼ 0 . 3 [64] ) . Binding of RNA-OUT to its target mRNA is characterized by a second-order binding constant in the range of k ∼ 1/50–1/20 ( nM min ) −1 . The pOUT promoter is a typical promoter , and we assume that αs is not very different from that of RyhB [65] . The pIN promoter , on the other hand , is atypically weak , and is only enhanced 10-fold upon methylation [65–67] . Values of the model parameters are summarized in Table 1 .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) accession numbers for the genes and gene products discussed in this paper are ryhB ( GeneID: 2847761 ) , sodB ( GeneID: 944953 ) , fumA ( GeneID: 2955664 ) , fur ( GeneID: 945295 ) , hfq ( GeneID: 948689 ) , oxyS ( GeneID: 2847701 ) , micC ( GeneID: 2847713 ) , micF ( GeneID: 2847742 ) , rprA ( GeneID: 2847671 ) , dsrA ( GeneID: 946470 ) , rpoS ( GeneID: 947210 ) , gadY ( GeneID: 2847729 ) .
|
The activation of stress response programs , while crucial for the survival of a bacterial cell under stressful conditions , is costly in terms of energy and substrates and risky to the normal functions of the cell . Stress response is therefore tightly regulated . A recently discovered layer of regulation involves small RNA molecules , which bind the mRNA transcripts of their targets , inhibit their translation , and promote their cleavage . To understand the role that small RNA plays in regulation , we have studied the quantitative aspects of small RNA regulation by integrating mathematical modeling and quantitative experiments in Escherichia coli . We have demonstrated that small RNAs can tightly repress their target genes when their synthesis rate is smaller than some threshold , but have little or no effect when the synthesis rate is much larger than that threshold . Importantly , the threshold level is set by the synthesis rate of the small RNA itself and can be dynamically tuned . The effect of biochemical properties—such as the binding affinity of the two RNA molecules , which can only be altered on evolutionary time scales—is limited to setting a hierarchical order among different targets of a small RNA , facilitating in principle a global coordination of stress response .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"microbiology",
"computational",
"biology",
"biophysics",
"molecular",
"biology",
"eubacteria"
] |
2007
|
Quantitative Characteristics of Gene Regulation by Small RNA
|
This paper introduces the concept of phase-locking analysis of oscillatory cellular signaling systems to elucidate biochemical circuit architecture . Phase-locking is a physical phenomenon that refers to a response mode in which system output is synchronized to a periodic stimulus; in some instances , the number of responses can be fewer than the number of inputs , indicative of skipped beats . While the observation of phase-locking alone is largely independent of detailed mechanism , we find that the properties of phase-locking are useful for discriminating circuit architectures because they reflect not only the activation but also the recovery characteristics of biochemical circuits . Here , this principle is demonstrated for analysis of a G-protein coupled receptor system , the M3 muscarinic receptor-calcium signaling pathway , using microfluidic-mediated periodic chemical stimulation of the M3 receptor with carbachol and real-time imaging of resulting calcium transients . Using this approach we uncovered the potential importance of basal IP3 production , a finding that has important implications on calcium response fidelity to periodic stimulation . Based upon our analysis , we also negated the notion that the Gq-PLC interaction is switch-like , which has a strong influence upon how extracellular signals are filtered and interpreted downstream . Phase-locking analysis is a new and useful tool for model revision and mechanism elucidation; the method complements conventional genetic and chemical tools for analysis of cellular signaling circuitry and should be broadly applicable to other oscillatory pathways .
Determining the circuit architecture of cellular signaling pathways is challenging . Analysis using perturbative tools including siRNA [1] , [2] , protein over-expression [3] , chemical inhibitors [4] , or caged compounds [5] usually reveal multiple plausible models that require further refinements and clarification , not just one definitive conclusion . Thus , there is always a need for additional tests and readouts that shed light on signaling circuit architecture in a robustly discriminating manner . Most perturbations applied to biochemical circuit analysis are genetic or chemical in nature and alters the circuit architecture itself . Furthermore , the analysis usually looks at how such perturbations change signaling in response to a single step change with no further time variation in stimulation parameters . While these types of analyses are very useful , the circuit-destructive and temporally non-varying nature limits information that can be obtained concerning dynamic properties of the intact signaling system [6] . We hypothesized that analysis of the frequency-dependent response characteristics of the intact biological oscillator circuit to periodic extracellular chemical stimulation would reveal critical activation and recovery properties of biological oscillators to enable elucidation of molecular mechanisms . Here we demonstrate and validate this concept for the oscillatory calcium pathway of the G-protein coupled receptor ( GPCR ) M3 muscarinic system . The biochemical recovery properties of this system were evaluated by reducing the rest period between pulses of the M3 ligand , carbachol ( CCh ) , and observing the resulting calcium responses . We noted the emergence of beat skipping upon periodic stimulation . The phenomenon whereby an oscillatory system becomes synchronized to a periodic stimulation input is referred to as phase-locking . As the rest period between stimulation pulses was decreased , the number of system responses of the signaling pathway of interest became less than the number of stimulatory inputs thereby revealing biochemical pathway recovery properties not attainable by continuous stimulation . Furthermore , the skipped beats often were not completely absent , but instead appeared as small calcium transients that we here termed “sub-threshold” spikes; these have been observed previously in electrical responses of cellular systems [7] . The sub-threshold spikes provided insight into the activation properties of the signaling system . The complete absence of a sub-threshold spike would suggest that a switch-like mechanism produced calcium spikes; their presence , however , would suggest that a graded mechanism was more plausible . These experimental observations of phase-locking properties were compared to the activation and recovery properties of nine models of oscillatory calcium signaling; while these models exclusively deal with the temporal dynamics of calcium signaling and we note that more elaborate models that also include spatial dynamics and IP3 receptor noise are available [8] , [9] . In the main text we focus upon two highly different models: the Chay et al . model [10] , and the positive feedback Politi et al . model [11] . The former model is the first that theoretically analyzed calcium dynamics in chemically-induced phase-locking; the latter model was recently published , features experimental work to support its proposed mechanisms , and carries dynamic features from previous models and experiments [12] , [13] , [14] . In addition , both models are able to account for a wide range of calcium oscillation periods ( 10s of seconds to minutes ) upon continuous stimulation . The activation properties of the Chay et al . model are characterized by switch-like activation of phospholipase C ( PLC ) by G-protein , and it also features basal inositol triphosphate ( IP3 ) production , which represents a recovery mechanism that ensures that IP3 quickly returns to its pre-stimulus levels . The Politi et al . model does not have such a recovery mechanism , and features graded PLC activation . To produce oscillations in the Chay et al . model , the products of the switch-like activation of PLC ( IP3 and diacylglycerol ) negatively feedback on upstream pathway components ( G-proteins ) . In the Politi et al . model , IP3 , produced by graded activation of PLC , feeds back on downstream elements ( IP3 receptor ) and calcium feeds back upon upstream elements ( PLC ) to create oscillations . A large number of oscillatory calcium models feature the aforementioned feedback mechanisms [15] , [16] , [17] , [18] , [19] . Under continuous stimulation , both models exhibit calcium oscillations with increasing frequencies upon increasing stimulation concentration , as seen in a host of experimental data [20] , [21] , [22] . Both models were thus appropriate but indistinguishable by conventional stimulation methods . The discriminating features provided by phase-locking analysis , however , revealed that neither of the calcium models correctly predicted all the experimental behaviors based upon their activation and recovery dynamics . Furthermore , by analyzing the sources of discrepancy between the predictions and experiments , we were able to propose a mechanism and parameter modification to account for all the experimental observations of phase-locking . Although phase-locking can be thought of as a general property of biological oscillators [23] , it has not been previously explored experimentally in the context of chemical stimulations . While recent reports have claimed that phase-locking events are largely independent of detailed mechanism [24] , we show that the properties of phase-locking can be employed for elucidation of some of the activation and recovery properties of an oscillatory calcium system . We demonstrate that phase-locking , which can only be observed using temporally patterned stimulation , complements conventional chemical and genetic tools for elucidating non-linear oscillatory pathways .
We assessed cellular responses to square-wave stimulation through use of a microfluidic platform [modification of 25] , which enabled exploration of phase-locked rhythms induced by chemical input signals ( Fig . 1a–c ) . With fixed stimulant concentration ( C ) and stimulation duration ( D ) , increases in the rest period ( R ) resulted in increases in the phase-locking ratio ( Fig . 1d ) ; phase-locking ratios were calculated by dividing the number of system responses by the number of chemical inputs ( See Fig . 1 , 2 in Text S1 ) . Analysis of the phase-locking rhythms also uncovered the existence of sub-threshold calcium spikes in individual cellular calcium responses ( Fig . 1b ) . In addition , we explored the phase-locking trends induced by varying C and D ( See Fig . 3a , b in Text S1 ) . These observations collectively provided robust discrimination markers for rigorous evaluation of mathematical models of oscillatory calcium signaling in order to elucidate molecular mechanisms . Nine oscillatory calcium models were chosen as a test set against our experimental results , based upon the inability to discriminate their behaviors using continuous stimulation despite significant differences in their activation and recovery mechanisms . Here we show phase-locking analysis of two of these models: the Chay et al . model [10] and the Politi et al . model [11] ( Fig . 2 ) . Under continuous stimulation , both the Chay et al . and Politi et al . models exhibited oscillatory calcium responses in physiologically relevant frequency ranges ( Fig . 3a–c ) ; furthermore , both depicted the same behavior as the strength of stimulation was increased , as depicted in Fig . 3a and b . We demonstrate that phase-locking analysis is able to effectively dissect the differences in recovery and activation properties between the models ( Fig . 3d–i ) . We first analyzed the Chay et al . model [10] ( Fig . 2a ) . As depicted in Fig . 3d , we found that as the rest period ( R ) between stimulation events was increased , the phase-locking ratio increased . Despite the agreement of the model with the effects of R on phase-locking ratio observed in our system ( compare Fig . 1d with Fig . 3d ) , it could not account for the presence of sub-threshold calcium spikes ( compare Fig . 1b with Fig . 3g ) , suggesting inaccuracies in its activation properties . We attributed the lack of sub-threshold spikes to the model mechanisms , and not model parameter values , as we used a sampling algorithm ( Latin Hypercube Sampling ( LHS ) ) to survey a range of parameter values and found no parameter set able to result in sub-threshold calcium spikes ( Fig . 4 ) . The Chay et al . model assumes that G-protein activation of PLC is a switch-like response with a Hill Coefficient of 4 . Therefore if activated G-protein levels are not sufficiently high to surpass the threshold for PLC activation , a calcium spike will not result . However , the presence of sub-threshold calcium spikes in our experiments suggested that such a sharp activation threshold does not exist . While some experiments suggest that Gq-protein activation of PLC is graded [26] , to our knowledge , there are no studies that have conclusively determined the nature of this interaction; furthermore , these activation properties may be cell type or signaling pathway dependent . When the Hill coefficient of the G-protein/PLC interaction was reduced below 3 . 5 in the Chay et al . model , calcium oscillations could not be obtained under continuous stimulation ( See Fig . 4a in Text S1 ) ; furthermore , periodic stimulation of the model with Hill coefficients between 3 . 5 and 4 did not yield sub-threshold calcium spikes for a wide range of stimulation conditions ( See Fig . 4b in Text S1 ) . These results have important implications in terms of how extracellular chemical signals are filtered and interpreted by downstream elements . In particular , intracellular calcium is not only frequency encoded [27] , but also amplitude encoded [28] , which means that sub-threshold calcium responses might affect cellular responses compared to the non-responses that were noted in the Chay et al . model . Therefore , from a mechanistic standpoint , the ability to capture behaviors such as sub-threshold spikes may prove critical . In addition , these findings show that the reaction mechanisms and model parameters need to be re-evaluated for the Chay et al . model , which has been used for analysis in many other studies [5] , [29] , [30] , [31] . Our experimental observations were then used to evaluate the Politi et al . model ( Fig . 2b ) . Individual calcium graphs portrayed sub-threshold calcium spikes upon exposure to square-wave stimulation pulses ( Fig . 3h ) . However , the model incorrectly predicted that larger R resulted in smaller phase-locking ratios ( Fig . 3e ) , suggesting that the recovery properties of the model are not accurate . LHS analysis indicated that the choice of model parameter values alone could not explain these inaccuracies , suggesting that reaction mechanisms used to formulate the model needed revision . Thus , neither of the calcium models tested was able to account for all of our experimental observations . We noted that the Politi et al . model showed continued IP3 decay between stimulation pulses , while in the Chay et al . model , IP3 levels exhibited recovery between stimulation pulses ( See Fig . 5 in Text S1 ) . In the latter model , IP3 recovery between stimulation pulses is due to a mechanism for basal IP3 production . Addition of basal IP3 production to the Politi et al . model was able to correct its deficiencies in recovery dynamics ( Fig . 3 right column ) ; the IP3 production value used in our study was similar to that of reference [32] . This model revision may provide crucial insight into physiological systems where cells or tissues require fidelity of its calcium signals to periodic chemical stimulation in order to carry out their function [33] . Accurate capture of the recovery properties of oscillatory pathways may also play a pivotal role in the entrainment of such systems [34] . We note that other mechanisms may be found that can account for our experimental observations , but basal IP3 production provides the simplest explanation and is supported by the literature [35] , [36] , [37] . Collectively , this would suggest that the activation and recovery mechanisms reflected in our revised Politi et al . model ( positive feedback mechanism of calcium upon PLC activity , graded PLC activation by G-proteins , and basal IP3 production ) are a good fit for the pathway studied here . We also analyzed seven additional calcium oscillation models . We first explicitly included ligand-receptor-G protein dynamics in both the Chay et al . and Politi et al . models analyzed above , to test whether this would affect our predictions . Those modifications did not change the outcomes of the phase-locking analysis ( phase-locking ratio vs . C , D , and R and presence of sub-threshold spikes ) ( See Fig . 6 in Text S1 ) , suggesting that the discrepancy between model and experiment did not lie in the simplified way stimulation was represented in the original models . We also tested a precursor to the Chay et al . model , a model by Cuthbertson and Chay [38] . Like the Politi et al . model described above , it did not contain a basal level of protein activity , and it too yielded a descending staircase as rest period ( R ) was increased ( See Fig . 7 in Text S1 ) . We next tested the model developed by Atri et al . [16] , and found that it produced the correct recovery behavior as well as sub-threshold spikes ( See Fig . 8 in Text S1 ) ; these results can be attributed to a basal flux term and graded activation , respectively . However , the calcium oscillation dynamics of the Atri et al . model are significantly faster than the range of oscillation periods we observed experimentally . As a result , we then analyzed a version of the Li and Rinzel model [18] that features slower dynamics , as presented in the study by Sneyd et al . [5] . While the model did exhibit calcium oscillation periods closer to what we saw experimentally , it exhibited a decrease in phase-locking ratio as both C and D were increased ( See Fig . 9 in Text S1 ) . This behavior was perhaps due to an augmented inhibitory effect of calcium upon the activation of the IP3 receptor; in addition , the model did exhibit sub-threshold spikes and showed the correct recovery properties , which could be attributed to a basal flux term and graded activation , respectively . Finally , we performed phase-locking analysis on the oscillatory calcium models developed by Dupont et al . [17] and Kummer et al . [39] . The former model features feedback of calcium upon PLC activity and IP3 metabolism , similar to the Politi et al . model , and the latter model features G-protein and PLC dyanmics . While the Dupont et al . model did exhibit sub-threshold spikes , phase-locking analysis revealed that it exhibited a decrease in phase-locking ratio for increases in R ( See Fig . 10 in Text S1 ) ; the Kummer et al . model exhibited sub-threshold spikes as well , but also did not show a change in phase-locking ratio with changes in C ( See Fig . 11 in Text S1 ) . Thus , although we have not performed an exhaustive search , the modified Politi et al . model developed here best describes the qualitative features of our data on the M3 pathway . In sum , we employed a combination of microfluidics , real-time imaging , and mathematical modeling in order to probe the circuit architecture of an oscillatory signaling pathway in mammalian cells . Here chemical-induced phase-locking was explored and analysis of its properties was used to test mathematical models and elucidate molecular mechanisms . Previous reports have claimed that phase-locking events are mostly robust to mechanism details [24] , [40]; this study reports that the properties of phase-locking , however , largely depend upon some of the recovery and activation properties of the molecular mechanisms of an oscillatory signaling system . As microfluidic setups become more elaborate in their ability to generate temporal stimulation patterns , we can expect even more discriminating markers for signaling studies [41]; the diverse waveform stimulation patterns generated by microfluidic setups such as the “chemical waveform synthesizer” [42] and the “chemical signal generator” [43] should prove useful to this end . While a single optical readout ( calcium ) was employed for this study , the experimental setup is amenable to the use of multiple real-time readouts of cellular signaling , thereby further enhancing the number of discriminating markers for elucidation of signaling pathways . Finally , although this paper focused on calcium oscillations , we believe our approach would be well-suited for studies on various biological oscillators such as ERK [44] , NFκB [45] , and components involved in cell cycle [24] , circadian [46] , and ultradian [47] rhythms . For example , we have performed phase-locking analysis of two popular circadian oscillator models [48] , [49] and seen dramatic differences in phase locking behavior between the two , despite similar behaviors under conventional stimulation conditions ( See Fig . 12 in Text S1 ) . Thus , these types of phase-locking analyses provide experimentally testable hypotheses for elucidating molecular mechanisms and show that the method is applicable to a broad range of oscillatory pathways .
HEK293 cells were cultured in Dulbecco's Modified Eagle's Medium ( DMEM ) ( Invitrogen ) supplemented with 10% Fetal Bovine Serum ( FBS ) ( Gibco ) and were maintained at 37°C with 5% CO2 in 24-well plates . 0 . 25% Trypsin/EDTA ( Gibco ) was used to detach cells from plates and transfer them to the microfluidic setup . These cells were stably transfected with the M3 muscarinic receptor ( selected with 0 . 4 mg/mL Geneticin ( Gibco ) ) . Cells were transiently transfected with the calcium FRET probe YC3 . 60 [50] . Transfections were carried out with Lipofectamine2000 ( Invitrogen ) using the manufacturer's protocol . Microfluidic device molds were fabricated based upon the ones described in Futai et al . [25] . Front-side photolithography [51] was used to construct the outlet channel where cells were cultured; the remaining channels ( inlets and “Braille” channels ) were constructed with backside photolithography [52] . With the resulting glass mold , PDMS ( 1∶10 ratio of curing agent to base ) was cast upon the positive relief features and allowed to cure for at least 2 hours in a 60°C oven . The resulting device was then irreversibly sealed against a thin ( ∼100 µm ) PDMS sheet through 30 s plasma oxidation . Once sealed , the device was filled with Phosphate Buffered Saline ( PBS ) and sterilized for 2 hrs in a UV oven . To ensure cell adhesion , the chip was subsequently filled with 100 µg/mL laminin ( Invitrogen ) and allowed to incubate at 37°C for two hours . After this , the chip was flushed and refilled with DMEM supplemented with 10% FBS . Transfected HEK293 cells were then seeded from the outlet port and were appropriately positioned in the outlet hydrodynamically . The cells were then allowed to attach overnight . A custom program written in Visual Basic was used to control the dynamic pumping mediated by Braille-actuation [53] , and thereby create the various temporal stimulation patterns used in experiments ( Fig . 1a ) ; experiments with fluorescein solution confirmed the nearly square-wave shape and reproducibility of these patterns . Carbachol ( CCh ) dissolved in imaging media [54] was added to one of the inlet reservoirs , and the other reservoir was filled with stimulant-free imaging media . Cells in the devices were maintained at 37°C via a transparent indium tin oxide heater [55] , situated between the objective and the thin PDMS-sheet upon which the cells were cultured . Fluid flow did not elicit detectable intracellular calcium responses . Cells were imaged with a TE2000-U Nikon inverted microscope , using a 20× objective , a standard 100W mercury lamp , and a 490 nm long pass dichroic mirror . A CoolSnap HQ2 camera ( Photometrics , Tucson , AZ ) was used to capture fluorescence images of YC3 . 60-transfected cells . Cells were excited at 450 nm and the emission signals were captured at 490 and 535 nm ( filters from Chroma Technology Corp , Rockingham , VT ) . An ND4 neutral density filter was used to reduce photo-bleaching . The excitation and emission filter wheels were controlled by the Lambda 10-3 Shutter Controller ( Sutter Instruments , Novato , CA ) . Images were acquired every 3 s , and an exposure time of 100 ms was used . The program MetaFluor ( Molecular Devices , Downington , PA ) was used for image acquisition and processing; for each emission image ( at 490 nm and 535 nm ) the background was subtracted , ratiometric images were constructed ( intensity at 535 nm/intensity at 490 nm ) , and calcium FRET ratios of individual cells were generated with this software . These FRET ratios ( I ) were normalized by the minimum FRET ratio obtained in the experimental run ( I0 ) , and accordingly I/I0 was plotted in our figures , as has been done previously [56] . The normalized ratio values of the calcium peaks fell between 1 . 2 and 7 . 5 , which was in accord with previously obtained values using the same FRET indicator [50] . The resulting images were then analyzed to calculate the phase-locking ratios by dividing the number of calcium spike events by the number of CCh stimulation inputs . Since at least several cells always responded to a particular stimulation pulse , we concluded that when cells did not respond , it was due to phase-locking and not a malfunction with the microfluidic setup ( Video S1 ) . Cells were exposed to 9–18 stimulation inputs , and the number of calcium responses for each run was recorded . For instance , for a cell that had been exposed to 12 CCh stimulation pulses and responded with 6 calcium spikes , the phase-locking ratio was computed as 0 . 5 . Calcium spikes that were above levels of background noise ( typically more than 10% maximum calcium spike height ) but did not reach an amplitude greater than 33% of the maximum calcium spike height were not counted as true calcium spikes and were deemed sub-threshold calcium spikes ( See Fig . 1 and Fig . 2 in Text S1 ) . Phase-locking ratios were computed for individual cells , and averages and standard errors of the mean were computed for each experimental condition . Statistics were based upon three experiments ( each of no less than 20 cells ) for each experimental condition . Between 85–106 cells were examined for each experimental condition . The unpaired Student t-test was used to statistically compare pairs of experimental conditions; p<0 . 05 was used as a threshold of statistical significance . Nine mathematical models of oscillatory calcium signaling were evaluated in our study: the Chay et al . model [10] ( Fig . 2a ) , the positive feedback Politi et al . model [11] ( Fig . 2b ) , the Cuthbertson and Chay model [38] , the Li and Rinzel model [13] , the Atri et al . model [16] , the Chay et al . and Politi et al . models with ligand/receptor/G-protein dynamics from Ref . [57] , the Dupont et al . model [17] , and the Kummer et al . model [39] . For all these mathematical models , we used the equations and initial conditions defined in the original publications ( except for the Li and Rinzel model , for which we used the adaption developed in Sneyd et al . [5] ) ; model equations , parameters , initial conditions , and brief model descriptions for all models used in this study are provided in Text S1 , starting on page 13 . For the Chay et al . model , it was assumed that receptor-mediated G-protein activation was proportional to stimulant concentration . For the Politi et al . model , it was assumed that the maximal rate of PLC-mediated IP3 production was proportional to stimulant concentration . These assumptions are based upon those from the original publications . For the Politi et al . model , we used calcium flux strength ε = 5 to reflect the role of extracellular flux in calcium oscillations [58] . The mathematical systems were exposed to 12 square-wave stimulation pulses and the corresponding number of calcium spike responses was counted in order to compute phase-locking ratios; the criteria for assessing the phase-locking ratio were the same as those for experiments , as described earlier in the Materials and Methods Section . To assess the effect of rest period on the phase-locking ratio , this parameter was varied , while stimulant concentration and stimulation duration were fixed; we then plotted the resulting phase-locking ratio against the rest period ( Fig . 3- middle row ) . The same procedure was applied to assess the effects of stimulant concentration and stimulation duration on the phase-locking ratio , respectively ( See Fig . 3 in Text S1 ) . Stimulation parameters for the mathematical models were chosen such that the range in behaviors under periodic stimulation matched those observed in experiments . The stimulation concentration ‘C’ is represented differently for each model , as noted in Text S1 . Original parameters were used for both circadian models [48] , [49] . All models were coded in MATLAB version 7 . 8 . 0 ( MathWorks Inc , Natick , MA ) and the system of ODEs was solved with the stiff solver ode15s . We used Latin Hypercube Sampling ( LHS ) to check if inaccuracies in model parameter values alone could account for differences between experimental results and model predictions . LHS is a highly effective method for exploring parameter spaces for mathematical models [59] , [60] , [61] , [62] . Using LHS code from Marino et al . [60] ( http://malthus . micro . med . umich . edu/lab/usadata/ ) , we varied model parameter values by sampling from a normal distribution with a 25% standard deviation; original parameter values were used as the mean . Larger standard deviations ( 100% ) did not yield results different from those at 25% standard deviation . We also sampled parameters from a uniform distribution; the boundaries of the distribution were set by using one tenth of the original parameter value as the minimum and ten times the original parameter value as the maximum . As was the case for sampling from a normal distribution , sampling from a uniform distribution did not yield any parameter sets that could account for the discrepancies between models and experiments . For the Chay et al . model , we varied all twelve independent parameters; for the Politi et al . model , we varied all 17 independent parameters , except for β , which represented the ratio of ER to cytoplasm volume . LHS was run for 500 iterations on each model , and each model output was analyzed to decipher whether the results matched experimental observations ( either by constructing ‘phase-locking ratio vs . rest period’ graphs for the Politi et al . model or by looking at individual model runs for the Chay et al . model , as depicted in Fig . 4 ) .
|
Key to robust discernment of cell circuit architecture is to have as many distinct response features as possible for comparison and evaluation . One under-appreciated characteristic of oscillatory circuits is that under periodic stimulation , these systems will exhibit responses synchronized to this stimulatory input , a phenomenon termed phase-locking . We demonstrate that phase-locked response characteristics vary noticeably depending on circuit activation and recovery properties; these response characteristics thereby provide a unique set of criteria for oscillatory circuit architecture analysis . The concept is validated through experiments on an oscillatory calcium pathway in mammalian cells; the experimental setup allowed us to explore , for the first time , the properties of chemically induced phase-locking of intracellular signals . Observations of this phenomenon were then used to test the predictions of several existing mathematical models of calcium signaling . Most of the models we evaluated were unable to match all our experimental observations , suggesting that current models are missing mechanistic elements in the context of calcium signaling for the cell type and receptor/stimulant tested . The observations of phase-locking further led us to identify one simple mechanistic modification that would account for all the experimental observations . The techniques and methodology presented should be broadly applicable to a variety of biological oscillators .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology/chemical",
"biology",
"of",
"the",
"cell",
"computational",
"biology/systems",
"biology",
"biotechnology/bioengineering",
"computational",
"biology/signaling",
"networks"
] |
2010
|
Phase-Locked Signals Elucidate Circuit Architecture of an Oscillatory Pathway
|
Intracellular protozoan parasites are causative agents of infectious diseases that constitute major health problems for developing countries . Leishmania sp . , Trypanosoma cruzi or Toxoplasma gondii are all obligate intracellular protozoan parasites that reside and multiply within the host cells of mammals , including humans . Following up intracellular parasite proliferation is therefore an essential and a quotidian task for many laboratories working on primary screening of new natural and synthetic drugs , analyzing drug susceptibility or comparing virulence properties of natural and genetically modified strains . Nevertheless , laborious manual microscopic counting of intracellular parasites is still the most commonly used approach . Here , we present INsPECT ( Intracellular ParasitE CounTer ) , an open-source and platform independent software dedicated to automate infection level measurement based on fluorescent DNA staining . It offers the possibility to choose between different types of analyses ( fluorescent DNA acquisitions only or in combination with phase contrast image set to further separate intra- from extracellular parasites ) , and software running modes ( automatic or custom ) . A proof-of-concept study with intracellular Leishmania infantum parasites stained with DAPI ( 4′ , 6-diamidino-2-phenylindole ) confirms a good correspondence between digital results and the “gold standard” microscopic counting method with Giemsa . Interestingly , this software is versatile enough to accurately detect intracellular T . gondii parasites on images acquired with High Content Screening ( HCS ) systems . In conclusion , INsPECT software is proposed as a new fast and simple alternative to the classical intracellular Leishmania quantification methods and can be adapted for mid to large-scale drug screening against different intracellular parasites .
Intracellular protozoan parasites are responsible for worldwide infectious diseases with a high impact in public health for developing countries . Among them , Leishmania parasites are causative agents of leishmaniasis , a worldwide endemic disease with diverse clinical manifestations ranging from self-healing skin ulcers to fatal outcome , depending on parasite species and host immune status or genetics [1] . In mammals , Leishmania parasites replicate as amastigotes within parasitophorous vacuoles ( PVs ) of macrophages . Without a vaccine and taking into account the limited number of existing drugs , the screening of new anti-leishmanial compounds or characterization of new drug target candidates is therefore a research priority . Drug resistance to available treatments has also been well documented in certain areas , and partially explained in natural or experimentally resistant laboratory strains [2] , [3] . Parasites isolated from patients refractory to treatment confirm the presence of circulating resistant parasites [4] and imply a careful surveillance of parasite drug susceptibility . All these research activities require the use of common calibrated and reliable procedures to monitor parasite proliferation inside host cell , maintaining physiological conditions as much as possible [5] . The most popular method still consists of manually counting intracellular parasites after Giemsa staining [6] . This direct counting approach is largely employed because Giemsa stain is cheap and only requires basic equipment ( i . e light microscope ) . Microscopic counting is , however , a laborious task merely providing a global estimation of the parasite burden that is further highly prone to operator experience and subjectivity . Numerous alternative indirect approaches have been proposed in Leishmania [7]–[11] . Most are based on reporter genes assays that increase screening capacities while limiting human intervention . Indirect reporter assays , however , average the biological response of thousands of cells without integrating critical factors such as the percentage of infection and the discrimination between intra- and extracellular parasites . Homogenous expression of reporter genes over time and biological stages in Leishmania further require genomic integration [7] , [8] . These transformation and selection steps can have a profound impact on the phenotypic traits of the original parasite population . This is particularly relevant when testing susceptibility of parasite strains isolated from drug unresponding patients . The development of image processing and analysis techniques ( together with the improvement of fluorescence microscopy instrumentation ) has recently emerged as a new powerful solution for drug screening and susceptibility tests against intracellular protozoan parasites . High Content Screening ( HCS ) systems have been indeed successfully adapted to major intracellular protozoan parasites and allow direct analysis of both parasite and host cell responses to drug candidates [12]–[15] . However , considering the cost of such equipment and the expertise required to manage HCS instrumentation and analysis , those approaches will remain restricted to few laboratories in the world . Extracting and quantifying information from biological images is a well-defined and common task in image processing . Some open source solutions such as ImageJ software ( http://rsb . info . nih . gov/ij ) are very helpful for biologists with more than 500 plugins available . Nevertheless , mastering image processing concepts require specific competences and , to our knowledge , no versatile open-access solutions for biologists are available to automate intracellular parasite quantification . We present INsPECT ( INtracellular ParasitE CounTer ) , the first open-source Java based software that uses fluorescent DNA staining and image processing framework to automate infection level measurement . This can be done through a user-friendly interface , where image files obtained from any fluorescent microscope and magnification , can be processed individually or as a batch , automatically or in a custom mode , without any experience required on image analysis . The software runs either with DNA fluorescent image files alone , or in combination with the corresponding phase contrast or DIC ( Differential Interference Contrast ) image set . Providing this complementary information allows automatic cell boundaries detection and discrimination between intra- and extracellular parasites without any additional use of fluorescent cytoplasm/membrane marker . Output files comprise annotated images and a report table with all information needed for most of experimental infection studies in vitro: total number of cells , total number of parasites , percentage of infected cells , mean number of parasite/cell , parasitic index . Software robustness was validated for the calculation of Ec50 toward intramacrophagic L . infantum treated with pentavalent antimonials ( glucantime ) as a proof-of-concept study . Alternative analyses performed on T . gondii infected fibroblasts further confirm that INsPECT software utilization may be enlarged to unrelated intracellular parasites .
The image-processing pipeline for cell , parasite and cytoplasm detection is illustrated in figure 1 and detailed below . Acquired fluorescent images first need to be saved in tiff format , 8bit type and inverted to obtain dark objects in white backgrounds . These steps can be performed directly from the acquisition software , or lately by different image analyzing softwares . A simple procedure with ImageJ software is described in INsPECT user manual ( File S1 ) .
Screen shots of the INsPECT interface are shown in Figure 2 , all frames are designed in such a way that a non-experimented user in image processing can easily work with them . A complete explanation of the software utilization can be found in the INsPECT user manual ( File S1 ) . The software comes with a basic functional image viewer that helps to see particles in images easily ( Figure 2A ) . Software can run either in an automatic or in a custom mode ( Figure 2B ) . In the former case , parameters and options are set to some default values that are in agreement with the nature of most input images; however , end users can also adapt their intended parameters and options for cell , parasite and cytoplasm pipelines manually ( see details in user manual ) . Users should then indicate input and output folders used for the rest of the analysis , and the nature of the images to analyze ( Figure 2C ) . In the same window , users further specify which result files need to be saved . A running log and progress bar is displayed during the analysis ( Figure 2D ) . Basically , the software takes images from the input folder , applies proposed algorithmic pipeline to each image , extracts needed information and finally saves the results in both text and visual forms in the specified output folder . If DAPI and phase contrast image pairs are available , the software extracts cell outlines to distinguish intra from extracellular parasites ( Figure 2E ) . An example of the accuracy of the proposed cell edges detection algorithm is illustrated in figure S1 for both phase contrast and DIC microscopic images . Alternatively , when DIC images are not available or of minor importance in final results , the software can deal with DAPI images only ( Figure 2E ) . The processing of each image takes less than 30 seconds , without any need of user interaction . To validate the software accuracy in detecting intracellular Leishmania parasites , we performed two parallel experiments with L . infantum infected macrophage THP-1 cell line treated by increasing concentration of glucantime ( 0-25-50-100 µg/ml ) , the mainstay treatment for leishmaniasis . After infection , drug incubation and cell fixation , one series was stained with Giemsa for manual counting and the other with DAPI , both procedures taking approximately similar time ( 30 minutes ) . In the manual counting , 300 cells ( 100 for each triplicate ) were counted under light microscope for each drug concentration to determine the parasitic burden ( parasitic index ) , calculated as the percentage of infected macrophages x the mean number of amastigotes per macrophage ( Table 1 ) . In parallel , random DAPI images were acquired with fluorescent microscope ( 40× objective ) until approximately 300 cells were reached by condition ( corresponding to 31 images and 1402 cells in total ) , together with their respective phase contrast images for cytoplasm detection ( File S2 ) . A batch analysis of all images was performed with INsPECT software ( automatic parameters ) and was completed in less than 7 minutes ( Table 1 ) . Direct comparison of output results shows that the number of detected intracellular parasites , and the subsequent calculated parasitic index ( PI ) , is markedly higher with INsPECT software than with Giemsa method ( Figure 3A ) . Verification of the INsPECT analyzed images in the output folder allows us to confirm that the detected particles accurately correspond to true parasites , excluding therefore an overestimation of the parasite load by the software . Conversely , the human operator naturally tends to select fields and cells offering the best visual resolution with Giemsa , generally excluding highly infected cells in which parasites cannot be well discriminated . The analysis of the “Cell Parasites Report” generated by INsPECT software confirms that highly infected cells are indeed frequently observed in untreated or low glucantime treatment conditions , and that this category of cells is mainly responsible for the differences observed between the two methods ( Figure S2 ) . Once normalized to the non-treated infected control however , scaled PI values are very similar with calculated Ec50 of 73 . 97 µg/ml ( 95% confidence interval [CI] , 60 . 31 to 90 . 71 ) and 79 . 53 µg/ml ( 95% confidence interval [CI] , 54 . 7 to 115 . 6 ) for INsPECT and Giemsa methods , respectively ( Figure 3B ) . To further validate software robustness against unrelated intracellular parasite , we performed analyses on HFF fibroblasts labeled with Hoechst fluorescent DNA stain and infected by fluorescent YFP-expressing T . gondii RH parasites ( File S3 ) . All input images were obtained from HCS system designed for fully automated image acquisition and analysis ( see details in Methods section ) . The analysis of representative images was performed with INsPECT software using only DNA fluorescent input images and choosing automatic mode . The size and distribution of Toxoplasma parasites strongly differ from that of L . infantum amastigotes , with tachyzoites parasites being mostly grouped in small aggregates inside cell cytoplasm . As shown in figure 4 , comparison of visual output annotated images to YFP labeled parasites confirms , however , a good correspondence between the two methods .
Monitoring intracellular parasite proliferation is an essential and quotidian activity for many academic or clinical diagnostic laboratories . The majority are still using manual microscopic counting , an unpopular and time-consuming task , that is further highly dependent on operator experience and objectivity thus leading to strong inter-laboratory variability . These facts make intracellular parasites counting a natural assignment for automation . In this study , we describe an innovative image analysis software allowing automatic detection of intracellular parasites and calculation of infection parameters such as the percentage of infected cells , the mean number of parasites per cell or the parasitic index . In a concept comparative study , we show that INsPECT software performs the complete analyze of 1402 Leishmania infected cells in less than 7 minutes , while counting the equivalent number of cells under microscope with Giemsa staining can take hours of effective work . This timesaving is furthermore directly proportional to the number of conditions/replicates to analyze . Because images are stored annotated in the output folder , users have the possibility to verify whether recognition of parasites and cells is satisfying enough and can optimize detection parameters accordingly ( see user manual , File S1 ) . The use of common and affordable fluorescent nucleic acid stains such as DAPI or Hoechst present the advantages to display a bright , homogenous and stable labeling of both intracellular parasite and host cell DNA . Furthermore , they allow working under physiological conditions with any wild-type parasite species , including clinical isolates . Leishmania , like all members of the kinetoplastid family , possess two large DNA containing organelles ( kinetoplast and nucleus ) . Kinetoplast is an A-T rich DNA structure binding DAPI and Hoechst stains with more affinity [13] , [30] and that consequently appears highly brighter than parasite nucleic DNA . Since kinetoplasts occupy very tiny portions of input images , and may be occasionally very similar to random generated noises , the task of extracting them with existing thresholding methods is complex because available methods often work efficiently for the objects with quite noticeable size and shape and investigable intensity functions . For that , we propose a new method which thresholds parasites with maximum accuracy by defining further discrimination steps than just the particle size . DNA structures of all kinetoplastids ( including T . cruzi and other Leishmania species ) appear very similar on images stained with DAPI [13] , [14] . Accordingly , we expect INsPECT software to be as effective with any other intracellular kinetoplastid parasite as with L . infantum . Besides , we confirm that input images of T . gondii intracellular parasites acquired with HCS instrumentation could also be efficiently processed , even in automatic mode , proving therefore that INsPECT software is versatile enough to work with a variety of unrelated parasites and host cells . Regarding host cell nuclei in DAPI images , illumination variance was handled well by firstly , fitting appropriate noise model which removes noise efficiently while preserving precious cell portions' edge data and secondly hiring adaptive threshold with logical and automatic assignment of local window size . Overlapping cells were also detected and separated using state of the art quantification and segmentation approaches . Cytoplasm boundaries of individual cells are visible structures in DIC or phase contrast images and we used these properties to create a new algorithm that automatically detects cell boundaries to discriminate intra- from extracellular parasites . This function allows considering only intracellular parasites in final calculated infection levels . This can be very useful in the case of Leishmania promastigotes infection since we experimented that numerous non-internalized parasites may stay attached to macrophages membrane following initial contact , even after extensive washes . Because their DNA will be as well labeled with DAPI stain , they can therefore induce a bias if analyze is based only on DAPI images . With Giemsa , these parasites are simply excluded from the counting by the human operator . In the case of indirect reporter gene assays using microplate readers however , these parasites can be the source of a false positive signal . In conclusion , we can state that INsPECT software accuracy and effectiveness is at least as reliable as the existing global estimation methods . Considering its flexibility , simplicity and the state of the art perspectives introduced , it could compete with available commercial and academic packages to become a new helpful tool for the scientific community working with intracellular parasites .
|
Research on intracellular parasites require using non-invasive technologies to follow up parasite proliferation inside their natural host cells by staying in the more physiological conditions as possible . High Content Screening ( HCS ) technology has recently emerged as a powerful image-based approach to screen new anti-parasitic compounds or to test parasite susceptibility to existing drugs in vitro . Nevertheless , such equipments will remain poorly accessible for most of academic and clinical diagnostic laboratories that mostly use more affordable , but laborious , microscopic counting procedures . The current work proposes new image-based , open-source software which provides a fast and accurate solution for investigating intracellular parasite quantification . Through an easy-to-use interface , cells' and parasites' information are dug out from DNA fluorescent images , and host cells' boundaries are extracted from corresponding phase contrast image set . Parasites are then reassigned to their related cells and intra/extracellular parasites are discriminated for each cell . The software further automatically calculates all data required for most of experimental infection studies . INsPECT software is proposed as a free substitute or complement to the available quantification methods for measuring Leishmania infection level in vitro . It may be enlarged , however , to different intracellular trypanosomatids or unrelated parasites such as T . gondii .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"engineering",
"and",
"technology",
"signal",
"processing",
"tropical",
"diseases",
"microbiology",
"parasitic",
"diseases",
"parasitology",
"neglected",
"tropical",
"diseases",
"veterinary",
"science",
"software",
"design",
"infectious",
"diseases",
"computer",
"and",
"information",
"sciences",
"veterinary",
"diseases",
"zoonoses",
"protozoan",
"infections",
"pathogenesis",
"toxoplasmosis",
"host-pathogen",
"interactions",
"image",
"processing",
"leishmaniasis",
"software",
"engineering",
"biology",
"and",
"life",
"sciences"
] |
2014
|
INsPECT, an Open-Source and Versatile Software for Automated Quantification of (Leishmania) Intracellular Parasites
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Pathogenic fungi must extend filamentous hyphae across solid surfaces to cause diseases of plants . However , the full inventory of genes which support this is incomplete and many may be currently concealed due to their essentiality for the hyphal growth form . During a random T-DNA mutagenesis screen performed on the pleomorphic wheat ( Triticum aestivum ) pathogen Zymoseptoria tritici , we acquired a mutant unable to extend hyphae specifically when on solid surfaces . In contrast “yeast-like” growth , and all other growth forms , were unaffected . The inability to extend surface hyphae resulted in a complete loss of virulence on plants . The affected gene encoded a predicted type 2 glycosyltransferase ( ZtGT2 ) . Analysis of >800 genomes from taxonomically diverse fungi highlighted a generally widespread , but discontinuous , distribution of ZtGT2 orthologues , and a complete absence of any similar proteins in non-filamentous ascomycete yeasts . Deletion mutants of the ZtGT2 orthologue in the taxonomically un-related fungus Fusarium graminearum were also severely impaired in hyphal growth and non-pathogenic on wheat ears . ZtGT2 expression increased during filamentous growth and electron microscopy on deletion mutants ( ΔZtGT2 ) suggested the protein functions to maintain the outermost surface of the fungal cell wall . Despite this , adhesion to leaf surfaces was unaffected in ΔZtGT2 mutants and global RNAseq-based gene expression profiling highlighted that surface-sensing and protein secretion was also largely unaffected . However , ΔZtGT2 mutants constitutively overexpressed several transmembrane and secreted proteins , including an important LysM-domain chitin-binding virulence effector , Zt3LysM . ZtGT2 likely functions in the synthesis of a currently unknown , potentially minor but widespread , extracellular or outer cell wall polysaccharide which plays a key role in facilitating many interactions between plants and fungi by enabling hyphal growth on solid matrices .
Micro-organisms have evolved many different mechanisms to enable them to cause diseases of plants . Some of these mechanisms are pathogen species-host plant specific . For example , the evolution and deployment of suites of secreted “effector” proteins , allow pathogens to manipulate components of plant immunity to support infection [1] . The molecular interplay which underpins this exquisite control of host-pathogen interactions has become the focus of considerable research aimed at improving disease resistance in crop plants [2] . However , prior to engaging fully with plant immunity , fungal pathogens must first adhere to , recognise , respond to , and then grow on or through plant surfaces to initiate infection [3] . All known fungi infecting the aerial tissues of plants ( flowers , leaves , stems ) are referred to as “filamentous” , meaning that although they arrive on plant surfaces as either air- or water-borne spores , they subsequently transition into filamentous hyphae to grow over or through tissues and cells [4] . This discriminates filamentous fungi from the model ascomycete yeast species Saccharomyces cerevisiae and Schizosaccharomyces pombe and their close relatives , which are free living and do not form true hyphae . Many fungi which are pathogenic on animals can switch between yeast and hyphal growth forms ( dimorphic ) , but most frequently in these cases the infectious module are often the spores , resulting in “yeast-like” infections [5–7] . In contrast , almost all ( if not all ) major plant infecting fungi use hyphal growth to initiate and/or complete infection . Therefore , hyphal growth over solid surfaces is a pre-requisite step for potentially all fungal diseases of plants . Fungal hyphae are structurally supported by complex outer cell walls , which have multiple layered components of proteins and sugars and provide the strength and flexibility which enable growth , and sensing of growth surfaces [8 , 9] . Major structural components of fungal cell walls include the complex polysaccharides chitin and beta glucans , which are present in most species . Both of these polysaccharides are also known to be triggers of plant defences , via their recognition by plant plasma membrane immune receptors [10] . However , fungal cell walls also contain various other polysaccharides including alpha glucans [11 , 12] and other structurally undefined , but likely important molecules . Moreover , as fungi grow across and through solid surfaces they are also likely to deploy some form of extracellular matrix ( ECM ) to counteract surface-derived frictional and shear stresses [13 , 14] . The importance of ECM is widely recognised for mammalian cells , which produce a matrix which contains a complex linear polysaccharide called Hyaluronan , as a key component [15 , 16] . In contrast , whilst the presence of ECM surrounding fungal cells and hyphae has been detected via various staining and microscopy methods [17 , 18] , the precise polysaccharide components therein have only in rare cases been structurally defined or functionally characterised [19 , 20] The ascomycete filamentous fungus Zymoseptoria tritici ( class Dothideomycete ) is the causal agent of Septoria tritici blotch ( STB ) disease of wheat leaves , which represents one of the most economically important crop diseases of wheat worldwide [21 , 22] . Z . tritici is also regarded as an emerging model fungus , due in part to its ability to grow in several different morphological states ( or forms ) depending on environmental conditions [23–25] . For this reason , Z . tritici is referred to as a pleomorphic fungus [26] , able to grow in at least two different growth forms . One advantage of experimenting with pleomorphic ( or dimorphic ) fungi is that genes which are essential for life ( or the recovery of stably transformed gene deletion strains ) for only one growth form can still be characterised in viable cells growing in the alternative growth form ( s ) . It is more problematic to define the functions of putative essential genes in fungi with only a single dominant growth form . Consistent with most plant pathogenic fungi , Z . tritici infection of wheat leaves begins with spores alighting , either through wind or rain splash , onto leaf surfaces . Initial infection then requires the spores to germinate and grow hyphae across the leaf surface and into stomatal pores [25 , 27] . Once inside leaves , Z . tritici secretes a plethora of important effector proteins during hyphal growth [25] , including a broadly conserved Lysin domain ( LysM ) effector protein which masks fungal chitin from plant immune receptors [28–32] . It is currently unknown what factors can trigger the up-regulation of effector production during phases of leaf infection . This current study derives from a forward’s genetic screen aimed to identify novel virulence factors from Z . tritici , which contribute to its ability to cause plant disease . The data we present reports identification of a widely conserved but discontinuously distributed glycosyltransferase which plays a crucial role in pathogenicity of not only Z . tritici , but also a distinct and taxonomically un-related pathogen of wheat , Fusarium graminearum . We demonstrate its key role in enabling pathogenicity is to allow hyphal filaments to extend over solid surfaces . The distribution of orthologous genes within the fungal kingdom , suggests that this role in virulence likely evolved “inadvertently” from a more generic need for fungi to scavenge for nutrients through extending hyphal filaments over solid surfaces . The current study also identified an unexpected role for this protein in the regulation of pathogen effector gene expression .
Z . tritici can grow in a “yeast-like” budding form or extend true hyphal filaments on agar plates depending on temperature and nutritional status . Fig 1 displays the growth phenotypes of the wild type ( WT ) fungus on different nutrient agar plates , at different temperatures , and in sterile liquid water . At 16°C on solid nutrient-rich Yeast extract peptone dextrose ( YPD ) agar , WT Z . tritici spores undergo yeast-like budding generating a mass of pink coloured spores ( Fig 1A ) . On the same plates incubated at 25°C the fungus subsequently forms extensive networks of melanised aerial hypha over the top of the colony , with no direct contact with the underlying agar ( Fig 1B ) . In nutrient limiting sterile shaking water flasks , spores germinate to form hyphal filaments in suspension ( Fig 1C ) and on solid water agar plates at 25°C spores germinate hyphae which extend radially from the spore droplet across the surrounding agar ( 1D ) . Fig 1E to 1H display the corresponding growth morphologies of a transposable DNA ( T-DNA ) tagged mutant of Z . tritici called 23–170 under the same conditions . The mutant had identical growth under all conditions ( Fig 1E to 1G ) except for growth on solid water agar , where hyphal growth was severely impaired ( Fig 1H ) . Yeast-like growth of 23–170 on YPD agar occurred at a rate comparable to the WT strain and spore morphologies were macroscopically indistinguishable ( S1 Fig ) . The filamentous growth defect of 23–170 on solid water agar was also seen at all tested agar strengths ( S2 Fig ) and during growth on Czapek-Dox and PDA agar , each of which can also support some level of filamentous growth by Z . tritici after protracted incubations ( S3 Fig ) . Closer inspection of the hyphal filaments of both WT and 23–170 on water agar by both light microscopy and scanning electron microscopy ( SEM ) revealed that , in contrast to the mostly long and straight filaments produced by the WT strain ( Fig 1L and 1K ) , the 23–170 filaments were both shorter and grew in a sinusoidal ( “wavy” ) manner ( Fig 1L–1N ) . These data suggested that the mutation ( s ) present in 23–170 rendered it incapable of normally extending hyphae when in contact with a solid surface . Significantly , this phenotype was supported by wheat leaf infection data . In contrast to the WT strain which could rapidly elaborate hyphal filaments on the surfaces of wheat leaves ( Fig 1O ) and subsequently cause full disease ( Fig 1Q ) , the 23–170 mutant displayed no clear hyphal growth on leaf surfaces ( Fig 1P ) . This resulted in a dramatic loss of disease causing ability ( Fig 1R ) . On occasions , some limited chlorosis was observed on leaves inoculated with 23–170 , but these never progressed into necrotic lesions and no fungal sporulation was ever observed ( Fig 1R ) . Thermal asymmetric interlaced ( TAIL ) -PCR was used to identify the T-DNA insertion site in 23–170 , with recovered sequences then subjected to a Blastn analysis against the fully sequenced genome of strain IPO323 [33] ( http://genome . jgi . doe . gov/pages/search-for-genes . jsf ? organism=Mycgr3 ) . All sequenced PCR products mapped an identical T-DNA insertion site within the second intron of a gene model present on the antisense strand of Chromosome 1 , between nucleotide positions 1786483-1788643 ( Fig 2A ) . This gene model was well supported by RNA sequencing ( RNAseq ) raw read mapping data ( Fig 2B ) , and the gene was annotated as encoding a type 2 glycosyltransferase ( GT2 ) represented by Genbank ID XP_003857553 . 1 ( Fig 2C ) . The T-DNA insertion likely resulted in a severely truncated protein ( Fig 2D ) . To validate whether loss of the Z . tritici GT2 ( ZtGT2 ) function was responsible for all the phenotypes seen for 23–170 , the wild-type gene plus upstream and downstream regions was transformed back into strain 23–170 for complementation analysis . In addition , the ZtGT2 gene was also subject to independent targeted deletion in the WT fungus . Full hyphal growth on water agar and plant disease causing ability were restored by complementation of 23–170 with the native gene , and the targeted ΔZtGT2 mutants displayed all the original 23–170 mutant phenotypes ( Fig 2D ) including short “wavy” hyphal formation on the surface of all tested agar plates ( S4 Fig ) . Thus , we conclude that the ZtGT2 protein plays an essential role in supporting the virulence of Z . tritici through enabling hyphal growth over solid surfaces . A Blastp analysis using the mature ZtGT2 protein sequence at expect ( e-value ) cut-off thresholds of 1 . 0e-100 , 1 . 0e-60 and 1 . 0e-5 was performed on the predicted proteomes of 823 fungi present at the JGI Mycocosm portal [34] representing 20 different taxonomic classes or sub phylum ( Fig 3A and S1 Table ) . Overall , proteins with varying degrees of similarity to ZtGT2 , were identified in many of the species analysed including some of the earliest evolved fungal groups ( Fig 3A and S1 Table ) . Phylogenetic analysis on a subset of transcripts representative of taxonomically distinct higher ( ascomycete and basidiomycetes ) and lower fungi , with parasitic , mutualistic or saprotrophic lifestyles ( S2 Table ) , confirmed that orthologues of ZtGT2 were found in accordance with the fungal species tree and were present in some lower fungi ( eg Chytrids and Mucor species ) as well as many , but not all , basidiomycetes ( Fig 3B ) . In contrast , most filamentous ascomycete fungi possess an orthologue of ZtGT2 irrespective of whether they were parasitic , mutualistic or saprotrophic . Numerous independent duplications were detected within basidiomycete species , but within ascomycetes , the 2nd Leotiomyceta paralogues grouped together , suggesting one duplication event there . ( Fig 3B and S1 Table ) . A single paralogue of ZtGT2 was identified in Z . tritici ( protein model 38873 in Fig 3B ) . The dramatic phenotypes observed for ΔZtGT2 mutants suggest this paralogue does not have an overlapping functionality . Interestingly no ascomycete species belonging to the class Pezizomycetes possessed orthologues , or any similar proteins whatsoever , to ZtGT2 , highlighting a discontinuous distribution within this kingdom most likely arising from gene loss ( Fig 3A and S1 Table ) . This distribution was further emphasised by the fact that no orthologues ( or proteins with any similarity even at Blastp e-5 ) were identified in the genomes ( proteomes ) of all 59 ascomycete yeasts including all tested members of the Saccharomycotina and Taphrinomycotina , which include the model species Saccharomyces cerevisiae , Schizosaccharomyces pombe and the yeast-like human pathogenic Candida species ( Fig 3A and S1 Table ) . The discontinuous distribution of proteins with similarity to ZtGT2 was also very evident within the tested Basidiomycetes ( Fig 3A and S1 Table ) with orthologues most frequently observed in the Agaricomycotina , represented by the gene model 6263 ( Cps1 ) from Cryptococcus neoformans in Fig 3B . To test whether orthologues of ZtGT2 conferred the same function in other ascomycete fungi , we attempted to generate deletion strains in the wheat ear pathogen , Fusarium graminearum , a member of the taxonomically distant Sordariomycete class of fungi ( Fig 3A and highlighted in Fig 3B ) . The gene selected for testing was FG00702 . 1 ( http://fungi . ensembl . org/Fusarium_graminearum/Info/Index ) with Blastp homology of 2 . 74e-142 to ZtGT2 , encompassing 51% amino acid identity spanning 78 . 9% of the protein model . Phylogenetic analysis on transcripts confirmed the likely 1 to 1 orthology between ZtGT2 and FgGT2 ( Fig 3B ) . F . graminearum is a rapidly growing filamentous fungus which unlike Z . tritici exhibits no known yeast-like growth form . Using standard protocols which select for hygromycin resistant transformants on solid agar [35] , we were unable to recover any homokaryotic FgGT2 deletion strains , but instead recovered numerous heterokaryons . PCR analysis on these strains demonstrated that they possessed both a wild-type allele in addition to a deleted allele of FgGT2 , and they grew hyphae at normal rates on agar plates ( S5 Fig ) . It is known that attempts to delete “essential” genes in multinucleate fungi places strong selective pressure on the formation of such heterokaryons [36] . In view of this , and the phenotype described for ΔZtGT2 mutants , we modified the transformation protocol to perform selection in liquid medium instead of on solid agar ( see Methods ) . This approach led to the acquisition of two independent homokaryotic deletion strains of FgGT2 ( Fig 4C ) . Significantly both mutant strains were severely impaired in radial hyphal growth on solid agar ( Fig 4D ) and completely impaired in virulence towards wheat ears when either point inoculated via droplet , or sprayed across the entire wheat ear ( Fig 4E ) . In addition to the radial growth defect seen on the surface of agar , the ΔFgGT2 mutants were also tested for ability to grow into ( penetrate ) agar plates through cellophane sheets [37] . This revealed that mutants were still capable of this type of invasive growth . However , this assay again demonstrated that subsequent radial growth on the agar surface was strongly affected ( S6 Fig ) . This data highlights that invasive growth , which represents a key additional feature of F . graminearum infection of plants not known to occur in Z . tritici , does not require functional FgGT2 ( S6 Fig ) . Quantitative Real-Time PCR ( qPCR ) gene expression profiling demonstrated that ZtGT2 was expressed most strongly under conditions which favour hyphal growth of Z . tritici , including elevated temperatures , low nutrients and early ( 48h ) growth on wheat leaf surfaces ( Fig 5A ) . To ascertain the likely protein localisation of ZtGT2 , we generated a peptide antibody . Western analysis demonstrated that this antibody cross reacted with a specific ~52kDa protein present in wild type cells but absent from ΔZtGT2 mutants ( S7 Fig ) . This protein was detected in the low speed cell wall pellet of fungal cells extracted in detergent-free buffer . In contrast , no protein was present in the soluble fraction , and little to none in the high speed microsomal fraction . This suggested that ZtGT2 may be present in , or attached to , the fungal cell wall ( S7 Fig ) . Analysis of cell wall ultrastructure by TEM , following collection of yeast-like spores from the surfaces of solid agar plates , revealed the frequent presence of outer wall surface irregularities , which appeared as bulges , protrusions or as breakages ( Fig 5B and S8 Fig ) . These were observed in over 50% of all sections examined from mutant cells and were never seen in sections of WT cells , suggesting that ZtGT2 plays a role in generating or maintaining an outer wall component . We then sought to determine whether there were any differences in monosaccharide levels ( glucose , mannose and galactose ) of the alcohol insoluble polysaccharides deriving from either the cell wall or culture filtrates of ΔZtGT2 relative to WT strains . We were unable to determine any quantitative differences in these monosaccharides deriving from total cell walls ( S9A Fig ) . Comparable analysis of the ethanol precipitated culture filtrates revealed an unexpected increase in monosaccharides from ΔZtGT2 relative to WT ( S9B Fig ) . Thus , overall the results were inconclusive but we detected no depletion of materials due to loss of ZtGT2 . This may suggest that the product of ZtGT2 is a relatively minor component of either the fungal cell wall or extracellular matrix . In this case its overall contribution to the total monosaccharide composition may be masked by the more abundant chitin , beta- and alpha- glucans . It is also possible that loss of ΔZtGT2 function could induce changes , such as the alterations seen in outer cell wall structure ( Fig 5B and S8 Fig ) that could result in / from changes in the levels of other polysaccharides , influenced indirectly by loss of ZtGT2 function . We also developed assays to determine whether the outer wall irregularities gave rise to changes in the ability of conidia to adhere to leaf surfaces ( S10 Fig ) , or to plastic ( hydrophobic ) surfaces ( S10 Fig ) . Like most fungi which are pathogenic on leaves of plants , the spores of Z . tritici adhere very strongly and rapidly to hydrophobic surfaces such as those posed by waxy leaf surfaces [3] . No change in the ability of ΔZtGT2 mutant spores to adhere to either of these hydrophobic surfaces was detected ( S10 Fig ) demonstrating that the macroscopic differences observed between the outer walls of each strain did not compromise surface attachment . Similarly , ΔZtGT2 mutant spores displayed no altered sensitivity to chemical de-stabilisation of chitin synthesis ( Calcoflour white ) , beta glucan function ( Caspofungin ) , temperature ( 30°C ) , oxidative ( H2O2 ) or osmotic ( sorbitol ) stress ( S11 Fig ) , suggesting that major cell wall and membrane components were not strongly affected by gene loss . Based upon the abnormalities seen on the outer wall surfaces of ΔZtGT2 mutants , and their failure to germinate hyphae on leaf surfaces , we speculated that leaf surface-sensing may be compromised in the mutants . To test this , we performed RNAseq based whole genome expression profiling of early leaf infection compared with growth in liquid culture . Materials were sampled as shown in S12 Fig to allow several pairwise analyses on the fungal and plant transcriptomes . Global Principle Components Analysis ( PCA ) on the fungal datasets highlighted that , relative to growth of WT and ΔZtGT2 strains in liquid culture , 48 h growth on leaf surfaces distinguished their respective transcriptomes the most ( Fig 6A and S3 Table ) . Much of this effect was attributed to relative growth rates on wheat leaf surfaces , as illustrated by the repression of many ribosomal protein encoding genes in ΔZtGT2 on the leaf surface . This feature was not observed when comparing the two strains transcriptomes in liquid culture , which further emphasised the key role of ZtGT2 in regulating contact-dependent fungal growth ( Fig 6B ) . Despite this , many genes normally expressed by the WT fungus on leaves relative to liquid culture , were also similarly expressed by ΔZtGT2 ( Fig 6C and S3 Table ) . These included the characteristic early transcriptional up-regulation of suites of secreted protein encoding genes including Lipases , Cutinases , Necrosis-inducing proteins ( NLP ) and Chloroperoxidases [24 , 38–40] ( Fig 6D ) . These data highlight that whilst hyphal growth is rapidly impaired , ΔZtGT2 can still sense and react transcriptionally to the leaf surface environment . The fungal leaf infection data also highlighted several protein families which are transcriptionally upregulated by the WT fungus on leaves , but which are not by ΔZtGT2 . One example being several CFEM domain containing membrane-spanning proteins [41] which were strongly negatively affected in ΔZtGT2 , both in liquid culture and even more so on leaf surfaces ( S13 Fig ) . This data suggests these genes may play key roles in regulating processes associated with elongating hyphae during normal plant infection by the WT fungus . The most strongly up-regulated genes in ΔZtGT2 growing in liquid culture encoded secreted or membrane bound proteins . Table 1 highlights that amongst the “top 30” genes upregulated in the ΔZtGT2 mutant relative to WT , twenty-eight fell into these categories . Seventeen of these 28 were also upregulated in the WT fungus at 48h on leaf surfaces relative to its growth in liquid culture ( Table 1 ) . This suggested that loss of ZtGT2 function stimulated some transcriptional changes to occur which are commonly induced early by the fungus on wheat leaf surfaces . Amongst this set of genes was one encoding a functionally validated effector protein , Zt3LysM ( Gene Id ZtritIPO323_04t03143 in Table 1 and S14 and S15 Figs ) , which binds to chitin fragments and mediates the evasion of plant chitin-triggered immunity [31 , 32] . This suggests that the loss of normal outer cell wall structure in ΔZtGT2 mutants may mimic changes which usually occur following inoculation onto plants , and that this may serve to trigger increased expression of the Zt3LysM effector . Also notable amongst the genes shown in Table 1 was a strong up-regulation ( >25 fold levels in the WT strain ) of a transcript predicted to encode an alpha-1 , 3-glucan synthase in the ΔZtGT2 mutant ( ZtritIPO323_04t10384 in Table 1 ) . In the filamentous fungal pathogen of animals Aspergillus fumigatus , alpha-1 , 3-glucan represents a major constituent of the outer cell wall and is required for full virulence [11 , 12] . In contrast to the putative Z . tritici alpha-1 , 3-glucan synthase gene , no other genes likely to be involved in the biosynthesis of either chitin or beta-glucans were differentially expressed in the mutant relative to the WT . Hence from the data here provided it may be that loss of the outer wall integrity in ΔZtGT2 triggers a specific compensatory activation of alpha glucan synthesis . Analysis of the wheat leaf transcriptome at 48h hpi with ΔZtGT2 and the WT fungus also revealed many differentially expressed genes ( S4 Table ) . Prominent amongst these were plant defence-associated genes including those encoding pathogenesis-related ( PR ) proteins , receptor-like kinases ( RLK ) and WRKY transcription factors , which were all more highly expressed in leaves infected with the WT fungus than those infected with ΔZtGT2 ( S16 Fig ) . This likely reflects the comparable lack of hyphal growth by the ΔZtGT2 mutants , relative to that by the WT fungus , over the growth period . In support of this , independent qPCR assessments on wheat defence gene expression incorporating mock-inoculated leaves ( no fungus ) indicated that the lower induction of plant defence genes by ΔZtGT2 was unlikely to be a consequence of loss of a specific elicitor activity ( S16 Fig ) .
All fungal pathogens of plants require the ability to grow hyphae over solid surfaces posed by host tissues and cells to initiate or complete infections . The present study identified a widely ( but not universally ) conserved glycosyltransferase ( GT2 ) as a likely key facilitator of this process . The pleomorphic growth characteristics of Zymoseptoria tritci enabled the identification of the specific growth state in which ZtGT2 plays a crucial role , that being hyphal growth when in physical contact with solid matrices . It is likely for fungi which exhibit a predominantly hyphal growth state on solid agar , that gene deletion mutants of ZtGT2 orthologues may be difficult to recover using standard protocols . This could be interpreted as either them being “essential” genes in filamentous fungi , or that hyphal growth is so severely affected in mutants that they cannot be recovered . In support of the latter interpretation was our requirement to perform selection in a liquid , rather than solid growth medium , to obtain homokaryotic ΔFgGT2 deletion mutants in the highly filamentous fungus , Fusarium graminearum . This highlights that the terminology “essential gene” should be used with caution and in consideration of the technical approaches used , when interpreting failed attempts to obtain specific gene deletion mutants from filamentous fungi . In contrast , the fact that yeast-like growth of Z . tritici ΔZtGT2 mutants occurs un-impeded on solid agar , highlights the utility of this species for characterising genes which may be “essential” for hyphal growth in fungi . The ability for Z . tritici to undergo “yeast-like” budding growth in the absence of ZtGT2 , agrees well with the observation that all true free-living ascomycete budding yeasts , particularly in the classes Saccharomycotina and Taphrinomycotina , lack any similar proteins . This suggests that ZtGT2 orthologues may be dispensable for the budding growth form of ascomycete fungi . The basidiomycete pathogen of mammals , Cryptococcus neoformans , is a dimorphic fungus pathogenic in the yeast form , but which can also grow as true hyphae during sexual reproduction [44] . C . neoformans possesses a ZtGT2 orthologue named CPS1 ( Blastp homology of 9 . 05e-90 to ZtGT2 with 47 . 3% identity covering 73 . 5% of the protein ) , shown in Fig 3B . This protein is predicted to function as a putative capsule polysaccharide synthase and is essential for full virulence of the yeast form against mammals [45 , 46] . In contrast to this , the yeast-like non-pathogenic growth form of Z . tritici is unaffected in ΔZtGT2 mutants , and it is instead the infectious hyphal growth form which is affected , and then only when in physical contact with surfaces . This re-emphasises a key distinguishing feature of infection of plants and animals by ascomycete and basidiomycete fungi which can replicate in either yeast-like or filamentous forms . The polysaccharide produced by CPS1 has been suggested to be similar to Hyaluronic acid , the key and ubiquitous extracellular matrix component of mammals , buts it’s precise structure has not yet been determined . The morphological abnormalities we detected in the ΔZtGT2 mutant suggests that ZtGT2 plays a key role in maintaining outer cell wall integrity , although there is currently no evidence to suggest that Z . tritici ( or any filamentous fungus ) , forms anything functionally equivalent to the unique C . neoformans yeast capsule [47] . Efforts to detect quantitative changes in cell wall or culture filtrate monosaccharides ( deriving from precipitated polysaccharide ) between WT and ΔZtGT2 mutants of Z . tritici were inconsistent . Neither of the analyses performed identified decreased levels of monosaccharides deriving from polysaccharides from ΔZtGT2 mutants . For total cell walls , the levels of glucose , mannose and galactose were unchanged . Conversely the culture filtrate of ΔZtGT2 mutants unexpectedly gave increased levels . As fungal cell walls possess chitin and beta glucan as some major components , it is possible that other functionally important , but more minor constituents might be beyond detection if only using comparisons between gene deletion strains with wild-type strains . It is also possible that such components may have been lost in preparation without prior knowledge of their physicochemical properties . However , identifying differences via the approach of comparing WT strains with gene deletion mutants in this case was also likely complicated by the fact that our transcriptome data demonstrated that ΔZtGT2 strains overexpress a conserved alpha-1 , 3- glucan synthase to ~25 fold higher levels than WT strains ( Table 1 ) . So its possible that the increased levels of polysaccharides derived from culture filtrates of ΔZtGT2 mutants might be a consequence of other polysaccharides being over-produced to compensate for the loss of ZtGT2 . It may also be that the irregularities seen in the outer cell wall surface may have also contributed to the increased levels of monosaccharides detected . This requires further study . However , future approaches aimed to identify and structurally characterise the polysaccharide produced by ZtGT2 and/or its orthologues may be better addressed using different approaches to circumvent these potential complications . At this point it is unclear why some filamentous fungi lack orthologues , or proteins with any similarity whatsoever , to ZtGT2 . For example , the model basidiomycete plant pathogens Puccinia graminis and Ustilago maydis have no similar proteins to ZtGT2 , but they do grow hyphae over plant surfaces ( including wheat leaves for the former ) . As presence / absence of proteins with similarity to ZtGT2 appears discontinuous , even within the ascomycete kingdom , it is possible that some fungal species use a different protein , or mechanism , to provide the equivalent functionality . This might again be reflected in the chemical composition of the outer cell wall surfaces in these fungi . However , for ZtGT2 and its close orthologues , some conclusions can be made . Firstly , the gene is present in almost all analysed ascomycete plant pathogens and in several opportunistic animal pathogens ( for example Aspergillus fumigatus ) . However , it is also frequently observed to be present in many saprophytic fungi including those which perform the primary events in leaf litter decay . Thus , we speculate that the importance of this gene for the evolution of fungal pathogenesis likely derived “inadvertently” from an ancient requirement for fungi to evolve the hyphal growth form , to extend filaments over solid surfaces for purposes such as nutrient acquisition . By acquiring this basic functionality , fungi also acquired a key competency which subsequently enabled them to develop parasitic ( as well as potentially mutualistic ) interactions with many plants and animals . Based upon all the available data it is likely that ZtGT2 synthesises or modifies a potentially widespread and essential extracellular matrix or outer cell wall polysaccharide component , which may be only a minor constituent ( Fig 7 ) . This may function to alleviate surface friction and shear stresses normally imposed on rapid hyphal tip growth over solid matrices [13 , 14] . This currently structurally undefined , polysaccharide may be functionally important for many plant ( and perhaps animal ) pathogens and may therefore represent a viable target for future widespread control of fungal diseases . This study also emphasises the tractability of Z . tritici as a model organism for isolating genes which may be essential for contact-dependent filamentous fungal growth .
The fully genome sequenced Z . tritici isolate IPO323 was used in all experiments ( http://genome . jgi-psf . org/Mycgr3/Mycgr3 . home . html ) . For all experiments , fungal spores were initially harvested from 5 day old cultures growing ( budding ) on Yeast extract peptone dextrose ( YPD ) plates ( Oxoid Ltd . , Hampshire , UK ) at 16°C . For RNAseq and qRT-PCR based gene expression analysis , duplicate or triplicate flasks containing 40 ml YPD broth ( Oxoid Ltd . , Hampshire , UK ) were inoculated for 96h at 18°C ( or other specified temperatures ) on an orbital shaking incubator at 120 rpm . After this time , fungal materials were collected via vacuum filtration and snap frozen in liquid N2 until RNA extraction . Wheat seedling infection assays ( cultivar Riband ) was performed as described previously [48] . Briefly the adaxial surfaces of leaves were inoculated with spore suspensions collected from YPD cultures , which were washed and re-suspended in dH20 +0 . 01% Tween 20 to a density of 1x106 spores / ml . After the indicated time , inoculated leaves were excised and snap frozen in liquid N2 until RNA extraction . Photographs displaying disease levels on leaves were taken twenty-one days after inoculation of the second leaf of three-week-old seedlings . All plant materials for RNAseq were prepared from duplicate ( two independent ) experiments performed three weeks apart . A total of six leaves were taken from six independent inoculated wheat seedlings and used to prepare a single total RNA preparation . This constituted a single biological replicate sample . The T-DNA mutagenesis screen of Z . tritici was previously described [49] . TAIL PCR was carried out on fungal genomic DNA isolated from the 23–170 mutant according to previous methods [49 , 50] . For targeted disruption of the ZtGT2 gene ( JGI protein model 65552; NCBI Reference Sequence: XP_003857553 . 1 ) two regions ( flanks ) of approximately 1000 bp of fungal genomic DNA were amplified by PCR . Flank1 was then cloned into vector pCHYG using SacI and KpnI ( primers P1 and P2 -S5 Table ) and Flank 2 using PstI and HindIII ( P3 and P4 ) and the resulting plasmid was transformed into A . tumefaciens strain Agl-1 via the freeze-thaw method [51] . For targeted gene deletion , a modified ΔKu70 strain of IPO323 was used [52] . To generate the complementation strain 23–170::GT2comp the entire open reading frame of ZtGT2 plus upstream and downstream sequences was amplified by PCR on genomic DNA ( primers P5 and P6 ) . The amplicon was cloned using SacI and KpnI into vector pCGEN for fungal transformation of strain 23–170 and selection used 100 μg/ml Geneticin ( Sigma St Louis ) . Agrobacterium-mediated transformation of Z . tritici was performed as previously described [24 , 53] . Targeted mutants were confirmed by PCR on genomic DNA . All oligonucleotide primer sequences are provided in S5 Table . The mature protein sequence of ZtGT2 ( Genbank XP_003857553 . 1 ) was used for a Blastp analysis against the contents of the JGI MycoCosm fungal genome portal ( http://genome . jgi . doe . gov/programs/fungi/index . jsf ) in July 2017 . Blastp searches at expect value homology cut-offs of 1e-100; 1e-60 and 1e-05 were performed for each taxonomic class / sub phylum . Species which returned one or more hits at each cut-off were included as a positive ( harbouring a protein with the indicated level of similarity ) . Species returning no hits at any cut-off were deemed negative ( lacking any similar proteins ) . Data was then presented in the form of pie charts for each taxonomic class/ sub phylum displaying number of species genomes having or lacking similar proteins at each e value cut-off , as a proportion of the total number of species genomes analysed in each category . Data from the protein similarity search was used for selection of sequences for phylogenetics analysis . For Maximum Likelihood gene tree analysis , the best Blastp hits were selected from 2–3 species representative of the different taxonomic subdivisions or classes from the fungal tree of life present within the JGI Mycocosm Genome portal [34] . For select species , including Z . tritici and F . graminearum , the next best Blastp hit was also included . To produce the most accurate nucleotide alignments , coding sequences were trimmed from each transcript to the conserved Type 2 glycosyltransferase domain as identified by the NCBI conserved domain database displayed in the Blastp output . The accession for the conserved domain sequence was cd06434 . Genes were then aligned using MAFFT v . 7 . 308 [54 , 55] , in Geneious 10 . 0 . 9 [56] . For phylogenetic reconstruction , the GTR+I+G nucleotide substitution model was selected by AIC in jModeltest 2 . 1 . 10 [57 , 58] . The Maximum Likelihood phylogeny was reconstructed using PhyML [59] , with the substitution model selected in jModeltest; starting tree with optimised topology , length and rate parameters; topology searching by the best of NNI and SPR; and 500 bootstraps . The FgGT2 gene , FGRRES_00702 ( http://fungi . ensembl . org/Fusarium_graminearum/Info/Index ) , was deleted in F . graminearum wild-type strain PH-1 ( NRRL 31084 ) for which the complete genome sequence is available [60] . A PCR-based split-marker gene deletion strategy was chosen [35] . The DNA flanks ( 1000 bp 5’- and 997 bp 3’-sequence ) of the FgGT2 gene were first amplified by PCR using primers ( S5 Table ) pairs U650/U651 and U656/U657 and cloned into the plasmid vector pGEM-T ( Promega ) using the Gibson assembly Master Mix kit ( New England BioLabs Inc . ) according to the manufacturer’s instructions to generate vectors pMU421 and pMU422 , respectively . Specific PCR was carried out in 25 μl volumes , containing 50 ng of DNA , 1 U of Expand High Fidelity Taq-Pwo polymerase mixture ( Boehringer Mannheim ) , 10 pmol of each primer and 0 . 25 mM each deoxynucleoside triphosphate , in a standard buffer for 35 cycles with the following cycling parameters: denaturation at 94°C for 30 s; annealing at 54°C for 30 s; and DNA synthesis at 72°C for 1 min . For transformation both split-marker constructs contained in pMU421 and pMU422 were quantitatively amplified by PCR using HotStar TAQ polymerase ( Qiagen ) following the manufacturer’s instructions . The concentration of the PCR products was adjusted to 2 μg μl-1 and 5 μl of each construct was mixed and transformed into 1x108 protoplasts of F . graminearum strain as previously described [61 , 62] . Recovery of transformants was accomplished in liquid TB3 medium containing hygromycin ( 75 μg/ml ) to allow selection of mutants which might otherwise be impaired in hyphal growth on selective solid agar . 0 . 2 ml transformation mix was added to 10 ml TB3 liquid medium containing 75 μg/ml hygromycin contained in 50 ml Falcon tubes . Cultures were grown for further 10 days in a shaking incubator set to 28 C and 180 rpm . Hygromycin resistant transformants were then transferred to PDA agar plates containing hygromycin ( 10 μg/ml ) for further analysis . Fungal genomic DNA was extracted from transformants grown in 10 ml potato dextrose medium in the presence of hygromycin ( 10 μg/ml ) as described [63] . In the two isolated gene replacement mutants MU426 and MU427 two diagnostic PCR fragments of 1 . 5kb and 1 . 3 kb size are detectable using oligomer pairs U659/U667 ( PCR 1 ) and U660/U668 ( PCR 2 ) respectively ( Fig 4 ) . In both mutants MU426 and MU427 , the FgGT2 gene is absent ( PCR 3 in Fig 4 ) . Plant infection and pathogenicity tests on wheat ( Triticum aestivum ) plants of cultivar Bobwhite were grown and infected by point and whole-wheat spike spray inoculation with spore solutions as previously described [64] . Each experiment was performed in triplicate with similar results . Fungal cells were collected from the surfaces of YPD agar plates using a sterile loop . Samples were then high pressure frozen using a Leica Microsystems EM HPM100 and stored in liquid nitrogen before staining with 1% osmium and freeze substitution using acetone in a Leica Microsystems EM AFS . Following freeze substitution , the samples were stored at -20C for 24 hours then 4C for 24 hours before resin infiltration . Samples were infiltrated with an acetone:Spurr resin series and polymerised at 60C overnight . 70nm thin sections were cut using a Leica Microsystems UC7 microtome and collected on copper grids coated with formvar and carbon . Micrographs were collected using a JEOL 2011 transmission electron microscope at 200kV and a Gatan Ultrascan CCD camera . Approximately 5mm square regions were cut from samples and attached to aluminium stubs using a 50:50 mixture of graphite:TissueTek . The samples were plunge frozen in liquid nitrogen and transferred to the GATAN ALTO 2100 cryo prep system . Samples were etched and coated in a thin layer of gold . Micrographs were collected using a JEOL 6360 scanning electron microscope at 5kV . Total RNA was isolated from frozen materials using the TRIZOL procedure ( Invitrogen ) . Library preparation and sequencing was performed at the Earlham Institute , Norwich Research Park , Norwich , UK . No pre-processing of the reads took place . Hisat2 ( v2 . 0 . 4 ) was used to map the reads to Zymoseptoria tritici IPO323 ( Ensembl fungi v30 ) or Triticum aestivum Chinese Spring ( Ensembl plants v32 ) using default settings . Cuffdiff ( v2 . 2 . 1 ) was used to produce FPKM and differential testing of counts with a classic-fpkm library normalisation method , pooled dispersion estimation method , and bias correction with effective length correction , for wheat using Ensembl plants v32 annotation but for fungi using a custom gff3 annotation file https://doi . org/10 . 6084/m9 . figshare . 4753708 . v1 [42] to count against only these models . CummeRbund ( v2 . 8 . 2 ) was used to produce the PCA plots in R ( v3 . 1 . 2 ) . Annotations were added to the cuffdiff output using Blast2Go ( v3 . 3 . 5 ) with Blast2Go GO database 05/2016 and default filtering settings , using input from InterPro ( v58 . 0 ) and Timelogic DeCypher with the NCBI NR database ( 23/06/16 ) using an e-value threshold of 1e-2 . For robust differential expression analysis , only genes with fold changes of Log2 >1 . 5 ( Padj 0 . 05 ) were considered further . Total RNA was isolated from freeze-dried , fungal material collected from the stated liquid culture or from fungal infected leaf tissues , was prepared using the TRIZOL procedure ( Invitrogen ) . Total RNA was used for all RT-PCR and Real-Time RT-PCR analyses . First-strand cDNA was synthesised from total RNA using the SuperScript III First_Strand Synthesis System for RT-PCR ( Invitrogen ) . A 5 μg aliquot of total RNA primed with oligo ( dT ) 20 was used in a 20 μl reaction , following the suppliers instructions . The resulting cDNA was analysed by Real-Time RT-PCR using a QuantiTect SYBR Green PCR Kit ( Qiagen ) , following the supplier’s instruction . A 0 . 5 μl aliquot of cDNA was used in each 20 μl PCR reaction , with an annealing temperature of 60°C . Primers were used at a final concentration of 0 . 25 μM . Real-Time RT-PCR reactions were run for 40 cycles and analysed using an ABI 7500 Real Time PCR System . The relative expression of each fungal or plant gene was determined by normalisation with the constitutively expressed Z . tritici beta-tubulin gene [48] or with the TaCdc48 gene for T . aestivum [31] . All oligonucleotides used are listed in S5 Table .
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All plant-pathogenic fungi must grow hyphae across host tissues and cells to establish diseases . We have identified a single glycosyltransferase enzyme from the pleomorphic wheat pathogen Zymoseptoria tritici which functions specifically to enable hyphal growth on solid surfaces , and is therefore essential for fungal disease of wheat plants . ZtGT2 orthologues are present in most ascomycete filamentous fungi , and we show that the orthologous gene from the distantly related wheat ear infecting fungus , Fusarium graminearum , is also required for hyphal growth and virulence . Conversely the gene is completely absent from the genomes of most ascomycete yeast species , which do not form true hyphae . The data suggests that ZtGT2 orthologues may have played an important role in the evolution of pathogenic fungi , by enabling hyphal growth on solid surfaces . It is likely that this capability , which is also a requirement for the establishment of mutualistic interactions and for saprophytic growth , arose “inadvertently” from the need for fungi to adopt the characteristic filamentous lifestyle which enables them to seek out and access distal nutrient sources .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Conclusions",
"Methods"
] |
[
"fungal",
"spores",
"plant",
"anatomy",
"medicine",
"and",
"health",
"sciences",
"fungal",
"genetics",
"fungi",
"plant",
"science",
"plant",
"pathology",
"fungal",
"diseases",
"plants",
"fungal",
"reproduction",
"infectious",
"diseases",
"mycology",
"grasses",
"leaves",
"wheat",
"fungal",
"genomics",
"eukaryota",
"plant",
"fungal",
"pathogens",
"plant",
"pathogens",
"genetics",
"biology",
"and",
"life",
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"genomics",
"organisms"
] |
2017
|
A conserved fungal glycosyltransferase facilitates pathogenesis of plants by enabling hyphal growth on solid surfaces
|
Children under two years of age are in the most critical window for growth and development . As mobility increases , this time period also coincides with first exposure to soil-transmitted helminth ( STH ) infections in tropical and sub-tropical environments . The association between malnutrition and STH infection , however , has been understudied in this vulnerable age group . A nested cross-sectional survey was conducted in 12 and 13-month old children participating in a deworming trial in Iquitos , an STH-endemic area of the Peruvian Amazon . An extensive socio-demo-epi questionnaire was administered to the child's parent . Length and weight were measured , and the Bayley Scales of Infant and Toddler Development were administered to measure cognition , language , and fine motor development . Stool specimens were collected to determine the presence of STH . The association between malnutrition ( i . e . stunting and underweight ) and STH infection , and other child , maternal , and household characteristics , was analyzed using multivariable Poisson regression . A total of 1760 children were recruited between September 2011 and June 2012 . Baseline data showed a prevalence of stunting and underweight of 24 . 2% and 8 . 6% , respectively . In a subgroup of 880 randomly-allocated children whose specimens were analyzed by the Kato-Katz method , the prevalence of any STH infection was 14 . 5% . Risk factors for stunting in these 880 children included infection with at least one STH species ( aRR = 1 . 37; 95% CI 1 . 01 , 1 . 86 ) and a lower development score ( aRR = 0 . 97; 95% CI: 0 . 95 , 0 . 99 ) . A lower development score was also a significant risk factor for underweight ( aRR = 0 . 92; 95% CI: 0 . 89 , 0 . 95 ) . The high prevalence of malnutrition , particularly stunting , and its association with STH infection and lower developmental attainment in early preschool-age children is of concern . Emphasis should be placed on determining the most cost-effective , integrated interventions to reduce disease and malnutrition burdens in this vulnerable age group .
Malnutrition is the leading cause of mortality in preschool-age children ( i . e . children under five years of age ) in low- and middle-income countries ( LMICs ) . Over 150 million children suffer from one or more forms of malnutrition , including stunting , underweight and wasting [1] , [2] . Malnutrition also predisposes to infection , creating a vicious infection-malnutrition cycle that contributes to over 35% of the disease burden of early childhood [1] , [3] . Infection and micronutrient and other deficiencies from an inadequate diet are the primary causes of malnutrition in childhood [4] . Early childhood before the age of two years is a particularly critical time for growth faltering [5] . This window of time corresponds to weaning and the introduction of complementary foods . As mobility increases , the risk of early acquisition of certain infectious pathogens also increases during this time . The soil-transmitted helminths ( STHs ) , or worm infections , are one such pathogen cluster that is transmitted through contaminated food , water and/or the environment in warm , tropical and subtropical climates . The STH disease cluster includes ascariasis ( caused by the roundworm Ascaris lumbricoides ) , trichuriasis ( caused by the whipworm Trichuris trichiura ) and ancylostomiasis or hookworm disease ( caused either by Ancylostoma duodenale or Necator americanus ) . The geographical distribution of these three diseases is overlapping , mainly in areas of poverty with poor sanitation and limited access to potable water . STHs are one of the most important Neglected Tropical Diseases ( NTDs ) and one of the most common infections worldwide . Recent estimates indicate that 1 . 45 billion people are infected with STHs in over 100 endemic countries [6] . It is estimated that they contribute 4 . 98 million years lived with disability ( YLD ) and 5 . 18 million disability-adjusted life years ( DALYs ) [6] . STHs are a significant contributor to poor health and nutritional status in all age groups , and especially in childhood . Traditionally , the occurrence of STH infection had been perceived to be low in children under two years of age . However , there has been increasing empirical evidence which shows that the opposite is true [7] . In Belén , a community of extreme poverty in the Peruvian Amazon , while the prevalence of Ascaris or Trichuris was only 4% in children at seven to nine months of age , it rose to almost 30% at 12 to 14 months of age [8] . In a cohort of preschool-age children in Ecuador , over 20% suffered from Ascaris or Trichuris infection at least once in the first two years of life , with infection first appearing around seven months of age [9] . There is also evidence to suggest that hookworm infection may be high in early preschool-age children . A study in Zanzibar by Stoltzfus et al ( 2004 ) , demonstrated that 31 . 3% of children under 30 months of age were infected with hookworm [10] . It is becoming increasingly recognized that STH infection in early childhood may have important adverse effects on health and nutrition [11] , [12] . One such reason for this is that the parasites take up a greater proportion of the body in younger children [13] . However , the importance of STH infection and its link with malnutrition in preschool-age children has been inadequately studied . Few studies have included preschool-age children in their study population . Even fewer studies have provided age-disaggregated data to examine differing effects and sequelae in the critical growth window before two years of age . Evidence from the World Health Organization ( WHO ) Child Growth Standards demonstrates that , with appropriate nutrition and health interventions provided early in life , all children have a similar potential for healthy growth and development [14]–[16]; however , children living in areas of greatest poverty suffer the most from health and social inequities due to increased disease burden and lack of access to necessary health interventions and services [17] . Improving the health of the youngest children has been a focus of many international efforts , including Canada's Muskoka Initiative , and the Millennium Development Goals ( MDGs ) which aim to reduce poverty worldwide by 2015 . With focus now shifting to the post-2015 MDG agenda , it is imperative to fill in knowledge gaps on the burden of disease and risk factors in early childhood to improve health in the short and the long term [18] . The principal objective of this study was to determine the association between malnutrition ( i . e . stunting and underweight ) and soil-transmitted helminth infection and other child , maternal and household characteristics in 12 and 13-month old children , living in an area of extreme poverty in the Peruvian Amazon .
This study received ethics approval in Peru from the Comité Institucional de Ética of the Universidad Peruana Cayetano Heredia and the Instituto Nacional de Salud , in Lima , and the local Ministry of Health office ( Dirección Regional de Salud Loreto ) in Iquitos . Ethics approval was obtained in Canada from the Research Ethics Board of the Research Institute of the McGill University Health Centre in Montréal , Québec . Written informed consent was obtained by the parents or guardian of each child that participated in the study . This study was conducted in neighbouring districts in and around the city of Iquitos , the capital of the Loreto region in the Peruvian Amazon ( Fig . 1 ) . The study area included four districts ( Belén , Iquitos , Punchana and San Juan ) where poverty is widespread , STH infections are highly endemic and malnutrition prevalence is high . Both malnutrition and STH prevalence have been identified as priority concerns by stakeholders in the community [19] . The study population included children attending their routine 12-month growth and development ( “Crecimiento y Desarrollo” or CRED ) clinic visit in the study area , and whose parents had agreed to their participation in a randomized controlled trial ( RCT ) to determine the benefit of deworming ( mebendazole ) on growth and development ( ClinicalTrials . gov #NCT01314937 ) . The current cross-sectional survey was nested within the deworming RCT , and describes information obtained at the baseline 12-month CRED trial visit . Preschool-age children are scheduled to attend routine government-sponsored CRED visits ( similar to well baby clinics ) at health clinics in Peru once-monthly from birth to 11 months of age ( with two visits before one month of age ) , and every two months from 12 to 24 months of age ( with less frequent visits thereafter to school age ) . During routine CRED visits , anthropometric measurements ( e . g . length and weight ) are taken , developmental milestones are recorded , and children receive routine age-appropriate vaccinations and micronutrient supplements . Parents also receive nutrition and other health counselling for their child [20] . Using information provided by the Peruvian Ministry of Health on health centre location and attendance , 12 study health centres ( “Centros de Salud” ( C . S . ) and “Puestos de Salud” ( P . S . ) ) were identified in the study area . These included: 1 ) P . S . America; 2 ) C . S . Belen; 3 ) C . S . Bellavista Nanay; 4 ) C . S . Cardozo; 5 ) P . S . 1 de Enero; 6 ) C . S . 6 de Octubre; 7 ) C . S . 9 de Octubre; 8 ) P . S . Masusa; 9 ) P . S . Porvenir; 10 ) C . S . Progreso; 11 ) C . S . San Juan; and 12 ) P . S . Tupac Amaru . Inclusion criteria for participating in the study were: 1 ) children attending any one of the study health centres for their 12-month CRED visit; and 2 ) children living in Belén , Iquitos , Punchana or San Juan districts . Exclusion criteria preventing participation in the study were: 1 ) children attending the health centre for suspected STH infection; 2 ) children who had received deworming treatment in the six months prior to the study; 3 ) children whose families planned to move outside of the study area within the next 12 months; 4 ) children under 12 months of age or 14 months of age or older; and 5 ) children with any serious congenital or chronic medical condition ( e . g . chronic severe malnutrition , extremely preterm birth ( i . e . < 28 weeks gestation ) , newborn hypoxia and neural tube defects ) . All inclusion and exclusion criteria were based on considerations related to participation in the deworming trial . All children who were enrolled in the deworming RCT were included in the current study . The sample size of the RCT was estimated to be 1760 children , or 440 children per intervention group ( MC4G Software© , GP Brooks , Ohio University , 2008 ) . This was based on detecting a minimum difference of 0 . 20 kg in mean weight gain among different deworming interventions ( 3 intervention groups , and 1 control group ) . Canvassing of the local population was undertaken between April 2011 and August 2011 prior to recruitment to assist in identifying potentially eligible children for the study . In households where any child under 12 months of age was present , information was recorded on the child's date of birth and address . Lists of CRED attendance from each health centre were also provided to identify children who would be potentially eligible to participate in the study based on place of residence and age of the child . Nine trained research assistants ( RAs ) , primarily nurses and nurse-midwives , were assigned to one or two health centres each to recruit study participants in the respective communities and health centres and to obtain all study outcomes . Additional nurse-technicians were hired and trained to assist in participant recruitment . For parents of eligible children , an informed consent form was administered and signed . A questionnaire , which included questions on socio-demographic and health information about the child and family , was then administered by the RA during a household interview with that parent who was the primary caregiver . The questionnaire was adapted from previous studies [8] , [21]–[23] , but included additional questions related to child nutrition . These included history and duration of breastfeeding , and first introduction of liquids and solid foods . The latter was confirmed by redundancy among the questions and a 24-hour dietary recall . At the end of the home visit , parents were also provided with the information and materials needed to collect a stool specimen from the child . Parents were then given an appointment at the health centre , at which time they would deposit the stool specimen and the child's anthropometric measures and development would be ascertained . All forms and questionnaires were returned to the study offices at the end of each work day , and reviewed by the Project Director , the local Study Coordinator , and , when needed , by the local Principal Investigator , to confirm the eligibility of each child . During the visit at the health centre , the quality of the stool specimen was first verified . If no specimen or an inadequate specimen ( i . e . liquid specimen and/or insufficient quantity ) was provided , then anthropometry was ascertained and a subsequent visit was scheduled to arrange for another stool specimen . If any child was discovered to be ill on the day of his or her health centre visit , the visit was postponed until the child had recovered . After verification of the quality and quantity of the stool specimen , the child was undressed and weighed ( in duplicate ) using a portable electronic scale ( Seca 334 , Seca Corp . , Baltimore , MD , USA ) . Length ( i . e . the recommended measurement for height in children less than two years of age ) was measured ( in duplicate ) as recumbent crown-heel length on a flat surface using a stadiometer ( Seca 210 , Seca Corp . , Baltimore , MD , USA ) . Cognition , receptive and expressive communication ( i . e . language ) and fine motor development were assessed using the Bayley Scales of Infant and Toddler Development , Third Edition ( Bayley-III ) ( Pearson Education Inc , Texas , 2006 ) . The latter instrument was translated into Spanish and adapted for local cultural appropriateness and validity by the Project Director ( SAJ ) and an experienced psychologist from the Instituto de Investigación Nutricional ( IIN ) in Lima , Peru . All RAs were trained on administration of the Bayley-III by SAJ and the IIN psychologist . As little variability was anticipated in gross motor skills at 12 months of age , and to reduce the length of time of assessment , the WHO gross motor milestones ( i . e . walking alone , standing alone , walking with assistance , hands and knees crawling , and standing with assistance ) was used instead of the Bayley-III Gross Motor subtest . Children's gross motor skill achievement was assessed by observation by RAs [24] . Upon completion of all baseline outcome measurements and the provision of an adequate stool specimen , participants were enrolled into the deworming trial and randomly assigned to one of three intervention groups or the control group . The stool specimen was labeled with a unique number between 1 and 1760 , corresponding to the randomly assigned treatment code for the deworming trial . Stool specimens were transferred to the laboratory at the local research facility ( Asociación Civil Selva Amazónica ) to be read by one of two experienced laboratory technologists . Two different techniques were required for reading stool specimens in the nested study based on the child's treatment allocation in the larger deworming RCT . Stool specimens from participants who were randomized to receive active deworming treatment were analyzed immediately by the Kato-Katz method , as recommended by WHO ( within 24 hours of initial collection , as a fresh specimen is required for this technique ) to determine both prevalence and intensity of STH infection [25] , [26] . This procedure of immediately analyzing stool specimens only of those randomly allocated to the intervention groups receiving active deworming treatment takes into account the ethical imperative of treating those who would be found to have positive results . Stool specimens of those receiving inactive placebo tablets were stored in 10% formalin and examined by the direct method upon completion of the trial , at which time all participants received deworming treatment . To maintain blinding , each specimen code was replaced with a laboratory code by the local study coordinator for use by the laboratory technologists . Laboratory technologists were provided with a list of those laboratory codes which would be analyzed and those which were to be stored . Each list was kept on a password-protected computer , one in the coordinator's office and one in the lab accessible only to the laboratory supervisor . A master list linking all information was stored at the research office in Canada ( Research Institute of the McGill University Health Centre ) . Quality control was conducted on 10% of all Kato-Katz slides to ensure agreement in species identification and egg counts between laboratory technologists . To classify child anthropometric measurements ( i . e . length and weight ) into categories of stunting , underweight and wasting , WHO Anthro software ( Version 3 , 2011 ) was used to calculate length-for-age z scores ( LAZ ) , weight-for-age z scores ( WAZ ) , and weight-for-length z scores ( WLZ ) , respectively . Z scores are calculated taking into account a child's sex and age and are based on a comparison to a WHO international standard population . Moderate-to-severe categories of stunting , underweight and wasting are based on LAZ , WAZ and WLZ of <−2SD . Severe stunting , underweight and wasting are defined as LAZ , WAZ and WLZ of <−3SD , respectively [27] . Categories of STH infection intensity were determined from established WHO guidelines [28] . For Ascaris infection , light , moderate and heavy intensity are based on egg counts per gram of feces ( epg ) of 1–4999 , 5000–49999 and 50000 and greater , respectively . For Trichuris infection , the categories for light , moderate and heavy intensity infection are an epg of 1–999 , 1000–9999 and 10000 and greater , respectively . Light , moderate and heavy intensity hookworm infection are based on epgs of 1–1999 , 2000–3999 and 4000 and greater , respectively . Both arithmetic and geometric mean epg were calculated and reported . The development score was calculated as the mean crude score for each subtest of the Bayley-III , as well as a composite score of all four subtests combined . The range of possible scores was 0 to 91 for cognition , 0 to 49 for receptive communication , 0 to 48 for expressive communication and 0 to 66 for fine motor skills . Scaled scores were derived from scaling the total raw score in each individual subtest to a metric between 1 ( i . e . the lowest possible score ) and 19 ( i . e . the highest possible score ) according to the subtest and age of the child in months and days [29] , [30] . As scaled scores are based on a study population that may not be representative of the general population , these scores were used for descriptive purposes only and not to quantify the level of developmental deficit . The WHO gross motor milestones were categorized into a dichotomous variable indicating whether the child had achieved the most advanced milestone of walking alone . The variable was coded as one , if the child was able to walk without any assistance or support , regardless of the other milestones achieved , and zero , if the child could not walk without assistance , but achieved at least one of the other gross motor milestones . Principal Component Analysis was used to create an asset-based index for socioeconomic status ( SES ) to be included in multivariable analyses ( StataCorp . 2013 . Stata Statistical Software: Release 13 . College Station , TX: StataCorp LP ) . Variables included in the index were house material , type of cooking fuel , television ownership , radio ownership and electricity in the home [31] , [32] . The socioeconomic status index explained 40 . 1% of the variance and was divided into quartiles for subsequent analyses . All associations with the outcomes of stunting and underweight were examined initially in univariable analyses . Variables with a p value<0 . 20 , or that were deemed to be important from previous published research , were included in multivariable modelling to determine the most parsimonious model . If variables were highly correlated , the most informative variable ( i . e . with more variation , more accurate measurements and/or important factors in previous literature ) was chosen to be included in multivariable model building . Multivariable associations with stunting and underweight were examined using a generalized linear model with a log link , a Poisson distribution , and a robust variance estimator to estimate the risk ratio for the dichotomous outcomes of moderate-to-severe stunting and moderate-to-severe underweight , where no and mild categories of stunting , and no and mild categories of underweight , respectively , comprised the reference groups [33] , [34] . Analyses were first restricted to children whose stool specimens were examined by the Kato-Katz method [26] . Analyses were then performed including all children in the study population . A complete case approach was used to analyze the data . All statistical analyses were performed using the Statistical Analysis Systems statistical software package version 9 . 3 ( SAS Institute , Cary , NC , USA ) .
Between September 2011 and June 2012 , parents of 2297 children 12 to 13 months of age were approached to participate in the study in order to meet the sample size requirements of 1760 eligible children . Three-hundred and eighty-five children did not meet the inclusion criteria , parents of 126 children declined to participate , and 26 children were recruited but the sample size was reached before they were enrolled in the study . Anthropometric and development measurements and stool specimens were obtained from all 1760 enrolled children . Baseline characteristics of the study population are described in Table 1 . The average number of CRED visits before enrolment in the study ( i . e . from birth to 11 months , inclusive ) was 7 . 6 ( ±3 . 5 ) . Less than 4% of children had no previous CRED attendance ( n = 62 ) . Only 25 . 5% ( n = 447 ) had all vaccinations up-to-date according to Peruvian Ministry of Health guidelines ( i . e . one dose of Bacille Calmette-Guérin ( BCG ) , one dose of hepatitis B , three doses of polio , three doses of pentavalent , two doses of rotavirus , three doses of pneumococcal and one dose of measles , mumps and rubella ( MMR ) vaccines ) [20]; however , as MMR vaccine and the third dose of pneumococcal vaccine are scheduled at the 12-month CRED visit , many children had not yet received these latter vaccinations . Including only vaccinations scheduled prior to 12 months , coverage of up-to-date vaccinations reached 80 . 3% . In terms of family and household characteristics , the average maternal age was 26 . 5 ( ±7 . 1 ) years . The average number of people living in the household was 6 . 6 ( ±2 . 7 ) . Sixty-nine percent of children had one or more siblings . Roughly half of the children ( 50 . 1% ) had received liquids ( other than water and water-based drinks ) or food before the age of six months . Baseline socio-demographic and epidemiological characteristics were similar in the 880 children whose stool specimens were examined by the Kato-Katz method compared to the entire study population of children ( n = 1760 ) ( results not shown ) . Twenty-five percent of the study population suffered from one or more forms of malnutrition . Prevalence of moderate-to-severe underweight , stunting and wasting were 8 . 6% , 24 . 2% and 2 . 3% , respectively ( Table 2 ) . Co-morbidity with two or three concurrent forms of malnutrition was present in 8 . 3% ( n = 146 ) of participants . Mean z scores for the study population were below average ( i . e . below 0 ) for all three indices ( i . e . LAZ , WAZ and WHZ ) . Severe malnutrition ( i . e . a z score of <−3 SD for LAZ , WAZ or WLZ ) affected 5 . 5% ( n = 96 ) of the population . The overall prevalence of any STH infection in children whose stool specimens were analyzed by the Kato-Katz method was 14 . 5% ( Table 3 ) . The prevalence of infection was 11 . 5% for Ascaris , 4 . 5% for Trichuris and 0 . 6% for hookworm . Eighteen children ( 2 . 1% ) were infected with two STH species , but none with all three . For those who had their stool specimens stored and analyzed by the direct method , the prevalence was lower for all three STH species ( i . e . 9 . 5% , 0 . 9% and 0 . 1% for Ascaris , Trichuris , and hookworm , respectively ) . Using the Kato-Katz method as the gold standard , and assuming equal STH prevalence due to randomization , the direct method , therefore , underestimated Ascaris infection by 17 . 4% , Trichuris infection by 80 . 0% , hookworm infection by 83 . 3% , and any STH prevalence by 29 . 0% . For the 880 children whose stool specimens were examined using the Kato-Katz method and who were found to be STH positive , most were found to have low intensity infection , with 86 . 1% , 92 . 5% and 100% harbouring light infections of Ascaris , Trichuris and hookworm , respectively ( Table 4 ) . There were no cases of heavy intensity infection of any STH species . In terms of developmental functioning in all 1760 children , the mean composite development score on the Bayley-III was 98 . 1 ( ± SD 6 . 0 ) with a range between 73 and 123 points . On individual subtests , the mean score was 42 . 5 ( ±3 . 0 ) for cognition , 12 . 9 ( ±1 . 6 ) for receptive communication , 13 . 5 ( ±2 . 1 ) for expressive communication and 29 . 2 ( ±1 . 5 ) for fine motor skills . This translated to a mean scaled score of 9 . 9 ( ±1 . 84 ) , 7 . 2 ( ±1 . 9 ) , 8 . 1 ( ±1 . 7 ) and 9 . 2 ( ±1 . 5 ) for the cognitive , receptive language , expressive language and fine motor subtests , respectively . The mean scores were slightly higher for 13-month old children compared to 12-month old children ( i . e . 43 . 2 vs . 42 . 5 for cognition , 13 . 3 vs . 12 . 9 for receptive communication , 13 . 8 vs . 13 . 4 for expressive communication , and 29 . 4 vs . 29 . 2 for fine motor skills , respectively ) . Twenty-three percent and 35 . 6% of 12 and 13-month old children , respectively , were able to walk without support . In determining the risk factors for malnutrition in the group of children whose specimens were analyzed by the Kato-Katz method , stunting was found to be statistically significantly associated with the presence of any STH infection , male sex , older age ( i . e . 13 months old ) , one or more hospitalizations since birth , lower SES , and lower birth weight in both unadjusted and adjusted analysis ( Table 5 ) . The crude score of each individual Bayley-III subtest was significantly associated with stunting in univariable analyses . The overall composite development score was included in the multivariable model , with a lower score associated with an increased risk of stunting in the adjusted model ( aRR 0 . 97; 95% CI: 0 . 95 , 0 . 99 ) . Risk factors for underweight in unadjusted and adjusted analyses included lower birth weight , lower development score , and lower SES ( Table 5 ) . Continued breastfeeding at one year of age was associated with a decreased risk of underweight in unadjusted and adjusted analyses . No statistically significant association was found between underweight and any STH infection in either unadjusted or adjusted analyses . No independent associations were found between malnutrition and up-to-date vaccinations , vitamin A supplementation , walking alone , maternal employment outside of the home , place of residence , place of delivery or antenatal care attendance ( Table 5 ) . The timing of introduction of liquids and foods was not associated with stunting or underweight in either unadjusted or adjusted analyses . STH infection was not associated with wasting in either unadjusted or adjusted analyses ( results not shown ) . Multivariable results for stunting , underweight and wasting were similar when analyses were extended to include participants with specimens analyzed by both the Kato-Katz and the direct method ( results not shown ) .
The scientific literature to date has provided insufficient evidence of an association between malnutrition and STH infection in early preschool-age children . This nested cross-sectional study in 1760 preschool-age children aged 12 and 13 months in a community of extreme poverty in the Peruvian Amazon contributes to filling this research gap . We demonstrate an important association between malnutrition and STH infection and developmental deficits . Previous studies in the area of Belen have found similar associations between malnutrition and STH infection in a wider age range of preschool-age children [8] , [23] . In contrast to previous studies , however , this association was apparent even with low intensity STH infection [8] . The current study updates previous estimates and provides in-depth data for that critical time period around one year of age when interventions are likely to be considered to be integrated into vaccination programs or well baby clinics . Consistent with previous studies , lower socioeconomic status and older child age were associated with a higher risk of malnutrition [8] , [23] , [35] . Nonetheless , the latter result is somewhat unexpected , as the age range was quite restricted in the present study . This finding , along with a greater number of children who were walking alone at 13 months of age , support the concept of a critical window in which children are rapidly developing and growing before two years of age [5] . This has the potential to translate to an even greater impact of parasite infection and nutritional deficits on child health in this time period . An interesting finding in this study was that STH prevalence ( from either detection method ) and malnutrition prevalence were lower compared to previous work in the area [8] . The current study was embedded within the existing health infrastructure of routine growth and development clinic visits . Although previous attendance was not an inclusion criterion , there may have been higher-risk populations with low CRED attendance that would not have been easily reached , but who may have been included in the previous community-based surveys . We attempted to solve this problem by conducting community canvassing prior to enrolment to identify all children in the eligible jurisdictions , not only those who had had the opportunity to access health services previously . An increase in research attention and community-based health and nutrition campaigns may also explain some of the improvements . In particular , deworming campaigns directed towards school-age children , may have contributed to a reduction in overall environmental contamination in the area . This could have resulted in lower infection rates in younger children not directly targeted by campaigns , as has been shown in other settings [36] . A recent study also demonstrated a decrease in the prevalence of stunting in preschool-age children in Peru from 1991 to 2011 , possibly due to economic growth and an increased emphasis on pro-poor social programs [37] . However , the overall prevalence of stunting has remained unacceptably high , with children between 12 and 23 months , those living in the Amazon or Andean region , and those of lower SES , suffering disproportionately from malnutrition [37] . Prevalence of stunting was also higher in males compared to females under the age of 36 months , which is consistent with our findings . Despite the positive trends in a reduction in stunting and STH infection in this and other studies , the current results demonstrate that even low STH prevalence and intensity of infection can be associated with poor growth in children in this vulnerable age group . This study benefits from a large sample size of children , representative of the wider population of children living in the STH high-risk flooding areas of Iquitos . This representativity was helped in part by the community-wide canvassing and by the inclusion of health centres from a wide catchment area . Nevertheless , hard-to-reach and hidden populations of children suffering from severe malnutrition or other chronic illnesses may be under-represented in the study . An additional strength of the study is the focus on children of a narrow age range in the critical growth window . Other studies have included populations of children at heterogeneous growth and development stages and have been unable to disaggregate outcome results by age . In-depth information on potential risk factors was also collected to ensure that the impact of other child , maternal and household characteristics were taken into account in the analysis . The ascertainment of nutritional information , such as when liquids and foods were first introduced and the age of weaning may have been limited by recall bias; however , the collection of information on the age of introduction of specific local foods and a 24-hour recall were used to increase validity of the responses . This study also incorporated comprehensive developmental testing . To our knowledge , this is the first study that has incorporated the Bayley-III , one of the most rigorous development tests available for preschool-age children , in conjunction with STH infection . We were also able to take into account the potential effects of SES by using an asset-based proxy index . The study was limited by the fact that , for ethical reasons , the Kato-Katz method could only be used to analyze half of the specimens from randomly-allocated participants ( i . e . those who were randomly assigned to receive active deworming treatment ) , and therefore intensity data were not available for all participants . The higher STH prevalence in specimens analyzed by the Kato-Katz technique suggests that the direct method likely underestimated STH prevalence , due to lower sensitivity and specificity , and/or the storage of specimens . In those with intensity data , a low prevalence of moderate-to-heavy intensity infection restricted the ability to detect differences in malnutrition risk according to the intensity of STH infection . In addition , although the malnutrition-infection association is known to be cyclical in nature [1] , [3] , the direction of the associations between various risk factors and malnutrition cannot be established due to the cross-sectional nature of the baseline survey . One additional limitation concerns the classification of development scores and their potential for generalization to other populations . These should be interpreted within the context of the current study population and would require further validation for use and for comparisons with populations in other settings' . Overall , this study demonstrates an important association between stunting , low birth weight , SES , STH infection and cognitive , language and motor development in early preschool-age children . This empirical evidence advances our knowledge of the risk factors for malnutrition in the critical growth and development window before two years of age . The results provide further evidence of the importance of determining the most cost-effective , integrated and multi-sectoral interventions to target this vulnerable age group , reduce health inequities , and prevent growth and development deficits in both the short and long-term .
|
Malnutrition , including stunting and underweight , is one of the leading causes of morbidity and mortality in preschool-age children . Children under two years of age are at a particularly critical period for growth and development , and for first exposure to worm infections in tropical and subtropical environments . The association between malnutrition and worm infection , however , is not well understood in this age group . A nested cross-sectional survey was therefore conducted between September 2011 and June 2012 in 1760 children 12 and 13 months of age living in a worm-endemic area of the Peruvian Amazon . Length , weight , development ( i . e . cognitive , language and motor development ) , worm infection , and socio-demographic information were obtained . Results showed a high prevalence of stunting , and a significant association with worm infection and lower development . Overall , these adverse effects have the potential to negatively impact short-term and long-term health and nutrition , and educational and social achievement , into school-age and adulthood . Emphasis is needed on determining the most appropriate and effective interventions to reduce poor health and nutrition outcomes in this age group .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"helminth",
"infections",
"medicine",
"and",
"health",
"sciences",
"nutrition",
"epidemiology",
"biology",
"and",
"life",
"sciences",
"soil-transmitted",
"helminthiases",
"parasitic",
"diseases",
"malnutrition"
] |
2014
|
Risk Factors Associated with Malnutrition in One-Year-Old Children Living in the Peruvian Amazon
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Chromosome segregation requires sister chromatid resolution . Condensins are essential for this process since they organize an axial structure where topoisomerase II can work . How sister chromatid separation is coordinated with chromosome condensation and decatenation activity remains unknown . We combined four-dimensional ( 4D ) microscopy , RNA interference ( RNAi ) , and biochemical analyses to show that topoisomerase II plays an essential role in this process . Either depletion of topoisomerase II or exposure to specific anti-topoisomerase II inhibitors causes centromere nondisjunction , associated with syntelic chromosome attachments . However , cells degrade cohesins and timely exit mitosis after satisfying the spindle assembly checkpoint . Moreover , in topoisomerase II–depleted cells , Aurora B and INCENP fail to transfer to the central spindle in late mitosis and remain tightly associated with centromeres of nondisjoined sister chromatids . Also , in topoisomerase II–depleted cells , Aurora B shows significantly reduced kinase activity both in S2 and HeLa cells . Codepletion of BubR1 in S2 cells restores Aurora B kinase activity , and consequently , most syntelic attachments are released . Taken together , our results support that topoisomerase II ensures proper sister chromatid separation through a direct role in centromere resolution and prevents incorrect microtubule–kinetochore attachments by allowing proper activation of Aurora B kinase .
Ordered segregation of the genome during cell division requires bipolar attachment to spindle microtubules [1] and maintenance of sister chromatid cohesion until anaphase onset [2] . Cohesin provides a physical link between sister chromatids , and cleavage of cohesin subunits results from separase activation after the spindle assembly checkpoint ( SAC ) is satisfied [3] . However , before segregation occurs , proper chromosome condensation and sister chromatid resolution must be completed . The condensin complex has been shown to play a key role in these processes by organizing an axial structure where topoisomerase II ( TOPO II ) localizes and decatenates entangled DNA strands that result from replication or transcription [4 , 5] . Indeed , the requirement of TOPO II activity in mitosis has been amply documented . In Saccharomyces cerevisiae , circular DNA molecules accumulate as catenated dimers in top2 mutants [6] , and TOPO II activity prevents nondisjunction and DNA breakage during mitosis [7–9] . Injection of antibodies against TOPO II in Drosophila embryos [10] , the addition of TOPO II inhibitors or RNA interference ( RNAi ) in mammalian culture cells and Xenopus extracts [11–14] caused severe defects in chromosome segregation during anaphase . More specifically , TOPO II activity has been suggested to affect normal centromere structure [15] where the protein normally accumulates in its catalytically active form [15–20] . These data strongly suggest that prior to segregation , TOPO II has a general role in promoting the resolution of sister chromatids . However , how this correlates with TOPO II activity at the centromeres remains a critically unanswered question .
To study the function of TOPO II during mitosis , we first analyzed the consequences of depleting the enzyme by RNAi or treating Drosophila S2 cells with specific inhibitors ( Figure 1 ) . Significant levels of TOPO II depletion were obtained by RNAi treatment as shown by western blot analysis in which the protein is barely detectable after 72 h ( Figure 1A ) . However , we found that these cells apparently progress normally through early stages of mitosis but show severe segregation defects during anaphase and telophase , and cell proliferation is significantly inhibited without altering the mitotic index ( Figure 1B–1D ) . Quantification of chromosome segregation abnormalities shows that after long RNAi treatment , a significant proportion of cells display either chromatin bridges or lagging chromatids during anaphase ( Figure 1B , 1E , and 1F ) . Immunofluorescence analysis of chromosome morphology with antibodies against condensin subunits reveals that depletion of TOPO II does not significantly affect mitotic chromosome structure ( Figure 1G ) . Cells were also treated with the TOPO II inhibitor ICRF-187 , a bisdioxopiperazine-type chemical that has been shown to interfere with the catalytic activity of TOPO II [21] . However , treatment of cells with ICRF-187 results in a more pronounced alteration in chromosome structure ( Figure 1H ) . The exact role of TOPO II in mitotic chromosome structure remains highly debatable . This is due to the fact that the use of different procedures to disrupt TOPO II function and localization in several model organisms has led to conflicting results [22] . Moreover , previous studies have shown that TOPO II inhibitors may also result in the activation of the G2 checkpoint because they block the activity of the enzyme in different conformation states [23] . Therefore , we have resorted to depleting TOPO II by RNAi for most of our study . To directly assess whether TOPO II function is required at centromeres , we depleted the single TOPO II isoform from Drosophila S2 cells stably expressing fluorescent markers for chromatin ( mRFP-H2B ) and centromeres ( CID-GFP ) [24] by RNAi treatment ( Figure 2 and Videos S1–S4 ) . In order to visualize individual sister centromeres at high temporal and spatial resolution , TOPO II–depleted cells were imaged by four-dimensional ( 4D ) time-lapse fluorescence microscopy in which the distance between the optical layers of the Z-stack was kept to less than 1 μm . In control cells , chromosomes congression occurs normally , and as anaphase starts , CID-GFP pairs disjoin and move poleward ( Figure 2A and Video S1 ) . However , in TOPO II–depleted cells , while chromosomes appear to exhibit normal congression , centromeres of sister chromatids remain on the same side of the metaphase plate , fail to disjoin , and move towards the same pole during anaphase ( Figure 2B and 2C and Video S2 ) . Nevertheless , in many cells after 72 h of RNAi treatment , chromatin bridges presumably linking chromosome arms are clearly observed ( Figure 2C and Video S3 ) . At later times after RNAi treatment ( 96 h ) , most cells show chromosome nondisjunction ( Figure 2D and Video S4 ) . Noteworthy , most of the analysis after TOPO II depletion was carried out in cells that did not show extensive polyploidy , as ascertained by chromosome and centromere labeling , indicating that they had not undergone multiple cell cycles . After long RNAi treatment , a small proportion of polyploid cells were observed ( Figure S1 ) . These cells are characterized by the presence of chromosomes that are attached by their nondisjoined centromeres , as would be expected if in the previous cycles , proper centromere separation failed . This effect is apparently not due to a failure to replicate centromeric DNA as shown by Southern blotting analysis ( Figure S1 ) . In order to determine why sister centromeres fail to disjoin after TOPO II depletion , cells stably expressing GFP-α-tubulin and CID-mCherry to specifically label spindle microtubules and centromeres were treated with RNAi , and mitotic progression was followed by time-lapse fluorescence microscopy ( Figure 3A and 3B and Videos S5 and S6 ) . As expected , in control cells , chromosomes show mostly amphitelic attachments , congression occurs normally , and bundles of spindle microtubules are easily observed associated with individual kinetochores ( Figure 3A and Video S5 ) . However , 72 h after TOPO II RNAi , most chromosomes show syntelic attachment and are oriented towards the same spindle pole ( Figure 3B and 3C and Video S6 ) . To confirm these observations , we performed an assay designed to quantify the nature of microtubule–kinetochore interactions in S2 cells [25] . For this assay , cells were arrested in mitosis with the proteasome inhibitor MG132 and subjected to a high dose of Taxol , which over a short period of time causes the collapse of the bipolar spindle into a monopolar configuration . This monopolar structure now contains the chromosomes distributed at the periphery of the aster , and microtubule–kinetochore interactions can be easily scored ( Figure S2 ) . We find that in control and TOPO II–depleted cells , more than 95% of the chromosomes had both kinetochores attached to microtubules in a syntelic configuration soon after significant depletion of TOPO II occurs . If these cells are analyzed when the two asters are in the process of collapsing , it is possible to ascertain whether the attachment is amphitelic , with chromosomes localized between the asters , or syntelic/monotelic , when located at the periphery of the aster ( Figure 3D–3H ) . In control cells ( n = 24 ) , most chromosomes ( 78% ) exhibited a clear amphitelic configuration , whereas in TOPO II–depleted cells ( n = 20 ) , most chromosomes ( 65% ) localized at the periphery of the two asters with a clear syntelic configuration ( Figure 3F and 3G ) . Consistently , CID fluorescence intensity for the centromere marker CID in TOPO II–depleted cells is almost double that of control cells ( unpublished data ) , and intercentromere distances never increase during mitotic progression ( Figure S3 ) . Analysis of microtubule–kinetochore interactions after TOPO II depletion was also performed in asynchronous cells . For this , either control or TOPO II–depleted cells were fixed and stained for CID , α-tubulin , and CENP-meta , the Drosophila CENP-E homolog , a kinetochore motor protein whose levels decrease significantly at kinetochores during anaphase [26] ( Figure 3I–3O ) . As expected , we find that CENP-E is present at kinetochores of control and TOPO II–depleted cells in prometaphase ( Figure 3I and 3L ) , but when chromosomes move poleward , CENP-E is undetectable ( Figure 3N ) , indicating that all kinetochores are attached to spindle microtubules . Rendering these images for the staining of CID and tubulin confirms that in the absence of TOPO II , kinetochore bundles are associated with pairs of CID dots , unlike control cells in which they associate with single CID dots ( Figure 3J , 3M , and 3O ) . Taken together , these results indicate that in the absence of TOPO II , sister centromeres fail to disjoin and chromosomes show mostly syntelic microtubule attachments . Long-term inhibition of TOPO II with drugs affects S2 cells during G2 and mitotic entry , resulting in severe abnormalities in chromosome structure that prevented us from using inhibitors to carry out a thorough analysis of the role of TOPO II during mitotic progression . However , we hypothesized that short incubations with the inhibitors might allow us to study its effects in living cells as they enter mitosis . Accordingly , cells were treated for short periods of time with ICRF-187 . S2 cells stably expressing GFP-α-tubulin and CID-mCherry were imaged by 4D microscopy during ICRF-187 treatment , before and after establishment of the metaphase plate ( Figure 4 and Videos S7–S9 ) . We find that inhibition of TOPO II activity after chromosomes have reached the metaphase plate and established bipolar attachment does not have an effect on the kinetochore–microtubule interaction ( Figure 4A and 4B ) . However , if the TOPO II inhibitor is added before nuclear envelope breakdown , as inferred by the exclusion of GFP-α-tubulin from the nucleus ( unpublished data ) , during prometaphase , we observe that 62 . 5% of chromosomes/cell ( n = 12 ) exhibit syntelic chromosome attachments , with kinetochore pairs moving poleward without separating their centromeres ( Figure 4C ) . These observations not only confirm our live-cell analysis of TOPO II–depleted cells after RNAi , but also indicate that TOPO II activity at early stages of mitosis plays an important role at centromeres to promote normal chromosome biorientation . However , once amphitelic attachments are achieved , TOPO II activity is not required for their maintenance . Previously , it has been demonstrated that as cells progress through late prometaphase and metaphase , cohesin is removed from the chromosome arms , remaining only at the centromere until the metaphase–anaphase transition [27–29] . Therefore , it remains possible that sister centromeres fail to disjoin after depletion of TOPO II due to an inappropriate accumulation of cohesin . In order to test this hypothesis , we determined the localization of the cohesin subunit RAD21/SCC1 in control and TOPO II–depleted cells before and after incubation with colchicine . In control cells at metaphase or after colchicine incubation , cohesin localizes as a clearly defined stripe between centromeres ( Figure 5A ) , but after depletion of TOPO II , cohesin shows a very abnormal distribution , which extends into the chromosome arms even after mitotic arrest ( Figure 5B ) . To test whether the inappropriate localization of cohesin accounts for the observed centromere nondisjunction in TOPO II–depleted cells , we performed simultaneous depletion of TOPO II and RAD21 by RNAi and followed mitotic progression and sister chromatid segregation ( Figure 5C ) . Although both proteins were efficiently depleted , cells progressed through mitosis , showing lagging chromatids or chromosomes ( Figure S4 ) . The 4D microscopy studies of mitotic cells stably expressing CID-GFP and RFP-H2B after simultaneous depletion of TOPO II and RAD21 show that the behavior of sister chromatids is identical to that of cells depleted of TOPO II alone and very different from RAD21 RNAi ( Figure 5F and Videos S10 and S11 ) . Cells depleted of RAD21 enter mitosis with separated sister chromatids and arrest in a prometaphase-like state ( Figure S5 , and Video S12 ) because they fail to inactivate the SAC [30 , 31] . However , in cells depleted of both TOPO II and RAD21 , closely paired sister chromatids reach the metaphase plate and , during anaphase , fail to disjoin , segregating together to the same spindle pole . These results demonstrate that inappropriate localization of RAD21 is unlikely to be responsible for the centromere nondisjunction phenotype observed after depletion of TOPO II . Together with the data , our results strongly suggest that depletion of TOPO II causes the formation of a physical linkage between sister centromeres , likely provided by DNA concatameres , which is not resolved during the metaphase–anaphase transition . As a consequence , TOPO II–depleted cells segregate entire chromosomes rather than sister chromatids . Apparently , this is not due to the inability of TOPO II–depleted cells to satisfy the SAC since they showed no significant mitotic delay and anaphase-promoting complex ( APC/C ) -dependent proteolysis of RAD21 and cyclin B occurs ( Figure S6 ) . If depletion of TOPO II causes failure to resolve sister chromatids and syntelic attachments , how then do these cells satisfy the SAC ? To address this question , we first determined the localization of SAC proteins after TOPO II RNAi . We find that in prometaphase , BubR1 accumulates strongly at kinetochores , while during anaphase , the level of BubR1 was significantly reduced , suggesting that TOPO II–depleted cells are able to inactivate the SAC , just like control cells ( Figure 6A and 6B ) . To further determine whether syntelic chromosomes undergo proper tension during mitosis , TOPO II–depleted cells were immunostained with the 3F3/2 monoclonal antibody that specifically detects kinetochore phosphoepitopes in the absence of tension [32–34] . We find that control and TOPO II–depleted cells behave very similarly so that 3F3/2 kinetochore phosphoepitopes are strongly labeled in prometaphase , become significantly reduced during prometaphase/metaphase , and are undetected in anaphase ( Figure 6C and 6D ) . These results indicate that SAC satisfaction in TOPO II–depleted cells with syntelic attachments correlates with the dephosphorylation of 3F3/2 epitopes but does not require extensive interkinetochore stretching . Previously , it has been shown that the correction of improper microtubule–kinetochore attachments requires Aurora B kinase activity as part of a tension-sensing mechanism on centromeres [1] . Aurora B is part of the chromosomal passenger complex ( CPC ) that localizes to the inner centromere during prometaphase/metaphase and transfers to the spindle midzone at anaphase onset and the mid body in telophase [35] . Therefore , we analyzed whether the localization of CPC proteins after TOPO II depletion was compromised and could be responsible for the inability of these cells to release syntelic attachments ( Figure 7 ) . In control cells , Aurora B localizes to the inner centromere region during prometaphase/metaphase and is transferred to the spindle midzone when cells initiate anaphase [36] ( Figure 7A ) . However , in TOPO II–depleted cells Aurora B localization is abnormal ( Figure 7B ) . In prometaphase , Aurora B remains associated with sister kinetochores , does not stretch across the metaphase plate , remains associated with inner centromeres of syntelic chromosomes during anaphase , and fails to transfer to the spindle midzone . A very similar abnormal pattern of localization was also observed for INCENP , a member of the CPC , which regulates Aurora B activity ( Figure 7C and 7D ) . These observations indicate that TOPO II is essential for the organization of the inner centromere so that the CPC can show a normal pattern of localization during mitosis . Previous studies in human cells have shown that chromosomes with syntelic attachments experience significant distortion of their centromeres and are not easily identified by the SAC , and that these errors are enhanced when aurora B kinase activity is inhibited [37] . Moreover , it has been shown that Aurora B is normally enriched at sites associated with erroneous microtubule attachments where it promotes microtubule depolymerization [38] . Since in TOPO II–depleted cells Aurora B remains associated with syntelic attachments throughout mitosis , it would be expected that these erroneous attachments would be corrected , unless Aurora B activity is compromised due to structural changes resulting from TOPO II depletion . To test this hypothesis , we analyzed the levels of phosphorylation of histone H3 at Ser 10 ( PH3 ) , a known Aurora B substrate [39] ( Figure 8A ) . Immunofluorescence analysis revealed that after TOPO II depletion at 96 h , PH3 levels were reduced almost by half ( 41% ) when compared with control cells ( Figure 8B ) and not much different from the reduction ( 62% ) observed after RNAi depletion of Aurora B ( Figure 8A and 8B ) . Similar results were obtained after treatment of cells with the TOPO II inhibitor ICRF-187 ( Figure S7 ) . Western blot of total protein extracts of TOPO II– and Aurora B–depleted cells confirmed that PH3 levels were significantly reduced ( Figure 8C ) . However , although TOPO II–depleted extracts show a significant reduction in PH3 reactivity , the total Aurora B levels appear unaffected , suggesting that depletion of TOPO II specifically affects the kinase activity of Aurora B . To directly address this possibility , Aurora B was immunoprecipitated from total protein extracts from control or TOPO II–depleted cells and its kinase activity tested in vitro with unphosphorylated histone H3 ( Figure 8D ) . We find that phosphorylation of histone H3 is reduced by half relative to controls when Aurora B is immunoprecipitated from TOPO II–depleted cells . This indicates that either directly or indirectly , TOPO II is required to promote Aurora B kinase activity . To further analyze how TOPO II regulates Aurora B activity at centromeres , we turned to HeLa cells . Cells were treated with the TOPO II inhibitor ICRF-187 , and Aurora B kinase activity was quantified by measuring the phosphorylation of Ser7 of the centromeric protein CENP-A that has been found to be a direct substrate of Aurora B [40] ( Figure 8E and 8F ) . In control cells , we find that P-Ser7CENP-A immunoreactivity is very high in most kinetochores during prometaphase , revealing the normal activity of Aurora B at this stage of mitosis . As cells reach metaphase and kinetochore–microtubule interactions become stabilized , P-Ser7CENP-A immunoreactivity is significantly reduced , suggesting that Aurora B kinase activity is normally down regulated at this stage . However , after inhibition of TOPO II , we find that cells in prometaphase display a significant reduction in the level of P-Ser7CENP-A immunoreactivity ( Figure 8E–8G ) , and more than 60% of chromosomes per cell ( n = 26 ) display syntelic attachment . These results indicate that TOPO II is also required to establish amphitelic attachment in HeLa cells , similar to what we observed for Drosophila , and further demonstrate that TOPO II activity regulates Aurora B kinase activity on chromosomes and more specifically at centromeres . The observations described above demonstrate that TOPO II activity at the centromere is required for the normal function of Aurora B . However , these studies do not distinguish whether TOPO II controls Aurora B kinase activity directly or indirectly . Previous studies showed that inhibition of Aurora kinase activity suppresses the misalignment/attachment defects in BubR1-depleted cells [41] . This effect was shown to be due to an increase in Aurora B kinase activity after BubR1 depletion [41] . Similarly , small interfering RNAi ( siRNAi ) depletion of Aurora B in cells where BubR1 was also knocked down , results in more stable kinetochore attachment [42] . Therefore , given that in the absence of TOPO II , sister centromeres appear unable to resolve , bind microtubules syntelically , and segregate to the same pole , we tested whether BubR1 might be responsible for negatively regulating Aurora B activity in these cells ( Figure 9 ) . To address this issue , we measured microtubule–kinetochore attachment using the Taxol-MG132 assay , as well as mitotic PH3 reactivity in control , BubR1- , TOPO II– , and TOPO II/BubR1–depleted S2 cells . Single or double RNAi treatments were carried out and the respective protein levels quantified by western blotting ( Figure 9G ) . As described before , the Taxol-MG132 assay shows that in control or TOPO II–depleted cells , most chromosomes are attached to spindle microtubules ( Figure 3 ) , but when BubR1 is depleted alone , many cells show either unattached or mono-oriented chromosomes [25] ( Figure 9A and 9B ) . Interestingly , when BubR1 and TOPO II are simultaneously depleted , we observed a large increase in the number of unattached kinetochores ( Figure 9A and 9B ) . This result indicates that removing BubR1 from TOPO II–depleted cells can reactivate the correction mechanism and allow the release of syntelic attachments . To determine whether this was the result of Aurora B kinase activity , we then analyzed PH3 levels by immunofluorescence microscopy . The results show that depletion of BubR1 in the absence of TOPO II is able to restore normal PH3 levels on chromatin , suggesting that Aurora B kinase is now active ( Figure 9C and 9D ) . We also analyzed the localization of Aurora B during mitotic exit of cells after simultaneous depletion of TOPO II and BubR1 ( Figure 9E ) . Interestingly , during early anaphase , Aurora B is found tightly associated with the centromeres but in late anaphase is no longer detected and accumulates in the spindle midzone ( Figure 9E and 9F ) . These results suggest that TOPO II is unlikely to have a direct role in regulating the kinase activity of Aurora B . Instead , the abnormal configuration of the centromere resulting from TOPO II depletion appears to cause inappropriate inhibition of Aurora B through BubR1 .
Our live-cell studies show that TOPO II has a central role in promoting structural changes of the centromeric DNA that are essential for their individualization and separation at metaphase–anaphase transition . This process is clearly independent of the cohesin complex since depletion of RAD21 causes a SAC-dependent prometaphase-like arrest with separated sister chromatids [4 , 43] , which can be overcome by simultaneous depletion of condensin [4] or TOPO II ( Figure 5 ) . Therefore , whereas the role of cohesin degradation in defining the initial steps of sister chromatid separation is well established , it is clear that these events must be tightly coordinated with TOPO II activity . Although it was previously suggested that TOPO II might have a role at the centromere [15 , 44] , previous functional studies have failed to detect any effect on centromere separation during mitotic exit [12 , 31 , 45] . Therefore , our results provide the first direct evidence that TOPO II activity is required for centromere disjunction during mitosis . Our results further show that the structural changes of centromeric DNA resulting from the decatenation activity of TOPO II appear to be essential for the establishment of amphitelic microtubule–kinetochore attachments . In the absence of TOPO II , the SAC appears unable to detect sister kinetochores that are attached to the same pole . One possible explanation is that in TOPO II–depleted cells , Aurora B kinase activity is down-regulated , and given its role in activating the SAC in response to loss of tension , cells cannot respond properly and therefore do not activate the correction mechanism . Interestingly , it has been shown that during exit from meiosis I , when sister chromatids do not disjoin , Aurora B and INCENP remain at the inner centromere [46] , similar to what we observe after depletion of TOPO II . Thus , chromosome segregation in TOPO II–depleted cells resembles the first meiotic division when both sister kinetochores are oriented towards the same pole , suggesting that TOPO II may play a role in modulating centromere structure required for proper bivalent biorientation . The functional interaction between TOPO II and Aurora B has been explored before . In human cells , TOPO II was demonstrated to be an in vitro substrate of Aurora B [47] . Here , we show that depletion of TOPO II causes a down-regulation of Aurora B kinase activity . We observed that the levels of chromosome-associated PH3 staining during prometaphase and metaphase are significantly reduced after depletion of TOPO II but also find that after treatment of S2 cells with a TOPO II inhibitor ICRF-187 , which compromises TOPO II activity without changing its chromosomal localization , PH3 reactivity is also significantly reduced . In agreement , inhibition of TOPO II catalytic activity in human cells also results in a dramatic reduction on the phosphorylation levels of Ser 7 CENP-A phosphoepitope , indicating that Aurora B activity is affected not only on chromosomes , but also specifically at centromeres . The reduction in Aurora B kinase activity could either result from a direct effect of TOPO II or , more likely , through an alteration of the structure of the centromere that occurs as a consequence of TOPO II depletion , and therefore likely represents an indirect effect . We addressed this issue by codepleting BubR1 , a SAC protein thought to be involved in inhibiting Aurora B at kinetochores , and find that indeed , codepletion of TOPO II and BubR1 restores normal Aurora B kinase activity and releases syntelic chromosome attachments . Previous work in HeLa cells has shown that either inhibition of Aurora B kinase activity [41] or depletion of Aurora B by RNAi [42] suppresses the misalignment/attachment defects observed in BubR1-depleted cells . In agreement , an increase in Aurora B kinase activity has been reported in the absence of BubR1 [41] . Taken together , our results suggest that BubR1 is able to inhibit Aurora B when in close proximity , so that in early stages of prometaphase , when microtubule attachment is being established and there is not sufficient tension , Aurora B is not activated . However , the increase in tension upon chromosome biorientation , which increases the distance between BubR1 and the centromere during prometaphase , allows Aurora B activation . Indeed , after TOPO II depletion , sister centromeres remain very close , and therefore BubR1 could be responsible for inhibiting Aurora B . Depletion of both BubR1 and TOPO II results in reactivation of Aurora B kinase activity , release of syntelic attachments , and the formation of unattached or mono-oriented chromosomes . These data support recent observations suggesting that activation of microtubule–kinetochore correction mechanisms during mitosis is dependent on centromere plasticity , but not on centromere elasticity [48] . In summary , our observations demonstrate that TOPO II is required for structural changes at the centromere during their resolution , and in turn , this allows normal function of Aurora B , maintenance of SAC activity , and eventual activation of the mechanisms that correct abnormal microtubule–kinetochore attachments .
RNAi was performed in Drosophila S2 tissue culture cells as previously described [49] . A 1 , 000-bp EcoRI-HindII and an 800-bp EcoRI-KpnI fragment from the 5′ end of TOPO II ( RE49802 ) and RAD21 cDNAs [4] , respectively , were cloned into both pSPT18 and pSPT19 expression vectors ( Roche ) . The recombinant plasmids were used as templates for RNA synthesis using the T7 Megascript kit ( Ambion ) , and 15 μg of double-stranded RNA ( dsRNA ) were added to 106 cells in all RNAi experiments . At each time point , cells were collected and processed for immunoblotting or immunofluorescence . For immunoblotting , cells were collected by centrifugation , washed in PBS supplemented with protease inhibitors ( Roche ) , and resuspended in 20 μl of SDS sample buffer before loading on a 5%–20% gradient SDS-PAGE . When required , cells were incubated with 30 μM colchicine prior to fixation ( Sigma ) . Live analysis of mitosis was done on S2 cells stably expressing GFP-CID and RFP-H2B [24] , as well as in CID-Cherry and GFP-α-tubulin . A cell line stably expressing both GFP-α-tubulin and CID-mCherry was created by transfecting S2 GFP-α-tubulin cells ( a kind gift from Gohta Goshima [50] ) with pMT_cid_mCherry_BLAST vector ( designed from the pMT_cid_gfp , a kind gift from Karpen ) , pCoBLAST ( Invitrogen ) , and pRSET-B_mCherry ( Invitrogen ) . Control or TOPO II RNAi-treated cells were incubated for 72–96 h and plated on glass coverslips treated with 30 μg/ml concanavalin A ( Sigma ) . Time-lapse images were collected every 20 s for CID-GFP and RFP-H2B and every 45 s for CID-mCherry and GFP-α-tubulin , by Scanning Confocal Microscope Leica SP2 AOBS SE ( Leica Microsystems ) , using the software provided by the manufacturer , Software LCS ( Leica Microsystem ) . Each Z-stack is composed of ten images at 0 . 8–1-μm intervals . Data stacks were deconvolved with the Huygens Essential version 3 . 0 . 2p1 ( Scientific Volume Imaging ) . Image sequence analysis and video assembly was done with ImageJ Software ( NIH ) and Quicktime 7 ( Apple Computer ) . For immunostaining , 2 × 105 cells were centrifuged onto slides , simultaneously fixed and extracted in 3 . 7% formaldehyde ( Sigma ) , 0 . 5% Triton X-100 in PBS for 10 min , and then washed three times for 5 min in PBS-T ( PBS with 0 . 05% Tween 20 ) . Blocking and incubating conditions were performed as described previously [4] . For immunofluorescence with the monoclonal antibody ( mAb ) 3F3/2 , cells were grown on glass coverslips , after which they were simultaneously lysed and fixed in lysis/fixation buffer for 2 min ( 1 . 5× PHEM , 2% Triton X-100 , 0 . 15% glutaraldehyde , 2% formaldehyde , 10 μM microcystin LR ) by 1:1 dilution directly in the culture dishes . Detergent-extracted S2 cells were fixed in 1% formaldehyde in 1× PHEM with 10 μM microcystin for 12 min at room temperature . Coverslips were then washed with 0 . 5× PHEM , and immunofluorescence was done as described previously [51] . Images Z-stacks were collected using the Scanning Confocal Microscope Leica SP2 AOBS SE ( Leica Microsystems ) and the software provided by the manufacturer , Software LCS ( Leica Microsystems ) . Data stacks were deconvolved , using the Huygens Essential version 3 . 0 . 2p1 ( Scientific Volume Imaging ) . HeLa cells were cultured in DMEM medium ( Invitrogen ) supplemented with 10% fetal bovine serum ( FBS ) and grown at 37 °C in a 5% CO2 humidified chamber . Cells were fixed for 12 min in freshly prepared 2% paraformaldehyde ( Sigma ) in 1× PHEM , permeabilized with 0 . 5% Triton X-100 in PBS 3 times for 5 min , washed in PBS , and blocked with 10% FBS . Incubation with primary and secondary antibodies was performed in 1× PBS with 10% FBS . Primary antibodies were anti–α-tubulin ( mouse mAb B512 ) , used at 1:3 , 000 ( Sigma-Aldrich ) ; antiphosphorylated histone H3 rabbit polyclonal , used at 1:500 ( Upstate Biotechnology ) ; anti-CID chicken polyclonal , used at 1:200 [52]; anti-SMC4 antibodies ( rabbit , 1:500 , or sheep , 1:200 ) , as described previously [53]; anti-Bubr1 rat polyclonal [25] used at 1:3 , 000; anti-RAD21 [29] rabbit polyclonal ( 1:500 ) ; anti-Polo mouse mAb MA294 , used at 1:50 [54]; anti–Aurora B polyclonal antibody [36] , used at 1:1 , 000; anti–Aurora B polyclonal antibody [55] , used at 1:500 in western blot; antiphospho ( Ser7 ) CENPA ( Upstate Biotechnology ) , used at 1:500; anti-3F3/2 mAb [56] , used at 1:1 , 000; anti-CENP-meta rabbit polyclonal ( Byron Williams ) used at 1: 1 , 000; anti–cyclin B rabbit polyclonal [57] , used at 1: 3 , 000; and anti–TOPO II mouse mAb P2G3 [58] , used at 1:20 . MG132 ( Sigma ) was used to inhibit the proteasome activity in S2 Drosophila cells according to the conditions previously described [25] . ICRF-187 at 50 μg/ml was used to inhibit TOPO II activity both in S2 Drosophila cells and in HeLa cells . Incubations were always performed for 2 h , excepted for in vivo–timed TOPO II inhibition . The TOPO II inhibition was done according to the description in each figure . Fluorescent in situ hybridization to mitotic chromosomes , 2 × 105 cells were centrifuged onto slides , after which cells were simultaneously fixed and extracted as described previously . Cells were dehydrated by incubation for 5 min in 70% , 80% , and 100% ethanol at 4 °C . Cells were air dried and denatured in 2× SSC; 70% formamide for 2 min at 70 °C . Cells were dehydrated once again as described before . We labeled the pericentromeric probe dodeca-satellite DNA with biotin-14-dATP using the BionickTM DNA labeling system ( Invitrogen ) . Detection of the biotinylated probe was done with avidin-D conjugated with fluorescein ( Vector Lab ) . A total of 30 ml of S2 cells were grown exponentially to 4–5 × 106 cells/ml , incubated on ice for 45 min , and centrifuged at 1 , 500g for 15 min at 4 °C . Cells were resuspended in 1 ml of cold 1× PBS in the presence of protease inhibitors , kept on ice for 45 min , and lysed in 1 ml of lysis buffer ( 15 mM Tris-HCl [pH 7 . 4] , 0 . 2 mM spermine , 0 . 5 mM spermidine , 2 mM K-EDTA , 1 mM EGTA , 150 mM KCl , 15 mM NaCl , 1 mM DTT , 1% [V/V] Triton X-100 ) with 2× protease inhibitors-EDTA free ( Roche ) and 1× phosphatase inhibitor cocktail I ( Sigma ) . Cells were lysed with a B-type pestle in a Dounce homogenizer after which they were incubated on ice for 1 h . Samples were precleared with Protein A-Sepharose CL-4B ( Sigma ) for 30 min at 4 °C . Antibody anti–Aurora B ( 5 μl ) and 100 μl of 10% Protein A-Sepharose beads were added to the samples and rotated for 1 h at 4 °C . The beads containing the immune complexes were washed three times in 1 ml of lysis buffer . The pellet was resuspended in 20 μl of serine/threonine kinase assay buffer ( 10 mM Tris-HCL [pH 7 . 4]; 0 . 1% Triton X-100; 10 mM MgCl2 ) with 1× phosphatase inhibitor cocktail I ( Sigma ) , 50 μCi ( 185 KBq ) of [γ32P]-adenosine 5′-triphosphate ( ATP; >5 , 000 Ci/mmol; Amersham ) and 10 μg of histone H3 ( Upstate Biotechnology ) . The preparation of the genomic DNA was done using 107 cells both for control and for TOPO II double-stranded RNAi ( dsRNAi ) cells . Cells were collected and spun down at 1 , 000g for 3 min . Pellets were resuspended in 300 μl of STE ( 150 mM NaCl; 30 mM Tris-HCl [pH 8 . 0]; 2 mM EDTA ) . We added 3 μl of 10% NP40 , 30 μl 10%SDS , and 30 μl of 10 mg/ml proteinase K ( Sigma ) and incubated at 55 °C for 3 h . We performed phenol/chloroform extraction . Aqueous fraction was recovered and extracted once again with chloroform . DNA was precipitated with 20 μl of 5 M NaCl and 400 μl of 100% ethanol . Genomic DNA was digested with the restriction enzyme HindIII ( Biolabs ) according to the manufacturer's conditions . Electrophoresis of the genomic DNA was performed in a 0 . 7% agarose gel , and the gel was prepared for standard alkaline Southern blotting . DNA probes were radioactively labeled with [α-32P]dCTP using a multiprime labeling kit ( Amersham ) .
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Successful cell division requires that chromosomes are properly condensed and that each sister chromatid is self-contained by the time the sister pairs are segregated into separate daughter cells . It is also essential that the kinetochores at the centromeres of each pair of sister chromatids bind microtubules from opposite spindle poles . Topoisomerase II is a highly conserved enzyme that removes interlinks from DNA and is known to be essential to proper chromosome segregation during cell division . In this work , we have used state-of-the-art four-dimensional fluorescent microscopy to follow progression through mitosis in living cells depleted of topoisomerase II . We find that when the enzyme is absent , the two sister centromeres do not separate , and chromosomes missegregate . Moreover , the inappropriate centromere structure that results prevents the correct activation of the Aurora B kinase , which forms part of a regulatory mechanism that monitors correct segregation of chromosomes; as a result , cells exit mitosis abnormally .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology"
] |
2008
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Dual Role of Topoisomerase II in Centromere Resolution and Aurora B Activity
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Replication of arboviruses in their arthropod vectors is controlled by innate immune responses . The RNA sequence-specific break down mechanism , RNA interference ( RNAi ) , has been shown to be an important innate antiviral response in mosquitoes . In addition , immune signaling pathways have been reported to mediate arbovirus infections in mosquitoes; namely the JAK/STAT , immune deficiency ( IMD ) and Toll pathways . Very little is known about these pathways in response to chikungunya virus ( CHIKV ) infection , a mosquito-borne alphavirus ( Togaviridae ) transmitted by aedine species to humans resulting in a febrile and arthralgic disease . In this study , the contribution of several innate immune responses to control CHIKV replication was investigated . In vitro experiments identified the RNAi pathway as a key antiviral pathway . CHIKV was shown to repress the activity of the Toll signaling pathway in vitro but neither JAK/STAT , IMD nor Toll pathways were found to mediate antiviral activities . In vivo data further confirmed our in vitro identification of the vital role of RNAi in antiviral defence . Taken together these results indicate a complex interaction between CHIKV replication and mosquito innate immune responses and demonstrate similarities as well as differences in the control of alphaviruses and other arboviruses by mosquito immune pathways .
Arthropod-borne viruses ( arboviruses ) replicate in both their vertebrate host and arthropod vector . This poses a unique problem for arboviruses as they are exposed to both the vertebrate and invertebrate immune systems . Arthropod vectors of arboviruses , such as mosquitoes , do not have the combination of innate and adaptive immune systems of vertebrates and must therefore rely on the innate immune system to control arbovirus replication . When the mosquito vector ingests a blood-meal from a viraemic vertebrate host , arboviruses infect the midgut epithelial cells . Successful replication and passage through the midgut allows the virus to disseminate into the hemocoel and infect other tissues such as the trachea , fat body , and salivary glands . Once the virus is detected in saliva , the mosquito becomes competent for transmission to the next vertebrate host [1] , [2] . Therefore , interactions between the replicating virus and the mosquito immune defence system produce an outcome that can influence subsequent viral transmission , as shown for the flavivirus dengue ( DENV ) [3] . This emphasizes the importance of further understanding innate immunity in arbovirus vectors . Several innate immune responses have been reported to have an antiviral effect in mosquitoes , and these include RNA interference ( RNAi ) , as well as responses mediated by Toll , Immune Deficiency ( IMD ) and JAK/STAT signaling pathways [4]–[6] . The exogenous ( antiviral ) RNAi pathway is induced by the presence of long double-stranded RNA ( dsRNA ) molecules , which in the case of RNA viruses may arise from secondary RNA structures in the viral genome , the viral genome itself ( in case of dsRNA viruses ) and/or viral replication intermediates . Much of our understanding of arthropod RNAi is based on research on Drosophila melanogaster , which has also proved very useful in providing insights into antiviral responses in mosquitoes [7] . In the exogenous RNAi pathway , these dsRNAs are recognized by the RNAse III enzyme Dicer 2 ( Dcr-2 ) [8] and cleaved into 21 nt small interfering RNA ( siRNA ) also known as virus induced small interfering RNAs ( viRNAs ) [9]–[15] , a hallmark of RNAi induction . The siRNAs/viRNAs are loaded into the multi-protein RNA Induced Silencing Complex ( RISC ) , of which the core catalytic component is the endonuclease Argonaute-2 ( Ago-2 ) [16] . Ago-2 unwinds the siRNAs/viRNAs and retains one strand ( guide strand ) to recognize single-stranded ( viral ) complementary sequence such as genomic RNA or mRNA , which triggers the endonucleic cleavage of the complementary RNA by Ago-2 [17] . A key role for mosquito Ago-2 and Dcr-2 in antiviral responses has also been demonstrated experimentally [18] , [19] . Sequence specific degradation of RNA results in repression of virus replication and virus production . Similarly , Toll , IMD and JAK/STAT signaling pathways have been described in Drosophila with pathway homologues also identified in mosquitoes . In the mosquito Aedes aegypti , the Toll and JAK/STAT signaling pathways have been shown to induce antiviral activities targeting DENV [20] , [21] . In culicine mosquitoes , West Nile virus ( WNV ) ( Flaviviridae ) was also shown to be inhibited by the JAK/STAT pathway and that this response is thought to be controlled by the cytokine Vago [22] . In the case of alphaviruses of the Togaviridae family , such as Sindbis virus ( SINV ) or Semliki Forest virus ( SFV ) , the data are less clear . At least in mosquito cells , antiviral activity against SFV was shown to be mediated by either IMD or JAK/STAT pathways following bacterial stimulation [23] . Recent research in Drosophila further implies those pathways in control of SINV infection [24] , [25] although no upregulation of target genes was shown in Ae . aegypti-derived Aag2 cells [26] . However , in the case of Anopheles gambiae infection by the alphavirus o'nyong-nyong ( ONNV ) , the contribution of the IMD pathway is only minor [27] . These pathways may therefore act in a virus-specific manner . In contrast to vertebrate cells where viral inhibition of innate immunity is an area of intense research , similar processes in arbovirus/vector interactions are poorly understood . Several arboviruses have previously been shown to inhibit or evade the antiviral action of these host responses in mosquitoes . An RNAi evasion strategy has been suggested for SFV , while flavivirus subgenomic RNA acts as an RNAi inhibitor [11] , [28] . Inhibition of immune signaling pathways such as JAK/STAT , Toll and IMD has been described for SFV [23] , while DENV has been shown to interfere with Toll and IMD signaling [29] . This suggests further complexity in arbovirus/vector interactions and the relevance of this is only just emerging , and needs to be assessed for each virus family and even each virus individually . Chikungunya virus ( CHIKV ) belongs to the genus Alphavirus of the family Togaviridae . It has become an increasingly important arbovirus in tropical and subtropical regions , resulting in febrile and arthralgic disease in humans [30] . After the large outbreak in the Indian Ocean in 2004 , CHIKV infections have spread throughout the Indian Ocean and Africa into Southern Europe [31] . Moreover , the virus has emerged for the first time in the Americas ( St . Martin/Martinique , Caribbean ) in December 2013 , thus potentially threatening other parts of the New World ( see WHO Website for information ) . CHIKV is spread by aedine mosquitoes with Ae . aegypti being the most important vector and more recently Ae . albopictus following changes in the E1 envelope protein [32] , [33] . The viral genome is a single stranded positive sense RNA containing two open reading frames: one expressing the non-structural polyprotein which will be cleaved into non-structural viral proteins ( nsP1-4 ) , and one structural polyprotein which will be cleaved into the structural proteins . The mRNA encoding the structural polyprotein is transcribed from the subgenomic promoter during virus replication . Little is known about innate immune responses induced by CHIKV during infection of mosquitoes . Identification of small RNAs derived from CHIKV in aedine mosquitoes and their derived cell lines proves the ability of the RNAi pathway to target CHIKV . Moreover , 21 nt viRNAs as well as another class of virus-derived small RNAs with characteristics of Piwi-interacting RNAs ( piRNAs ) were discovered [12] . However , nothing is known about the functional ability of this RNAi response to limit CHIKV replication in mosquito cells or mosquitoes , or the involvement of other innate immune responses . In this study , we investigated the major antiviral Ae . aegypti immune pathways and assayed their ability to control CHIKV replication in cell culture , where experimental conditions are easily controlled and manipulated . The exogenous RNAi pathway was identified as a key control mechanism of CHIKV replication and this was further confirmed in mosquitoes . Moreover , CHIKV repressed Toll , IMD and JAK/STAT pathway stimulation in vitro by induction of host cell shut off and none of the pathways were able to mediate antiviral responses against CHIKV replication . These data indicate the importance of RNAi as a mosquito antiviral response also targeting CHIKV replication as well a complex interplay with other host responses .
CHIKV E1-226V strain ( provided by the French National Reference Center for Arboviruses , Institut Pasteur ) was used for mosquito infections . The strain was isolated from a patient on La Reunion Island in November 2005 as part of a survey during an outbreak , as described in [34] . Vero cells were cultured in DMEM ( Dulbecco's Modified Eagle Medium ) supplemented with 10% fetal bovine serum ( FBS ) , 1000 units/ml penicillin , 1 mg/ml streptomycin , and maintained at 37°C and 5% CO2 . Ae . aegypti Aag2 and Ae . albopictus C6/36 cells ( for virus propagation ) were grown in Leibovitz L-15 insect medium with 10% FBS , 10% tryptose phosphate broth , 1000 units/ml penicillin , and 1 mg/ml streptomycin at 28°C . For CHIKV titer determination , Vero cells were grown to confluent monolayers in 6-well plates , infected with 10-fold serial dilutions of virus for 1 h , and then overlaid with an agarose-nutrient mixture . After 3 days incubation at 37°C , cells were stained with a solution of crystal violet ( 0 . 2% in 10% formaldehyde and 20% ethanol ) . The total number of plaques was counted and the titer was calculated . Titers are indicated as plaque forming units ( PFU ) /ml . Use of plasmids pAct-Renilla ( coding sequence of Renilla luciferase ( RLuc ) under control of the Drosophila actin 5C promoter ) , p6×2DRAF-Luc ( coding sequence of firefly luciferase ( FFLuc ) under control of a multimerised Drosophila STAT-responsive element ) , pJL195 ( coding sequence of FFLuc under control of the Drosophila attacin A promoter ) and pJM648 ( coding sequence of FFLuc under control of the Drosophila drosomycin promoter ) in signaling pathway experiments have been previously described [23] . Luciferase SP6 Control DNA plasmid ( Promega ) expressing FFLuc under control of SP6 promoter was used as a template for in vitro transcription of a control RNA . Nonstructural open reading frame of CHIKVRep ( 3F ) RLuc-SG-FFLuc replicon contains RLuc encoding sequence inserted between codons for amino acid residues 383 and 384 of CHIKV nsP3 . In place of the structural open reading frame , mRNA encoding for FFLuc is transcribed following replication by use of the subgenomic promoter . Replicon CHIKVRep-SG-eGFP contains the nonstructural open reading frame without any reporter genes and mRNA of eGFP is transcribed following replication by use of the subgenomic promoter . Replicon DNA was linearized by digestion with NotI , purified using a PCR purification kit ( Roche ) and 1 µg of the linearized DNA was in vitro transcribed using MEGAscript SP6 polymerase kit ( Ambion ) in the presence of Cap analog ( Ambion ) . Control Luciferase SP6 DNA was linearized by digestion with XmnI , purified by gel extraction and in vitro transcribed as before . For all subsequent experiments , 2 µl of in vitro transcription was transfected . Total RNA was extracted from adult Ae . aegypti using the RNA II Nucleospin kit ( Macherey-Nagel GmbH & Co . ) according to the manufacturer's instructions . cDNAs were generated from 60 ng of total RNA by reverse transcription using SuperScript II Reverse Transcriptase ( Invitrogen ) and oligo dT . To synthesize dsRNA , cDNA was amplified with gene specific primers ( table 1 designated as being for use in vivo ) incorporating the T7 RNA polymerase promoter sequences ( in bold ) at the 5′ ends . Primers were designed to amplify a unique ∼500 bp region in the PIWI domain for Ago-2 . PCR was carried out using the KOD Hot Start DNA Polymerase ( Novagen ) . PCR products were purified with the QIAquick Gel Extraction kit ( Qiagen ) and dsRNA was produced using the MEGAscript RNAi kit ( Ambion ) according to the manufacturer's instructions . RNA was extracted from Aag2 cells using TRIzol and reverse transcribed using Superscript III Reverse Transcriptase ( Invitrogen ) following the manufacturer's instructions . PCR products were generated with T7 promoter sequences at either end of the fragment using the primers listed in table 1 and designated as for use in vitro . The PCR product was blunt end cloned into a pJet1 . 2 vector ( Thermo Scientific ) following the manufacturer's instructions . Cloned PCR fragments were verified by sequencing . PCR was performed on the cloned fragments and the products purified using the PCR purification kit ( Roche ) . peGFP-C1 ( Clontech ) was used as a template for the amplification of control dsRNA , targeting eGFP . dsRNA was synthesized using the Megascript RNAi kit ( Ambion ) following manufacturer's instructions . 24 h prior to transfection , 1 . 7×105 Aag2 cells were seeded in 24-well plates to reach 70% confluence the following day . For knockdown experiments , 500 ng of dsRNAs were transfected into Aag2 cells using Opti-MEM and Lipofectamine2000 ( Invitrogen ) according to manufacturer's instructions . At 24 h post dsRNA transfection , cells were transfected with capped in vitro transcribed replicon RNA derived from CHIKVRep ( 3F ) RLuc-SG-FFLuc using Lipofectamine2000 . Cells were lysed in 1× passive lysis buffer ( Promega ) 24 h post replicon transfection and luciferase activities determined . For the signaling pathway stimulation experiments , bacterial stocks were prepared by inoculation of 1 µl E . coli JM109 in 5 ml L-Broth and incubation at 37°C for 16 h or 5 µl B . subtilis inoculated in 5 ml L-Broth incubated at 37°C for 8 h . E . coli is used to stimulate the JAK/STAT and IMD pathways and B . subtilis is used to stimulate the Toll pathway . Cultures were centrifuged at 1174 g at 4°C for 10 minutes . The bacterial pellet was then resuspended in 500 µl PBS and heat inactivated at 80°C for 10 minutes . At 24 h post seeding , Aag2 cells were transfected with 12 . 5 ng pAct-Renilla , 25 ng p6×2DRAF-Luc , 25 ng pJL195 or 500 ng pJM648 and capped in vitro transcribed CHIKV replicon RNA ( derived from CHIKVRep-SG-eGFP ) at 24 h post seeding , using Lipofectamine2000 according to the manufacturer's protocol . Cells were stimulated at 24 h post transfection with heat inactivated E . coli JM109 or B . subtilis for 1 hour at 28°C . 12 h post stimulation , cells were lysed in 1× passive lysis buffer and luciferase activities determined . Ae . aegypti Liverpool strain ( provided by D . Severson , University of Notre Dame , IN , USA ) were maintained on 10% sugar solution at 28°C with a photophase of 16 h and 80% relative humidity according to the standard rearing procedures . RNAi-based gene-silencing assays were conducted by injecting 500 ng of dsRNA ( dsAgo-2 or dsFFLuc ) in water into the thorax of cold-anesthetized 4 day-old females using a Nanoject II microinjector ( Drummond Scientific Company ) . Blood feeding was carried out 48 h post dsRNA injection . Gene silencing validations were performed at day 1 , 2 , 3 , 4 and 7 after ingestion of the infectious blood-meal . As controls , mosquitoes injected with PBS and non-injected unfed mosquitoes were used . Adult female mosquitoes were deprived of sugar source 24 h before infection and allowed to feed on artificial blood-meals consisting of a virus suspension ( 1/3 vol/vol ) , washed rabbit erythrocytes ( 2/3 vol/vol ) , and 5 mM ATP . The artificial blood-meal was provided in glass feeders covered with a chicken skin membrane and maintained at 37°C . Females placed in plastic boxes were allowed to feed for 15 minutes . Engorged females were selected , transferred into cardboard containers , provided with sugar solution and maintained in BSL-3 insectaries until analysis . To determine the titer of the infectious blood-meal which would be used for subsequent RNAi-based gene-silencing assays , three different virus titers: 106 , 107 , 108 PFU/ml were tested . At indicated time points , 10 females were tested for the presence of CHIKV by immunofluorescence assay ( IFA ) on head squashes using mouse ascitic fluid raised against the virus [35] . Disseminated infection rates ( DIR ) corresponding to the number of females with disseminated infection among tested females , were determined . At various days post infection , mosquitoes were analyzed as follows: midgut and head were isolated from each individual and ground in 150 µl DMEM before being homogenised and filtered . 50 µl of the filtrate was titrated by plaque assay on Vero cells to estimate the number of infectious viral particles . The remaining 100 µl was used for quantification of Ago-2 and ribosomal S7 RNA by real time quantitative RT-PCR . Dissected mosquito organs ( midgut and head ) were homogenized separately and used for RNA extraction using the NucleoSpin 96 RNA kit ( Macherey-Nagel GmbH & Co ) . An equal amount of RNA extracted from each organ was examined for each time point . Quantification was carried out by real time quantitative RT-PCR using the Power SYBR Green RNA-to-CT one step kit ( Applied Biosystem ) . Total RNA was extracted from cultured cells using TRIzol ( Invitrogen ) according to the manufacturer's instructions . Polyadenylated RNAs were reverse transcribed using the Superscript III kit ( Invitrogen ) and oligo dT primers ( Promega ) according to the manufacturer's recommendation . Quantification was carried out by real time quantitative PCR using the Fast SYBR green master mix ( Invitrogen ) . All PCR reactions were done in triplicate . Specificity of the PCR reactions was assessed by analysis of melting curves for each data point . Values were normalized against the Aedes aegypti ribosomal protein S7 gene . Following real-time quantitative PCR assays , analysis of relative expression of Ago-1 , Ago-2 and S7 was performed according to the 2−ΔΔCt method [36] . Luciferase expression was determined using the Dual Luciferase kit ( Promega ) . Luciferase activities were determined on a Glomax Multi+ Microplate Multimode reader ( Promega ) . Virus titer means were compared using the Kruskall-Wallis test from the STATA software ( StataCorp LP ) . Statistical significance for replicon replication after knockdown was determined by a paired Student's t-test and statistical significance in the stimulation experiments was determined using a general linear mixed model .
Aag2 cells have proven to be a reliable model for the study of aedine immune responses including those against viruses given the presence of all major immune signaling pathways and small RNA pathways in the cell line [10] , [11] , [14] , [26] , [29] , [37] . Not only have mosquito cell lines been shown to have intact immune pathways but they also provide a robust system where experiments can be performed in a controlled manner and have been used extensively in the mosquito immunity field [10]–[12] , [23] , [29] , [38]–[40] . Viral replicons are useful tools to investigate innate immune responses in a tightly controlled manner in cell culture experiments . We first engineered two CHIKV reporter replicons on the basis of the CHIKV E1-226V strain . The first replicon , named CHIKVRep ( 3F ) RLuc-SG-FFLuc , encodes a non-structural polyprotein with RLuc inserted into the C-terminal region of nsP3 and mRNA of FFLuc expressed from the viral subgenomic promoter instead of the mRNA of structural proteins . A second replicon is expressing mRNA of eGFP from the viral subgenomic promoter: CHIKVRep-SG-eGFP ( Fig . 1A ) . This replicon was designed with a fluorescent protein as a reporter rather than luciferase to allow a multitude of different experiments to be performed . To determine if these replicons are functional in mosquito cells and to characterize their kinetics with regards to the expression of the reporter proteins , Aag2 cells were transfected with in vitro transcribed capped CHIKVRep ( 3F ) RLuc-SG-FFLuc , lysed at different time points post transfection ( 24 , 48 and 72 hours post transfection; hpt ) and luciferase expression determined . Luciferase expression ( both FFLuc [data not shown] and RLuc ) can be measured by 24 hpt with a peak at 48 hpt and a slight decrease at 72 hpt ( Fig . 1B ) . These time points were chosen to allow sufficient time for replicon replication but also taking into consideration the transient nature of transfections . These data suggest that the engineered replicons are replicating in Aag2 cells . The RNAi pathway has been identified as being the major antiviral pathway in control of replication of a number of arboviruses in mosquitoes [4] , [5] . The generally accepted method of confirming antiviral activity in mosquito cell culture and mosquitoes is RNAi mediated knockdown of components of individual small RNA pathways [3] , [18] , [19] , [37] . Therefore , in order to determine whether the exogenous RNAi pathway also limits CHIKV replication in Aag2 cells , unique dsRNAs were designed and validated against the exogenous RNAi pathway component Ago-2 and the miRNA pathway component Ago-1 [4] , [5] . Knockdown was determined by quantitative RT-PCR ( Fig . 2A ) . Having shown efficient knockdown of both Ago-1 and Ago-2 ( 42% or 25% respectively ) , their contribution in control of CHIKV replication was assessed . Aag2 cells were transfected with dsRNA ( Ago-1 and Ago-2 specific or eGFP-specific as a control ) , followed by transfection of in vitro transcribed capped CHIKVRep ( 3F ) RLuc-SG-FFLuc and lysed after 24 hpt of replicon . A significant ( Student's t-test; p = 0 . 045 ) 9-fold increase in RLuc expression was observed in cells treated with Ago-2 specific dsRNA compared to cells with control dsRNA . In contrast , no significant increase in RLuc expression was observed for cells transfected with Ago-1 specific dsRNA ( Fig . 2B ) . These data suggest that the miRNA pathway is not involved in the inhibition of CHIKV replication in Ae . aegypti-derived cells , in contrast to the exogenous RNAi response that is able to inhibit CHIKV replication in vitro . Having shown an inhibitory effect of the exogenous RNAi pathway on CHIKV replication in mosquito cells , similar experiments were performed in mosquitoes to determine if Ae . aegypti RNAi components are required for defence against CHIKV in vivo . First , CHIKV infection kinetics was determined in Ae . aegypti mosquitoes , infected with bloodmeals containing different virus titers , by subsequent determination of dissemination rates by IFA on head squashes at several time points ( Fig . 3 ) . Maximum dissemination was observed at day 7 post infection ( pi ) with 52% for a blood-meal titer of 106 PFU/ml , at day 6 pi ( 100% ) for 107 PFU/ml , and at day 3 pi ( 100% ) for 108 PFU/ml . Based on these patterns , the intermediate titer of 107 PFU/ml was chosen for further assays on RNAi-based gene-silencing . Gene silencing efficiency was then tested . 500 ng dsRNA ( Ago-2 specific or FFLuc specific as a control ) was injected into mosquitoes 48 h prior to infectious blood-meal ( CHIKV at 107 PFU/ml ) . Ago-2 expression ( relative to S7 expression ) in midguts or heads of injected mosquitoes was determined by real-time quantitative PCR at time points indicated ( 1 , 2 , 3 , 4 and 7 days pi ) . The S7 gene was chosen as a control housekeeping gene due to its stability in infected and non-infected conditions [data not shown] . Mosquitoes were analyzed at days 0 , 1 , 2 , 3 , 4 and 7 pi . Injection of Ago-2 specific dsRNA strongly decreased the expression of Ago-2 in midguts ( Fig . 4A: ranging from 80% silencing at day 2 to 70% silencing at day 7 ) and heads ( Fig . 4C: ranging from 68% at day 2 to 50% at day 7 ) compared to the controls ( FFLuc specific dsRNA dsFFLuc , PBS or non-injected mosquitoes ) . Silencing was effective from day 2 up to 7 pi . To test whether silencing of Ago-2 expression would increase CHIKV replication in Ae . aegypti following an infectious blood-meal , midguts and heads of the previous experiments were examined for CHIKV production . The number of infectious viral particles was determined by plaque assay of midguts and heads of 10 females at day 1 , 2 , 3 , 4 , and 7 pi ( Fig . 4 B and D ) . A non-significant increase of virus in midguts was observed between mosquitoes injected with Ago-2 specific dsRNA and controls ( dsFFLuc , PBS and non-injected ) at day 1 , 2 and 3 pi ( Kruskall-Wallis Test: p>0 . 05 ) . However , at day 4 and 7 pi a significant increase in the number of virus particles in midguts was detected following Ago-2 knockdown ( Kruskall-Wallis test: p<0 . 05 ) ( Fig . 4B ) . To determine the effect of gene silencing on viral dissemination , the number of viral particles in heads was also determined . At day 4 pi , heads of mosquitoes injected with Ago-2-specific dsRNA contained significantly more infectious viral particles ( 103 . 1±102 . 3 ) ( Fig . 4D ) than controls ( Kruskall-Wallis test: p<0 . 05 ) . This effect was transitory as at day 7 pi , viral loads remained similar when compared to other treatments . Taken together the in vitro and in vivo data imply a control of virus replication by the exogenous RNAi pathway with the virus being unable or not efficiently able to avoid this antiviral response . Similar to other arboviruses , CHIKV may have evolved mechanisms which allow the virus to evade or suppress the induction of innate immune signaling pathways [6] . To investigate this possibility , signaling assays were performed in Aag2 cells similar to those described previously [23] . First , inhibition of innate immune pathways by CHIKV RNA was determined . Gene expression studies have shown the presence of JAK/STAT , IMD and Toll in Aag2 cells , which make them suitable models for the subsequent experiments [26] , [29] . Preliminary experiments indicated the suitability of reporter gene expression studies and bacterial stimulation to study these pathways also in our Aag2 cells [not shown] . Therefore , Aag2 cells were co-transfected with in vitro transcribed capped CHIKVRep-SG-eGFP ( not expressing luciferase ) and plasmids encoding FFLuc under control of promoters that are activated in response to immune signaling of the JAK/STAT ( p6×2DRAF-Luc ) , IMD ( pJL195 ) and Toll ( pJM648 ) pathways and an internal transfection control plasmid pAct-Renilla . This was followed by stimulation of the different pathways by either heat inactivated E . coli or B . subtilis and expression of FFLuc and RLuc was detected . Stimulated cells without CHIKV replicon showed a significant increase in FFLuc expression in case of E . coli or B . subtilis respectively for both JAK/STAT , IMD and Toll stimulations ( Fig . 5 A–C control versus E . coli or B . subtilis [p = <0 . 001] ) . Cells co-transfected with the CHIKV RNA and pathway specific constructs , showed a much reduced level of stimulation compared to cells lacking the CHIKV replicon after IMD stimulation and a highly significant reduction in the level of stimulation after JAK/STAT and Toll stimulation ( 2 . 5 fold less JAK/STAT stimulation , 3 . 4 fold less IMD stimulation and 4 . 9 fold less Toll pathway stimulation in the presence of CHIKV ) ( Fig . 5 A–C control versus CHIKV+E . coli or CHIKV+B . subtilis [p = <0 . 001] ) . However , presence of the CHIKV replicon RNA also significantly reduced RLuc expression , regardless of which pathway was tested , compared to cells lacking the CHIKV replicon ( Fig . 5 A–C control vs CHIKV [p = <0 . 001] ) . Moreover , inclusion of the CHIKV replicon RNA reduced the RLuc expression levels without stimulation of other pathways ( Fig . 5 A–C control versus CHIKV [p = <0 . 001] ) . The reduction in RLuc expression is specifically due to the presence of the CHIKV replicon as co-transfection of pACT-Renilla plasmid and a control luciferase RNA expressing FFLuc did not result in a reduction of RLuc expression ( Fig . 5D ) . These data suggest that , similar to findings for the related alphavirus SFV [23] , CHIKV can shut down , albeit incompletely , the host cell transcription/translation systems and this general targeting mechanism also interferes with immune pathway stimulation , especially with regards to the Toll pathway which cannot be stimulated in the presence of CHIKV . In order to determine on which cellular process CHIKV is exerting its effect , CHIKV replicon RNA was co-transfected with control FFLuc expressing RNA and the translation of FFLuc assessed by luciferase assay . While there is a small reduction in FFLuc expression upon co-transfection of CHIKVRep-SG-eGFP replicon , there is not the same significant effect on the control RNA in the presence of CHIKV ( Fig . 5E ) as there is during the co-transfection with the pACT-Renilla plasmid . Therefore , similar to the related SINV and SFV , it is likely that CHIKV host cell shut-off occurs at a transcriptional level [41]–[43] . It should be noted that this incomplete or weak shut-off does not appear to have the detrimental effects often observed in alphavirus-infected vertebrate cells , but nonetheless affects host cell signaling . As CHIKV was shown to inhibit immune signaling via host cell shut off , we hypothesized these pathways could induce antiviral activities . In order to investigate these possibilities , Aag2 cells were stimulated with either heat inactivated E . coli ( to induce JAK/STAT and IMD signaling ) or B . subtilis ( to induce Toll signaling ) prior to transfection of in vitro transcribed and capped CHIKVRep ( 3F ) RLuc-SG-FFLuc . Replication was determined by measuring by FFLuc expression ( Fig . 6 ) . Stimulation of cells prior to replicon transfection had no effect on CHIKV replication regardless of whether cells were stimulated with gram negative or gram positive bacteria . This suggests the Toll , IMD and JAK/STAT pathways cannot affect CHIKV replicon replication .
Innate immune responses are important for regulating arboviral replication in mosquitoes . The JAK/STAT , IMD and Toll immune signaling pathways have been individually implicated in the control of arbovirus replication in mosquitoes in a virus/pathway-dependent manner [20] , [21] , [23] , [25] . In addition , the exogenous RNAi pathway is a key mosquito antiviral pathway [4] , [5] . The re-emerging CHIKV is known to induce an RNAi response in infected mosquitoes [12] . CHIKV infection of mosquitoes and their derived cell lines results in the production of small RNAs indicating that small RNA pathways are activate against CHIKV and that the presence of an RNAi inhibitor resulted in increased virus replication and virus production [12] . This study , however , did not identify which small RNA pathways have antiviral activity . Expression of the RNAi inhibitor B2 by CHIKV affected both exogenous RNAi and piRNA pathways thus making it unclear which of these pathways affected viral replication . Indeed , our study identifies one such pathway specifically . Here we have demonstrated that Ago-2 plays an important role in the antiviral RNAi response to CHIKV both in vitro and in vivo , and that the induced exogenous RNAi response mediates effective antiviral activities . The exogenous RNAi pathway has previously been shown to mediate antiviral activities against three other alphaviruses , SINV , SFV and ONNV [18] , [19] , [37] . In the case of CHIKV infection , production of small RNAs produced by the exogenous RNAi and piRNA pathways has also been demonstrated [12] and our data demonstrate that the exogenous RNAi does indeed mediate antiviral activity . In the case of SINV infection of Ae . aegypti mosquitoes , knockdown of Ago-2 resulted in a transient increase in virus replication and titer early in infection; however , similar to our findings , the effect was lost by day 7 [18] . Additionally , engineering the Flock House Virus RNAi inhibitor B2 protein into SINV increased the mortality rate , indicating that RNAi is vital in controlling virus replication to levels that are non-pathogenic to the arthropod vector [44] . A similar effect was reported for exogenous RNAi control of DENV replication in Ae . aegypti [3] . Our results extend the importance of the exogenous RNAi pathway to another virus of medical importance , CHIKV . Knockdown results for Ago-2 directly show that CHIKV replication is inhibited by the RNAi pathway and that the small RNAs produced in the exogenous RNAi pathway do mediate antiviral activity . It has been postulated that arboviruses may in fact subject themselves to RNAi control to ensure vector survival , which may explain why no RNA silencing suppressor ( RSS ) proteins have been identified to date in arboviruses . Recently , an RSS was found in the flaviviruses DENV and WNV . The RSS was not a viral protein but a highly structured region in the 3′ UTR of the flavivirus genome called subgenomic flavivirus RNA ( sfRNA ) which is proposed to act as an RNAi inhibitor [28] . It is yet to be seen if a similar mechanism is employed by alphaviruses to escape replication control by RNAi although an evasion mechanism has been suggested for SFV [11] . Antiviral responses other than RNAi in mosquitoes are beginning to emerge , and these innate immune pathways play a vital role in antiviral defence in a virus dependent manner . We have identified that in particular the Toll immune signaling pathway is strongly inhibited by CHIKV in vitro , most likely by a mechanism involving host shut-off . The JAK/STAT and IMD pathways have previously been reported to be involved in anti-viral defense against SINV in Drosophila [24] , [25] . On the other hand , gene array studies suggested inhibition of Toll signaling late in infection and possibly activation of IMD signaling in SINV-infected Ae . aegypti mosquitoes [45] . However , in Aag2 cells infected with SINV gene expression studies revealed no activation of these pathways ( with the exception of weak upregulation of STAT itself ) [26] . The design of these experiments with SINV makes it difficult to come to any conclusion with regards to viral inhibition , but recent work has shown that SFV as well as DENV have been able to inhibit innate immune signaling in mosquito cells [23] , [29] . Despite this , IMD and/or JAK/STAT but not Toll can mediate antiviral activity against SFV , while Toll and JAK/STAT induce antiviral responses against DENV [20] , [21] , [23] . We found no effect of any of these pathways on CHIKV replicon replication either because they do not act antivirally or because viral inhibition of any antiviral effects is strong enough to mask these . This points to virus specific differences or highly efficient inhibition of the immune pathways by CHIKV . Interestingly , only a minor activity by the IMD pathway was shown against ONNV [27] , which is closely related to CHIKV therefore these viruses may respond in a more similar manner . Overall , this interplay between antiviral signaling and viral inhibition is strongly reminiscent of viral interactions with the innate immune responses , such as the interferon system of vertebrate cells [46]–[48] . At least for SFV , and our observations with CHIKV , shut off of host gene expression appears to mediate signaling inhibition in mosquito cells , as it does in vertebrate cells [23] . The mechanism by which shut-off occurs is not clear , although our data ( Fig . 5E ) suggest an effect prior to translation , possibly at the transcriptional level as occurs in vertebrate cells [41]– . We cannot rule out competition between host and viral mRNAs , differences in mRNA stability or other processes that would lead to differences in translation , however , as reporter mRNAs are not affected by CHIKV replicon this appears unlikely . Other inhibitory strategies employed by arboviruses may also resemble those used in vertebrate cells . In the presence of RNAi , virus replication may still be controlled to a non-harmful level . Recent work has indicated candidates for antimicrobial peptides , attC and dptB , that inhibit SINV replication in Drosophila [24] . Similar mechanisms in mosquito cells remain to be determined . Clearly , there is no direct effect of these classical immune signaling pathways on CHIKV replication under our experimental conditions . In conclusion , we demonstrated that the exogenous RNAi pathway plays a vital role in limiting CHIKV replication in cell culture and in mosquitoes . Knockdown of Ago-2 resulted in a significant increase in RNA replication and virus titers . Additionally , we have shown that CHIKV significantly represses Toll pathway stimulation , and stimulation of major insect immune pathways did not limit CHIKV replication . This indicates that antiviral immunity is a complex process which needs more research to tease out the complexities of the virus/host interactions and differences exist even between closely related alphaviruses . Exogenous RNAi is also active against CHIKV while analysis of immune signaling pathways indicates differences to other arboviruses including other alphaviruses . Taken together this and other studies suggest that the RNAi response is the most generally active antiviral pathway in mosquitoes .
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Chikungunya virus ( CHIKV ) is a mosquito-borne human-pathogenic arbovirus of the Togaviridae family , genus Alphavirus . Arbovirus replication in vectors , such as mosquitoes , is not passively tolerated but leads to immune responses , that control virus infection . These responses therefore represent interesting targets for novel intervention strategies . Mosquito antiviral immune responses , such as small RNA pathways or immune signaling pathways , are increasingly well studied but it is not known which one mediate antiviral effects against CHIKV in particular . Here we screened four key immune responses in vitro for their antiviral potential against CHIKV and only the exogenous RNA interference was found to mediate antiviral activity . This was confirmed in vivo in Aedes aegypti mosquitoes . Immune signaling pathways were not found to mediate antiviral activity but were inhibited by CHIKV . This shows interesting differences and similarities to other mosquito-borne alphaviruses that increase our understanding of alphavirus-mosquito interactions .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"epidemiology",
"virology",
"vector",
"biology",
"biology",
"and",
"life",
"sciences",
"microbiology",
"disease",
"vectors"
] |
2014
|
Characterization of Aedes aegypti Innate-Immune Pathways that Limit Chikungunya Virus Replication
|
PRP4 encodes the only kinase among the spliceosome components . Although it is an essential gene in the fission yeast and other eukaryotic organisms , the Fgprp4 mutant was viable in the wheat scab fungus Fusarium graminearum . Deletion of FgPRP4 did not block intron splicing but affected intron splicing efficiency in over 60% of the F . graminearum genes . The Fgprp4 mutant had severe growth defects and produced spontaneous suppressors that were recovered in growth rate . Suppressor mutations were identified in the PRP6 , PRP31 , BRR2 , and PRP8 orthologs in nine suppressor strains by sequencing analysis with candidate tri-snRNP component genes . The Q86K mutation in FgMSL1 was identified by whole genome sequencing in suppressor mutant S3 . Whereas two of the suppressor mutations in FgBrr2 and FgPrp8 were similar to those characterized in their orthologs in yeasts , suppressor mutations in Prp6 and Prp31 orthologs or FgMSL1 have not been reported . Interestingly , four and two suppressor mutations identified in FgPrp6 and FgPrp31 , respectively , all are near the conserved Prp4-phosphorylation sites , suggesting that these mutations may have similar effects with phosphorylation by Prp4 kinase . In FgPrp31 , the non-sense mutation at R464 resulted in the truncation of the C-terminal 130 aa region that contains all the conserved Prp4-phosphorylation sites . Deletion analysis showed that the N-terminal 310-aa rich in SR residues plays a critical role in the localization and functions of FgPrp4 . We also conducted phosphoproteomics analysis with FgPrp4 and identified S289 as the phosphorylation site that is essential for its functions . These results indicated that FgPrp4 is critical for splicing efficiency but not essential for intron splicing , and FgPrp4 may regulate pre-mRNA splicing by phosphorylation of other components of the tri-snRNP although itself may be activated by phosphorylation at S289 .
Pre-mRNA splicing is mediated by the spliceosome that is formed by ordered interaction of the U1 , U2 , U4/U6 , U5 snRNPs , and non-snRNP proteins [1] . U1 and U2 first interact with the 5’-splice site ( 5’-ss ) and the branch point ( BP ) of the introns in pre-mRNA to generate the A complex . The A complex is then converted to the pre-catalytic B-complex by the integration of the preformed U4/U6-U5 tri-snRNP . Activation of the B-complex involves the unwinding of U4/U6 and dissociation of U1 and U4 . Whereas the activated B-complex catalyzes the first step of splicing , the C complex catalyzes the second step of splicing to form mature mRNA [2] . Unwinding of U4/U6 , a critical step during B-complex activation , is catalyzed by the Brr2 DExD/H-box family RNA helicase that recognizes the single-stranded region of U4 next to the Stem I of the U4/U6 [2] . The helicase activity of Brr2 is regulated by Prp8 and Snu114 to prevent premature unwinding of U4/U6 [1 , 2] . Prp6 and Prp31 also are two essential components of the U4/U6-U5 tri-snRNP . However , unlike Prp8 and Brr2 , they lack structural domains with defined biochemical functions . Prp6 and Prp31 are associated with pre-catalytic spliceosomal complexes [3] but not with the activated- or post-catalytic spliceosomal complexes [4–7] . Prp6 interacts with the U4/U6 specific protein Prp31 and the U5 proteins Brr2 and Prp8 [8 , 9] . Many components of the spliceosome are conserved in eukaryotic organisms [10] . However , the budding yeast Saccharomyces cerevisiae , a model for studying spliceosome and intron splicing , lacks a distinct ortholog of Prp4 , which is the only serine/threonine protein kinase among the spliceosome components [11] . In the fission yeast Schizosaccharomyces pombe , prp4 is an essential gene required for intron splicing [11] . It phosphorylates the non-SR protein Prp1 and its kinase activity is essential for G1-S and G2-M transition in the cell cycle [12] . In humans , hPrp4 is specifically associated with the U4/U6 and U4/U6-U5 RNPs . It functionally interacts with hPrp6 ( human ortholog of S . pombe Prp1 ) , Prp31 , Brr2 , and Prp8 , and plays an essential role in the catalytic activation of B-complex [3] . Phosphorylation of hPrp6 and hPrp31 by hPrp4 is required for stable integration of the tri-snRNP into the B-complex , and it has been characterized by phosphoproteomics analysis [11] . Whereas Prp4 is essential in S . pombe , deletion of its orthologous gene appears to be not lethal in Fusarium graminearum because the putative Fgprp4 deletion mutant was identified in a systematic characterization study of its protein kinase genes . F . graminearum is the predominant species causing Fusarium head blight ( FHB ) , one of the most important diseases on wheat and barley [13 , 14] . It causes severe yield losses and contaminates infested grains with harmful mycotoxins , including zearalenone and trichothecene mycotoxin deoxynivalenol ( DON ) , a potent inhibitor of eukaryotic protein synthesis [15 , 16] . The PRP4 orthologs are well conserved in filamentous fungi but none of them have been functionally characterized , including the model organisms Neurospora crassa and Aspergillus nidulans . To our knowledge , Fgprp4 is the only null mutant that is available for this well-conserved protein kinase gene among all the eukaryotic organisms . In this study , we further characterized the function of FgPRP4 in intron splicing and suppressor mutations of the Fgprp4 mutant . Our results showed that FgPrp4 is critical for splicing efficiency and FgPrp4 may regulate pre-mRNA splicing by phosphorylation of other tri-snRNP proteins . FgPrp4 itself may be phosphorylated at the N-terminal region by autophosphorylation or other protein kinases .
The Prp4 ortholog in F . graminearum ( Fg04053 ) shares 57% identity with Prp4 of S . pombe but their homology is mainly in the kinase domain . Although it is conserved in other ascomycetes , a distinct Prp4 ortholog was absent in Saccharomycotina species except Yarrowia lipolytica ( S1 Fig ) . Most of Saccharomycotina species , including S . cerevisiae and Candida albicans , may have lost the PRP4 ortholog during evolution after massive intron loss [17] . Unlike prp4 in S . pombe , the putative FgPrp4 mutant was viable in F . graminearum [18] . In this study we first confirmed the Fgprp4 mutant by Southern blot analysis ( S2 Fig ) . Careful examinations showed that the Fgprp4 mutant had severe growth defects ( Fig 1A ) and rarely produced morphologically abnormal conidia ( Fig 1B ) . The length of Fgprp4 conidia ( 28 . 3 ± 7 . 1 μm ) was approximately 45% shorter than that of wild-type conidia ( 51 . 2 ± 8 . 9 μm ) . Deletion of FgPRP4 also reduced conidiation . Whereas the Fgprp4 mutant produced 2 . 4 ± 1 . 7x104 conidia/ml in 5-day-old CMC cultures , the wild type strain produced over 106 conidia/ml under the same conditions . In addition , the Fgprp4 mutant failed to produce perithecia on mating plates ( Fig 1C ) and was non-pathogenic in infection assays with flowering wheat heads ( Fig 1D ) . To confirm its function , we re-introduced the full-length FgPRP4 allele into the Fgprp4 mutant strain FP1 . The resulting Fgprp4/FgPRP4 transformant FPC1 ( Table 1 ) was similar to the wild type in growth rate , conidiation , sexual reproduction , and virulence ( Fig 1 ) . Therefore , deletion of FgPRP4 is responsible for all the phenotypes observed in the Fgprp4 mutant . To determine its subcellular localization , we fused GFP to the carboxyl-terminus of FgPRP4 and transformed the FgPRP4-GFP construct into the Fgprp4 mutant FP1 . The resulting FgPRP4-GFP transformant FPN1 ( Table 1 ) was normal in growth ( S3 Fig ) , reproduction , and pathogenesis . When examined by epifluorescence microscopy , GFP signals of similar strength were observed in the nucleus in conidia , germlings , and hyphae ( Fig 2A ) . When assayed by qRT-PCR , FgPRP4 had similar expression levels in conidia , germlings , perithecia , and infected wheat heads ( Fig 2B ) . These results indicate that FgPRP4 is constitutively expressed in F . graminearum and its localization to the nucleus may be associated with its functions in the spliceosome . Like hPrp4 , FgPrp4 has a long N-terminal region that is rich in serine and arginine ( SR-rich ) and contains one putative nuclear localization signal ( NLS ) . This N-terminal SR-rich domain of FgPrp4 is absent in its orthologs from S . pombe ( S4 Fig ) . To determine its function , we generated the FgPRP4ΔN310-GFP construct deleted of the N-terminal 310 aa and transformed it into the Fgprp4 mutant . The resulting FgPRP4ΔN310-GFP transformant had similar phenotypes with the original mutant ( Fig 2C ) and GFP signals in the cytoplasm ( Fig 2D ) . These results indicate that the N-terminal region of FgPrp4 is essential for its localization and function in F . graminearum . Interestingly , FgPRP4 has two isoforms based on our RNA-seq data ( S5A Fig ) [19] . Isoform A encodes the full-length FgPrp4 kinase as predicted by automated annotation . Isoform B has the retention of the forth intron and encodes a protein with the predicted C’-terminal 73 aa region replaced with 66 aa encoded by the retained intron ( S5B Fig ) . The protein encoded by isoform B should have no kinase function because the protein kinase domain was disrupted ( S5B ) . Nevertheless , isoform A accounted for over 85% of the FgPRP4 transcripts in RNA-seq data of hyphae , conidia , and perithecia ( S5A Fig ) . This observation was verified by qRT-PCR analysis ( S5C Fig ) , indicating that isoform A is the predominant transcript of FgPRP4 . To determine the defects of Fgprp4 in intron splicing , RNA samples were isolated from aerial hyphae of 9-day-old PDA cultures for RNA-seq analysis . RNA-seq data from two independent experimental replicates were obtained and analyzed . Among the total of 13 , 321 genes in the Fusarium graminearum genome [20] , 10 , 268 have at least one intron and the average intron size is 83 bp . In our RNA-seq data , the expression of 8 , 028 genes ( CPM≥10 ) was detected in both replicates and 6 , 359 of them have introns . Although deletion of FgPRP4 did not completely block intron splicing , the level of retained introns ( un-spliced introns ) was significantly higher in the mutant than in the wild type ( P<0 . 0001 , t-test ) ( Fig 3A ) . In comparison with the wild type , over 38% of the introns in 47% of the genes with detectable transcripts were increased in intron retention over 2-fold in Fgprp4 ( Fig 3B ) . Approximately 76% of them ( 7 , 837 ) were identified in both RNA-seq data ( Fig 3C ) , confirming that retention of these introns was related to FgPRP4 deletion . A third of these introns had over 4-fold reduction in splicing efficiency in both replicates . Nevertheless , splicing of approximately 60% of the predicted introns was not significantly affected ( <2-fold ) by FgPRP4 deletion ( Fig 3B ) . Therefore , FgPRP4 is not essential for intron splicing but it affects splicing efficiency . Based on GO analysis , genes with over 4-fold reduction in splicing efficiency in the mutant belong to various functional categories , which may contribute to its pleiotropic phenotype . A number of them are known to be functionally related to DNA recombination and repair ( S1 Table ) based on the functions of their yeast orthologs , including the FgPHR1 ( FGSG_00797 ) , FgNHP6A ( FGSG_00385 ) , and FgEAF1 ( FGSG_05512 ) genes that were confirmed to be reduced in splicing efficiency in the Fgprp4 mutant by RT-PCR analysis ( Fig 3D ) . Therefore , the Fgprp4 mutant may be compromised in DNA repair . We then compared sequences of the introns that were not affected by FgPRP4 deletion with those with over 4-fold reduction in splicing efficiency in the mutant . No differences were identified in the sequences of the branch point ( BP ) , 5’ss , and 3’ss ( S6 Fig ) . However , introns with reduced splicing efficiency in the Fgprp4 mutant tend to be longer ( p<0 . 001 ) than introns unaffected by FgPRP4 deletion ( S7A Fig ) , mainly due to longer distance between the BP and 5’ss sequences ( S7B Fig ) . We also noticed that genes with intron splicing efficiency affected by FgPRP4 deletion tend to have fewer introns that those unaffected in the Fgprp4 mutant ( S7C Fig ) . Because it is not directly involved in the recognition of 5’ss , BP , and 3’ss sequences , Prp4 likely affects intron splicing by interacting with other spliceosome proteins such as Prp8 [21] or phosphorylation of its substrates in F . graminearum . The Fgprp4 mutant was not stable . Approximately 10% of Fgprp4 cultures produced fast-growing sectors after incubation for 2 weeks ( Fig 4A ) . We randomly collected 49 subcultures of spontaneous sectors and categorized them into two types based on their growth rate and colony morphology ( Fig 4B ) . Thirty two type I suppressor strains ( >65% ) had similar growth rate and colony morphology with the wild type . The other 17 type II suppressors grew slower than the wild type but faster than Fgprp4 ( Fig 4B ) . For the 32 type I suppressors , we also assayed their defects in conidiation , sexual reproduction , and plant infection ( S2 Table ) . Twenty four of them were still defective in plant infection ( Fig 4C ) . The other 8 were pathogenic on wheat heads but still impaired in sexual reproduction ( Fig 4D ) or conidiation . ( S2 Table ) . These results indicate that none of these suppressor strains were fully rescued in the defects of Fgprp4 . We selected two type I suppressor strains , S2 and S47 , for RNA-seq analysis . In comparison with the original Fgprp4 mutant , only 74 . 3% and 34 . 7% of the introns with over 8-fold splicing deficiency were recovered in splicing efficiency in S2 and S47 ( S8 Fig ) , respectively . Therefore , these spontaneous suppressor strains may be not fully recovered in splicing efficiency for all the introns that were affected in the Fgprp4 mutant . FgPRP4 must be important for proper regulation of intron splicing and expression of various genes involved in different biological processes . To identify suppressor mutations , we sequenced 10 genes orthologous to the known components of the U4/U6 and U4/U6 . U5 tri-snRNPs [2 , 3] amplified from 18 type I and 2 type II suppressor strains ( Table 2 ) . Whereas 11 of them had no mutations in these candidate genes , 9 type I suppressor strains had mutations in the FgPRP6 ( FGSG_10242 ) , FgPRP31 ( FGSG_01299 ) , FgPRP8 ( FGSG_02536 ) , and FgBRR2 ( FGSG_01210 ) genes ( Table 2 ) . However , we failed to identify mutations in the rest 11 suppressor strains , suggesting that suppressor mutations may occur in other FgPrp4-targets or tri-snRNP components . In suppressor S30 , the G308E mutation was identified in the FgBRR2 gene ( FGSG_01210 ) . G308 is located in the long N-terminal region of Brr2 that has no known motifs but is required for the in vitro helicase activity [22] . Sequence alignment showed that G308 of FgBrr2 is at the same position with A311 of Spp41 ( Fig 5A ) . In S . pombe , the A311E mutation in spp41 is known to suppress the temperature sensitive prp4-73 mutant [3] . Therefore , the G308E and A311E mutations that changed a neutral amino acid residue ( G or A ) to a charged one ( E ) must have similar effects on the structure and function of the Brr2 helicase . Two suppressor mutations , D1153G and E1429K ( Table 2 ) were identified in FGSG_02536 that is orthologous to S . cerevisiae PRP8 and S . pombe spp42 . Sequence alignment revealed that both D1153 and E1429 are well conserved in Prp8 orthologs ( Fig 5B ) . D1153 of FgPrp8 is at the same position with D1192 of yeast Prp8 , which is in the RT fingers/palm domain . In S . cerevisiae , the D1192G mutation is a suppressor of the U4-cs1 ( cold sensitive ) mutant that is defective in U4/U6 unwinding due to a mutation in the U4 RNA [23] . In F . graminearum , the same D to G mutation in FgPRP8 suppressed the growth defects of Fgprp4 , further indicating the role of FgPrp4 in the activation of B-complex and U4-U6 unwinding . The E1429K mutation occurs in the linker region ( Fig 5B ) . Structural modeling based on yeast Prp8 showed that E1429 and E1412 ( = E1450 of yeast Prp8 ) of FgPrp8 are in the same α-helix that is involved in the formation of the catalytic cavity binding to pre-mRNA ( boxed in Fig 5C ) . E1429K mutation in FgPrp8 may have similar effects with E1450K mutation in yeast on the interaction of Prp8 with the RNA catalytic core . Four suppressor strains had mutations in the ortholog of S . cerevisiae PRP6 ( = Prp1 of S . pombe ) . The FgPrp6 protein has an N-terminal PRP6_N domain and 19 tetratricopeptide repeats ( TPRs ) . Whereas strains S39 and S46 had the same △E308 mutation , suppressor strains S47 and S22 had R230 changed to H and C , respectively ( Fig 6A ) . In humans , five hPrp4-phosphorylation sites have been identified in the linker region of hPrp6 between the PRP6_N domain and TPR repeats [11] . Sequence alignment showed that two of them , T252 and T261 , are conserved in FgPrp6 and its orthologs from other filamentous fungi ( Fig 6A ) . Whereas R230 is in the linker region , E308 is in the first TPR repeat and not far away from the conserved Prp4-phosphorylation sites ( Fig 6A ) . The R230C/H and △E308 mutations may have similar effects on FgPrp6 functions as phosphorylation by FgPrp4 in F . graminearum . Arginine methylation is known to affect the nucleocytoplasmic localization of the hnRNP protein A2 [24] and the RNA helicase A [25] . The suppressor mutation in site R230H/C of FgPrp6 is located in a putative non-GAR methylarginine motif GXXR [26 , 27] that is conserved between FgPrp6 orthologs from filamentous fungi , humans , and S . pombe ( Fig 6A ) . This non-GAR methylarginine motif is not conserved in Prp6 of S . cerevisiae ( Fig 6A ) , which lacks Prp4 kinase . To determine whether mutations at R230 will interfere with its subcellular localization , we generated the FgPRP6- and FgPRP6R230H-GFP fusion constructs and transformed them into the wild-type strain . In the resulting transformants , GFP signals were mainly observed in the nucleus ( Fig 6B ) . No obvious difference was observed in the strength or localization of GFP signals between the FgPRP6- and FgPRP6R230H-GFP transformants ( Fig 6B ) . Therefore , R230H mutation had no effect on the localization of FgPrp6 . In suppressor mutant S17 , the L532P mutation was identified in FgPRP31 ( FGSG_01299 ) . Interestingly , the non-sense mutation at R464 in suppressor S2 resulted in the truncation of the C-terminal 130 aa residues of FgPrp31 , including L532 ( Fig 7A ) . In RNA-seq data with suppressor strain S2 , the FgPRP31 transcripts also had the G1392A mutation that caused the change of R464 ( CGA ) to a stop codon ( UGA ) . Whereas the NOSIC and NOP domains ( spanning the 93–368 aa region ) are well-conserved and known to interact with Prp6 and the U4 RNA , the R464* and L532P suppressor mutations occurred in or after the less-conserved PRP31_C ( Fig 7A ) . Although the exact phosphorylation site or function is not clear , nine hPrp4-phosphorylation sites have been identified in hPrp31 by phosphoproteomics analysis [11] . Five of them , S485 , S486 , S520 , S521 , and T525 are conserved in FgPrp31 ( Fig 7A ) . The nonsense mutation at R464 eliminated all of these putative Prp4-phosphorylation sites in FgPrp31 . These data suggest that the C-terminal region of FgPrp31 likely plays a negative role in B-complex activation , possibly by inhibitory binding to its own N-terminal region or other Prp31-interacting proteins . Phosphorylation by FgPrp4 in the phosphorylation or modulation region may result in conformational changes and release the inhibitory self-binding . We selected FgPRP31 for further characterization because of interesting features of the R464* truncation mutation . The geneticin resistant FgPRP31R464* and FgPRP31L532P gene replacement constructs were generated and co-transformed with the hygromycin-resistant FgPRP4 knockout cassette [18] into protoplasts of PH-1 . Transformants resistant to both hygromycin and geneticin were screened by PCR for deletion of FgPRP4 . In the resulting Fgprp4 mutants , the replacement of endogenous FgPRP31 with the FgPRP31R464* or FgPRP31L532P mutant allele was confirmed by PCR amplification and sequencing analysis . Similar to suppressor strains S2 and S17 , the Fgprp4/FgPRP31R464* and Fgprp4/FgPRP31L532P transformants were normal in growth rate and sexual reproduction ( Fig 7B ) but still defective in plant infection ( Fig 7C ) . Therefore , the R464* and L532P mutations are directly responsible for the recovery of growth rate in suppressor strains S2 and S17 . Because mutations were not identified in 11 type I suppressors that were analyzed , we selected suppressor S3 for whole genome sequencing analysis . After aligning the sequences of S3 ( approximately 50 coverage ) generated by Illumina Hi-seq with the genome sequence of PH-1 , the C to A mutation at 305 was identified in FGSG_11793 , which is orthologous to yeast MSL1 , a U2B component of the U2 SNP [28] . The resulting Q to K mutation occurred at the Q86 residue that is conserved in MSL1 orthologs from filamentous fungi ( S9 Fig ) . The Q86K mutation is in the predicted RNA recognition motif ( RRM ) domain ( S9 Fig ) and will likely affect its interaction with pre-mRNA or other components of sn-RNP during B-complex activation . To determine whether FgPrp4 kinase itself is activated by phosphorylation , we generated the FgPRP4-3xFLAG construct and transformed it into PH-1 . The resulting transformant FPF1 ( Table 1 ) had the expected 88-KD Prp4-3xFLAG fusion protein band on western blots detected with the anti-FLAG antibody ( Fig 8A ) . To assay FgPrp4 phosphorylation , total proteins isolated from the FgPRP4-3xFLAG transformants were incubated with anti-FLAG beads . Proteins eluted from anti-FLAG beads were treated with trypsin and enriched for phosphopeptides with the PolyMac approach as described [29] . The resulting peptides were analyzed by MALDI-TOF/TOF MS analysis . In three independent biological replicates , phosphorylation of S289 was detected in the peptide AAS289PASTLP of FgPrp4 . Because S289 was the only phosphorylation site identified in FgPRP4 , we generated the FgPRP4S289A-GFP mutant allele and transformed it into the Fgprp4 mutant . The resulting Fgprp4/FgPRP4S289A transformant FPA2 ( Table 1 ) had GFP signals in the nucleus ( Fig 8B ) but , like the original mutant , displayed severe growth and conidiation defects ( Fig 8C ) , indicating that FgPRP4S289A failed to complement the Fgprp4 mutant in growth and conidiation . Therefore , phosphorylation at S289 is essential for FgPrp4 functions . It is possible that FgPrp4 is activated by phosphorylation at S289 by itself or other protein kinases for spliceosome activation in F . graminearum .
Among all the spliceosome components , Prp4 is the only protein kinase and it is conserved in humans , plants , and S . pombe [21 , 30] . Interestingly , all the sequenced Saccharomycotina species except Y . lipolytica lack a distinct Prp4 ortholog ( S1 Fig ) . Whereas S . cerevisiae has only 376 introns , Y . lipolytica , a dimorphic yeast , has over 1 , 500 introns [31] . Because lower fungi such as Rhizopus oryzae and Batrachochytrium dendrobatidis have this kinase gene , Saccharomycotina species may have lost the PRP4 ortholog after massive intron loss during evolution [17 , 32] . In F . graminearum , the Fgprp4 mutant was viable although it had severe growth defects . To our knowledge , null mutants of the Prp4 kinase have not been reported in any other eukaryotic organisms except in N . crassa , in which the putative stk-57 mutant deleted of the PRP4 ortholog ( NCU10853 ) generated in a large-scale protein kinase gene knockout study had no defects in hyphal growth , asexual reproduction , and sexual development but could not be purified by isolation of ascospores [33] . Because of its striking difference from the Fgprp4 mutant , we obtained the putative stk-57 mutant ( stock number FGSC17970 ) from Fungal Genetics Stock Center ( www . fgsc . net ) and conducted PCR analyses . Both the STK-57 kinase gene and the hygromycin-resistant marker could be amplified in this putative knockout mutant ( S10 Fig ) . Furthermore , we failed to amplify any PCR products with the anchor primers that were designed to amplify the upstream and downstream fragments resulted from gene replacement events ( S10 Fig ) . These results indicate that this putative stk-57 knockout mutant was not a true mutant but likely an ectopic transformant . Considering the fact that many essential genes have introns in F . graminearum , the viability of Fgprp4 mutant suggests that deletion of FgPRP4 does not block spliceosome activation and intron splicing . This hypothesis was confirmed by RNA-seq data . FgPrp4 kinase is not essential for RNA splicing but it regulates splicing efficiency . Consistent with its pleiotropic defects , splicing efficiency of introns in over 39% of the F . graminearum genes involved in various physiological and developmental processes were reduced significantly in the Fgprp4 mutant . Although no unique 5’ss , BP , and 3’ss sequences were identified in introns affected in the mutant , we noticed that splicing of larger introns with longer distance between the BP and 5’ss sequences is more sensitive to FgPRP4 deletion . In addition , intron splicing efficiency in the Fgprp4 mutant was not related to predicted gene functions . In fact , it is often that the splicing efficiency was only affected by FgPRP4 deletion for some but not all the introns in the genes with multiple introns in F . graminearum . Furthermore , we noticed that the position of introns in mRNA has no effects on intron splicing affected by FgPRP4 deletion . The budding yeast has approximately 300 genes with small introns although it lacks the Prp4 ortholog . Among 136 of them with orthologs in F . graminearum , only two of them had normal intron splicing efficiency in the Fgprp4 mutant . Therefore , the function and evolutional relationship of genes have no effect on whether intron splicing was affected or not by deletion of FgPRP4 in F . graminearum . The Fgprp4 mutant was unstable and produced fast growing sectors . Our RNA-seq and RT-PCR results showed that deletion of FgPRP4 resulted in splicing defects in a number of genes important for DNA recombination and repairing , which may be responsible for the production of spontaneous suppressors . Among the 49 sectors we isolated , over 60% were fully recovered in the growth rate and colony morphology , the others grew faster than the original mutant but still slower than the wild type , and may had additional defects in aerial hyphal growth or colony pigmentation , indicating that suppressor mutations may occur in different genes . Even for spontaneous suppressors with the wild-type growth rate and colony morphology , none of them were normal in all the other phenotypes assayed , including virulence , conidiation , and sexual reproduction . Therefore , although suppressor mutations suppressed the defects of Fgprp4 mutant in vegetative growth , they failed to rescue all the other defects associated with FgPRP4 deletion . This observation may explain why F . graminearum still keep the FgPRP4 gene although suppressor mutations occur at such a high frequency in its deletion mutant . Prp6 , Prp8 , Prp31 , and Brr2 are key components of the U4/U6-U5 tri-snRNP [34 , 35] ( Fig 9 ) . Suppressor mutations identified in these genes may have similar effects with phosphorylation by Prp4 on the interactions among these tri-snRNP components . In S . pombe , suppressor mutations of the prp4-73ts mutant have been identified in the Brr2 ( Spp41 ) and Prp8 ( Spp42 ) orthologs [3 , 36] . The G308E mutation of FgBrr2 is the same to A311E of Spp41 , changing from a neutral , non-polar residue ( G or A ) to an acidic , polar one ( E ) . G308 is in the N-terminal region of Brr2 required for the in vitro helicase activity [22] . The N-terminal region , RecA-1 , and RecA-2 of hBrr2 also may be involved in interacting with hPrp6 [9] . Therefore , G308E and A311E mutations may have similar effects on Brr2 helicase activity or its interaction with Prp6 to suppress prp4 mutant . For the G2248D suppressor mutation characterized in spp42 [36] , we failed to identify mutations at the same residue in FgPRP8 . However , D1153G , one of the two suppressor mutations identified in FgPRP8 , is the same to D1192G of PRP8 that could suppress the yeast U4-cs1 mutant [23] . When modeled after the crystal structure of yeast Prp8885-2413 , D1153 is at the tip of the exposed loop following the RTα12 . Interestingly , this region of Prp8 also contains other suppressor mutations of U4-cs1 and suppressor mutations of brr2-1 [37 , 38] , suggesting its involvement in interaction with Brr2 and other proteins or RNA . The D to E mutation may affect the interaction of Prp8 with other tri-snRNP components . Nevertheless , the D1153E mutation in FgPRP8 suppressed the Fgprp4 mutant , indicating that FgPrp4 may play a critical role in U4/U6 unwinding by affecting the interactions between different tri-snRNP components . Like D1153G , E1429K did not change the overall structure of FgPrp8 . In yeast Prp8 , the E1450K mutation suppresses the 3’ss mutation [38 , 39] , indicating its involvement in RNA binding . In FgPrp8 , E1429 and E1412 ( = E1450 of yeast Prp8 ) are in the same α-helix that is involved in the formation of the catalytic cavity [38] . Because they are in the same cleft and have the same E to K change , the E1429K and E1450K mutations may have similar effects on the catalytic cavity of tri-snRNP and B-complex activation . In S . pombe , suppressor mutations of prp4-73ts mutant have not been reported in Prp6 . In humans , phosphorylation of Prp6 that occurs after the tri-snRNP being integrated into the B-complex may release the inhibition of Brr2 by Prp8 and is important for spliceosomal B-complex activation [3 , 11] . In this study , we identified four suppressor strains with mutations in the PRP6 ortholog . In RNA-seq data with suppressor strains , the transcripts of FgPRP6 had the C690U mutation in suppressor strain S47 , which is consistent with the FgPRP6R230H mutation detected by DNA sequencing analysis . Because both R230 and E308 are in the proximity of T252 and T261 , two conserved Prp4-phosphorylation sites , it is likely that mutations at these two residues have similar effects with phosphorylation by FgPrp4 on FgPrp6 functions . Among 20 suppressor mutants that were sequenced , we only identified suppressor mutations in nine of them . For the other 11 suppressor strains , none of them had mutations in the candidate tri-snRNP components that were selected for sequencing analysis . Suppressor mutations likely occur in other tri-snRNP components , such as orthologs of Snu13 , Snu66 , Snu114 , Cpr1 , Sad1 , and Dib1 [40 , 41] . However , in suppressor S3 , the Q86K mutation in FgMSL1 was identified by whole genome sequencing . To our knowledge , suppressor mutations in MSL1 orthologs have not been reported in other organisms . In S . cerevisiae , MSL1 is an nonessential gene that encodes a U2 snRNP-specific protein [42] . In Drosophila , the U2B protein is part of a protein network that is important for splicing accuracy and efficiency [43] . In F . graminearum , the Q86K mutation suppressed the defects of the FgPrp4 mutant in growth . It is possible that this mutation in FgMSL1 may affect the U2-U6 coupling and complex B activation . Unlike Prp4 in S . pombe , FgPrp4 has a long N-terminal SR-rich region/domain that is conserved in metazoan Prp4 kinases [44] . Expression of the FgPRP4ΔN310-GFP allele failed to complement the Fgprp4 mutant and GFP signals became localized to the cytoplasm instead of the nucleus , indicating that this N-terminal region is important for the function and subcellular localization of FgPrp4 in F . graminearum . This region contains two putative NLS sequences conserved among the FgPrp4 orthologs . To our knowledge , the NLS sequence responsible for the localization of Prp4 kinases to the nucleus has not been characterized . It will be important to further characterize the function of these two NLS sequences in the N-terminal region of FgPrp4 . In humans , a number of other splicing factors , such as SRSF1 and ASF/SF2 , also have the N-terminal RS-rich region that may be phosphorylated by SR protein kinases such as CLK and SRPK [45] . In this study , we showed that S289 is a phosphorylation site important for FgPrp4 functions . Because auto-phosphorylation of Prp4 has been reported in humans [44] , it will be important to determine whether phosphorylation of S289 is catalyzed by FgPrp4 itself or other protein kinases .
The wild-type strain PH-1 , Fgprp4 mutants , and all the transformants generated in this study were routinely cultured on potato dextrose agar ( PDA ) [46] or complete medium ( CM ) at 25°C and preserved in 20% glycerol at -80°C [47] . Growth rate , conidiation , and sexual reproduction were assayed as described [18] . Protoplasts prepared from 12 h germlings were used for PEG-mediated transformation [48] . For infection assays , flowering wheat heads of cultivar XiaoYan 22 were drop-inoculated with 10 μl of conidium suspensions ( 2 . 0×105 conidia/ml ) as described [49] . Scab symptoms were examined 14 days post-inoculation ( dpi ) . RNA was isolated with the TRIzol reagent ( Invitrogen ) from conidia , 12 h germlings , perithecia at 10 days post-fertilization , and infected wheat heads collected at 7 dpi as described [50 , 51] . For qRT-PCR analysis , first-strand cDNA was synthesized with the Fermentas 1st cDNA synthesis kit ( Hanover ) following the instructions provided by the manufacturer . The β-tubulin gene FGSG_06611 of F . graminearum was used as the internal control [52] . The mean and standard deviation were calculated with data from three biological replicates . For complementation assays , the FgPPR4 gene was cloned into pFL2 [48] by gap repair [53] . The resulting FgPRP4 construct carrying the geneticin-resistant marker was transformed into the Fgprp4 mutant FP1 . The same gap repair approach was used to generate the FgPRP4-GFP , Fgprp4/FgPRP4S289A , and FgPRP4ΔN310-GFP construct with primers showed in S3 Table . The resulting constructs were confirmed by sequencing analysis and transformed into protoplasts of FP1 to generate the complemented transformants . Fast-growing sectors of the Fgprp4 mutant were transferred with sterile toothpicks to fresh PDA plates . After single spore isolation , each sub-cultures of spontaneous suppressors were assayed for defects in growth , differentiation , and plant infection [18] . To identify suppressor mutations in the candidate tri-snRNP components , PCR products amplified with primers listed in S3 Table were sequenced at BGI-Beijing . Mutation sites were identified by sequence alignment and confirmed by re-sequencing analysis . Vegetative hyphae of PH-1 , Fgprp4 mutant FP1 , S2 , and S47 were harvested from 9-day-old PDA colonies formed over sterile dialysis membrane and used for RNA isolation with the TRIzol Reagent ( Life technologies , US ) . Poly ( A ) mRNA was isolated with the Oligotex mRNA mini kit ( Qiagen , Germany ) . Library construction and sequencing with an Illumina Hiseq 2000 sequencer were performed at Shanghai Biotechnology Corporation ( Shanghai , China ) . For each sample , at least 25 Mb high-quality reads were obtained . The resulting RNA-seq reads were mapped onto the reference genome of F . graminearum strain PH-1 with the Tophat2 program ( ccb . jhu . edu/software/tophat/index . shtml ) . To filter out weakly expressed genes , only genes with a minimum expression level of 1 count per million were included in the analysis . The intron retention level was defined as the number of reads that aligned to the predicted intron divided by the number of reads aligned to the corresponding transcript . RNA was isolated with the TRIzol Reagent ( Life technologies ) from vegetative hyphae of PH-1 and the Fgprp4 mutant . The Fermentas 1st cDNA synthesis kit ( Hanover , MD , USA ) was used to synthesize the first-strand cDNA following the instruction provided by the manufacturer . The primers used for PCR amplification of the FgPHR1 ( FGSG_00797 ) , FgNHP6A ( FGSG_00385 ) , and FgEAF1 ( FGSG_05512 ) genes were listed in S3 Table . The FgPRP4-3xFLAG fusion construct was generated by the gap repair approach by co-transformation of the full-length FgPRP4 fragment and XhoI-digested pFL7 into yeast strain XK1-25 [48] . The resulting fusion construct rescued from Trp+yeast transformants was confirmed by sequence analysis and transformed into the wide-type strain PH-1 . Geneticin-resistant transformants expressing the fusion constructs were identified by PCR and confirmed by western blot analysis with the anti-FLAG antibody ( Sigma ) . Total proteins isolated from the resulting transformant were incubated with the anti-FLAG M2 beads ( Sigma ) as described [54] . Proteins eluted from anti-FLAG beads were digested with proteomics grade trypsin ( Sigma ) and enriched for phosphopeptides with the polymer-based metal ion affinity capture ( PolyMAC ) as described [29] . Phosphopeptides enriched by PolyMac were analyzed with an ABI 4800 MALDI-TOF/TOF mass spectrometer . Proteome Discoverer ( version 1 . 0; Thermo Fisher Scientific ) was used to identify peptide sequences and phosphorylation sites as described [29] . Multiple alignments of protein sequences were constructed with COBALT ( www . ncbi . nlm . nih . gov/tools/cobalt ) and manually modified . The analysis of type I and type II functional divergence was performed with the Diverge 3 . 0 software [55] . Maximum likelihood ( ML ) phylogenies were estimated with PhyML3 . 0 assuming 8 categories of γ-distributed substitution rate and SPRs algorithms . For phylogeny of protein sequences , the bestfit model for each datasets selected by ProtTest2 . 4 [56] was used . The reliability of internal branches was evaluated based on SH-aLRT supports . The 3D-structural model of FgPrp8 was modeled after that of Prp8 in S . cerevisiae ( PDB ID: 3SBT and 2OG4 ) and displayed with Chimera 1 . 8 . 1 [57] . To identify mutations in suppressor S3 , DNA isolated from 12 h germlings were sequenced by Illumina platform at Shanghai Biotechnology Corporation ( Shanghai , China ) to 50x coverage with pair-end libraries . The sequence reads were mapped onto reference genome of strain PH-1 by using Bowtie 2 . 23 [58] . Mutation sites were called by SAMtools with the default parameters . Annotation of the mutation sites was performed with Variant Effect Predictor ( VEP ) [59] . RNA-seq data generated in this study were deposited in the NCBI Sequence Read Archive database under the accession code of SRP062439 .
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In eukaryotic organisms , many genes containing introns that need to be spliced by the spliceosome after transcription . Among all the spliceosome components , Prp4 is the only protein kinase . Unlike other organisms , deletion of the FgPRP4 kinase gene was not lethal in the wheat scab fungus Fusarium graminearum . In this study , we found that FgPRP4 is not essential for intron splicing but important for splicing efficiency . The Fgprp4 mutant was not stable and produced spontaneous suppressors recovered in growth rate . Suppressor mutations were identified in the PRP6 , PRP31 , BRR2 , and PRP8 orthologs , key components of the U4/U6-U5 complex in the spliceosome and FgMSL1 by candidate gene or whole genome sequencing . We also showed that the N-terminal 310 amino acid region of FgPrp4 plays a critical role in its localization and functions of FgPrp4 and identified S289 as a critical phosphorylation site . Overall , our result indicated that FgPrp4 is important for splicing efficiency , possibly by phosphorylation of other spliceosome components .
|
[
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"and",
"Methods"
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2016
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FgPrp4 Kinase Is Important for Spliceosome B-Complex Activation and Splicing Efficiency in Fusarium graminearum
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The double-stranded RNA-activated protein kinase R ( PKR ) is a key regulator of the innate immune response . Activation of PKR during viral infection culminates in phosphorylation of the α subunit of the eukaryotic translation initiation factor 2 ( eIF2α ) to inhibit protein translation . A broad range of regulatory functions has also been attributed to PKR . However , as few additional PKR substrates have been identified , the mechanisms remain unclear . Here , PKR is shown to interact with an essential RNA helicase , RHA . Moreover , RHA is identified as a substrate for PKR , with phosphorylation perturbing the association of the helicase with double-stranded RNA ( dsRNA ) . Through this mechanism , PKR can modulate transcription , as revealed by its ability to prevent the capacity of RHA to catalyze transactivating response ( TAR ) –mediated type 1 human immunodeficiency virus ( HIV-1 ) gene regulation . Consequently , HIV-1 virions packaged in cells also expressing the decoy RHA peptides subsequently had enhanced infectivity . The data demonstrate interplay between key components of dsRNA metabolism , both connecting RHA to an important component of innate immunity and delineating an unanticipated role for PKR in RNA metabolism .
The primary detection of viral infection is by the host innate immune system , with the recognition of viral double-stranded RNA ( dsRNA ) a crucial early function . Responses to dsRNA are mediated by several protein receptors that recognize this pathogen-associated molecular pattern ( PAMP ) . Most important of these receptors are the Toll-like receptor-3 ( TLR3 ) , two caspase recruitment domain ( CARD ) -containing helicases , retinoic acid inducible gene-I ( RIG-I ) and the related IFN inducible helicase-I ( IFIH-I ) , and the protein kinase R ( PKR ) . These dsRNA receptors are spatially separated within the cell to respond to either intra- or extra-cellular dsRNA . Moreover , the outcome of the ensuing antiviral response triggered by each receptor differs between cell compartments [1] . Consequently , a full contingent of pattern recognition receptors is required for immune competence . TLR3 is located on the cell surface or in the endosome compartment , and upon sensing dsRNA recruits the cytoplasmic adaptor Toll/IL-1R ( TIR ) domain-containing adaptor-inducing IFNβ ( TRIF ) , via shared TIR homologous regions to mediate antiviral responses [2]–[4] . Adaptor signaling leads to IFN regulatory factor ( IRF ) 3 and IRF7 activation and type-I IFN production [5] , [6] . RIG-I and IFIH-I are cytoplasmic receptors which sense dsRNA and subsequently transmit a signal via helicase and CARD domains , respectfully . Activated RIG-I/IFIH-I associate with a mitochondrial anchored CARD adaptor , IPS-1 ( also called MAVS , Cardif , or VISA ) , to activate NFκB and IRF3 and induce IFNβ [7]–[10] . Alternatively , dsRNA-binding at the amino terminus of PKR activates the kinase , resulting in the phosphorylation of the α subunit of the eukaryotic translation initiation factor 2 ( eIF2α ) and inhibition of protein translation within infected cells [11] . In addition , PKR evokes cellular responses by modulating cell-signaling pathways . The mechanisms by which PKR functions as a signaling molecule have not been fully delineated . However , PKR has been shown to mediate the responses to other PAMPs , including bacterial LPS , as well as stress stimuli such as IFNγ , TNFα , mitomycin C , and serum deprivation by inducing degradation of inhibitor κB ( IκB ) , IRF1 expression , indirectly mediating STAT1 phosphorylation , and triggering apoptotic pathways [12] , [13] . These broad responses are not reconciled with a narrow mechanism involving translational control through eIF2α . However , few other PKR substrates are known that account for these cellular responses . PKR has two domains , a C-terminal catalytic domain and an N-terminal regulatory domain . The N-terminus encodes tandem RNA-binding motifs ( RBMs ) . The RBMs not only recognize dsRNA to activate PKR , but also serve as an autoinhibitory domain , as well as mediating dimerization to form the fully active kinase molecule . These observations suggest an additional function for RBMs as protein–protein interaction domains . Support for this comes from other proteins identified to interact with PKR . The protein activator of PKR ( PACT ) encodes three RBMs , and the structurally similar transactivating response ( TAR ) -RNA binding protein ( TARBP ) interacts with PKR to , conversely , inhibit the kinase . That RBMs might mediate interactions between proteins , particularly other RBMs , highlights an emerging concept that has consequence for coordinating the dsRNA response in cells . In this way the RBM might be considered as a signaling domain , analogous to the TIR domain of TLRs or the CARD domain of RIG-I and IFIH-I to mediate homo- or heterotypic protein interactions . Here , we identify an interaction between PKR and another protein encoding RBMs , the RNA helicase A ( RHA ) . RHA is an essential DEAH-box protein that exhibits both RNA and DNA helicase activity [14] . The association is demonstrated to be via the helicase RBMs . Importantly , biochemical analysis shows RHA is a substrate for PKR , and demonstrates that phosphorylation modulates the helicase association with its nucleic acid substrate . The consequences of these observations are examined in relation to RHA's previously established role in retroviral infection . PKR is shown to mediate transcriptional activity and HIV-1 infectivity via phosphorylation of RHA . These findings identify a novel function for PKR , delineating a new cell signaling pathway to target in anti-HIV-1 therapy , and highlighting a process by which proteins that respond to dsRNA may be coordinated .
To detect proteins interacting with PKR , the kinase was immunoprecipitated from isogenic , pkr-null mouse embryonic fibroblasts ( MEFs ) either silenced or expressing human PKR from the native promoter elements at physiologic levels . The cells were stimulated with the synthetic , dsRNA mimic polyinosinic-polycytidylic acid ( pIC ) to activate the kinase . A 140 kDa protein band was coimmunoprecipitated with a monoclonal anti-PKR antibody ( Figure 1A ) . Mass spectrometric analysis of this protein identified 11 different peptide sequences that matched the amino acid sequence of murine RHA ( Table 1 ) . Immunoprecipitation and Western blot analysis were conducted to verify this protein association . The protein interaction was confirmed in MEFs isolated from wild-type and pkr null mice by performing the reciprocal immunoprecipitation , using a anti-RHA monoclonal antibody , then detecting coimmunoprecipited PKR ( Figure 1B ) . This experiment was repeated in the rescued pkr null MEFs expressing the human kinase , with and without stimulation of the cells with pIC to activate PKR . Coimmunoprecipitation of PKR and RHA only occurred with pIC treatment , demonstrating that the protein interaction is dependent upon activation of the kinase ( Figure 1C ) . Notably , pIC appears to modulate the conformation of RHA as the protein is not immunoprecipitated by its own antibody from untreated cell lysates . This supports a previous suggestion that the constitutively expressed helicase is maintained in an inactive conformation , likely via the protein's RBM , under basal conditions [15] . In vitro binding studies were conducted to map the association between PKR and RHA . Six different glutathione-S-transferase ( GST ) fusion constructs , together spanning the full helicase , were used in a GST-pull down experiment to determine which region of RHA associated with PKR . This experiment showed PKR associated exclusively with the first 263 amino acids of RHA that encodes its two RBMs ( Figure 2A ) . Since the first RBM , encoded between amino acids 1 to 79 , did not associate with PKR , it appears that the second RBM is the interacting region , or both RBMs are required . Efforts to map the region of PKR that interacts with RHA were inconclusive . Binding assays with the isolated RBM or kinase domains of PKR showed the N-terminal RBMs interacted specifically with RHA . However , the truncated C-terminus of PKR bound non-specifically to the control ( beads only ) in the assay conditions . Subsequent analysis suggests the RBM of RHA interacts with both C- and N-terminal domains of PKR ( see below ) . RHA encodes several domains that bind dsRNA , located in the helicase domain , the C-terminal RGG box , as well as the two RBMs at the N-terminus , that did not associate with PKR . This implies that the PKR-RHA interaction is direct and not through mutual association with dsRNA . Moreover , it has been established that PKR dissociates from dsRNA upon activation , autophosphorylation and dimerization [16] . Consequently , an indirect association between PKR and RHA , bridged by dsRNA , is unlikely . However , to unequivocally establish that the two proteins interact directly , several experiments were conducted . First the ability of a 16 bp dsRNA molecule to block the association between in vitro synthesized PKR and the N-terminal 263 amino acid GST-fusion construct of RHA was measured . This short dsRNA , although able to bind to a single RBM , is not long enough to interact with two RBMs from separate proteins [17] . The results ( Figure 2B ) showed that the 16 bp dsRNA did not perturb the association between PKR and RHA , even when present at considerable molar excess . Next , alternative cell treatments that did not use pIC to activate PKR were evaluated and coimmunoprecipitations performed . Accordingly , treatment of the human monocytic cell line , THP-1 , with LPS , demonstrated to activate PKR [18] , induces the association between the kinase and RHA ( Figure 2C ) . Finally , triggering of PKR with its protein activator PACT [19] effected association between PKR and RHA . HEK293T cells were cotransfected with expression constructs for RHA and wild-type PACT or a mutant ( ΔPACT ) that does not activate PKR and treated with actinomycin-D to stimulate PACT , then PKR was immunoprecipitated . Figure 2D shows the wild-type and not the mutant PACT increased the association between PKR and the helicase . Together these data demonstrate that PKR interacts directly with the N-terminal region of RHA , and that this interaction is dependent upon activation of the kinase . To investigate the possible consequences of this interaction , we tested whether RHA is a substrate for PKR-mediated phosphorylation . An in vitro kinase assay was performed with proteins coimmunoprecipitated from MEFs using a monoclonal antibody against PKR . The results ( Figure 3A ) show RHA bound to PKR at physiological ratios was phosphorylated by the associated PKR in a subsequent kinase assay . To measure phosphorylation in vivo RHA was directly immunoprecipitated with antibodies to RHA , or coimmunoprecipitated with an anti-PKR antibody from HeLa cells treated with pIC . The resulting immune complexes were probed by western blot for phosphorylated residues using anti-phosphoserine and anti-phosphothreonine antibodies . Figure 3B shows only the RHA in complex with PKR had detectable phosphorylated serine and threonine residues . Detection of phosphorylated RHA specifically associated with the kinase , strongly suggesting direct phosphorylation of RHA by PKR . The antibody used to immunoprecipitate RHA in this experiment ( ab2627 from Abcam ) was raised against a synthetic peptide derived from within residues 100 to 200 of human RHA . This is within the region of RHA demonstrated to associate with PKR ( between residues 80 to 263 , Figure 2A ) . As this antibody and PKR interacts with the same region of RHA mutual association is excluded . Consequently , PKR is not coimmunoprecipitated with this anti-RHA antibody ( Figure 3B ) . This confirms the GST-pull down experiments and further narrows the region mediating the interaction between the RHA and PKR . Phosphorylation of RHA by PKR was confirmed in an in vitro kinase assay , using purified recombinant PKR and RHA ( Figure 3C ) . To examine the possible functional consequences of phosphorylation by PKR we mapped the region of RHA that is modified . Accordingly , an in vitro kinase assay was conducted with truncated GST-fusion constructs of RHA and recombinant PKR . Figure 3D demonstrates that RHA is phosphorylated within the 263 amino acid region previously demonstrated to interact with PKR . Hence , the RBM of RHA must interact with the catalytic kinase domain of PKR . This is consistent with previous evidence showing other RBMs , for instance from PACT , are phosphorylated by PKR [20] , [21] . As the N-terminal 263 amino acid region of RHA regulates the association with dsRNA , it seemed evident that addition of a negatively charged phosphate group to this region would perturb RHA's interaction with dsRNA . To test this hypothesis we measured the relative affinity of the phosphorylated or unphosphorylated RHA peptide for pIC . The 263 amino acid RHA peptide was either taken directly from an in vitro synthesis reaction , or subsequently phosphorylated by PKR in an in vitro kinase assay following synthesis . Approximately 19% of the total 35S-labeled RHA peptide was recovered in a pIC pull down . In contrast , none of the phosphorylated RHA peptide , evidenced as a 32P-labeled product , bound pIC ( Figure 4 ) . Consequently , phosphorylation of the RHA peptide by PKR inhibited pIC binding . Given that phosphorylation perturbs RHA's association with its nucleic acid substrate , we would expected PKR should have a profound effect upon the function of the helicase in vivo . Our data supports a model in which PKR regulates RHA by phosphorylating its RBD thereby decreasing its affinity for RNA . To determine the in vivo consequence of such regulation , we investigated PKR's effect on the reported ability of RHA to regulate transactivation of the HIV-1 LTR [22] . Accordingly , transcription of an LTR-EGFP reporter construct was measured in HEK293T cells in which PKR was depleted by RNA interference ( RNAi ) . Additional control small interfering RNAs ( siRNAs ) against RHA , EGFP , and as an alternative target Lamin A/C , were cotransfected with the reporter construct to gauge the RHA dependence of transactivation , the efficacy of RNAi , and to account for non-specific effects of RNAi , respectively . Since the HIV-1 LTR RNA has been reported to bind and activate PKR , no further activating stimulus was used [23] . As depletion of PKR can increase gene expression by reduced phosphorylation of the eIF2α translation factor , we delineated specific regulation of LTR-transactivation by normalizing reporter protein levels to an internal constitutive Renilla reporter . Western blot analysis confirmed the specific release of the reporter gene ( GFP ) relative to the constitutively expressed GAPDH , and verified appropriate targeting of each siRNA against PKR , RHA , and as a control GFP ( Figure 5B ) . The control siRNA to Lamin A/C did not affect reporter protein levels and is not shown . Depletion of RHA by siRNA confirms the role of the helicase in LTR-regulated gene expression ( Figure 5A ) . Significantly , depletion of PKR increased EGFP expression . The timing of transcriptional release , beginning at 48 hours , conforms to the anticipated kinetics of the removal of PKR from the cell , as the kinase has an approximate half-life of 48 hours ( B . R . G . Williams , unpublished results ) . This effect of PKR on the LTR reporter system was further tested using three PKR constructs with different catalytic activity . The requirement for kinase activity for PKR control of RHA-mediated LTR expression was assessed by comparing the relative affect of wild-type PKR , and two mutant PKR proteins either; catalytically active but modified to preclude eIF2α regulation by substitution of the threonine residue to an alanine at position 487 ( T487A ) [24] , or a kinase dead construct modified by substitution of a lysine residue to a arginine at position 296 ( K296R ) . Expression of wild-type PKR reduced RHA-dependent transcription of the LTR-EGFP reporter . Conversely , expression of the catalytically inactive PKR-K296R promotes RHA-dependent transcription of the reporter ( Figure 5C ) . This construct ( K296R ) dimerizes with endogenous PKR , so acts as a dominant negative to directly inhibit PKR's regulation of RHA as well as general protein translation , via wild-type PKR phosphorylation of eIF2α . The relative contribution of these two mechanisms was explored by expressing the mutant PKR-T487A that mediates association with eIF2α . This construct is catalytically active , so will phosphorylate RHA , but is incapable of regulating translation . Accordingly , expression of the PKR-T487A showed an intermediate affect on the LTR-driven reporter , reflecting direct inhibition of RHA-mediated induction of the reporter without the wild-type PKR-mediated regulation of global protein translation ( Figure 5C ) . The relative contribution of PKR's direct regulation of RHA juxtaposed to indirect effect upon translation , demonstrated with either PKR mutation ( K296R or T487A ) , is made more clear when the RHA-dependent transcription of the HIV-1 LTR reporter ( EGFP ) is normalizing against a constitutive reporter ( Renilla luciferase ) . This normalization shows the catalytically inactive PKR-K296R does not affect RHA-mediated transactivation of the HIV-1 LTR , while the catalytically active PKR-T487A construct reduces transactivation of the HIV-1 LTR . This data shows that PKR negatively regulates RHA transactivation of the retroviral reporter gene by direct phosphorylation control of RHA . The preceding data predicts over expression of the RBD of RHA would perturb PKR function by acting as a decoy substrate . This prediction is supported by reporter assays in HEK293T cells that show RHA-regulated LTR expression increases with increasing amounts of the RHA RBD ( Figure 6A ) . This rescue effect of RHA's RBD in the reporter assays should extend to full viral infection . To test this , we measured the capacity of constructs that encoded RHA's RBD , and two truncated constructs , of each separate RBM within this domain , to enhance HIV-1 infection in the peripheral blood mononuclear cell line MT-2 . As a previous report had demonstrated that RHA becomes incorporated into the HIV-1 virion during replication [25] , infectious virus was produced in cells co-expressing the three RHA peptides ( RBD , RBM1 , and RBM2 , encoding residues 1-263 , 1-76 , and 169-263 , respectively ) , and the virions produced were titrated onto the mononuclear cells . In keeping with the reporter assays , expression of the RBD significantly increased HIV-1 infectivity . Notably , a truncation construct from the first RBM ( RBM1 ) that did not associate with , and was not phosphorylated by , PKR ( Figures 2A and 3D ) , did not alter HIV-1 infectivity . In contrast , the construct encoding the second RBM ( RBM2 ) , predicted to be the substrate for PKR , did enhance viral infectivity . In fact , this peptide was more potent than the domain that encompassed both motifs ( Figure 6B ) . As these truncated constructs have no helicase activity and lack other domains demonstrated to enhance retroviral replication , increased virus infectivity is presumed to be due to the demonstrated association and inhibition of PKR . However , an alternative mechanism is conceivable whereby RHA's RBD might recruit other cellular factors to enhance viral replication . This was assessed by measuring the activity of reverse transcriptase in infections with HIV-1 produced with the control plasmid or each of the RHA constructs . Importantly , the RHA peptides did not increase viral replication , as measured by the activity of the viral reverse transcriptase enzyme ( Figure 6C ) . These experiments validate the preceding data in a cell infection system , substantiating a consequence of the interaction between PKR and RHA for the cell's innate immune response to HIV-1 infection .
The innate immune response is the primary shield against microbial infection and directs the subsequent adaptive response . The protein kinase PKR was identified some 30 years ago as a sentinel kinase that is constitutively expressed in all cells as a monomer that subsequently dimerizes to form the active enzyme . We show here that RHA is a novel substrate for PKR and explore points of significance that arise from the finding . PKR's interaction with RHA identifies a novel mechanism by which the previously established translational regulator can also modulate transcription . This function of PKR identifies an antiviral pathway that represents a plausible target for treatment of established retroviral infections . Consistent with this antiviral mechanism , RHA is positively associated with viral replication . RHA transactivates the Bovine Viral Diarrhea virus by binding to the terminal nontranslated regions of the viral RNA genome [26] . RHA also positively regulates expression of the HIV-1 transactivation response region [27] ( and in this study ) . In addition , RHA mediates release of retroviral transcripts from the splicesome and transports the RNA from the nucleus . Correspondingly , the helicase has been shown to associate with the constitutive transport element ( CTE ) of type D-retroviruses and Rev Response elements of HIV-1 and to associate with cellular mRNA export receptors TAP , SAM68 , and HAP95 [28] , [29] . Of particular relevance to this study , RHA also associates with the HIV-1 gag protein and becomes incorporated into the HIV-1 particle [25] . Our data demonstrates that coexpression of HIV-1 provirus with RHA peptides that are substrates for PKR subsequently enhances viral infectivity . Significantly , the truncated RHA peptides do not encode any helicase activity and are therefore incapable of transactivating the HIV-1 LTR sequences . Appropriately , no benefit to virus replication was observed by co-expressing RHA peptides . We contend the observed increased infectivity , without increased replication , is due to inhibition of the ensuing antiviral response mediated by PKR , through the interaction between RHAs second RBM and PKR . Therefore an additional function of RHA possibly exploited by HIV-1 is to dampen the primary host immune response . Such a role adds weight to the previous observed incorporation of RHA into the virion . RHA was initially identified as a homolog of the Drosophila melanogaster maleless gene that regulates chromosomal dosage compensation , a function essential for survival of male larvae [30] . Deletion of rha-1 in Caenorhabditis elegans indicates that the helicase controls germ cell proliferation and development [31] . Embryonic lethality of rha null mice at day 11 of gestation shows that the helicase is also essential for development in mammals [32] . Several lines of evidence suggest that RHA may also have a role in the immune response . The helicase appears as an auto-antigen in the auto-immune disease systemic lupus erythematosus [33] . In addition , RHA associates with the transcription cofactor and histone acetyltransferase CBP , and the transcription factor NFκB , both potent factors in immune responses [34] , [35] . Furthermore , the rha gene promoter contains regulatory elements that control induction of this constitutively expressed protein during cellular immune responses , including an Interferon Stimulatory Response Element . Interestingly , immunohistochemistry of IFNα-treated cells shows accumulation of the helicase within promyelocytic leukemia nuclear bodies that are involved in transcription of IFN-induced genes [35] . Accordingly , RHA may not only be induced by IFN , but could also regulate its downstream effects . Therefore appropriation of RHA by viruses during their replication would not only boost viral transcripts , but may also blunt the innate immune response . Our observations of interplay between RHA and PKR strengthen the perspective that helicases are key signaling molecules . Helicases had been thought of as terminal proteins in signal cascades that elicit appropriate responses by remodeling RNA and DNA . The data here underpin previous findings with RIG-I and IFIH-I to support a primary role for helicases as immediate players in the innate immune response [36] . We demonstrate that just as the CARD domains of these helicases and their associated adaptor molecules mediate signal transduction , the RBM of RHA mediates the association with PKR . Importantly , by identifying an inhibitory effect of phosphorylation on the function of RHA , we present compelling evidence of this association , with resulting effect upon the enzyme's function . Correspondingly , peptides within RHA's RBD , that interacts with PKR , enhance the infectivity of HIV-1 . The data support a paradigm by which the function of a class of RNA-responsive proteins are coordinated or exacerbated by interaction via their RBMs . The consequence of this could be considerable , as at least 17 human proteins have been described that encode RBMs . Moreover , gene deletion studies highlight the importance of these proteins . Disruption of the RBM-containing ribonuclease Dicer , TARBP , the adenosine deaminase ADAR-1 , and , as discussed , RHA , is embryonically lethal in murine models [32] , [37]–[39] . Similarly , mice null for the PKR-activator PACT , spermatid perinuclear RNA-binding protein ( STRBP ) , and the testis-specific mRNA editor TENR , which all encode RBMs , have retarded growth , increased mortality and/or reduced fertility ( G . C . Sen , unpublished results; L . Saunders and G . N . Barber , unpublished results; [40]–[42] . PKR has previously been reported to associate with four members of this family of proteins . As mentioned the kinase is activated by PACT , and conversely inhibited by TARBP , in addition to the nuclear factor of activated T-cells , NF90 , as well as the antiviral protein ADAR1 [43] , [44] . The association here between PKR and RHA via their RBMs strengthens an emerging paradigm whereby this motif acts as a signaling domain to coordinate the dsRNA-response as has been identified for the CARD domains of the cytoplasmic helicases RIG-I and IFIH-I , or the TIR domains of TLRs and their adaptor molecules .
Full-length RHA and truncated GST-RHA fusion plasmids were constructed as described by Nakajima et al . [34] . RHA was expressed for protein purification as a recombinant baculovirus as described by Lee et al . [30] . Wild-type PKR was expressed as described previously by Gabel et al . [16] . Truncated RHA constructs , encoding the N-terminal 262 amino acid ( pRHARBD ) , the first 76 amino acids ( pRHARBM1 ) , or residues 169 to 262 ( pRHARBM2 ) , were generated in pCMVFlag ( Sigma ) . Other plasmid constructs were gifted by others as listed in the acknowledgments . Gene silencing was achieved though RNA interference using the chemically synthesized siRNAs , AAAUUUUCUGUAUGCCUGG , CAGCCAAAUUAGCUGUUGA , AATGTTCTTCTGGAAGTCCAG , and GCUGACCCUGAAGUUCAUCUU , targeting rha , pkr , lamin A/C , and egfp transcripts ( Dharmacon ) . All other reagents were purchased from Sigma unless otherwise indicated . Adherent cells were maintained in DMEM , while suspension cells were cultured in RPMI supplemented with 10% fetal bovine serum and cells were grown at 37°C with a humidified 95% air , 5% CO2 atmosphere . Murine ( C57/BL6 ) PKR null MEFs were transformed with the pBeloBAC construct encoding an approximately 60 kbp genomic fragment that encompassed the gene and promoter elements of human pkr as described previously [45] , [46] . Reporter assays were performed in HEK293T cells at 20–60% confluency in 6-well dishes ( Falcon ) . Cells were transfected using the calcium phosphate method with 300 ng pLTR-EGFP , 2 ng pSV2tat72 , 10 ng of a control reporter pβactin-RL and 4 nM siRNA per well . Cells were collected 24 , 48 , and 72 hours after transfection , Assays to measure the effect of the pRHARBD were performed in HEK293T cells cultured in 24 well dishes transfected with 50 ng of pLTR-EGFP , 10 ng pβactin-RL and 0 , 10 , 20 , 40 , 80 , 160 , or 640 ng of either pRHARBD or pCMVFlag DNA . The cells were cultured for 62 hours . HEK293T cells were washed with phosphate-buffered saline ( PBS ) , and lysed in Promega's passive lysis buffer for fluorescence and luciferase measurements using a Wallac Victor3 plate reader ( Perkin-Elmer ) . Fluorescence values were normalized to the total protein level quantified using the Bradford assay ( BioRad ) and also compared to an internal reporter quantified by Renilla luciferase assay ( Promega ) . All experiments were performed in triplicate and independently replicated a minimum of three times . PKR was activated in MEFs by adding 100 μg/ml pIC to the culture supernatant for 2 hours . Alternatively , THP-1 cells were treated with 10 μg/ml E . coli LPS for 2 hours as described by Gusella et al . [47] . Finally , HEK293T transfected with pcDNA-PACT/ΔPACT were temporally treated with Actinomycin-D as described by Peters et al . [20] . HIV-1 particles were produced by polyethylenimine transfection of HEK293T cells with 5 μg of pNL4-3-Luc-RE proviral DNA , 2 . 5 μg of pNLA1 , and 2 μg of each RHA construct ( pRHARBD , pRHARBM1 , or pRHARBM2 ) . Viral particles were harvest after 36 hours , purified from the supernatant and concentrated by ultracentrifugation through 20% sucrose , using ultracentrifuge at 87 , 000×g for 1 hour at 4°C in a Beckman centrifuge , and virus pellets were eluted in PBS , and quantitated with the HIV-1 Antigen p24-CA MicroELISA Vironostika system ( Organon Teknika ) . Equivalent amounts of virus were used to infect 1×106 MT-2 cells maintained in RF10 ( Gibco/BRL ) , supplemented with 2 mM L-glutamine and 24 μg/ml gentamicin for 2 hours at 37°C . Residual virus was removed by washing with PBS and cells were resuspended in RF10 , then cultured at 37°C for 48 hours , before washing with PBS and harvesting in Cell Culture Lysis Reagent ( Promega ) . The success of a single round of infection was determined by the level of luciferase activity , measured by luciferase assay ( Promega ) using a Fluorostar plate reader ( BMG ) . HIV-1 infectivity was assessed in three independent experiments with two or four replicates at each occasion . Ten μl of non-concentrated viral supernatant was mixed with 10 μL of 0 . 3% NP40 , followed by addition of 40 μL reverse transcriptase ( RT ) reaction cocktail containing 5 μg/ml of the template primer poly ( rA ) - ( dT ) 15 ( Amersham Pharmacia Biotech ) , in 50 mM Tris-HCl ( pH 7 . 8 ) , 2 mM DTT , 5 mM MgCl2 , 7 . 5 mM KCl , and 0 . 5 mCi α33P-dTTP . Following incubation for 2 hours at 37°C , 8 μL of the reaction mixture was spotted onto DEAE81 ion-exchange paper ( Whatman ) and washed six times in 300 mM NaCl and 30 mM sodium citrate to remove unincorporated α33P-dTTP . RT activity was determined by the level of α33P-dTTP using a Wallac 1450 Microbeta-Plus liquid scintillation counter ( Perkin-Elmer ) . Human PKR was immunoprecipitated using the mouse monoclonal antibody 71/10 [48] . PKR was detected in Western blot with multiple redundant antibodies , including a rabbit monoclonal antibody YE350 from Abcam ( for human PKR ) , and rabbit polyclonal antibodies D20 , and B10 from Santa Cruz Biotechnology . Activation of PKR in vivo was confirmed by detecting phosphorylation of eIF2α using a rabbit anti-phospho-eIF2α ( Ser51 ) antibody from Stressgen . GAPDH and GFP were detected in Western blots using mouse monoclonal antibodies from Chemicon and Roche , respectively . Endogenous RHA was detected in Western blot analysis and immunoprecipitated from whole-cell lysates using a rabbit polyclonal antibody [30] , a rabbit polyclonal antibody ab26271 , and a mouse monoclonal ab54593 from Abcam . Recombinant HA-tagged RHA was immunoprecipitated and detected in Western blots using the monoclonal antibody HA . 11 from Covance . Phosphorylated amino acids were detected using rabbit polyclonal anti-phosphoserine , and mouse monoclonal anti-phosphothreonine antibodies from Zymed Laboratories ( Invitrogen ) . Cells were collected in lysis buffer ( 50 mM Tris-HCL [pH 7 . 4] , 150 mM NaCl , 50 mM NaF , 10 mM β-glycerophosphate , 0 . 1 mM EDTA , 10% glycerol , 1% Triton X-100 , and protease inhibitors ) . Immune complexes were resuspended in loading buffer ( 125 mM Tris-HCl [pH 6 . 8] , 4% SDS , 20% glycerol , 10% β-mercaptoethanol , 1% Bromophenol Blue ) for separation by SDS-polyacrylamide electrophoresis ( SDS-PAGE ) . Separated proteins were visualized by staining with BioRads Coomassie , or Silver Stain Plus reagents . Stained protein bands were excised from the gel and analyzed by Maldi-ToF . Alternatively , separated proteins were electrophoretically transferred to either Immobilon-P , or Immobilon-FL membrane ( Millipore ) for immunoblotting using horseradish peroxidase-linked secondary antibody and ECL from Amersham , or fluorescently labeled ( 680 and 800 nm ) secondary antibodies ( Invitrogen , Molecular Probes ) , respectively . Fluorescently labeled antibodies were detected and quantitation using the Odyssey infrared imaging system ( Li-Cor ) . Replicate experiments to quantitate RHA phosphorylated by PKR in vivo recorded mean values of phosphorylated residues on RHA coimmunoprecipitated with PKR of 1 . 6+/−0 . 9 for phosphoserine and 5 . 7+/−1 . 3 for phosphothreonine . No phosphorylated residues were detected with these phospho-specific antibodies in RHA directly immunoprecipitated . The values of total RHA coimmunoprecipitated with the anti-PKR antibody were 55 . 3+/−5 . 8 , while that directly immunoprecipitated with the anti-RHA antibody was 111 . 5+/−13 . 2 in this experiment . GST and His-tagged proteins were purified from E . coli and Sf-9 insect cells on either glutatione-Sepharose 4B beads ( Amersham ) or Ni-NTA agarose ( Qiagen ) according to the manufacturer's protocols . To map protein interactions , PKR was synthesized in an in vitro coupled transcription–translation system ( Promega ) with 35S-methionine ( NEN-DuPont ) , then incubated in cleared E . coli lysate with protease inhibitors with GST-fused RHA constructs for 2 hours at 4°C . Supernatant , containing unbound proteins , was removed after 500×g centrifugation . Recovered beads were rinsed five times with bead-binding buffer ( 50 mM K3PO4 [pH 7 . 5] , 150 mM KCl , 1 mM MgCl2 , 10% glycerol , 1% Triton X-100 and protease inhibitors ) . The resin-bound proteins were eluted with loading buffer and separated by SDS-PAGE , then visualized by autoradiography . The experiment on the effect of RNA on the interaction between PKR and the 263 amino acid GSTRHA-fusion peptide was conducted as above with an additional step . Approximately 20 μg of the GSTRHA peptide was incubated with a 16 bp dsRNA at 10 , 100 , or 1000-fold excess for one hour prior to addition of 35S-labeled PKR . The 16 bp dsRNA was synthesized in vitro using T7 RNA polymerase then gel purified from an SDS-PAGE gel . To measure the relative affinity of unphosphorylated or phosphorylated RHA for RNA , the 263 amino acid N-terminus of RHA was synthesized in vitro with either 35S-methionine during the synthesis reaction or γ32P-ATP in a PKR kinase assay . Labeled proteins were incubated in binding buffer ( 20 mM Tris-HCL [pH 7 . 4] , 200 mM NaCl , and 5 mM DTT ) with pIC conjugated to agarose beads ( Promega ) for an hour , then washed with binding buffer five times , eluted with loading buffer and separated by SDS-PAGE . Recovered proteins were detected by exposure to a phosphor screen , imaged with a Storm-840 scanner , and quantified with ImageQuant software ( Molecular Dynamics ) . For kinase assays , full-length RHA , truncated GSTRHA fusion proteins , and the PKR substrate B56α [49] were incubated with recombinant PKR in 30 μl DBGA buffer ( 10 mM Tris-HCl [pH 7 . 6] , 50 mM KCl , 2 mM [CH3COO]2Mg4H2O , 7 mM β-mercaptoethanol , 20% glycerol ) , 20 μl of DBGB buffer ( 2 . 5 mM MnCl2 in DBGA ) , 5 μl of ATP mixture ( 10 μM ATP and 1 . 5 μCi of γ32P-ATP/ml ) , and 5 μl of pIC ( 12 ng/μl ) at 30°C for 10 minutes . Phosphorylated proteins were denatured in loading buffer and separated by SDS-PAGE , then visualized by autoradiography . The levels of total proteins in the SDS-PAGE gel were visualized by staining with Coomassie blue . In vivo phosphorylation of RHA was detected as described above ( Immune analysis ) . Protein were excised from SDS-PAGE gels and washed in 50% ethanol , 5% acetic acid , reduced and alkylated with DTT and iodoacetamide . The gel slices were dehydrated in acetonitrile and dried in a speed-vac , then digested in 20 ng/ml Trypsin in 50 nM ammonium bicarbonate overnight at room temperature . Released peptides were extracted from the polyacrylamide with 50% acetonitrile with 5% formic acid . The extract was evaporated for LC-MS analysis using a Finnigan LTQ linear ion trap mass spectrometer . Two μl volumes of the extract were injected and the peptides eluted from the column by acetonitrile in a 50 mM acetic acid gradient at a flow rate of 0 . 2 μl/minute . The microelectrospray ion source was operated at 2 . 5 kV . Samples were also analyzed by Maldi-ToF . Data collected in the experiment was used to search the NCBI non-redundant database with the search program TurboSequest .
|
Our manuscript explores the immune response to viral infection by investigating events triggered by the protein kinase PKR . This sentinel kinase is constitutively expressed in all cells as an inactive protein that is subsequently activated by viral RNA produced during an infection . The active kinase perturbs viral replication by phosphorylating protein substrates in the cell . In this manuscript we identify a novel substrate for PKR , an essential helicase , RHA . Viruses usurp this helicase to replicate their own genome . We demonstrate that phosphorylation of RHA by PKR perturbs the ability of the helicase to bind viral RNA . Correspondingly , PKR prevents the capacity of RHA to enhance expression of genetic elements encoded by the human immunodeficiency virus ( HIV ) . Juxtaposed to this , HIV virions packaged within cells that also express protein fragments of RHA , demonstrated to interact with PKR as decoy substrates , have enhanced infectivity . These fragments of RHA occur within a protein domain previously established to bind RNA but increasingly recognized to mediate protein–protein interactions . This supports an emerging role for these protein domains to coordinate the cell's response to pathogen-associated RNA . The findings identify a new cell-signaling pathway important in the response to viral infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/host",
"antiviral",
"responses",
"molecular",
"biology/rna-protein",
"interactions",
"immunology/innate",
"immunity",
"cell",
"biology/cell",
"signaling"
] |
2009
|
An Antiviral Response Directed by PKR Phosphorylation of the RNA Helicase A
|
Asthma is a complex phenotype influenced by genetic and environmental factors . We conducted a genome-wide association study ( GWAS ) with 938 Japanese pediatric asthma patients and 2 , 376 controls . Single-nucleotide polymorphisms ( SNPs ) showing strong associations ( P<1×10−8 ) in GWAS were further genotyped in an independent Japanese samples ( 818 cases and 1 , 032 controls ) and in Korean samples ( 835 cases and 421 controls ) . SNP rs987870 , located between HLA-DPA1 and HLA-DPB1 , was consistently associated with pediatric asthma in 3 independent populations ( Pcombined = 2 . 3×10−10 , odds ratio [OR] = 1 . 40 ) . HLA-DP allele analysis showed that DPA1*0201 and DPB1*0901 , which were in strong linkage disequilibrium , were strongly associated with pediatric asthma ( DPA1*0201: P = 5 . 5×10−10 , OR = 1 . 52 , and DPB1*0901: P = 2 . 0×10−7 , OR = 1 . 49 ) . Our findings show that genetic variants in the HLA-DP locus are associated with the risk of pediatric asthma in Asian populations .
Asthma is the most common chronic disorder in children , and asthma exacerbation is an important cause of childhood morbidity and hospitalization . The prevalence of childhood asthma in Japan is 5 . 0% among school children in 2006 [1] , and an estimated 300 million people worldwide have asthma [2] . Asthma is characterized by airway hyperresponsiveness and inflammation , tissue remodeling , and airflow obstruction . Infiltration of lymphocytes , mast cells , and eosinophils in the airways cause airway inflammation , and T helper ( Th ) type 2 cytokines play crucial roles in orchestrating the inflammatory responses; thus , asthma is considered a Th2-type immune disease . Previously conducted genome-wide association studies ( GWAS ) for asthma identified association with the loci on chromosomes 17q21 ( ORMDL3 for Caucasian pediatric asthma , odds ratio ( OR ) = 1 . 45 , P = 1×10−10 ) [3] , 5q21 ( PDE4D for pediatric asthma , OR = 0 . 6 , P = 4 . 7×10−7 ) [4] , 9q21 . 31 ( TLE4 for Hispanic pediatric asthma , OR = 0 . 6 , P = 6 . 8×10−7 ) [5] , and 1q31 ( DENND1B for Europeans and African ancestries [6] , OR = 0 . 77 and 1 . 41 , respectively; combined P = 1 . 7×10−13 ) . A GWAS for severe asthma identified association with the region between RAD50 and IL5 on chromosome 5q ( OR = 1 . 64 , P = 3 . 0×10−7 ) and HLA-DR/DQ ( OR = 0 . 68 , P = 9 . 6×10−6 ) , but they did not include a replication dataset [7] . Recently , Moffatt et al . conducted a large-scale GWAS in Caucasian populations and identified 6 loci ( IL18R1 , HLA-DQ , IL33 , SMAD3 , GSDMB/GSDMA , and IL2RB ) associated with asthma [8] . In the present study , we conducted the first GWAS in Asian population for pediatric asthma by using Illumina HumanHap550/610-Quad BeadChip ( Illumina , San Diego , USA ) .
The GWAS flow chart is shown in Figure 1 . We analyzed 450 , 326 SNPs in 938 cases and 2 , 376 controls , using standard quality control practices ( Table S1 ) . The genotypes in cases and controls were compared using the Cochran–Armitage trend test ( Figure 2 ) . There was only minor inflation of the genome-wide statistical results owing to population stratification ( genomic control ( λGC ) = 1 . 048; Figure 3 ) . Five SNPs ( rs3019885 , rs987870 , rs2281389 , rs2064478 , and rs3117230 ) showed strong association with pediatric asthma with P<1×10−8 . Of these , rs2064478 and rs3117230 were in complete linkage disequilibrium ( LD ) ( r2 = 1 ) with rs2281389 . In order to validate the results of the GWAS , we tested the remaining 3 SNPs ( rs3019885 , rs987870 , and rs2281389 ) in 2 independent replication cohorts comprising Asians ( Japanese and Koreans ) , considering P<0 . 05 as significant replication . Of these 3 SNPs , significant associations were noted at rs987870 in both cohorts ( Table 1 ) . To merge the findings of these studies , we conducted meta-analysis with a fixed-effects model by using the Mantel–Haenszel method . As shown in Table 1 , the Mantel–Haenszel P value of 2 . 3×10−10 was noted for rs987870 ( OR = 1 . 40 , confidence interval ( CI ) = 1 . 26–1 . 55 ) . The rs987870 is located between HLA-DPA1 and HLA-DPB1 . Genotype imputation using MACH [9] revealed association between asthma and the SNPs that were in strong LD with rs987870 ( Figure 4 , Table S2 ) . Moreover , rs987870 C allele was in complete LD with DPA1*0201 ( r2 = 1 ) . We determined HLA-DPA1 genotypes by using direct sequencing and MACH imputation of the data from 1135 cases and 2376 controls and found that DPA1*0201 was strongly associated with pediatric asthma ( P = 5 . 2×10−10 , OR = 1 . 52 , Table 2 ) . Then , we determined the HLA-DPB1 genotypes in 1135 cases and 2296 controls and found that DPB1*0901 was associated with pediatric asthma ( P = 2 . 0×10−7 , OR = 1 . 49 , Table 3 ) . DPB1*0901 was in strong LD with DPA1*0201 and rs987870 C allele ( D prime = 0 . 93 ) . Because more than 90% of pediatric asthma patients were allergic to house dust mites , it is possible that the association was due to IgE reactivity ( sensitization ) against mites . We performed an association study for mite sensitization using independent adult subjects without allergic respiratory diseases such as asthma and perennial allergic rhinitis ( 367 subjects with house dust mite-specific IgE and 1633 subjects without mite-specific IgE ) . Subjects with house dust mite-specific IgE were non-allergic in terms of symptoms but possessed mite-specific IgE . Subjects without mite-specific IgE did not exhibit allergic symptoms . We did not find an association between rs987870 and mite sensitization ( P = 0 . 54 , OR = 1 . 07 , Table S3 ) .
Our GWAS in Asian populations found HLA-DP as susceptibility gene for pediatric asthma . Majority of pediatric asthmas are atopic ( i . e . , familial tendency to produce IgE antibodies against common environmental allergens ) and possess specific IgE against the house dust mite . Mite sensitization is more prevalent in Asia than in Europe and is observed in 39% of the general adult population in Japan [10] . High prevalence of mite sensitization in asthmatic children has also been reported in Taiwan , where 94 . 2% of children with asthma are sensitized against Dermatophagoides pteronyssinus [11] . However , only a small subset of subjects with house dust allergy develop asthma [12] . We performed an independent association study for mite sensitization in adult subjects without allergic respiratory diseases and did not find an association between rs987870 and mite sensitization without symptoms . If the relative risk for mite sensitization in the individuals carrying a putative risk allele was 1 . 4 and the allele frequency was 0 . 15 compared to that in individuals without the allele , the statistical power of the sample size for mite sensitization study was 0 . 92 at an alpha level of 0 . 05 . These results suggested that DPA1*0201 and DPB1*0901 may be associated with asthma rather than IgE production against house dust mite . The association signal was stretched in the region of HLA-DPB2 , collagen , type XI , alpha 2 ( COL11A2 ) , and Retinoid X receptor beta ( RXRB ) ( Figure 4 ) . Because of LD in this region , it is difficult to specifically identify causative variants . HLA-DPB2 is a pseudogene . COL11A2 encodes a component of type XI collagen called the pro-alpha2 ( XI ) chain . Mutations in COL11A2 have been associated with non-syndromic deafness , otospondylomegaepiphyseal dysplasia , Weissenbacher-Zweymüller syndrome , and Stickler syndrome ( OMIM ID *120290 ) . RXRB belongs to the RXR family and is involved in mediating the effects of retinoic acid . RXRB forms a heterodimer with the retinoic acid receptor and thus preferentially increases its DNA binding and transcriptional activity at promoters containing retinoic acid [13] . All SNPs showing strong association with asthma ( P<1×10−10 ) were located in introns or intergenic regions . LD of these associated SNPs with rs987870 was not strong; therefore , it is likely that the functional effect is due to DPA1*0201 and DPB1*0901 . In HLA-DP , Caraballo et al . reported that DPB1*0401 is significantly decreased in patients with allergic asthma in Mulatto population ( an admixture population of European and African ancestries ) [14] . Apart from the study of Caraballo et al . , the association between HLA-DP alleles and asthma was restricted to occupational [15] or aspirin-induced asthma [16] . Howell et al . reported associations between HLA-DR genotype and asthma and between HLA-DPA1*0201 and IgE specific to grass pollen mix and the pollen allergen Phl p 5 [17] . Grass pollen allergy is not a major cause of asthma in Japan [18]; therefore , the HLA-DPA1*0201 association in the present study was less likely to be due to sensitization to grass pollen . DPA1*0201 has also been reported to be positively associated with lower levels of rubella-induced antibodies [19] , cytokine immune responses against measles vaccine [20] , and ulcerative colitis [21] , and negatively associated with type 1 diabetes [22] . DPB1*0901 was shown to be associated with systemic sclerosis [23] , non-permissive mismatches for hematologic stem cell transplantation [24] , ulcerative colitis [21] , and Takayasu's arteritis [25] . HLA-DP molecules present short peptides of largely exogenous origin to CD4-positive helper T cells and other T cells , leading to subsequent immunological responses . T cells recognize complex formation between a specific HLA type and a particular antigen-derived epitope . Therefore , HLA molecules capable of binding a particular epitope can restrict T cell induced-immune responses , leading to association between particular HLA types and immune-related diseases . Type 1 diabetes is a Th-1 type immune disease . Varney et al . studied 1 , 771 type 1 diabetes multiplex families , analyzing them by the affected family-based control method [26] , and found that DPA1*0201 has a protective effect on the development of type 1 diabetes ( adjusted P = 5×10−4 , OR 0 . 7 ) [22] . Epidemiologic studies have associated type 1 diabetes with lower prevalence of asthma and other allergic diseases [26] , [27] . Also , the previous GWAS of rheumatoid arthritis , other Th-1 type immune disease , has shown that rs987870 C allele confers protection against rheumatoid arthritis [28] . These findings suggest that HLA-DPA1*0201 could determine Th1/Th2 dominance and could partially explain the inverse relationship between asthma and Th-1 type immune diseases . Previous GWAS involving European , Mexican , and African populations showed association of asthma with SNPs located in several newly discovered genes . Our GWAS dataset supported an association between identical SNPs reported in ORMDL3/GSDMB/GSDMA , IL5/RAD50/IL13 , HLA-DR/DQ , and SMAD3 and pediatric asthma ( P<0 . 05 , Table S4 ) . Two asthma GWA studies revealed an association of HLA-DQ with pediatric/adult asthma in Caucasians [7] , [8] . HLA-DQ , like HLA-DP , is an αβ heterodimer of the MHC Class II type . Like HLA-DP , HLA-DQ recognizes and presents foreign antigens , but is also involved in recognizing common self-antigens and presenting those antigens to the immune system . We failed to replicate the top SNPs of PDE4D , TLE4 , DENND1B , IL18R1 , and IL2RB that were reported in the original articles , but several SNPs in the regions surrounding PDE4D and IL2RB showed significant association when we set the significance level at P = 0 . 05 ( Table S4 ) . The different LD patterns/allele frequencies observed in PDE4D and IL2RB in Asians and Caucasians may explain the different SNP associations observed in different ethnic populations . rs1342326 in IL33 was not polymorphic in the Asian population . There were several limitations of the present GWAS . The controls for the GWAS and 1st replication samples were from adult populations . Information regarding history of asthma in early childhood or other asthma-related information ( i . e . , status of allergic sensitization and lung function ) was not collected for these controls . Therefore , we cannot exclude the possibility that our control samples may include subjects who outgrew asthma . The prevalence of pediatric asthma in Japan is around 5%; therefore , our GWAS samples have reduced power compared with that of selected controls . In the 1st replication Japanese controls , subjects with present and past history of allergic diseases were excluded , and Korean controls in the 2nd replication were non-allergic pediatric controls ( Table S5 ) . The genomic control value in the present study was 1 . 053 , indicating minor population stratification . The Japanese population comprises 2 clusters ( Hondo and Ryukyu; Hondo is the mainland of Japan and Ryukyu is the name of the island south of Japan ) . We performed principal component analysis using EIGENSTRAT software [29] to identify subjects belonging to Ryukyu . Because 2nd or 3rd generation Chinese live in Japan , and the genetic population structure in Chinese differs from that in Japanese , we also performed principal component analysis to exclude Chinese subjects . Although hidden population stratification may exist , its influence on the final results is not expected to be significant . rs3019885 is located in intron 2 of solute carrier family 30 ( SLC30A8 ) , and showed strong association in the GWAS population . The association was replicated in the independent Japanese samples , but not in the Korean population . SLC30A8 is a zinc efflux transporter expressed at high levels only in the pancreas; the GWAS revealed that variants of SLC30A8 are associated with type 2 diabetes [30] . Japanese and Koreans are genetically close but we cannot exclude the possibility that the association of rs3019885 with pediatric asthma is population specific . In conclusion , we performed the first GWAS in Asian population for pediatric asthma and found that DPA*0201/DPB1*0901 is strongly associated with pediatric asthma . The association with the HLA-DP locus emphasizes the importance of the HLA-class II molecules on the biological pathways involved in the etiology of pediatric asthma , and suggests that HLA-DP can be a therapeutic target for asthma .
The study was approved by the institutional review board and the ethics committee of each institution . Written informed consent was obtained from each participant in accordance with institutional requirements and the Declaration of Helsinki Principles . Characteristics of pediatric asthma cases and controls are summarized in Table S5 . Genotyping for GWAS was performed using the Illumina HumanHap550v3/610-Quad Genotyping BeadChip ( Illumina ) , as per manufacturer's instruction . In replication analyses , genotyping of each individual was performed with the TaqMan genotyping system ( Applied Biosystems ) on an ABI PRISM 7900HT Sequence Detection System ( Applied Biosystems ) . PCR was performed on a 384-well format , and automatic allele calling was performed using ABI PRISM 7900HT data collection and analysis software , version 2 . 2 . 2 ( Applied Biosystems ) . HLA-DPB1 genotyping of 1135 cases , 794 controls ( control 1 ) and 1475 controls ( control 2 ) were performed with the WAKFlow HLA typing kit ( Wakunaga , Hiroshima , Japan ) , as per manufacturer's instruction . First , the target DNA was amplified by polymerase chain reaction ( PCR ) with biotinylated primers specifically designed for each HLA-DPB1 locus . Then , the PCR product was denatured and hybridized to complementary oligonucleotide probes immobilized on fluorescent-coded microsphere beads . Concurrently , the biotinylated PCR product was labeled with phycoerythrin-conjugated streptavidin and immediately examined with the Luminex 100 system ( Luminex , Austin , TX ) . Genotype determination and data analysis were performed with the WAKFlow typing software ( Wakunaga ) . HLA-DPA1 genotyping was performed with direct sequencing of exon 2 with forward primer 5′-TCAGGATGCCCAGACTTTCAA-3′ and reverse primer 5′-CAGGGGGCACTTAGGCTTCC-3′ , and with the sequencing primer 5′-TCAGGATGCCCAGACTTTCAA-3′ using the BigDye Terminator v . 1 . 1 Cycle Sequencing Kit ( Applied Biosystems ) on an ABI PRISM 3130 Genetic Analyzer ( Applied Biosystems ) . In the GWAS , we examined the potential genetic relatedness on the basis of pairwise identity by state for all of the successfully genotyped samples using the EIGENSTRAT software [29] . In the GWAS , we genotyped 978 cases with pediatric asthma and 2402 controls using Illumina HumanHap550v3/610-Quad Genotyping BeadChip ( Illumina , San Diego , USA ) . Samples of duplicated ( identical individual or monozygotic twin ) , first- , second- , and third-degree pairs were detected , and the individual with a lower call rate was excluded from further analysis . PCA was performed , and the results were combined with those obtained for our in-house Ryukyu and Han Chinese reference samples . Yamaguchi-Kabata et al . characterized the Japanese population structure using the genotypes for 140 , 387 SNPs in 7003 Japanese individuals , along with 60 European , 60 African , and 90 East-Asian individuals , in the HapMap project and found that the Japanese population is composed of 2 clusters ( Hondo and Ryukyu ) [36] . Hondo is the biggest island of Japan , and the island of Ryukyu is located in southern Japan . Also , we have 2nd or 3rd generation Chinese living in Japan , and Chinese present a different genetic population structure from Japanese . Therefore , we excluded samples belonging to Han Chinese or Ryukyu , and 938 cases and 2376 controls were considered for further analysis . Cluster plots of SNPs were checked by visual inspection and SNPs with ambiguous calls were excluded . We excluded SNPs with a low genotyping rate ( <90% ) , minor allele frequency less than 0 . 01 in either pediatric asthma cases or controls , or with Hardy-Weinberg equilibrium P value<10−4 in controls . Finally , 450 , 326 SNPs were used for the GWAS . Details regarding the exact number of remaining SNPs after applying each quality control criterion are available in Table S1 . The genomic control inflation factor ( λGC ) , defined as the median association test statistic across all SNPs divided by its expected value , was calculated by the method proposed by Devlin et al . [37] . GWAS and replication analyses were performed using the Cochran–Armitage trend test and χ2 test . The meta-analysis was performed with the Mantel–Haenszel approach as a fixed-effects model [38] . All statistical findings were reported without correction . The results of GWAS were plotted with GWAS GUI v0 . 0 . 2 [39] . HLA-DP region was plotted with LocusZoom [40] . The power calculation was performed with Genetic Power Calculator [41] . Quantile-quantile ( Q-Q ) plot was plotted with ggplot2 package [42] in R version 2 . 10 . 0 ( http://www . r-project . org/ ) . Imputation of genotypes in the DP region was performed with MACH version 1 . 0 [9] with 1000 Genome Project data ( 1000G 2010-6 release , http://www . sph . umich . edu/csg/yli/mach/download/1000G-2010-06 . html ) . The HLA-DP region was in strong linkage disequilibrium and some DPB1 alleles were known to be linked with particular DPA1 alleles . First , we imputed HLA-DPA1 alleles by using the actual genotype data of samples obtained from Illumina HumanHap550v3/610-Quad ( Illumina ) and 1000 Genome Project data of Asian origin ( JPT+CHB ) ( http://www . sph . umich . edu/csg/abecasis/MaCH/download/1000G-2010-06 . html ) . The accuracy of the imputated data was confirmed by direct sequencing . The error rate of imputation was 1/352 ( 0 . 003 ) .
|
Asthma is the most common chronic disorder in children , and asthma exacerbation is an important cause of childhood morbidity and hospitalization . Here , taking advantage of recent technological advances in human genetics , we performed a genome-wide association study and follow-up validation studies to identify genetic variants for asthma . By examining 6 , 428 Asians , we found rs987870 and HLA-DPA1*0201/DPB1*0901 were associated with pediatric asthma . The association signal was stretched in the region of HLA-DPB2 , collagen , type XI , alpha 2 ( COL11A2 ) , and Retinoid X receptor beta ( RXRB ) , but strong linkage disequilibrium in this region made it difficult to specifically identify causative variants . Interestingly , the SNP ( or the HLA-DP allele ) associated with pediatric asthma ( Th-2 type immune diseases ) in the present study confers protection against Th-1 type immune diseases , such as type 1 diabetes and rheumatoid arthritis . Therefore , the association results obtained in the present study could partially explain the inverse relationship between asthma and Th-1 type immune diseases and may lead to better understanding of Th-1/Th-2 immune diseases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"genetics",
"of",
"the",
"immune",
"system",
"clinical",
"immunology",
"genetics",
"genetics",
"and",
"genomics",
"biology",
"human",
"genetics",
"immunology",
"respiratory",
"medicine",
"pulmonology",
"asthma"
] |
2011
|
Genome-Wide Association Study Identifies HLA-DP as a Susceptibility Gene for Pediatric Asthma in Asian Populations
|
The leaves of angiosperms contain highly complex venation networks consisting of recursively nested , hierarchically organized loops . We describe a new phenotypic trait of reticulate vascular networks based on the topology of the nested loops . This phenotypic trait encodes information orthogonal to widely used geometric phenotypic traits , and thus constitutes a new dimension in the leaf venation phenotypic space . We apply our metric to a database of 186 leaves and leaflets representing 137 species , predominantly from the Burseraceae family , revealing diverse topological network traits even within this single family . We show that topological information significantly improves identification of leaves from fragments by calculating a “leaf venation fingerprint” from topology and geometry . Further , we present a phenomenological model suggesting that the topological traits can be explained by noise effects unique to specimen during development of each leaf which leave their imprint on the final network . This work opens the path to new quantitative identification techniques for leaves which go beyond simple geometric traits such as vein density and is directly applicable to other planar or sub-planar networks such as blood vessels in the brain .
Our topological metric quantifies the hierarchical nesting of loops within the network as well as the topological lengths of tapered veins . The analysis follows an existing hierarchical decomposition algorithm [25 , 26 , 28] , constructing from a weighted network a binary tree graph termed the nesting tree which contains information about nesting of loops . The algorithm is schematically shown in Fig 1g and discussed in the supplement . We stress that the method depends not on exact measurements of vein diameters but only on relative order . Similarly , transformations which slightly alter node positions do not affect the outcome ( see Fig 1h ) . Once the binary nesting tree ( see Fig 1g ) has been obtained , its structure can be quantified . Here , for each node j in the nesting tree , we calculate the nesting ratio q j = s j r j[29] , where rj ≥ sj are the numbers of leaf nodes in the right and left subtrees of node j . We then define the nesting number as a weighted average i = ∑j wj qj , where ∑j wj = 1 . We employ an unweighted nesting number iu , with wj = 1 , and a degree-weighted nesting number iw , with wj ∝ dj − 1 = rj + sj − 1 , where dj is called subtree degree . A high value of iu , w qualitatively represents graphs that are highly nested such as those in the top row of Fig 1i . The presence and extent of tapered veins is quantified as follows . Starting from some edge e , we find the next edge by taking the maximum width edge amongst all with smaller width than e . We count how many steps can be taken until no more edges with smaller width are adjacent , resulting in a topological length Le assigned to each edge in the network . The mean topological length L top = 1 N E ∑ e L e , where NE is the number of edges , characterizes tapered veins in a network . Fig 1i shows a qualitative representation of various example network topologies using mean topological length and nesting number . Instead of using just the nesting number , we additionally calculate pairwise topological distances between networks as the two-sample Kolmogorov-Smirnov statistic DKS between the cumulative distributions of nesting ratios in order to quantify the statistical similarity between nested loop topologies . Other methods to quantify the degree of topological dissimilarity between binary trees representing biological systems have been proposed on the basis of a “tree edit distance” [30] . Despite promise , this distance suffers from being dominated by differences in the size of the compared trees . In its local form [31] , it suffers from the opposite problem , quantifying only the similarity between the n most similar subtrees . In contrast , our method is designed to capture statistical similarities between nesting trees , making it more suitable for dissimilarly sized , noisy networks .
From the vectorized data , we obtained for each leaf five local geometric quantities: vein density σ ( total length of all veins/leaf area ) , mean distance between veins a , mean areole area A , areole density ρA , and average vein diameter weighted by length of venation between junctions d . The ( un ) weighted nesting number i ( u ) w was calculated from all subtrees of the nesting tree with degree d ≤ 256 in order to remove leaf size effects for the full networks; the mean topological length was calculated from the whole network . Together , these metrics form a “leaf venation fingerprint” encompassing local features of the network , that can be estimated from leaf segments alone if necessary . Fig 1a shows the complete dataset plotted in the space of unweighted nesting number and mean topological length . We plot the most abundant genera Protium ( 98 specimen in the dataset ) , Bursera ( 21 specimen ) , and Parkia ( 8 speciment ) as different symbols . Although the dataset does not allow for firm conclusions at this taxonomic level , both Protium and Parkia appear to show a modest trend towards clustering around characteristic nesting numbers . We then employed Principal Component Analysis ( see Fig 2b ) and found that together , the first two principal components explain 81% ( = 52% + 29% ) of the total variance in the dataset . Component 1 can be interpreted as containing mostly metrics derived from geometry , whereas Component 2 contains mostly metrics from topology . Topological lengths contribute roughly equally to either . Even though small correlations between them exist , this reveals local geometrical and topological leaf traits as approximately orthogonal traits for the description of the phenotype of leaf venation ( see S1 Text , also for further analysis of the data in terms of latent factors ) . Pairs of leaves ( see Fig 2a and Fig 2e and 2f ) which are close according to the topological distance defined by the DKS metric applied to the nesting ratio statistics can possess similar “by eye” venation traits . In the samples in Fig 2e and 2f , cycle nestedness and vein thickness are traits that appear correlated . However , the topology of leaf venation constitutes a new phenotypic trait that provides information orthogonal to geometric traits . Topological information significantly helps in identifying leaf samples to species , especially when only a segment of the leaf is available . We fragmented all leaf samples in silico into equally sized segments of ca . 1 . 2 × 1 . 2cm and calculated all venation traits for the individual pieces ( see S2 Table ) . Here , we thresholded the nesting ratios at subtree degree d ≤ 128 . We employed Linear Discriminant Analysis ( LDA ) [35] to classify the fragments based on specimen membership ( see also S1 Text ) . We then calculated the the probability of correctly identifying a segment as belonging to one of the 186 leaves and leaflets ( the accuracy , see Fig 2c ) . Using only geometrical degrees of freedom , we found a 10-fold cross-validated accuracy of 0 . 35 ( 95% CI: [0 . 31 , 0 . 39] ) . Adding topology improves the accuracy to 0 . 54 ( 95% CI: [0 . 48 , 0 . 60] ) . Additionally , for each pair of individual leaves in the dataset , the same procedure was applied to obtain a mean pairwise accuracy score ( the probability of correctly identifying a fragment as belonging to one of two leaves . ) Again , using topological traits significantly improved the summary result ( see Fig 2d and S1 Text ) . The same classification was applied towards identification of segments to species , as opposed to samples , with quantitatively similar results ( see S1 Text ) . It must be noted that there can be considerable variance among leaf traits , even when comparing among specimen from a single plant—in particular between sun- and shade leaves [6 , 36]— that should be taken into account if the information is available . In order to explain the nesting ratio and topological length distributions measured in our dataset , we examine a developmental model for the formation of higher-order venation in which the interplay between strictly hierarchical loop genesis and random noise is the major factor affecting nestedness . Empirically , during the expansion growth phase of the leaf lamina , high order vein loops grow and are subdivided by the appearance of new veins , subsequent vein orders appearing discretely one after the other [11 , 37] . Our model intends to capture this phenomenological fact ( see Fig 3a for an illustration ) . The model is compatible with models of vein morphogenesis that invoke either auxin canalization [38] or mechanical instabilities [39] , or a combination . It is similar in spirit to that described in the supporting information of [39] or [40] but adds fine-grained control of stochasticity . We stipulate that each leaf is subject to a species dependent characteristic amount of noise during development , resulting in unique characteristic statistics of minor venation patterns . The model as a whole is controlled by four dimensionless parameters ( see Methods section ) . In Fig 3b and 3c we show the distributions of normalized areole size , mean topological lengths and nesting ratios for the same two leaves as in Fig 2e and 2f . The real distributions can be explained well by tuning two of the parameters . Thus , noise during growth of cycles can explain the observed local hierarchical nesting characteristics . It should be noted that different mechanisms may underlie the organization of low order veins . Indeed , both models [41] and empirical observations [42] have found strong links between low order vein structure and leaf shape that may be connected to the overall growth pattern and developmental constraints of the lamina [43] .
The leaf vasculature is a complex reticulate network , and properly chosen and defined topological metrics can quantify and highlight aspects of the architecture that have been ignored until now . The topological metrics presented in this work provide a new , independent dimension in the phenotypic space of leaf venation , allowing for more precise characterization of leaf features and improved identification accuracy , including identification of fragments . The extensive nomenclature for characterization of the vascular morphology [20] offers a discrete set of attributes that is mathematically insufficient to properly quantify a continuum of leaf venation phenotypes . However , this descriptive terminology can be incorporated as additional topological dimensions in the phenotypic space and alongside the metrics presented in this work can provide a tool to quantify inter- and intra- species diversity . In addition , we show that the local hierarchy of nested loops in the leaf venation network can be explained by very simple stochastic processes during development , pointing toward a universal mechanism governing ( minor ) vein morphogenesis . The topological measures we employ have possible applications that range far beyond the leaf data set explored here , being usable on any loopy complex weighted network which possesses an embedding on a surface . Examples of systems that could benefit from an analysis along the lines of this work include the blood vessels in the retina , liver or brain , anastomosing foraging networks built by slime molds and fungi , lowland river networks , human-made street networks , force chain networks in granular materials , and many more , thereby possibly opening up an entire new line of research .
The extraction the networks from the original high-resolution scans ( 6400 dpi ) can be divided into two main steps: segmentation of the image to create a suitable binary representation and skeletonization of the shapes . To segment the image we use a combination of Gaussian blurring to reduce noise , local histogram equalization and recombination with the original image to increase contrast , and Otsu thresholding [44] to find the optimal threshold for the creation of the binary image . For the skeletonization we use a vectorization technique known from optical sign recognition [45 , 46] . The approach relies on the extraction and approximation of the foreground feature’s contours using the Teh-Chin dominant point detection algorithm [47] and subsequent triangulation of the contours via constrained Delaunay triangulation [48] . Therefore the foreground is partitioned into triangles which can be used to create a skeleton of the shape . Each triangle contributes a “center” point to the skeleton which is determined by looking for local maxima in the euclidean distance map [49] of the binary and together these center points approximate the skeleton . By looking at edges shared between two triangles , neighborhood relations can be established and an adjacency matrix can be created . This adjacency matrix defines a graph composed of nodes ( the former triangle centers ) and edges ( the connections between two adjacent triangles ) . In addition to the topology of the graph the original geometry of the network including coordinates of the nodes and lengths and radii of edges are preserved and stored in the graph . The processing is done using algorithms implemented in python . The framework uniting all the aforementioned functionality is freely available at [50] . A complete and detailed description of the hierarchical decomposition algorithm to extract the nesting tree from leaf network graphs can be found in the supplement S1 Text . The software package used to calculate nesting numbers , topological lengths , and geometric metrics is freely available at [51] . The model starts from a single rectangular loop of veins ( Fig 3a ) . The loops grow and subdivide when they reach a threshold size A0 by introduction of a new vein . Not all loops subdivide at exactly the same size: the probability of subdivision as a function of areole area is a sigmoidal of width σA ( Fig 3 ) . All veins start with a fixed small width and grow linearly with time . The relative growth rate of vein lengths and widths is controlled by the nondimensional parameter α . The areole subdivision is only approximately symmetric: the new vein is randomly positioned close to the midline of the areole and the extent of the asymmetry is controlled by a parameter ρ ∈ [0 , 1] ( see S1 Text ) . After the growing leaf has a certain size , the simulation is terminated and random Gaussian noise with zero mean and standard deviation proportional to the parameter fn is added to the vein diameters . The model is controlled by the four dimensionless parameters ρ , β = σA/A0 , α and fn .
|
Planar reticular networks are ubiquitous in nature and engineering , formed for instance by the arterial vasculature in the mammalian neocortex , urban street grids or the vascular network of plant leaves . We use a topological metric to characterize the way loops are nested in such networks and analyze a large database of 186 leaves and leaflets , revealing for the first time that the nesting of the networks’ cycles constitutes a distinct phenotypic trait orthogonal to previously used geometric features . Furthermore , we demonstrate that the information contained in the leaf topology can significantly improve specimen identification from fragments , and provide an empirical growth model that can explain much of the observed data . Our work can improve understanding of the functional significance of the various leaf vein architectures and their correlation with the environment . It can pave the way for similar analyses in diverse areas of research involving reticulate networks .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks
|
Viruses often encode proteins with multiple functions due to their compact genomes . Existing approaches to identify functional residues largely rely on sequence conservation analysis . Inferring functional residues from sequence conservation can produce false positives , in which the conserved residues are functionally silent , or false negatives , where functional residues are not identified since they are species-specific and therefore non-conserved . Furthermore , the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study . Here , we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein . We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types . Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone . More importantly , this technique can be adapted to any viral ( and potentially non-viral ) protein where structural information is available .
To comprehensively describe the functional roles of a given protein , which are often diverse for many viral proteins and include catalytic activity , intermolecular interactions , and/or cofactor binding , it is necessary to identify the individual functional residues that carry out the biochemical mechanism . Sequence conservation analysis is a common strategy to search for functional residues and is facilitated by the availability of public protein sequence databases [1–3] . The underlying logic is composed of two parts . First , functional residues are essential . Second , essential residues are conserved . However , the reverse may not hold true − conserved residues are not necessary essential . With the extensively studied influenza A virus , several groups have experimentally demonstrated that conserved residues need not be essential for viral replication [4–6] . In addition , a residue shown to be essential for viral replication can also be the result of stability constraints , where the residue is essential for protein stability and expression levels , rather than due to functional constraints [7–10] . Another caveat of sequence conservation analysis is the inefficacy for identifying species-specific functional residues . This issue is often overlooked . During natural evolution , continuous diversification and adaptation leads to the acquisition of new functions . For example , NS1 from influenza B but not influenza A interacts with ISG15 [11]; NS1 from influenza A but not influenza B interacts with CPSF30 [12] . Furthermore , certain phosphorylation sites are not conserved across influenza A and B viruses [13] . In fact , non-conserved functional residues have been demonstrated in various organisms [14–17] . Consequently , when comparing the sequence identities of a set of diverse homologs , as is the case when comparing influenza types A , B , and C , species-specific functional residues may appear as non-conserved residues and be classified as non-functional . As a result , development of a sequence conservation-independent approach is needed to provide an unbiased assessment for the functionality of individual residues and to permit a systematic interrogation of the relationship between functionality and evolutionary conservation . The influenza A virus PA polymerase subunit consists of a ∼ 25 kD N-terminal domain and a ∼ 55 kD C-terminal domain [18 , 19] . Structural information for both domains is available [20–23] . PA forms a heterotrimer complex with two other influenza virus proteins , PB1 and PB2 . Together , they function as an RNA-dependent RNA polymerase . The three subunits perform distinct functions , which contribute to the replication and transcription of the viral RNA genome . PB1 binds to the viral promoter and is the catalytic subunit for viral RNA synthesis [24] . PB2 is essential for the transcription of viral RNA and can bind to the 5’ cap of host pre-mRNAs for “cap-snatching” [25–27] . PA is required for both replication and transcription of the viral RNA and contains an endonuclease catalytic site for cleaving the capped RNA primer [28–31] . It has also been reported that PA may be involved in other viral processes , such as viral assembly [32 , 33] , and may possess protease activity [34 , 35] . Recently , several groups have proposed targeting the influenza PA polymerase subunit for antiviral drug development as it is an essential component for viral replication [36–42] . In this study , we have developed a systematic approach that is independent of any prior knowledge in sequence conservation to identify functional residues at single amino acid resolution . In this strategy , we coupled a high-throughput fitness profiling platform with an in silico mutant stability prediction . We employed the influenza A virus PA polymerase subunit as the target protein , due to the availability of structural information and the extensive information available for natural sequence variants . The fitness effects of amino acid substitutions were profiled across 94% of all PA protein residues using a novel “small library” approach . Computational modeling predicted the stability effect of all individual substitutions , thus uncovering the structural constraints for individual residues . By integrating the fitness and structural information , we identified known functional sites previously documented in the literature and provide additional insight into the structure-function relationship of the influenza PA protein . We further examined the relationship between evolutionary conservation and functional constraints and show that functional residues are not necessarily conserved . This study not only describes a novel functional annotation platform that provides insight into the relationship between functionality and sequence conservation , but also presents valuable information for drug development and future functional studies of the influenza A virus PA protein . More importantly , this approach has the potential to be adapted for any protein where structural information is available .
High-throughput genetic approaches have been applied to the study of various proteins ( reviewed in [43] ) , which include several from influenza virus and HIV [44–49] . Generally , a mutant library is monitored using deep sequencing , and the relative fitness of each mutation can be inferred by changes in the frequency of mutation occurrence throughout the fitness selection process . Mutant library construction represents a key step in these high-throughput genetic approaches . An ideal mutant library should contain only one point mutation per genome , which poses a challenge for high-throughput mutagenic strategies . Existing approaches have used viral genomes that contain multiple mutations within the mutant library . However , the short read length in current deep sequencing technologies disallows the examination of any possible linkage between distantly placed mutations within each genome . Consequently , genetic interactions between mutations may exist during the selection process , but are not accounted for during the fitness calculation for individual point mutations . To resolve this drawback in existing high-throughput genetic approaches , we have developed a “small library” strategy ( Fig 1A ) . Each mutant library contains a mutated region that can be covered by a single sequencing read . Here , we generated a 240 bp mutated amplicon by error-prone PCR , which is then cloned into a PCR-generated vector using type IIs restriction enzymes ( BsaI or BsmBI ) . The resulting plasmid mutant library was constructed from ∼ 50 , 000 clones . A total of nine different “small libraries” for influenza A/WSN/33 PA were constructed . Together , these nine “small libraries” covered the entire PA gene . Each viral mutant library was rescued by transfecting the plasmid mutant library with the other seven wild type ( WT ) plasmids of the influenza A/WSN/33 eight-plasmid reverse genetic system [50] . A549 cells were then infected with the viral mutant library for 24 hours . The plasmid mutant libraries ( DNA library ) , post-transfection viral mutant libraries ( transfection ) , and post-infection viral mutant libraries ( infection ) were subjected to deep sequencing . In this study , we included a technical replicate for sequencing the DNA library , a biological replicate for transfection , and a biological replicate for infection to estimate the reproducibility of individual steps ( S1 Fig ) . In addition , we also sequenced the WT PA plasmid as a control . The amplicon sequencing library was prepared for the Illumina MiSeq 250 bp paired-end sequencing , using either DNA ( DNA library or WT plasmid ) or cDNA ( transfection or infection ) ( Fig 1B ) . For each “small library” , the 240 bp mutated region was amplified by a primer pair that contained a BpmI restriction site . A subsequent BpmI digestion excised the primer region from the PCR amplicon . As a result , the entire 240 bp mutated region would be covered by both forward and reverse reads ( S2 Fig ) . This enabled sequencing error correction by read-pairing . We obtained a coverage of at least 20 , 000 ( range = 20 , 128 to 965 , 488 ) for each sequencing library ( S3 Fig ) . The design of our high-throughput genetic platform enables us to examine the mutation in individual genomes . On average , 44% ( range = 25% to 76% ) of viral genomes contain no mutation ( i . e . WT ) , 33% ( range = 20% to 36% ) of viral genomes contain a single mutation , and 23% ( range = 3% to 42% ) of viral genomes contain at least two or more mutations ( S4 Fig ) . While a fraction of the genomes in the mutant library contain more than one mutation due to the nature of error-prone PCR , they were filtered out for downstream analysis . Occurrence frequency for each point mutation was computed from genomes that contained only one mutation . This allowed a precise fitness calculation for individual point mutations without complication by genetic interactions that may exist with additional mutations . Individual point mutations exhibited an occurrence frequency of 0 . 04% ( range = 0% to 0 . 3% ) across all DNA libraries . Whereas the mutation frequency obtained from sequencing the WT plasmid , which served as a control for sequencing error rates , was 0 . 005% ( range = 0% to 0 . 07% ) ( S5 Fig ) . Comparison of the relative frequency of individual point mutations between replicates was performed to assess the reproducibility of our “small library” high-throughput genetic platform ( see Materials and Methods for the calculation of relative frequency ) . A Pearson’s correlation of 0 . 95 was obtained for the technical replicate of DNA library , 0 . 76 for the biological replicate of transfection , and 0 . 96 for the biological replicate of infection ( Fig 2A ) . The strong correlations between replicates validated the design of our high-throughput genetic platform . Only those point mutations with an occurrence frequency of ≥ 0 . 03% in the DNA library were included in the downstream analysis , which covered 42% of all possible point mutations on the PA gene , to avoid fitness calculations being obscured by sequencing errors . The relative fitness index ( RF index ) was used as a proxy to estimate the fitness effect for each point mutation . The RF index of silent mutations ( mean = 0 . 98 ) was significantly higher than that of nonsense mutations ( mean = 0 . 09 ) ( P < 2e−16 , two-tailed Student’s t-test ) . Furthermore , the RF index distributions of silent mutations versus nonsense mutations were well-separated ( Fig 2B ) , validating that fitness selection was taking place . The fitness effects of substitutions were profiled across 94% of all amino acid residues in PA . The fitness profiling data is shown in Fig 2C . Next we aimed to identify amino acid residues that were functionally essential , but not structurally important . Essential residues in viral replication can be systematically mapped by high-throughput fitness profiling experiments [46–48 , 51–53] . However , fitness profiling only quantifies essentialness , but does not partition the structural versus functional role of individual residues . Several studies have shown that mutating functional residues imposed minimum stability cost to the proteins in which they reside [54–58] , suggesting that functional residues can be pinpointed by identifying substitutions that are deleterious to the virus but not destabilizing to the protein . Using Rosetta software we predicted the effect of individual substitutions on protein stability . We used the parameters from row 16 of Table I in Kellogg et al . , which has been shown to give a correlation of 0 . 69 with experimental data and a stability-classification accuracy of 0 . 72 [59 , 60] . We were able to identify substitutions that had a low RF index , but did not destabilize the protein ( Fig 3A ) . We hypothesized that these residues had large functional constraints with little structural effects to the protein upon substitution . To identify the substitutions of interest , a cutoff was set at an RF index < 0 . 15 ( based on the separation point of silent mutations and nonsense mutations ) and a predicted ΔΔG < 0 ( not destabilizing ) . A total of 32 substitutions ( 22 unique residues ) in the PA N-terminal domain and 110 substitutions ( 81 unique residues ) in the PA C-terminal domain satisfied these criteria . A number of functional residues in the PA protein have been experimentally characterized in the literature ( S1 Table ) . Out of 32 substitutions of interest in the PA N-terminal domain , eight were at residue positions that carried known biological functions . This included five substitutions in the endonuclease active site ( E80V , E80G , E80K , E119V , K134 ) [20 , 21] , and six substitutions in the cRNA promoter binding site ( E166D , R170W , R170M , R170K , T173I , T173A ) [18 , 61] . We also found multiple residues with known biological functions among the 110 substitutions of interest in the C-terminal domain . This included a substitution at a residue required for endonuclease activity ( H510R ) [28] , a substitution at a residue required for small viral RNA ( svRNA ) binding ( R566W ) [62] , four substitutions at residues required for viral genome replication ( E410V , E524V , K539M , K539E ) [28] , and six substitutions at the PB1-binding site ( N412I , N412Y , Q670R , Q670L , F710I , F710Y ) [22 , 23] . For all residues that carry a deleterious substitution ( RF index < 0 . 15 ) , residues identified as functional residues ( ΔΔG < 0 ) had a larger relative SASA ( solvent accessible surface area ) versus amino acid positions that were not ( P = 4 . 2e−9 , two-tailed Student’s t-test ) ( Fig 3B ) . This indicates that the identified functional residues were mostly surface exposed , as expected if they mediate possible interactions with biomolecules . In fact , ∼ 50% of the solvent exposed residues that carried a deleterious mutation ( relative SASA > 0 . 2 and RF index < 0 . 15 ) were identified as functional residues ( Fig 3C ) . Since our mutagenesis technique was based on error-prone PCR , which results in a non-comprehensive sampling of all the possible amino acid substitutions at each site , there may be some functional substitutions that were not sampled in our study . Nonetheless , these results demonstrate the feasibility of combining high-throughput fitness profiling with mutant stability prediction to identify functional sites at single amino acid resolution . Since the PA C-terminal region’s structure-function relationship remains largely unclear , we aimed to identify functional residues in this region to provide additional insight into the role of PA during viral replication . Ten previously uncharacterized substitutions with an RF index < 0 . 15 and a predicted ΔΔG < 0 were individually reconstructed and analyzed . Their spatial locations were distributed throughout the PA C-terminal domain ( Fig 4A and S6 Fig ) . The effect of these substitutions on the influenza polymerase activity was tested using an influenza A virus-inducible luciferase reporter assay [63] ( Fig 4B ) . Three substitutions , K281I , K413M , and F681S , completely abolished the influenza polymerase activity . This defect is unlikely to be a protein destabilizing effect since all ten mutants analyzed did not alter protein expression levels as compared to WT ( Fig 4C ) . The fact that nine out of ten mutants had a decrease in polymerase activity as compared to WT further validated our high-throughput approach in identifying deleterious mutations . Interestingly , we found six substitutions ( D426G , E427V , G429E , E430G , L470H , and R512W ) that retained > 10% of the WT influenza polymerase activity ( Fig 4B ) . A rescue experiment was performed using the influenza A/WSN/33 eight-plasmid reverse genetic system [50] . Unexpectedly , R512W , which had ∼ 60% of the WT polymerase activity , completely abolished the production of viral particles ( Fig 4D ) . In addition , E430G , which had a polymerase activity comparable to WT , displayed a four-log drop in virus titer as compared to WT . In contrast , although D426G and E427V displayed a polymerase activity that was only ∼ 10%-20% of WT , each could produce a much higher amount of infectious virus as compared to other substitutions in this set ( one-log to two-log higher titers as compared to E430G ) . Our results suggest that the E430G and R512W substitutions each had a functional defect that is unrelated to the polymerase activity . E430G and R512W were selected for further functional characterization because they exhibited the strongest polymerase activity among all the individually analyzed substitutions , despite their defect in producing infectious virus . During a viral rescue experiment , there was an accumulation of viral copy number in the supernatant for WT , but not for the E430G and R512W viral mutants ( S7A Fig ) . In contrast , both mutants displayed an accumulation of intracellular viral copy number similar to WT ( S7B Fig ) . At 72 hours post-transfection , the HA titer of R512W and E430G was undetected , indicating viral particles were present at a very low amount , if present ( S7C Fig ) . These results further confirm that E430G and R512W have a defect that is unrelated to polymerase activity . When this study was initiated , PA was the only influenza polymerase subunit with structural information available . The structural information for the other two influenza polymerase subunits , PB1 and PB2 , were largely unknown . Nonetheless , after the completion of this study , the crystal structure of the complete influenza A virus polymerase complex bound to the viral RNA promoter has been published [64] , which provides an independent reference to validate and interpret our data . Our functional profile identified a subset of PA residues that interact with PB1 ( S8A Fig ) , PB2 ( S8B Fig ) , and the viral RNA promoter ( S9 Fig ) . Moreover , six out of the 10 validated functional residues participate in these interaction interfaces: − D426 , E427 , and F681 interacted with PB1; L470 interacted with PB2; K281 and R512 interacted with the viral RNA promoter . Our data also identified functional residues that were not involved in polymerase complex formation or RNA binding activity . For example , E430 did not interact with either PB1 , PB2 , or the viral RNA promoter ( S10 Fig ) . This is consistent with our data that E430 is involved in a non-polymerase function . In addition , a putative functional subdomain independent of the polymerase-interacting surface was identified in our functional profiling data . This putative functional subdomain is composed of a series of charged or polar residues − D286 , N412 , K413 , R454 , D529 , K559 , and K635 . Interestingly , this patch of functional residues was adjacent to residue 552 , which has been shown to be a host-specific determinant [65] . This indicates a possible biological significance of the putative functional subdomain we identified . Consistently , substitutions at positions D286 , N412 , K413 , R454 , D529 , and K635 were shown to abolish the polymerase activity in our validation experiment ( Fig 5B-5C ) , further confirming the functional importance of this subdomain in viral replication . Overall , our profiling data is consistent with the polymerase complex-viral RNA promoter complex structural data , which provides an independent validation of our approach . There are three types of influenza viruses , namely type A , B , and C . Phylogenetic analysis indicates that PA displays a high inter-type diversity ( evolutionary distance among viral strains within the same influenza type ) , while the intra-type diversity is limited ( evolutionary distance between viral strains of different influenza types ) ( S11 Fig ) . The average inter-type amino-acid sequence identity is < 40% and that of intra-type is > 95% . The huge divergence among different types of influenza viruses leads us to hypothesize that a significant number of functional residues are type-specific and are non-conserved across different influenza types . Consequently , we aimed to interrogate the relationship between functional constraints , structural constraints and evolutionary conservation . In this study , sequence conservation for each residue was computed using Shannon’s entropy [66] . The higher the entropy , the less conserved a residue is . Here , we divided all profiled residues into three groups: 1 ) Functional residues , which had at least one substitution that displayed an RF index < 0 . 15 and a predicted ΔΔG < 0 . 2 ) Structural residues , which did not satisfy the condition of functional residues but had at least one substitution that displayed an RF index < 0 . 15 . 3 ) “Other” residues , which contained all other profiled residues that were neither functional nor structural residues ( i . e . all profiled substitutions at “other” residues displayed an RF index ≥ 0 . 15 ) . The entropy calculation was performed on a multiple sequence alignment of 3837 strains from different influenza types ( Fig 6A ) . In general , functional residues were more conserved than structural residues ( P = 0 . 032 , Wilcoxon rank-sum test ) , and structural residues were more conserved than “other” residues ( P = 2 . 9e−9 , Wilcoxon rank-sum test ) ( S12 Fig ) . From this analysis , 58% of functional residues , 43% of structural residues , and 26% of “other” residues were highly conserved ( entropy < 0 . 1 ) . This indicates that a significant number of functional residues are not conserved across the different types of influenza virus . We further computed a phylogenetic-based dN/dS analysis on each codon across the influenza A virus PA coding sequence with FUBAR [67] ( Fig 6B ) . A mild , yet statistically significant , correlation was detected between dN/dS and RF index ( Spearman’s rank correlation = 0 . 38 , P < 2 . 2e−16 ) ( S13A Fig ) . On average , functional residues and structural residues had a lower dN/dS as compared to “other” residues ( P = 7 . 2e−8 and P = 1 . 5e−8 respectively ) ( S13B Fig ) . However , the difference of dN/dS between functional residues and structural residues was not significant ( P = 0 . 57 ) . This result shows that dN/dS may not be a good indicator to distinguish functional residues from structural residues . In addition , some functional residues exhibited a dN/dS that was well within the range of “other” residues , demonstrating that some functional residues could not be identified by dN/dS analysis alone . The utility of dN/dS is largely determined by the phylogenetic depth of the sequences being analyzed . In fact , it has been shown that when the genetic diversity is low , as is the case of PA protein sequences from type A influenza virus , dN/dS becomes less sensitive to purifying selection [68] , and may not be able to identify functional residues . We next examined individual residues validated in this study . Among the 13 validated functional residues , three ( K281 , K413 , and E430 ) had both entropy and dN/dS at the median level ( Fig 6C ) . Moreover , these residues are not conserved across different influenza types . These results confirm that functional residues may not be identified by phylogenetic-based analysis alone . As expected , sequence conservation-based functional site prediction software was unable to predict these functional residues . We tested three software approaches , firestar [69] and two classification schemes under FRpred [70 , 71] , namely FRcons and FRsubtype . FRcons and FRsubtype were each able to identify only one of our validated functional residues ( D286 for FRcons and K413 for FRsubtype , respectively ) using a category cutoff of ≥ 8 . Firestar was not able to identify any of our validated functional residues . Furthermore , out of a set of 28 functional residues identified in the literature ( S1 Table ) , our approach identified 12 , whereas FRcons , FRsubtype , and firestar were only capable of identifying 4 , 2 and 5 functional residues , respectively . This comparison demonstrates that our methodology can outperform phylogenetic approaches in identifying functional residues . We aimed to further investigate the structural basis of type-specific functional residues . The RNA binding function is required for viral replication and is conserved among type A and B influenza viruses . In the validation above , substituting lysine [K] to isoleucine [I] at residue 281 completely abolished the polymerase activity . This highlights the importance of the hydrogen bond formed between K281 and the RNA phosphate backbone in the influenza A virus ( Fig 7A and boxed in S14 Fig ) . However , PA K281 is not conserved between type A and B influenza viruses . All influenza B viruses carry an alanine [A] at residue 281 , which is unable to form a hydrogen bond with the RNA backbone . The critical hydrogen bond mediated by K281 in influenza A virus is replaced by the main chain of G569 in the influenza B virus ( Fig 7B and boxed in S15 Fig ) . In fact , structural analysis indicates that type A [64] and B [72] influenza viruses display different hydrogen bonding patterns between PA and the viral RNA promoter ( S14 Fig and S15 Fig ) . Thus , conserved functions may not necessarily require conserved functional residues . Together , these analyses show that while certain functional residues were completely conserved among different types of influenza viruses , a significant number of residues that mediate critical viral functions may not be conserved , and suggests that some residues may have acquired functionality in recent evolutionary history .
Traditionally , sequence conservation is the common approach for identifying functional residues . In this study , we coupled two high-throughput techniques , experimental fitness profiling and in silico mutant stability prediction , to systematically identify functional residues in the influenza A virus PA protein . This strategy provided a direct measure of essentialness and enabled the partitioning of functional constraints versus structural constraints at each residue position . This approach is independent of any prior knowledge of sequence conservation . Therefore , it is devoid of the caveats associated with sequence conservation analysis and possesses the power to identify species-specific functional residues . A number of functional residues identified in this study , are not completely conserved across different types of influenza viruses , suggesting that even functional residues may not be conserved . This disparity between conservation and function highlights the power of our approach to identify functional residues that may not be identified by traditional sequence conservation analysis alone . We anticipate that this method can be further improved as the accuracy of mutant stability prediction methodology improves . It has been shown that although most force fields exhibit a correct trend in ΔΔG prediction , the precision is still lacking as compared to experimental methods [73] . For example , in this study , N412I decreases protein expression levels , despite being predicted as a stabilizing mutant . In addition , it is known that most proteins are able to buffer a small destabilizing effect without becoming unfolded , and hence without attenuating the fitness [74 , 75] . As a result , understanding the stability buffer margin will help to determine the optimal ΔΔG cutoff in our approach . It is also known that many proteins have multiple conformations , which may further complicate the ΔΔG prediction . Together , these caveats may explain the weak correlation between the predicted ΔΔG and RF index in this study . To obtain a more accurate measurement of protein stability , high-throughput experimental analysis on protein stability may provide an alternative [76 , 77] . All the advances stated above will improve the accuracy of our platform in identifying functional residues within a target protein . During natural evolution , continuous accumulation of protein mutations drives speciation and divergence from the common ancestor . The genomic plasticity of an evolving species permits the acquisition of new function through mutations [78] . Evolution of a new function has been demonstrated in bacteriophage λ within an experimental timescale [79] , and a long-term evolution experiment on Escherichia coli[80] . Therefore , it is not surprising to see species-specific function even in recently separated species . Based on the sequence comparison of hemagglutinin , it was estimated that type A and B influenza virus diverged from type C ∼ 8 , 000 years ago , whereas type A influenza virus diverged from type B ∼ 4 , 000 years ago [81] . This length of time is sufficient for the influenza virus to develop a type-specific function as exemplified by type-specific virus-host interactions in NS1 [11 , 12] . Furthermore , conservation of protein function does not necessarily support that sequence conservation exists at the primary sequence level , which is evidenced by the differences between the nuclear localization signal of influenza A and B NP proteins [82 , 83] . In fact , this study reveals that type-specific functional residues are prevalent in the influenza virus PA protein . These results not only provide insight into how functional residues evolve through species diversification , but also highlight the caveats encountered when identifying functional sites from conservation-based approaches . In the past decade , proteins from different medically important viruses , such as influenza , HIV , and HCV , have been crystallized [84–86] . The approach described in this study systematically integrates the available structural information with mutation fitness information to examine the structure-function relationship of a viral protein of interest and to map functional subdomains . Profiling datasets will facilitate functional characterization of the protein of interest , and will promote targeted drug discovery and rational drug design . The emergence of drug resistant mutations is a major challenge for antiviral drug development . Therefore , it is important to target functional subdomains that are less tolerable to substitution to increase the genetic barrier for developing drug resistant mutations . Our profiling technique can help locate such functional subdomains that are suitable for drug development . More importantly , our technique can potentially be adapted to study any protein , provided the relevant structural information is available .
The PA plasmid mutant libraries were created by performing error-prone PCR on the PA segment of the eight-plasmid reverse genetics system of influenza A/WSN/1933 ( H1N1 ) [50] . To generate the mutated insert , we PCR-amplified regions of the PA gene from pHW2000-PA plasmid with error-prone polymerase Mutazyme II ( Stratagene , La Jolla , CA ) according to the manufacturer’s instructions . The following primers were used: Library 1 insert: 5’-CAG GTC TCA TCA AAA TGG AAG ATT TTG TGC GA-3’ and 5’-CAG GTC TCA ATA CTG TTT ATT ACT GTC CAG GC-3’ Library 2 insert: 5’-CAG GTC TCA TCG AGG GAA GAG ATC GCA CAA TA-3’ and 5’-CAG GTC TCA CTG GTT TTG ATC CTA GCC CTG CT-3’ Library 3 insert: 5’-CAG GTC TCA CCG ACT ACA CTC TCG ATG AAG AA-3’ and 5’-CAG GTC TCA TTT ACT TCT TTG GAC ATT TGA GA-3’ Library 4 insert: 5’-CAG GTC TCA ACG GCT ACA TTG AGG GCA AGC TT-3’ and 5’-CAG GTC TCA TAA TTT GGA TTT ATT CCC TTT TC-3’ Library 5 insert: 5’-CAG GTC TCA AAC CCA ATG TTG TTA AAC CAC AC-3’ and 5’-CAG GTC TCA GCC TTG TTG AAC TCA TTC TGA AT-3’ Library 6 insert: 5’-CAG GTC TCA AAT TGA GGT CGC TTG CAA GTT GG-3’ and 5’-CAG GTC TCA CCC TCC TTA GTT CTA CAC TTG CT-3’ Library 7 insert: 5’-CAG GTC TCA ATT TCC AAT TAA TTC CAA TGA TA-3’ and 5’-CAG GTC TCA TTA ATT TTT GAG GTT CCA TTT GT-3’ Library 8 insert: 5’-CAG GTC TCA GGC CTA TGT TCT TGT ATG TGA GG-3’ and 5’-CAG GTC TCA TGT GGA GAT GCA TAC AAG CTG TT-3’ Library 9 insert: 5’-CAG GTC TCA GAA GGT CTG CAG AAC TTT ATT GG-3’ and 5’-CAG GTC TCA GGA CAG TAT GGA TAG CAA ATA GT-3’ The corresponding vector for each of the nine mutant library was generated by PCR using the following primers: Library 1 vector: 5’-CAC GTC TCT TTG AAT CAG TAC CTG CTT TCG CT-3’ and 5’-CAC GTC TCA GTA TTT GCA ACA CTA CAG GGG CT-3’ Library 2 vector: 5’-CAC GTC TCC TCG ATT ATT TCA AAT CTG TGC TT-3’ and 5’-CAC GTC TCA CCA GGC TAT TCA CCA TAA GAC AA-3’ Library 3 vector: 5’-CAC GTC TCG TCG GCC TTT GTG GCC ATT TCC TC-3’ and 5’-CAC GTC TCG TAA ATG CTA GAA TTG AAC CTT TT-3’ Library 4 vector: 5’-CAC GTC TCG CCG TTC GGT TCG AAT CCA TCC AC-3’ and 5’-CAC GTC TCA ATT ATC TTC TGT CAT GGA AGC AA-3’ Library 5 vector: 5’-CAC GTC TCG GGT TCC TTC CAT CCA AAG AAT GT-3’ and 5’-CAC GTC TCA AGG CAT GTG AAC TGA CCG ATT CA-3’ Library 6 vector: 5’-CAC GTC TCC AAT TCT GGT TCA TCA CTA TCA TA-3’ and 5’-CAC GTC TCG AGG GAA GGC GAA AGA CCA ATT TG-3’ Library 7 vector: 5’-CAC GTC TCG AAA TCA TCC ATT GCT GCA CAG GA-3’ and 5’-CAC GTC TCA TTA AAA TGA AAT GGG GGA TGG AA-3’ Library 8 vector: 5’-CAC GTC TCA GGC CTT GAC ACA TGG CCT ATG GC-3’ and 5’-CAC GTC TCC CAC AAC TAG AAG GAT TTT CAG CT-3’ Library 9 vector: 5’-CAC GTC TCC CTT CCC AAT GGA ACC TTC CTC CA-3’ and 5’-CAC GTC TCT GTC CAA AAA GTA CCT TGT TTC TA-3’ The PCR was performed using KOD DNA polymerase ( EMD Millipore , Billerica , MA ) with 1 . 5 mM MgSO4 , 0 . 2 mM of each dNTP ( dATP , dCTP , dGTP , and dTTP ) , 20 ng pHW2000-PA plasmid , and 0 . 6 uM of forward and reverse primer . The thermocycler was set as follows: 2 minutes at 95°C , then 20 three-step cycles of 20 seconds at 95°C , 15 seconds at 58°C , and 3 . 5 minutes at 68°C , and a 2 minutes final extension at 68°C . The PCR product was digested by DpnI ( New England Biolabs ) to remove the input plasmid . The insert was then digested by BsaI ( New England Biolabs , Ipswich , MA ) , whereas the vector was digested by BsmBI ( New England Biolabs ) . Ligation was performed for each of the nine libraries with T4 DNA ligase ( Life Technologies , Carlsbad , CA ) using the corresponding insert and vector . Transformations were carried out with electrocompetent MegaX DH10B T1R cells ( Life Technologies ) . For each of the nine mutant libraries , ∼ 50 , 000 colonies were scraped and directly processed for plasmid DNA purification ( Qiagen Sciences , Germantown , MD ) . Point mutations for the validation experiment were constructed using the QuikChange XL Mutagenesis kit ( Stratagene ) according to the manufacturer’s instructions . ∼ 35 million 293T ( human embryonic kidney ) cells in a 175 cm2 flask were used for transfection to rescue each viral mutant library from the plasmid mutant library as described [44–46] . Transfections were performed using Lipofectamine 2000 ( Life Technologies ) according to the manufacturer’s instructions . Supernatant was replaced with fresh cell growth medium at 24 hours and 48 hours post-transfection . At 72 hours post-transfection , supernatant containing infectious virus was harvested , filtered through a 0 . 45 um MCE filter , and stored at -80°C . The TCID50 was measured on A549 cells ( human lung carcinoma cells ) . For infection , ∼ 10 million A549 cells in a 50 cm2 plate were used with an MOI of 0 . 05 . At 2 hours post-infection , infected cells were washed three times with PBS followed by the addition of fresh cell growth medium . Virus was harvested at 24 hrs post-infection . Viral RNA was extracted using QIAamp Viral RNA Mini Kit ( Qiagen Sciences ) and treated with DNaseI ( Life Technologies ) to digest any residual plasmid DNA from transfection . The DNA-free RNA was then reverse transcribed to cDNA using Superscript III reverse transcriptase ( Life Technologies ) . The plasmid mutant libraries or cDNA from the viral mutant libraries ( transfection or infection ) were amplified using the following primers: Library 1: 5’-CTG ATT CTG GAG GGA AGA TTT TGT GCG A-3’ and 5’-TGC AAA CTG GAG TTA TTA CTG TCC AGG C-3’ Library 2: 5’-AAT AAT CTG GAG AAG AGA TCG CAC AAT A-3’ and 5’-ATA GCC CTG GAG TGA TCC TAG CCC TGC T-3’ Library 3: 5’-AAA GGC CTG GAG CAC TCT CGA TGA AGA A-3’ and 5’-TAG CAT CTG GAG CTT TGG ACA TTT GAG A-3’ Library 4: 5’-ACC GAA CTG GAG CAT TGA GGG CAA GCT T-3’ and 5’-GAA GAT CTG GAG GAT TTA TTC CCT TTT C-3’ Library 5: 5’-GAA GGA CTG GAG TGT TGT TAA ACC ACA C-3’ and 5’-CAC ATG CTG GAG TGA ACT CAT TCT GAA T-3’ Library 6: 5’-ACC AGA CTG GAG GTC GCT TGC AAG TTG G-3’ and 5’-GCC TTC CTG GAG TAG TTC TAC ACT TGC T-3’ Library 7: 5’-GGA TGA CTG GAG ATT AAT TCC AAT GAT A-3’ and 5’-TCA TTT CTG GAG TTG AGG TTC CAT TTG T-3’ Library 8: 5’-GTC AAG CTG GAG GTT CTT GTA TGT GAG G-3’ and 5’-CTA GTT CTG GAG ATG CAT ACA AGC TGT T-3’ Library 9: 5’-ATT GGG CTG GAG TGC AGA ACT TTA TTG G-3’ and 5’-TTT TTG CTG GAG ATG GAT AGC AAA TAG T-3’ The PCR was performed using KOD DNA polymerase ( EMD Millipore ) with 1 . 5 mM MgSO4 , 0 . 2 mM of each dNTP ( dATP , dCTP , dGTP , and dTTP ) and 0 . 6 uM of forward and reverse primer . The thermocycler was set as follows: 2 minutes at 95°C , then 30 three-step cycles of 20 seconds at 95°C , 15 seconds at 54°C , and 20 seconds at 68°C , and a 1 minute final extension at 68°C . The resulting PCR amplicons were digested with BpmI ( New England Biolabs ) . End repair and 3’ dA-tailing were performed by end repair module and dA-tailing module respectively ( New England BioLabs ) . dA-tailed amplicons were ligated to sequencing adapters using T4 DNA ligase ( Life Technologies ) . Adapters were generated by annealing two oligos: 5’-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT NNN T-3’ and 5’-/5Phos/NNN AGA TCG GAA GAG CGG TTC AGC AGG AAT GCC GAG-3’ . The location of multiplex ID for distinguishing different samples is underlined . The nucleotide sequences for the multiplex ID were the reverse complement in the two oligos ( S2 Table ) . The adapter-ligated products were enriched by a final PCR using primers: 5’-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC-3’ and 5’-CAA GCA GAA GAC GGC ATA CGA GAT CGG TCT CGG CAT TCC TGC TGA ACC-3’ . This final PCR was performed using KOD DNA polymerase ( EMD Millipore ) with 1 . 5 mM MgSO4 , 0 . 2 mM of each dNTP ( dATP , dCTP , dGTP , and dTTP ) and 0 . 6 uM of forward and reverse primer . The thermocycler was set as follow: 2 minutes at 95°C , then 18 three-step cycles of 20 seconds at 95°C , 15 seconds at 56°C , and 20 seconds at 68°C , and a 1 minute final extension at 68°C . Deep sequencing was performed using two lanes of the Illumina MiSeq with 250 bp paired-end reads . Raw sequencing data have been submitted to the NIH Short Read Archive ( SRA ) under accession number: BioProject PRJNA254185 . Sequencing data were de-multiplexed by the three-nucleotide barcode . A paired-end read was filtered and removed if the corresponding forward and reverse reads did not match . Each mutation was called by comparing individual reads to the WT reference sequence . All analysis was performed by custom python scripts , which are available upon request . For the RF index calculation , only mutants that carried a single mutation were considered . RF index for a given mutation was computed as follows: For a mutation i in mutant library n of sample t ( where t could be input plasmid library transfection or infection ) : Occurrence frequencyi , n , t = Read counti , n , t/Coveragen , t , where Read counti , n , t represented the number of read in mutant library n of sample t that carried mutation i and coveragen represented the sequencing coverage of the mutant library n of sample t . Similarly , Occurrence frequencyWT , n , t = Read countWT , n , t/Coveragen , t , where Read countWT , n represented the number of read that has a complete match with the reference sequence in mutant library n of sample t and coveragen represented the sequencing coverage of the mutant library n of sample t . For a mutation i in mutant library n of sample t: Relative frequencyi , n , t = ( Occurrence frequencyi , n , t ) / ( Occurrence frequencyWT , n , t ) . Subsequently , RF index = ( Relative frequencyi , n , infection ) / ( Relative frequencyi , n , plasmidlibrary ) To avoid fitness calculations being obscured by sequencing errors , only the point mutations with an occurrence frequency of ≥ 0 . 03% in the DNA library were included in the downstream analysis unless otherwise stated . The RF index for individual mutations is shown in S1 Dataset . PDB: 4M5Q ( PA N-terminal endonuclease domain ) [37] and PDB: 2ZNL ( PA C-terminal domain ) [23] were used for ΔΔG prediction of single amino acid substitution . ΔΔG prediction was performed by the ddg_monomer application in Rosetta software [59] . Parameters from row 16 of Table I in Kellogg et al . were used [60] . Briefly , a “soft-rep” energy function was used for side chain repacking for all residues , in which the Lennard-Jones repulsive interactions at short atomic separations were damped . After repacking , a restrained quasi-Newton minimization step was performed for both side chain and backbone using a “hard-rep” energy function , in which the repulsive interactions were not damped . All options followed the high resolution protocol flags of the ddg_monomer application . The ΔΔG prediction result is shown in S2 Dataset . Minimal , if any , destabilizing effect is expected if predicted ΔΔG is < 0 . DSSP ( http://www . cmbi . ru . nl/dssp . html ) was used to compute the SASA from the PDB structure [87] . SASA was then normalized to the empirical scale reported in [88] . All terminal residues were excluded from this analysis . An influenza A virus-inducible luciferase reporter assay was used to measure the virus polymerase activity [63] . 293T cells seeded on 48-well plates were transfected with 100 ng each of PB2 , PB1 , PA , NP , 50 ng of vLuciferase reporter plasmid and 5 ng of PGK-renilla-luciferase using Lipofectamine 2000 ( Life Technologies ) according to the manufacturer’s instructions . Luciferase activity measurement was performed at 24 hours post-transfection using Promega Dual-Luciferase Assay Kit according to the manufacturers instructions ( Promega , Madison , WI ) . Relative luciferase activity was calculated by normalizing the firefly-luciferase activities to their internal renilla luciferase controls . 293T cells seeded on a 12-well plate were transfected with pHW2000-PA plasmid using Lipofectamine 2000 ( Life Technologies ) according to the manufacturer’s instructions . At 24 hours post-transfection , cells were lysed and heated with SDS loading buffer for five minutes . Lysates were loaded onto a 10% polyacrylamide gel and subjected to immunoblot analysis . Rabbit anti-PA antibody ( catalog number: GTX125932 , GeneTex , Irvine , CA ) , mouse anti-Flag antibody ( Sigma ) , mouse anti-actin antibody ACTN05 ( C4 ) ( Abcam , Cambridge , MA ) , sheep horseradish peroxidase-conjugated anti-mouse Immunoglobulin G ( GE Healthcare , Pasadena , CA ) , and donkey horseradish peroxidase-conjugated anti-rabbit Immunoglobulin G ( GE Healthcare ) were used for protein detection . Viral RNA was extracted using QIAamp Viral RNA Mini Kit ( Qiagen Sciences ) and treated with DNaseI ( Life Technologies ) to digest any residual plasmid DNA from transfection . The DNA-free RNA was then reverse transcribed to cDNA using Superscript III reverse transcriptase ( Life Technologies ) . The cDNA was subjected to qPCR analysis . qPCR was performed on a DNA Engine OPTICON 2 system ( Bio-Rad , Irvine , CA ) using SYBR Green ( Life Technologies ) with primers: 5’-GAC GAT GCA ACG GCT GGT CTG-3’ and 5’-ACC ATT GTT CCA AC TCC TTT-3’ . HA titer was measured by HA assay . A round-bottom 96-well plate was employed for this assay . A 2-fold serial dilution of the virus was performed using PBS . Different dilutions were then inoculated with a final concentration of 0 . 25% of turkey red blood cell ( Lampire Biological Laboratories , Pipersville , PA ) for 30 to 60 minutes at room temperature . Those wells with a uniform reddish color were scored as a positive result . PA protein sequences of type A and B influenza virus and P3 protein sequences of type C influenza virus were retrieved from the Influenza Research Database [89] . A total of 3271 PA protein sequences from type A influenza virus , 562 PA protein sequences from type B influenza virus , and 4 P3 protein sequences from type C influenza virus were obtained using the following parameters: human host , all geographical locations , complete segment only , include pH1N1 , remove duplicate sequences . Multiple sequence alignment was performed along with the A/WSN/33 PA sequence using MAFFT ( version 7 . 157b ) [90] using the “–nofft” option . Shannon’s entropy for each residue position was then calculated by: Entropy=−∑i=1MPilog2 ( Pi ) [66] , where Pi is the fraction of residues of amino acid type i , and M is the number of amino acid types ( i . e . 20 ) . Amino acid sequences were align with MAFFT ( version 7 . 157b ) [90] using default parameters . Phylogenetic tree was generated by FastTree ( version 2 . 1 . 8 ) [91] from the sequence alignment using default parameters and displayed in FigTree ( version 1 . 3 . 1 ) ( http://tree . bio . ed . ac . uk/software/figtree/ ) . 8726 influenza A PA coding sequences ( CDS ) were retrieved from the Influenza Research Database [89] using the following parameters: human host , all geographical locations , complete segment only , include pH1N1 , remove duplicate sequences , length of 2151 bp . Due to the large amount of computational power required to process such a large number of sequences , 3000 sequences were randomly sampled for dN/dS calculation . Multiple sequence alignment was performed along with the A/WSN/33 PA CDS using MAFFT ( version 7 . 157b ) [90] using the “–nofft” option . Phylogenetic tree was generated by FastTree ( version 2 . 1 . 8 ) [91] from the sequence alignment using default parameters . The sequence alignment and the phylogenetic tree were analyzed by FUBAR [67] using HyPhy [92] . From the FUBAR output , dN/dS for each codon was calculated by beta/alpha , where beta was the posterior mean non-synonymous substitution rate and alpha was the posterior mean synonymous substitution rate . WSN PA protein sequence was used as the input for the firestar server ( http://firedb . bioinfo . cnio . es/Php/FireStar . php ) [69] using default parameters . For the functional site prediction using FRpred [70 , 71] , two classification schemes were employed , FRcons and FRsubtype , respectively . For FRcons , a random subset of 2000 aligned PA protein sequences from type A influenza virus was used as input due to the limitation of computational cost . For FRsubtype , 1702 PA protein sequences from type A influenza virus , 294 PA protein sequences from type B influenza virus , and 4 P3 protein sequences from type C influenza virus were used as input . Default parameters were used . Each PA residue was assigned a FRcons category and a FRsubtype category , ranging from 1 to 9 , with 1 being least likely to be a functional residue , and 9 being most likely to be a functional residue . In this study , residues that were assigned a category of ≥ 8 were identified as a hit . A total of 72 residues were identified under each of the FRcons classification and the FRsubtype classification .
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The analysis of sequence conservation is a common approach to identify functional residues within a protein . However , not all functional residues are conserved as natural evolution and species diversification permit continuous innovation of protein functionality through the retention of advantageous mutations . Non-conserved functional residues , which are often species-specific , may not be identified by conventional analysis of sequence conservation despite being biologically important . Here we described a novel approach to identify functional residues within a protein by coupling a high-throughput experimental fitness profiling approach with computational protein modeling . Our methodology is independent of sequence conservation and is applicable to any protein where structural information is available . In this study , we systematically mapped the functional residues on the influenza A PA protein and revealed that non-conserved functional residues are prevalent . Our results not only have significant implication on how functionality evolves during natural evolution , but also highlight the caveats when applying conservation-based approaches to identify functional residues within a protein .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality
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Myotonic dystrophy type 1 ( DM1 ) is associated with one of the most highly unstable CTG•CAG repeat expansions . The formation of further repeat expansions in transgenic mice carrying expanded CTG•CAG tracts requires the mismatch repair ( MMR ) proteins MSH2 and MSH3 , forming the MutSβ complex . It has been proposed that binding of MutSβ to CAG hairpins blocks its ATPase activity compromising hairpin repair , thereby causing expansions . This would suggest that binding , but not ATP hydrolysis , by MutSβ is critical for trinucleotide expansions . However , it is unknown if the MSH2 ATPase activity is dispensible for instability . To get insight into the mechanism by which MSH2 generates trinucleotide expansions , we crossed DM1 transgenic mice carrying a highly unstable > ( CTG ) 300 repeat tract with mice carrying the G674A mutation in the MSH2 ATPase domain . This mutation impairs MSH2 ATPase activity and ablates base–base MMR , but does not affect the ability of MSH2 ( associated with MSH6 ) to bind DNA mismatches . We found that the ATPase domain mutation of MSH2 strongly affects the formation of CTG expansions and leads instead to transmitted contractions , similar to a Msh2-null or Msh3-null deficiency . While a decrease in MSH2 protein level was observed in tissues from Msh2G674 mice , the dramatic reduction of expansions suggests that the expansion-biased trinucleotide repeat instability requires a functional MSH2 ATPase domain and probably a functional MMR system .
Myotonic dystrophy type 1 ( DM1 ) is a neuromuscular disease characterized by highly variable clinical manifestations including skeletal muscle , cardiorespiratory and brain defects and endocrine abnormalities [1] . DM1 results from the expansion of an unstable CTG trinucleotide repeat in the 3′-untranslated region of the dystrophia myotonica protein kinase ( DMPK ) gene [2]–[4] . In normal individuals , the CTG repeat at the DM1 locus varies from 5 to 37 repeats and remains stable over generations . Tract lengths of >50 CTG repeats are dramatically unstable in successive generations , with a strong bias towards expansions , often reaching thousands of CTGs in the most severe form of the disease [1] . The level of CTG repeat instability depends on the sex and repeat length of the transmitting parent [5] , [6] . The size of the expanded repeat inversely correlates with the age at onset and is positively correlated with the severity of symptoms providing the molecular basis for anticipation [7] . In addition to intergenerational instability , the CTG repeat is also somatically unstable in DM1 patients . Somatic instability , which is age-dependent , biased towards expansions of CTG repeat length , presents variable expansion patterns between tissues , and is probably associated with the progression of disease symptoms in DM1 patients [8] . A better understanding of the molecular mechanisms underlying trinucleotide repeat instability is fundamental for the development of therapies aimed at controlling trinucleotide repeat instability . Such therapeutic approaches would hopefully reduce the disease severity and progression of the symptoms . Extensive analysis of repeat instability in various model systems in vitro and in vivo has revealed crucial insights into the role of cis-elements , DNA replication and genome-maintenance repair [9]–[12] . Numerous transgenic mouse models and their derived cell lines revealed that the repeat expansion rate does not always correlate with cell division rate , suggesting the involvement of various replication-independent mechanisms [13] , [14] . Studies have reported that several mismatch repair ( MMR ) proteins are involved in expansion-biased trinucleotide instability in transgenic mouse models carrying expanded CTG•CAG repeat tracts . Accumulation of CTG•CAG expansions in mitotic , non-mitotic and germline tissues clearly depends on the MMR proteins MSH2 , MSH3 and to a lesser extent on PMS2 ( postmeiotic segregation homologue 2 ) [15]–[20] . Precisely how MutS homologues MSH2 and MSH3 mediate CTG•CAG repeat expansions has not yet been clearly determined . MMR proteins are required to maintain genomic integrity in prokaryotes and eukaryotes , by correcting single mismatches and short unpaired regions , such as small insertions and deletions [21] , [22] . In eukaryotes , three proteins are involved in mismatch recognition , MSH2 , MSH3 and MSH6 . The three proteins form two heterodimers MutSα ( MSH2-MSH6 ) and MutSβ ( MSH2-MSH3 ) . MutSα is thought to be involved primarily in the recognition and repair of base-base mismatches and small insertion/deletion loops . MutSβ acts preferentially on insertion/deletion loops up to 12 nucleotides in length [23] . MMR is tightly linked with the mismatch-binding-dependent ATPase activities of the MSH complexes but the precise roles of these activities are still in debate . Upon binding to the mismatch , the MSH complex exchanges a bound ADP for an ATP , which permits a change in the proteins conformation and the signaling of downstream repair events [21] , [22] , [24] , [25] . The MSH2 , MSH3 , and PMS2 mismatch repair proteins are also involved in other DNA repair pathways such as single-strand annealing and homologous recombination , anti-recombination , DNA damage signaling , apoptosis , as well as site-specific mutagenesis during immunoglobin somatic hypermutation and class switch recombination [26]–[29] . While the MSH2 ATPase activity is known to be required for some of these non-MMR functions , the specific roles of ATP-binding and ATP-hydrolysis remain to be elucidated . Different scenarios have been suggested to explain the role of MMR proteins in trinucleotide repeat instability . It has been shown that CTG•CAG repeat tracts can form hairpin structures and/or slipped-strand structures in vitro [30] , [31] . These structures are suspected to disturb DNA metabolism in vivo , leading to trinucleotide repeat instability [9] . An initial study reported that in vitro MSH2 can recognize and bind to slipped-strand ( S-DNA ) structures formed by expanded CTG•CAG repeat tracts [32] . More recently , a detailed study of MutSβ binding in vitro to long CAG hairpin structures was reported [18] , as well as an in vitro assay to process slipped-DNAs [33] . Three models explaining the mechanism through which the MMR proteins MSH2 and MSH3 act to generate CTG•CAG expansions have been put forth . The first model proposes that the role of the MMR proteins MSH2-MSH3 ( MutSβ ) in trinucleotide repeat expansions is through protecting CAG loops from repair by binding the loops [18] . This model was based upon the reduced ability of the human MutSβ complex to hydrolyze ATP when bound to hairpins formed by either ( CAG ) 13 or ( CTG ) 13; suggesting that the protein complex would be stuck on the loop , unable to translocate along the helix , and unable to signal or interact with downstream repair proteins . In this manner binding of MutSβ to CAG or CTG hairpins would abort their repair , thereby allowing the loops to be incorporated into the genome as expansions . Thus , in this model MutSβ binding but not ATP hydrolysis would be critical for expansions . The second model posits that the MutSβ complex is not required for CTG or CAG slip-out processing or protecting them from repair , but rather MutSβ may be involved in expansion prior to the processing of the slip-outs [33] . An in vitro repair assay using slipped-DNAs with slip-outs of either ( CAG ) 20 or ( CTG ) 20 revealed that these can be correctly repaired , escape repair , or be repaired in an error-prone manner ( only partially excising the slip-outs ) . All of these processes occurred in human cell extracts deficient in either MSH2 or MSH3 . These authors suggested that rather than acting in the processing or protecting CTG or CAG slip-outs from repair , MutSβ may be involved in forming the slip-outs . It is unknown if such an activity would require the endogenous ATPase activity of the protein . The third model suggests that the mismatch repair process itself is involved in trinucleotide repeat expansions [17] . This study demonstrated that the MutL MMR homologue PMS2 is also involved in CTG•CAG repeat somatic expansions in mice . Some repeat expansions are still formed in PMS2-deficient mice suggesting a partial role of PMS2 in trinucleotide repeat instability [17] . The authors proposed that the MMR activity is required to generate somatic expansions implying that the role of MMR proteins would not be limited to the binding of MutSβ complex to the A-A mismatches in CAG hairpins and/or to the MutSβ-mediated formation of slipped-strand structures . In this model the ATPase activity of the MutSβ would be critical to expansions . In an attempt to distinguish between the different hypotheses and to clarify the role of the ATPase activity of MSH2 in the mechanism of CTG•CAG expansion , we crossed Msh2 ATPase-defective mice ( Msh2G674A mice ) with mice carrying a large unstable CTG expansion ( DM300-328 ) [27] , [34] , [35] . The DM300-328 mice carrying >300 CTG repeats in the context of the human genomic DM1 locus of over 45 kb display very high levels of intergenerational and somatic instability similar to that observed in DM1 patients [36] . The Msh2-mutant mice carry a missense mutation of a glycine-to-alanine within the Walker “type A” motif GXXXXGKS/T of the MSH2 ATPase domain [27] , [34] . This ATPase domain in MSH2 and in many proteins with ATPase function , is known to coordinate the phosphate groups of ATP [34] . Mismatch binding experiments using Msh2G674A/G674A ES cell extracts demonstrated that the mutant MSH2G674A-MSH6 complex retained normal mismatch binding activity . However , the complex was resistant to ATP-mediated mismatch release and had lost its capacity to signal mismatch repair resulting in MMR-deficiency similar to that observed in Msh2−/− null mutant extracts . Moreover , the homozygous ATPase mutation caused a mutator phenotype , increased genome-wide mononucleotide and dinucleotide instability in the genome of mutant mice similar to that observed in Msh2−/− mice . However , in contrast to the Msh2-null allele , the Msh2G674A mutation did not significantly affect the cellular response to DNA damage-inducing agents , indicating that normal ATP processing with subsequent repair is not essential for the apoptosis signalling function of MSH2 [34] . Therefore , Msh2G674A mutant mice appeared to be an excellent model to determine if MSH2 ATPase activity is required for CTG•CAG repeat expansions or if MSH2 binding to the trinucleotide repeats is sufficient to generate expansions in transgenic mice . We assessed intergenerational repeat instability over successive generations and somatic mosaicism in CTG repeat containing mice deficient for MSH2 ATPase activity . The Msh2G674A/G674A mice showed a dramatic decrease in CTG repeat expansions and an increase in contractions; a pattern paralleling that observed in Msh2−/− transgenic mice . Our data indicate that the ATPase-defective Msh2G674A mutation affects intergenerational and somatic instability of CTG•CAG repeats in transgenic mice .
To assess the consequence of the G674A Msh2 missense mutation in the generation of expanded trinucleotide repeats , we crossed ATPase-defective Msh2 mutant mice [27] , [34] with DM300-328 mice carrying the human DMPK gene with >300 CTG repeats , which show high levels of intergenerational and somatic instability that is biased towards expansions and age-dependent [36] . We analyzed the length of the CTG repeat inherited from males and females , after weaning , in tail DNA from mice with different Msh2 genotypes , as previously described [36] . The CTG instability in the various transmissions are reported in Table 1 and Figure 1 . In Msh2+/+ transgenic mice , we observed a high level of intergenerational instability biased towards expansions ( 100% and 62 . 3% expansions in offspring from male and female transmissions , respectively ) . In the Msh2G674A/+ transmissions , the frequency of expansions significantly decreased for both male and female transmissions ( from 100% to 75% and from 62 . 3% to 37 . 9% , respectively ) . For both paternal and maternal Msh2G674A/G674A transmissions , the frequency of expansions dramatically decreased ( down to 12 . 1% and 0% in male and female transmissions , respectively ) . At the same time , the frequencies of CTG contractions increased in Msh2G674A/+ and Msh2G674A/G674A transgenic mice . The distributions of the magnitudes of intergenerational CTG repeat length changes were significantly different between Msh2+/+ and Msh2G674A/+ and between Msh2+/+ and Msh2G674A/G674A transmissions; p<0 . 005 and p<0 . 0001 , respectively , both for male and female transmissions ( assessed by the Mann-Whitney test ) . Interestingly , much larger contractions are observed in Msh2G674A/G674A female transmissions ( with a mean of −41 . 9 CTG units ) . The sizes of contractions were positively correlated with the age of the transmitting female ( r2 = 0 . 718 , p<0 . 0001 ) . In summary , these data support the idea that the G674A mutation of one or both Msh2 alleles significantly decreased the frequency of repeat expansions in paternal and maternal transmissions , suggesting that a functional MSH2 ATPase domain is required to generate expansions . Furthermore , these data indicate that the ATPase-mutant MSH2 is sufficient to lead to a preferential formation of CTG contractions that also arise in the Msh2-null and Msh3-null DM1 mice [19] , [20] . Together these results support a requirement of a functional MSH2 ATPase domain for the expansion-biased parent to offspring CTG transmissions , and that this defect is sufficient to cause the formation of transmitted CTG contractions , which also arise in the absence of either MSH2 of MSH3 [19] , [20] . Both MSH2 and MSH3 proteins are required for CTG expansions in somatic tissues [19] , [20] , [37] , yet the requirement of the MSH2 ATPase domain is unknown . Thus , we analysed the somatic CTG instability in several tissues collected from 8-month-old transgenic mice with different Msh2 genotypes . Wild-type mice show inter- and intra-tissue CTG length mosaicism biased towards expansions , pronounced in pancreas and germinal tissues , as previously observed [19] , [36] ( Figure 2 and data not shown for female mice ) . A striking change in somatic instability was not readily observed by standard PCR for the Msh2G674A/+ mice . In contrast , expansions were clearly not detected in Msh2G674A/G674A tissues and contractions were easily observed in testis and sperm , but not in somatic tissues . Somatic instability was assessed with greater sensitivity using small-pool PCR ( SP-PCR ) . Tissues assessed by SP-PCR include ovaries , testis , brain and cerebellum; tissues known to display a range of instability levels in repair-proficient mice . The inherited size in each mouse is represented by the CTG repeat length in blood , liver or in tail at weaning , in which somatic instability is minimal throughout the animal's life [19] . In Msh2G674A/+ mice , the degree of somatic mosaicism seems to be unchanged in all tissues analysed , and where present showed an expansion bias ( Figure 3 ) . In contrast , the degree of somatic CTG mosaicism was strikingly decreased in all tissues tested from the Msh2G674A/G674A mice . Neither expansions nor high levels of contractions were evident by either standard PCR or SP-PCR for the ATPase mutant mice , except in brain , where very small expansions could be detected . The absence of expansions in Msh2G674A/G674A mice parallels their loss in Msh2−/− mice . However , the absence of contractions in somatic tissues of Msh2G674A/G674A mice contrasts with their detection in tissues of Msh2−/− mice [19] . These results show that the homozygous Msh2G674A mutation reduces the formation of CTG•CAG expansions in somatic tissues , while mutation of a single allele has no significant effect . These results also indicate that the Msh2G674A/G674A mutation does not lead to high levels of CTG•CAG contractions in somatic tissues , which contrasts with their formation in tissues of Msh2−/− mice [19] . We have shown that CTG instability is dramatically decreased in Msh2G674A/G674A , supporting the idea that the G674A mutation in MSH2 ATPase domain affects CTG repeat instability . Lin et al . demonstrated that this mutant ATPase domain alters the MMR pathway in Msh2G674A/G674A mice without affecting the mismatch binding activity [34] . The stability of MSH2 , MSH6 and MSH3 proteins depends on the presence of each partner involved in the formation of the MutSα and MutSβ complexes [38] , [39] . In embryonic fibroblasts from Msh2G674A/G674A mice , expression levels of MSH2 and MSH6 proteins were normal , suggesting that the ATPase mutation did not alter the stability of these proteins [34] . We assessed the amount of MSH2 protein in germinal and cerebral tissues from 3 to 4-month-old transgenic mice with different Msh2 genotypes , by western blot . We observed a decrease of MSH2 protein in Msh2G674A/G674A germinal tissues ( to 13% in ovaries , to 50% in testis , p<0 . 001 , t-test ) , cerebellum ( to 19% p<0 . 001 , t-test ) and brain ( to 62% , p = 0 . 005 , t-test ) ( Figure 4 ) . In mice heterozygous for the G674A mutation , MSH2 protein levels were also decreased only in germinal tissues ( to 37% and 80% in ovaries and testis respectively , p<0 . 001 , t-test ) . No detectable variation in protein levels was observed in brain and cerebellum of heterozygous mice . To ensure that the observed decrease of MSH2 protein was not due to a decreased sensitivity/avidity of the mutant MSH2 to the anti-MSH2 antibody , we also assessed the levels of MSH3 and MSH6 proteins and found these to be decreased in tissues of the Msh2 ATPase-mutant mice with a pattern similar to the decrease of MSH2 ( Figure 5 ) . To determine if the mutation decreases MSH2 expression at the RNA level , we measured transcript levels in Msh2G674A/G674A and wild-type tissues by qRT-PCR . Our analysis showed that the quantity of Msh2G674A transcripts is unchanged in homozygous mutant mice compared to wild-type mice ( data not shown ) . Therefore , our results suggest that the missense mutation in MSH2 ATPase domain probably destabilizes MSH2 protein in adult mice .
Since the discovery of dynamic mutations , which are responsible for more than 20 neurological and/or neuromuscular diseases , many studies have focused on understanding the molecular mechanism of instability . Various data have clearly revealed that MSH2 , MSH3 , MSH6 and PMS2 are required for CTG•CAG expansions in mouse model systems [15]–[20] . However , the mode of action of these proteins in the generation of trinucleotide expansions remains unclear . It has been reported that MSH2-MSH3 ATPase activity was reduced upon binding to CAG hairpins , this was interpreted as impairing the repair of the CAG hairpin permitting its integration as an expansion [18] . In contrast , MutSβ was not required for either the correct or protected repair of slipped-CTG•CAG repeats , suggesting that MutSβ may be required to form these DNA structures with an unknown involvement of ATP hydrolysis [33] . Nevertheless , the role of the MutL homologue PMS2 in the formation of somatic expansions suggests that MMR pathway could be involved in the mechanism of instability [17] . In our study , we have shown that the G674A mutation in MSH2 ATPase domain strongly affects the formation of intergenerational expansions . Msh2G674A/G674A transgenic mice showed a strong bias towards contractions compared to Msh2+/+ transgenic mice which showed a strong bias towards expansions in their offspring . In the homozygous Msh2G674A/G674A mice , we observed only 12 . 1% of expansions for paternal transmissions and no expansions for maternal transmissions ( versus 100% and 62 . 3% in both male and female wild-type transmissions , respectively ) . These results suggested that a functional MSH2 ATPase domain is necessary to generate intergenerational expansions . These results also suggest that the defective ATPase domain is sufficient to yield a contraction bias similar to the Msh2-null mice . In testis , where 89 . 3% of seminiferous tubules are germinal cells [40] , we observed a decrease of MSH2 levels of ∼50% in the Msh2G674A/G674A mice . This coupled with the severe reduction of expansions upon male Msh2G674A/G674A transmissions ( from 100% for Msh2+/+ down to 12% for the mutant mice ) compared to the negligible effect for Msh2−/+ mice ( 92% , Table S1 and [19] ) strongly argue that a functional ATPase activity is required for expansions . Then , the deficiency of the ATPase domain in conjunction with decreased protein levels are responsible for the decrease of expansion frequency . Furthermore , it demonstrates that the apoptosis signaling function of MSH2 , described to be non affected by the G674A mutation [34] , is not involved in the mechanism generating CTG repeat expansions . We also observed that the G674A mutation of one Msh2 allele was sufficient to decrease the frequency of expansions in both male and female transmissions ( from 100% to 75% expansions and from 62 . 3% to 37 . 9% for male and female transmissions , respectively ) . Such a decrease in the frequency of expansions was not observed for male transmissions in heterozygous Msh2−/+ mice which displayed 92% expansions similar to Msh2+/+ mice; indicating that in this context a single functional Msh2 allele was sufficient to drive the same level of expansions as two functional alleles [19] . In contrast , our results show that a functional MSH2 protein in the presence of an ATPase mutant MSH2G674A protein was not sufficient to drive the same levels of expansions; but yielded reduced expansion levels for paternal and maternal transmissions – further supporting a requirement of the ATPase domain . To this degree our data suggest that the MSH2G674A mutant protein could act as a dominant-negative mutant , effectively blocking the ability of the wild-type MSH2 protein from driving CTG•CAG expansions . This purported dominant negative function would be specific to CTG•CAG expansions , as a dominant-negative mutator effect was not observed for either mono- or dinucleotide microsatellites in the heterozygous Msh2G674A/+ mice [34] . In concordance with our observed CTG•CAG effect , the S . cerevisiae yMsh2 protein with the G693A ( equivalent to the mammalian G674A mutation ) or G693D mutated ATPase domains cause a dominant mutator phenotype [41] , [42] . The mutant yMsh2G693A-yMsh6 complex binds to DNA mismatches and is not released in the presence of ATP . This could block access of wild-type proteins and subsequently inhibit normal repair [43] . In the case of CTG•CAG repeats , the MSH2G674A mutant protein could partially block the access of the wild-type MSH2-MSH3 complex to unpaired CAG repeats , therefore protecting it from being processed to expansions . There is no data concerning the ability of the MSH2G674A-MSH3 complex to bind trinucleotide repeats . The analogous mutation in the conserved Walker domain of the prokaryote MutS protein ( K620M ) results in a weak mismatch binding activity and in a decrease in ATP binding and hydrolysis [24] , [44] . However , in the case of MutS homoduplex in bacteria , both subunits of the complex are affected by the mutation . In eukaryotes , MSH2 partners ( MSH3 or MSH6 ) retain their capacity to bind and hydrolyse ATP and it has been shown that MSH6 binds ATP with higher affinity and hydrolyses ATP faster than MSH2 [45] . Mispair binding is regulated by mismatch-stimulated ADP to ATP exchange inducing conformational changes to the N-terminus mispair binding domain [24] , [46] . Recent data have shown that human MutSα and MutSβ have different ADP binding and ATPase activities in vitro that could explain the preferential processing of base-base and insertion/deletion mispairs by these two complexes [25] . In the mice used in this study , the MSH2G674A-MSH6 complex is able to bind to mismatches but the ATP-mediated disassociation from the substrate containing a G/T mismatch is reduced [34] . It is not known if the binding of MSH2G674A-MSH3 is affected by the mutation . However , the possible dominant-negative function of MSH2G674A on CTG•CAG expansions suggest that MSH2G674A-MSH3 may still be able to bind to trinucleotide repeat expansions . In Msh2G674A/G674A mice , we observed a dramatic decrease of somatic CTG expansions together with a decrease of MSH2 protein levels in adult somatic tissues , such as brain and cerebellum . Because the stability of MSH2 , MSH3 and MSH6 depends on the ability of these proteins to form heterodimers , the levels of MSH3 and MSH6 were also found decreased in mutant tissues . However , MSH3 and MSH6 were clearly detected in homozygous mutant mice showing that the mutant MSH2G674A protein was still able to form the MutSβ and MutSα complexes . While this reduction varied between tissues , a role of the ATPase domain in somatic instability seemed to be implicated . The dramatic loss of CTG instability in Msh2G674A/G674A mouse brains , which show as much as 62% of MSH2 protein strongly suggests that a functional ATPase domain is required for the formation of somatic expansions . The decrease of protein levels in the germinal and somatic tissues of Msh2G674A/G674A mice was unexpected as no decrease was observed in MEF cells from these mice [34] . Furthermore , Ollila et al recently observed that the production of human recombinant MSH2G674A-MSH6 complex was expressed in amount similar to the wild-type hMutSα suggesting that the G674A mutation did not affect protein stability [47] . However , a lowered interaction of MSH2G674A with MSH6 was reported in this study . Interestingly , the difference in hMutSα expression levels between various cell lines was shown to result from cell-dependent differences in the degradation rate of both proteins forming the complex , by the ubiquitin-proteasome pathway [48] . Furthermore , the protein kinase PKCζ is able to phosphorylate hMutSα and regulates MSH2 and MSH6 protein stability and levels by inhibiting the ubiquitin-proteasome dependent degradation of these proteins [49] . It is unknown if the stability of MSH3 is regulated by the same pathway but the regulation of MSH2 stability has a direct impact on the regulation of MutSβ . It is possible that the Msh2G674A mutation affects the sensitivity of MSH2 to the ubiquitin-proteasome degradation pathways possibly by altering its ability to be phosphorylated . In this manner a tissue-specificity in the efficiency of the ubiquitin-proteasome degradation pathways would account for the different decrease of MSH2G674A protein that we observed in the various mouse tissues . Interestingly , we found a putative PKC phosphorylation site at threonine 677; proximal to the G674A mutation , in the human and mouse MSH2 proteins using the NetPhosK 1 . 0 server ( output score 0 . 89 using the Evolutionary Stable Sites filter ) [50] . While tissue-specific regulation of MSH2 protein levels is beyond the scope of the present study , it is clear that the altered CTG instability we observe with the Msh2G674A mutation indicates that an ATPase-competent MSH2 protein is necessary to drive CTG•CAG expansions and that in its absence contractions predominate in parent-to-offspring transmissions . It is noteworthy , that the levels of CTG•CAG contractions in either Msh2G674A/G674A , or Msh3-null mice is considerably greater in parent-offspring transmissions than in their somatic tissues ( [20] and this study ) . The formation of high levels of contractions of expanded CTG•CAG repeats transmitted from parent-to-offspring depends upon the homozygous loss of Msh2 or Msh3 , or a homozygous Msh2 G674A/G674A mutation , and occur to some degree in a heterozygous Msh2G674A/+ mutant . One interpretation is that the contractions would normally have been prevented by the presence of MSH3 and MSH2 with a functional ATPase domain . It would be of interest to learn if the germline-specific MSH2–MSH3:MLH3-MLH1/PMS2 complexes , detected at centromeric and Y chromosome repeats and proposed to prevent deleterious crossover events at repetitive DNAs [28] , also form at expanded CTG•CAG transgenes in germ cells of transgenic mice . They may be linked to transmitted repeat contractions . In conclusion , our finding that a functional MSH2 ATPase domain is required for CTG•CAG expansion mutations makes this the third mutagenic process for which its activity is required . The ATPase activity of MSH2 has been previously demonstrated to be required for both somatic hypermutation and class switch recombination at immunoglobin genes [26] , [27] . Our data demonstrate that the Msh2G674A mutation affects CTG•CAG repeat instability in transgenic mice and almost completely abolishes the formation of intergenerational and somatic expansions and leads to a high level of contractions in parental transmissions . The G674A mutation also decreases the levels of MSH2 protein in a tissue-specific manner . The decrease of MSH2 protein cannot account totally for the dramatic decrease of expansions . Therefore , CTG•CAG repeat expansions are likely affected by cumulative effects of its defective ATPase domain and reduced MSH2 protein levels . If we assume that the binding of MSH2G674A-MSH3 is not affected by the mutation , our data would suggest that the binding of MutS complexes to the trinucleotide repeats is not sufficient to drive instability towards expansion and that a fully functional MSH2 ATPase domain is required . This does not support the hypothesis that a decrease of ATP hydrolysis by MSH2-MSH3 bound to trinucleotide repeats and aborted repair of DNA hairpin loops could cause expansions . On the contrary , our data sustain the hypothesis according to which a functional MMR activity is required to generate expansions .
DM300-328 transgenic mice ( >90% C57BL/6 background ) carrying very large human genomic sequences ( 45 kb ) and >300 CTG repeats [36] were crossed over successive generations with Msh2G674A mutant mice [27] , [34] . Genotype was determined by PCR on 25 ng of DNA extracted from tail at weaning . Transgenic status of mice was performed with 0 . 5 µM of oligonucleotide primers HS3 ( 5′-TGAAGATGAAGCAGACGGG-3′ ) and HS4 ( 5′-TCCCCATTCACCAACAC-3′ ) , 1× ReddyMix PCR Master Mix ( Thermo Scientific ) . The DNA was denatured by heating to 94° for 6 min . Reactions involved 30 cycles of 94°C ( 30 s ) , 55°C ( 30 s ) and 72°C ( 30 s ) with a chase of 10 min at 72°C . Msh2 genotype was determined with 0 . 35 mM of oligonucleotide primers in12/13 ( 5′-GTGGGTTTGTCTGACTGAATG-3′ ) and ex13/3 ( 5′ -GGATGGAAGAAGTCTCCAGC-3′ ) , 1× ReddyMix PCR Master Mix and 2 . 5 mM of MgCl2 . After initial denaturation for 10 min at 94°C , reactions were performed for 30 s at 94°C , 30 s at 60°C and 45 s at 72°C for 35 cycles followed by a chase of 7 min at 72°C . The sizes of predicted PCR products were 596 and 411 bp for mutant and wild-type alleles , respectively . Msh2−/+ ( 129/OLA , FVB background ) mutant mice and genotype by multiplex PCR have been described by de Wind et al . [51] . Housing and handling of mice were performed according to French government ethical guidelines . Intergenerational instability was investigated by comparison of the CTG repeat lengths in transgenic parent and offspring . DNA was extracted from tail collected at weaning by isopropanol precipitation . To determine the CTG repeat length in transgenic mice , 15 ng of tail DNA samples were amplified in 25 µl reaction using 0 . 4 µM 101 and 102 primers [36] , 1× Custom master mix ( Thermo Scientific ) and 0 . 04U Thermoperfect Taq polymerase ( Integro BV ) . The following cycling conditions were used: 5 min at 96°C; 45 s at 96°C , 30 s at 68°C and 3 min at 72°C ( 30 cycles ) ; 1 min at 68°C and 10 min at 72°C ( 1 cycle ) . Electrophoresis of PCR products was performed using a previously described method [52] . The amplified product ( from 2 µl to 5 µl ) was mixed with DNA-loading dye and subjected to electrophoresis in 0 . 7–0 . 8% agarose gels at 300 V for 30 min and then 160 V overnight at 4°C . After electrophoresis , the gel was incubated in depurinating solution ( 0 . 25 M HCl ) for 10 min , denaturing solution ( 0 . 5 M NaOH , 1 . 5 M NaCl ) for 30 min and neutralization solution ( 1 . 5 M NaCl , 0 . 5 M Tris-HCl , pH 7 . 5 ) for 30 min . DNA was blotted onto positive nylon membrane ( MP Biomedicals ) , UV-crosslinked ( UV Stratalinker 2400 , Stratagene ) and hybridised with a double-strand CTG DNA probe radiolabelled with α-32P-dCTP using Amersham ready-to-go™ DNA labelling beads ( -dCTG ) ( GE Healthcare ) . CTG repeat lengths were compared with a 250 bp DNA ladder loaded on the same gel and analysed by Quantity One-1-D Analysis Software ( Bio-Rad Laboratories ) . Somatic instability was analysed in various tissues collected from 8-month-old mice . DNA was phenol/chloroform-extracted from tissues [14] and sperm DNA was extracted according to Seznec et al . [36] . CTG repeat instability was determined as described above . DNA extraction , SP-PCR amplifications and PCR product electrophoretic analyses were performed using the methods previously described [52] . DNA samples were digested with HindIII and SP-PCR was performed with DM-C and DM-BR primers [52] . SP-PCR products from mice with different Msh2 genotypes and tail/blood or tail/liver PCR products were loaded on the same 0 . 7% agarose gel to compare instability between Msh2+/+ , Msh2G674A/+ and Msh2G674A/G674A mice . The different tissues were collected from 3 to 4-month-old-mice with different Msh2 genotypes and pooled for protein extraction ( three mice for each genotype ) . Proteins were extracted by mechanical homogenisation in lysis buffer ( 0 . 125 M Tris-HCl pH 6 . 8 , 4% SDS , 10% glycerol ) containing complete Mini 7× protease inhibitor cocktail ( Roche ) and 1 mM PMSF ( Sigma ) . Protein concentration was determined using the Bio-Rad protein assay . 20 µg of proteins were denatured 5 min at 95°C in Laemmli sample buffer ( Bio-Rad ) supplemented with 5% β-mercaptoethanol added extemporaneously , resolved by electrophoresis on a 10% polyacrylamide SDS-PAGE gel and electroblotted onto Millipore Immobilon-P membranes ( Millipore ) in tranfer buffer ( 25 mM Tris-HCl pH 8 . 0 , 192 mM glycine , 20% methanol and 0 . 1% SDS ) at 350 mA at 4°C for 1h30 . Membranes were blocked for one hour at room temperature in 5% blotto in TBST pH 7 . 5 ( 10 mM Tris-HCl , 0 . 15 mM NaCl and 0 . 05% Tween 20 ) , then incubated overnight at 4°C in primary antibody . The membranes were washed once for 5 min , 10 min and 15 min each in TBST , incubated for 1h in secondary antibody ( Jackson Immuno-research , sheep anti-mouse-HRP , 1∶5000 ) at room temperature for MSH2 , MSH3 , MSH6 and for actin , and washed one time for 5 min , 10 min and 15 min each in TBST . Antibody binding was visualized using ECL™ Western blotting analysis system and ECL plus Western blotting detection system ( Amersham ) . MSH2 , MSH6 , and actin were detected using antibodies mouse anti-MSH2 ( Oncogene , Ab-2 , 1∶200 ) , mouse anti-MSH6 ( BD Laboratories , 1∶500 ) , and mouse monoclonal β-actin ( gift from Dr Manuel Hernàndez , 1∶400 ) . The MSH3 monoclonal antibody was raised against an N-terminal fragment of mouse MSH3 ( amino acids 24 to 308 of NCBI sequence NP_034959 ) and its specificity was confirmed using Msh3+/+ and Msh3−/− mouse tissues ( dilution 1∶500 ) . Western blotting was reproduced at least three times for each tissue to make semi-quantitative analysis . Densitometric analysis of protein levels was performed using Quantity One-1-D Analysis Software ( Bio-Rad Laboratories ) using non-saturated exposures . Statistical analyses for instability distributions were performed with StatView software using the Mann-Whitney test ( SAS Institute Inc . ) . The statistical analysis of the levels of MSH2 protein was performed with StatView software using t-test . The detected differences in total transmissions and in MSH2 protein levels were considered statistically significant only if p<0 . 05 .
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Myotonic dystrophy type 1 is a neuromuscular disease characterized by highly variable clinical manifestations , including muscular and neuropsychological symptoms . DM1 results from the dramatic expansion of an unstable CTG repeat in the DMPK gene . Longer CTG repeats cause a more severe form of the disease and an earlier age of onset . The DNA mismatch repair proteins MSH2 and MSH3 are known to be major players in the formation of trinucleotide expansions . Nevertheless , the mode of action of these proteins remains elusive . In order to get further insight into the role of MSH2 in the formation of CTG expansions , we used a mouse model carrying a mutation in the conserved ATPase domain of Msh2 . This mutation affects the function of this domain and alters the DNA repair mismatch activity . After breeding of these mice with mice carrying highly unstable CTG repeats , we found that the ATPase domain mutation of MSH2 strongly affects the formation of CTG expansions . Our findings show that expansion-biased trinucleotide repeat instability requires a functional MSH2 ATPase domain and support the hypothesis , according to which a functional MMR activity is required to generate expansions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neurological",
"disorders/neuromuscular",
"diseases",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"molecular",
"biology/dna",
"repair"
] |
2009
|
MSH2 ATPase Domain Mutation Affects CTG•CAG Repeat Instability in Transgenic Mice
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Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo . Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization . However , estimating and interpreting large correlation matrices is statistically challenging . Estimation can be improved by regularization , i . e . by imposing a structure on the estimate . The amount of improvement depends on how closely the assumed structure represents dependencies in the data . Therefore , the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically . Importantly , the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system . We sought statistically efficient estimators of neural correlation matrices in recordings from large , dense groups of cortical neurons . Using fast 3D random-access laser scanning microscopy of calcium signals , we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep ( 150–350 cells ) in mouse visual cortex . We hypothesized that in these densely sampled recordings , the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs . Indeed , in cross-validation tests , the covariance matrix estimator with this structure consistently outperformed other regularized estimators . The sparse component of the estimate defined a graph of interactions . These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive ‘excitatory’ interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative ‘inhibitory’ interactions were less selective . Because of its superior performance , this ‘sparse+latent’ estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix .
Functional connectivity is a statistical description of observed multineuronal activity patterns not reducible to the response properties of the individual cells . Functional connectivity reflects local synaptic connections , shared inputs from other regions , and endogenous network activity . Although functional connectivity is a phenomenological description without a strict mechanistic interpretation , it can be used to generate hypotheses about the anatomical architecture of the neural circuit and to test hypotheses about the processing of information at the population level . Pearson correlations between the spiking activity of pairs of neurons are among the most familiar measures of functional connectivity [1–5] . In particular , noise correlations , i . e . the correlations of trial-to-trial response variability between pairs of neurons , have a profound impact on stimulus coding [1 , 2 , 6–11] . In addition , noise correlations and correlations in spontaneous activity have been hypothesized to reflect aspects of synaptic connectivity [12] . Interest in neural correlations has been sustained by a series of discoveries of their nontrivial relationships to various aspects of circuit organization such as the physical distances between the neurons [13 , 14] , their synaptic connectivity [15] , stimulus response similarity [3–5 , 15–22] , cell types [23] , cortical layer specificity [24 , 25] , progressive changes in development and in learning [26–28] , changes due to sensory stimulation and global brain states [21 , 29–33] . Neural correlations do not come with ready or unambiguous mechanistic interpretations . They can arise from monosynaptic or polysynaptic interactions , common or correlated inputs , oscillations , top-down modulation , and background network fluctuations , and other mechanisms [34–39] . But multineuronal recordings do provide more information than an equivalent number of separately recorded pairs of cells . For example , the eigenvalue decomposition of the covariance matrix expresses shared correlated activity components across the population; common fluctuations of population activity may be accurately represented by only a few eigenvectors that affect all correlation coefficients . On the other hand , a correlation matrix can be specified using the partial correlations between pairs of the recorded neurons . The partial correlation coefficient between two neurons reflects their linear association conditioned on the activity of all the other recorded cells [40] . Under some assumptions , partial correlations measure conditional independence between variables and may more directly approximate causal effects between components of complex systems than correlations [40] . For this reason , partial correlations have been used to describe interactions between genes in functional genomics [41 , 42] and between brain regions in imaging studies [43 , 44] . These opportunities have not yet been explored in neurophysiological studies where most analyses have only considered the distributions of pairwise correlations [2 , 4 , 5 , 13] . However , estimation of correlation matrices from large populations presents a number of numerical challenges . The amount of recorded data grows only linearly with population size whereas the number of estimated coefficients increases quadratically . This mismatch leads to an increase in spurious correlations , overestimation of common activity ( i . e . overestimation of the largest eigenvalues ) [45] , and poorly conditioned partial correlations [41] . The sample correlation matrix is an unbiased estimate of the true correlations but its many free parameters make it sensitive to sampling noise . As a result , on average , the sample correlation matrix is farther from the true correlation matrix than some structured estimates . Estimation can be improved through regularization , the technique of deliberately imposing a structure on an estimate in order to reduce its estimation error [41 , 46] . To ‘impose a structure’ on an estimate means to bias ( ‘shrink’ ) it toward a reduced representation with fewer free parameters , the target estimate . The optimal target estimate and the optimal amount of shrinkage can be obtained from the data sample either analytically [41 , 45 , 47] or by cross-validation [48] . An estimator that produces estimates that are , on average , closer to the truth for a given sample size is said to be more efficient than other estimators . Although regularized covariance matrix estimation is commonplace in finance [47] , functional genomics [41] , and brain imaging [44] , surprisingly little work has been done to identify optimal regularization of neural correlation matrices . Improved estimation of the correlation matrix is beneficial in itself . For example , improved estimates can be used to optimize decoding of the population activity [48 , 49] . But reduced estimation error is not the only benefit of regularization . Finding the most efficient among many regularized estimators leads to insights about the system itself: the structure of the most efficient estimator is a parsimonious representation of the regularities in the data . The advantages due to regularization increase with the size of the recorded population . With the advent of big neural data [50] , the search for optimal regularization schemes will become increasingly relevant in any model of population activity . Since optimal regularization schemes are specific to systems under investigation , the inference of functional connectivity in large-scale neural data will entail the search for optimal regularization schemes . Such schemes may involve combinations of heuristic rules and numerical techniques specially designed for given types of neural circuits . What structures of correlation matrices best describe the multineuronal activity in specific circuits and in specific brain states ? More specifically , are correlations in the visual cortex during visual stimulation best explained by common fluctuations or by local interactions within the recorded microcircuit ? To address these questions , we evaluated four regularized covariance matrix estimators that imposed different structures on the estimate . The estimators are designated as follows: Csample—the sample covariance matrix , the unbiased estimator . Cdiag—linear shrinkage of covariances toward zero , i . e . toward a diagonal covariance matrix . Cfactor—a low-rank approximation of the sample covariance matrix , representing inputs from unobserved shared factors ( latent units ) . Csparse—sparse partial correlations , i . e . a large fraction of the partial correlations between pairs of neurons are set to zero . Csparse+latent—sparse partial correlations between the recorded neurons and linear interactions with a number of latent units . First , we used simulated data to demonstrate that the selection of the optimal estimator indeed pointed to the true structure of the dependencies in the data . We then performed a cross-validated evaluation to establish which of the four regularized estimators was most efficient for representing the population activity of dense groups of neurons in mouse primary visual cortex recorded with high-speed 3D random-access two-photon imaging of calcium signals . In our data , the sample correlation coefficients were largely positive and low . We found that the most efficient estimator of the correlation matrix in these data was Csparse+latent . This estimator revealed a sparse network of partial correlations ( ‘interactions’ ) , between the observed neurons; it also inferred a number of latent units interacting with the observed neurons . We analyzed these networks of partial correlations and found the following: Whereas significant noise correlations were predominantly positive , the inferred interactions had a large fraction of negative values possibly reflecting inhibitory circuitry . Moreover , the inferred positive interactions exhibited a substantially stronger relationship to the physical distances and to the differences in preferred orientations than noise correlations . In contrast , the inferred negative interactions were less selective .
The covariance matrix is defined as Σ = E [ ( x − μ ) ( x − μ ) T ] , μ = E [ x ] ( 1 ) where the p × 1 vector x is a single observation of the firing rates of p neurons in a time bin of some duration , E [ ⋅ ] denotes expectation , and μ is the vector of expected firing rates . Given a set of observations {x ( t ) : t ∈ T} of population activity , where x ( t ) contains observed firing rates in time bin t , and an independent estimate of the mean firing rates x¯ , the sample covariance matrix , C sample = 1 n ∑ t ∈ T ( x ( t ) − x ¯ ) ( x ( t ) − x ¯ ) T , ( 2 ) where n is the number of time bins in T , is an unbiased estimate of the true covariance matrix , i . e . E [ C sample ] = Σ . In all cases when the unbiasedness of the sample covariance matrix matters in this paper , the mean activity is estimated independently from a separate sample . Given any covariance matrix estimate C , the corresponding correlation matrix R is calculated by normalizing the rows and columns of C by the square roots of its diagonal elements to produce unit entries on the diagonal: R = ( diag ( C ) ) − 1 2 C ( diag ( C ) ) − 1 2 , ( 3 ) where diag ( C ) denotes the diagonal matrix with the diagonal elements from C . The partial correlation between a pair of variables is the Pearson correlation coefficient of the residuals of the linear least-squares predictor of their activity based on all the other variables , excluding the pair [40 , 51] . Partial correlations figure prominently in probabilistic graphical modeling wherein the joint distribution is explained by sets of pairwise interactions [40] . For multivariate Gaussian distributions , zero partial correlations indicate conditional independence of the pair , implying a lack of direct interaction [40 , 52] . More generally , partial correlations can serve as a measure of conditional independence under the assumption that dependencies in the system are close to linear effects [40 , 53] . As neural recordings become increasingly dense , partial correlations may prove useful as indicators of conditional independence ( lack of functional connectivity ) between pairs of neurons . Pairwise partial correlations are closely related to the elements of the precision matrix , i . e . the inverse of the covariance matrix [40 , 52] . Zero elements in the precision matrix signify zero partial correlations between the corresponding pairs of variables . Given the covariance estimate C , the matrix of partial correlations P is computed by normalizing the rows and columns of the precision matrix C−1 to produce negative unit entries on the diagonal: P = − ( diag ( C − 1 ) ) − 1 2 C − 1 ( diag ( C − 1 ) ) − 1 2 ( 4 ) Increasing the number of recorded neurons results in a higher condition number of the sample covariance matrix [45] making the partial correlation estimates more ill-conditioned: small errors in the covariance estimates translate into greater errors in the estimates of the partial correlations . With massively multineuronal recordings , partial correlations cannot be estimated without regularization [41 , 45] . We considered four regularized estimators based on distinct families of target estimates: Cdiag , Cfactor , Csparse , and Csparse+latent . In probabilistic models with exclusively linear dependencies , the target estimates of these estimators correspond to distinct families of graphical models ( Fig . 1 Row 1 ) . The target estimate of estimator Cdiag is the diagonal matrix D containing estimates of neurons’ variances . Regularization is achieved by linear shrinkage of the sample covariance matrix Csample toward D as controlled by the scalar shrinkage intensity parameter λ ∈ [0 , 1]: C diag = ( 1 − λ ) C sample + λ D ( 5 ) The structure imposed by Cdiag describes a population with no linear associations between the neurons ( Fig . 1 Row 1 , A ) . If sample correlations are largely spurious , Cdiag is expected to be more efficient than other estimators . Estimator Cfactor approximates the covariance matrix by the factor model , C factor = L + D , ( 6 ) where L is a p × p symmetric positive semidefinite matrix with low rank and D is a diagonal matrix . This approximation is the basis for factor analysis [51] , where matrix L represents covariances arising from latent factors . The rank of L corresponds to the number of latent factors . Matrix D contains the variances of the cells’ independent activity from the latent factors . The estimator is regularized by selecting the rank of L and by shrinking the independent variances in D toward their mean . The structure imposed by Cfactor describes a population whose activity is linearly driven by a number of latent factors that affect many cells while direct interactions between the recorded cells are insignificant ( Fig . 1 Row 1 , B ) . Estimator Csparse is produced by approximating the sample covariance matrix by the inverse of a sparse matrix S: C sparse = S − 1 . ( 7 ) The estimator is regularized by adjusting the sparsity ( fraction of off-diagonal zeros ) of S . The problem of finding the optimal set of non-zero elements in S is known as covariance selection [52] . The structure imposed by Csparse describes conditions in which neural correlations arise from direct linear effects ( ‘interactions’ ) between some pairs of neurons ( Fig . 1 Row 1 , C ) . Estimator Csparse+latent is obtained by approximating the sample covariance matrix by a matrix whose inverse is the difference of a sparse component and a low-rank component: C sparse + latent = ( S − L ) − 1 , ( 8 ) where S is a sparse matrix and L is a low-rank matrix . The estimator is regularized by adjusting the sparsity of S and the rank of L . See Methods for more detailed explanations . The structure imposed by Csparse+latent favors conditions in which the activity of neurons is determined by linear effects between some observed pairs of neurons and linear effects from several latent units ( Fig . 1 Row 1 , D ) [54 , 55] . We refer to the sparse partial correlations in estimators Csparse and Csparse+latent as ‘interactions’ . We next demonstrated how the most efficient among different regularized estimators can reveal the structure of correlations . We constructed four families of 50 × 50 covariance matrices , each with structure that matched one of the four regularized estimators ( Fig . 1 Row 2 , A–D and Methods ) . We used these covariance matrices as the ground truth in multivariate Gaussian distributions with zero means and drew samples of various sizes . The sample correlation matrices from finite samples ( e . g . n = 500 in Fig . 1 Row 3 ) were contaminated with sampling noise and their underlying structures were difficult to discern . The evaluation of any covariance matrix estimator , C , is performed with respect to a loss function ℓ ( C , Σ ) to quantify its discrepancy from the truth , Σ . The loss function is chosen to attain its minimum when C = Σ . Here , in the role of the loss function we adopted the Kullback-Leibler divergence between multivariate normal distributions with equal means , scaled by 2 p to make its values comparable across different population sizes: ℓ ( C , Σ ) = 2 p D K L ( N Σ ‖ N C ) = 1 p [ Tr ( C − 1 Σ ) + lndetC − lndetΣ − p ] ( 9 ) Thus ℓ ( C , Σ ) is expressed in nats/neuron per time bin . When the ground truth is not accessible , the loss cannot be computed directly but may be estimated from data through validation . In a validation procedure , a validation sample covariance matrix C sample ′ is computed from a testing data set that is independent from the data used for computing C . Then the validation loss ℒ ( C , C sample ′ ) measures the discrepancy of C from C sample ′ . Here , in the role of validation loss , we adopted the negative multivariate normal log likelihood of C given C sample ′ , also scaled by 2 p and omitting the constant term: L ( C , C sample ′ ) = 1 p [ Tr ( C − 1 C sample ′ ) + /lndetC ] ( 10 ) Since L ( ⋅ , ⋅ ) is additive in its second argument and C sample ′ is an unbiased estimate of Σ , then , for given C and Σ , the validation loss is an unbiased estimate of the true loss , up to a constant: E [ L ( C , C sample ′ ) ] = L ( C , E [ C sample ′ ] ) = L ( C , Σ ) = ℓ ( C , Σ ) + const . ( 11 ) Therefore , the validation procedure allows comparing the relative values of the loss attained by different covariance matrix estimators even without access to the ground truth . We drew 30 independent samples with sample sizes n = 250 , 500 , 1000 , 2000 , and 4000 from each model and computed the loss ℓ ( C , Σ ) for each of the five estimators . The hyperparameters of the regularized estimators were optimized by nested cross-validation using only the data in the sample . All the regularized estimators produced better estimates ( lower loss ) than the sample covariance matrix . However , estimators whose structure matched the true model outperformed the other estimators ( Fig . 1 Rows 4 and 5 ) . The validation loss computed by ten-fold cross-validation ( see Methods ) accurately reproduced the relative values of the true loss as well as the rankings of the estimators even without access to the ground truth ( Fig . 1 Row 6 ) . Note that when the ground truth had zero correlations ( Column A ) , Cfactor performed equally well to Cdiag because it correctly inferred zero factors and only estimated the individual variances . Similarly , when the number of latent units was zero ( Column C ) , Csparse+latent performed nearly equally well to Csparse because it correctly inferred zero latent units . With increasing sample sizes , all estimators converged to the ground truth ( zero loss ) but the estimators with correct structure outperformed the others even for large samples . In Gaussian models , the pairwise partial correlations perfectly characterize the conditional dependencies between the variables . To demonstrate that estimator rankings were robust to deviations from Gaussian models , we repeated the same cross-validated evaluation using pairwise Ising models to generate the data . Ising models have been used to infer functional connectivity from neuronal spike trains [56] . Conveniently , the Ising model has equivalent mathematical form to the Gaussian distribution , x ∼ 1 Z ( J , h ) exp ( 1 2 x T J x + h T x ) ( 12 ) but the Ising model is defined on the multivariate binary domain rather than the continuous domain . Both models are maximum-entropy models constrained to match the mean firing rates and the covariance matrix [57] . The partition function Z ( J , h ) normalizes the distributions on the models’ respective domains . In the Gaussian model , the matrix −J−1 is the covariance matrix; and the mean values are μ = J−1 h . For the Ising model , J is the matrix of pairwise interactions and h is the vector of the cells’ individual activity drives , although they do not have a simple relationship to the means and the covariance matrix . Both distributions have the same structure of pairwise conditional dependencies: zeros in the matrix J indicate conditional independence between the corresponding pair of neurons . Indeed , despite their considerable departure from strictly linear conditional dependencies , Ising models yielded the same relationships between the performances of the covariance estimators as the Gaussian models in cross-validation ( Fig . 2 ) . Identical interaction matrices J of the joint distributions over the observable and latent variables were used for both the Gaussian and the Ising models . This simulation study demonstrated that cross-validated evaluation of regularized estimators of the covariance matrices of population activity can discriminate between structures of dependencies in the population . The selection of the most efficient covariance estimators for particular neural circuits is therefore an empirical finding characteristic of the nature of circuit interactions . We recorded the calcium activity of densely sampled populations of neurons in layers 2/3 and upper layer 4 in primary visual cortex of sedated mice using fast random-access 3D scanning two-photon microscopy during visual stimulation ( Fig . 3 A–B ) [58–60] . This technique allowed fast sampling ( 100–150 Hz ) from large numbers ( 150–350 ) of cells in 200 × 200 × 100 μm3 volumes of cortical tissue ( Fig . 3 C and D ) . The instantaneous firing rates were inferred using sparse nonnegative deconvolution [61] ( Fig . 3 C ) . Only cells that produced detectable calcium activity were included in the analysis ( see Methods ) . First , 30 repetitions of full-field drifting gratings of 16 directions were presented in random order . Each grating was played for 500 ms , without intervening blanks . This stimulus was used to compute the orientation tuning of the recorded cells ( Fig . 3 D ) . To estimate the noise correlation matrix , we presented only two distinct directions in some experiments or five directions in others with 100–300 repetitions of each condition . Each grating lasted 1 second and was followed by a 1-second blank . The traces were then binned into 150 ms intervals aligned on the stimulus onset for the estimation of the correlation matrix . The sample correlation coefficients were largely positive and low ( Fig . 3 E and F ) . The average value of the correlation coefficient across sites ranged from 0 . 0065 to 0 . 051 with the mean across sites of 0 . 018 . In these densely sampled populations , direct interactions between cells are likely to influence the patterns of population activity . We therefore hypothesized that covariance matrix estimators that explicitly modeled the partial correlations between pairs of neurons ( Csparse and Csparse+latent ) would have a performance advantage . However , the observed neurons must also be strongly influenced by global activity fluctuations and by unobserved common inputs to the advantage of estimators that explicitly model common fluctuations of the entire population: Cfactor and Csparse+latent . If both types of effects are significant , then Csparse+latent should outperform the other estimators . To test this hypothesis , we computed the validation loss of estimators Csample , Cdiag , Cfactor , Csparse , and Csparse+latent in n = 27 imaged sites in 14 mice . The hyperparameters of each estimator were optimized by nested cross-validation ( See S1 Fig . and Methods ) . Indeed , the sparse+latent estimator outperformed the other estimators ( Fig . 4 ) . The respective median differences of the validation loss were 0 . 039 , 0 . 0016 , 0 . 0029 , and 0 . 0059 nats/cell/bin , significantly greater than zero ( p < 0 . 01 in each comparison , Wilcoxon signed rank test ) . We examined the composition of the Csparse+latent estimates for each imaged site ( Fig . 5 and Fig . 6 ) . Although the regularized estimates were similar to the sample correlation matrix ( Fig . 5 A and B ) , the corresponding partial correlation matrices differed substantially ( Fig . 5 C and D ) . The estimates separated two sources of correlations: a network of linear interactions expressed by the sparse component of the inverse and latent units expressed by the low-rank components of the inverse ( Fig . 5 E ) . The sparse partial correlations revealed a network that differed substantially from the network composed of the greatest coefficients in the sample correlation matrix ( Fig . 5 F , G , H , and I ) . In the example site ( Fig . 5 ) , the sparse component had 92 . 8% sparsity ( or conversely , 7 . 2% connectivity: connectivity = 1−sparsity ) with average node degree of 20 . 9 ( Fig . 5 G ) . The average node degree , i . e . the average number of interactions linking each neuron , is related to connectivity as degree = connectivity⋅ ( p−1 ) , where p is the number of neurons . The low-rank component had rank 72 , denoting 72 inferred latent units . The number of latent units increased with population size ( Fig . 6 A ) but the connectivity was highly variable ( Fig . 6 B ) : Several sites , despite their large population sizes , were driven by latent units and had few pairwise interactions . This variability may be explained by differences in brain states and recording quality and warrants further investigation . The average partial correlations calculated from these estimates according to Eq . 4 at all 27 sites were about 5 times lower than the average sample correlations ( Fig . 6 C ) . This suggests that correlations between neurons build up from multiple chains of smaller interactions . Furthermore , the average partial correlations were less variable ( p = 0 . 002 Brown-Forsythe test ) : the coefficient of variation of the average sample correlations across sites was 0 . 45 whereas that of the average partial correlations was 0 . 29 . While the sample correlations were mostly positive , the sparse component of the partial correlations ( ‘interactions’ ) had a high fraction ( 28 . 7% in the example site ) of negative values ( Fig . 5 F ) . The fraction of negative interactions increased with the inferred connectivity ( Fig . 6 D ) , suggesting that negative interactions can be inferred only after a sufficient density of positive interactions has been uncovered . Thresholded sample correlations have been used in several studies to infer pairwise interactions [26 , 62–64] . We therefore compared the interactions in the sparse component of Csparse+latent to those obtained from the sample correlations thresholded to the same level of connectivity . The networks revealed by the two methods differed substantially . In the example site with 7 . 2% connectivity in Csparse+latent , only 27 . 7% of the connections coincided with the above-threshold sample correlations ( Fig . 5 F , H , and I ) . In particular , most of the inferred negative interactions corresponded to low sample correlations ( Fig . 5 F ) where high correlations are expected given the rest of the correlation matrix . We then examined how the structure of the Csparse+latent estimates related to the differences in orientation preference and to the physical distances separating pairs of cells ( Fig . 7 ) . Five sites with highest pairwise connectivities were included in the analysis . Partial correlations were computed using Eq . 4 based on the regularized estimate , including both the sparse and the latent component . Connectivity was computed as the fraction of pairs of cells connected by non-zero elements ( interactions ) in the sparse component of the estimate , segregated into positive and negative connectivities . First , we analyzed how correlations and connectivity depended on the differences in preferred orientations ( Δori ) of pairs of significantly ( α = 0 . 05 ) tuned cells . The partial correlations decayed more rapidly with Δori than did sample correlations ( Fig . 7 A and D . p < 10−9 in each of the five sites , two-sample t-test of the difference of the linear regression coefficients in normalized data ) . Positive connectivity decreased with Δori ( p < 0 . 005 in each of the five sites , t-test on the logistic regression coefficient ) whereas negative connectivity did not decrease ( Fig . 7 G ) : The slope in the logistic model of connectivity with respect to Δori was significantly higher for positive than for negative interactions ( p < 0 . 04 in each of the five sites , two-sample t-test of the difference of the logistic regression coefficient ) . Second , we compared how correlations and connectivity depended on the physical distance separating pairs of cells . We distinguished between the lateral distance , Δx , in the plane parallel to the pia , and the vertical distance , Δz , orthogonal to the pia . When considering the dependence on Δx , the analysis was limited to cell pairs located at the same depth with Δz < 30 μm; conversely , when considering the dependence on Δz , only vertically aligned cell pairs with Δx < 30 μm were included . Again , the partial correlations decayed more rapidly both laterally and vertically than sample correlations ( Fig . 7 B , C , E , F . p < 10−6 in each of the five sites , for both lateral and vertical distances , two-sample t-test of the difference of the linear regression coefficients in normalized data ) . Positive connectivity decayed with distance ( p < 10−6 in each of the five sites for positive interactions , t-test on the logistic regression coefficient in normalized data ) ( Fig . 7 E , H , I ) , so that cells separated laterally by less than 25 μm were 3 . 2 times more likely to be connected than cells separated laterally by more than 150 μm . Although the positive connectivity appeared to decay faster with vertical than with lateral distance , the differences in slopes of the respective logistic regression models were not significant with available data . The negative connectivity decayed slower with distance ( Fig . 7 H and I ) : The slope in the respective logistic models with respect to the lateral distance was significantly higher for positive than for negative connectivities ( p < 0 . 05 in each of the five sites , two-sample t-test of the difference of the logistic regression coefficients ) .
Functional connectivity is often represented as a graph of pairwise interactions . The goal of many studies of functional connectivity has been to estimate anatomical connectivity from observed multineuronal spiking activity . For example , characteristic peaks and troughs in the pairwise cross-correlograms of recorded spike trains contain statistical signatures of monosynaptic connections and shared synaptic inputs [12 , 14 , 34 , 35 , 65] . Such signatures are ambiguous as they can arise from network effects other than direct synaptic connections [66] . With simultaneous recordings from more neurons , ambiguities can be resolved by inferring the conditional dependencies between pairs of neurons . Direct causal interactions between neurons produce statistical dependency between them even after conditioning on the state of the remainder of the network and external input . Therefore , conditional independence shown statistically can signify the absence of a direct causal influence . Conditional dependencies can be inferred by fitting a probabilistic model of the joint population activity . For example , generalized linear models ( GLMs ) have been constructed to include biophysically plausible synaptic integration , membrane kinetics , and individual neurons’ stimulus drive [67] . Maximum entropy models constrained by observed pairwise correlations are among other models with pairwise coupling between cells [68–72] . Assuming that the population response follows a multivariate normal distribution , the conditional dependencies between pairs of neurons are expressed by the partial correlations between them . Each probabilistic model , fitted to the same data may reveal a completely different network of ‘interactions’ , i . e . conditional dependencies between pairs of cells . It is not yet clear which approach provides the best correspondence with anatomical connectivity . Little experimental evidence is available to answer this question . The connectivity graphs inferred by various statistical methods are commonly reported without examining their relation to anatomy . Topological properties of such graphs have been interpreted as principles of circuit organization ( e . g . small-world organization ) [62–64 , 70] . However , the topological properties of functional connectivity graphs can depend on the method of inference [73] . Until a physiological interpretation of functional connectivity is established , the physiological relevance of such analyses remains in question and we did not attempt applying graph-theoretical analyses to our results . Inference of the conditional dependencies also depends on the completeness of the recorded population: To equate conditional dependency to direct interaction between two neurons , we must record from all neurons with which the pair interacts . Unobserved portions of the circuit may manifest as conditional dependencies between observed neurons that do not directly interact . For this reason , statistical models of population activity have been most successfully applied to in vitro preparations of the retina or cell cultures where high-quality recordings from the complete populations were available [67] . In cortical tissue , electrode arrays record from a small fraction of cells in a given volume , limiting the validity of inference of the pairwise conditional dependencies . Perhaps for this reason , partial correlations have not , until now , been used to describe the functional connectivity in cortical populations . Two-photon imaging of population calcium signals presents unique advantages for the estimation of functional connectivity . While the temporal resolution of calcium signals is limited by the calcium dye kinetics , fast imaging techniques combined with spike inference algorithms provide millisecond-scale temporal resolution of single action potentials [74] . However , such high temporal precision comes at the cost of lower accuracy of inferred spike rates . Better accuracy is achieved when calcium signals are analyzed on scales of tens of milliseconds [60 , 75] . The major advantage of calcium imaging is its ability to characterize the spatial arrangement and types of recorded cells . Recently , advanced imaging techniques have allowed recording from nearly every cell in a volume of cortical tissue in vivo [59 , 60] and even from entire nervous systems [76 , 77] . These techniques may provide more incisive measurements of functional connectivity than electrophysiological recordings . The low temporal resolution of calcium signals limits the use of functional connectivity methods that rely on millisecond-scale binning of signals ( cross-correlograms , some GLMs , and binary maximum entropy models ) . Hence , most studies of functional connectivity have relied on instantaneous sample correlations [23 , 26 , 29 , 63] . Although some investigators have interpreted such correlations as indicators of ( chemical or electrical ) synaptic connectivity , most used them as more general indicators of functional connectivity without relating them to underlying mechanisms . In this study , we sought to infer pairwise functional connectivity networks in cortical microcircuits . We hypothesized that partial correlations correspond more closely to underlying mechanisms than sample correlations when recordings are sufficiently dense . Since neurons form synaptic connections mostly locally and sparsely [78] , we a priori favored solutions with sparse partial correlations . Under the assumptions that the recorded population is sufficiently complete and that the model correctly represents the nature of interactions , the network of partial correlations can better represent the functional dependencies in the circuit than correlations . Another approach to describing the functional connectivity of a circuit is to isolate individual patterns of multineuronal coactivations . Depending on the method of their extraction , coactivation patterns may be referred to as assemblies , factor loadings , principal components , independent components , activity modes , eigenvectors , or coactivation maps [79–84] . Coactivation patterns could be interpreted as signatures of Hebbian cell assemblies , i . e . groups of tightly interconnected groups of cells involved in a common computation [79 , 82] . Coactivation patterns could also result from shared input from unobserved parts of the circuit , or global network fluctuations modulating the activity of the local circuit [32 , 85] . Coactivation patterns and pairwise connectivity are not mutually exclusive since assemblies arise from patterns of synaptic connectivity . However , an analysis of coactivation shifts the focus from detailed interactions to collective behavior . In our study , the functional connectivity solely through modes of coactivations was represented by the factor analysis-based estimator Cfactor . In the effort to account for the joint activity patterns that are poorly explained by pairwise interactions , investigators have augmented models of pairwise interactions with additional factors such as latent variables , higher-order correlations , or global network fluctuations [32 , 86–89] . In our study , we combined pairwise interactions with collective coactivations by applying the recently developed numerical techniques for the inference of the partial correlation structure in systems with latent variables [54 , 55] . The resulting estimator , Csparse+latent , effectively decomposed the functional connectivity into a sparse network of pairwise interactions and coactivation mode vectors . Inferring the conditional dependencies between variables in a probabilistic model often becomes an ill-posed problem: small variations in the data can produce large errors in the inferred network of dependencies ( Fig . 5 C and D ) . The problem becomes worse as the number of recorded neurons increases until such models lose their statistical validity [90] . As techniques have improved to allow recording from larger neuronal populations , experimental neuroscientists have addressed this problem by extending the recording durations to keep sampling noise in check and verified that existing models are not overfitted [87] . However , ambitious projects already underway , such as the BRAIN initiative [50] , aim to record from significantly larger populations . Simply increasing recording duration will be neither practical nor sufficient , and the problem must be addressed by using regularized estimators . Regularization biases the solution toward a small subspace in order to counteract the effects of sampling noise in the empirical data . However , biasing the solution to an inappropriate subspace does not allow significant estimation improvement and hinders interpretation . Several strategies have been developed to limit the model space in order to improve the quality of the estimate . For example , Ganmor et al . [86] developed a heuristic rule to identify the most significant features that must be fitted by a maximum entropy model for improved performance in the retina . As another example of regularization , generalized linear models typically employ L1 penalty terms to constrain the solution space and to effectively reduce the dimensionality of the solution [67] . Our study demonstrates regularization schemes empirically optimized for specific types of neural data . Various model selection criteria have been devised to select between families of models and the optimal subsets of variables in a given model family based on observed data . Despite its high computational demands , cross-validation is among the most popular model selection approaches due to its minimal assumptions about the data-generating process [91] . We evaluated the covariance matrix estimators using a loss function derived from the normal distribution . However , this does not limit the applicability of its conclusions to normal distributions . Other probabilistic models , fitted to the same data , could also serve as estimators of the covariance matrix . If a different model yields better estimation of the covariance matrix than the estimator proposed here , we believe that its structure should deserve consideration as the better representation of the functional connectivity . The results of model selection must be interpreted with caution . As we demonstrated by simulation , even models with incorrect forms of dependencies can substantially improve estimates ( Fig . 1 ) . Therefore , showing that a more constrained model has better cross-validated performance than a more complex model does not necessarily support the conclusion that it reveals a better representation of dependencies in the data . This caveat is related to Stein’s Paradox [92]: The biasing of an estimate toward an arbitrary low-dimensional target can consistently outperform a less constrained estimate . We showed that among several models a sparse network of linear interactions with several latent inputs yielded the best estimates of the noise covariance matrix for cortical microcircuits . This finding is valuable in itself: improved estimates of the noise covariance matrix for large datasets are important in order to understand the role of noise correlations in population coding [1 , 6 , 7 , 9 , 11] Moreover , this estimation approach provides a graphical representation of the dependencies in the data that can be used to formulate and test hypotheses about the structure of connectivity in the microcircuit . Importantly , the inferred functional interactions were substantially different from the network of the highest sample correlations . For example , the Csparse+latent estimator reveals a large number of negative interactions that were not present in the sample correlation matrix ( Fig . 5 F ) and may reflect inhibitory circuitry . Distances between cells in physical space and in sensory feature space had a stronger effect on the partial correlations estimated by the Csparse+latent estimator than on sample correlations ( Fig . 7 A–F ) . These differences support the idea that correlations are built up from partial correlations in chains of intermediate cells positioned closer and tuned more similarly to one another , with potentially closer correspondence to anatomical connectivity . These differences may also be at least partially explained by a trivial effect of regularization: the L1 penalty applied by the estimator ( Eq . 18 ) suppresses small partial correlations to a greater extent than large partial correlations , enhancing the apparent effect of distance and tuning . Still , the distinct positive and negative connectivity patterns ( Fig . 7 G–I ) may reflect geometric and graphical features of local excitatory and inhibitory networks . Indeed , the relationships between patterns of positive and negative connectivities inferred by the estimator resembled the properties of excitatory and inhibitory synaptic connectivities with respect to distance , cortical layers , and feature tuning [23 , 78 , 93–98] . For example , while excitatory neurons form synapses within highly specific local cliques [78] , inhibitory interneurons form synapses with nearly all excitatory cells within local microcircuits [23 , 96 , 99] . To further investigate the link between synaptic connectivity and inferred functional connectivity , in future experiments , we will use molecular markers for various cell types with follow-up multiple whole-cell in vitro recordings [23 , 28] to directly compare the inferred functional connectivity graphs to the underlying anatomical circuitry . Finally , the latent units inferred by the estimator can be analyzed for their physiological functions . For example , these latent units may be modulated under different brain states ( e . g . slow-wave sleep , attention ) and stimulus conditions ( e . g . certain types of stimuli may engage feedback connections ) [100 , 101] .
All procedures were conducted in accordance with the ethical guidelines of the National Institutes of Health and were approved by the Baylor College of Medicine IACUC . The surgical procedures and data acquisition were performed as described in [60]: C57BL/6J mice ( aged p40–60 ) were used . For surgery , animals were initially anesthetized with isoflurane ( 3% ) . During the experiments , animals were sedated with a mixture of fentanyl ( 0 . 05 mg/kg ) , midazolam ( 5 mg/kg ) , and medetomidine ( 0 . 5 mg/kg ) , with boosts of half the initial dose every 3 hours . A craniotomy was performed over the right primary visual cortex . Membrane-permeant calcium indicator Oregon Green 488 BAPTA-1 AM ( OGB-1 , Invitrogen ) was loaded by bolus injection . The craniotomy was sealed using a glass coverslip secured with dental cement . Calcium imaging began 1 hour after dye injection . All imaging was performed using 3D-RAMP two-photon microscopy [60] . First , a 3D stack was acquired and cells were manually segmented . Then calcium signal were collected by sampling in the center of each cell at rates of 100 Hz or higher , depending on the number of cells . The visual stimulus consisted of full-field drifting gratings with 90% contrast , 10 cd/m2 luminance , 0 . 08 cycles/degree spatial frequency , and 2 cycles/s temporal frequency . Two types of stimuli were presented for each imaging site: First , directional tuning was mapped using a pseudo-random sequence of drifting gratings at sixteen directions of motion , 500 ms per direction , without blanks , with 12–30 trials for each direction of motion . Second , to measure correlations , the stimulus was modified to include only two directions of motion ( in 9 datasets ) or five directions ( in 22 datasets ) and the gratings were presented for 1 second and were separated by 1-second blanks , with 100–300 trials for each direction of motion . All data were processed in MATLAB using the DataJoint data processing chain toolbox ( http://datajoint . github . com ) . The measured fluorescent traces were deconvolved to reconstruct the firing rates for each neuron: First , the first principal component was subtracted from the raw traces in order to reduce common mode noise related to small cardiovascular movements [60] . The resulting traces were high-pass filtered above 0 . 1 Hz and downsampled to 20 Hz ( Fig . 3 C ) . Then , the firing rates were estimated using by nonnegative deconvolution [61] . Orientation tuning was computed by fitting the mean firing rates for each direction of motion ϕ using two-peaked von Mises tuning functions f ( ϕ ) = a + bexp [ 1 w ( cos ( ϕ − θ ) − 1 ) ] + cexp [ 1 w ( cos ( ϕ − θ + π ) − 1 ) ] where b ≥ c are the amplitudes of the two respective peaks , w is the tuning width , and θ is the preferred direction . The significance of the fit was determined by the permutation test: the labels of the direction were randomly permuted 10 , 000 times; the p-values of the fits were computed as the fraction of permutations that yielded R2 equal to or higher than that of the original data . Cells were considered tuned with p < 0 . 05 . For covariance estimation , the analysis was limited to the period with two or five stimulus conditions and lasted between 14 and 27 minutes ( mean 22 minutes ) . Cells that did not have substantial spiking activity ( those whose variance was less than 1% of the median across the site ) or whose activity was unstable ( those whose variance in the least active quarter of the recording did not exceed 1% of the variance in the most active quarter ) were excluded from the analysis . To compare the performance of the estimators , we used conventional 10-fold cross-validation: Trials were randomly divided into 10 subsets with approximately equal numbers of trials of each condition in each subset . Each subset was then used as the testing sample with the rest of the data used as the training sample for estimating the covariance matrix . The average validation loss over the 10 folds was reported . Since each of the regularized estimators had one or two hyperparameters , we used nested cross-validation: The outer loop evaluated the performance of the estimators with the hyperparameter values optimized by cross-validation within the inner loop . Hyperparameters were optimized by a two-phase search algorithm: random search to find a good starting point for the subsequent pattern search to find the global minimum . The inner cross-validation loop subdivided the training dataset from the outer loop to perform 10-fold cross-validation in order to evaluate each choice of the hyperparameter values . Thus the size of the training dataset within the inner loop comprised 81% of the entire recording . S1 Fig . illustrates the dependence of the validation loss on the hyperparameters of the Csparse+latent estimator for the example site shown in Figs . 3 and 5 and the optimal value found by the pattern search algorithm . When the validation loss was not required , only the inner loop of cross-validation was used on the entire dataset . This approach was used to compute the covariance matrix estimates and their true loss in the simulation study ( Fig . 1 Rows 4 and 5 ) and to analyze the partial correlation structure of the Csparse+latent estimator ( Fig . 5–7 ) . Within the inner loop of cross-validation , regularized covariance matrix estimation required only the sample covariance matrix Csample of the training dataset and the hyperparameter values provided by the outer loop . Estimator Cdiag ( Eq . 5 ) used two hyperparameters: the covariance shrinkage intensity λ ∈ [0 , 1] and variance shrinkage intensity α ∈ [0 , 1] . The variances ( the diagonal of Csample ) were shrunk linearly toward their mean value 1 p Tr ( C sample ) : D = ( 1 − α ) diag ( C sample ) + α 1 p Tr ( C sample ) I ( 13 ) The Cdiag estimate was then obtained by shrinking Csample toward D according to Eq . 5 . In estimator Cfactor ( Eq . 6 ) , the low-rank matrix L and the diagonal matrix D were found by solving the minimization problem ( L , D ) = arg min L ̂ , D ̂ ℒ ( L ̂ + D ̂ , C sample ) , ( 14 ) using an expectation-maximization ( EM ) algorithm for a specified rank of L . After that , the diagonal of D was linearly shrunk toward the its mean diagonal value similar to Eq . 13 . In estimator Csparse ( Eq . 7 ) , the sparse precision matrix S was found by minimizing the L1-penalized loss with regularization parameter λ: S = arg min S ̂ ≻ 0 L ( S ^ − 1 , C sample ) + λ ‖ S ^ ‖ 1 ( 15 ) where S^≻0 denotes the constraint that S^ be a positive-definite matrix and ‖S^‖1 is the element-wise L1 norm of the matrix S^ . This problem formulation is known as graphical lasso [102 , 103] . To solve this minimization problem , we adapted the alternative-direction method of multipliers ( ADMM ) [55] . Unlike Cdiag and Cfactor , this estimator does not include linear shrinkage: the selection of the sparsity level provides sufficient flexibility to fine-tune the regularization level . Estimator Csparse+latent ( Eq . 8 ) estimates a larger sparse precision matrix S* of the joint distribution of the p observed neurons and d latent units . S * = ( S S 12 S 12 T S 22 ) , ( 16 ) where the p × p partition S corresponds to the visible units . Then the covariance matrix of the observed population is C sparse + latent = ( S − S 12 S 22 − 1 S 12 T ) − 1 ( 17 ) The rank of the p×p matrix L = S 12 S 22 − 1 S 12 T matches the number of the latent units in the joint distribution . Rather than finding S12 and S22 separately , L can be estimated as a low-rank positive semidefinite matrix . To simultaneously optimize the sparse component S and the low-rank component L , we adapted the loss function with an L1 penalty on S and another penalty on the trace of L [54 , 55]: ( S , L ) = arg min S ̂ , L ̂L ( S ^ − L ^ ) − 1 , C sample + α ‖ S ^ ‖ 1 + β Tr ( L ^ ) ( 18 ) The trace of a symmetric semidefinite matrix equals the sum of the absolute values of its eigenvalues , i . e . its nuclear norm; penalty on Tr ( L ) favors solutions with few non-zero eigenvalues or , equivalently , low-rank solutions while keeping the convexity of the overall optimization problem [104 , 105] . This allows using convex optimization algorithm such as ADMM to be applied with great computational efficiency [55] . The partial correlation matrix ( Eq . 4 ) computed from Csparse+latent includes interactions between the visible and latent units and was used in Fig . 5 C and D and Fig . 6 C , and Fig . 7 D–F ) . The partial correlation matrix computed from S alone expresses strengths of pairwise interactions P sparse = − ( diag ( S ) ) − 1 2 S ( diag ( S ) ) − 1 2 ( 19 ) and were used in Fig . 5 F , G , H . The MATLAB code for these computations is available online at http://github . com/atlab/cov-est . Special attention was given to estimating the variances . All evaluations and optimization in this study were defined with respect to the covariance matrices . However , neuroscientists often estimate a common correlation matrix across multiple stimulus conditions when the variances of responses are conditioned on the stimulus [106 , 107] . In this study , we too conditioned the variances on the stimulus but estimated a single correlation matrix across all conditions . Here we describe the computation of the validation loss ( Eq . 10 ) when the variances were allowed to vary with the stimulus condition . Let Tc and T c ′ denote the sets of time bin indices for the training and testing samples , respectively , limited to condition c . Similar to Eq . 2 , the training and testing sample covariance matrices for condition c are C c , sample = 1 n c ∑ t ∈ T c ( x ( t ) − x ¯ c ) ( x ( t ) − x ¯ c ) T ( 20 ) and C c , sample ′ = 1 n c ′ ∑ t ∈ T c ′ ( x ( t ) − x ¯ c ) ( x ( t ) − x ¯ c ) T ( 21 ) Here nc and n c ′ denote the sizes of Tc and T c ′ , respectively . Note that x ‾ c = 1 n c ∑ t ∈ T c x ( t ) is estimated from the training sample but used in both estimates , making C c , sample ′ an unbiased estimate of the true covariance matrix , Σ . As such , C c , sample ′ can be used for validation . The common correlation matrix Rsample is estimated by averaging the condition-specific correlations: R sample = 1 n ∑ c n c ( V c , sample − 1 2 C c , sample V c , sample − 1 2 ) = 1 n ∑ c ∑ t ∈ T c z ( t ) z ( t ) T , ( 22 ) where n = ∑ c n c and Vc , sample = diag ( Cc , sample ) is the diagonal matrix containing the sample variances . Then Rsample is simply the covariance matrix of the z-score signal z ( t ) =Vc , sample−12 ( x ( t ) −x¯c ) of the training sample . For consistency with prior work , we applied regularization to covariance matrices rather than to correlation matrices . The common covariance matrix was estimated by scaling Rsample by the average variances across conditions V sample = 1 n ∑ c n c V c , sample: C sample = V sample 1 2 R sample V sample 1 2 ( 23 ) Note that Csample differs from the sample covariance matrix computed without conditioning the variances on c and this computation helps avoid any biases that would be introduced by ignoring changes in variance . The covariance matrix estimators Cdiag , Cfactor , Csparse or Csparse+latent convert Csample into its regularized counterpart denoted here as Creg . To evaluate the estimators , we regularized the conditioned variances by linear shrinkage toward their mean value across all conditions . This was done by scaling Creg by the conditioned variance adjustment matrix Q c = δ I + ( 1 − δ ) V sample − 1 V c , sample to produce the conditioned regularized covariance matrix estimate: C c , reg = Q c 1 2 C reg Q c 1 2 ( 24 ) The variance regularization parameter δ ∈ [0 , 1] was optimized in the inner loop of cross-validation along with the other hyperparameters . The overall validation loss is obtained by averaging the validation losses across all conditions: 1 ∑ c n c ′ ∑ c n c ′ L ( C c , reg , C c , sample ′ ) ( 25 ) With negative normal log-likelihood as the validation loss ( Eq . 10 ) and the unbiased validation covariance matrix Cc , sample , the loss function in Eq . 25 is an unbiased estimate of the true loss . Hence , it was used for evaluations reported in Fig . 4 . For simulation , ground truth covariance matrices were produced by taking 150 independent samples from an artificial population of 50 independent , identically normally distributed units . The covariance matrices were then subjected to the respective regularizations to produce the ground truth matrices for the simulation studies ( Fig . 1 Row 2 ) . Samples were then drawn from multivariate normal distributions models with the respective true covariance matrices to be estimated by each of the estimators . For Ising models , the negative inverse of the true covariance matrix was used as the matrix of coupling coefficients and the sampling was performed by the Metropolis-Hastings algorithm .
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It is now possible to record the spiking activity of hundreds of neurons at the same time . A meaningful statistical description of the collective activity of these neural populations—their ‘functional connectivity’—is a forefront challenge in neuroscience . We addressed this problem by identifying statistically efficient estimators of correlation matrices of the spiking activity of neural populations . Various underlying processes may reflect differently on the structure of the correlation matrix: Correlations due to common network fluctuations or external inputs are well estimated by low-rank representations , whereas correlations arising from linear interactions between pairs of neurons are well approximated by their pairwise partial correlations . In our data obtained from fast 3D two-photon imaging of calcium signals of large and dense groups of neurons in mouse visual cortex , the best estimation performance was attained by decomposing the correlation matrix into a sparse network of partial correlations ( ‘interactions’ ) combined with a low-rank component . The inferred interactions were both positive ( ‘excitatory’ ) and negative ( ‘inhibitory’ ) and reflected the spatial organization and orientation preferences of the interacting cells . We propose that the most efficient among many estimators provides a more informative picture of the functional connectivity than previous analyses of neural correlations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Improved Estimation and Interpretation of Correlations in Neural Circuits
|
A new approach for dengue control has been proposed that relies on life-shortening strains of the obligate intracellular bacterium Wolbachia pipientis to modify mosquito population age structure and reduce pathogen transmission . Previously we reported the stable transinfection of the major dengue vector Aedes aegypti with a life-shortening Wolbachia strain ( wMelPop-CLA ) from the vinegar fly Drosophila melanogaster . Here , we report a further characterization of the phenotypic effects of this virulent Wolbachia infection on several life-history traits of Ae . aegypti . Minor costs of wMelPop-CLA infection for pre-imaginal survivorship , development and adult size were found . However , we discovered that the wMelPop-CLA infection dramatically decreased the viability of desiccated Ae . aegypti eggs over time . Similarly , the reproductive fitness of wMelPop-CLA infected Ae . aegypti females declined with age . These results reveal a general pattern associated with wMelPop-CLA induced pathogenesis in this mosquito species , where host fitness costs increase during aging of both immature and adult life-history stages . In addition to influencing the invasion dynamics of this particular Wolbachia strain , we suggest that the negative impact of wMelPop-CLA on embryonic quiescence may have applied utility as a tool to reduce mosquito population size in regions with pronounced dry seasons or in regions that experience cool winters .
Aedes aegypti , the primary vector of dengue viruses throughout the tropics , is a mosquito species that has strong associations with human habitation [1] . In the past , control of dengue has been complicated by an inability to eradicate Ae . aegypti from urban environments and implement sustained vector control programs [2] . These challenges have highlighted the critical need for new approaches to curb a worldwide resurgence in dengue activity [3] . A novel approach for dengue control that has been proposed involves the introduction of the obligate intracellular bacterium Wolbachia pipientis into field populations of Ae . aegypti . Wolbachia are maternally inherited bacteria that naturally infect a wide diversity of invertebrate species [4] , [5] , and can rapidly spread through arthropod populations by manipulations to host reproduction such as cytoplasmic incompatibility [6] . Wolbachia infections could limit dengue transmission through two distinct mechanisms . The first by introducing Wolbachia strains that reduce the survival rate and associated vectorial capacity of the mosquito population [7] , [8] . The second mechanism relies on the ability of some Wolbachia strains to interfere with the ability of RNA viruses to form productive infections in insects [9] , [10] and potentially modulate the vector competence of Ae . aegypti for dengue viruses . Towards this aim , we previously reported the stable transinfection of Ae . aegypti with a life-shortening Wolbachia strain wMelPop-CLA ( a mosquito cell-line adapted isolate of wMelPop ) [11] , originally derived from the vinegar fly Drosophila melanogaster [12] . In this mosquito host , wMelPop-CLA has been shown to both reduce adult life span [11] and directly interfere with dengue virus infection [13] , suggesting that this Wolbachia strain may have applied utility as a biological tool to reduce dengue transmission . However , prior to application in a field setting , a thorough understanding of any fitness effects that occur in wMelPop-CLA infected mosquitoes is required to accurately model infection dynamics and the impact of wMelPop-CLA on Ae . aegypti populations . To further characterize this novel symbiosis and identify any fitness parameters likely to influence its spread throughout mosquito populations , we examined the phenotypic effects of wMelPop-CLA infection on several life-history traits across embryonic , pre-imaginal and adult stages of Ae . aegypti . We compared the developmental time and survivorship of pre-imaginal stages from infected and uninfected Ae . aegypti strains , and the effect of this infection on adult body size . We also considered the effect of wMelPop-CLA infection on embryonic viability during egg quiescence and reproductive fitness as mosquitoes age .
The work reported in this manuscript used human volunteers for mosquito feeding as approved by the University of Queensland Human Ethics Committee - Approval 2007001379 . Written consent was obtained from each participant used for blood feeding . wMelPop-CLA infected PGYP1 and tetracycline-cured PGYP1 . tet strains of Ae . aegypti [11] were maintained at 25°C , 75–85% relative humidity , with a 12∶12 h light∶dark photoperiod . Larvae were reared in plastic trays ( 30×40×8 cm ) at a set density of 150 larvae in 3 L distilled water , and fed 150 mg fish food ( TetraMin Tropical Tablets , Tetra , Germany ) per pan every day until pupation . Adults were kept in screened 30×30×30 cm cages , and provided with constant access to 10% sucrose solution and water . Females ( 5 days old ) were blood-fed using human blood . For routine colony maintenance , eggs from PGYP1 were hatched 5–7 days post-oviposition ( i . e . without prolonged desiccation ) to initiate the next generation . All fitness experiments with PGYP1 were conducted at G20 to G22 post transinfection . The tetracycline-cured PGYP1 . tet strain , generated at G8–G9 post-transinfection , was re-colonized with resident gut microflora from wild-type larvae as previously described [11] . Eggs ( 120 h old ) from PGYP1 and PGYP1 . tet strains were hatched synchronously in nutrient-infused deoxygenated water for 1 h . After hatching , individual first instar larvae ( n = 156 per strain ) were placed into separate plastic 30 mL plastic cups with 20 mL of water , and fed 1 mg powdered TetraMin suspended in distilled water each day until pupation . The number of days spent in each pre-imaginal life stage ( i . e . , 1st , 2nd , 3rd and 4th instars , pupae ) , mortality at each stage , and sex of eclosing adults were recorded every 24 h . Stage-specific development and eclosion times for each strain were compared using Mann-Whitney U ( MWU ) tests conducted in Statistica Version 8 ( StatSoft , Tulsa , OK ) . As an indicator of adult body size , wing lengths of PGYP1 and PGYP1 . tet mosquitoes ( n = 50 of each sex ) derived from the pre-imaginal development time assay were measured ( distance from the axillary incision to the apical margin excluding the fringe of scales ) [14] . Wing lengths of males and females from each strain were compared using MWU tests . Individual PGYP1 and PGYP1 . tet population cages ( 30×30×30 cm ) , each containing 200 males and 200 females per strain , were maintained over multiple gonotrophic cycles , with ad libitum access to 10% sucrose solution and water for the duration of their life span . During each cycle , females were provided with a human blood meal for 2×10 min periods on consecutive days , and 96 h post-blood meal a random sample of females ( n = 48 ) was collected from each cage and isolated individually for oviposition . Following a set 24 h period for oviposition , females were returned to their respective cages and the proportion of females laying eggs determined . Eggs were conditioned and hatched 120 h post-oviposition as described above , and the total number of eggs ( fecundity ) and hatched larvae ( fertility ) from each female were recorded . To ensure that gravid females not sampled for oviposition could also lay eggs every cycle , oviposition cups were introduced into each stock cage ( 96 h post-blood meal ) for a period of 48 h . Females were then blood fed to initiate the next gonotrophic cycle . Cages were sampled until all females in the population were dead , which occurred after 7 and 16 gonotrophic cycles for PGYP1 and PGYP1 . tet strains respectively . To ensure PGYP1 . tet females did not become depleted of sperm , young males ( 3 days old ) were supplemented to this cage after 8 gonotrophic cycles . Multiple linear regression analysis was used to detect trends in fecundity/fertility of mosquitoes from each strain over their lifespan . Student's t-test was used to compare the fecundity/fertility of mosquitoes from both strains of the same age . PGYP1 and PGYP1 . tet females were blood-fed on human blood , and 96 h post-blood meal isolated individually for oviposition in plastic Drosophila vials with wet filter paper funnels . After oviposition , egg papers were kept wet for 48 h , after which time they were removed from vials , wrapped individually in paper towel , and conditioned for a further 72 h at 25°C and 75–85% relative humidity . Egg batches were then moved to their respective storage temperature of 18°C , or 25°C in glass desiccator jars; maintained at a constant relative humidity of 85% with a saturated KCl solution [15] . For each temperature , 20 oviposition papers from each strain were hatched at seven time points at 7 day-intervals ( 5 to 47 days post-oviposition ) by submersion in nutrient-infused deoxygenated water for 48 h . To hatch any remaining eggs , oviposition papers were dried briefly then submersed for a further 5 days and before the final number of hatched larvae was recorded . Multiple linear regression analysis was used to detect trends in the viability of eggs from each strain over time . MWU tests were used to compare viability of eggs between strains at the same storage age .
No significant differences in development times for larval stages of wMelPop-CLA infected PGYP1 or tetracycline-cured PGYP1 . tet males were found ( MWU , P>0 . 05 for all comparisons ) ( Table 1 ) . In contrast , the mean development time for male PGYP1 pupae ( 64 . 88±1 . 38 h ) was significantly greater relative to PGYP1 . tet ( 57 . 00±1 . 25 h ) ( MWU , U = 1892 . 00 , P<0 . 001 ) , resulting in a longer cumulative time to eclosion for this strain ( MWU , U = 1484 . 50 , P<0 . 001 ) . For females , development times for immature stages were not significantly different between strains; except for third instar larvae where PGYP1 development times were increased by ∼5 h relative to PGYP1 . tet ( MWU , U = 1929 . 00 , P = 0 . 013 ) ( Table 1 ) . Despite this delay , eclosion times for PGYP1 females were not significantly different from PGYP1 . tet ( MWU , U = 2185 . 50 , P = 0 . 15 ) . Overall , the survivorship of immature stages from both strains to adulthood was identical ( 96 . 15% ) . A comparison of the wing lengths of newly emerged adults from both strains revealed a minor , yet statistically significant adult size cost to wMelPop-CLA infection for both sexes . Wing lengths of PGYP1 males ( 2 . 36±0 . 01 mm , n = 50 ) were significantly shorter than those of PGYP1 . tet males ( 2 . 46±0 . 02 mm , n = 50 ) ( MWU , U = 661 . 50 , P<0 . 0001 ) . A smaller size difference ( MWU , U = 955 . 00 , P = 0 . 04 ) was found between PGYP1 females ( 3 . 03±0 . 03 mm , n = 50 ) and PGYP1 . tet females ( 3 . 09±0 . 03 mm , n = 50 ) . PGYP1 and PGYP1 . tet females had similar reproductive outputs in terms of the number of eggs oviposited and the number of viable larvae hatched per female during their first gonotrophic cycle ( Fig . 1A and B ) . However , during subsequent cycles both fecundity and fertility of PGYP1 females decreased at an accelerated rate ( fecundity: R2 = 0 . 5068 , F1 , 299 = 307 . 30 , P<0 . 001; fertility: R2 = 0 . 3517 , F1 , 299 = 162 . 20 , P<0 . 001 ) relative to females from the PGYP1 . tet strain ( fecundity: R2 = 0 . 3167 , F1 , 602 = 278 . 95 , P<0 . 001; fertility: R2 = 0 . 1506 , F1 , 602 = 106 . 76 , P<0 . 001 ) . For example , as PGYP1 females aged the average number of larvae produced per female decreased such that by the second cycle a 15% cost to reproductive output was observed relative to uninfected PGYP1 . tet females , which progressively declined to a 40% cost by the fifth cycle ( t-tests , P<0 . 05 for all comparisons ) . A large proportion of PGYP1 females that were randomly sampled for oviposition at the six and seventh gonotrophic cycles did not produce eggs ( Fig . 1C ) , leading to a further decline in fecundity and fertility of this strain ( Fig . 1A and B ) . This appeared to be due to defects in feeding behaviour , as many of these older PGYP1 females were observed to be unsuccessful in obtaining a blood meal ( data not shown ) . Such a dramatic decrease in oviposition rates was not evident for PGYP1 . tet females as they aged ( Fig . 1C ) . The viability of quiescent embryos from the wMelPop-CLA infected PGYP1 strain decreased over time at 25°C and 18°C , whereas viability of embryos from the tetracycline-cured PGYP1 . tet strain was relatively stable at both storage temperatures ( Fig . 2 ) . At 25°C ( Fig . 2A ) , there was no significant difference in embryonic viability between PGYP1 ( 80 . 93±5 . 12% ) and PGYP1 . tet strains ( 74 . 96±4 . 37% ) at 5 days post oviposition ( MWU , U = 146 . 50 , P = 0 . 1478 ) . As quiescent embryos aged , however , PGYP1 embryonic viability decreased rapidly over time ( R2 = 0 . 6539 , F1 , 138 = 260 . 73 , P<0 . 001 ) , such that by 40 days post oviposition very few PGYP1 eggs hatched ( 0 . 44±0 . 24% ) . In contrast , PGYP1 . tet embryonic viability remained relatively constant over time ( R2 = 0 . 0005 , F1 , 138 = 0 . 07 , P = 0 . 7897 ) with ∼75% of quiescent eggs hatching at each time point . An analogous trend was observed at 18°C ( Fig . 2B ) , where initially hatch rates were comparable between the two strains , but subsequently a greater loss in embryonic viability was observed for PGYP1 ( R2 = 0 . 4035 , F1 , 138 = 93 . 34 , P<0 . 001 ) relative to PGYP1 . tet ( R2 = 0 . 0803 , F1 , 138 = 12 . 05 , P<0 . 001 ) . This was particularly evident at 12 days post oviposition where embryonic viability declined more rapidly in PGYP1 ( 9 . 88±2 . 96% ) compared to PGYP1 . tet ( 68 . 06±4 . 12% ) after being moved to a cooler storage temperature ( MWU , U = 5 . 00 , P<0 . 0001 ) .
In its native D . melanogaster host , wMelPop induces minor phenotypic effects during pre-imaginal life-history stages [12] , [16] . However , after adult emergence , somatic and nervous tissues of flies gradually become densely populated with Wolbachia leading to overt pathology and shortened life span [12] . Similarly , in this study we observed minor costs of wMelPop-CLA infection during Ae . aegypti pre-imaginal development , with the phenotypic effects of this virulent Wolbachia strain increasing as adult mosquitoes aged . A small delay in the mean time to eclosion was observed for wMelPop-CLA infected Ae . aegypti males , but not females relative to their tetracycline-cured counterparts . Increased egg-to-adult development times have previously been characterized for certain D . melanogaster genotypes infected by wMelPop [16] . Differences in development time were also reflected by variations in adult body size , where size costs to wMelPop-CLA infection were more pronounced for infected males than infected females . Taken together , results from development time , immature survivorship and adult size assays suggest a minor physiological cost to wMelPop-CLA infection during Ae . aegypti pre-imaginal development . Additional studies that introduce larval competition [17] , and which utilise a wide variety of potential nutrient sources likely to be encountered in field environments are required to fully evaluate the impact of wMelPop-CLA infection on this stage of Ae . aegypti life-history . A common trait observed in many mosquito species , including Ae . aegypti , is a general decline in the numbers of eggs laid by females over successive gonotrophic cycles , which is thought to be caused by increasing ovarian follicle degeneration as mosquitoes age [18] , [19] . Fecundity of both wMelPop-CLA infected and tetracycline-cured mosquito strains was initially comparable , consistent with previous assays using the PGYP1 Ae . aegypti strain [11] . Over subsequent gonotrophic cycles , however , fecundity declined at an accelerated rate in PGYP1 relative to the PGYP1 . tet strain suggesting that wMelPop-CLA infection contributed to a reduction in reproductive fitness . This may be related to a progressive increase in pathology induced by this Wolbachia strain in reproductive tissue , as commonly observed in somatic and nervous tissue [12] , as mosquitoes age . In Drosophila simulans , fecundity costs of wMelPop infection were initially high after transinfection of this strain from D . melanogaster , but attenuated over subsequent generations [20] . It remains possible that such costs to reproductive fitness will also diminish for PGYP1 , as wMelPop-CLA and Ae . aegypti further adapt to each other . Interestingly , as wMelPop-CLA infected females aged we observed a rapid decrease in the number of randomly sampled PGYP1 females that would oviposit in gonotrophic cycles 5 to 7 . This time range correlates with the onset of wMelPop-CLA induced life-shortening in Ae . aegypti [11] . Such a decline in oviposition rate may be directly related to pathology induced in reproductive tissues , or most likely be due to unsuccessful blood feeding behaviour observed in wMelPop-CLA infected mosquitoes as they age [21] . Such an age-related decline in fecundity may limit or influence the rate at which the wMelPop-CLA infection to spreads through an Ae . aegypti population , and should therefore be considered in the development of models predicting invasion dynamics of this Wolbachia strain . A complete understanding of this magnitude of this effect will require further determination of the relative reproductive contribution of different age-classes of Wolbachia-infected and uninfected Ae . aegypti to mosquito population size in a more ecologically relevant field cage setting . In addition to the previously characterized life-shortening [11] and viral interference phenotypes [13] of wMelPop-CLA infection in Ae . aegypti , a third major effect described in this study is the observation that this infection decreases the viability of quiescent embryos over time . The viability of eggs laid by tetracycline-cured Ae . aegypti remained high over the 1 . 5 month test period . In contrast , the viability of the wMelPop-CLA infected PGYP1 strain declined rapidly over time . This decrease in embryonic viability was particularly evident after PGYP1 eggs were moved to a cooler storage temperature , possibly reflecting decreased levels of cold tolerance in the presence of infection . Such decreases in embryonic viability are not observed in the closely related mosquito species Aedes albopictus , which is infected by two avirulent Wolbachia strains ( wAlbA and wAlbB ) [22] . Moreover , reductions in embryonic viability are also not seen in Ae . aegypti lines transinfected with wAlbB from Ae . albopictus [23] . The impact of wMelPop-CLA on survival of quiescent eggs may have important implications for the spread and maintenance of this infection in Ae . aegypti populations , as well as mosquito population dynamics . Larval habitats of container breeding mosquito species such as Ae . aegypti and other members of the subgenus Stegomyia , are often subject to high selection pressures due to drying during certain seasonal periods [24] . In this context , the effects of wMelPop-CLA on Ae . aegypti populations are likely to be highly dependent on geographical location where field releases occur . In tropical regions , such as Thailand and Vietnam , where an abundance of both permanent and transient larval breeding sites exist and rainfall occurs on a regular basis or containers are maintained full of water by human intervention , it is likely that under certain release thresholds wMelPop-CLA will be able to spread and persist in local Ae . aegpyti populations . However , in regions with a pronounced dry season , such as northern Australia , where drying of eggs may occur , it would be expected that this effect would significantly reduce mosquito population size at the beginning of the following wet season due to wMelPop-CLA induced embryonic mortality . The magnitude of such an effect will be dependent on the ability of the wMelPop-CLA infection to invade an area under the action of CI before the onset of the dry season , as a concurrent decrease in Wolbachia prevalence in the mosquito population would also be expected if the infection had not spread to fixation prior to dry season onset . From an applied perspective , we suggest that the ability of wMelPop-CLA to decrease mosquito viability during periods of embryonic quiescence may have potential utility in certain geographic locations as a tool to reduce mosquito population size at the beginning of each wet season . An analogous genetic strategy for population suppression has previously been proposed , involving the release of Ae . albopictus males adapted to tropical regions into temperate field populations of this mosquito species to reduce their over-wintering ability [25] . Given the importance of seasonal fluctuations in mosquito population density in influencing dengue epidemics [26] , this phenotype may act synergistically with described effects of this infection on mosquito lifespan [11] and vector competence [13] to further reduce the probability of virus transmission in several disease-endemic countries worldwide . However , the observation that wMelPop-CLA influences fitness of both embryonic and adult life-history stages , also suggests that the invasion dynamics of this virulent Wolbachia strain are likely to be complex and highly sensitive to the ecological setting where field releases occur .
|
A virulent strain of the vertically-inherited bacterium Wolbachia pipientis ( wMelPop-CLA ) from the vinegar fly Drosophila melanogaster has been established in the dengue vector Aedes aegypti as part of a biological strategy for dengue control . In this medically important disease vector , wMelPop-CLA infection shortens mosquito lifespan and effectively blocks dengue productivity within the mosquito – two powerful effects that could decrease the vectorial capacity of mosquito populations for transmission of dengue viruses . Here , we further characterize the phenotypic effects of wMelPop-CLA on several life-history traits of Ae . aegypti , and report that this infection influences the survival of this mosquito species during sustained periods of embryonic quiescence . From an applied perspective , we suggest that this novel phenotype may be a useful tool to reduce mosquito population size in regions where embryonic quiescence contributes towards survival of this species through seasonal changes in rainfall or temperature , and thus further reduce the probability of dengue transmission at the beginning of each wet season . This study also highlights key fitness parameters needed to accurately model invasion dynamics of this virulent Wolbachia strain .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"ecology/environmental",
"microbiology",
"infectious",
"diseases/viral",
"infections",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases"
] |
2010
|
A Virulent Wolbachia Infection Decreases the Viability of the Dengue Vector Aedes aegypti during Periods of Embryonic Quiescence
|
Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences . However , a majority of cancer variants arise in noncoding regions , and some of them are thought to play a critical role through transcriptional perturbation . Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions . The identified genes showed high connectivity in the cancer type-specific transcription regulatory network , with high outdegree and many downstream genes , highlighting their causative role during tumorigenesis . In the protein interactome , the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers . The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples . Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes .
Recent efforts to understand noncoding variation through epigenomic annotation have shown that disease-associated variation is frequently located in regulatory DNA marked by DNase I hypersensitive sites ( DHSs ) or particular histone modifications [1–4] . Noncoding somatic variants in cancer have been a focus of interest since the recent discovery of TERT promoter variants [5 , 6] , which was followed by efforts to systematically analyze the whole noncoding genome [7–9] . Epigenomic dissection of cancer genomes revealed that chromatin accessibility and histone modifications in corresponding cell types shape the noncoding variant landscape [10] . DNA repair activity was found to be a determinant of variant density within DHSs [11–13] . Identifying driver variants is one of the greatest challenges currently facing cancer genomics . Probably the most robust way to find driver variants is by leveraging large cohorts of samples and using recurrence as an indicator of selection [14] . Efforts to identify recurrent variants in cancer have focused on protein-coding sequences . However , a sizeable fraction of tumor samples lack variants in highly recurrent genes , indicating that the single gene-based approach may miss a large number of true driver genes [14] . In this light , protein interaction networks or signaling pathways were dissected to identify drive modules or driver pathways based on a combinatorial recurrence of coding variants [15–19] . Inferring the driver status of noncoding variants can be more complicated than coding variants . Noncoding recurrence was previously examined within single promoters or at the same sites . However , a majority of variants reside in distal enhancers , which scatter across a long distance while converging on the same target transcript . Therefore , target gene identification is crucial for estimating regulatory variant recurrence . To this end , it is essential to determine three-dimensional chromatin structure [20] . For example , a novel metabolic regulator was discovered by surveying long-range interactions that engage an obesity-associated variant [21] . From breast and liver cancer genomes , we first identify regulatory driver variants and their associated genes , referred to as transcriptional drivers ( TDs ) , based on combinatorial recurrence over the chromatin interactome . We then characterize the TD genes at the systems level in comparison with coding driver ( CD ) genes by projecting them onto the gene regulatory network and protein interactome . In particular , we utilize a Bayesian network that models causal ( directional ) regulatory relationships [22] , a transcription network that contains direct co-regulatory interactions [23] , an integrated physical protein interaction network [24] , and a probabilistic functional protein association network [25] .
The workflow of our analyses is summarized in S1 Fig . We first identified regulatory variants in 119 breast and 88 liver cancer samples as illustrated in Fig 1A . In this example , four different samples carry motif-changing variants at different positions in cis-regulatory regions , whose convergence on a common transcriptional target is revealed by the chromatin interactome . In this case , the combinatorial measure of variant recurrence for this gene should be four although none of the four variants arose at the same site . For this type of recurrence analysis , we employed enhancer-promoter maps constructed by RNA polymerase II-mediated chromatin interaction analysis by paired-end tag ( ChIA-PET ) sequencing [26–28] , integrated methods for predicting enhancer targets ( IM-PET ) [29] , DHS tag density correlations [1] , and cap analysis gene expression ( CAGE ) -based RNA correlations [30] . We also applied additional filters for enhancer-promoter mapping ( see Methods ) . The different criteria and resulting number of chromatin interactions are described in S2 Fig . The list of genes with the resulting recurrence level in each cancer is provided in S1 Table . The genes recurrently mutated in this manner ( i . e . , TD genes ) in either breast or liver cancer were enriched for cancer-related biological processes such as cell cycle , differentiation , and apoptosis ( Fig 1B ) . This enrichment was more pronounced with higher recurrence than lower recurrence . In addition , these genes appeared to play a highly causative function in the cancer regulatory network . When applied to the directional regulatory network in breast cancer [22] , the TDs of breast cancer exhibited a high causal score; in other words , they have a relatively high outdegree in the network while positioned upstream of the causal path ( Fig 1C; see Methods ) . This means that they tend to exert regulatory effects rather than be regulated by other genes . Their causal score was so high as the known CDs that were identified based on the 20/20 rule [14] or retrieved from the Cancer Gene Census ( CGC ) database [31] . This pattern was not found when the TDs of liver cancer were projected onto the breast network ( S3A Fig ) . We also constructed other types of regulatory networks that show regulatory associations but not regulatory directions [23 , 32 , 33] for each cancer . Again , a high connectivity of the TDs was observed ( Fig 1D , S3B~S3D Fig ) . Taken together , the TDs identified based on combinatorial cis-regulatory variant recurrence seem to play a crucial oncogenic role through an extensive perturbation of the regulatory network . We performed permutation-based statistical tests on the cis-recurrence measures ( Fig 1E ) . First , two types of variant simulations were performed . We first randomly generated the same number of variants while maintaining the distribution of per-sample variant counts ( in silico simulation ) . We also performed a clinical simulation in which the same number of variants was retrieved from the control set of variants derived from samples of other cancer types . In both breast cancer and liver cancer , and from both simulations , the observed level of recurrence was significantly higher than that expected by chance ( S4 Fig ) . Selected examples of individual genes are provided in S5 Fig . Next , we mapped variants to an irrelevant chromatin interactome with comparable data types and size ( “Other epigenome” in Fig 1E ) . Based on K562 data , we generated control epigenomic datasets against MCF7 and HepG2 . In contrast to non-recurrent genes , recurrently mutated genes were 2~3 times more frequently detected when using the matched epigenome ( S6 Fig ) , implying the tissue specificity of the recurrent cis-regulatory variants . Together , these results suggest the combinatorial recurrence patterns we identified were of biological relevance rather than from technical artifacts . The 20/20 and CGC CDs also showed high connectivity in the transcription network despite some exceptions ( S3B Fig ) , meaning that they may be able to act through transcriptional perturbation as well as through protein malfunction . By contrast , in the protein-protein interaction network [24] and functional association network [25] , the TDs were not so highly connected as the CDs ( S7 Fig ) . These suggest that unlike the CDs , the oncogenic effects of the TDs may be confined to the transcription network . However , disease proteins are not scattered randomly in biological networks , but tend to interact with each other and form network modules [34] . Therefore , we tested whether the TDs frequently interact with the CDs in the protein interactome . Indeed , we observed a positive correlation between the recurrence level of the TDs and their agglomeration with the CD genes ( Fig 2 and S8 Fig ) . In other words , genes with a high recurrence of regulatory variants tend to interact frequently with genes with a high recurrence of coding variants . This finding , in concert with the high degree of the CDs in the protein interactome , led us to test whether the CDs have modular relationships with the TDs ( Fig 3A ) . For a given gene and all its neighbors in the network , we computed the combinatorial chromatin-based measure of cis-recurrence as described above . Then , we examined the degree to which the cis-recurrence levels of the given gene itself and all its neighbors can predict the coding driver status of the given gene ( see Methods ) . The CDs themselves had a higher level of cis-recurrence than other genes as indicated by the gray receiver operating characteristic ( ROC ) curves in Fig 3B . This is consistent with the high connectivity of the CDs in the regulatory network . However , the modular extension of the recurrence levels considerably improved the performance of CD prediction ( colored ROC curves in Fig 3B ) . The TP53 network module is illustrated in Fig 3C with the coding recurrence levels in breast and liver cancer ( yellow and blue bars at the center ) and regulatory recurrence levels in each cancer ( violet and green bars at the circumferences ) . It is notable that this approach performs better for the prediction of the CDs than for the prediction of all known cancer genes ( S9 Fig ) . For example , compare the CGC CDs identified by point variants ( Fig 3B ) with all CGC genes ( S9 Fig ) . This implies evolutionary interactions between protein-coding and cis-regulatory point variants during cancer development . We examined complementary recurrence patterns of interacting coding and regulatory variants . We computed variant complementarity as described in Fig 4A for each pair of genes . As shown in Fig 4B , this measure was significantly higher for the interacting CD-TD pairs ( red boxplots ) than all CD-TD pairs ( blue boxplots ) and all background coding-regulatory variant pairs ( gray boxplots ) . Complementary variant patterns between coding variants of TP53 and regulatory variants of its interacting genes with the greatest degrees of variant complementarity are illustrated in Fig 4C . In the given breast cancer samples , MYC , CEBPB , CCND1 , and TFAP2C regulatory variants showed clear mutual exclusivity between themselves as well as with TP53 coding variants . Mutual exclusivity of the coding variants of proteins on the same signaling pathways has been a focus of interest . However , such relationships between coding and regulatory variants have never been investigated before . In summary , we search the chromatin interactome and protein interactome for combinatorial regulatory variant recurrence with aim to prioritize cancer-driving genes . Candidate transcriptional driver genes , ones that are recurrently affected by cis-regulatory variants via chromatin interactions , showed functional and network features that could be shared with cancer-driving genes . The gene transcription network , especially the Bayesian causal regulatory network , exhibited the potential effects of these genes on extensive network perturbation . Genes with recurrent coding variants also stood out in the regulatory network . For example , tumor suppressors and oncogenes can perturb the regulatory network through transcriptional silencing or activation . In fact , these genes were high in cis-regulatory recurrence , indicating that they may be recurrent for both coding and regulatory variants . For the first time , we systematically investigated interactions between genes associated with coding variants and those with regulatory variants . The regulatory recurrent genes are not hubs per se in the protein interactome but frequently interact with genes of high coding variant recurrence . The variant occurrence patterns support the complementary evolution of the coding and interacting regulatory variants during cancer development . Therefore , the recurrent regulatory variants may act not only through an extensive perturbation of the regulatory network but also by altering the protein network through interactions with coding variants . To directly estimate the effect of a cis-regulatory variant , the regulatory network of the sample that carries the given variant should be interrogated . For example , a personalized characterization of regulatory variants can be conducted by using sample-specific networks [35] . This approach will be useful when one is interested in a specific driver gene and would like to know which particular genes are affected by the variants of this driver gene . For this , we need a large number of whole-genome sequenced samples from which TDs can be identified reliably and matched gene expression data based on which sample-specific networks can be constructed . It should be noted that recurrence is not an absolute indicator of cancer-driving variants . For example , harmfulness of amino acid substitutions can be directly measured [36] . Cancer-related genes identified in this fashion showed high connectivity in protein interaction networks [36] as the CDs identified on the basis of recurrence . However , there is currently no such method for noncoding regulatory variants . In conclusion , our results illustrate that various types of biological networks can deepen our understanding of the cancer genome and promote the discovery of novel cancer genes .
We downloaded variant calls for whole genome sequences of 507 cancer samples across 10 different cancer types from ftp://ftp . sanger . ac . uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data [37] . The variants detected by the filters of the Sanger pipeline were excluded from our analysis . In total , we used 647 , 695 point variants in 119 breast cancer samples and 899 , 449 point variants in 88 liver cancer samples . Variants of other tumor types were used for our clinical simulation , in which the same number of variants as in breast or liver cancer were retrieved and subjected to the computation of combinatorial recurrence . To single out functional variants , we applied the position weight matrix of transcription factor binding to the variant sites . The transcription factor binding information was obtained from the human CIS-BP ( Catalog of Inferred Sequence Binding Preferences ) database [38] and TRANSFAC [39] . The screening of transcription factor binding sites was performed by the FIMO tool [40] . Gain or loss of the binding sites by variants was evaluated based on the P value differences from the FIMO outputs . The P value cutoff of 10–5 was used . To map the target genes of the identified regulatory variants , we used four chromatin interactome datasets in each cancer type: ( 1 ) ChIA-PET [26–28] , ( 2 ) Distal-proximal DHS tag correlation [1] , ( 3 ) CAGE-based enhancer RNA-messenger RNA correlation [30] and ( 4 ) IM-PET [29] . As for ChIA-PET , we focused on RNA polymerase II-mediated chromatin interactions measured in MCF-7 and K562 [27] . For a filtering purpose , PET counts ≥ 3 were used to avoid false positive interactions . CAGE-detected enhancer RNA varied 2 ~ 2 , 860 bp in length , so we defined enhancers as 100 bp upstream and downstream of the center of an enhancer RNA . For gene annotation , we used protein coding genes from the GENCODE v19 [41] . Promoters were defined as 2 kb upstream ~ 500 bp downstream of the transcription start site . Finally , we merged all chromatin interactome data separately for each cell type ( MCF-7 , HepG2 , and K562 ) after filtering out promoter-promoter interactions . Because the DHS correlation and CAGE correlation data provide a universal set of enhancer-promoter mappings , we intersected the DHS regions of the relevant cell type to reconstruct the cell type-specific chromatin interactome . The cell type-specific subsets were combined with ChIA-PET and IM-PET in MCF7 for breast cancer analysis , IM-PET in HepG2 for liver cancer analysis , and ChIA-PET and IM-PET in K562 for the epigenome simulation described later . We computed combinatorial cis-regulatory recurrence levels by projecting the regulatory variants in breast cancer and liver cancer onto the merged chromatin interactome . Recurrent genes were defined as having a co-occurrence of > = 2 . Furthremore , we grouped all genes into three categories according to their combinatorial cis-recurrence levels: no recurrence for a co-occurrence of 1 , low recurrence for a co-occurrence of 2 ~ 4 , and high recurrence for a co-occurrence of > = 5 . The two regions linked by chromatin interactome data per se can be assumed to be cis-regulatory regions; the ChIA-PET , DHS correlation , CAGE correlation , and IM-PET interactome are based on RNA polymerase II binding , chromatin accessibility , enhancer and messenger RNA expression , and various enhancer and promoter features , respectively . However , we applied additional filters for the detection of enhancers and promoters by using histone modification ( H3K27ac and H3K4me3 ) , RNA polymerase II binding , p300 binding , and RNA expression . The different criteria and resulting number of chromatin interactions are shown in S2 Fig . The resulting recurrence level with each criterion for each gene is provided in S1 Table . We performed two types of genomic simulation and one type of epigenomic simulation to assess the statistical significance of the observed combinatorial cis-recurrence levels . First , random variant sets were constructed for each cancer in silico by generating the same number of variants while maintaining the distribution of per-sample variant counts . Second , instead of randomly generating in silico variants , the same number of variants for each sample was retrieved from the other 506 clinical samples across various tumor types . These two genomic simulations were repeated 1 , 000 times to generate a null distribution of recurrence levels . Third , we mapped the real variants to an irrelevant chromatin interactome . The same number of the ChIA-PET , IM-PET , DHS correlation , CAGE correlation interactions as the original data ( MCF-7 and HepG2 ) was retrieved randomly from the matching K562 data . We generated two gold-standard sets of the CDs: one from the CGC database [31] and the other based on the 20/20 rule [14] . From the CGC , we retained frameshift , missense , nonsense , and splicing variants while excluding amplifications , large deletions , and translocations , in order to focus our analysis on point variants . The 20/20 set was constructed based on the hypothesis that > 20% variants in an oncogene should be at recurrent positions and > 20% variants in a tumor suppressor gene need to be inactivating or truncating . We used three more inclusive gene sets . First one was CGCAll , which included all genes in the CGC database . Second , AllOnco is a master set of other seven cancer gene sets [42] . Third , MouseIns is a set consisting of genes identified by insertional mutagenesis in mice [43 , 44] . For a directional , causal gene regulatory network , we employed our previously constructed breast cancer Bayesian network [22] . This global network was constructed at an unprecedented level of biological coverage and accuracy based on precise modeling of genomic regulatory interactions . We computed a causal score for each gene on the basis of its outdegree ( the number of outgoing links ) in the network and relative position in the causal chain . The causal chain was defined as the longest ( or shortest ) path connecting the head and tail nodes via the gene of interest . The causal score is proportional to the relative outdegree in the network ( the number of outgoing links divided by the total number of links of the given node ) and the relative distance to the tail node of the causal path . The relative distance was obtained by considering the length of the causal path . Choice of the longest or shortest path did not make significant differences . For non-directional association networks , we employed ARACNe ( Algorithm for the Reconstruction of Accurate Cellular Networks ) [23] and PCA-PMI ( Part Mutual Information-based PC-algorithm ) [33] . We applied the available tools ( http://califano . c2b2 . columbia . edu/aracne [45] and http://www . sysbio . ac . cn/cb/chenlab/software/PCA-PMI/ ) for gene expression data in breast cancer and liver cancer separately . For this analysis , we downloaded gene expression data ( Illumina HiSeq-based ) for 1215 breast cancer and 423 liver cancer samples from the Cancer Genomics Browser ( https://genome-cancer . ucsc . edu/ ) . We employed two different types of the protein interactome: ( 1 ) an integrated physical interaction network constructed by adding interactions from Stitch-seq mapping [46] and the HINT database [47] to the basal data [24] consisting of the yeast-two-hybrid interaction pairs and integrated literature-based protein-protein interactions , and ( 2 ) a probabilistic functional network [25] constructed by a modified Bayesian integration of various types of data from multiple organisms . Only direct links between the CDs and TDs were considered . The expected number of interactions was estimated by generating the null distribution through 1 , 000 random permutations of nodes or links of each network . The real ( observed ) number of interactions was divided by the 1 , 000 randomized ( expected ) numbers of interactions; thus we calculated enrichment scores as the observed-to-expected ratios . We sought to examine modular relationships between the CDs and TDs . First , for a given gene and all its neighbors in the network , we computed the combinatorial chromatin-based measure of cis-regulatory recurrence . Then , we examined the degree to which the cis-recurrence levels of the given gene itself and its neighbors can predict the coding driver status of the given gene . We used three metrics for recurrence combination at the modular level . Let M ( v ) be the number of cases ( patients ) in which gene ( node ) v has cis-regulatory variants , in other words , the combinatorial cis-regulatory recurrence of gene v . Let L ( v ) be the set of linked neighbors of gene v and deg ( v ) be the number of linked neighbors of gene v . With these , the three scoring metrics for gene v are defined as follows . Average of the neighbor variant occurrences: Scoreaverage ( v ) =M ( v ) + ∑u∈L ( v ) M ( u ) deg ( v ) . Weighted max of the neighbor variant occurrences: ScoreMax ( v ) =M ( v ) +max{x|x=M ( u ) ×W ( v , u ) , u∈L ( v ) } , where W ( v , u ) is the normalized edge weights between gene v and gene u , which indicate the degree of functional association [25] . Degree-normalized sum of the neighbor variant occurrences: Scoresum ( v ) =M ( v ) + ∑u∈L ( v ) M ( u ) deg ( u ) . Considering the edge weights did not improve the predictability of the method in the case of the average and sum of the neighbor variant occurrences .
|
Identifying driver variants is a current challenge facing cancer genomics . A well-established and robust method for this is to find recurrence in large cohorts of samples . Recurrence patterns of amino acid-changing variants can reveal oncogenes and tumor suppressor genes . However , such single-gene approaches have limitations because of rare variants . Therefore , recurrently affected protein complexes , network modules , or signaling pathways have been identified based on network-level recurrence . Here we dissect chromatin interactome to identify cis-regulatory variants that show high gene-level recurrence . We then employ the gene regulatory network and protein interactome to characterize putative cancer genes with cis-regulatory variant recurrence . These genes were located at critical positions in the regulatory network . By contrast , they are at the circumference in the protein interactome; instead , they form a network module with coding cancer genes located at hub positions . Furthermore , the coding and regulatory variants associated via these interactions showed exclusive and complementary occurrence patterns across tumor samples . Therefore , we suggest that transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Material",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"breast",
"tumors",
"genetic",
"networks",
"gene",
"regulation",
"protein",
"interaction",
"networks",
"carcinomas",
"cancers",
"and",
"neoplasms",
"gastrointestinal",
"tumors",
"liver",
"diseases",
"dna",
"transcription",
"oncology",
"network",
"analysis",
"gastroenterology",
"and",
"hepatology",
"epigenetics",
"chromatin",
"computer",
"and",
"information",
"sciences",
"chromosome",
"biology",
"gene",
"expression",
"breast",
"cancer",
"proteomics",
"hepatocellular",
"carcinoma",
"biochemistry",
"cell",
"biology",
"gene",
"regulatory",
"networks",
"gene",
"identification",
"and",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology"
] |
2017
|
Network perturbation by recurrent regulatory variants in cancer
|
The specific binding of regulatory proteins to DNA sequences exhibits no clear patterns of association between amino acids ( AAs ) and nucleotides ( NTs ) . This complexity of protein-DNA interactions raises the question of whether a simple set of wide-coverage recognition rules can ever be identified . Here , we analyzed this issue using the extensive LacI family of transcriptional factors ( TFs ) . We searched for recognition patterns by introducing a new approach to phylogenetic footprinting , based on the pervasive presence of local regulation in prokaryotic transcriptional networks . We identified a set of specificity correlations –determined by two AAs of the TFs and two NTs in the binding sites– that is conserved throughout a dominant subgroup within the family regardless of the evolutionary distance , and that act as a relatively consistent recognition code . The proposed rules are confirmed with data of previous experimental studies and by events of convergent evolution in the phylogenetic tree . The presence of a code emphasizes the stable structural context of the LacI family , while defining a precise blueprint to reprogram TF specificity with many practical applications .
The search for principles describing how specific nucleotide sequences are recognized by proteins remains one of the most fundamental problems to be solved in Biology [1]–[5] . The relevance of this question is linked to the wide breadth of basic cellular processes to be better understood with its resolution , like how genomes respond to stress by accurately activating/inactivating groups of genes , or how cells differentiate into separate classes following a program of precise spatio-temporal gene expression . Additionally , these principles could turn into genuine rules to engineer protein production , either in isolation or as part of elaborated molecular circuits or networks , with many practical applications . Given the relevance of this search , when could one say that principles have been actually identified , or that this goal failed ? Answers to these questions changed over the years , e . g . , [2] , [6]–[10] , as the knowledge of how transcriptional factors ( TFs ) recognize their cognate binding sites ( BSs ) did . Two mechanistic aspects of this recognition are relevant in this regard [11]–[13] , i . e . , how selected AA/NT binding partners determine specificity ( direct readout ) and how specificity could be influenced by additional structural features ( indirect readout ) . Within this second aspect , both the protein structural context in which the contacting AAs are embedded [7] , [14] , [15] and the conformational characteristics of DNA upon TF binding [11] , [12] appear as particularly important modifiers . In fact , the relative strength of direct and indirect readouts can greatly influence the nature of the recognition rules to be identified . The most simplistic situation could be one in which ( simple ) direct readouts for the contacting positions were dominant specificity determinants . In this case , one could conceive the presence of deterministic codes of wide applicability . However , the rich repertoire for AA/NT interactions , which includes hydrogen or water-mediated bonds and also van der Waals contacts [16] , and the context dependence of these interactions rule out the appearance of deterministic codes [6] , [7] , [17] . Instead , one should rather look for probabilistic recognition codes restricted to similar protein structures [3] , [8] , [14] , [18] . The applicability of these principles to large protein groups might ultimately depend on the conservation of the modifiers linked to indirect readouts . Interestingly , some of these issues can be studied with the use of mutational experiments –either in vivo [19] , [20] or in vitro [8] , [10] , [21] , [22] – which start with a known TF/BS relationship to characterize changes in specificity once selected AA and/or NT positions are mutated . Since the number of possible sequences grows exponentially with the number of positions to be explored , this approach usually requires the use of large mutant libraries . Consequently , even when the sequence space is explored in a random way [8] , or by screening methods [20] , the positions to be mutated are always selected among those corresponding to direct readouts . Since the rest of positions remains fixed , the conservation of the structural context within the library directly follows . This implies that any set of recognition rules deduced from the mutational approach is restricted , in principle , to the library elements . The existence of a natural version of such synthetic code would require a strong conservation of the mode of binding within the family of proteins to which the focal mutated protein belongs –despite the variability in the non-contacting positions [23] , [24] . Mutational studies can estimate this conservation only in an indirect manner , by finding natural correspondences of some of the synthetic AA/NT relationships studied [8] , [19] . Regardless of the existence or absence of such correspondences , those mutants with differential specificities could constitute useful tools for Synthetic Biology [20] , [25] . An alternative approach to this problem , in which the role of indirect readouts is evaluated , deduces the recognition rules by using genomic tools applied to natural sequences of both TFs and BSs [14] , [15] , [26]–[28] . In this case , each residue/base contact is embedded in its own structural context and the possibility of family codes can be explicitly examined . The finding of consistent recognition rules , whereby the sequences of the contacting AAs and NTs correlate , would imply that variations on the rest of residues do not compromise the conservation of the binding mode within the considered set . Moreover , such natural recognition code would suggest that the evolution of new specificities is mainly achieved by alteration of base contacting residues ( direct readouts ) [14] . Recognition rules following this approach were formulated for several sets which , in each case , involved a limited number of related TFs [14] , [27] , [28] . In this work , we asked to what extent a natural wide-coverage recognition code could exist . From the arguments before , this code could be considered as such when it fulfills two important requirements . First , the determinants of the indirect readout should not prevent the identification of consistent sequence correlations between the contacting AAs and NTs for a given regulator family ( or a substantial fraction of it ) . Second , most of these natural associations should be reproducible by mutating the specificity-associated AAs of a particular focal member of the family . Note that these features do not include that the recognition correlations should be expressed in terms of a few deterministic rules –although strong general trends are expected . We considered as a model system to approach this question the extensive LacI family of transcriptional regulators [26] , whose helix-turn-helix ( HTH ) domain ( Fig . 1 . A ) interacts with a set of cognate BSs [29] . Within this set , we examined a dominant group ( involving more than half of the LacI family members ) composed by regulators exhibiting the sequence threonine-valine-serine-arginine ( TVSR ) in the recognition helix of the HTH domain . We searched for recognition rules by introducing a new strategy based on comparative genomics and the use of a pervasive characteristic of prokaryotic regulation: the local control of gene expression [30]–[34] . Our analysis suggests that the determinants of the indirect readout are substantially conserved throughout the TVSR group , in which a set of relatively consistent recognition rules applies . Moreover , the phylogenetic tree associated to this group exhibited several convergence events for the recognition relationships , i . e . , distant proteins in the tree sharing the same recognition AA sequence tend to bind similar NT sequences . The natural recognition correlations identified could be reproduced with a synthetic approach , as suggested by comparing the theoretical predictions with previous mutational experiments [19] , [20] and by the finding of natural BSs previously considered as simple laboratory constructs [35] .
We aligned non-redundant HTH-LacI domain sequences using information from MicrobesOnline [36] , a database that contains approximately one thousand prokaryotic genomes ( Methods ) . The resulting sequence logo ( Fig . 1 . B ) suggested that the binding patterns previously identified with structural studies could potentially apply to the whole LacI family . Specifically , these studies solved the binding-domain/DNA complex of Escherichia coli's LacI [37]–[39] and PurR [40] , [41] , and Bacillus megaterium's CcpA [42] , clearly distinguishing a contrast between structural and DNA-binding residues in the corresponding domains . Indeed , positions exhibiting a strong conservation in our comparative analysis corresponded to proposed structural residues . In particular , the conservation of the hydrophobic residues in AA-54 ( mostly leucine , 82% ) indicated that the BS pattern in the family could be dominated by a conserved central CG group ( although we did not use this prior knowledge in our analysis ) . In every structural study , this residue of the hinge-helix inserts into a central CG group located in the minor groove and bends the DNA ( Fig . 1 . A ) . The conserved alanine in AA-51 is similarly related in these analyses to the hinge-helix/CG union by non-specific interactions with the phosphate groups , or through direct contacts with the bases [43] . Exceptions to this union are rare [44] , [45] . To identify the potential DNA-binding residues resolving BS specificity , we selected those domains in the alignment which were univocally associated to BSs in the RegTransBase v5 [46] ( 370 domains ) . These BSs were aligned to produce the logo in Fig . 1 . C . Note the palindromic nature of this logo , which manifests the symmetrical contacts made by the monomers that constitute the dimeric regulators on the corresponding left ( L ) /right ( R ) half site location of the BSs [in the following , we usually simplify the notation of symmetrical positions , and palindromic sequences , by those in the left half site , e . g . , as ( NT-5 , NT-4 ) = TG] . We then calculated the mutual information ( covariance dependency ) between the alignment of these 370 domains and that of their corresponding BSs [47] ( Fig . S1 ) . This computation identified three main patterns . First , the extensive linkage between the non-conserved nucleotide pair ( NT-5 , NT-4 ) and the ( AA-15 , AA-16 ) residues located in the recognition helix ( this helix includes residues AA-15 to AA-22 , see Fig . 1 . B ) . Second , the presence of a strong connection between NT-6 and AA-20 ( also in the recognition helix ) ; these coordinates exhibited no other appreciable interdependences suggesting a mode of recognition relatively independent to the previously discussed pair . Finally , the correlation of NT-2 with AA-55 , AA-15 and AA-5 , in decreasing order of importance . The mutual information analysis also generalized previous experimental results obtained with a few members of the LacI family , this time with respect to the proposed specificity residues . In particular , the association of the pair ( NT-5 , NT-4 ) to ( AA-15 , AA-16 ) was demonstrated by structural models [29] and mutational studies [19] . The independent nature of the recognition interaction between NT-6 and AA-20 was also suggested by previous mutational studies of E . coli's LacI [19] , [29] . In addition , the link between NT-2 and the hinge-helix residue AA-55 ( Fig . 1 . B ) was proposed in [41] . Moreover , although AA-20 was related to recognition processes , it is a strongly conserved residue –with arginine ( R ) linked to the presence of a guanine in NT-6 ( , , Yates-corrected -test ) . This resulted in the same AA sequence ( a TVSR sequence for the range AA-17 to AA-20 ) in 1490 instances of a total of 2639 included domains ( , Fig . S2 ) . We thus restricted the following analysis to the TVSR dominant subgroup . From all the above , we hypothesized that the distinction among the different BSs associated to the TVSR set would rely mostly on the ( AA-15 , AA-16 ) pair . We further considered a stronger version of this hypothesis assuming that regulators sharing the same ( AA-15 , AA-16 ) sequence would tend to bind similar BSs regardless of their evolutionary distance . In the following , we tried to confirm these conjectures by analyzing the possible presence of a recognition code assigning specific nucleotides ( NT-5 , NT-4 ) to residues ( AA-15 , AA-16 ) . The search of a wide-coverage recognition code required a large scale identification of the native BSs for each TF , with independence of its location in the LacI family phylogenetic tree . This requirement might become problematic if we were to apply the standard protocols of BS search . These methods often rely on the identification of orthologs of experimentally determined target genes to look for conserved upstream BSs –for example , by applying phylogenetic footprinting ( PF ) techniques [48] . As evolutionary distance between TFs increases , this approach lacks precision because of the complications to define orthologs , e . g . , due to events of duplication and loss of genes [49] . We decided to use a complementary strategy to search for BSs . This strategy was based on the hypothesis of the conservation of binding mode and also on the widespread presence of local transcriptional control in bacteria ( including both auto- and neighbor-regulation [34] ) . Thus , we first grouped regulators sharing the same sequence of recognition residues ( AA-15 , AA-16 ) , regardless of the evolutionary distance among the full TF sequences . Within each of these groups , or recognition classes , we looked for potential BSs in the intergenic regions located before the operon encoding the TF itself , and before the downstream operon , respectively ( Fig . 2 . A ) . We applied PF for BS search on these sequences with a subsequent refinement based on iterated position weight matrices ( PWMs ) ( this protocol was aimed to minimize the rate of false positives linked to bioinformatic BS searches [49] , see Methods ) . We obtained in this way a nucleotide logo from each alignment of BSs associated to a recognition class ( Figs . 2 . B–D and Appendix in Text S1 for the complete set ) . We also computed the consensus logo of the full TVSR group ( Fig . 2 . E ) , where the contrast between conserved and non-conserved NTs is especially apparent . Although we used uninformed priors in the BS-finding algorithms to avoid circularity biases , the obtained consensus logo corresponded to the one expected from a situation where the TF binding mode is conserved ( compare Fig . 1 . C , computed from a previously known BS set [46] , to Fig . 2 . E ) . Note the conservation of G in ( and C in ) , for we considered a group of domains with arginine in AA-20 . Computation of the familial binding profile [23] , [24] –a method that can also suggest the conservation of the binding mode within a TF family– for the TVSR set produced the same qualitative patterns in the consensus logo . Once we obtained the BS logos associated to each AA recognition class , we could naively suppose that the presence of logos with high information content in both NT-4 and NT-5 would confirm the hypothesis of a recognition code . In the same vein , ambiguities in these nucleotides would reject the hypothesis ( for example , in the set , Fig . 2 . B , where both T and A are found in ) . However , low-information positions could alternatively be explained by degeneracies in the recognition process , an expected attribute of extant codes [3] . In this latter case , the code conjecture would still hold true . How can we distinguish these contrasting situations ? Imagine a simplistic scenario in which a particular recognition AA sequence corresponds to a ( recognition ) class uniquely constituted by two different TFs . Imagine also that there were only two types of half site with different ( NT-5 , NT-4 ) sequences in the BSs observed for this TF class . Consequently , the corresponding BS logo would exhibit low-information ( NT-5 , NT-4 ) positions . This ambiguity could be caused because the particular ( AA-15 , AA-16 ) sequence for this class showed some degeneracy in recognition ( as discussed above; we termed this intrinsic degeneracy ) , or because each TF exhibited a precise specificity to either type of half site , i . e . , the recognition AA pair is not acting as the only determinant of specificity . We can further illustrate this with the help of Figure 3 . In principle , the two species of half sites involved could be combined into palindromic ( P1 , P2 in Fig . 3 . A ) or non-palindromic architectures ( M1 , M2 in Fig . 3 . A ) . When each TF monomer had a high affinity for both half sites ( Fig . 3 . B left ) , they could bind efficiently to P1 , P2 and either mixture ( we considered both mixtures to have the same binding energy ) . In a second situation ( Fig . 3 . B , center ) both TFs had again similar affinities , but this time the monomers bound preferentially to one type of half site and , consequently , to one palindrome . Although a mixed configuration could still be compatible with ( weaker ) regulatory tasks , the probability of binding to the other palindrome strongly decreased . These are two instances of intrinsic degeneracy . Finally , in a third scenario each TF was very specific to a single half site type; so that only P1 or P2 were accessible ( no mixtures ) , an example of logo ambiguity due to an extrinsic degeneracy ( Fig . 3 . B , right ) . Ambiguities explained as intrinsic degeneracies are compatible with our starting hypothesis and would only reflect a degenerate code . The code hypothesis must be revised or even rejected when extrinsic degeneracies are common . This would presumably reflect critical changes in the determinants of the indirect readout . A BS logo can thus be degenerate because i ) the recognition process is degenerate in itself ( intrinsic degeneracy ) or ii ) the logo is computed from BSs recognized by TFs with different specificities ( extrinsic degeneracy ) . To distinguish between these two scenarios , we identified and classified degeneracies ( Methods ) . Fig . 3 . C shows the notation used for the different degeneracies . One could simultaneously observe several of these degenerate scenarios for any alignment involving more than two different types of half sites . Table S1 included all correlations obtained between the pair of residues ( AA-15 , AA-16 ) and the nucleotides NT-4 and NT-5 , together with the corresponding degeneracies when observed . This table contains 48 different recognition classes , involving a total of 38 intrinsic and 6 extrinsic degeneracies ( some classes exhibiting both ) . The different types of identified degeneracies corroborated the potential of this protocol to detect distinct BSs within a TF class . The extrinsic degeneracies observed constitute a small number of exceptions to an otherwise consistent confirmation of the code conjecture . We showed a subset of these results , with only significant palindromic combinations , in Fig . 4 . A . Recognition sequences were sorted by the left semisequence of the palindromes they recognize , and connected according to their resolved degeneracies . For instance , shows an extrinsic degeneracy between ( NT-5 , NT-4 ) = CA and ( NT-5 , NT-4 ) = GG . The variability of the recognition correlations in AA-15 became manifest also in this figure , a flexibility previously pointed out by mutational studies [19] . Our genomic approach confirmed then that the role of AA-16 as the strongest determinant of specificity applies throughout the TVSR group [19] . Since the general mode of binding in the LacI family involves DNA bending , one could expect that the direct readout of the contacting residues would be strongly conditioned by the characteristics of this specific type of indirect reading [11] , [12] , [50] . This would directly imply that TFs with the same contacting residues could recognize different NT sequences . However , the small number of extrinsic degeneracies found suggests that the degree of bending remains substantially conserved throughout the TVSR group . The consistent next step after proposing an AA/NT recognition code was to validate its predictions . We approached this issue in the next sections in three complementary ways . First , we compared the theoretical predictions with experimental data from LacI mutants ( Fig . 4 . B and Fig . S3 ) [19] , [20] . Second , we confirmed the existence of natural counterparts of BSs previously interpreted only as synthetic constructs ( Fig . 4 . C ) . Finally , by computing a gene tree including all TFs with at least one BS in Table S1 , we identified several convergence events in the recognition process –the same AAs/NTs association in different tree locations ( Fig . 5 ) – that additionally supported the hypothesis of the conservation of the mode of binding , and that overall indicated the presence of a relatively consistent recognition code . We compared the theoretical predictions with two experimental studies analysing the DNA binding specificities of Escherichia coli's LacI repressor [19] , [20] . Fig . 4 . B shows a comparison between the recognition rules in Table S1 and data from the first of these studies , the pioneering work of Müller-Hill and colleagues [19] in which several repressor mutants where isolated and characterized . In this figure , the experimentally measured repression of ( NT-5 , NT-4 ) -palindromes by different ( AA-15 , AA-16 ) -LacI mutants is shown in boxes ( with being the wild type interaction ) , where the theoretical predictions are superimposed . These predictions are indicated by arrows , following Table S1 , with dots denoting non-degenerate associations [links to a single ( NT-5 , NT-4 ) pair] . The agreement between theory and experiments emphasizes the presence of an intrinsically degenerate code , with the only discrepancy of the wild type . This inconsistency of the wild type class is due to the difference between the BSs considered in our study and those examined experimentally . Theoretical correlations were derived from natural BSs exhibiting variations over the asymmetric O1 site for E . coli's LacI ( Fig . 4 . C ) . This specific BS presents an intervening base ( NT-2bis , Fig . 4 . C ) which introduces an asymmetry between the protein contacts made over the left and right half sites [29] , [38] . However , LacI can bind a palindromic BS lacking the intervening nucleotide . This BS is called SymL ( Fig . 4 . C ) because it is synthetically built from the symmetrization of the left half site of O1 [29] . The mutational studies were based on variations over SymL [19] –for example , the SymL' site in Fig . 4 . C . In such synthetic constructs the palindromic affinity of LacI is severely restricted to ( NT-5 , NT-4 ) = TG . Moreover , LacI is unable to bind the SymL/SymL'-like mixture ( Table S2 ) obtained from the delection of in the natural O1 site [51] . In a more recent work , Lewis and colleagues [20] characterized the associations between a set of E . coli's LacI mutants for the triplet ( AA-15 , AA-16 , AA-20 ) –corresponding to the AA coordinates 17 , 18 and 22 of LacI , respectively– and the palindromic ( NT-6 , NT-5 , NT-4 ) -variants of the SymL operator . We plotted in Fig . S3 a comparison between the recognition pairs obtained in these experiments ( corresponding to the TVSR group ) and the theoretical predictions involving significant NT palindromic combinations ( Fig . 4 . A ) . We noticed again a strong agreement between theory and experiment , which becomes more evident when considering that regulators sharing the same AA-16 sequence tend to bind similar NT sequences . Note also that some of the theoretical correlations could remain untested due to the specific mutant sampling of the screening protocol . Our predictions appeared nevertheless at odds with some experiments done with lac family members in the latter study [20] . In this case , the recognition triplet ( AA-15 , AA-16 , AA-20 ) of LacI was swapped to that of nine different members of the family , i . e . , MalR , RbtR , FruR , PurR , RbsR , GalR , CytR , RafR and ScrR ( the last four in the TVSR group ) . The sequence of ( NT-6 , NT-5 , NT-4 ) in SymL was changed accordingly for these regulators to that of a natural BS in which they were known to bind . Only the mutants associated to GalR and FruR worked [20] . This seemingly contradiction is partly linked to the presence of members out of the TVSR group ( see below ) and the use of single BSs in the repressor-operator characterization ( see Text S1 , section 3 for a detailed discussion ) . The agreement between the familial ( genomic-based ) specificity predictions and the corresponding mutational experiments in the TVSR set ( Fig . 4 . B and Fig . S3 ) , this set being of the whole family , suggests that the preferential binding of arginine in AA-20 to guanine in NT-6 might turn the structural environment under which the recognition partners ( AA-15 , AA-16 ) / ( NT-5 , NT-4 ) operate with strong stability , so that indirect readouts did not prevent the emergence of a consistent recognition code . The binding of LacI to the synthetic site SymL was believed to be a laboratory construct , not representative of the characteristic binding mode of this regulator [35] . However , two observations from our study supported the presence of a natural counterpart of this synthetic binding mode . First , the natural BSs for the related recognition sequence resembled either the perfect palindromic sequences of SymL and SymL' , or their mixture ( Table S2 , see the corresponding logo in Fig . 2 . D ) . Second , although every BS involved in the logo in Fig . 2 . C incorporated the inserted nucleotide , we also found several BSs related to the synthetic SymL construction ( Fig . 4 . C and Table S2 ) in the first BS search based on PF . In agreement with the mutant model [19] , [51] , neither natural SymL'-like BSs nor mixtures were detected for in this PF scan . That the recognition sequences of - and -TFs are highly related was also suggested by its location in the gene tree . Fig . 5 shows the gene tree of all TFs with at least one BS in the table of correlations ( 623 TFs for 811 BSs in Table S1 ) and the three TFs with binding to SymL-like BSs . In this tree , branches corresponding to these two recognition classes appeared closely located . In fact , a recent mutational work [52] demonstrated that the LacI-mutant exhibits a stronger affinity to SymL than the wild type . If only a restricted number of specificity determinants ( AA to NT pairs ) were possible within a particular regulatory family , we should expect instances of convergent evolution for the same recognition AAs in divergent backgrounds . This is indeed what we observed . In the gene tree plotted in Fig . 5 ( see also Fig . S4 ) , branches corresponding to several of the largest recognition classes were highlighted . We identified convergence events in the recognition process ( i . e . , same AAs associated to the same NTs throughout the tree ) . These findings validated the initial hypothesis that the binding mode was highly conserved and that , as a consequence , evolution finds the same solutions repeatedly ( the presence of relatively consistent recognition rules ) . Such structural stability of the TVSR set could apply to other regulator families . This work reveals the first comprehensive resolution of a recognition code for a large group of proteins within a family of transcriptional regulators . This resolution is based on the use of comparative genomics [15] , the identification of local transcriptional regulation as a fundamental regulatory architecture in prokaryotes [30]–[34] and the hypothesis of the stability –in the large phylogenetic distances considered– of the domain structure around the recognition sites [10] , [14] . This last hypothesis is confirmed by the patterns of differential residue and BS conservation obtained . Indeed , we only found a few instances of TFs that would invalidate our conjecture , i . e . , TFs with the same sequence in the specificity pair ( AA-15 , AA-16 ) but recognizing incompatible BSs ( extrinsic degeneracies ) . Moreover , the convergence events and the agreement of the correlations with mutational data ( including the extension of the rule of the AA-15 flexibility to become a dominant family attribute ) support the assumption that the mode of binding is conserved for a large fraction of the family . A few caveats to our approach should be noticed . First , we considered a stringent protocol to select for BSs . This method combined PF , iterated PWM refinement , and further removal of BSs with potential spurious nucleotides exhibiting no special affinity ( see Text S1 , section 2 ) . In this way , those AA/NT relationships incorporated into the code should exhibit at least a minimal moderate affinity . Of course , any false positive removal is made at the cost of losing some true positives . An example of this was the loss of the BS for RafR [26] , which was detected in the initial PF search but removed after the processing protocol . In any case , this was a consequence of the dominance within the TVSR set of a canonical mode of binding associated to an ideal BS backbone given by the conserved pattern ( T ) G–A-CG-T–C ( A ) in Fig . 2 . E . A second limitation to our approach is the reliability of the extrinsic/intrinsic degeneracy analysis . The most reliable ones correspond to TF classes with many members and many detected BSs , e . g . , the TF class corresponding to ( see the Appendix in Text S1 ) . This second limitation could be overcome as more genomes become available . In contrast to what appears to happen with the LacI family as a whole [20] , the natural recognition correlations within the TVSR subfamily could be largely reproduced by mutational experiments . Thus , the genomically-derived correlations will be useful to complete the specificity map derived with mutational approaches only [19] , [20] . Moreover , the use of natural correlations will be probably essential to guide the redesign of a library of regulators that can target the maximal number of arbitrary sequences in the non-conserved positions of the consensus sequence . Note that , beyond the code established between the pairs ( AA-15 , AA-16 ) and ( NT-5 , NT-4 ) , the mutual information analysis of Fig . S1 suggested that there existed alternative AA and NT positions also involved in specificity tasks . In particular , the sequence in NT-2 was associated in this analysis to those of AA-5 , AA-15 and AA-55 . The same applies for a mutual information analysis restricted to the TVSR set ( data not shown ) . This specificity role of AA-55 was demonstrated in the particular case of the purine repressor [41] . As AA-15 could be coupling the recognition of NT-2 to that of the pair ( NT-5 , NT-4 ) , the resolution of the specificity map for the triad ( NT-5 , NT-4 , NT-2 ) could be beyond the scope of any mutational approach without a previous genomic blueprint . In summary , the main advantage of the BS search based on local regulation is its potential applicability to any annotated genome and TF family , without the limitations linked to orthology and functionality definitions , i . e . , the functional relationship between the TF and the regulated operon trivially exists in the case of autoregulation . The explicit correlations obtained in this analysis can thus be refined with sequence data from newly sequenced genomes , and could ultimately act as a blueprint for the synthetic redesign of TFs with new specificities . These correlations constitute the first candidate to a relatively consistent recognition code applicable to an extensive subfamily of transcriptional regulators .
5597 AA sequences for HTH-LacI domains ( Smart SM00354 ) were obtained from MicrobesOnline [36] . The median length value of this domain ( including both the HTH and hinge-helix regions ) is AAs . To guarantee the functionality of the domains , we selected from the starting set every sequence whose length is inside the range of AAs , and removed those lacking the 26-AA Pfam domain PF00356 –this label corresponds to the HTH core of the HTH-LacI domain . We also discarded three cases of proteins containing two SM00354 domains . Finally , we removed overrepresented sequences due to strain variations in the database to get a final set of 2639 sequences . We use Muscle [53] to add each of the HTH-LacI domains to a previous Smart curated alignment involving 49 SM00354 domains [54] . After the removal of columns exhibiting gaps in more than 80% of its sequences , we obtained a seed-alignment with 71 AA positions . Then , for each of the 2639 sequences we applied the following protocol: i ) the sequence is added to the seed-alignment using the mentioned option of Muscle; ii ) all those positions that imply the insertion of a gap in the seed-alignment are removed from the sequence; and iii ) the sequence ( in its aligned configuration ) is removed from the seed-alignment and saved . After the process was completed , none of the 71 positions in the final alignment of the 2639 domains ( Fig . 1 . C ) exhibited gaps in more than 5% of sequences . We extracted all the recognition helix sequences from the alignment . 1490 out of 2639 domains belonged to the TVSR group ( Dataset S1 ) . We could extract from the operons predictions included in MicrobesOnline [36] the non-coding region located upstream of the operon encoding the HTH-LacI domain ( up to 200 bp ) , and also the non-coding region located before the downstream neighbor operon ( Fig . 2 . A , Dataset S1 ) . When the regulated operon is located downstream of the regulator , both operons are usually encoded in the same strand ( unidirectional architecture [31] ) . Thus , in the case of downstream regulation we only considered the unidirectional orientation –this occurs in of domains . We did not included alternative convergent orientation ( downstream operon encoded in the opposite strand ) because under this architecture neighbor regulation is much less common [31] . Sequences were truncated if the next upstream coding region was reached ( Fig . 2 . A , red lines ) . From every region we also obtained an extended version of 250 bp that includes the range of coding positions from +1 to +50 . These extended regions were never truncated ( Fig . 2 . A , green lines ) . Within the TVSR group we divided the intergenic regions in groups associated to domains sharing the same ( AA-15 , AA-16 ) sequence . On each group ( recognition class ) , we made a first BS scan using PF techniques as implemented in the Gibbs Motif Sampler [55] , with the following parameters: estimated total number of BSs in a given group of regions equals the number of these regions; one BS per region at the most; palindromic BSs of 14 bp without fragmentation . Results were robust to changes in these parameters , including the estimated BS length and the palindromic nature of the sites . To avoid circularity we did use uninformed priors based on the average background composition [56] . The PF scan was applied over the truncated version of the intergenic regions to avoid coding zones , which , as it happens with BSs , are more conserved than the non-functional intergenic sequences . Finally , we discarded BSs with confidences below 40% . After the first BS scan we had at most one BS per intergenic region . We refined and extended our results through an iterative process of PWM construction and BS selection . This time , we considered that there might be multiple BSs per intergenic region and BSs located in the coding zone . Firstly , we built a PWM from the BSs found in the PF scan using a constant pseudocount function [49] ( results were robust under variations on this parameter ) . Secondly , we slided this PWM over the extended version of the intergenic regions and generously selected all those sites with a score over the minimal one in the starting BS set . The sites selected in this search is what we called the candidate sites . Finally , we applied the following protocol to look for the most significant candidates: i ) generation of a null set of scores that was obtained by sliding the PWM over random versions of the intergenic regions; ii ) selection of every candidate whose score had a p-value below when compared to the null set; iii ) construction of a new PWM from the candidates selected in ii ) ; and iv ) computation of the score for all candidates under the new PWM . Using the new PWM to generate a new null set , these four steps were iterated until convergence . The resulting set of 942 BSs was the end product of the whole process of search ( Dataset S1 ) . All the found BSs exhibited Z-scores above . Each BS was read in the sense strand –consequently , its left and right semisequences were univocally determined . See Text S1 , section 1 and Fig . S5 for a comparison with more standard approaches to BS search . We extracted the consensus sequence of BSs associated to a same recognition class and then aligned the whole set of consensus sequences to obtain the consensus logo ( Fig . 2 . E ) . Using the alignment of consensus sequences instead of the raw alignment of all found BSs avoids the over-representation of those BSs corresponding to the most populated classes . The raw alignment exhibited the same qualitative behavior to that of Fig . 2 . E . We successively applied the following protocol to each set of BSs associated with the same recognition AAs ( see section 2 of Text S1 , Table S3 , Fig . S6 , and Fig . S7 for a more detailed description ) . First , a triangular matrix F containing the frequencies of the 136 possible combinations for the quartet of positions was computed . Second , a matrix S was extracted from F by selecting combinations found to be statistically significant ( with respect to those observed in the genomic background ) . Third , significantly under-represented mixtures were identified in S , as the absence of mixed combinations is linked to extrinsic degeneracies ( Fig . 3 . B , right ) . Finally , each extrinsic degeneracy partitioned S into two submatrices in which the two types of intrinsic degeneracy were resolved . In the absence of any significant high frequency in a submatrix we kept the symmetrical recognition scenario of the null model ( Fig . 3 . B , left ) . Moreover , the presence of a significant frequency usually corresponded to a palindromic combination . In this case , we considered an asymmetrical recognition process with a dominant palindrome ( Fig . 3 . B , center ) . The full AA-sequences of the 626 TFs with at least a BSs in Table S1 ( 623 TFs ) plus the 3 TFs with binding to natural SymL-like BSs ( Fig . 4 . C and Table S2 ) were aligned and refined with Muscle . This alignment was trimmed with Gblocks [57] . Finally , we use PhyML to build the tree in Fig . 5 . Supplementary Fig . S4 contains a more detailed version of this tree in which each protein is labeled with its VIMSS ID plus the recognition AA pair . In this larger version , we plotted all the BSs associated to each TF ( we found four BSs per TF at most ) .
|
Transcriptional factors ( TF ) are proteins that bind specific short DNA sequences adjacent to the genes whose transcription they regulate . Although the nucleotide sequence recognized by a given regulator depends on the amino acids contacting the DNA , the mode in which amino acids and nucleotides interact is strongly influenced by the overall protein structure . This prevents the existence of a universal amino acid/nucleotide recognition code . However , recognition rules could be formulated for regulators sharing a similar structure , i . e . , for a family or subfamily of TFs . In fact , such rules have already been described for several sets which , in each case , involved a limited number of related TFs . In this study , we ask to what extent a wide-coverage recognition code might actually be found . To answer this question , we use the extensive LacI family of transcriptional regulators . Our analysis suggests that a set of relatively consistent recognition rules does apply within a major subset of this family . These rules could ultimately act as a blueprint for the synthetic redesign of TFs with new specificities .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"computational",
"biology/synthetic",
"biology",
"computational",
"biology/transcriptional",
"regulation"
] |
2010
|
Local Gene Regulation Details a Recognition Code within the LacI Transcriptional Factor Family
|
Cooperation and competition between human players in repeated microeconomic games offer a window onto social phenomena such as the establishment , breakdown and repair of trust . However , although a suitable starting point for the quantitative analysis of such games exists , namely the Interactive Partially Observable Markov Decision Process ( I-POMDP ) , computational considerations and structural limitations have limited its application , and left unmodelled critical features of behavior in a canonical trust task . Here , we provide the first analysis of two central phenomena: a form of social risk-aversion exhibited by the player who is in control of the interaction in the game; and irritation or anger , potentially exhibited by both players . Irritation arises when partners apparently defect , and it potentially causes a precipitate breakdown in cooperation . Failing to model one’s partner’s propensity for it leads to substantial economic inefficiency . We illustrate these behaviours using evidence drawn from the play of large cohorts of healthy volunteers and patients . We show that for both cohorts , a particular subtype of player is largely responsible for the breakdown of trust , a finding which sheds new light on borderline personality disorder .
We use the data set shown in King-Casas et al . ( see [6] ) , consisting of 93 healthy investors , paired with 93 trustees , of which 55 were BPD diagnosed trustees ( BPD Group , “BPD” ) and 38 were healthy trustees , matched in age , gender , IQ and socio-economic status ( SES ) with the BPD trustee group ( healthy control group , “HC” ) . The precise demographics can be found in King-Casas et al .
Fig 1C shows the average investments and returns in the data from King-Casas et al . [6] . The dark blue and dark red lines in Fig 1C show the respective average investments and returns for healthy investors playing BPD trustees . The lighter blue and red lines show average investments and returns for healthy investors and healthy trustees who matched the BPD trustees in socio-economic status ( SES ) , IQ , age and gender . Investments averaged about half the initial endowment and evolved over trials . In the second half of the game , investors paired with BPDs invested considerably less than investors paired with healthy trustees . This effect was a central topic in King-Casas et al , and was explained by BPD trustees not heeding warning signals from their investor partners , indicating investor dissatisfaction with the BPD patients’ lack of reciprocation . The strongest difference ( p = 0 . 05 , two sided permutation t-test , Bonferroni corrected for 10 time step comparisons , indicated by an asterisk in Fig 1C in trustee reciprocation at step 6 also indicates the time at which the average investment trajectories have persistently diverged . This gave rise to the difference in early vs late investment between the two groups that was reported in King-Casas et al . [6] . The solid bars in Fig 1D show the average total investments in the real data for the two groups . The hatched bars show the result of generating data from the model in our earlier work ( see [10] ) ( using the extensions discussed above to higher theory of mind and lower maximal planning ) . Model data is generated for each dyad , using that dyad’s best fitting parameters . The model overestimates the investments of the BPD-paired investors by about 30% . Fig 1E demonstrates a similar issue for the modelled trustees . The simulated HC trustees ( hatched bars ) return less than the actual HC trustees . Although it may seem that the simulated BPD trustees return similar proportions to the actual BPD trustees , this actually flatters the model , since this repayment is based on the over-generous model investment ( the hatched bars in part D ) rather than the true , more miserly , investment . A second model failure concerns the detailed dynamics of investment across the task . The solid lines in Fig 1F and 1G show a selected sample interaction between a healthy investor ( see Fig 1F ) and a BPD trustee ( see Fig 1G ) . The trustee provides a poor return in trial 3 , and is met by zero investment in trial 4 . The same pattern repeats in trials 6 and 7 . The trustee is then far more generous in trial 8; this then coaxes ( to adopt a term from a former study , see [6] ) the investor to continue investing , though after 2 breaks , the investors is unwilling to much increase their investment above a low level . The trustee then defects on trial 10 , returning nothing . The conclusion was that a significant portion of the BPD group lacked mechanisms that could consistently repair the faltering interactions that occur when subjects become what we will describe as being irritated . Thus tentative ruptures ( in the form of drops in investment level ) turned into complete breaks , with the investor using their position of power in the game to punish the trustee . The dashed lines in Fig 1F and 1G show the result of simulating 200 trajectories using parameters fit to the actual data , and also making predictions at each step based on the actual investments and returns of the dyad prior to each step ( explaining why the model return is also 0 on trials 4 and 7 ) . The shaded areas show the empirical standard deviations—which are evidently very wide . In fact , the specific reductions are not only absent in the averages; the modelled investment following the trustee’s defection on trials 3 and 6 decreased to 0 on only 11% and 13 . 5% of the sample runs; compared with the collapse to 0 apparent in the actual data . We addressed these sources of model failure by introducing two new parameters , associated with risk aversion and irritation . The investor is in charge in the MRT , since she could simply keep her endowment on each round . It has been noted since the advent of this kind of trust game ( see [17] ) that a lack of investment could represent a social form of risk aversion rather than a lack of trust ( see [12] ) . This could account for differences in levels of investment regardless of the cooperativity of either partner . We parameterize such risk aversion as a multiplicative factor ωI in the payoff functions , increasing or decreasing the evaluation of money that the investor keeps for herself compared to the money returned by the trustee: χ ω I ( a I , a T ) = ω I ( 20 - a I ) + a T , ( 7 ) with ωI ∈ [0 . 4 , 1 . 8] ( in 7 steps of 0 . 2 ) . The trustee is subordinate in the task , and so does not have a risk parameter of their own . Instead , the trustee makes an assumption about the investor’s degree of risk aversion , at one of the above mentioned 8 values . We capture intentional aspects of trust through guilt , and so treat risk aversion as a non-intentional parameter . However , in keeping with Harsanyi [15] , both players are assumed to be consistent , with the investor believing the trustee to know her risk aversion , and to know that she believes this; and the trustee believing that the investor believes this too . We write bT ( ωI ) for the trustee’s belief about the investor’s value of ωI . Depending on the trustee’s belief bT ( ωI ) , there will be either earlier or later attempts at exploitation . If bT ( ωI ) < 1 , then the trustee infers the investor will keep investing , even if the trustee has been relatively uncooperative ( i . e . the investor will be risk-seeking ) . Conversely , if bT ( ωI ) > 1 , then the trustee will infer that any investment is contingent on their behavior , and there could be negative consequences of poor return . For values bT ( ωI ) > 1 . 4 , the trustee expects the investor to invest so little that building up trust will not be worthwhile in the first place . In this case , the interaction will rupture . Illustrations can be found in the supplemental material S1 Text “Risk Aversion” , along with additional detail on the workings of this parameter . Including risk aversion allows the model to account for the behavioural data much more proficiently , with the average Investor NLL improving from 12 . 96 to 9 . 68 . The average trustee NLL improves from 11 . 37 to 9 . 5 . In terms of likelihood ratio tests the model with risk aversion is a better model for the observed data at a threshold of p < 10−46 on the investor side and p < 10−11 on the trustee side . The average BIC for the investors improves from 27 . 3 to 21 . 68 , and for the trustees , from 24 . 1 to 21 . 4 . A BIC based comparison of all models in this work can be found in the supplementary material ( S1 Text “Model Selection” ) . We explained the breakdown in cooperation evident in Fig 1F and 1G as arising when the participants become irritated . Formalizing this leads to four considerations: ( i ) what do subjects do differently when irritated; ( ii ) what leads a subject to become irritated; ( iii ) how can irritation be repaired; ( iv ) and what do subjects know about their own irritability ? We offer a highly simplified characterization of all four of these . Individual interactions in the 10 round MRT are too short to license more complex treatments . Definition 1 ( Irritability ) We define the irritated state as associated with planning P = 0 , guilt α = 0 , temperature β = 1 2 and complete disregard of beliefs about the other player that have hitherto been established . Additionally , for investors , the value of the risk aversion under irritation ( ω ι I ) is bounded below at 1 . 0 i . e . ω ι I = max { 1 . 0 , ω I } , since otherwise “irritated” investors may not show punishing behaviour . We model the players’ policy π as being a mixture between irritated πι and the nonirritated π ι ˜ choices , with irritation weight vι π ( a , h ) = ( 1 - v ι ) π ι ˜ ( a , h ) + v ι π ι ( a , h ) . A participant’s irritation weight is assumed to start at vι = 0 , and to increase when their partner’s action ( investment or return ) falls short of the value expected on the basis of the current model they have of the partner ( including the partner’s potential irritation ) : v ι = min { v ι + ζ , 1 . 0 } g i v e n u n f a v o r a b l e i n v e s t m e n t o r r e t u r n ( 8 ) where ζ is a subject-specific parameter . Irritation decreases through a process of repair when the action exceeds this expected value v ι = max { v ι - ζ , 0 . 0 } g i v e n f a v o r a b l e i n v e s t m e n t o r r e t u r n ( 9 ) Definition 2 ( Intentional Inference about Irritation ) Players maintain and constantly update beliefs about the partner’s irritability during the interaction in exactly the same way as about the partner’s guilt: that is , they employ a Dirichlet prior on a multinomial distribution over five possible irritation values ζ ∈ {0 , 0 . 25 , 0 . 5 , 0 . 75 , 1} ( dubbed respectively “nonirritable” and four different“irritable” types in the following ) and use the same approximate inference rule as is used for guilt . In particular , this means that they represent the possible current values of v ι partner , that is , the partner’s current degree of irritation at the given choice , marginalizing over the posterior distribution over ζ . However , unlike guilt , for which we consider only one ( uniform ) initial belief setting , we consider a discrete set of possible prior beliefs about irritability . That is , irritability awareness is treated as an additional discrete new parameter ( qI ( ζT ) ; qT ( ζI ) ∈ {0 , 1 , 2 , 3 , 4} ) . The investor’s value qI ( ζT ) determines prior weights of his belief over the trustee’s actual irritability ζT . The trustee’s value qT ( ζI ) determines prior weights of her belief over the investor’s actual irritability ζI . These priors are intended to cover a suitable range of possibilities; as noted , the MRT involves too few choices to license a richer depiction . Table 2 lists the four particular prior beliefs q ( ζ ) over the values of irritation . Players range from being ignorant about the possibility that their partners might be irritable , through stages of optimism that they are not , realism that they could be , pessimism that they likely are and fatalistic that they certainly are . Finally , although we assume that players infer both their partner’s inequality aversion and their partner’s irritability level during the interaction , we do not allow subjects to consider their own future irritation . This follows famous ( see [25] ) observations of subjects’ inability whilst engaging in ‘cold’ cognition to contemplate the possibility of one’s own behaviour under ‘hot’ cognition ( i . e . in the affective state ) . “Cold” cognotion describes a emotional state , in which the subject is not under influence of particular strong emotions or cravings ( hunger , thirst , fear , anger for example ) , while “hot” states are a model of a subject acting under the influence of such factors . In the case of our model , all agents start in the “cold” ( nonirritated ) state , yet irritable agents can transition into the “hot” state of decision making under irritation . An irritable , but not currently irritated , agent is not modeled to consider their prospective actions under irritation in our case , indeed our approach makes them unaware of their own irritability . This is an direct example of the “prospective” variety of the “cold-to-hot” empathy gap mentioned on p49 . of the cited work [25] . A detailed example of the general workings of irritation in the case of a single trajectory with potentially aware participants ( qI ( ζT ) = qT ( ζI ) = 2 ) is shown in Fig 2A . The golden line depicts the evolution of the irritation weight v ι I . At step 2 , a subpar repayment by the trustee was introduced by fiat to irritate the investor ( the expected repayment by the trustee would have been 50% ) . The investor’s irritation duly rose to v ι I = 0 . 5 . At this point the trustee’s belief about the investor’s irritability is still at 0 . 5 , as they have not observed the investor’s response to their action . At step 3 the investor retaliated against the earlier defection of the trustee . The aware trustee thus updated their irritation beliefs , inferring that the investor was more likely to be irritable ( at a marginal probability of p = 0 . 58 ) . Noting the potential cost to the interaction of further irritating the investor , the trustee ensured a better than expected response in the next interaction at step 4 . Not only did the trustee repair the interaction , they also ensured that they did not further irritate the investor , at least until the very end of the interaction , as can be seen in the remainder of Fig 2A , from step 4 . This exactly captures the “coaxing”-type repair mechanism that King-Casas et al . suggested to explain differences in investment behaviours elicited by healthy control and BPD trustees . Fig 2B shows the consequence of a lack of irritation inference in the presence of an irritable investor . The players had the same parameter values as in Fig 2A , except for being irritability ignorant ( qI ( ζT ) = qT ( ζI ) = 0 ) . After the same two initial actions ( again introduced by fiat ) , without a notion of the partner being irritable , the trustee missed the chance to repair the interaction at step 3 and the investor’s irritability weight rose to v ι I = 1 . From this point on the investments stayed low and the trustee did not placate the investor , thus receiving only a paltry total income . Both players failed to extract anything like the full return available from the experimenter . Quantitative effects of irritability on the group level can be found in the supplementary material S1 Text “Quantitative Illustration of Irritability” . Fig 2C and 2D show that including these various features removes the discrepancies between data generated from the full model and the subject data . There is no longer a significant difference between generated and original investments or repayments . The complete model predicts 43% of the investor choices ( chance is 20% ) or equivalently an average NLL of 8 . 4 on 10 investor choices ( from 9 . 68 ) and an average NLL of 7 . 6 or 47% of choice predicted for trustee choices ( from 9 . 5 ) . The richer model is accepted in a likelihood ratio test at a threshold of p = 0 . 006 on the investor side and p < 10−12 on the trustee side . The final average BIC for the investors is 20 . 05 and for the trustees is 18 . 5 . A BIC based comparison of all models in this work can be found in the supplementary material ( S1 Text “Model Selection” ) . Fig 2E and 2F demonstrates that the model qualitatively captures ruptures and repair occurring in real interactions , with the investment decreasing to 0 on 43% and 53% of the sample runs on trials 4 and 7 respectively . Further , the spread of the predictions is greatly reduced from those in Fig 2E and 2F . The investor NLL of this interaction improves from 7 . 4 to 5 . 4 , while the trustee improves from NLL 11 . 6 to an NLL of 10 . 3 ( with ζI = 0 . 5; ζT = 1 ) . The main intent of refining the model was to use it to make inferences about the two investor and two trustee groups that generated the data . In the supplemental material S1 Text “Parameter Recoverability” , we show that such inferences are legitimate in that the parameters are broadly identifiable in self-generated data . Our prior hypothesis was that either irritability or irritation inferrence would show a significant difference between controls subjects and BPD subjects . This is revealed to be the case , in the form of an irritation belief difference . Additionally , we explored whether previous differences in investor planning and trustee guilt , reported in earlier work [10] , would be reproduced in the new model . This turned out to be true for trustee guilt , while the investor planning difference is no longer significant . We then derived an hypothesis that characterized the difference between the two groups , at a level of significance that survived correction for the multiple comparisons undertaken in the derivation of the hypothesis . The distributions of the new parameters ( risk aversion , irritability , awareness ) across the groups are shown in Fig 3A–3F . We extend a finding reported in earlier work ( see [10] ) , namely that even in the extended model , the average guilt in BPD trustees is significantly lower in BPD trustees compared to matched ( in IQ and socio-economic status ) healthy controls ( p = 0 . 04 , αT: 0 . 32 < 0 . 49 , uncorrected for multiple comparisons ) . This can be traced back to a significantly higher proportion of guilt αT = 0 subjects ( p = 0 . 02 , χ2-test for equal proportions , uncorrected for multiple comparisons ) . Additionally , the irritation ignorant awareness setting ( qT ( ζ ) = 0 ) is significantly more common in BPD trustees , compared to HC trustees ( p = 0 . 03 , χ2-test for equal proportions , uncorrected for multiple comparisons ) . We therefore considered a model-based characterization of the subjects in which we combined together the two key differences between HC and BPD trustees in the model: trustees who are either totally guilt-less ( αT = 0 ) or who are irritation unaware ( qT ( ζ ) = 0 ) , or both . Either of these leads to trustees who may exploit the investor ( deliberately for αT = 0 or accidentaly at qT ( ζ ) = 0 ) , and so create problems in the context of an interaction in which latter is in charge . We describe these trustees as being ‘perilous’ . This combined group turns out to be present at a significantly higher proportion ( 60 . 0% ) in the BPD group , compared with the HC group ( 29% ) ( p = 0 . 003 , χ2-test for equal proportions ) . The difference remains significant ( p < 0 . 05 ) even when Bonferroni correcting for the 7 ( 4 parameters plus the 2 proportion tests and the derived “perilous group” hypothesis ) comparisons that we undertook . Fig 4 shows investment and repayment profiles for dyads in our data set including perilous ( A ) and non-perilous ( B ) trustees . These interaction profiles are evidently different ( confirmed in two-sided t-tests at p < 0 . 05 , Bonferroni corrected for the 10 time points ) . Yet , having adjusted for this by sorting healthy controls and BPD trustees according to perilousness , there is no longer a difference between the average investment and return profiles for BPD versus HC dyads ( p > 0 . 05 using an uncorrected two-sided t-test ) . Fig 4C compares investment and return profiles for investors with little ( ωI ≤ 1 . 0 ) or substantial risk aversion ( ωI > 1 . 0 ) . Splits based on trustee risk aversion profiles bT ( ωI ) do not appear significantly different ( which is also a testament to the dominant role of the investor ) and are not shown here . Finally , Fig 4D shows the distributions over the guilt parameters for BPD and HC trustee subjects .
Informed consent was obtained for all research involving human participants , and all clinical investigation was conducted according to the principles expressed in the Declaration of Helsinki . The procedures were approved by the Institutional Board of Baylor College of Medicine . Programs were run at the local Wellcome Trust Center for Neuroimaging ( WTCN ) cluster using Intel Xeon E312xx ( Sandy Bridge ) processor cores clocked at 2 . 2 GHz; no process used more than 0 . 8 GB of RAM . We used R [26] and Matlab [27] for data analysis and the boost C++ libraries [28] for code generation . Our earlier approach ( in [10] ) utilized a sampling based method to explore the decision tree during planning in the trust game , drawing from approximate solution methods for tree search from machine learning ( see [29–32] ) . However , if lower levels of calculation are all part of the same hierarchy , as well as kept in memory and so are immediately available for higher level calculations , then the problem scales linearly in the theory of mind level parameter , rather than exponentially ( as for other computational approaches to I-POMDPs , [33] , p . 325 , 9 . 2 . ) . This trade off of memory for computation is only practical if the planning horizon is reduced to at most 4 steps into the future . A more detailed discussion of the used algorithm can be found in the supplementary material S1 Text “Algorithmic Representation” . The net result is that it takes less than 2 minutes per generated 10 step interaction , to calculate deterministically ( i . e . , avoiding approximations from the stochasticity of Monte Carlo-based tree evaluation ) a 10 step exchange of a level kI = 4 investor with a level kT = 3 trustee , both having horizons of P = 4 steps . This comes at the cost of having to commit 0 . 8 Gb of RAM to the tree calculation . Trust games of various kinds have been used in behavioural economics and psychology research ( see [34] ) . In particular , the MRT we used was based on variants in several earlier studies ( see examples in [17 , 35 , 36] ) . The current MRT was first modeled using regression models ( see [16] ) of various depths: one step models for the increase/decrease of the amount sent to the partner and models which track the effects of more distant investments/repayments . These models generated signals of increases and decreases in investments and returns that were correlated with fMRI data . One seminal study on the effect of BPD in the trustgame by King-Casas et al . ( see [6] ) included the concept of “coaxing” ( repaying substanially more than the fair split ) the partner ( back ) into cooperating/trust whenever trust was running low , as signified by small investments . Furthermore , an earlier study ( see [37] ) used clustering to associate trustgame investment and repayment levels to various clinical populations . An I-POMDP generative model for the trust task which included inequality aversion , inference and theory of mind level was previously proposed [8] . This model was later refined rather substantially to include faster calculation and planning as a parameter [10] . The I-POMDP framework itself has been used in a considerable number of studies . Notable among these are investigations of the depth of tactical reasoning directly in competitive games ( see [38–40] ) . It has also been used for deriving optimal strategies in repeated games ( see [41] ) . The benefits of a variant of the framework for fitting human behavioural data were recently exhibited in [42] .
Although we provided an additional characterisation of the difference between the healthy and clinical populations studied in [6] , the psychiatric validity of our model parameters has yet to be established . In particular , we lacked additional clinical scales and personality measures for these populations; we are presently collecting new data that will allow a more comprehensive assessment . In section S1 Text “Predictive Validity through Comparison with other Games” , we provide a reason to expect the inferred parameters to characterize something generalizable about a different group of subjects by showing how they relate to parameters derived from a model of subjects’ performance of the ultimatum game . In addition , the notion of perilousness and its effects have been derived post hoc on the well studied data of earlier works [6] . We are working to test on newly collected data , whether this notion and its effects can be reproduced . In terms of parameter identifiability , we illustrate the internal consistency of the model in section S1 Text “Parameter Recoverability” . When looking at section S1 Text “Model Selection” , we see that the correct model is identified on generated data on the group level in almost all cases , the exception being the difference between trustee data generated from the original model , being estimated to have come from the model with variable temperature and risk aversion . Computationally , the costs of planning limit our exact calculation to a planning horizon of 4 . While we consider this sufficient , as we were able to reproduce all behaviours seen at a planning horizon of 7 in our earlier work , some more complex behaviour may have been eliminated through the restricted planning horizon . Finally , computational limitations force us to use a coarser representation of the MRT than might be optimal , both in terms of representing possible subject actions and in terms of the number of discrete parameter settings .
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In multi-round games in which players can benefit by trusting each other , swift and catastrophic breakdowns can arise amidst otherwise efficient cooperation . We present a model that quantifies this as a form of anger , and we exploit novel algorithmic improvements in inference based on the model to examine exchanges involving healthy volunteers and people suffering from personality disorders . This provides a new view on the problems that can underlie social interactions .
|
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"Abstract",
"Introduction",
"Results",
"Materials",
"and",
"methods",
"Discussion"
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2018
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A model of risk and mental state shifts during social interaction
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Topoisomerase inhibitors such as camptothecin and etoposide are used as anti-cancer drugs and induce double-strand breaks ( DSBs ) in genomic DNA in cycling cells . These DSBs are often covalently bound with polypeptides at the 3′ and 5′ ends . Such modifications must be eliminated before DSB repair can take place , but it remains elusive which nucleases are involved in this process . Previous studies show that CtIP plays a critical role in the generation of 3′ single-strand overhang at “clean” DSBs , thus initiating homologous recombination ( HR ) –dependent DSB repair . To analyze the function of CtIP in detail , we conditionally disrupted the CtIP gene in the chicken DT40 cell line . We found that CtIP is essential for cellular proliferation as well as for the formation of 3′ single-strand overhang , similar to what is observed in DT40 cells deficient in the Mre11/Rad50/Nbs1 complex . We also generated DT40 cell line harboring CtIP with an alanine substitution at residue Ser332 , which is required for interaction with BRCA1 . Although the resulting CtIPS332A/−/− cells exhibited accumulation of RPA and Rad51 upon DNA damage , and were proficient in HR , they showed a marked hypersensitivity to camptothecin and etoposide in comparison with CtIP+/−/− cells . Finally , CtIPS332A/−/−BRCA1−/− and CtIP+/−/−BRCA1−/− showed similar sensitivities to these reagents . Taken together , our data indicate that , in addition to its function in HR , CtIP plays a role in cellular tolerance to topoisomerase inhibitors . We propose that the BRCA1-CtIP complex plays a role in the nuclease-mediated elimination of oligonucleotides covalently bound to polypeptides from DSBs , thereby facilitating subsequent DSB repair .
CtIP was isolated as a binding partner of CtBP ( C-terminal binding protein ) , and has subsequently been shown to interact with a number of molecules , including BRCA1 ( Breast Cancer Susceptibility Gene 1 ) [1] . CtIP is a functional homolog of yeast Sae2 ( Sporulation in the Absence of Spo Eleven ) , and acts at the initial step of homologous recombination ( HR ) -dependent double-strand break ( DSB ) repair [2] , [3] . HR is initiated by forming 3′ single-strand ( ss ) overhangs at DSBs . In this resection step , Sae2/CtIP works together with a complex composed of Mre11/Rad50/Xrs2 in budding yeast , or with Mre11/Rad50/Nbs1 in mammals [4]–[7] . The Rad51 recombinase protein polymerizes on the ss DNA overhang , and the resulting ssDNA-Rad51 complex undergoes homology search . Resection activity is upregulated by phosphorylation of a conserved residue in Sae2 by the cyclin-dependent kinase ( CDK ) [8] . This phosphorylation site is conserved in human CtIP ( Thr847 ) , and is also phosphorylated by CDK [7] . BRCA1 was originally identified as a tumor suppressor gene associated with familial breast and ovarian cancer [9] . BRCA1 contains an N-terminal RING-finger domain , and is associated with structurally related BARD1 to form an E3-ubiquitin ligase . BRCA1/BARD1 forms three distinct complexes with Abraxas , Bach1 and CtIP , and plays a role in DNA repair [10] . BRCA1 binds to CtIP in a manner that is dependent on the phosphorylation of CtIP at Ser327 [11] , [12] . Following DNA damage , the ubiquitylation of CtIP by BRCA1 causes the migration of CtIP towards a chromatin fraction [12] . However , the biological significance of the complex formed between BRCA1 and CtIP has not yet been clarified . Topoisomerases 1 and 2 ( Topo1 and Topo2 ) have been drawing increasing attention as important targets for cancer therapy , since the inhibition of these enzymes causes DSBs during DNA replication [13] . Topo1 and Topo2 induce single-strand breaks ( SSBs ) and DSBs , respectively . Covalent bonds are transiently formed between Topo1 and the 3′ end of the SSB and between Topo2 and the 5′ end of the DSB [14] . The anti-cancer agent camptothecin ( CPT ) inhibits Topo1 by stabilizing the Topo1-cleavage complex , which interferes with replication , and thereby induces DSBs in one of the sister chromatids [15] . Topo2 inhibitors such as etoposide ( VP16 ) and ICRF-193 also kill cycling cells and are used in cancer therapy . VP16 stabilizes the Topo2-cleavage complex , while ICRF-193 stabilizes the closed clamp which forms after the strand passage [16] , [17] . Topo1-mediated DNA damage caused by CPT is repaired primarily by homologous recombination ( HR ) , while Topo2-mediated DNA damage caused by VP16 or ICRF-193 is mainly repaired by nonhomologous end joining ( NHEJ ) [18] , [19] . It should be noted that the repair of CPT- and VP16-induced DSBs requires an additional step: the elimination of covalently bound polypeptides from the DNA ends . Hartsuiker et al . demonstrated that Topo2 is removed from DNA by the collaborative action of the MRX complex and ctp1 ( the ortholog of CtIP ) in fission yeast [20] . It remains to be seen whether vertebrate CtIP shares the same function as in yeast , which does not have BRCA1 ortholog . To understand the role of the BRCA1-CtIP interaction , we substituted the Ser332 residue ( equivalent to human Ser327 ) of CtIP with alanine in the chicken DT40 B lymphocyte line [21] , [22] . In addition , to analyze the function of CtIP , we conditionally depleted CtIP in DT40 cells . We here show that the depletion of CtIP is lethal to cells as is the inactivation of Mre11 , Rad50 , and Nbs1 [23] , [24] , indicating the critical role played by CtIP in HR . Remarkably , although the CtIP S332A mutation had no significant impact on HR , it made cells hypersensitive to CPT and VP16 but not to ICRF193 . These observations unmasked an unexpected function of the BRCA1-CtIP interaction: cellular tolerance to the DSBs that are covalently associated with the polypeptides . Our data therefore support two distinct functions of CtIP: the resection of DSBs in HR and the elimination of polypeptides from the cleavage complex .
In order to determine the function of CtIP , we conditionally disrupted the CtIP gene in chicken DT40 cells , using a chicken CtIP transgene under the control of a tetracycline-repressible promoter ( tetCtIP transgene , Figure S1A ) . We designed CtIP gene-disruption constructs , so that the amino acid sequences from 96 to 335 would be replaced by selection-marker genes . Since the gene is encoded on chromosome 2 , which is in trisomy in DT40 , we disrupted three CtIP alleles ( Figure S1B and S1C ) . The resulting CtIP−/−/−tetCtIP cells tended to grow more slowly than did wild-type cells , presumably due to overexpression of the tetCtIP transgene ( Figure 1A and 1B ) . To deplete the CtIP in the CtIP−/−/−tetCtIP cells , we added doxycycline ( modified tetracycline ) to the culture medium . One day after the addition of doxycycline , the amount of CtIP was reduced to around 20% of wild-type cells ( Figure 1B ) , and the cells started dying as evidenced by an increase in the sub-G1 fraction ( Figure 1C ) . This lethality can be attributed to abolished HR , because the cells showed a significant increase in the number of spontaneous chromosomal breaks ( Table 1 ) , as do Mre11- and Rad51-depleted cells [23] , [25] . By day 3 , the vast majority of the CtIP−/−/−tetCtIP cells had stopped growing and died ( Figure 1A and 1C ) . We therefore conclude that CtIP is essential for maintenance of chromosomal DNA and cellular proliferation . To assess the HR capability of CtIP−/−/−tetCtIP cells , we monitored the recruitment of Rad51 and RPA to DNA damage sites one day after addition of doxycycline . Clear Rad51 foci appeared in wild-type cells one hour after ionizing radiation ( IR ) , whereas Rad51 foci were hardly detectable in the CtIP-depleted cells ( Figure 2A ) . Likewise , the depletion of CtIP abolished the accumulation of RPA on DNA lesions induced by microlaser treatment ( Figure 2B ) . This is consistent with a phenotype shown in the previous report [3] . Thus , CtIP plays an essential role in the resection of DSBs during HR in DT40 cells as well as in mammalian cells . We next investigated whether or not CtIP facilitates the activation of BRCA1 at DSBs . To this end , we measured the formation of conjugated-ubiquitin foci at DSBs , since Brca1 promotes extensive ubiquitylation at IR-induced DSBs [26] . Previous studies showed that BRCA1−/− DT40 cells exhibit a prominent defect in the formation of conjugated-ubiquitin foci [27] . In contrast , CtIP depletion did not reduce the ubiquitylation of DNA damage sites ( Figure 2C ) , suggesting that CtIP is not required for the activation of Brca1 . To functionally analyze the interaction of CtIP with BRCA1 , we generated CtIPS332A/−/− cells , in which the critical amino acid in the binding interface has been mutated ( Figure S2A , S2B , S2C ) . The CtIPS332A/−/− DT40 clones were capable of proliferating at a rate similar to the CtIP+/−/− cells without a prominent change in the cell-cycle profile ( Figure 3A and Figure S2D ) . Western blot analysis showed that the S332A CtIP proteins were expressed at the similar level to the wild-type protein , indicating that amino acid substitutions do not affect the stability of the CtIP protein ( Figure S2E ) . As expected , given the results of a previous study [12] , the S332A mutation of CtIP indeed inhibited its interaction with BRCA1 ( Figure S2F ) . To evaluate the capability of HR in CtIPS332A/−/− cells , we integrated an artificial substrate , SCneo , in the Ovalbumin locus [28] , and measured the efficiency of I-SceI-induced gene conversion . The CtIPS332A/−/− clones showed no significant decrease in the appearance of neomycin-resistant colonies compared to CtIP+/−/− cells ( Figure 3B ) . The proficient HR in CtIPS332A/−/− DT40 clones is in marked contrast to the severe phenotype of the Nbs1p70 hypomorphic mutant , which exhibited a 10-fold reduction of the gene-targeting frequency and a 103-fold decrease in the efficiency of HR in the SCneo substrate [29] . Next , we measured the frequency of gene targeting at the CENP-H and Ovalbumin loci . In contrast to I-SceI-induced gene conversion , the gene-targeting frequency of the CtIPS332A/−/− clones decreased moderately in comparison with CtIP+/−/− cells ( Table 2 ) . We speculate that this is because unknown recombination intermediates that require processing by CtIP/BRCA1 may arise during gene targeting event ( see Discussion ) . Fluorescent immunostaining revealed that the kinetics of Rad51 focus formation after γ-irradiation was indistinguishable between CtIPS332A/−/− cells and the CtIP+/−/− control cells , while BRCA1−/− cells showed the significant reduction in the Rad51 focus formation at 1–6 h after irradiation ( Figure 4A ) . Furthermore , the CtIPS332A/−/− mutants displayed laser-induced RPA accumulation as did the CtIP+/−/−cells ( Figure 4B ) . Laser-generated RPA accumulation following BrdU incorporation largely arises from the resection rather than other routes of single strand formation such as the damage caused by laser itself or replication-associated single strand formation , because RPA accumulation is abolished specifically in Ubc13 deficient cells [27] . This suggests that CtIPS332A/−/− cells are proficient in resection at DSB sites . Taken together , we conclude that the S332A mutation of CtIP does not significantly compromise HR . To determine the role of CtIP in the cellular response to DNA damage , we measured the sensitivity of the CtIP mutant cells to various genotoxic agents using a colony survival assay . CtIP+/−/− cells exhibited the slightly elevated sensitivity toward CPT and VP16 ( Figure 5A and 5B ) , though they expressed the similar level of CtIP protein to the wild-type cells ( Figure S2E ) . It is possible that the difference in the amount of CtIP protein between CtIP+/−/− and wild-type cells is too subtle to detect , and that even the suboptimal level of CtIP protein renders the cells sensitive to genotoxic stimuli . A compensatory post-translational regulation may be present because CtIP+/−/− cells exhibited about 80% reductions in CtIP mRNA level compared to the wild-type level ( Figure S2G ) . In contrast to CtIP+/−/− cells , CtIPS332A/−/− mutants showed a significantly increased sensitivity to VP16 and MMS ( Figure 5B and 5D ) , but not to γ-rays ( data not shown ) . Furthermore , the sensitivity to CPT was dramatically elevated in the CtIPS332A/−/− mutants , in comparison with the CtIP+/−/− cells ( Figure 5A ) . The contribution of CtIP to the cellular tolerance to VP16 indicated that CtIP might play a role in NHEJ [18] . To test this hypothesis , we evaluated NHEJ by measuring the sensitivity of CtIP mutant cells to ICRF-193 , because ICRF193-induced DNA lesions are repaired exclusively by NHEJ , whereas a fraction of the VP16-induced DSBs are repaired by HR [18] . The CtIPS332A/−/− clones exhibited no increased ICRF193 sensitivity ( Figure 5C ) . NHEJ can also be evaluated by measuring the IR sensitivity of the cell population at the G1 phase , where NHEJ plays a dominant role in DSB repair [30] . The CtIP hypomorphic mutants synchronized at the G1 phase did not show significant IR hypersensitivity ( Figure S3 ) . These observations indicate that NHEJ is not impaired in CtIPS332A/−/− clones . In summary , in comparison with CtIP+/−/− cells , CtIPS332A/−/− clones exhibited a significantly higher sensitivity to CPT and VP16 , although these clones exhibited no decrease in the efficiency of HR or NHEJ . We conclude that CtIP can therefore contribute to cellular tolerance to CPT and VP16 , independently of HR or NHEJ , most likely by eliminating covalently bound polypeptides from the DSBs . CtIP physically interacts with BRCA1 in a manner dependent on phosphorylation of Ser332 [12] . In order to assess the functional relationship between Ser332 phosphorylation of CtIP and BRCA1 , we disrupted the BRCA1 gene in the CtIPS332A/−/− and CtIP+/−/− clones ( Figure S4A and S4B ) , as was done previously [31] . Both the CtIPS332A/−/−BRCA1−/− and the CtIP+/−/−BRCA1−/− clones proliferated with similar rates at significantly reduced growth rates , in comparison with BRCA1−/−cells ( doubling time ± SD: 8 . 3±0 . 2 h for wild-type , 9 . 3±0 . 3 h for BRCA1−/− , 11±0 . 3 h for CtIP+/−/−BRCA1−/− , 12 . 6±0 . 9 h for CtIPS332A/−/−BRCA1−/− ) . The viability of CtIPS332A/−/−BRCA1−/− cells is in marked contrast with the lethality of CtIP-null cells , supporting the idea that the CtIP-BRCA1 interaction works independently from the function of CtIP in resection . We next examined the sensitivity of double mutant cells to CPT and VP16 . To this end , we measured the number of live cells after 48-hour continuous exposure to the DNA-damaging agents [32] , during which the double mutant cells are able to divide four to five times . We did not use a conventional colony formation assay for this purpose , because CtIP+/−/−BRCA1−/− and CtIPS332A/−/−BRCA1−/− clones grew very badly from a single cell in semi-solid methylcellulose medium . The number of viable cells cultured in the presence of CPT was significantly decreased for CtIPS332A/−/− and BRCA1−/− cells compared to the wild-type cells , whereas CtIP+/−/− cells grew to the similar extent to the wild-type cells in the presence of CPT ( Figure 6A ) . The sensitivity of CtIP+/−/−BRCA1−/− cells to CPT was greater than that of BRCA1−/− clones . This observation is in agreement with the idea that BRCA1 and CtIP can independently contribute to HR , where CtIP promotes the resection of DSBs , while BRCA1 subsequently loads Rad51 at resected ssDNA overhang . Importantly , although the CtIPS332A mutation significantly increased cellular sensitivity to CPT in the presence of BRCA1 , the CtIPS332A/−/−BRCA1−/− and CtIP+/−/−BRCA1−/− clones exhibited a very similar sensitivity to CPT ( Figure 6A ) . Likewise , the CtIPS332A/−/−BRCA1−/− and CtIP+/−/−BRCA1−/− clones exhibited indistinguishable cellular sensitivities to VP16 ( Figure 6B ) . These observations suggest that CtIP and BRCA1 can act in collaboration to repair DSBs that are chemically modified by topoisomerases .
We here show that conditional depletion of CtIP protein led to cellular lethality with increased frequency of chromosomal aberrations in DT40 cells . CtIP depletion abolished the accumulation of RPA and Rad51 at DNA damaged sites , suggesting that it is required for the resection of DSBs during HR , and that this function is essential for the proliferation of cells . These results are in agreement with previous reports [3] . In contrast , the DT40 cells harboring S332A mutation in CtIP showed the accumulation of RPA and Rad51 upon DNA damage , and were able to proliferate with normal kinetics . Remarkably , compared to the CtIP+/−/− cells , the CtIPS332A/−/− clones exhibited significantly increased sensitivity to CPT and VP16 , both of which stabilize the Topo-DNA cleavage complex . These observations support the proposition that , in additon to the resection of DSBs , CtIP has the second function , most likely the removal of covalently-bound polypeptides from DSBs . Hence , CtIPS332A/−/− clones are the novel separation-of-function mutants where CtIP-dependent resection is proficient , whereas the second function required for the tolerance to topoisomerase inhibitors is deficient . In this study , we demonstrated that the inactivation of CtIP in DT40 cells results in cellular death . We speculate that the defective DSB repair during S phase is the primary cause of cellular death rather than the misregulation of RB/E2F pathway [33] , [34] . It has been reported that CtIP promotes G1/S progression by releasing RB-imposed repression and by upregulating the genes required for S phase entry such as cyclin D1 . MEF from CtIP-deficient mice and NIH3T3 cells transfected with CtIP siRNA arrest at G1 phase of cell cycle . In contrast , DT40 cells that are depleted of CtIP showed a marked reduction in S phase and an increase in sub-G1 population with the spontaneous chromosomal aberrations . We speculate that DT40 cells have a lower threshold to enter the S phase in the presence of DNA damage compared to the other types of cells owing to their character that they lack p53 expression [35] and overexpress c-myc [36] . The phenotype of our CtIP-depleted DT40 cells was remarkably different from that of the CtIP-deficient DT40 cells generated by Hiom's group [37] . Surprisingly , their CtIP-depleted DT40 cells were capable of proliferating . However , we believe that CtIP is essential for cellular proliferation because it has been shown that CtIP works together with Mre11/Rad50/Nbs1 complex in budding and fission yeasts as well as in mammalian cells [3] , [6] , [38] , and the increased spontaneous chromosomal aberrations and cellular death observed in our CtIP-depleted cells are consistent with our previous reports that deficiency of either one of Mre11 , Rad50 , or Nbs1 was all lethal to DT40 clones [23] , [24] . The viability of the CtIP-deficient DT40 cells generated by Hiom's group might be due to the occurrence of suppressor mutations during the disruption of the three allelic CtIP genes . Another possibility is that the disruption of exons 1 and 2 in Hiom's group might still allow the residual expression of an N-terminal-truncated CtIP protein , as is observed for the expression of an N-terminal-truncated Nbs1 protein in patients with Nijmegen syndrome [39] . Another critically different point between our study and Hiom's group is that they conclude that the phosphorylation of CtIP-S332 promotes the resection of DSBs , whereas our data do not support this conclusion . The discrepancy between the two studies may be attributable to the different ways of introducing the S332A mutation into the DT40 cells . They randomly integrated wild-type and CtIPS332A transgenes at different loci in their “CtIP-null” cells , while we inserted the S332A mutant into one of the CtIP allelic genes . This knock-in approach is essential for the accurate quantitative evaluation of HR and NHEJ , because the endogenous promoter expresses CtIP transcripts differently in each phase of the cell cycle , and this differential expression accounts for the reduced usage of HR in the G1 phase in fission yeast [38] . Alternatively , the difference between our results could be because Hiom's group re-introduced human CtIP cDNA ( wild type or mutants ) instead of that derived from chicken into DT40 cells to create individual clones . The human protein may act differently or incompletely in chicken DT40 cells . The exact function of BRCA1 in HR is controversial . The discovery of the BRCA1-CtIP interaction has led to a proposal that BRCA1 might facilitate the resection step of HR [11] , [37] , [40] . However , RPA foci are not completely abolished in BRCA1 mutant cells in these reports , suggesting that ssDNA does form in the absence of functional BRCA1 . We found that RPA accumulated at the sites of laser microirradiation in BRCA1−/− and CtIPS332A/−/− cells , while Rad51 focus formation is impaired in BRCA1−/− cells . These results indicate that the BRCA1-CtIP interaction is not involved in the promotion of HR including the resection step , and are in agreement with the idea that BRCA1 facilitates the loading of Rad51 on resected ssDNA as does BRCA2 [1] , [29] , [41] . Recently , it was found that BRCA1 forms a complex with BRCA2 [42] , further supporting the collaborative and overlapping function of BRCA1 and BRCA2 . Although we cannot formally exclude the possibility that the RPA accumulation is delayed in BRCA1−/− cells ( the extent of RPA accumulation induced by laser irradiation cannot be quantified , and we failed to induce RPA foci by other genotoxic stimuli in DT40 cells ) , our data , together with the fact that BRCA1 deficiency does not lead to cellular lethality in DT40 cells , indicate that BRCA1 has only a minor role , if any , in the resection step . The discrepancies among researchers may arise from different experimental settings including how BRCA1 is inactivated ( by gene targeting , siRNA knockdown , or C-terminal truncation ) , the cell cycle distribution of each cell type , and the extent of DSB end modifications induced by laser or γ-ray irradiation . Further studies will clarify the differences among each group . Accumulating evidence indicates that there are two parallel pathways to eliminate chemical modifications from single-strand breaks and DSBs ( Figure 7 ) . Firstly , tyrosyl-DNA phosphodiesterase1 ( Tdp1 ) removes polypeptides covalently bound at the 3′ end of DSBs [43] . Polynucleotide kinase 3′-phosphatase ( PNKP ) and AP endonuclease I ( APE1 ) are also involved in this process . Likewise , PNKP , DNA polymerase β , and aprataxin remove aberrant chemical modifications from the 5′ ends of DSBs [44] . These enzymes may be capable of accurately repairing damaged bases at DSBs . On the other hand , the second pathway involves endonucleases and removes damaged bases along with proximal intact oligonucleotides from the 3′ or 5′ ends of DSBs . Our study showed that this pathway could contribute to cellular tolerance to alkylating agents such as MMS as well as to topoisomerase inhibitors . A well-known precedent involving the second pathway is the Mre11/Rad50/Nbs1-complex-dependent elimination of oligonucleotides as well as the covalently associated topoisomerase-like protein ( Spo11 ) from DSBs during meiotic HR in S . cerevisiae [2] . A more recent study of the S . pombe CtIP mutant ( ctp1Δ ) showed that the level of Top2 protein covalently bound to DNA in the ctp1Δ mutant increased during treatment with TOP-53 , one of the VP16 derivatives , suggesting that Ctp1 plays a role in the endonuclease-dependent removal of covalently-bound polypeptides from the 5′ end of DSBs [20] . Our study indicates that this conclusion is also relevant to vertebrate cells although there are significant differences between vertebrate and yeast systems . First , yeast Ctp1 or Sae2 seem to be important only for the removal of the peptide covalently bound to 5′ of DSB ends as demonstrated for DNA damage induced by TOP-53 or Spo11 [2] , [20] . Second , yeast does not have BRCA1 counterpart . BRCA1 is involved in degradation of trapped Topo1 cleavage complexes along with proteasome [45] . We hypothesize that BRCA1 may facilitate the removal of Topo1 by degrading them to small polypeptides , which in turn are removed with oligonucleotides by the nuclease activity of CtIP . In summary , we here show compelling evidence that the collaborative action of BRCA1 and CtIP plays a critical role in the endonuclease-dependent removal of damaged nucleotides from DSBs , and acts on the processed DSBs for subsequent HR and NHEJ .
DT40 cells were cultured in RPMI-1640 medium supplemented with 10−5 M β-mercaptoethanol , penicillin , streptomycin , 10% fetal calf serum ( FCS ) , and 1% chicken serum ( Sigma , St Louis , MO , USA ) at 39 . 5°C . To generate CtIP gene disruption constructs , genomic DNA sequences of DT40 cells were amplified using primers 5′-GGATGCGGAGAGGCTTGAAGAGTTTTACAC-3′ and 5′-TTACAGCACAACGATCACATAATCCCGCTC-3′ for the 5′ arm , and 5′-GGAGCTTCTAGCAATACGCGGAACAACTCA-3′ and 5′-GCTTCCCCTCCAATTCTTGACTGAGAATCA-3′ for the 3′ arm . The amplified PCR products were cloned into the pCR2 . 1-TOPO vector ( Invitrogen , CA , USA ) . The BamHI site in the plasmid that contains the 5′ arm was disrupted by blunt-self ligation . The 1 . 6-kb HindIII fragment was ligated into the partially-digested HindIII site of the 3 . 0-kb 3′ arm containing the plasmid . A drug-resistance gene ( hisD or bsr ) was inserted into the BamHI site of the pCR2 . 1 vector containing both the 5′ and 3′ arms . To generate CtIP+/−/− cells , linearized CtIP gene-disruption constructs were transfected sequentially by electroporation ( BioRad ) . The genomic DNA of the transfectants was digested with SacI and the targeted clones were confirmed by Southern blot analysis . The 0 . 5-kb fragment was amplified using primers 5′-GATTGTATGCTTCAGAGGCTCCTGC-3′ and 5′-GAAATTCCCAACTTTAGCTCCCCTTGAC-3′ and used as a probe . To construct the CtIP expression plasmid , chicken the CtIP open reading frame was amplified by PCR , using the primers 5′-GGGGACAAGTTTGTACAAAAAAGCAGGCTTCGAACCATGAATGCGTCTGGGGGAACTTGTG-3′ and 5′-GGGGACCACTTTGTACAAGAAAGCTGGGTCTTATGTCTTCTGCTCTTTGCCTTTTGG-3′ , and cloned into a Gateway donor vector , pDONR207 ( Invitrogen , CA , USA ) , by BP reaction . The CtIP gene in the donor vector was transferred to an expression vector ( pA-puro ) containing the Gateway conversion cassette under the β-actin promoter by LR reaction . To construct a CtIP expression vector under the control of a tetracycline-repressible promoter , CtIP cDNA was amplified by PCR , using the primers 5′-CTCGAGATGAATGCGTCTGGGGGAACTTGTG-3′ and 5′-GTCGACTTATGTCTTCTGCTCTTTGCCTTTTGG-3′ , and cloned into pCR2 . 1-TOPO vector . The XhoI-SalI fragment containing the CtIP cDNA was blunted and cloned into the EcoRV site of a modified pTRE2 vector ( Clontech , CA , USA ) containing a loxP-flanked puromycin-resistant cassette . CtIP+/−/− cells were introduced with the tetracycline-controlled trans-activator ( tTA ) gene through retrovirus infection . Infected cells were sub-cloned , and tTA expression was confirmed by western blot analysis . The resulting tTA-expressing CtIP+/−/− cells were transfected with the pTRE2 puroR/CtIP , and puromycin-resistant clones were selected to isolate the CtIP+/−/−tetCtIP cells . The puromycin-resistance gene was then deleted by transiently expressing the Cre recombinase ( Amaxa solution T , program B-23 ) . Puromycin-sensitive CtIP+/−/−tetCtIP cells were transfected with the CtIP gene-disruption construct carrying the puromycin-resistant cassette to generate CtIP−/−/−tetCtIP cells . The targeting vectors for the CtIP S332A mutants were generated by site-directed mutagenesis . To generate the S332A knock-in vector , genomic DNA was amplified by PCR , using primers 5′-ATTATGCCCCTGAAAGAAGGGAAAC-3′ and 5′-TTTCCTGGGTTTGCTCTTGATTTT-3′ , and cloned into the pCR2 . 1-TOPO vector ( Invitrogen , CA , USA ) . Site-directed mutagenesis was performed using primers 5′-GATTCTCAGGTAGTTGCTCCTGTTTTCGGA-3′ and 5′-TCCGAAAACAGGAGCAACTACCTGAGAATC-3′ . The puromycin-resistance gene was inserted into the HpaI site of the resulting plasmid . After transfection of the S332A knock-in vector into the CtIP+/+/− cells , the targeted clones were selected against puromycin and then identified by Southern blot analysis of genomic DNA digested with HindIII . To make probe DNAs , the 0 . 6-kb fragments were amplified using primers 5′-GACTAACAAAGATCAACCTGTC-3′ and 5′-GTGCATGAGATTTTGGTCGTTG-3′ . After the deletion of the puromycin-resistance gene by transiently expressing Cre recombinase by nucleofection ( Amaxa , Germany ) , the third allele of the CtIP gene was disrupted by transfecting the CtIP gene-disruption construct carrying the puromycin-resistance gene . The insertion of the S332A mutation into the endogenous CtIP gene was confirmed by RT-PCR followed by sequencing amplified DNA . The puromycin-resistant cassette in the targeting vector for the BRCA1 gene [31] was replaced with the neomycin-resistant cassette . CtIP+/−/− and CtIPS332A/−/− cells were sequentially transfected with targeting vectors containing the puromycin- and neomycin-resistant gene , and selected against G418 and puromycin , respectively . The clones with the disrupted BRCA1 gene were identified by Southern blot analysis as described previously [31] . Quantitative real-time PCR was performed in an ABI Prism 7000 sequence detector ( Applied Biosystems ) using SYBR Green PCR Master Mix reagent ( Applied Biosystems ) according to the manufacturer's instruction . CtIP cDNA was amplified using primers 5′-GGAATTGGAGGAGCAAAAGCAAC-3′ and 5′-GAAACTCACTGTTGCTCTTTG-3′ . The expression level of CtIP was normalized against β-actin using the comparative CT method . For Western blot analysis , the antibodies specific for CtIP ( BL1914 , Bethyl , TX , USA ) , β-actin ( Sigma , MO , USA ) , Rad51 ( Ab-1 , Calbiochem , CA , USA ) were used for detection of each protein . Secondary antibodies were horseradish peroxidase ( HRP ) -conjugated antibodies to mouse Ig ( GE Healthcare , MA , USA ) and HRP-conjugated antibody to rabbit Ig ( Santa Cruz , CA , USA ) . Karyotype analysis was performed as described previously [25] . To measure the number of γ-ray-induced chromosome breaks in mitotic cells , we exposed cells to 2 Gy γ-rays and immediately added colcemid . At 3 hours after irradiation , mitotic cells were harvested and subjected to chromosome analysis . Methylcellulose colony formation assays were performed as described previously [30] , [46] . Since in this assay the plating efficiency of BRCA1-deficient cells was less than 50% , we used a different assay to measure cellular sensitivity to DNA-damaging agents . Cells ( 1×103 ) were seeded into 24-well plates containing 1 ml culture medium per well and the DNA-damaging agents , and then incubated at 39 . 5°C for 48 hours . To assess the number of live cells , we measured the amount of ATP in the cellular lysates . We confirmed that the number of live cells was closely correlated with the amount of ATP . This ATP assay was carried out with 96-well plates using a CellTiter-Glo Luminescent Cell Viability Assay Kit ( Promega Corporation , WI , USA ) . Briefly , we transferred 100 µl of cell suspension to the individual wells of the plates , placed the plates at room temperature for approximately 30 minutes , added 100 µl of CellTiter-Glo Reagent , and mixed the contents for 2 minutes on an orbital shaker to induce cell lysis . The plate was then incubated at room temperature for 10 minutes to stabilize the luminescent signal . Luminescence was measured by Fluoroskan Ascent FL ( Thermo Fisher Scientific Inc . , MA , USA ) . The measurement of homologous recombination frequencies using a SCneo cassette [28] and CENP-H-EGFP was performed as described previously [47] . After the I-Sce-I vector was transfected into the cells , the frequency of neomycin-resistant colony formation was measured . To enrich DT40 cells in the G1 phase , cells were synchronized by centrifugal counterflow elutriation ( Hitachi Industrial , Japan ) . The cell suspension ( ∼5×107 ) was loaded at a flow rate of 11 ml/min into an elutriation chamber running at 2 , 000 rpm . The first 50 ml was discarded , and the following 100 ml was used as a G1-phase cell fraction . Fluorescence microscopy was carried out and images were obtained and processed using the IX81 ( Olympus , Japan ) . Cells were cultured in medium containing BrdU ( 10 µM ) for 24–48 h to sensitize them to DSB generation by means of a 405 nm laser from a confocal microscope ( FV-1000 , Olympus , Japan ) . During laser treatment , cells were incubated in phenol red-free Opti medium ( GIBCO , NY , USA ) to prevent the absorption of the laser's wavelength . γ-irradiation was performed using 137C ( Gammacell 40 , Nordion , Kanata , Ontario , Canada ) . Antibodies against Rad51 ( Ab-1 , Calbiochem , CA , USA ) , FK2 ( Nippon Biotest Laboratories , Japan ) , RPA p32 ( GeneTex , TX , USA ) , rabbit Ig ( Alexa 488-conjugated antibody , Molecular Probe , OR , USA ) , and mouse Ig ( Alexa 594-conjugated antibody , Molecular probe , OR , USA ) were used for visualization .
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Induction of double-strand breaks ( DSBs ) in chromosomal DNA effectively activates a program of cellular suicide and is widely used for chemotherapy on malignant cancer cells . Cells resist such therapies by quickly repairing the DSBs . Repair is carried out by two major DSB repair pathways , homologous recombination ( HR ) and nonhomologous end-joining . However , these pathways cannot join DSBs if their ends are chemically modified , as seen in the DSB ends that would arise after the prolonged treatment of the cells with topoisomerase inhibitors such as camptothecin and etoposide . These anti-cancer drugs can produce the polypeptides covalently attached to the 3′ or 5′ end of DSBs . It remains elusive which enzymes eliminate these chemical modifications prior to repair . We here show evidence that the BRCA1-CtIP complex plays a role in eliminating this chemical modification , thereby facilitating subsequent DSB repair . Thus , BRCA1 and CtIP have dual functions: their previously documented roles in HR and this newly identified function . This study contributes to our ability to predict the effectiveness of chemotherapeutic agents prior to their selection by evaluating the activity of individual repair factors . Accurate prediction is crucial , because chemotherapeutic agents that cause DNA damage have such strong side effects .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/dna",
"repair",
"genetics",
"and",
"genomics/gene",
"function",
"genetics",
"and",
"genomics/chromosome",
"biology"
] |
2010
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Collaborative Action of Brca1 and CtIP in Elimination of Covalent Modifications from Double-Strand Breaks to Facilitate Subsequent Break Repair
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Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks . The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing . So far the predictive capability of these methods has not been critically evaluated and compared . This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models . A subset of these methods is then systematically evaluated using published data from three different case studies in E . coli and S . cerevisiae . The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes ( from 13C-labeling data ) . The sensitivity of the results to method-specific parameters is also evaluated , as well as their robustness to noise in the data . The results show that none of the methods outperforms the others for all cases . Also , it is observed that for many conditions , the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data . We further discuss the differences in the mathematical formulation of the methods , and their relation to the results we have obtained , as well as the connection to the underlying biological principles of metabolic regulation .
The methods presented in this survey tend to fall into one of two categories . One encompasses all the methods that use transcript levels in order to improve the prediction of metabolic flux distributions . On the other hand are the methods for creating tissue ( or context ) specific models from more generic organism-specific models . A typical example is the creation of models for different kinds of human cells using the global human metabolic reconstruction , which can be used for the study of tissue specific diseases [16] , [17] . Note that some methods fall into both categories , i . e . they return both a context-specific model and a metabolic flux distribution for the complete model consistent with the gene expression data . At the implementation level , the methods differ mainly in the way they use the expression data , by integrating either discrete or continuous expression levels , and by using absolute values for a single condition , or relative expression levels between different conditions ( Fig . 2 ) .
This case study uses a comprehensive omics dataset published by Ishii et al [40] . The experimental setup consists of E . coli strains growing aerobically in a chemostat at a dilution rate of 0 . 2 h−1 . The different experiments include variations of the dilution rate ( from 0 . 1 to 0 . 7 h−1 ) , and several single-gene knockout mutants growing at the reference dilution rate . For this dataset , the gene expression data is limited to the central carbon metabolism and is measured by microarray analysis . The assessed methods were applied to a genome-scale metabolic reconstruction of E . coli [43] , to predict the complete phenotype ( growth , secretion and intracellular fluxes ) from the gene expression data , given only the measured glucose and oxygen uptake rates as constraints . Figure 3a shows the error distribution for the different methods ( see Methods section for a description of the normalized error calculation ) . It can be observed that the median error for each method is higher than that of pFBA . Furthermore , many of the methods also have a higher variation in the error distribution compared to pFBA . In order to understand how the phenotype predictions vary across the different methods , we analyze in detail a particular case , namely the experiment at the highest dilution rate ( 0 . 7 h−1 ) . This is a typical case where FBA simulations are less accurate , since the assumption of growth yield maximization no longer holds true due to overflow metabolism . This is one of the experiments where pFBA gives a higher prediction error , and a likely scenario where alternative methods , such as those studied herein , will be most useful . The measured and predicted flux phenotypes are shown in Figure 4 . It can be observed that , in most cases , the results differ significantly from the measured values . Since the oxygen uptake rate is constrained , pFBA is able to predict the secretion of fermentation products , namely lactate and acetate . However , it predicts higher values than the experimental ones . All the methods predict some level of lactate production , although not all were able to predict the production of acetate ( iMAT , E–Flux , Lee–12 ) . The residual amounts of CO2 and pyruvate produced were either not predicted by most of the methods , or overestimated by some methods ( GIMME , iMAT , GX–FBA ) . Lee–12 incorrectly predicted a large production of ethanol . None of the methods predicted the production of succinate , and all correctly predicted the absence of formate production . Regarding growth rate prediction , there are essentially two cases . The methods that maximize biomass production ( pFBA , E–Flux ) and the methods that impose some predefined threshold of biomass production ( GIMME , MADE ) , predict values close to the maximum theoretical level . On the other hand , methods that do not impose any constraints regarding the growth rate simply predict no growth at all ( iMAT , Lee–12 , RELATCH* , GX–FBA ) . In order to understand the influence of imposing experimental measurement constraints on the predictive ability of the methods , all the simulations were repeated using the complete set of measured uptake , growth and secretion rates as constraints ( Fig . 3d ) . As expected , a decrease in the prediction error can be observed for many of the methods , with a higher impact on those that do not make assumptions regarding the growth rate ( iMAT , RELATCH* , GX–FBA ) . On the other hand , Lee–12 exhibited an unexpected significant deterioration in performance when constraints were added . A comparison between the predicted and measured fluxes across conditions for all the methods is given as supplementary material ( Fig . S1 ) . The experimental conditions are sorted by increasing error obtained by pFBA . Although there seems to be no correlation between the prediction errors across conditions , it can be observed that some of the methods exhibit a few biases towards systematically predicting higher or lower fluxes than experimental measurements for particular reactions . Finally , we test whether integration of proteomic data ( also included in this dataset ) results in more accurate predictions than the use of gene expression data ( Fig . S4 ) . Despite some differences , there is no improvement in predictive ability when proteomics data is used instead of transcriptomics data . This case study uses a dataset from Holm et al [41] , whose experimental setup consists of E . coli strains growing aerobically in batch cultures . The study compares the phenotype of the wild-type strain with two over-expression mutants , nox ( NADH oxidase ) and atpAGD ( F1-ATPase ) , with the goal of understanding global transcriptional responses to lowered levels of NADH and ATP . The dataset contains gene expression data measured at the genome scale using microarray analysis and 13C-flux data . The methods were tested using the same metabolic model as in the previous case study . In this dataset glucose uptake is the only measured uptake rate . The error distributions are shown in Figure 3b . Again , it can be observed that all methods show a higher median prediction error than pFBA . In this case , GX–FBA exhibits a much higher variation across conditions compared to the other methods . The predicted phenotypes for the over-expression mutants are analyzed in more detail ( Fig . 5 ) . Unlike gene knockouts or gene insertions , over-expression targets do not change the topology of the metabolic network . Therefore , this is a typical case where the flux-balance formulation is insufficient to predict phenotypic changes . In fact , it can be observed that pFBA does not predict the decrease in growth rate and the increase in acetate secretion that characterizes these mutant strains . Only E–Flux was able to predict acetate production in both conditions , although in the first case the quantitative prediction is incorrect . As in the previous case study , only the methods that define a biomass objective or requirement predict positive growth rates . In this case E–Flux successfully predicted the growth rate to be below the theoretical maximum . The impact of including the measured growth and secretion rates as constraints was also measured ( Fig . 3f ) . As expected , most of the median error values decreased . Again , this impact is more significant for the methods that do not make any assumptions regarding the growth rate . A significant decrease in variation is observed for GX–FBA . A comparison between the predicted and measured fluxes for all conditions is given as supplementary material ( Fig . S2 ) . It is interesting to observe that , especially in the cases of pFBA and E–Flux , the biases in flux prediction towards certain reactions are the same as observed in the previous case study . This case study uses a dataset from Rintala et al [42] , whose experimental setup consists of S . cerevisiae strains growing in a glucose-limited chemostat at a dilution rate of 0 . 1 h−1 with different oxygenation levels . These include intermediate levels from fully anaerobic to fully aerobic . The dataset contains genome-wide gene expression data . Fluxomic data for the same conditions could be obtained from a separate publication [44] . The assessed methods were used to integrate the gene expression data into a recent genome-scale metabolic reconstruction of S . cerevisiae [45] . Measured oxygen and glucose uptake rates were set as constraints . The error distribution for the different methods is shown in Figure 3c . As already observed in the previous case studies , all of the methods ( with the exception of E–Flux ) present a median prediction error above that of pFBA . We analyze in more detail the results for the two extreme conditions , complete aerobiosis and complete anaerobiosis ( Fig . 6 ) . For the aerobic case , the growth rate is very close to the maximum theoretical value , and no fermentation products are secreted . This is the typical case where the underlying assumptions of FBA are valid , as can be observed by the accuracy of the predictions . However , some of the methods incorrectly predict the secretion of some fermentation products . Under anaerobic conditions , the strain produces ethanol at high rates , and also a small amount of glycerol . All methods were able to predict ethanol production at rates similar to the experimental values , with the exception of GX–FBA that predicted a lower level of ethanol secretion accompanied with secretion of acetate and glycerol . GIMME and Lee–12 also incorrectly predicted the formation of acetate . On the other hand , Lee–12 predicted the glycerol secretion rate more accurately . As in the previous case studies , we analyze the impact of including the complete physiological measurements ( uptake , secretion and growth rates ) as constraints ( Fig . 3f ) . A decrease in the median prediction error is observed for most methods . Furthermore , a significant decrease in variability is observed for RELATCH* and GX–FBA . The comparison between the predicted and measured fluxes for all conditions is given as supplementary material ( Fig . S3 ) . In this case , very few systematic biases can be observed . Finally , we tested whether integration of proteomic data ( also included in this dataset ) results in more accurate predictions than the use of gene expression data ( Fig . S4 ) . Since the number of transcripts whose levels could be measured is one order of magnitude above the number of proteins whose levels were measured , we recalculated the prediction error from transcript data using the subset of genes that match measured protein levels . Using only a subset of the transcriptomic data results in a small decrease in the variability of the prediction error , without affecting the median error . Furthermore , with the exception of E–Flux , there are no significant changes in the flux predictions when proteomics data is used instead of transcriptomics data . Three of the methods evaluated , namely GIMME , iMAT and MADE are parameterized , which makes the results presented so far dependent on the particular choice of the parameter configuration . Therefore , the sensitivity of the prediction error with respect to the parameter values was analyzed . For each case , one parameter was varied at a time while the others remained fixed ( see Methods ) . In order to ensure that the results are not dependent on the case study , the analysis was performed for two datasets ( Holm and Rintala ) . The results show that for most parameters the variation is not monotonic with respect to the parameter value , and that the variance for one particular value can be larger than the average variation across the whole parameter range ( Figs . S5 , S6 ) . Nevertheless , some trends can be observed . In general , higher cutoff thresholds for the gene expression data seem to be preferred , leading to the deactivation of more genes . A lower flux activation threshold is preferable for iMAT , and higher values of the required fraction of the biological objective seem to be favorable for GIMME and MADE . All these choices lead to parsimony in enzyme usage and maximization of the biological objective , which are the same principles used in pFBA . This is not surprising , considering that pFBA had in general better predictive power than other methods for all the case studies presented herein . Finally , we tested the robustness of all the methods towards noise in the data ( Fig . S7 ) . The level of noise was gradually increased by a weighted combination of the original data with random data ( see Methods ) . By gradually varying the noise weight from 0 to 1 , the methods were given increasing levels of noise , including completely random data at the last step . This allows studying the robustness of the methods towards small levels of noise , as well as possible biases in the flux predictions in response to randomized data . The analysis was performed using the anaerobic condition from the Rintala dataset . This is a test case where all the methods have low error levels to begin with . One would expect a smooth increase in the average prediction error with increasing noise level as an indicator of robustness . This increase in the error should also be accompanied by a gradual increase in the variance in flux predictions ( made using different noisy transcript patterns generated at the same level of noise ) as an indicator of the absence of systematic bias in flux predictions . However , only E–Flux exhibited this pattern . GIMME and Lee–12 show a gradual increase in the variance , although the average prediction error is the same for the original and the random data . MADE and iMAT show small changes in the average prediction error , coupled with a mostly constant level of variance . GX–FBA shows a smooth increase in the average prediction error , coupled with a sharp increase in variance , and fails to compute for very high levels of noise . RELATCH* shows an apparent constant level of the prediction error , with an increasing variation that is many orders of magnitude lower compared to the other methods . Hence the solution is biased regardless of the gene expression levels .
One of the main features distinguishing the surveyed methods is the discretization of the gene expression data . It would seem preferable to make use of the continuous expression data in order not to lose the fine-grained data on the individual gene expression levels . Also , this avoids the definition of arbitrary threshold parameters . However , it is not possible to conclude that the methods that use continuous expression data ( E–Flux , Lee–12 , RELATCH* , GX–FBA ) provide more accurate flux predictions than the ones that discretize the expression levels ( GIMME , iMAT , MADE ) . Discretization also presents a few advantages , such as robustness to noise in the data , seamless integration with the logic-based gene-protein-reaction ( GPR ) associations , and avoiding data normalization issues . Furthermore , coarse-graining the gene expression data reduces the reliance on a direct proportionality between the fluxes and the transcript levels . Another major distinction between the surveyed methods is the choice between using absolute gene expression levels for one condition , or using differential gene expression between two or more conditions . One of the limitations of using absolute expression levels is the lack of proportionality between transcript and flux levels . A recent review from Hoppe highlights the multiple steps between gene expression and reaction rates [50] . Although some level of correlation can be observed between mRNA and protein levels , these are not directly proportional due to differences in translation , degradation rates , and post-translational modifications . Furthermore , enzyme concentrations do not necessarily reflect enzyme activity levels , as enzyme turnover numbers ( ) can vary by several orders of magnitude . Finally , metabolite concentrations , enzyme kinetics , and network level effects can influence the reaction flux as well . Altogether it seems that enforcing a correspondence between absolute transcript and flux levels does not reflect the underlying biochemical mechanisms . In that sense , accounting for relative expression changes as an indicator of the intended flux reconfiguration may provide a more meaningful description . However , the methods that use relative expression levels ( MADE and GX–FBA ) , did not generally give more accurate flux predictions . Another distinction among the presented methods is the utilization of a biological objective function . The mathematical definition of a biological objective is the key step that transforms a metabolic network reconstruction into a model that can simulate the cellular phenotype . The maximization of growth yield , determined from the cellular biomass composition , has been a commonly assumed objective for microbial organisms . Although the validity of this assumption has been experimentally confirmed under some conditions [51] , there are cases ( such as overflow metabolism ) where this assumption is not valid . Also , it has been shown that the biomass composition can vary across different experimental conditions [52] . Furthermore , in the case of multicellular organisms it is not trivial to define a biological objective . All of the methods evaluated , with the exception of E–Flux , replace the biological objective function with a function that relies on the gene expression data . Nevertheless , some of these methods still use the original objective to define a minimum growth requirement constraint ( GIMME , MADE ) or to calculate a reference flux distribution ( GX–FBA ) . Methods that do not make any assumptions regarding a biological objective ( iMAT , Lee–12 and RELATCH* ) should be suitable for a larger scope of organisms and experimental conditions . However , these methods incorrectly predicted a zero growth rate in all cases , with the exception of RELATCH* for the yeast case study . In order to evaluate the effect of imposing a biological objective on all methods , we repeated all the tests , adding a minimum growth rate constraint , corresponding to 90% of the maximum theoretical growth rate , to all simulations ( Fig . S8 ) . We observed that the average error decreased for all the methods that do not impose any restrictions on the growth rate otherwise ( iMAT , Lee–12 , RELATCH* , GX–FBA ) . This decrease is similar to that observed by adding the experimental growth and secretion rates as constraints . Therefore , in the absence of experimental measurement , the imposition of constraints related to assumed cellular objectives may still be necessary for accurate flux predictions . Despite the high number of proposed methods , the prediction of flux levels from gene expression data is far from being solved . Although some of the methods evaluated give reasonable predictions under certain conditions , there is no universal method that performs well under all scenarios . Regardless of the mathematical formulation proposed to address the problem , the mapping of transcripts to fluxes is intrinsically hampered by the fact that gene expression levels do not necessarily reflect flux levels , which are systemic properties of the cellular metabolism . Nonetheless , the transcriptome should provide cues to guide the determination of the correct phenotype among the space of solutions that results from the large number of degrees of freedom in metabolic networks . It has been proposed that the metabolic phenotype of microbial cells results from a trade-off between optimality and flexibility towards adaptation [53] . The optimality principles can be further decomposed into three distinct goals: growth yield , energy ( ATP ) yield , and parsimonious use of metabolic reactions . Hence , there are fewer inherent degrees of freedom in metabolism than the ones given by the network topology . Our study showed that growth yield and parsimony alone could be better predictors of metabolic fluxes than the transcriptome for most experimental sets . The ideal formulation to combine gene expression with fundamental biological principles governing metabolic flux distributions is yet to be found . This may require the integration of approaches that consider the interplay between transcripts and other metabolic components , by combining multiple omics data [20] , [54] and kinetic parameters [55] , [56] into constraint-based models . Alternatively , careful measurement of physiological parameters and intracellular fluxes coupled with separate analysis of transcript and flux patterns may be the most suitable strategy to uncover the principles of metabolic regulation [57] . These types of data can also be used to parameterize next generation of whole-cell models that explicitly represent proteins and transcripts in addition to metabolic fluxes [58] , Finally , we would like to acknowledge the authors who published their source code with the respective articles . We would like to reiterate the importance of providing published methods in a usable format , a fundamental step for reproducible research [15] . With this in mind , all the scripts , datasets , and results generated from this work are freely available at: https://github . com/cdanielmachado/transcript2flux .
The simulations for the E . coli and S . cerevisiae case studies were performed using , respectively , the iAF1260 and iTO977 genome-scale models [43] , [45] . For all simulations , any constraints given in the original models were discarded and ( depending on the test scenario ) overridden with experimental values from the respective datasets . All method-specific configuration details are given in the following . All methods evaluated in this study have available implementations in MATLAB ( The Mathworks; Natick , MA , USA ) . These were tested using MATLAB R2012b with Gurobi Optimizer 5 . 5 ( Gurobi Optimization , Inc . ) running on a 1 . 7 GHz Intel Core i5 processor .
|
Constraint-based modeling has become one of the most successful approaches for modeling large-scale biochemical networks . There are nowadays hundreds of genome-scale reconstructions of metabolic networks available for a wide variety of organisms ranging from bacteria to human cells . One of the limitations of the constraint-based approach is that it describes the cellular phenotype simply in terms of biochemical reaction rates , in a way that is disconnected from other biological processes such as genetic regulation . In order to overcome this limitation , different approaches for integration of gene expression data into constraint-based models have been developed during the past few years . However , all the methods developed so far have only been tested using isolated case studies . In this work , we elaborate a detailed survey of these methods , and perform a critical and quantitative evaluation of a selected subset of methods , using experimental datasets that include different organisms and conditions . This study highlights some of the current limitations in many of these methods , and reveals that no method published so far systematically outperforms the others .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"systems",
"biology",
"biochemistry",
"biochemical",
"simulations",
"computer",
"and",
"information",
"sciences",
"network",
"analysis",
"biology",
"and",
"life",
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"metabolic",
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2014
|
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism
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Despite the crucial role of the liver in glucose homeostasis , a detailed mathematical model of human hepatic glucose metabolism is lacking so far . Here we present a detailed kinetic model of glycolysis , gluconeogenesis and glycogen metabolism in human hepatocytes integrated with the hormonal control of these pathways by insulin , glucagon and epinephrine . Model simulations are in good agreement with experimental data on ( i ) the quantitative contributions of glycolysis , gluconeogenesis , and glycogen metabolism to hepatic glucose production and hepatic glucose utilization under varying physiological states . ( ii ) the time courses of postprandial glycogen storage as well as glycogen depletion in overnight fasting and short term fasting ( iii ) the switch from net hepatic glucose production under hypoglycemia to net hepatic glucose utilization under hyperglycemia essential for glucose homeostasis ( iv ) hormone perturbations of hepatic glucose metabolism . Response analysis reveals an extra high capacity of the liver to counteract changes of plasma glucose level below 5 mM ( hypoglycemia ) and above 7 . 5 mM ( hyperglycemia ) . Our model may serve as an important module of a whole-body model of human glucose metabolism and as a valuable tool for understanding the role of the liver in glucose homeostasis under normal conditions and in diseases like diabetes or glycogen storage diseases .
The human plasma glucose is kept in a narrow range between minimum values of ∼3 mM after prolonged fasting or extensive muscle activity and maximum values of ∼9 mM reached postprandially [1] , [2] . Homoeostasis of plasma glucose is crucial for the organism: Hyperglycemia results in non-enzymatic glycosylation ( glycation ) and thus loss-of-function of proteins [3] , glucose induced oxidative damage [4] , [5] and other adverse effects [6] , [7] . Hypoglycemia leads to an under-supply of tissues with glucose and is thereby of particular danger for neuronal cells , erythrocytes and fibroblasts , using glucose as dominant or even exclusive energy-delivering fuel under normal physiological conditions . The liver is a central player in buffering plasma glucose contributing either by net hepatic glucose utilization ( HGU ) or net hepatic glucose production ( HGP ) depending on the plasma glucose level exceeding or falling below a critical threshold value ( in the following referred to as ‘set point’ ) of ∼6 mM . Switching between HGP and HGU is therefore a switch between positive ( i . e . export of glucose ) and negative ( i . e . import of glucose ) net hepatic glucose balance . This crucial metabolic function of the liver is performed by hepatocytes which exhibit high capacity of glycogenesis , glycogenolysis , glycolysis and gluconeogenesis enabling them to transiently store substantial amounts of glucose as glycogen , to synthesize glucose from lactate , glycerol and glucoplastic amino acids and to convert excess glucose into triglycerides [2] , [8] , [9] . Glucose homeostasis is controlled by several hormones , with insulin and glucagon being the main counteracting players [1] , [10] . Insulin is the only known hormone lowering blood glucose , whereas multiple glucose increasing hormones exist . Glucagon plays the primary role in counter-regulation to hypoglycemia . Epinephrine has a secondary role , becoming critical under impaired glucagon responses , but with reduced effectiveness compared to glucagon [11]–[13] . Other counter-regulatory hormones like cortisol or thyroxin play only a minor role for the liver [10] , [12] , [13] . The plasma concentrations of insulin , glucagon and epinephrine change as direct response to varying blood glucose [1] , [10] . In the liver insulin increases the activity of glucose utilizing pathways ( HGU , glycolysis , glycogenesis ) and decreases glucose producing pathways ( HGP , gluconeogenesis , glycogenolysis ) , whereas glucagon and epinephrine have contrary effects . Main targets of the gluco-regulatory hormones are key interconvertible enzymes of glucose metabolism like pyruvate kinase or glycogen synthase . The kinetics of the interconvertible enzymes , and consequently also the hepatic glucose metabolism , depends on their phosphorylation state [2] , [14] which is altered by the hormones . Despite the crucial role of the liver for glucose homeostasis , a detailed mathematical model of human glucose metabolism of the liver , indispensable for understanding the hepatic role under normal and impaired conditions like occurring in diabetes , has not been developed yet . Available kinetic models of hepatic glucose metabolism are either minimal models [15]–[18] , concentrate on parts of the glucose metabolism [19] , or lump reactions [20] , [21] . Furthermore , all available models concentrate on the glucose-insulin system , and ignore the crucial insulin antagonists glucagon and epinephrine [15]–[19] , or do not include hormonal regulation at all [20] , [21] . All mentioned models with exception of [19] ignore the crucial dependency of enzyme kinetics on the respective phosphorylation state of the enzyme completely , an essential mechanism for short term regulation in hepatic glucose metabolism . Here , we present the first model of hepatic glucose metabolism in molecular detail , which includes the crucial control of hepatic glucose metabolism by insulin , glucagon and epinephrine via changes in phosphorylation state of key enzymes based on a new concept of linking hormonal regulation with metabolism .
The detailed kinetic equations for the reactions and transporters ( Text S3 ) are specific for human liver and based on extensive literature research . All kinetic parameters except the maximal velocities are based on literature data with references given in the respective rate equation . All values in the model were determined by fitting simulated model fluxes to experimentally determined fluxes and simulated model metabolite concentrations to experimentally measured concentrations . The fit procedure minimizes the sum of quadratic relative differences in fluxes and concentrations , divided by the total number of experimental fluxes or respectively the total number of experimental concentrations ( Equation 1 ) . Relative differences were used to avoid a domination of the optimization by large absolute values and to have dimensionless quantities . The contributions of fluxes and concentrations to least-square fit function were weighed equally ( ) assigning same relevance to fluxes and concentrations . ( 1 ) The experimental flux data was human specific ( Dataset S1 ) , the metabolite concentrations were taken from human and rat liver and are given in ( Text S1 , Dataset S1 ) . Equation 1 was minimized using the MATLAB® Optimization Toolbox ( constraint nonlinear optimization ) and resulted in a final value of , indicating that the overall remaining relative deviations of theoretical values from experimental ones were lower than a factor of 2 . The fitted values are given in Text S2 . For the time course and steady state simulations the differential equation system ( Text S3 ) was integrated with a variable-order solver for stiff problems based on numerical differentiation formulas with absolute integration tolerance and relative integration tolerance ( ode15s MATLAB® R2011a , MathWorks ) . Initial concentrations for all simulations are given in Text S1 . Variation in blood glucose and glycogen are given in the respective figure legends . The external concentrations of blood glucose and lactate were kept at fixed values in all simulations as their time evolution in the blood depends on the metabolic activity of various organs not considered in this model . The cellular redox state ( given by the concentrations of NAD and NADH ) and cellular energy state ( given by the concentrations of the adenine nucleotides ) were kept constant during all simulations . Steady state solutions are defined as states with absolute changes in every concentration smaller than for a time interval . Steady state solutions were tested to be stable against small changes in initial concentrations ( 1% ) . All conservation entities were tested to be constant within the tolerances over the integration . As the release of hormones and their elimination from the plasma are not part of the model , the relationships between plasma glucose level and hormone levels ( Figure 2 ) are described by phenomenological functions called glucose-hormone responses ( GHR ) . The GHRs are sigmoid functions ranging between basal hormone concentration hbase and maximal hormone concentration hmax , monotonically increasing with increasing blood glucose for insulin ( Equation 2 ) , monotonically decreasing for glucagon and epinephrine ( Equation 3 ) . The GHRs were determined by least-square fit to oral glucose tolerance tests and hypoglycemic , hyperinsulinemic clamp studies ( Table 1 , Dataset S1 ) . The parameters correspond to , , the inflection point and the Hill coefficient which determines the steepness of the sigmoidal hormone response . ( 2 ) ( 3 ) Experimental data and standard deviations ( Dataset S1 ) were extracted from figures , tables and supplemental information . Data points correspond to mean data for multiple subjects from the studies . No data points were omitted . The short-term effects of insulin , glucagon and epinephrine result from changes in the phosphorylation state of key interconvertible enzymes of glucose metabolism , namely GS , GP , PFK2 , FBP2 , PK and PDH . The interconvertible enzymes exhibit different kinetic properties in the phosphorylated state ( P ) and dephosphorylated state ( DP ) , thus carrying different fluxes in the phosphorylated state ( ) and dephosphorylated state ( ) . The resulting effective kinetics ( Equation 4 ) is the linear combination of and dependent on the phosphorylation state , with being the phosphorylated and the dephosphorylated fraction of the enzyme . ( 4 ) The phosphorylation state is a phenomenological function of insulin , glucagon and epinephrine ( Equation 5 ) . Insulin decreases , whereas glucagon and epinephrine increase . Epinephrine acts as a backup system for glucagon with reduced effectiveness . Only the currently dominating hormone ( glucagon or epinephrine ) was taken into account ( max ) . The hormonal dependencies on the phosphorylation state were modeled by a Michaelis-Menten like hyperbolic function with half-saturation at whereby the saturation parameter was set to for all hormones . The maximal basal hormone concentrations of the three hormones ( ) result from the respective GHR curves and are given in Table 1 . ( 5 ) We assumed that for given hormone concentrations the fraction of all interconvertible enzymes is equal . Experimental data ( Table 2 , Dataset S1 ) was extracted from figures and tables of 25 independent studies with different tracer methods . See [2] for review and Table 2 for detailed references , used methods and experimental data . Every data point is the mean for multiple subjects from one of the studies . No data points were omitted . Experimental data ( Dataset S1 ) was extracted from figures and tables from [22] ( individual data ) and [23] ( mean person data with STD ) for the glycogenolysis and from [8] , [24] , [25] ( mean person data ) for the glycogen synthesis . The only difference between model simulations in states of either net glycogen synthesis or net glycogenolysis simulations are differences in the initial glycogen and blood glucose concentrations . Experimental data for the mixed meal and hyperglycemic , hyperinsulinemic clamp simulations were extracted from [26] using the experimental time courses of glucose as model input . Insulin and glucagon time courses were predicted with the respective hormone dose response curves ( Equation 2 and Equation 3 ) . Simulations were started the from initial glycogen concentrations reported in the experiments . Experimental data ( Dataset S1 ) were extracted from figures and tables from [27] . Basal hormone concentrations of dogs and humans differ due to difference in human and dog physiology . Therefore , in model simulations of the human liver typical insulin and glucagon concentrations for the basal glucose production rate in humans were used ( insulin and glucagon ) and concentration changes for the hormones expressed relative to these basal values Changes in blood glucose were calculated from the simulated HGP , for a mean bodyweight of , blood volume of and whole-body basal glucose utilization rate of . In Simulation 1 no experimental data was taken into account , but only a drop in the respective hormones during the infusion period ( 135–200 min ) assumed . In Simulation 2 the experimentally observed changes in hormones and GU relative to basal levels ( mean of values during the pre-infusion period ) were used . The HGRC defined by Equation 6 measures the change in net hepatic glucose balance defined through the net rate of glucose exchange between hepatocytes and the blood ( ) elicited by a small change in blood glucose concentration . In model simulations , the HGRC was approximated by a difference quotient and numerically calculated from the steady state GLUT2 fluxes . For the HRGC corresponds to the changes in HGU , for to the change in HGP . ( 6 ) The reactions for citrate efflux , oxaloacetate influx and acetyl-CoA flux were included in the model , to test the model under various TCA cycle loads and changes in acetyl-CoA demand and production being a necessary condition in the model development of a functioning model of hepatic glucose metabolism under typical physiological TCA cycle loads . The malate-aspartate shuttle ( MALT ) including cytosolic and mitochondrial malate dehydrogenase ( MDH ) was not modeled in detail . Thus , the rate of the cytosolic isoform of the PEPCK was put to directly depend on mitochondrial oxaloacetate concentration . For the actual simulations these boundary fluxes where set to zero . To characterize the regulatory importance of various reactions in different states of hepatic glucose metabolism we used metabolic flux response coefficients ( Equation 7 ) , describing how the flux rate of an arbitrary reaction changes in response to a small change in the maximal activity of an arbitrary reaction . ( 7 ) Response coefficients with respect to the maximal activities of all enzymes were calculated at varying values of external glucose and cellular glycogen content for key fluxes of hepatic glucose metabolism ( Figure S9 ) : hepatic glucose uptake/release ( ) , gluconeogenesis/glycolysis ( ) and glycogenolysis/gluconeogenesis ( ) .
The plasma concentrations of the gluco-regulatory hormones change with changes in plasma glucose concentration . With increasing glucose the insulin level increases ( Figure 2C ) , whereas the levels of glucagon ( Figure 2A ) and epinephrine ( Figure 2B ) decrease . Consequently , the phosphorylation state of the interconvertible enzymes ( Figure 2D , Equation 5 ) changes from phosphorylated ( 94% at 2 mM ) to dephosphorylated ( 5% at 14 mM ) with increasing blood glucose . Changes in the phosphorylation state are accompanied by changes of the kinetic properties of the respective enzymes ( see Equation 4 ) . Due to the higher activity of the interconvertible enzymes of HGP ( GP and FBP2 ) in the phosphorylated form and the increased activity of the enzymes of HGU ( GS , PFK2 , PDH , PK ) in their dephosphorylated form ( Text S5 ) , the hepatic glucose metabolism is shifted from a glucose producing phenotype ( HGP ) under low glucose concentrations to a glucose consuming phenotype ( HGU ) at hyperglycemia . This change from an anabolic metabolism of glucose production ( HGP ) to a catabolic mode of glucose utilization ( HGU ) is a short term adaptation via changes in the kinetic properties of crucial enzymes of glucose metabolism . By adapting the HGP/HGU , gluconeogenesis/glycolysis and glycogen metabolism to the current hormonal signals and blood glucose concentration , the liver is able to fulfill its important role in glucose homeostasis in a variety of physiological states ranging from hypoglycemic states in overnight and short term fasting to hyperglycemic states postprandial . The liver is the main glucose supplier in overnight fasting and short term fasting . The produced hepatic glucose ( HGP , Figure 3A ) results either from de novo synthesis via gluconeogenesis ( GNG , Figure 3B ) or from degradation of hepatic glycogen via glycogenolysis ( GLY , Figure 3C ) . The relative contributions of these two processes to HGP change over the time course of fasting with gluconeogenesis becoming more and more important , whereas the share of glycogenolysis to HGP decreases ( GNG/HGP , Figure 3D ) . The simulations for short term fasting under constant glucose concentrations between 5 mM ( green ) and 3 mM ( red ) , the normal range of fasting glucose concentration [22] , [23] , are in agreement with the experimental data ( Figure 3 , Table 2 ) . Taking into account the gradual decrease in blood glucose concentration during fasting from 5 mM to 3 . 6 mM ( blue ) , an agreement between simulation and experimental data is observed . HGP decreases with ongoing fasting to a constant basal rate of 7–8 at around 40 h ( Table 3D , Table 3F ) . With increasing blood glucose HGP decreases . Gluconeogenesis rate is constant for given blood glucose concentrations ( see [2] for review ) . Taking into account the gradual decrease in blood glucose over fasting the rate of gluconeogenesis increases gradually ( blue ) . In contrast , glycogenolysis decreases sharply during fasting due to the emptying glycogen stores ( see also Figure 4A ) . As a consequence , the fractional contributions of glycogenolysis and glycogen synthesis to HGP shift from initially equal contribution of both processes to glucose finally completely synthesized de novo . Whereas after an overnight fast ( ∼10 h ) half of the HGP comes from glycogenolysis [2] , after 40 h fasting only 10% of HGP result from glycogen , 90% from gluconeogenesis . Liver glycogen has an important role as a short term glucose buffer for glucose homeostasis . At low blood glucose concentrations , like in the fasting state or during extensive muscle activity , glucose is released from glycogen ( Figure 4A ) , whereas during periods of high blood glucose like postprandial glycogen is synthesized from glucose ( Figure 4B ) . Over 40–50 h short term fasting the glycogen stores are emptied . During an overnight fast glycogen is utilized and the resulting glucose from glycogenolysis is exported resulting in half-filled glycogen stores after ∼16 h ( Table 3E ) . The rate of glycogenolysis is almost constant and decreases only at low glycogen concentrations ( Table 3E ) . The simulations for glycogen depletion under hypoglycemia ( Figure 4A ) for glucose concentrations between 3 . 6 mM ( red ) and 5 mM ( green ) are in agreement with experimental data [22] , [23] . The rate of glycogenolysis depends on the blood glucose concentration ( see also Figure 3C ) . With decreasing blood glucose concentration , the rate of glycogenolysis increases , glycogen stores are emptied faster . As a consequence of the drop in glucose concentration over fasting , simulations at low glucose concentration overestimate the decrease in glycogen , simulations at high glucose concentrations underestimate the depletion . When taking the gradual drop of blood glucose from 5 to 3 . 6 mM into account ( blue ) , the concordance of simulations experimental data further improved . Blood glucose levels are elevated postprandial and glycogen is stored via glycogen synthesis ( Figure 4B ) . The simulations for glycogen synthesis under hyperglycemia for different glucose concentrations between 5 . 5 mM ( red ) and 8 . 0 mM ( green ) ( normal range of postprandial glucose concentrations ) are in good agreement with the experimental data [8] , [24] , [25] , especially the simulation at 7 mM ( blue ) representing a normal blood glucose value postprandial . With increasing blood glucose the rate of glycogen synthesis increases and the hepatic glycogen stores are filled faster . For medium filled glycogen stores between 200 and 300 mM the rate of glycogen synthesis is constant for a given blood glucose concentration . To further evaluate the predictive capacity of our model , we performed time-course simulations of experimentally determined glycogen levels in dogs monitored under conditions of hyperglycemic , hyperinsulinemic clamps and administration of a mixed meal diet [26] , as can be seen from the respective figures in the supplement ( Figure S1 , S2 , S3 , S4 , S5 , S6 ) , our model simulation were in good agreement with the measured time courses of insulin , glucagon and glycogen . Of note , in these simulations none of the data was used for the calibration of the model . Moreover , successful simulation of these experiments under conditions where the two hormones insulin and glucagon could be varied independently from each other while in vivo their levels are coupled by the glucose level of the blood demonstrates the validity of the phenomenological glucose-hormone response functions ( Equation 2 and 3 ) and interconversion –versus-hormone function γ ( Equation 5 ) used in our model . To analyze the effects of insulin and glucagon on HGP classical hormone perturbation experiments of hepatic glucose metabolism were simulated [27] . In these experiments a deficiency in either insulin or glucagon or both was achieved by somatostatin infusion , an inhibitor of insulin and glucagon secretion , in combination with hormone replacement infusions . Figure 4C depicts exemplarily the effect of a transient drop of insulin which is characterized by a marked increase in HGP and a consequent rise in plasma glucose concentration . HGP and plasma glucose return to normal after the perturbation . Simulations of other cases ( saline control , insulin and glucagon depletion , glucagon depletion and somatostatin in combination with insulin and glucagon restoration ) are depicted in Figure S1 , S2 , S3 and S5 , respectively . For all hormone perturbations the time courses of HGP as well as glucose are in good agreement with the experimentally observed changes . Predicted changes in basal glucose production ( HGP ) are very similar to the experimentally observed changes ( Figure 4C ) . Insulin and glucagon depletion or glucagon depletion alone reduce the HGP to around 70% of basal values , insulin depletion increases HGP to around 130% . Insulin and glucagon have a dramatic effect on the human hepatic glucose metabolism , with basal glucagon being responsible for about 30% of HGP and basal insulin preventing increased HGP as a consequence of an unrestrained glucagon action [27] . The special role of the liver for glucose homeostasis results from the ability to switch between an anabolic glucose producing mode ( HGP ) to a catabolic glucose utilizing mode ( HGU ) depending on the blood glucose concentration and hormonal signals . In this process the hepatic metabolism is altered from glucose production via gluconeogenesis and glycogenolysis at hypoglycemia to glucose utilization via glycolysis and glycogen synthesis at hyperglycemia – a short term switch between metabolic pathways occurring in the range of minutes . To analyze the temporal response of hepatic glucose metabolism to rapid variations in the external glucose concentrations we performed time-dependent simulations with a step-wise constant concentration profile of blood glucose and constant internal glycogen concentrations . ( Figure S7 and S8 ) . To evaluate the impact of hormone induced fast changes in the phosphorylation state of key regulatory enzymes on metabolic regulation we performed these simulations also in the absence of this mode of regulation in the glycogen metabolism , i . e . frozen phosphorylation state of glycogen synthase and glycogen phosphorylase and regulation of these enzyme activities only by allosteric effects . We found clear differences in the simulated time-courses ( black and red curves in Figure S7 and S8 ) indicating that hormonal regulation contributes substantially in the rapid adaptation of the network to abrupt changes of blood glucose . In both cases ( i . e . presence or absence of hormonal control ) the simulation revealed that even after changing the external glucose concentrations abruptly by 2 mM and more a new steady state was reached within several minutes . Based on the above finding that the a new metabolic steady-state ( except for glycogen ) is achieved within a few minutes after abrupt changes of the blood glucose level it appeared justified to treat the metabolic network as being in a quasi-steady state as long blood glucose changes are slow and the glycogen pool is quasi constant . Hence , the quasi-steady state approximation was applied to simulate HGP/HGU at varying concentrations of blood glucose and cellular glycogen ( Figure 5 ) . In these calculations , the external glucose concentration was varied between constant values of 2 to 14 mM and the glycogen concentration kept at constant values between 0 and 500 mM . For each couple of glucose/glycogen values the resulting HGP/HGU ( Figure 5A ) , the contribution of gluconeogenesis/glycolysis to HGP/HGU ( Figure 5C ) , the contribution of glycogenolysis/glycogen synthesis to HGP/HGU ( Figure 5D ) and the ability of the liver to respond to changes in blood glucose expressed through the response coefficient HGRC ( Figure 5B ) in this quasi steady-state where analyzed . The fluxes depicted Figure 5 for a given constant glucose and glycogen concentrations are the reached steady state fluxes for this glucose and glycogen concentrations . The hepatic glucose metabolism switches between anabolic HGP and catabolic HGU at blood glucose concentrations of 6 . 6 mM for half-filled glycogen stores ( Figure 5A ) . The set point above the normal glucose concentration of around 5–5 . 5 mM [1] , [28] is in line with the role of the liver as glucose producer under normoglycemic conditions [1] , [29] . The liver contributes to glucose homeostasis by exporting glucose below the HGP/HGU set point ( red ) and importing glucose above this threshold ( green ) ( Table 3A ) . With increasing blood glucose levels , HGP decreases and HGU increases , in accordance with reported suppression of glucose production and increase in HGU with increasing postprandial glucose level [8] . Glycogen has almost no influence on HGU whereas for low blood glucose concentrations HGP strongly depends on glycogen due to increased contribution of glycogenolysis to HGP with increasing glycogen content ( Figure 5D ) . For low glucose concentrations the increase in glycogenolysis with increasing glycogen is strongest for low glycogen levels , more moderate for glycogen concentrations above 200 mM . HGP is maximal for filled glycogen store and low blood glucose . The switch between gluconeogenesis and glycolysis occurs at 8 . 5 mM glucose for half-filled glycogen stores ( Figure 5C ) . Gluconeogenesis and glycolysis rate are mainly determined by the prevalent blood glucose and only marginally depend on glycogen . Below 8 mM blood glucose gluconeogenesis is remarkably constant [2] ( see also Figure 3B ) . For plasma glucose levels below 5 . 1 mM glucose is released from glycogen stores via glycogenolysis , above 5 . 1 mM glycogen is synthesized for half-filled glycogen stores ( Figure 5D , Table 3B ) with rates of glycogenesis and cumulative glycogen as reported ( Table 3C ) . In contrast to gluconeogenesis/glycolysis , glycogen metabolism is markedly affected by the glycogen content . Glycogenolysis is almost constant for partially filled glycogen stores and decreases only for low glycogen concentrations ( Table 3H ) . Glycogenesis increases with increasing glucose concentration ( Figure 5D ) , which is in accordance with the view that blood glucose determines the maximal rate of glycogenesis [8] . Most interestingly the switching behavior for glycogenesis/glycogenolysis and glycolysis/gluconeogenesis is very distinct , with different set points and different dependencies on glucose . Gluconeogenesis is almost constant for low blood glucose and glycolysis increases with increasing glucose . In contrast glycogenolysis is almost constant for high blood glucose and increases with decreasing blood glucose at low blood glucose . In combination of the two processes a HGP/HGU output of the liver is generated with a set point at normal blood glucose concentrations which is able to react over the whole range of physiological glucose concentrations ( Figure 5A ) . As a consequence of the different set points for gluconeogenesis/glycolysis and glycogenesis/glycogenolysis , hepatic glycogen can be synthesized via two alternative pathways: the direct pathway , in which glucose taken from the blood ( HGU ) is directly stored as glycogen above the gluconeogenesis/glycolysis set point of ∼8 . 5–8 . 8 mM; the indirect pathway , in which glucose synthesized via gluconeogenesis , is stored as glycogen for blood glucose concentrations between 5 mM and the HGP/HGU set point of ∼6 . 6–7 mM; In the intermediate region between the set points of HGP/HGU and gluconeogenesis/glycolysis glycogen is synthesized via the direct and indirect pathway with varying contributions [8] ( Table 3G ) . The effects of changes of values on HGP/HGU , gluconeogenesis/glycolysis and glycogenolysis/gluconeogenesis were analyzed via response coefficients [30] ( Figure S9 , Equation 7 ) . The important findings of this analysis are as follows: ( i ) The effect of changes in the is strongly dependent on the external concentration of glucose and to a much lesser extent on the glycogen level . ( ii ) Under hypoglycemic conditions main control of hepatic glucose metabolism is exerted by a completely different set of reactions than in hyperglycemia . ( iii ) The response of the opposing pathway couples gluconeogenesis/glycolysis is very different to the glycogenolysis/gluconeogenesis . ( iv ) A small group of enzymes catalyzing irreversible reaction steps ( GK , G6PASE , GP , PFK1 , FBPASE1 , PFK2 , and FBPASE2 ) have a significant influence on the hepatic glucose metabolism whereas the majority of enzymes have only marginal effects . This control analysis is of particular interest for gene expression studies of hepatic glucose metabolism like for instance studies of circadian or feeding induced changes in hepatic expression [31] . The hepatic counter-regulatory capacity to changes in blood glucose was evaluated using the HGRC ( Equation 6 , Figure 5B ) , a measurement of the ability of the liver to react to changes in the blood glucose with changes in HGP or HGU , respectively . Our simulations showed that the liver is able to respond over the whole range of physiological blood glucose concentrations with being the lower limit , i . e . a change of 0 . 01 mM in blood glucose results in a change of HGP/HGU of at least . Intriguingly , our analysis suggest the counter-regulatory response of the liver to variations in the external glucose concentration to be particularly pronounced around the HGP/HGU set point at ∼6 . 6 mM which is flanked by a strong counter-regulatory response to hypoglycemia and a weaker response to elevated blood glucose levels . The strong counter-regulation to a decrease of blood glucose below 5 mM results in an effective increase in HGP whereby the rise of HGP depends on the glycogen content , with most effective counter-regulation for filled glycogen stores . The liver counteracts falling blood glucose levels to avoid hypoglycemia with a strong response . Furthermore , an increased response to elevated blood glucose levels of above 7 . 5 mM is seen enabling the hepatocyte to react efficiently to elevated blood glucose levels as occurring postprandially ( 6–10 mM ) . The hepatic glucose metabolism has ideal regulatory properties to react to the typical physiological challenges to glucose homeostasis: counter regulation to hypoglycemia in fasting and under extensive muscle activity and counter regulation to postprandial increase in blood glucose .
We present the first detailed model of human hepatic glucose metabolism integrating hormonal regulation by insulin , glucagon and epinephrine based on a novel concept to couple the level of these hormones with the phosphorylation state of interconvertible enzymes . This model enables for the first time the analysis of the hepatic carbohydrate metabolism at molecular level , including hormonal regulation . Furthermore , we provide a novel method to integrate hormonal signals with metabolism based on changes in phosphorylation state . Model simulations are in good agreement with experimental data from a multitude of studies by different laboratories , researchers and methods . We want to emphasize , that the model was not fitted to single study data , but instead , data from a multitude of different studies covering various aspects of glucose metabolism were used . Thereby , the fundamental properties of human hepatic glucose metabolism and not individual study properties could be captured . Remarkably , the agreement of model simulations with numerous experimental and clinical findings was achieved without any re-fitting of model parameters and under neglect of other gluconeogenic substrates than lactate and regulatory phenomena on slow timescales as insulin-dependent changes in the expression level of metabolic enzymes . The model clearly underlines the importance of short term regulation of metabolism by interconvertible enzymes , being able to adapt hepatic metabolism to hormonal signals and glucose levels , and in this process being able to switch between anabolic and catabolic modes even within metabolic pathways . We simulated the response of the human liver to changes in blood glucose under varying glycogen concentrations and assessed the contributions of glycogen metabolism and glycolysis/gluconeogenesis to HGP/HGU . Thereby we provide essential data for the understanding of the role of the liver in glucose homeostasis , which is not accessible experimentally . The underlying metabolic network reaches quasi-steady state within minutes after perturbations in plasma glucose ( see Figure S7 and Figure S8 ) . The dynamics of hepatic glucose metabolism is therefore mainly determined by the depletion/filling of the glycogen stores and the external glucose concentrations under normal conditions . As a consequence , the quasi-steady state system responses , shown for the key fluxes of the glucose metabolism in Figure 5 , provide a good approximation of the state of hepatic glucose metabolism at given concentrations of blood glucose and temporary filling state of the glycogen store . Furthermore , we analyzed the hepatic response to changes in blood glucose over the whole physiological range of blood glucose concentrations ( 3–11 mM ) , thereby integrating the available experimental data from the research field of hypoglycemia ( accessible with hypoglycemic , hyperinsulinemic clamps <5 . 5 mM ) and of elevated glucose concentrations ( accessible with oral glucose tolerance tests OGTT>5 mM ) . A model is always an abstraction of reality describing a certain subset of biological phenomena . The underlining assumptions are crucial for to understand the range of application of the model and the limitations . Main model assumptions and simplifications are ( i ) usage of phenomenological functions to incorporate hormone-induced signal transduction , ( ii ) a constant cellular redox- and energy status ( iii ) modeling of an ‘average’ hepatocyte , i . e . neglecting metabolic zonation of hepatocytes along the sinusoid [32] and ( iv ) no inclusion of changes in the gene expression of metabolic enzymes . ( i ) A crucial part of this model is the phenomenological description of signal transduction ( ) , which takes into account a substantial body of qualitative knowledge about the effects of the individual hormones on hepatic glucose metabolism . Due to the lack of quantitative experimental data on signaling processes as , for example , concentrations , activities and phosphorylation states of key kinases and phosphatases at varying concentrations of glucose , insulin , glucagon and combinations of these factors , a more detailed description was not possible . The model reproduces the experimentally observed dependency of glucose metabolism on the hormones . Especially , the reproduction of the classical insulin and glucagon perturbation experiments of [27] , performed as independent validation without taking into account in the modeling process , underlines the validity of such a simplified treatment of the signaling network in the case of the hepatic glucose metabolism . Throughout the model the phosphorylation state of all regulatory enzymes is the same at given concentration of glucagon and insulin . This simplification could be the reason why the activation of the glycogen synthase was overestimated in mixed meal and clamp simulations . A second simplification was that the fraction ( ) of phosphorylated inter-convertible enzymes follow instantaneously the hormone concentrations in the plasma although signal transduction occurs in the range of some minutes [33] . Including time-dependent changes of proteins involved in the cAMP-dependent signaling cascade in a later and more advanced version of the model should result in slightly different time-courses of fluxes and metabolite concentrations elicited by abrupt changes of external metabolite- and hormone concentrations . However , the main results of this study refer to the normal physiological situation where significant changes of the blood glucose level occur in a time window of hours ( Figure 3 , 4 and 5 ) with the metabolic network always being in quasi-steady state . ( ii ) The model is limited to physiological states of the liver where changes in the energy- and redox state can be neglected . Normally , the hepatic energy state is decoupled from the hepatic glucose metabolism , with -oxidation of fatty acids providing ATP and reduced redox equivalents ( NADH ) . Consequently , the model is not able to simulate conditions where this assumption of energy decoupling is not valid , for example , under hypoxic or ischemic conditions . ( iii ) The presented model describes the metabolic net behavior of the liver of a hepatocyte averaged over the liver and is therefore able to simulate the net effect of the liver on glucose balance , namely net HGP and net HGU . In reality the liver exhibits a zonated structure with varying capacities of gluconeogenesis and glycolysis of the hepatocytes along the sinusoid [32] . Some of the effects brought about by an ensemble of hepatocytes equipped with differing capacities of glucose metabolism cannot be simulated with a model of a ‘mean’ hepatocyte . For example , the remaining difference between absolute glucose production and net glucose production ( Figure S6 ) are due to the simultaneous presence of hepatocytes with net HGP ( peri-portal hepatocytes ) and HGU ( peri-central hepatocytes ) . ( iv ) Remarkably , the anabolic/catabolic switch was achieved by fast changes in the phosphorylation state and allosteric regulation of key enzymes of glucose metabolism without temporal changes in gene expression , i . e . changes in the protein levels of enzymes . In our opinion , changes in gene expression as observed to occur with different period lengths during the day [31] play no crucial role in short-term glucose homeostasis , a system which has to react to fast changes in the minute range of glucose supply ( postprandial ) and glucose demand ( muscle activity ) by an adaptation of metabolism . Changes in gene expression play a role in adapting hepatic metabolism on longer time scales to for example reduce futile cycles in counteracting pathways ( like down-regulation of glucokinase and up-regulation of glucose-6 phosphatase under hypoglycemia ) or match glucose metabolism to physiological requirements ( up-regulating of glucose-6 phosphatase under hypoglycemic conditions to increase gluconeogenesis ) . The strong effects of such changes on hepatic glucose metabolism could be seen in the analysis of the response coefficients . Finally , it has to be noted , that our model comprises fairly active futile cycles in the glycogen metabolism , the PFK1/FBPase1 system and between PEPCK and PK . During model building it was tried to minimize these cycles . Reducing and removing individual cycles in the modeling process compromised the ability of the model to switch between glucose consuming and glucose producing pathways , namely HGP/HGU , gluconeogenesis/glycolysis and glycogen synthesis/glycogenolysis . An essential property of the model is the simultaneous activity of glycogen synthase and glycogen phosphorylase and the accompanied futile cycle , necessary to reproduce the experimental time-courses for glycogen synthesis in fasting and postprandially . Substrate cycling allows the system to react to fast changes in metabolite levels , the concomitant adaptation of key enzymes via phosphorylation or dephosphorylation causes an additional shift of the net flux in the right direction ( see Figure S7 ) . Taken together , metabolic regulation via phosphorylation/dephosphorylation of key enzymes in combination with futile cycles plays a key role in our model , in line with the results presented by Xu et al . [34] . Future work will apply the presented model to diseases of glucose homeostasis like diabetes , integrate the model in models of whole-body glucose metabolism and use individual patient data to generate individualized hepatic glucose models and analyze inter-individual differences in glucose metabolism .
|
Glucose is an indispensable fuel for all cells and organs , but at the same time leads to problems at high concentrations . As a consequence , blood glucose is controlled in a narrow range to guarantee constant supply and on the other hand avoid damages associated with elevated glucose levels . The liver is the main organ controlling blood glucose by ( i ) releasing newly synthesized or stored glucose in the blood stream when blood glucose is low ( ii ) using and storing glucose when blood glucose is elevated . These processes are regulated by hormones , in particular insulin , glucagon and epinephrine . We developed the first detailed kinetic model of this crucial metabolic system integrated with its hormonal control and validated the model based on a multitude of experimental data . Our model enables for the first time to simulate hepatic glucose metabolism in depth . Our results show how due to the hormonal control of key enzymes the liver metabolism can be switched between glucose production and utilization . We provide an essential model to analyze glucose regulation in the normal state and diseases associated with defects in glucose homeostasis like diabetes .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"metabolic",
"networks",
"anatomy",
"and",
"physiology",
"hormones",
"endocrine",
"physiology",
"physiological",
"processes",
"homeostasis",
"insulin",
"endocrinology",
"diabetes",
"and",
"endocrinology",
"biology",
"systems",
"biology",
"biochemical",
"simulations",
"physiology",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Quantifying the Contribution of the Liver to Glucose Homeostasis: A Detailed Kinetic Model of Human Hepatic Glucose Metabolism
|
Many targets of plant microRNAs ( miRNAs ) are thought to play important roles in plant physiology and development . However , because plant miRNAs are typically encoded by medium-size gene families , it has often been difficult to assess their precise function . We report the generation of a large-scale collection of knockdowns for Arabidopsis thaliana miRNA families; this has been achieved using artificial miRNA target mimics , a recently developed technique fashioned on an endogenous mechanism of miRNA regulation . Morphological defects in the aerial part were observed for ∼20% of analyzed families , all of which are deeply conserved in land plants . In addition , we find that non-cleavable mimic sites can confer translational regulation in cis . Phenotypes of plants expressing target mimics directed against miRNAs involved in development were in several cases consistent with previous reports on plants expressing miRNA–resistant forms of individual target genes , indicating that a limited number of targets mediates most effects of these miRNAs . That less conserved miRNAs rarely had obvious effects on plant morphology suggests that most of them do not affect fundamental aspects of development . In addition to insight into modes of miRNA action , this study provides an important resource for the study of miRNA function in plants .
MicroRNAs ( miRNAs ) are a class of small RNA ( sRNA ) molecules that has recently emerged as a key regulator of gene activity . In plants , miRNAs are released from larger precursors ( pri-miRNAs ) in the nucleus mainly , by DICER-LIKE1 ( DCL1 ) [1] . The resulting sRNA duplex is methylated and translocated to the cytoplasm where it can be loaded into an RNA-induced silencing complex ( RISC ) that includes a member of the ARGONAUTE ( AGO ) family as catalytic component . The RISC can then recognize mRNAs containing sequences complementary to the loaded miRNA [2] . In plants , cleavage of the target mRNA is an important mechanism for plant miRNA action , but there are also direct effects on protein accumulation , as reported for many animal miRNAs [3]–[11] . The spatio-temporal expression pattern of miRNA genes is regulated to a large extent at the transcriptional level , and different members of a miRNA family can have distinct , specialized expression domains [12]–[17] . An additional layer of regulation in miRNA action has been reported by Franco-Zorrilla and colleagues [18] . IPS1 ( INDUCED BY PHOSPHATE STARVATION 1 ) encodes a non-coding RNA with a short motif that is highly complementary to the sequence of miR399 , which like IPS1 is involved in the response to phosphate starvation [19]–[23] . In contrast to regular miRNA target sites , the IPS1 sequence contains a three-nucleotide insertion in the center , corresponding to the position where normally miRNA-guided cleavage takes place , and this bulge in the miRNA/target pair prevents endonucleolytic cleavage of IPS1 transcripts . This results in sequestration of RISCmiR399 , leading to a reduction of miR399 activity . A similar phenomenon , negative regulation of small RNA activity by a partially complementary mRNA , has been recently described in bacteria as well [24] , [25] . MiRNA target mimicry can be exploited to study the effects of reducing the function of entire miRNA families [18] . Simultaneous inactivation of all miRNA family members by constructing multiply mutant lines has so far been achieved for only two relatively small families [16] , [26] . Plant target mimics are conceptually similar to miRNA sponges , used to reduce miRNA activity in animals . MiRNA sponges are transcripts containing multiple miRNA binding sites that compete with endogenous target mRNAs , thereby reducing the efficiency of the corresponding miRNA [27] . Although in animals perfect-match miRNA binding sites seems sufficient to sequester miRNAs [28] , such optimal sites would be generally cleaved in plants , and they would not succeed in sequestering the miRNA-loaded RISC . Consistent with this , plants overexpressing non-modified versions of miR156 and miR319 target genes show much milder phenotypes than plants expressing the corresponding target mimics [18] , [29] , [30] . Modifications of the miRNA binding site that prevent cleavage but still allow miRNA binding are therefore required to reduce miRNA activity in plants . Here , we present a collection of transgenic plants expressing artificial target mimics designed to knockdown the majority of Arabidopsis thaliana miRNA families . One fifth of these lines have obvious morphological defects , which is in the same range as the approximately 10% of miRNA knockouts that caused phenotypic abnormalities or lethality in Caenorhabditis elegans [31] . We found a clear correlation between the evolutionary conservation of plant miRNA families and their effect on aerial plant morphology .
We generated artificial target mimics for 73 different families or subfamilies of miRNAs and expressed them in Arabidopsis thaliana plants under the control of the constitutive 35S CaMV promoter . As described [18] , we modified the 23 nucleotide , miR399-complementary motif in IPS1 . The different constructs , and the corresponding transgenic lines , are named “MIM” , followed by the numeric identifier of the targeted miRNA family or subfamily . We targeted all miRNA families reported in miRBase ( http://microrna . sanger . ac . uk/sequences/index . shtml ) and ASRP ( http://asrp . cgrb . oregonstate . edu ) [32] at the beginning of 2007 , plus some of the miRNAs described subsequently [33] . The majority of the analyzed families have only been described in Arabidopsis thaliana and Arabidopsis lyrata [34] , [35] . The remaining families are shared with other angiosperms , and less than a quarter has been detected in non-flowering plants , including gymnosperms , ferns or mosses [32] , [33] , [36] , [37] . A complete list of MIM constructs , and the primer pairs used to generate them , can be found in Table S1 . For miRNA target predictions , see [8] , [33] , unless stated otherwise . A single artificial target mimic could be designed for most miRNA families . The mature miRNAs produced by members of the miR169 and miR171 families differ slightly , and different target mimics were designed for these subfamilies . Two target mimics were also designed for the miR161 family , which produce two mature miRNAs that have only partially overlapping sequences , and that target similar subsets of the PPR gene family [38] . Conversely , some miRNA families have very similar sequences and overlapping in vivo targets ( e . g . , miR159/319 , miR156/157 and miR170/171a ) , and artificial target mimics might not be able to unambiguously discriminate between different miRNAs . In some cases , the sequence of the bulge in the miRNA/target mimic pair had to be modified . For example , maintaining the original central sequence of IPS1 in MIM172 could have reconstituted a cleavage site for miR172 . Consistent with such modifications being important , plants expressing the appropriately modified version of MIM172 showed an altered phenotype ( see below ) , whereas plants expressing an initial version of MIM172 in which a putative miR172 cleavage site was present ( MIM172cs ) did not . Moreover , plants expressing a MIM172 version with only a single-nucleotide mismatch corresponding to position 11 of the mature miRNA ( MIM172sn ) did not show any abnormal phenotype either , suggesting that the three-nucleotide bulge is required for target mimic activity ( Figure 1 ) . We generated at least 20 independent transformants for each of 75 separate constructs . Of these , 15 , targeting 14 different families , caused reproducible phenotypes in the shoot system of the plants , which are described below . Phenotypic alterations were consistent across most , if not all , independent transformants examined for each construct . An example of the phenotypic variation among primary transformants is shown in the histograms in Figure 1 . An overview of all lines with morphological defects is given in Table 1 , together with the main target genes of the corresponding miRNA family and a list of other taxa in which they can be found . The phenotypes of MIM156 and MIM319 plants have been briefly described before [18] , [39] . All miRNA families whose inactivation resulted in visible phenotypical alterations are conserved among the angiosperms , and most of them are also found in non-flowering plants . MIM156 and MIM157 plants ( Figure 2 ) had reduced leaf initiation rates , such that they flowered at about the same time as wild type , but with only two or three true leaves . This phenotype is similar to what is seen in plants carrying non-targetable versions of SPL9 or SPL10 , two of the miR156/157 targets , and opposite of plants overexpressing miR156b or spl9 spl15 double mutants [10] , [40]–[42] . In addition , these plants had bent , spoon-shaped cotyledons . The few rosette leaves were characterized by serrated margins , indicating adult leaf identity , consistent with a role of miR156 and its targets in controlling phase change [30] . MIM159 plants had extensive pleiotropic defects , and similar phenotypes were observed in most MIM319 lines . These plants had reduced stature , with rounder , upward curled leaves ( Figure 2 ) , shorter stem internodes , and smaller flowers with short sepals , reduced petals and anthers that did not develop completely . More severe MIM319 lines were progressively smaller , had warped leaves and lacked well-developed petals ( Figure 3A ) . Stem elongation was often completely suppressed ( Figure 3B ) . Most plants had reduced fertility , and this phenotype was particularly severe in MIM319 plants , for which only a few viable seeds could be recovered after they were grown for prolonged periods at 16°C in long days . Both vegetative and floral phenotypes reminiscent of MIM159 defects have been reported for plants that express non-targetable forms of miR159 target genes [29] , and in plants doubly mutant for miR159a and miR159b [26] . In particular , upward curled leaves have been observed in plant expressing non-targetable forms of MYB33 , which can be targeted both by miR159 and miR319 [43] . Milder MIM319 lines showed different leaf defects , with leaves curled downward ( Figure 2 ) . This is consistent with what has been reported for plants that express non-targetable forms of TCP2 and TCP4 , which are both exclusive miR319 targets [29] , suggesting that target mimics can at least partially discriminate between these two miRNA families . Serrated and hyponastic leaves were seen in MIM160 plants ( Figure 2 ) , in agreement with the phenotype of plants that express non-targetable versions of ARF10 or ARF17 , two of the three miR160 targets [44] , [45] . In addition , MIM160 plants were smaller than wild type . Compared to other constructs , fewer transformants were recovered , consistent with the known requirement of miR160 for seed viability or germination [44] . A different type of leaf serration was caused by MIM164 ( Figure 2 ) , similar to what has been reported for plants expressing a non-targetable version of CUC2 , one of the miR164 targets , and for plants lacking one of the miR164 isoforms , miR164a [13] . While expression of MIM160 affected the entire leaf , with the serrations being regular and jagged , MIM164 caused mainly serration of the basal part of the leaf , with more irregular and rounded sinuses and teeth ( Figure 3C ) . Although carpel fusion defects have been described for plants lacking miR164c [12] , the carpel defects in MIM164 plants seemed to be different , with ectopic growths forming at the valve margins ( Figure 3D ) , resembling those seen in the cuc2-1D mutant , in which a point mutation affects the miR164 complementary motif in CUC2 [46] . In some cases , this tissue could develop into adventitious pistil-like structures ( Figure 3E ) . Rounder leaves with an irregular surface , which appeared to be hollowed out between the main veins , were caused by MIM165/166 . Younger leaves tended also to be cup-shaped ( Figure 2 ) . Targets of miR165/166 , including the transcription factor-encoding genes PHAVOLUTA and PHABULOSA , control leaf polarity , and dominant mutations that disrupt the miRNA target site in these genes cause severe alterations in leaf morphology [47]–[49] . A substantial delay in flowering was observed in MIM167 plants , which flowered with 20 . 8±4 . 2 ( mean ± standard deviation; n = 30 ) leaves in long days , compared to 13 . 0±0 . 9 rosette leaves in wild-type plants ( Figure S1A and Figure S2 ) . These plants had in addition twisted leaves ( Figure 2 ) , as well as defects in the maturation of anthers ( Figure 3F ) and in the development and shattering of seeds , which often remained attached to the dehiscent siliques ( Figure 3G ) , resulting in reduced seed production and germination ( not shown ) . This is consistent with what has been observed in plants that express a non-targetable form of the miR167 target ARF6 or ARF8 . Such plants have smaller leaves and are often sterile due to defects both in ovule and anther development [17] . Effects on flowering time have not been previously associated with miR167 [17] , [50] , and the late-flowering phenotype of MIM167 plants reveals a new role for this miRNA family . Two constructs were used to downregulate different subfamilies of miR169 family , whose main targets are HAP transcription factors . MIM169 was designed for miR169a , b , c , h , i , j , k , l , m and n , and MIM169defg for miR169d , e , f and g . Both target mimics reduced the size of transgenic plants ( Figure 2 ) . MiR170 and miR171 target a group of SCARECROW-like transcription factor genes [9] , and both MIM170 and MIM171A plants had round , pale leaves ( Figure 2 ) , as well as defective flowers , with sepals that did not separate properly , resulting in reduced fertility ( Figure 3H and 3I ) . Expression of target mimics against the b and c members of the miR171 family did not confer any phenotype , suggesting less important roles for these two miRNAs . MIM172 plants were also late flowering , with 20 . 0±3 . 5 ( n = 30 ) rosette leaves in long days ( Figure S1B ) , consistent with the flowering time phenotype of plants that have increased expression of miR172 targets [4] , [6] , [51] . In addition , leaves of MIM172 plants appeared to be somewhat narrower than those of wild type , and mildly curled downward , and severe MIM172 lines presented reduced apical dominance ( not shown ) . In contrast to plants that express a non-targetable version of AP2 [52] , flowers of MIM172 plants were normal . These differential effects could be due to the particularly high levels of miR172 levels during early flower development [6] . MiR393 targets a small group of auxin receptor genes . MIM393 plants had mild defects in leaf morphology , with narrow leaves that were curled downward ( Figure 2 ) . Leaf epinasty is often associated with high auxin levels [53] , and is consistent with an increase of auxin signaling caused by downregulation of miR393 activity . Finally , epinastic leaves were observed also in MIM394 plants ( Figure 2 ) . MiR394 is predicted to target a gene encoding an F-box protein . Artificial target mimics are thought to sequester their target miRNAs , presumably by stably binding to miRNA-loaded RISCs . To obtain additional evidence for such interactions , we embedded a functional MIM159 site in the 3′-UTR of a triple- Enhanced Yellow Fluorescent Protein ( EYFP ) reporter; stable recruitment of RISCmiR399 to the mimic site could be expected to interfere with EYFP translation . In 80% of MIM159 expressing T1 plants , as in control plants , the EYFP transgene was completely silenced . In the remaining 20% , we detected EYFP signal that was strongly reduced in the region where MIR159 genes are known to be expressed ( Figure 4A ) [26] . In addition , these plants presented the typical phenotypic defects of MIM159 plants , confirming that the EYFP:MIM159 construct functions properly as a target mimic . RISCmiRNA sequestration in turn should relieve target genes from miRNA-dependent regulation , resulting in increased levels of the encoded protein . In agreement with such a scenario , activity levels of a genomic MYB33:GUS reporter were markedly increased in MIM159 plants ( Figure 4A ) . In analogy with EYFP:MIM159 , reporter activity was increased in the tissues expressing MIR159 genes [26] , as expected . Sequestration of RISCmiR399 by the natural target mimic IPS1 prevents miR399-guided cleavage of PHO2 mRNA , thus increasing PHO2 mRNA levels [18] . To assess the effects of artificial target mimics on the levels of mRNA of miRNA target genes , we tested them by reverse transcription followed by quantitative PCR ( qRT-PCR ) in a subset of MIM lines . We preferentially analyzed organs in which miRNA abundance was high according to the ASRP database [32] , [54] , or organs with major phenotypic alterations in MIM lines . Two independent lines were tested for each construct . Among the miRNA targets , we chose ones known to induce phenotypic defects when expressed as non-targetable forms [44] , [45] , [47] and ones that show altered expression in miRNA biogenesis mutants [32] , [54] , [55] . PCR products spanned the miRNA target sequence , allowing quantification of the attenuation in slicing activity by the corresponding miRNA . Surprisingly , in most cases there were no major changes in target transcript levels ( Figure 4B and Figure S3 ) . For comparison , we examined the expression of the same miRNA target genes in seedlings of several mutants impaired in small RNA biogenesis and function , including dcl1-100 , se-1 , hyl1-2 and ago1-27 , and in plants overexpressing viral silencing suppressors that are known to counteract the action of the small RNA machinery , including P1/HC-Pro , P0 , P19 and p21 [56]–[60] . In most cases , the changes seen in MIM lines correlated with those seen in miRNA biogenesis mutants . Stronger effects were observed only in dcl1-100 plants ( Figure 4C ) . These results are consistent with what has been observed in microarray studies of miRNA biogenesis mutants , including other dcl1 alleles , se and hyl1 [55] , . As in animals , inhibition of translation is an important component of miRNA function in plants [4] , [6] , [11] . To test whether artificial mimics impact miRNA effects independent of changes in target transcript accumulation , we monitored the protein levels produced by CIP4 , a gene that is regulated by miR834 through translational inhibition [5] , [62] . In MIM834 lines , CIP4 levels were appreciably increased , while CIP4 mRNA levels were unchanged ( Figure 4D ) . Direct effects on protein translation could explain the absence of a clear correlation between target mRNA levels and plant phenotype in plants expressing artificial target mimics . Finally , we investigated the levels of mature miRNAs in plants expressing artificial target mimics . In all MIM lines we examined , levels of the targeted miRNA were decreased , suggesting that unproductive interaction of RISCmiRNA with a decoy affects miRNA stability ( Figure 4E ) . Although such an effect has not been observed in case of the endogenous IPS1-miR399 interaction [18] , a similar reduction in small RNA levels triggered by a target mimic has been reported in bacteria [24] , [25] . We have generated a collection of transgenic plants expressing artificial target mimics designed to reduce activity for most of the known miRNA families in Arabidopsis thaliana . Inhibiting the function of 14 out of 71 miRNA families with target mimics led to morphological abnormalities . All of these families belong to the more abundant and widely conserved miRNA families , which were the first ones to be discovered ( Table 1 ) . This agrees with results from experiments in which miRNAs were overexpressed , miRNA target genes were mutated , or miRNA genes were inactivated by conventional knockouts [reviewed in 63] . Together , these findings are consistent with the scenario of frequent birth and death of miRNA genes , with only a few becoming fixed early on during evolution because they acquired a relevant function in plant development [33] , [36] . More recently evolved , species-specific miRNAs could instead play a role in adaptation to certain abiotic or biotic challenges , or have no discernable function at all . Some miRNAs are known to regulate physiological traits , and they do not cause morphological abnormalities under standard benign conditions [20] , [21] , [64] . Such conditional effects would have escaped our screen , as would have defects in the root system of the plant . Moreover , compared to expression of non-targetable forms of miRNA target genes , or miRNA loss-of-function mutants , the defects of MIM plants were often weaker . Examples are the absence of an altered floral phenotype in MIM172 plants , which is seen in plants that express a non-targetable version of AP2 under the control of normal regulatory sequences [52] , or the extra-petals phenotype seen in mir164c mutants , but not in MIM164 plants [12] . Another caveat is that some miRNAs might be required for embryonic development , in which case only lines with relatively weak expression of the artificial target mimic might have survived . Such limitations could be overcome by tissue-specific or inducible expression of target mimics . On the other hand , while artificial mimics increase levels of individual miRNA target genes less strongly than what can be achieved by expression of miRNA-resistant forms , mimics have the advantage that they affect all targets simultaneously [18] . Apart from translational regulation [3]–[7] , another possibility for the absence of a clear correlation between phenotypic severity and change in mRNA levels of miRNA targets could be that many miRNAs affect their targets only in a small set of cells . In these cases , assaying expression in whole organs would obscure the effects of miRNA downregulation on mRNA levels . It has recently been suggested that plant miRNAs could also repress the translation of target mRNAs that have only limited sequence complementarity , as often happens in animals [5] . Support for the existence of miRNA binding sites with reduced complementarity in plants comes from an analysis of miR398 , which regulates COPPER SUPEROXIDE DISMUTASE ( CSD ) genes . Certain mutations in the miR398 complementary motif site reduced the effects of miR398 on CSD mRNA , but not on protein levels [3] . We have shown that mimic-like sites , when introduced into the 3′-UTR of a protein-coding gene , not only are active in sequestering the targeted miRNA , but can also reduce protein levels produced by the mRNA linked in cis . This reduction likely occurs at the translational level , since mimic sites are not subject to miRNA-dependent slicing [18] . This observation opens an intriguing scenario in which mRNAs containing mimic-like sites , or possibly other sites with reduced complementarity to miRNAs , are regulated by miRNAs exclusively through translational inhibition . A further level of complexity is added by such sites reducing the effects of an miRNA on other mRNA with a sliceable miRNA targeting motif , similarly to what has been recently proposed in animal systems [65] . Nevertheless , as pointed out before [43] , miRNA overexpression and knockout of major target genes normally produce very similar phenotypes , and these are generally the opposite of what is seen in plants with reduced activity of the miRNA . These observations are supported by our finding of extensive similarities between phenotypes caused by target mimics and by expressing resistant forms of individual targets . We conclude that , at least for the instances in which developmental defects could be observed , target genes with extensive complementarity likely account for the majority of miRNA effects , but that in certain cases targets regulated solely through translational inhibition via diverged target sites might be important as well .
Plants were grown on soil in long days ( 16 h light/8 hours dark ) under a mixture of cool and warm white fluorescent light at 23°C and 65% humidity . The se-1 , ago1-27 , and hyl1-2 and dcl1-100 mutants have been described [66]–[69] . MIM834 plants were grown on MS media plates supplemented with 1% sucrose for 14 days in long days at 23°C . Plants overexpressing viral proteins Hc-Pro , P0 , P19 and P21 were a kind gift from the Carrington lab . Artificial target mimics were generated by modifying the sequence of the IPS1 gene [18] . All target mimics constructs were placed behind the constitutive CaMV 35S promoter in the pGREEN vector conferring resistance to BASTA [70] . For the MYB33:GUS reporter , a MYB33 genomic fragment was PCR amplified , cloned into the TOPO-PCR8 Gateway vector ( Invitrogen ) , and recombined through LR clonase reaction into pGWB433 [71] to generate a GUS translational fusion . The MIM159 construct was introduced into three independent MYB33-GUS T2 lines . A MIM159 site was placed in the 3′-UTR of a triple-EYFP sequence linked to a fragment encoding a nuclear localization signal ( NLS ) and driven by a CaMV 35S promoter . Constructs were introduced into A . thaliana ( accession Col-0 ) plants by Agrobacterium tumefaciens-mediated transformation [72] . Nine-day-old seedlings from three independent T2 lines for all the GUS reporter backgrounds were fixed in acetone 90% . GUS activity was assayed as described [73] . Total RNA was extracted from 11-day old seedlings and 30-day old inflorescences ( 47 days for the MIM172 lines ) , using TRIzol Reagent ( Invitrogen ) . For dcl-100 , 13-day old seedlings were collected , to obtain a similar developmental stage compared to the other plants . For real time RT-PCR , two biological replicates with tissue pooled from 8 to 10 plants were assayed from two independent MIM lines per miRNA family or subfamily . Complementary DNA was produced with the RevertAid First Strand cDNA Synthesis Kit ( Fermentas ) , using as starting material 4 µg of total RNA that had been treated with DNase I ( Fermentas ) . PCR was carried out in presence of SYBR Green ( Invitrogen ) and monitored in real time with the Opticon Continuous Fluorescence Detection System ( MJR ) . Oligonucleotide primers are given in Table S2 . Small RNA blots were performed on the same RNA used as template for real time RT-PCR , with DNA oligonucleotides as probes . Proteins were extracted from four MIM834 lines using a Tris buffer ( 50 mM Tris pH 7 , 5; 150 mM NaCl; 1 mM EDTA; 10% [v/v] Glycerol; 1 mM DTT; 1 mM Pefablock and 1 complete protease inhibitor cocktail [Roche] ) . Protein concentration was measured using a commercial Bradford assay ( BioRad ) . 50 µg of raw protein extract per sample were resolved on an 8% acrylamide gel . Blotting and antibody incubation were performed as described [5] , except that the secondary antibody was incubated for 8 hours at 4°C . Two biological replicates from 4 independent lines were analyzed . Band intensity was measured using the ImageJ software ( http://rsbweb . nih . gov/ij/ ) .
|
MiRNAs are small RNA molecules that play an important role in regulating gene function , both in animals and in plants . In plants , miRNA target mimicry is an endogenous mechanism used to negatively regulate the activity of a specific miRNA family , through the production of a false target transcript that cannot be cleaved . This mechanism can be engineered to target different miRNA families . Using this technique , we have generated artificial target mimics predicted to reduce the activity of most of the miRNA families in Arabidopsis thaliana and have observed their effects on plant development . We found that deeply conserved miRNAs tend to have a strong impact on plant growth , while more recently evolved ones had generally less obvious effects , suggesting either that they primarily affect processes other than development , or else that they have more subtle or conditional functions or are even dispensable . In several cases , the effects on plant development that we observed closely resembled those seen in plants expressing miRNA–resistant versions of the major predicted targets , indicating that a limited number of targets mediates most effects of these miRNAs . Analyses of mimic expressing plants also support that plant miRNAs affect both transcript stability and protein accumulation . The artificial target mimic collection will be a useful resource to further investigate the function of individual miRNA families .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"discovery",
"genetics",
"and",
"genomics/functional",
"genomics",
"plant",
"biology/plant",
"growth",
"and",
"development",
"genetics",
"and",
"genomics/plant",
"genetics",
"and",
"gene",
"expression"
] |
2010
|
A Collection of Target Mimics for Comprehensive Analysis of MicroRNA Function in Arabidopsis thaliana
|
Modulation of interactions among neurons can manifest as dramatic changes in the state of population dynamics in cerebral cortex . How such transitions in cortical state impact the information processing performed by cortical circuits is not clear . Here we performed experiments and computational modeling to determine how somatosensory dynamic range depends on cortical state . We used microelectrode arrays to record ongoing and whisker stimulus-evoked population spiking activity in somatosensory cortex of urethane anesthetized rats . We observed a continuum of different cortical states; at one extreme population activity exhibited small scale variability and was weakly correlated , the other extreme had large scale fluctuations and strong correlations . In experiments , shifts along the continuum often occurred naturally , without direct manipulation . In addition , in both the experiment and the model we directly tuned the cortical state by manipulating inhibitory synaptic interactions . Our principal finding was that somatosensory dynamic range was maximized in a specific cortical state , called criticality , near the tipping point midway between the ends of the continuum . The optimal cortical state was uniquely characterized by scale-free ongoing population dynamics and moderate correlations , in line with theoretical predictions about criticality . However , to reproduce our experimental findings , we found that existing theory required modifications which account for activity-dependent depression . In conclusion , our experiments indicate that in vivo sensory dynamic range is maximized near criticality and our model revealed an unanticipated role for activity-dependent depression in this basic principle of cortical function .
Cortical neuronal network dynamics shift among myriad states to cope with the changing needs of the organism [1–3] . Strikingly different cortical states are observed during different behaviors such as sleep [4] , wakeful resting [5] , active movement [6] , or vigilant attention [7] . Externally-imposed manipulations of interactions among cortical neurons , like neuromodulators [7–9] , anesthetics [10–12] , and other drugs [13 , 14] , also alter the cortical state . Which cortical states are optimal for gathering information about the world through sensory input ? Answers to this question are only beginning to be understood . For example , previous studies have shown that changes in cortical state impact sensory adaptation [5] , variability of cortical response [9 , 12 , 15 , 16] , and the ability to track fast stimulus changes [12 , 17] . Here we focused on the ability of cortical neuronal networks to distinguish a wide range of stimulus intensities , i . e . sensory dynamic range . We sought to delineate how sensory dynamic range depends on cortical state . To meet this goal , we took advantage of changes in cortical state that occur naturally [15 , 18] without experimental control and we also imposed changes in cortical state by tuning cortical inhibitory interactions [19] . Our approach was motivated , in part , by theory [20–22] and in vitro experiments [19] which point to a potential general principle governing cortical dynamic range . They proposed that dynamic range is maximized by tuning the cortex to operate at criticality . Criticality is a boundary regime separating two distinct regimes of cortical state [23 , 24] . On one side of the critical boundary , the ‘subcritical’ cortical state is characterized by asynchronous population activity , low firing rates , and low sensitivity to stimuli . On the other side , the ‘supercritical’ cortical state is marked by large-scale , coordinated population activity and tends to be hyperexcitable in response to stimulation . Cortical dynamic range is thought to be low in the subcritical state due to insensitivity to weak stimuli . In contrast , existing theory suggests that dynamic range is low in the supercritical regime because the system tends to saturate with most neurons in the network firing at high rates , even without external input . Criticality is thought to be optimally balanced between these extremes , able to detect weak stimuli without saturating . However , this potentially fundamental relationship between criticality and sensory dynamic range has not been tested in an intact sensory system . Indeed , the theory may be irrelevant because in vivo cortical networks never reach the saturated firing regime that has been theoretically shown to be responsible for low dynamic range in the supercritical state . Synaptic depression and other mechanisms serve to prevent such saturated firing . Thus , it remains unclear if in vivo sensory dynamic range will indeed be highest when the cortex operates near criticality . Here we directly measured the in vivo relationship between cortical state and somatosensory dynamic range in the rat whisker system . We found that dynamic range is highest in cortical states that exhibit signs of criticality . However , our experimental observations were not well-explained by existing theories , particularly in the experimentally observed supercritical regime . To account for our experimental results we used a model with strong activity-dependent depressive effects , thus avoiding the saturated response in the supercritical regime . Thus , we conclude that , for reasons not anticipated by previous theory , in vivo sensory dynamic range is maximized near criticality .
First , we parameterized the cortical state based on the prevalence of different spatiotemporal scales of population spiking activity . Our approach accounts for the relative importance of diverse scales , avoiding bias for any particular scale . Motivated by previous studies of spatiotemporal cascades of population activity called ‘neuronal avalanches’ [25–27] , we began by making a population MUA spike count time series including spikes recorded on all electrodes . Then , ‘avalanches’ were defined as periods of time when the MUA spike count exceeded a threshold ( Fig 2A ) . We note that our results were robust to variation ( up or down by a factor of 2 ) in the choice of threshold and time bin duration ( S1 and S2 Figs ) . The ‘size’ of each avalanche was defined as the total number of spikes occurring during the avalanche . To determine the prevalence of different spatiotemporal scales , for each recording , we examined the distribution of avalanche sizes ( Fig 2B and 2D ) . Examining avalanche size distributions over all of our experiments , we found that a continuum of different network states occurred ( Fig 2D ) . At one end of the continuum , distributions were bimodal , indicating that large-scale avalanches played a prominent role in the cortex dynamics . This situation often occurred for pharmacologically reduced inhibition ( Fig 2B and 2C ) . At the opposite end of the continuum , we observed cortical states in which small scales were dominant , often occurring when inhibition was enhanced ( Fig 2B and 2C ) . The cortical state varied continuously between these extremes ( Fig 2C ) . Near the middle of the continuum , we observed highly diverse avalanches with heavy-tailed distributions [23 , 28] of avalanche size , close to a power-law distribution with exponent -1 . 5 ( Fig 2D ) . To quantitatively index the observed continuum of cortical states , we employed the parameter κ , which measured the deviation between the observed avalanche size distribution and a power law with exponent -1 . 5 , as in previous work [11 , 19 , 27] . In brief , large κ entailed a cortical state with strongly coordinated population activity , commonly sweeping across the entire recording area ( Fig 2E ) . For small κ , population activity was weakly coordinated , typically confined to small spatial extents ( Fig 2E ) . Separating these extremes , the cortical state with κ = 1 exhibited more diverse population activity with power law distributed spatiotemporal scales . Power law avalanche size distributions have additional significance because they are predicted to occur in a specific cortical state called ‘criticality’ , as discussed in the introduction section . The particular power law exponent -1 . 5 is associated with a particular type ( universality class ) of criticality , namely , directed-percolation [29] and has also been studied in other excitable networks [30] . The degree of correlations among cortical neurons plays an important role in population coding [31] and cortical state [1] . Our second approach for assessing the cortical state was based on pairwise correlations of MUA recorded at different electrodes . For this , we created MUA spike count time series for each electrode , excluding the times when whiskers were stimulated . Then , we computed the Pearson correlation coefficient between all pairs of electrodes . We found that correlations were closely related to our state index κ . The distribution of pairwise correlation coefficients was broadest for states near κ = 1 ( Fig 3A and 3B ) , which reflects the diversity of avalanche sizes that occur in such states . States with either small or large κ had relatively narrow distributions of correlations with decreased or increased average correlations , respectively ( Fig 3A and 3B ) . The average pairwise correlation of the population increased sigmoidally as κ is increased ( Fig 3C ) . This demonstrates that the state with κ = 1 lies at the boundary separating two distinct dynamical regimes–one with low correlations , the other with high correlations . Our ultimate goal was to determine how cortical somatosensory dynamic range depends on cortical state . For this , dynamic range was calculated based on average cortical neural response to a range of whisker stimulus intensities ( Fig 4 ) . We defined neural response to be the MUA spike rate during the 100 ms following stimulus onset ( Figs 1 and 4A ) . We defined the stimulus to be the average whisker speed during the 100 ms following stimulus onset ( Figs 1 and 4B ) . Repeated identical puff pressures generally resulted in different whisker motion . Therefore , we parameterized the stimulus based on measurements of the actual whisker speeds for each puff . Whisker speeds ranged from 0 to about 30 mm/s . For cortical states with small κ , the response curves tended to rise gradually with increasing whisker speed ( Fig 4C ) . For states with large κ , the response curve tended to rise sharply and saturate for a relatively small whisker speed ( Fig 4C ) . Dynamic range was defined based on the range of whisker speeds over which the response increased from 10% to 90% of the response range ( Fig 4D , inset ) . The main result of our work is that dynamic range was low in experiments with either low κ or high κ; dynamic range was maximized for cortical states with κ≈1 ( Fig 4D ) . We remind the reader that κ is based solely on ongoing activity; periods of stimulus-evoked activity are excluded when computing κ . Comparing dynamic range ( Fig 4D ) to correlations ( Fig 3C ) , our results establish that cortical dynamic range is maximized for cortical states with an intermediate degree of correlations . In the model , we obtained stimulus-response curves and dynamic range trends similar to those we observed in our experiments ( Fig 4E ) . To obtain this match , we had to limit the range of stimulus intensities to be below 10−3 stimulus driven spikes per model time step . As discussed further below , further increases in stimulus intensity resulted in a further rise in the response curve and disagreement between the model and experiment .
Our findings offer a specific answer to a long standing debate concerning what degree of correlations among neurons is optimal for sensory information processing [31–33] . If correlations are too strong—many neurons firing synchronously—then coding is redundant and metabolically inefficient . At the other extreme , sufficiently weak correlations may compromise the robustness of information transfer among cortical circuits . Thus , functionally effective correlations must lie between these extremes , but pinpointing the optimal level of correlations has been an elusive goal . In the context of sensory dynamic range , our results demonstrate that the specific intermediate level of correlations that coincides with power law distributed avalanches ( κ = 1 ) is optimal . Our study was motivated by predictions of maximized dynamic range at criticality based on pioneering analytical and computational studies [20–22 , 34] . At first glance , these previous predictions appear to agree with our main findings here . However , taking a closer look , we found that these previous models and theories did not explain our experimental observations . The discrepancies were in the putative supercritical regime ( κ>1 ) , experimentally observed when inhibition was suppressed . In this case , we observed large bursts of synchronous spiking activity occurring at irregular intervals , emerging from a mostly inactive baseline activity ( red , Fig 2A ) . In contrast , in the supercritical regime of most previously studied models , ongoing activity manifests as persistent activity with nearly all neurons active at all times , with no quiet periods and no synchronous bursts ( Fig 5A ) . We found that a simple way to modify previous models to produce more realistic , bursty dynamics was to introduce activity dependent depression–spiking probability was reduced in proportion to how may spikes occurred in the recent past ( see Materials and Methods ) . This naturally resulted in large bursts of population activity separated by times of relative silence ( Fig 2E and Fig 5A ) , as seen experimentally when inhibition was reduced . Without such depressive adaptation , our model produced sustained , saturated activity in the supercritical regime and dynamic range was maximized near criticality like in previous models ( Fig 5A and 5B ) . Including depressive adaptation dramatically altered the shape of the stimulus response curve compared to that of a model without activity dependent depression ( Fig 5B and 5C ) . In fact , if the full range of stimuli studied in previous models was considered , including those large enough to activate a large fraction of the network , then dynamic range was no longer maximized at criticality , as shown in Fig 5C . However , such large stimuli are not relevant in real sensory systems; even the most intense whisker stimulation does not result in a neural response that approaches the system size , i . e . all neurons firing . Thus , the most plausible comparison to our experiments was to exclude the large-stimulus section of the response curve . This limitation leads to response curves ( Fig 4E ) which match well with our experimental observations ( Fig 4C ) and , most importantly , recovers the result that dynamic range is maximized near criticality . In conclusion , our results indicate that a different mechanism than previously predicted is responsible for the experimental observation of peak somatosensory dynamic near criticality in vivo . A natural question arises due to the fact that activity dependent depression dramatically changes the nature of network dynamics in the supercritical regime . Does activity-dependent depression change the nature of the phase transition; do we expect a continuous phase transition or some other type of phase transition ? We leave this question to be answered by future theoretical work , but we speculate , based on the following reasoning , that the phase transition remains continuous . The activity-dependent depression has no effect on the model dynamics if spike rates are sufficiently low ( < 1 spike per 80 time steps ) . Indeed , in the subcritical regimes where spike rates are relatively low , the stimulus response curves in Fig 5C ( with depression ) are not significantly different than those in Fig 5B ( without depression ) . Since the tipping point of the phase transition is close to this low activity regime , it is likely that a continuous phase transition remains continuous when the model includes depression . This speculation is partially supported by the fact that our model produces power-law distributed avalanches ( Fig 2G ) when inhibitory modulation is near 1 . Such power-laws are expected for continuous phase transitions . Finally , our findings highlight a promising hypothesis for future research; changes in cortical state due to changes in behavioral context [1] may tune sensory dynamic range to suit the needs of the organism . For example , a highly focused task may benefit from a state with lower dynamic range , away from criticality . In contrast , a critical cortical state with high dynamic range may be optimal when vigilance or readiness for unknown input is important . Confirmation of this hypothesis would establish a general principle of sensory information processing: sensory dynamic range can be optimized by tuning the cortical state and maximized specifically in the critical cortical state .
All procedures were carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by University of Arkansas Institutional Animal Care and Use Committee ( protocol #12025 ) . We studied adult male rats ( n = 13 , 328±54 g; Rattus Norvegicus , Sprague-Dawley outbred , Harlan Laboratories , TX , USA ) . Anesthesia was induced with isoflurane inhalation and maintained with urethane ( 1 . 5 g/kg body weight ( bw ) dissolved in saline , intraperitoneal injection ( ip ) ) . Dexamethasone ( 2 mg/kg bw , ip ) and atropine sulphate ( 0 . 4 mg/kg bw , ip ) were administered before performing a 2 mm x 2 mm craniotomy over barrel cortex ( 1 to 3 mm posterior from bregma , 5 to 7 mm lateral from midline ) . Extracellular voltage was recorded using 32-channel microelectrode arrays . The electrode arrays were comprised of 8 shanks with 4 electrodes per shank , 200 μm inter-electrode distance , 400 μm inter-shank distance ( A468-5 mm-200–400-177-A32 , NeuroNexus , MI , USA ) . Each shank was made of silicon and each electrode contact was made of iridium . Shanks were 50 μm x 15 μm in cross section . Electrode impedances were approximately 1 MΩ at 1 kHz . Insertion depth was 650 μm , centered 2 mm posterior from bregma and 6 mm lateral from midline . Voltages were measured with respect to an AgCl ground pellet placed in the saline-soaked gel foams , which protect the exposed tissue surrounding the insertion site . Voltages were digitized with 30 kHz sample rate ( Cereplex + Cerebus , Blackrock Microsystems , UT , USA ) . Recordings were filtered between 300 and 3000 Hz and thresholded at -3 SD to detect multi-unit activity ( MUA ) . All whiskers were trimmed except 2–4 whiskers from rows A-C and arcs 1–4 . A computer-automated , pressure-controlled air puff ( 1 s duration ) was used to deliver 10 different puff intensities , each repeated 20 times in pseudorandom order at 7 s intervals . As shown in Fig 1 and previously described [35] , two-dimensional ( rostrocaudal and mediolateral ) multi-whisker motion was measured with millisecond , micron precision using two line cameras ( LC100 , Thorlabs Inc , NJ , USA ) . Response curves were based on the speed of the whisker which evoked the largest MUA neural response , which we call the dominant whisker . Up to nine 20 min recordings were conducted with each rat . First , three recordings were performed with no direct manipulation of inhibition ( n = 32 , indirect effects may be imposed by anesthetics [36] and atropine sulfate ) . Then , three recordings were performed with a drug topically applied via gel foam pieces soaked in saline mixed with drug . Finally , three wash experiments were performed with drug-free gel foams . Three drug conditions were studied ( one condition per rat ) : 1 ) 20 μM muscimol ( 6 rats , 15 recordings ) , 2 ) 20 μM bicuculline methiodide ( 4 rats , 10 experiments ) , 3 ) 40 μM bicuculline methiodide ( 3 rats , 8 experiments ) . The wash condition for bicuculline typically returned to activity similar to that measured in pre-drug experiments , but this was not the case for muscimol . The model was comprised of N = 1000 binary , probabilistic , integrate-and-fire neurons . At each time t , the state si ( t ) of neuron i was 0 ( quiescent ) or 1 ( firing ) . At each time there was a probability pext that each neuron would fire due to external input and a probability pi ( t ) that it would fire due to input from other neurons Ii ( t ) , pi ( t ) ={1forIi ( t ) >1Ii ( t ) for0≤Ii ( t ) ≤10forIi ( t ) <0 , Ii ( t ) = ( ∑j≠iWijsj ( t−1 ) ) 1hi ( t ) , where hi ( t ) depends on recent firing history hi ( t ) =∑τ=t−Ttsi ( τ ) . In cases where this sum was zero , we set h to 1 . Note that T plays an important role determining the character of the ongoing network dynamics as well as the shape of the stimulus response curves presented in Results ( see Fig 5 ) . For the results shown in Figs 2 , 3 , 4 and 5C , we set T = 80 ms ( assuming that one model time step takes 1 ms ) to obtain good qualitative fit with observed experimental results . For the results shown in Fig 5B , we set T = 0 , i . e . there was no history-dependent depression . The default synaptic weight matrix W is constructed as follows . First , all entries are drawn from a uniform distribution [0 , 1] . Second , 20% of neurons are designated as inhibitory and the corresponding columns of W are multiplied by -1 . Third , all entries are multiplied by a constant to enforce that the largest eigenvalue of W is 1 . This third step ensures that the network operates at criticality [21] . Thus , the network topology is all-to-all coupling , but with non-uniform coupling strength . To model pharmacological manipulation of inhibition we multiply all the negative entries of W by a constant ranging from 0 ( model of strong GABA antagonist ) to 3 ( model of strong GABA agonist ) . This is the quantity labeled ‘inhibitory modulation’ in Figs 2 , 3 , 4 and 5 . These manipulations change the largest eigenvalue of W , thus pushing the system away from criticality . To simulate the onset of sensory stimulation , pext undergoes a step increase . The pre-stimulus low rate ( pext = 5 × 10−6 ) resulted in 5 externally-driven spikes per second for the entire network , assuming that one model time step was 1 ms . The during-stimulus high rate was fixed during a single trial , but varied across trials to model different intensities of sensory stimulation . High rates ranged from pext = 5 × 10−6 to pext = 1 × 10−3 , i . e . generating 5 to 1000 externally-driven spikes per second for the entire network . Each level of pext we repeated 20 times , just as each experimental stimulus intensity was repeated 20 times . We note that increasing pext to values approaching 1 does result in a saturation of network activity , which changes the shape of the stimulus-response curves and changes dynamic range . However , such highly saturated response-curves were not observed in our experiments . The chosen range of pext , better fits our experimental observations . MUA spike count time series were based on time bins of duration DT = 7 . 5±3 ms ( mean±SD ) , depending on the number of good electrodes , signal/noise , and baseline spike rates for each animal . The threshold for avalanche detection was TH = 11±5 spikes per time bin . For experiments with overall higher MUA spike rates , presumably due to differences in experimental details like electrode quality , we chose smaller DT and smaller TH . However , this experiment-specific tuning was not necessary to support our conclusions . Indeed , we found that changes in the choice of time bin durations ( in the range 5 ms > DT > 20 ms ) and avalanche thresholds ( in the range 5 > TH > 20 ) can cause small changes in the shape of the avalanche distribution ( S1 Fig ) and , consequently , small changes in κ . However , we emphasize that our primary conclusion–peak dynamic range near κ = 1 –was robust to such parameter variations ( S2 Fig ) . Deviation from the reference power-law ( -1 . 5 exponent ) was quantified with κ , which is a previously developed non-parametric measure with similarities to a Kolmogorov-Smirnov statistic [19]; κ equals 1 plus the sum of 10 differences ( logarithmically spaced ) between the observed avalanche size distribution ( recast as a cumulative distribution ) and a perfect power-law ( in cumulative form ) . In the summary plots of κ versus correlation and κ versus dynamic range , experiments were grouped according to their κ values into 13 equally spaced κ bins . For dynamic range calculations , each point in the response curve was the average MUA response for a range of whisker speeds . The binning of whisker speeds was based on 10 equally spaced values spanning the range of observed speeds . Finally , the response curve was fit with a sigmoid function , f ( x ) =Rmax1+e−b ( x−c ) +Rmin where Rmin was defined as the ongoing spike count per time bin and the constants Rmax , b , and c were fitting parameters . This fit function was used to compute dynamic range , as defined in previous studies [19 , 20] . First , two stimulus levels , S10 and S90 are defined as those stimuli which give rise to response R10 and R90 as illustrated in Fig 4D ( inset ) . R10 is defined as the response level Rmin+0 . 1 ( Rmax- Rmin ) and R90 is defined as the response level Rmin+0 . 9 ( Rmax- Rmin ) . Finally , we define dynamic range as Δ=10log10S90S10 Analysis of model data paralleled the experimental data analysis with a few exceptions . MUA spike count time series were based on time bins of duration 1 time step . The threshold for avalanche detection was 10 spikes per time bin . For computing stimulus-response curves , the response was defined as the total number of spikes from the entire network during the first 200 time steps following onset of stimulation ( increase in pext ) . For dynamic range calculations based on model data , we did not fit the response curves with a sigmoid because they were less noisy than the experimental response curves . The correlation coefficients in Fig 3 were computed in a way to mimic the experiments , as follows . First , the 1000 model neurons were broken up into 32 groups , like the 32 electrodes in experiments . Then , a spike count timeseries was created for each group . Finally , all pairwise zero-lag Pearson correlation coefficients were computed and averaged together .
|
When many simple parts interact , the collective behavior of the whole can be astonishingly complex . A particularly striking example is our capacity for sensory perception , which results from the collective interactions of billions of relatively simple neurons . Another example is found in physical systems which undergo a phase transition–for example , liquid water turning to solid ice . When collective interactions among the water molecules are changed , the system transitions from a disordered state ( liquid ) to an ordered state ( crystalline solid ) . At the tipping point of a critical phase transition , i . e . at criticality , physical systems exhibit very complex behavior . In this study , we show that phase transitions may occur in the cerebral cortex changing the neural activity from a disordered to an ordered state . Moreover , this neural phase transition may be intimately linked with sensory perception . We experimentally manipulate the interactions among neurons and show that sensory dynamic range is maximized when the cerebral cortex of a rat is closest to criticality .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality
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Ebola and Marburg viruses ( family Filoviridae , genera Ebolavirus and Marburgvirus ) cause haemorrhagic fevers in humans , often associated with high mortality rates . The presence of antibodies to Ebola virus ( EBOV ) and Marburg virus ( MARV ) has been reported in some African countries in individuals without a history of haemorrhagic fever . In this study , we present a MARV and EBOV seroprevalence study conducted amongst blood donors in the Republic of Congo and the analysis of risk factors for contact with EBOV . In 2011 , we conducted a MARV and EBOV seroprevalence study amongst 809 blood donors recruited in rural ( 75; 9 . 3% ) and urban ( 734; 90 . 7% ) areas of the Republic of Congo . Serum titres of IgG antibodies to MARV and EBOV were assessed by indirect double-immunofluorescence microscopy . MARV seroprevalence was 0 . 5% ( 4 in 809 ) without any identified risk factors . Prevalence of IgG to EBOV was 2 . 5% , peaking at 4% in rural areas and in Pointe Noire . Independent risk factors identified by multivariate analysis were contact with bats and exposure to birds . This MARV and EBOV serological survey performed in the Republic of Congo identifies a probable role for environmental determinants of exposure to EBOV . It highlights the requirement for extending our understanding of the ecological and epidemiological risk of bats ( previously identified as a potential ecological reservoir ) and birds as vectors of EBOV to humans , and characterising the protection potentially afforded by EBOV-specific antibodies as detected in blood donors .
Marburg and Ebola viruses ( family Filoviridae , genera Marburgvirus and Ebolavirus ) cause severe Viral Haemorrhagic Fever ( VHF ) in humans , with a high fatality rate in symptomatic cases [1 , 2 , 3] . They appear to infect and persist in some species of fruit bats , that may serve as natural reservoirs for these viruses [4 , 5 , 6 , 7 , 8 , 9] . Non-human primates have been a source of human infections however they are not thought to be the reservoir as they develop severe , fatal illness when infected [10] . The genus Marburgvirus currently consists of a single species Marburg marburgvirus , of which the recognised members are Marburg virus ( MARV ) and Ravn virus [11 , 12] . The first cases of Marburg haemorrhagic fever ( MHF ) occurred in Germany and Serbia ( in the former Yugoslavia ) in 1967 and were linked to laboratory work using tissues dissected from African green monkeys imported from Uganda [13 , 14] . The first major outbreak of MHF occurred in Democratic Republic of the Congo ( DRC , formerly Zaire ) , from 1998 to 2000 [15 , 16] . A second , even more devastating outbreak occurred in Angola in 2004–2005 with a reported case fatality rate ( CFR ) of almost 90% [17 , 18] . In Nigeria and DRC , seroprevalence studies identified antibodies to MARV in less than 2% of apparently healthy people selected in general population in Nigeria and amongst healthcare workers and general population in DRC [19 , 20] . In the Central African Republic ( CAR ) , antibodies to MARV were observed in both Pygmy ( 0 . 7–5 . 6% ) and non-Pygmy ( 0 . 0–3 . 9% ) populations [21] . An African serosurvey of VHF ( Crimean-Congo haemorrhagic fever , Rift Valley fever , Lassa , Hantaan , EBOV and MARV ) , conducted in the 1980s in the Central African general population , reported low prevalence values: 0 . 3% in N’Djamena ( Tchad ) , 2 . 6% in Bioco Island ( Equatorial Guinea ) and , in the Republic of Congo , 3% in Pointe-Noire but no seropositive sera to MARV detected in people in Brazzaville [22] . To date , no case of MHF has been reported in the Republic of Congo . The genus Ebolavirus includes five species: Zaire ebolavirus ( Ebola virus: EBOV ) , Sudan ebolavirus , Taï Forest ebolavirus , Reston ebolavirus and Bundibugyo ebolavirus [11 , 12] . The genus Ebolavirus is primarily African in origin , with the exception of the species Reston ebolavirus which is Asian [23] . EBOV was first identified in 1976 , in Southern Sudan [24] and in the North of DRC [25 , 26] . Since then , outbreaks have been described in several other African countries ( the Republic of Congo , Ivory Coast , DRC , Gabon , Sudan , Uganda , Guinea , Sierra Leone and Liberia ) [1 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34] , with reported ( CFR ) frequently exceeding 50% amongst symptomatic patients . In the Republic of Congo where the current study took place , several outbreaks of ( Zaire ) EBOV were reported in the North of the country ( 2001 in Olloba-Mbomo , 2002 in Kéllé , 2003 in Mbandza-Mbomo ) , with 75 to 89% reported fatality rates [35 , 36 , 37] . In previous seroprevalence studies , amongst 1 , 517 apparently healthy persons tested in five regions of the Cameroon , a positive rate of 9 . 7% was found with highest rates amongst Pygmies ( 14 . 5% ) , young adults ( 11 . 6% ) and rain forest farmers ( 13% ) [38] . In CAR , the seropositivity rate was 5 . 3% and Pygmies appeared to have a higher seroprevalence than non-Pygmies ( 7% versus 4 . 2% ) [21] . During the 1995 outbreak of Ebola virus disease in the region of Kikwit ( Democratic Republic of Congo ) , villagers had a greater chance of exposure ( 9 . 3% ) than forest and city workers ( 2 . 2% ) [39] . In a large study conducted in 220 villages in Gabon ( 4 , 349 individuals enrolled ) , antibodies against EBOV were detected in 15 . 3% of those tested , with the highest levels in forested regions ( 17 . 6% and 19 . 4% respectively in forest and deep forest areas ) , suggesting the occurrence of mild or asymptomatic infections [40 , 41] . In the Republic of Congo , seroprevalence values reported in the late 1980's were 7 . 8% in Pointe-Noire and 6 . 2% in Brazzaville [22] . In Sierra Leone , in 2006–2008 , among 253 febrile patients negative for Lassa fever and malaria , antibodies against EBOV and MARV were detected in respectively 8 . 2% et 3 . 2% of the samples [42] . In this study , we present an analysis of MARV and EBOV seroprevalence amongst blood donors in the Republic of Congo in 2011 and we report associated risk factors for contact with EBOV .
A MARV and EBOV seroprevalence study was performed in 2011 in the Republic of Congo , using a prospective cohort of blood donors . Field samples for the study were collected from March to July 2011 , in the Republic of Congo ( Fig 1 ) in urban areas ( Brazzaville and Pointe-Noire ) and in rural locations ( Gamboma , Owando , Oyo and Ewo ) . Ewo is the capital of the Department of Cuvette-Ouest , where all previous EBOV outbreaks occurred . This study was performed in collaboration with the Centre National de Transfusion Sanguine ( CNTS ) of Congo; the Virology Laboratory UMR_D 190 "Emergence des Pathologies Virales" ( Aix-Marseille University , IRD French Institute of Research for Development , EHESP French School of Public Health ) , Marseille , France and the Virology Laboratory of Bernhard-Nocht-Institut für Tropenmedizin , Hamburg , Germany . Blood donors of both genders were included . The criteria for enrollment were eligibility for blood donation and provision of informed consent without specific limiting factors . The age of blood donors ranged from 18 to 65 years . Serum samples for serological analyses were collected in collaboration with the CNTS . Informed , written consent was obtained from each person enrolled in the study and the consent procedure was approved by the Congolese Research in Health Sciences Ethics Committee ( N° 00000065 DGRST/CERSSA ) . A structured questionnaire was administered face-to-face , in the official language ( French ) and/or in national languages ( Lingala or Kutumba ) . All questionnaires were completed by the medical personnel conducting the interviews . The following data were collected: socio-demographic circumstances , domestic characteristics ( age , gender , occupation , residence , size of household , type of house , water resource , usage of mosquito nets ) , environmental characteristics ( animal contacts and/or consumption ) , travel outside the country during their lifetime , history of haemorrhagic fever ( in family or personal ) . Venous blood samples were drawn using two 4 mL plain tubes which were immediately centrifuged . Sera were kept at -80°C until use . Aliquots were inactivated at 56°C for 30 min and transferred to the Virology Laboratory of the Bernhard-Nocht-Institut ( BNI ) in Hamburg for serological assays . Serum IgG antibodies specific for EBOV and MARV were titrated using indirect double-immunofluorescence microscopy assays which were recorded as positive if reciprocal end point titres were ≥20 [43] . Antigens consisted of acetone-fixed Vero cells infected with Ebola virus ( strain ATCC 1978 ) or Marburg virus ( strain Popp 1967 ) . Cultivation of the viruses was carried out in an approved and compliant BLS4 laboratory in BNI . Serum samples were tested as serial twofold dilutions from 1:20 to ≥ 1:160 and antibodies were detected with a Fluorescein isothiocyanate ( FITC ) labelled anti-human IgG antibody-conjugate . Cell smears were counterstained with specific anti-Ebola or anti-Marburg nucleocapsid monoclonal antibodies ( provided by the Institute of Virology , University of Marburg ) using a rhodamine-anti-mouse conjugate as secondary antibody [44] . As positive controls for Ebola virus , we used ( i ) a human polyclonal antibody for the first IF with a titre at 2 , 560 ( IgG ) and ( ii ) a mouse monoclonal antibody with a titre at 1 , 280 ( IgG ) . As positive controls for Marburg virus , we used ( i ) a human polyclonal antibody for the first IF with a titre at 1 , 280 ( IgG ) and ( ii ) a mouse monoclonal antibody with a titre at 640 ( IgG ) . This “double immunofluorescence” protocol provides a much higher specificity than regular immunofluorescence assays , since only antibodies that detect filoviral antigens in co-localisation with a monoclonal antibody are considered . Statistical analyses were performed using the IBM SPSS statistic 21 software . Analyses aiming at analysing risk factors for seropositivity included univariate , stratified and multivariate analyses . The Fisher’s exact test was used to compare proportions in univariate analysis and the ANOVA test to compare means . The Pearson’s test was used for stratified analysis . All statistical analyses were performed at the 95% confidence level . The association between anti-EBOV IgG seropositivity and risk factors was determined by binary logistic regression analysis . Stratified analysis based on sex , age and area were performed . The significant variables in univariate analysis were entered in the multivariate model . The quality of the multivariate model was assessed with Hosmer-Lemeshow’s test .
IgG to MARV was identified in 0 . 5% of the donors tested ( 4 in 809 ) . Seropositivity could not be significantly associated with any of the risk factors investigated in the individual questionnaire . Antibodies to MARV were detected exclusively in male blood donors from Brazzaville who had all been in contact ( touching and catching ) with rodents ( mice and rats ) , but this is a common feature in out cohort ( >80% of the donors reported such contact ) . Three out of four were students ( median age 23 ) and one ( 55yo ) was an office worker . None had been in contact with ( or had eaten ) forest animals or bats . The titre of IgG in positive donor ranged from 80 to 160 . The overall prevalence of positivity for IgG when tested against EBOV was 2 . 5% ( 20 in 809 ) . It was 1 . 6% ( 8 in 509 ) in Brazzaville , 4% ( 3 in 75 ) in the rural locations ( Gamboma , Owando , Oyo and Ewo ) and 4% ( 9 in 225 ) in Pointe-Noire . Table 3 summarises the association between potential risk factors and anti-EBOV IgG seropositivity using univariate analysis . Amongst the populations studied there was no statistically significant relationship between gender ( p = 0 . 79 ) , age ( p = 0 . 96 ) , travel ( p = 0 . 62 ) , household size ( p = 0 . 39 ) , exposure to rodents ( p = 0 . 63 ) and the presence of IgG to EBOV . However , being a hunter ( p = 0 . 01 ) was a risk factor , whereas the other occupations showed no statistical significance in univariate analysis . The use of simple mosquito nets had a protective effect . Importantly , significant association with bat contact or eating birds ( p values respectively of <0 . 001 and 0 . 01 ) was identified . Seropositivity in age-groups was as follows: 2 . 7% in 18–29yo , 2 . 1% in 30–39yo , 2 . 3% in 40–49yo , 3 . 2% in 50–59yo and 0 . 0% in >60yo , with no evidence for an increase of seroprevalence with age . In the 18–29yo age-group , higher seroprevalence was associated with touching bats ( p<0 . 001 ) . In the second group ( 30–39yo ) , higher seroprevalence was associated with the military profession ( p = 0 . 04 ) . No significant association was found in the other age-groups . No significant association was identified in a stratified analysis by occupation , including military profession . Regarding stratification by areas: in Pointe-Noire , to be a student ( p = 0 . 002 ) or to have exposure to bats ( p<0 . 001 ) was statistically associated with anti-EBOV IgG . Exposure to bats ( p<0 . 001 ) was also found to be a risk factor in Brazzaville . Other variables ( sex , age , household size , type of house , travel risk , exposure to rodents , and consumption of forest animals ) were not significantly associated with the presence of IgG , regardless of the area in which donors lived . Concerning stratification by gender ( S1 Table ) , seropositivity was 2 . 6% in males and 2 . 0% in females . No significant association was identified in females . Amongst the male subpopulation , being a hunter ( p = 0 . 009 ) , having contact with bats ( p<0 . 001 ) or monkeys ( p = 0 . 01 ) , or consuming birds ( p = 0 . 002 ) was statistically associated with EBOV IgG positivity . Other variables ( age , household size , type of house , travel risk , exposure to rodents ) had no statistically significant relationship with the presence of IgG to EBOV . In the multivariate model ( Table 3 ) , the only variables independently associated with Ebola antibody detection were contact with bats ( p<0 . 001 ) and bird consumption ( p = 0 . 04 ) .
Our results imply that in the Republic of Congo , the circulation of Marburg virus occurs at a very low rate without any identified risk factor , but that human exposure to Ebola virus without consequent disease is not infrequent . Living in Pointe Noire or in a rural area , and having contact with bats and birds is associated with a higher risk of exposure to Ebola virus . Unfortunately , little is known about the natural history and biological properties of EBOV antibody in individuals without haemorrhagic fever . Here , we did not observe an increase of seroprevalence with age , which may suggest that , in some individuals , the antibody titre decreases and becomes undetectable with time . This would in turn imply the absence of iterative contacts with Ebola virus antigens or that of a strong antibody response following such secondary antigenic exposure . Similarly , the protection afforded by antibodies detected in blood donors against Ebola virus infection remains completely unknown . How individuals without any history of haemorrhagic fever acquire specific antibodies to EBOV and what are the biological properties of such antibodies ( in particular what is their seroneutralising capacity ) deserve further investigations in African populations .
|
Ebola and Marburg viruses cause haemorrhagic fevers often fatal to humans . Here , we looked for antibodies to Ebola and Marburg viruses ( i . e . , markers of previous contact with these viruses ) in Congolese blood donors with no previous history of haemorrhagic fever . We found serologic evidence for contact with Marburg and Ebola viruses in 0 . 5% and 2 . 5% of blood donors , respectively . The circulation of Marburg virus occurs at a very low rate without any identified risk factor . In contrast , prevalence to Ebola virus was peaking at 4% in rural areas and in Pointe Noire city . Importantly , we identified that contacts with bats and birds constituted two independent environmental determinants of exposure . This study confirms that contact with Ebola virus is not infrequent in Congo and can occur in the absence of haemorrhagic fever . It highlights the requirement for further investigating the role of bats and birds in the ecological cycle of Ebola , and for determining whether asymptomatic contact with Ebola virus can provide subsequent protection against severe forms of the Ebola disease .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Risk Factors Associated with Ebola and Marburg Viruses Seroprevalence in Blood Donors in the Republic of Congo
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DNA polymerase ν ( pol ν ) , encoded by the POLN gene , is an A-family DNA polymerase in vertebrates and some other animal lineages . Here we report an in-depth analysis of pol ν–defective mice and human cells . POLN is very weakly expressed in most tissues , with the highest relative expression in testis . We constructed multiple mouse models for Poln disruption and detected no anatomic abnormalities , alterations in lifespan , or changed causes of mortality . Mice with inactive Poln are fertile and have normal testis morphology . However , pol ν–disrupted mice have a modestly reduced crossover frequency at a meiotic recombination hot spot harboring insertion/deletion polymorphisms . These polymorphisms are suggested to generate a looped-out primer and a hairpin structure during recombination , substrates on which pol ν can operate . Pol ν-defective mice had no alteration in DNA end-joining during immunoglobulin class-switching , in contrast to animals defective in the related DNA polymerase θ ( pol θ ) . We examined the response to DNA crosslinking agents , as purified pol ν has some ability to bypass major groove peptide adducts and residues of DNA crosslink repair . Inactivation of Poln in mouse embryonic fibroblasts did not alter cellular sensitivity to mitomycin C , cisplatin , or aldehydes . Depletion of POLN from human cells with shRNA or siRNA did not change cellular sensitivity to mitomycin C or alter the frequency of mitomycin C-induced radial chromosomes . Our results suggest a function of pol ν in meiotic homologous recombination in processing specific substrates . The restricted and more recent evolutionary appearance of pol ν ( in comparison to pol θ ) supports such a specialized role .
In mammalian cells , a diverse group of DNA polymerases carry out genomic DNA replication and genome maintenance . These include the core enzymes for semi-conservative DNA replication ( pols α , δ , ε and telomerase ) , base excision repair ( pol β ) , mitochondrial DNA replication and repair ( pol γ and Primpol ) , non-homologous end-joining and immunological diversity ( pols λ , μ , θ and terminal-deoxynucleotidyl transferase ) , and DNA damage tolerance by translesion synthesis ( η , ι , κ , ζ , and Rev1 ) . Some of these enzymes have roles in more than one pathway of DNA processing [1 , 2] . The consequences of genetic disruption of an individual DNA polymerase vary widely , ranging from embryonic lethality to no discernable phenotype . Pol ν , encoded by the POLN gene in mammalian cells , is not assigned in the preceding list , and it is the only identified polymerase for which an analysis of knockout animals remains to be described . The catalytic domain of pol ν belongs to the A-family of DNA polymerases , and is related to the pol domain of pol θ ( encoded by mammalian POLQ / Drosophila Mus308 ) [3–6] . Pol θ participates in a pathway of DNA double-strand break repair by alternative end joining [7 , 8] , and consequently defects in Mus308 or POLQ confer hypersensitivity to various DNA damaging agents [8–10] . The function of pol ν is currently uncertain , but several roles have been suggested . For example , it was reported that siRNA-mediated knockdown of POLN sensitizes human cells to DNA crosslinking agents [11 , 12] . Mammalian genomes encode three Mus308 homologs: POLN , POLQ , and DNA helicase HELQ [3 , 13 , 14] . Pol θ possesses both a helicase-like and a DNA polymerase domain while pol ν has only a DNA polymerase domain and HELQ has only a helicase domain . Our phylogenetic analysis of the distribution of these three genes is shown in Fig 1 . POLQ genes are found throughout most of the eukaryotic lineage , in both animals and in plants , and are inferred to be present in the last eukaryotic common ancestor , 1500 million years ago . Even the unicellular green algae Ostreococcus ( the smallest free-living eukaryote ) contains POLQ . HELQ is found in most eukaryotic branches , but not in plants . HELQ orthologs are also present in both euryarchaea and crenarchaea [15] . In contrast , POLN genes are restricted to a much more limited range of genomes , in some metazoan groups . POLN appeared during genomic evolution much more recently than POLQ . The POLN gene arose , either by gene duplication or horizontal transfer , in the last common ancestor of the metazoa , around 600 million years ago . POLN genes are distinguished by characteristic sequence insertions in the polymerase domain ( S1 Fig ) , with the location of insert 3 distinct from that of POLQ [5] . Human pol ν ( 900 amino acid residues ) has a strong strand-displacement activity and low fidelity , with exceptionally efficient insertion of T opposite template G in the steady state [4 , 16 , 17] . Purified pol ν can bypass the major groove DNA lesion 5S-thymine glycol ( Tg ) by inserting A , and bypasses major groove DNA-peptide and some DNA-DNA crosslinks [4 , 18] , but does not bypass other DNA modifications including an abasic ( AP ) site , a cisplatin-induced intrastrand d[GpG] crosslink , a cyclobutane pyrimidine dimer , a 6–4 photoproduct , or minor groove DNA-peptide or DNA-DNA crosslinks [4 , 18] . A unique cavity in the polymerase domain allows pol ν to generate and accommodate a looped-out primer strand [6] . However , it is currently unknown how these unique biochemical properties are implemented in vivo . To help illuminate pol ν function , we examined POLN gene expression patterns in mice , and phenotypes of mice and cells where POLN was disrupted . The results suggest a specific function in germ cells , but do not support a role for pol ν in tolerance of DNA crosslinks .
To explore possible biological functions of pol ν , we established two different Poln knockout mouse models . In one model , the second exon was deleted in mice of C57BL/6J;129Sv background ( Fig 2 and S2 Fig ) . This exon encodes the ATG initiation codon and the first 46 amino acids , including an N-terminal protein domain that is highly conserved in vertebrates [21] . We avoided targeting the first exon , because POLN shares this exon with HAUS3 , an essential gene encoded within the first intron of POLN [21] . In another model , a zinc finger nuclease ( ZFN ) -mediated 4 or 13 bp frameshift was introduced in the DNA polymerase domain of pol ν ( FVB/NCrl background ) ( Fig 3A and S3 Fig ) . ZFN-mediated Poln mutant mice are missing critical parts of the DNA polymerase domain including the highly conserved motifs 5 and 6 ( Fig 3B ) . A universally conserved Asp residue in motif 5 of A-family DNA polymerases ( D804 in pol ν ) that coordinates bivalent metal ions for interaction with an incoming nucleotide , is lost [22] . In all of the engineered mice studied here , a long noncoding RNA gene that overlaps with Poln exons 3–6 remains intact . Both homozygous Poln-deficient mouse models were viable and fertile . Comparing them to their wild-type littermates , no reproductive or developmental differences were observed . Heterozygous Poln Ex2 mutant mouse crosses yielded pups with normal litter sizes and within expected proportions of genotypes and genders ( Table 1 ) . Homozygous Poln knockout mouse crosses for the ΔEx2 , del4 and del13 constructs all yielded normal litter sizes and with normal sized pups at birth . A phenotype analysis , which included a complete gross necropsy , hematology and serum chemistry , full histopathology on all tissues , and survey radiographs was completed with adult ZFN-mediated Poln knockout mice . A comparison between wild-type mice and Poln-deficient mice did not reveal any obvious differences in size , body weight , organ development ( Table 2 ) , or skeletal appearance . Additionally , no significant differences between genotypes were observed in hematology or serum chemistry analysis ( Table 2 ) . Poln+/+ , Poln+/ΔEx2 , and PolnΔEx2/ΔEx2 mice were monitored for over 2 years to assess survival , general health , and tumor incidence . Loss of functional Poln did not affect overall survival compared to wild-type and heterozygous littermate controls ( Fig 4 ) . There were no significant differences in tumor prevalence , multiplicity or distribution of tumor types ( Tables 3 and 4 ) . Non-neoplastic lesions were of types and incidence commonly found in aging laboratory mice , and there appeared to be no pattern of susceptibility related to genotype ( Table 5 ) or sex . All mice had a variety of age-related lesions that were considered incidental and not included in non-neoplastic diagnoses . These included atrophies of testicles , ovaries , uterus , and thymus , mild multifocal lymphoid infiltrates in various organs , and mild glomerulopathy . Similarly , no obvious alterations in lifespan , general health or cause of death were noted in Poln del4 and del13 mice . We previously reported that the POLN transcript is most prominent in adult testis from the human , mouse , and zebrafish [3 , 21] . To quantify absolute transcript levels , we compared expression of Poln , Polq , and the replicative polymerase Pold1 in different mouse tissues . Poln expression was highest in testis by an order of magnitude , much higher than Polq and Pold1 in this organ . However , Poln transcript was almost undetectable in other tissues , in contrast to Polq and Pold1 ( Fig 5A ) . To investigate when Poln starts expressing during testis development , we isolated testes from young male mice of various ages . Poln expression gradually increased and was higher than Polq and Pold1 after 16-days , when the population of spermatocytes in the pachytene stage increases [23] . In contrast , the amounts of mRNA of Polq and Pold1 were not markedly increased during testis development ( Fig 5B ) . Fractions enriched in spermatids and pachytene spermatocytes were prepared by centrifugal elutriation , and Poln expression was higher in the pachytene-enriched fraction ( Fig 5C ) . These data suggested a possible meiosis-related function of pol ν in testis . Testis morphology was normal in Poln knockout mice ( Fig 6A ) , and testes size measurements , histology and breeding experiments described above did not suggest a marked perturbation of spermato- and spermiogenesis . To test for an overt role of pol ν in meiotic recombination , chromosome spreads were stained with SYCP3 ( a component of the synaptonemal complex ) and MLH1 ( a crossover marker ) to assess progression through meiotic prophase I ( Fig 6B ) . MLH1 foci numbers per nucleus were not statistically different between pol ν proficient and Polndel4/del4 mice ( Fig 6C ) . PolnΔEx2/ΔEx2 spermatocytes had one less MLH1 focus per nucleus than controls ( p = 0 . 03 ) ( Fig 6D ) . The results suggest that while pol ν is not essential for crossing over or meiosis , it may be required for efficient crossing over at specific loci , at least in some genetic backgrounds ( see legend to S4 Fig ) We examined the potential role of pol ν in meiotic homologous recombination-associated DNA synthesis . Crossovers , arising by recombination at one meiotic hotspot A3 [24 , 25] , were isolated from sperm of PolnΔEx2/ΔEx2 C57BL/6J x DBA/2J and Polndel4/del4 FVB/NCrl x DBA/2J F1 hybrid mice , and control animals . The crossover frequency at A3 in FVB/NCrl x DBA/2J is half that of C57BL/6J x DBA/2J ( Fig 6E and 6F ) . The A3 locus in FVB/NCrl lacks a high affinity PRDM9 binding site ( S4 Fig ) and , therefore it likely does not receive meiotic DSBs to initiate recombination [26] . As such , the FVB/NCrl x DBA/2J F1 hybrid receives meiotic DSBs on only the DBA/2J allele , rather than at both the C57BL/6J and DBA/2J alleles as in the C57BL/6J x DBA/2J F1 hybrid . Consistent with this model , the A3 locus shows reciprocal crossover asymmetry , an altered distribution of crossovers depending upon their orientation , which is a hallmark of meiotic hotspots with biased DSB formation on only one of the two parental alleles ( S4 Fig ) . A total of 155 , 157 , 185 , 180 crossovers from Polndel4/del4 , Poln+/del4 , PolnΔEx2/ΔEx2 , and Poln+/ΔEx2 males were isolated and mapped . Consistent with reduced MLH1 foci , the Poisson corrected crossover frequency in male PolnΔEx2/ΔEx2 mice was 80% of that in heterozygous littermates ( p = 0 . 04 ) ( Fig 6F ) . The crossover frequency in Polndel4/del4 mutants was not significantly reduced compared to controls ( p = 0 . 09 , Fig 6E ) , but combining both datasets showed a statistically significant ( p = 0 . 01 ) reduction in Poln homozygous mutants to about 80% of the control frequency ( S5 Fig ) . We further examined the so-called ‘crossover refractory zone’ ( CRZ ) at the A3 hotspot in Poln mutant mice . Crossover formation in this 258 bp region of the A3 hotspot is strongly inhibited , even though DSBs are formed there abundantly [24 , 27] . The region contains secondary structure-forming sequence that may inhibit heteroduplex formation [24] . The combined Poln mutant data ( S5 Fig ) showed a statistically significant ( p = 0 . 04 ) 50% reduction in crossover frequency at this region ( 10 of 340 crossovers in knockouts , 21 out of 337 crossovers in controls ) . Whole genome transcriptome analysis by RNAseq was performed to determine whether Poln disruption influences gene expression in testis from PolnΔEx2/ΔEx2 mice . Using rRNA-depleted total RNA , we obtained an average of 65 million reads per sample ( range 53 to 73 million ) with an average mapping rate of 90% to the reference mouse genome . Poln disruption did not perturb overall transcription ( Fig 7A ) . Only a few genes had significantly altered expression , at a false discovery rate ( FDR ) ≤ 0 . 05 . Expression of only four of these genes was increased more than 2-fold ( S1 Table ) , comprising two that encode proteins ( Lgi2 and 9330182L06Rik ) , and two specifying noncoding RNAs ( BB283400 and Speer4cos ) . Expression of only two genes was reduced more than 2-fold , Egfem1 and Poln itself . Because these genes have no close physical linkage and their functions are unknown , we have not pursued them further at present . As expected , expression of the targeted exon 2 of Poln was not detected in PolnΔEx2/ΔEx2 mice . All other Poln exons showed greatly reduced or absent expression , likely related to the disruption of normal splicing . Haus3 expression was not influenced by deletion of the targeted Poln exon ( Fig 7B ) . We asked whether pol ν influences levels of ionizing radiation-induced DNA damage in testis . This could be the case if pol ν is directly involved in DSB repair ( by analogy with the related pol θ ) or if pol ν is necessary for translesion DNA synthesis of radiation-induced lesions such as thymine glycol ( Tg ) . Pol ν is proficient in bypass of Tg , which blocks the progression of replicative DNA polymerases [4] . A dose dependent increase in γ-H2AX foci ( a surrogate for DNA damage including DSB ) was observed in wild-type , PolnΔEx2/ΔEx2 , and Polq-/- mice ( Fig 8A ) . The number of γ-H2AX foci in the absence of irradiation was higher in Polq-/- mice than in wild-type or PolnΔEx2/ΔEx2 mice . This is consistent with the higher basal level of γ-H2AX foci in pol θ-suppressed human cells [28] . Following irradiation of mice with 1 or 2 Gy , the average γ-H2AX foci numbers in round spermatids of testis sections was measured [29] . After 5 hr , the number of foci was reduced to a similar level in all mice ( Fig 8B ) , irrespective of Poln and Polq status . As a control experiment , blood was taken from killed mice after irradiation and micronuclei were measured in reticulocytes . Frequencies of micronuclei were increased in Polq-/- mice after IR , as previously reported [10 , 30 , 31] . Poln status did not influence the level of micronuclei in reticulocytes ( Fig 8C ) . We also examined PolnΔEx2/ΔEx2 Polq-/- double knockout mice and found that Poln deletion did not further increase the frequency of micronuclei above that in Polq-/- reticulocytes ( Fig 8C ) . Because pol θ can participate in end-joining of DNA breaks produced during immunoglobulin class-switch recombination ( CSR ) [8] , we tested whether pol ν is also involved in CSR . Naïve B cells isolated from the spleens of wild-type and PolnΔEx2/ΔEx2 mice were stimulated for IgM to IgG class switching , and then the fraction of IgG1-positive B cells was measured by flow cytometry . Parallel B-cell cultures were incubated with NU7026 , an inhibitor of DNA-PKcs that increases the proportion of CSR with >1 bp insertion at the junction [8 , 32] . We found that stimulated B cells from Poln-proficient and deficient mice had similar overall frequencies of IgG1 ( 6 . 1% ) . Inhibition of DNA-PKcs increased the frequency of CSR in both genotypes by 2-fold ( 11 . 7% in wild-type , 12 . 5% in PolnΔEx2/ΔEx2 ) ( S2 Table ) , as observed previously with wild-type and Polq-/- mice [8] . The Sμ-Sγ1 junction was then sequenced from 100 clones of each group of IgG1-positive B cells . Insertions of >1 bp at Sμ-Sγ1 junctions were pol θ-dependent [8] , but pol ν-independent ( Fig 9A and 9B ) . We also performed qPCR analysis of transcript expression in splenic B cells for Polq , Poln , Helq , Haus3 , and Pold1 . Interestingly , among those genes only Polq expression was increased after B-cell activation by lipopolysaccharide and interleukin 4 treatment ( Fig 9C ) . Cells with inactivation of pol θ or its ortholog HELQ show increased sensitivity to DNA damaging agents , such as bleomycin ( pol θ ) [8] and mitomycin C or cisplatin ( HELQ ) [33–35] . To determine whether inactivation of pol ν also sensitizes cells to DNA damage , we examined the relative sensitivity of PolnΔEx2/ΔEx2 MEFs to mitomycin C ( MMC ) , cisplatin , bleomycin , hydroxyurea , olaparib , formaldehyde and acetaldehyde . Aldehyde sensitivity was tested because of the observation that purified pol ν can bypass some DNA-peptide crosslinks [18] . However , we detected no hypersensitivity of immortalized MEFs to any of the tested DNA damaging agents ( Fig 10 ) . We also examined primary Polndel4/del4 MEFs and found no hypersensitivity to MMC , olaparib , etoposide or 5-fluorouracil ( S6 Fig ) . Rev3L null MEFs were used as positive controls in the same experiment , and they showed sensitivity to MMC as reported [36] . We generated PolnΔEx2/ΔEx2 Polq−/− MEFs by crossing PolnΔEx2/ΔEx2 and Polq−/− mice , isolating double mutant MEFs from the progeny and immortalizing with SV40 Tag . Polq−/− MEFs were more sensitive to bleomycin , as reported [8] . PolnΔEx2/ΔEx2 MEFs were not more sensitive to bleomycin and Poln ablation did not further increase the bleomycin sensitivity of Polq−/− MEFs ( S7A Fig ) . The cellular sensitivity to MMC was not increased in Poln or Polq single knockout MEFs or in the double homozygous mutants ( S7B Fig ) . These results contrast with previous reports that siRNA directed against POLN causes an increased sensitivity to MMC and cisplatin [11 , 12] . Therefore , we performed siRNA-mediated knockdown in human cells using the previously reported targeting sequence [11] . In that study , the efficiency of siRNA-mediated knockdown was assessed using an antibody raised against pol ν . We have developed ~80 Pol ν monoclonal antibodies and two polyclonal antibodies against pol ν -77 [37] , and identified several of them that can immunoblot and immunoprecipitate overexpressed pol ν in human cells ( e . g . Mab#40 in S8 Fig ) . However , we have not been able to detect endogenous pol ν with these or other antibodies , and so we turned to a different approach to monitor the efficiency of siRNA-mediated POLN knockdown . Pol ν was expressed in a doxycycline-inducible manner in 293T-Rex cells . We confirmed complete knockdown of tagged protein expression , showing that the POLN-specific siRNA construct very effectively inactivates POLN mRNA ( Fig 11A , top panel ) . We also performed quantitative PCR assays for POLN and HAUS3 as described [21] and detected significant reduction of doxycycline-induced POLN transcript ( P = 0 . 01 ) as well as endogenous POLN transcript ( P = 0 . 03 ) by the POLN-specific siRNA ( S9 Fig ) . Note that only incomplete transcripts of POLN have been detected in 293T cells [21] . HAUS3 expression was not affected by this siRNA ( S9 Fig ) . Nevertheless , neither pol ν-depletion ( siN ) nor pol ν-overexpression ( 293T-REx ( POLN ) + Dox , siC ) had any influence on mitomycin C sensitivity in human cells ( Fig 11B and S8 Fig ) . It was also proposed that pol ν depletion caused an increased formation of radial chromosomes in human cells treated with MMC [11] . We therefore analyzed metaphase spreads of 293T-REx depleted of pol ν , and 293FT cells depleted of pol ν , FANCA , or FANCD2 ( Fig 11A ) . Suppression of FANCA and FANCD2 resulted in an increased frequency of radial chromosomal formation after treatment with mitomycin C as expected for these Fanconi anemia gene products . However , pol ν depletion did not increase the frequency of radial chromosome formation ( Fig 11C and 11D ) .
The investigations presented here answer many long-standing questions about pol ν function and expression . An analysis of mice with genetic disruptions of Poln has not been reported previously . Because Poln is expressed only in some metazoan lineages , many widely-used model organisms are not relevant for biological analysis of pol ν . We found that Poln is not essential for mouse embryonic development or for viability . Three independently established and targeted mouse models that disrupt Poln were fully viable , in C57BL/6J;129Sv and FVB/NCrl backgrounds . The Poln deficient mice have a normal lifespan and lack abnormalities for many investigated anatomic features . Consistent with this , the International Mouse Phenotyping Consortium ( mousephenotype . org ) has recently listed mice with a homozygous knockout of exon 6 . No abnormalities in adults or embryos were detected in a battery of anatomical , physiological and behavioral tests . No expression of Poln was detected in embryos , also consistent with our reported observations [21] . Our observations point to a function of pol ν in the testis . We discovered that Poln is uniquely upregulated during mouse testicular development and that it is enriched in spermatocytes . This may be a conserved feature in organisms where POLN occurs . For example , we found that POLN expression was essentially undetectable in somatic adult cells or embryonic cells from the zebrafish , but was present in adult zebrafish testis [21] . Further , the phylogenetic analysis described here shows that POLN genes exist only in metazoan animals that use sexual reproduction and deploy sperm . This is consistent with a specialized function for pol ν in discrete spermatocyte-forming organs . The evolutionary distribution of POLN is a contrast to the wide distribution of POLQ genes in animals and plants , including many unicellular species and some that reproduce asexually . The DNA polymerase activity of pol ν appears to be critical for its function , as all organisms encoding the enzyme retain the six conserved DNA polymerase motifs with the residues necessary for catalytic activity . Expression of recombinant protein from the human , mouse and zebrafish POLN cDNAs all produce active DNA polymerase [21] . Importantly , it is uncertain whether the pol ν protein is expressed significantly in mammalian somatic cells or tissues . Most POLN transcripts in somatic human cells are inactive , alternatively spliced variants [3] . The protein is not detectable with the antibodies currently available , although mass spectrometry experiments detect a few peptides representing pol ν in various mouse , human , and rat tissues including brain , heart , kidney , liver , lung , pancreas , spleen , and testis [38] . The low level expression of mostly inactive transcripts is consistent with the lack of phenotypes in somatically derived cells , described here . Pol ν may be important at specific recombination hot spots where DNA forms unique DNA secondary structures which provide challenges for other DNA polymerases . We detected a modest but significant reduction in meiotic recombination frequencies at the A3 hot spot in Poln deficient mice . The frequency was lower in the crossover refractory zone of A3 in Poln deficient mice . This zone harbors insertion/deletion polymorphisms and possibly forms a hairpin structure inhibiting replicative DNA polymerases [24] . The strand displacement activity of pol ν [4] may be effective in helping to synthesize the secondary structure-forming sequence . In fact , the crystal structure of pol ν reveals a cavity in the polymerase domain , which could accommodate a looped-out primer strand [6] . Such a looped-out primer could be formed during annealing of insertion/deletion polymorphisms during meiotic recombination . Further , we previously reported proteins associated with pol ν when it was overexpressed in human cells . Mass spectrometry analysis showed associations of pol ν with homologous recombination factors including BRCA1 , FANCJ , BRCA2 , and PALB2 [21] . This is consistent with the participation of pol ν in a specialized homologous recombination reaction . Our results show that mammalian cells have normal responses to DNA crosslinking agent exposure following pol ν elimination or depletion . This contrasts with the reported increased sensitivity to the DNA crosslinking agents MMC and cisplatin in human cells after an siRNA-mediated knockdown of POLN [11 , 12] , Our conclusions are based on several independent knockouts of Poln function in the mouse , and on controlled siRNA and shRNA-depletion of POLN in human cells . These cells showed normal survival responses to crosslinks , and no evidence of increased frequency of MMC-induced radial chromosomes . We did not detect cellular crosslink-sensitivity after suppression of POLN . In studying pol ν , it is also crucially important to insure , as we have done here , that knockdown of POLN does not interfere with the expression of the essential HAUS3 gene encoded in intron 1 of vertebrate POLN . Further , we emphasize that antibodies cannot be used currently to monitor endogenous knockdown of POLN . Finally , we have described that there is little evidence for expression of pol ν in somatic cells , which would explain the lack of phenotypes in non-germline cells when the gene is disrupted . Current evidence points towards a specific germ cell function . Although pol ν shares homology with the DNA polymerase domain of Drosophila Mus308 , which is involved in resistance to DNA crosslinking agents [9] , it appears that a different Mus308 homolog is more involved in crosslink sensitivity in mammalian cells . HELQ , which shows similarity to the Mus308 helicase domain , is involved in DNA crosslink tolerance [34 , 33 , 35] . Our results are more in line with the lack of sensitivity to DNA crosslinking agents that was reported for POLN−/− chicken DT40 cells [39 , 40] . The ability of pol ν to perform limited bypass of a major groove DNA-peptide adduct ( N6-A-peptide ) and a DNA-DNA ( N6-A-N6-A ) crosslink residue [18] may be more broadly related to properties of pol ν that allow it to bypass blocks such as the secondary DNA structures that exist within meiotic recombination hotspots . In many organisms , it is a common observation that knocking out a single DNA repair protein generates little or no DNA damage or growth-related phenotype . Double mutations , or disruption of backup pathways , are frequently necessary to reveal significant phenotypes . For example , no phenotype of pol ι disruption has been detected as a single mutant . In contrast , pol η /pol ι double-deficient mice show an altered spectrum of UV radiation-induced tumors [41 , 42] . We constructed pol ν /pol θ double-defective mice , and as described here , this double mutation did not exacerbate cellular sensitivity to DNA damaging agents beyond what we observed with pol θ defective animals . Further screening of double mutants seems a possible approach to unveil the biological function of pol ν in vertebrates . The present work provides a major resource and foundation that paves the way for further exploration of the function of this unique human enzyme .
Research mice were handled according to the policies of the MD Anderson Cancer Center Institutional Animal Care and Use Committee , under approved protocol number 00001119-RN00 . The strategy for cre-loxP based knockout and targeting vector construction was designed and performed by genOway ( Lyon , France ) . The genomic region of interest containing the murine Poln locus was isolated by PCR from genOway's 129Sv BAC library . PCR fragments were subcloned into the pCR4-TOPO vector ( Invitrogen ) . The following primers were used . For the short homology arm: 5’-CATAACAGGTCAGAGTCACAAAACAGATATGC-3’and 5’- TATCTCACAGACATCAAAACCTACACATGCC-3’; for proximal long homology arm: 5’-CCAGGTAATTTAGATGTGTGAACCAGATGC-3’ and 5’- ACAAACTTTCCAGAACAAGGACAATGACC-3’; for distal long homology arm: 5’-GGGACAGAATAGAAACAAAATGACAAAATAGACC-3’ and 5’-GCTTATGCAAGTAGAGATTCAAAGTTGATGTAAGG-3’ . The resulting sequenced clones ( containing intron 1 to intron 2 ) were used to construct the targeting vector . A region including exon 2 was flanked in the adjacent introns by a Neo cassette ( LoxP site—FRT site—PGK promoter—Neo cDNA—FRT site ) and by a distal loxP site ( Fig 2A ) . This allowed generation of constitutive and conditional knockout lines which had deleted the 145 bp Poln exon 2 containing the translational initiation codon , 760 bp of the upstream intron and 63 bp of the downstream intron . In the knockout , the deleted region is replaced by a 50 bp fragment containing the distal loxP site , simultaneously disrupting an endogenous AflII restriction enzyme site . Linearized targeting vector was transfected into 129Sv ES cells ( 5 x 106 ES cells in presence of 40 μg of linearized plasmid , 260 V , 500 μF ) . Positive selection was started 48 hr after electroporation , by addition of 200 μg/mL of G418 . 2547 resistant clones were isolated and amplified in 96-well plates . Duplicates of 96-well plates were made . The set of plates containing ES cell clones propagated on gelatin were genotyped by both PCR and Southern blot analysis . For PCR analysis , one primer pair was designed to amplify sequences spanning the 5’ homology region . This primer pair designed to specifically amplify the targeted locus was: sense , 5’ GAAAAGCCTCGAAGATATGGGCACC-3' , anti-sense ( Neo cassette ) 5'- GCCTCCCCTACCCGGTAGAATTAGATC-3' . Targeting was confirmed by Southern blot analysis using internal and external probes on both 3’ and 5’ ends . Two clones were identified as correctly targeted at the Poln locus . Clones were microinjected into C57BL/6J blastocysts , and gave rise to male mosaics with a significant ES cell contribution ( as determined by an agouti / black coat color ) . Mice were bred to C57BL/6J mice expressing Flp recombinase to remove the Neo cassette ( B6;129Sv-Polntm1 . 1Rwd designated as Polnlox allele ) and to female C57BL/6J mice expressing Cre recombinase to generate a germline deletion of Poln ( B6;129Sv-Polntm1 . 2Rwd designated as PolnΔEx2 allele ) . The following genotyping primers were used . Polnlox: forward , 5'- GAACCAGATGCTTGTTTGTTCTTTTCACC-3'; reverse , 5'- GTGTACTGAAATACTCCTCAGTTCTAAAAACGACC-3'; wild-type 152-bp product , floxed 266-bp product . PolnΔEx2: forward , 5'- CAGGTAATTTAGATGTGTGAACCAGATGCTTG-3'; reverse , 5'- GGCAGTACAATAACAGAAACACTTCTCTTATGACC-3'; wild-type 1402-bp product , deleted 484-bp product ( S2 Fig ) . Animals were validated by Southern blot analysis of AflII-digested DNA using a 5’ probe ( Fig 2 ) of 364-bp probe for the Southern blot was generated by PCR on genomic DNA using the primer set: 5’-TGGGCAGTTAACTTAGTGGCAACTCTACT-3’ and 5’- GTGTACCTGATCTGTTCCATGTCTTCATATAATC-3’ . Sixteen ZFN pairs targeting Poln were designed and assembled by PCR subcloning into the pZFN plasmid by Sigma . All pairs were tested for efficiency of generating double strand breaks using the Surveyor Mutation assay in cultured mouse Neuro2A cells . The selected ZFN pair , targeting exon 21 , was used for subsequent microinjections into FVB/NCrl embryos . The ZFN target site ( cleavage site in lowercase ) is: 5’-GGCCTCTCCCGAGGAtctgtGCACAGGACCAGCAG-3’ . Validation used forward primer: 5’-CCCTGGGAATACTTGGGACT and reverse primer: 5’-ACTACCAGGCAGGACAGGTG . Embryos were obtained from superovulated Charles River FVB/NCrl female mice . ZFN pairs were microinjected and transferred into pseudopregnant foster females . Thirty-six pups were born . The pups were sampled for genotyping at approximately 3 weeks of age . Four founders ( 11% efficiency ) have been identified . Three of the four founders resulted in early stop codons . Founder #22 has a 4 base pair deletion ( FVB/NCrl . Cg-Polnem1Rwd designated as Polndel4 allele ) . Founder #26 has a 13 base pair deletion ( FVB/NCrl . Cg-Polnem2Rwd designated as Polndel13 allele ) and #27 is chimeric with a 4 and 13 base pair deletion . An ApaL1 restriction site is present in the ZFN targeted site and the sequence is lost in the 4-bp and 13-bp-deleted allele . This is used to validate the genotyping result ( S3 Fig ) . Thirty mice ( 15 males and 15 females ) of each genotype ( PolnΔEx2/ΔEx2 , Poln+/ΔEx2 , and Poln+/+ ) were monitored over time for tumor incidence and survival . Mice were housed in an SPF modified barrier facility under standard light cycle and temperature conditions . Sentinal animals were free of multiple murine infectious and parasitic agents including Helicobacter and mouse parvoviruses . Mice were fed irradiated standard rodent diet ( Harlan Laboratories Irradiated Teklad 22/5 Rodent Diet #8940 ) ad libitum and provided with reverse osmosis purified water via an automated system . Mice were monitored from approximately 3 weeks of age until they reached a moribund state . Mice were monitored daily for overall health status . Additionally , body condition score [43] and body weight were assessed weekly and recorded . Mice were removed from the study if any of the following criteria were met: Loss of greater than 20% body weight in one week , body condition score of 1 or 2 [43] , respiratory or ambulatory difficulties , or non-healing dermatitis . Mice removed from study were killed by carbon dioxide inhalation followed by cervical dislocation before complete necropsies were performed . Body weight and weights of the following organs were obtained: spleen , kidneys , liver , adrenals , testes , heart , thymus , and brain . All tissues were examined grossly , fixed for 24–48 hr in neutral buffered formalin , and stored in 70% ethanol . For all mice , H&E-stained slides of the following tissues were prepared by standard techniques and examined by a board-certified veterinary pathologist: kidneys , liver , heart , lungs , thymus , spleen , testes or ovaries , brain , all gross lesions . For all suspected lesions of histiocytic sarcoma or lymphoma , immunohistochemistry was performed to identify T cells ( CD3 ) , B-cell ( CD45R ) , and macrophages ( F4/80 or MAC ) . Additional immunohistochemistry was performed as required for specific diagnosis . Physiological serum parameters were measured using the VetScan VS2 Analyzer with the Comprehensive Diagnostic Profile reagent rotor ( Abaxis ) . The primary mouse embryonic fibroblasts ( MEFs ) were derived from e13 . 5 embryos with genotypes Poln+/+ , Poln+/ΔEx2 and PolnΔEx2/ΔEx2 ( C57BL/6J mouse , in which exon 2 encoding the first methionine was deleted ) and Poln+/+ , Poln+/del4 and Polndel4/del4 ( FVB/NCrl mouse , in which a Zinc Finger Nuclease-mediated 4 bp frameshift was introduced in the DNA polymerase domain of Poln ) . Primary MEFs were cultured in medium containing high glucose , glutamax-DMEM ( Invitrogen ) , 15% Hyclone FBS ( Thermo Scientific ) , non-essential amino acids , sodium pyruvate , MEM vitamin solution , penicillin/streptomycin ( Invitrogen ) and maintained in air-tight containers filled with a gas mixture containing 93% N2 , 5% CO2 and 2% O2 ( Praxair ) at 37°C . MEFs were immortalized with SV40 Tag as reported [36] . Immortalized MEFs were cultured in medium containing high glucose , glutamax-DMEM ( Invitrogen ) , 10% FBS ( Atlanta Biologics ) and penicillin/streptomycin and maintained in a humidified 5% CO2 incubator at 37°C . We prepared a cDNA library was prepared from 129Sv mouse testis . The full-length open reading frame ( ORF ) was amplified with primers ( 5’-caccaaaatggaaaattatgaggcatgtg and 5'-caggagatcctgggctcagccgactacaaagacgatgacgacaagtaggaattcatatat ) , and cloned into pENTR/D-TOPO vectors and then recombined into pDEST17 vectors ( Invitrogen ) . Total RNA was extracted using TRIzol ( ThermoFisher Scientific ) , and RNA integrity assessed using the Agilent 2100 bioanalyzer ( Agilent Technologies , Inc . ) . Total RNA ( 1 μg ) was used as template to synthesize cDNA with the High-Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) . qPCR was then performed on the ABI 7900HT Fast Real-Time PCR System ( Applied Biosystems ) . Custom assays for mouse Poln , Polq , and Pold1 were designed using FileBuilder 3 . 1 software ( Applied Biosystems ) and ordered from Applied Biosystems . TaqMan primer and probe sets for each gene are shown in Table 6 . The absolute quantity ( AQ ) of transcripts for Poln and Polq was determined using the generated standard curves and Applied Biosystems’ Sequence Detection Software version 2 . 2 . 2 ( ABI ) . Standard curves for each gene were determined using the plasmids pDEST17 carrying cDNA coding full length of Poln , and Polq clone ( MGC:189905 , IMAGE:9088092 ) . For relative quantification , TaqMan primers and the probe set for Gapdh were purchased from Applied Biosystems . Triplicate qPCR reactions each containing cDNA representing 40 ng of reverse-transcribed total RNA were then assayed for transcript quantity with Gapdh serving as endogenous controls to normalize input RNA levels . Cell suspensions were prepared from 25 adult mice ( ~100 days old ) by previously published methods [44 , 45] . Seminiferous tubules were isolated by incubating the decapsulated testes with collagenase ( 0 . 5 mg/mL ) and DNase I ( 200 μg/mL ) in enriched DMEM/F12 ( Invitrogen ) , to which 0 . 1 mM non-essential amino acids ( Invitrogen ) , 1 mM sodium pyruvate ( Invitrogen ) , and 5 mM sodium lactate ( Sigma ) were added . This decapsulated testis tissue was shaken for 15 min at 35°C in a water bath , until it was mostly dispersed into tubules . The dispersed tubules were allowed to settle and , after removal of the supernatant , were resuspended in 32 mL of DMEM/F12 solution . In each of four 50 mL tubes , 8 mL of this suspension was layered onto 40 mL of 5% Percoll solution ( Sigma ) , and the tubules were allowed to settle until most of the larger tubules and clumps were at the bottom . The supernatants were removed and the settled tubules were washed with DMEM/F12 solution and then further digested with trypsin ( 1 mg/mL ) and DNase I ( 200 μg/mL ) in enriched DMEM/F12 for 20 min at 35°C with shaking . Fetal bovine serum was added to 10% , and the cells were dispersed by pipetting . Total cell suspensions were separated by centrifugal elutriation ( JE-6B rotor , Beckman ) to obtain fractions enriched in spermatids ( flow rate: 15 . 6 mL/min , rotor speed: 2250 rpm ) and pachytene primary spermatocytes ( 37 mL/min , 2250 rpm ) . The purified pachytene fraction was obtained by plating the Percoll-enriched fraction on DSA ( Sigma ) coated dishes , to which Sertoli cells bind strongly , and the pachytene spermatocytes were recovered in the unbound cell fraction . The Sertoli cells recovered from the elutriator were purified by plating them on the DSA-coated dishes , removing the unbound and loosely bound cells , and directly extracting the bound Sertoli cells with RLT lysis buffer ( QIAGEN ) . The purity of each fraction was initially determined by cell smears stained with periodic acid Schiff-hematoxylin . Testes were removed from killed mice and fixed in neutral-buffered formalin for 24 hr before being processed for histologic sectioning ( 4 μm ) and stained with hematoxylin and eosin ( H&E ) . The samples were analyzed using a BX41 Olympus microscope with 10X objective . In order to detect recombinant molecules at the A3 locus in sperm , F1 hybrid animals carrying heterozygous A3 alleles were generated . The PolnΔEx2 allele in the C57BL/6J background and the Polndel4 allele in the FVB/NCrl background were backcrossed into DBA2/J strain . The C57BL/6J or FVB/NCrl strains share an ~1 . 8% polymorphism density at A3 with the DBA2/J strain allowing detection of recombinant molecules from sperm DNA . F1 hybrid males were generated from matings of parents carrying each of the Poln mutant alleles in one of the background strains . Spermatocyte spreads were prepared according to previously published protocol [46] . Briefly , slides rinsed with PBS were blocked for 30 min at room temperature in Gelatin Block Solution ( GBS: 0 . 2% v/v fish gelatin ( Sigma G-7765 ) , 0 . 2% v/v IgG-free BSA ( Jackson ImmunoResearch 001-000-162 ) , 0 . 05% w/v Tween-20 ) . Next , covered slides were incubated overnight at 4°C with 100 μl of primary antibody diluted in GBS . Slides were then washed with GBS on a rotating platform shaker ( one quick rinse followed by 5 , 10 and 15 min rinses ) . Next , covered slides were incubated for 45 min at 37°C with 100 μl of secondary antibody diluted ( 1:200 ) in GBS . Slides were washed with GBS as above , followed by two 5 min washes with 0 . 4% PhotoFlo 200 ( Kodak 1464510 ) . Slides were then dried in the dark and mounted with Prolong® Gold antifade with DAPI . Antibodies were used in two combinations in this study ( dilution ) : Rabbit anti-SCP3 ( 1:500; Santa Cruz Biotechnology sc-33195 ) and Mouse anti-MLH1 ( 1:20; BD Pharmingen 551092 ) or Mouse anti-SCP3 ( 1:200; Santa Cruz Biotechnology sc-74569 ) ; and Rabbit anti-SCP1 , ( 1:200; Abcam ab15090 ) ; secondary antibodies ( all 1:200 ) were: Goat anti-Rabbit 594 ( Life Technologies A11037 ) and Goat anti- Mouse 488 ( Life Technologies A11029 ) or Goat anti-Mouse 594 ( Life Technologies A11029 ) and Goat anti-Rabbit 488 ( Life Technologies A11034 ) . Images were acquired on a Zeiss Axio Imager M2 with a Plan-Apochromat 100x/1 . 4 oil immersion objective . DNA samples were isolated from sperm from epididymides of adult ( 2–5 m . o . ) F1 hybrid animals , as previously described [24 , 25] . Two animals were analyzed for each allele , per genotype . Briefly , a standard phenol/ chloroform/ isoamyl alcohol DNA isolation protocol was followed by ethanol precipitation . An aliquot of each DNA sample was used to quantify DNA concentration by UV absorbance and by comparison to a dilution series by agarose gel electrophoresis , using high quality sperm DNA of defined concentration . The number of amplifiable DNA molecules/pg was determined by performing 12–24 PCR reactions per sample seeded with 12 pg per reaction ( equivalent to 2 amplifiable molecules/well ) . The crossover assay for A3 was as described previously [24 , 25] . Briefly , recombinant molecules were identified after two rounds of nested PCR by 0 . 8% agarose gel electrophoresis and positive reactions ( putative crossovers ) were then PCR-amplified , together with positive and negative controls . Products in a 96-well format were dot-blotted onto nylon hybridization membrane ( Roche , 11417240001 ) and genotyped by Southern blotting with allele-specific oligonucleotide ( ASO ) probes . Somatic DNA from spleen or liver for each assayed animal was used as a negative control at total DNA inputs equivalent to or higher than sperm DNA . No crossovers were detected in somatic controls ( frequency < 1 . 05 x 10−5 ) . A detailed version of the Southern blotting protocol for the A3 hotspot has been described [24 , 25] . Briefly , ASO probes were radiolabeled using T4 polynucleotide kinase and hybridized with nylon membranes containing PCR products from the crossover assay . After hybridization , blots were exposed for 4–5 hr on phosphorimager screens and scanned using the Typhoon FLA 9500 phosphorimager ( GE Healthcare ) . Scans were then scored and positive signals used to generate crossover breakpoint maps . Each ASO is an 18-bp oligonucleotide designed to specifically hybridize with one of the two parental genotypes . At the A3 locus , most of the FVB/NCrl sequence is identical to the C57BL/6J strain . Wild-type and PolnΔEx2/ΔEx2 mice were generated from Poln heterozygous knockout crosses , and Polq-/- mice were generated from Polq heterozygous knockout crosses . 8–12 week-old mice received whole-body irradiation with 1 Gy or 2 Gy at 2 Gy/min , 160 kV peak energy ( Rad Source 2000 irradiator , Suwanee , GA ) . For DSB induction , three wild-type mice per dose were analyzed at 30 min post-irradiation . For the DSB repair kinetics three mice per time-point were analyzed at 0 . 5 , 5 , 24 , and 48 hr after irradiation with 2 Gy . In each experiment three mock-irradiated mice served as controls . After animals were sacrificed , testes were immediately removed and placed in fixative . Formalin-fixed tissues were embedded in paraffin and sectioned at an average thickness of 4 μm . Tissue sections were incubated with primary antibody against γ-H2AX ( Bethyl ) followed by Alexa Fluor-488-conjugated secondary antibody ( Invitrogen ) . Finally , sections were mounted in VECTASHIELD mounting medium with 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Vector Laboratories ) . For quantitative analysis , radiation-induced foci were counted by eye using a Leica DMI 6000 microscope equipped with a 63X oil objective . Tissue sections were incubated with primary antibody against γ-H2AX . To evaluate potential differences in DSB repair kinetics , the two-way ANOVA test was performed for each dose and repair-time . The criterion for statistical significance was p ≤ 0 . 05 . For RNA-seq analysis , four biological replicates were prepared for Poln+/+ and PolnΔEx2/ΔEx2 mice . RNA was purified from testis of 68-day old male mice with RNeasy kit ( QIAGEN ) with on-column DNase treatment . The libraries were prepared using the Illumina TruSeq stranded total RNA kit according to the manufacturer’s protocol , except that the PCR amplification cycle was reduced to 10 . The libraries were sequenced on HiSeq 2000 ( Illumina ) , generating 53–73 million pairs of 75 bp reads per sample . Each pair of reads represents a cDNA fragment from the library . The reads were mapped to mouse genome ( mm10 ) by TopHat ( Version 2 . 0 . 7 ) [47] . By reads , the overall mapping rate is 90–96% . 83–93% fragments have both ends mapped to mouse genome . The number of fragments in each known gene from RefSeq database 48 ( downloaded from UCSC Genome Browser on March 6 , 2013 ) was enumerated using htseq-count from HTSeq package ( version 0 . 5 . 3p9 ) [48] . Genes with less than 10 fragments in all the samples were removed before differential expression analysis . The differential expression between conditions was statistically accessed by R/Bioconductor package edgeR ( version 3 . 0 . 8 ) [49] . Genes with false discovery rate ≤0 . 05 and fold change ≥2 were called significant . Gene clustering and heatmap were done by Cluster 3 . 0 and TreeView . GO analysis was performed using Ingenuity Pathway Analysis ( IPA ) software . The transcriptional profiling plot ( Fig 7A ) was made using R software . Approximately 100 μL of blood obtained from individual mice by cardiac puncture were collected into tubes containing 350 μL of heparin solution and fixed in ultra-cold methanol according to the protocol in the Mouse MicroFlowBasic Kit ( Litron Laboratories ) . The fixed samples were stored at −80°C until the flow cytometry analysis was performed . Methanol-fixed blood samples were washed and labeled with anti-CD71-FITC , anti-CD61-PE and PI for high speed flow cytometry using CellQuest software , v5 . 2 ( Becton Dickinson , San Jose , CA ) . For each sample , 2 × 104 CD71-positive reticulocytes were analyzed for the presence of micronucleated reticulocytes . Flow cytometers were calibrated by staining Plasmodium berghei-infected rodent blood ( malaria biostandards ) in parallel with test samples on each day of analysis . Statistical analysis was performed using the Student's t-test or one-way ANOVA followed by Tukey's test . B cells were isolated from mouse spleens , purified by negative selection with anti-CD43 depletion ( Miltenyi ) and stimulated with IL-4 and Lipopolysaccharide and IL-4 ( Sigma ) for 72 hr . Where indicated , cultures were incubated with DNA-PKcs inhibitor 20 μM NU7026 ( Tocris ) dissolved in DMSO , or mock-treated . Three mice were analyzed in triplicate and cell count numbers and viability were similar for all groups . The culture , flow-cytometric analysis for CSR analysis and junction analysis has been described [32 , 50] . Sμ-Sγ1 CSR junctions were amplified by PCR using the following conditions for 25 cycles at 95°C ( 30 s ) , 55°C ( 30 s ) , 68°C ( 180 s ) using the primers ( FWD 5′-AATGGATACCTCAGTGGTTTTTAATGGTGGGTTTA-3′; REV 5′ CAATTAGCTCCTGCTCTTCTGTGG-3′ ) and Pfu Turbo ( Stratagene ) . To the PCR reaction , 5 U of Taq polymerase ( Promega ) was added and incubated at 72°C for 10 min . The resulting product was TOPO TA cloned and transformed into Top10 E . coli cells ( Life Technologies ) and plasmids were purified and sent for sequencing using M13 FWD and REV primers in addition to the amplification primers for sequencing . 100 clones for each group were analyzed for mutations , deletions , insertions , and sequence overlaps at the junction and both 30 nt upstream and downstream of the junction . T-REx 293 cells ( Invitrogen ) were transfected with the pcDNA5/FRT/TO TOPO TA construct . Full-length pol ν with six His residues at the N-terminus and a FLAG tag at the C-terminus [4 , 21] was inserted into the plasmid vector . Stable clones were selected with hygromycin and blasticidin . Expression of the Flag-tagged pol ν was induced with 0 . 1 μg/mL doxycycline ( SIGMA ) for 24 hr . Basal repression and doxycycline-induced expression of pol ν were confirmed by immunoblotting . The POLN-specific RNAs ( designated ‘siN’ , 5’- AAGCACCCAAUUCAGAUUACU ) ( Dharmacon ) , the FANCA-specific RNAs ( designated ‘siA’ , 5’-AAGGGUCAAGAGGGAAAAAUA-3’ ) ( Invitrogen ) , the FANCD2-specific Stealth RNAs ( designated ‘siD2’ , 5’-CCAUGUCUGCUAAAGAGCGUUCAUU-3’ ) ( Invitrogen ) and ON-TARGETplus Non-Targeting siRNAs as a negative control designated ‘siC’ ( Thermo Scientific ) were used . The siRNAs were introduced into 293FT or 293T-REx POLN cells . 24 hr prior to transfection , cells were plated in a 6-well plate at 2 . 0 x 105 cells/well . For each well , 5 pmol of siRNAs was diluted into 250 μl of Opti-MEM ( Invitrogen ) . In a separate tube , 5 μl of Lipofectamine RNAiMAX reagent ( Invitrogen ) was diluted into 250 μl of Opti-MEM and incubated at room temperature for 10 min . The Lipofectamine RNAiMAX dilution was added into the diluted siRNA duplex and incubated at room temperature for 20 min . Before the transfection , medium was replaced with fresh 2 . 5 mL of DMEM supplemented with 10% fetal bovine serum for each well . The Lipofectamine RNAiMAX-siRNA complex was added dropwise to the cells and incubated at 37°C . After 24 hr the cells were washed , trypsinized , and plated with fresh DMEM medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin ( Invitrogen ) . To measure the levels of proteins , whole cell crude extracts were prepared 48 hr after the RNA transfection and analyzed by immunoblotting with anti-pol ν ( PA434 ) [3] , anti-FANCA ( Bethyl , A301-980A , 1:10 , 000 dilution ) , anti-FANCD2 ( GeneTex Inc . , EPR2302 , 1:2000 dilution ) , and anti-α-tubulin ( Sigma , T5168 , 1:8000 dilution ) antibodies . The following sequence was cloned into pSIF-H1-copGFP vector to target POLN: 5’-GATCCTCTTTGGCGAGTTAGAGCTGTACTTCCTGTCAGATGCAGCTTTAACTTGCCAAAGAGGATTTTTT-3’ , underlined sequence indicates the target sequence ( System Biosciences ) . Lentiviral particles were generated by transfection of three plasmids ( the expression plasmid , e . g . , pSIF-H1-copGFP-hPOLN . 1; plus pFIV-34N and pVSV-G ) into 293FT cells using FuGene 6 . Culture media from transfected cells was collected 48 hr after transfection to isolate the viral particles , passed through 0 . 45 μm filters , used immediately or stored at −80°C in single-use aliquots . Lentiviral transduction was completed as follows: Briefly , 6 . 0 × 104 cells were seeded into 6-well plate and incubated for 24–30 hr at 5% CO2 at 37°C . Cells were transduced for 18 hr with shRNA-expression lentiviral stocks at 32°C and cultured for 72 hr at 37°C as described [51] . Stable cell lines were selected by detecting GFP expression ( a co-expressed marker gene ) . To measure the levels of proteins , immunoblotting was performed with anti-pol ν ( Mab#40 ) and anti-PCNA ( Santa Cruz , sc-56 , 1:1 , 000 dilution ) antibodies . For the ATPlite assay ( PerkinElmer ) , 1 , 250 cells were plated per well into white 96 well plates and incubated twenty-four hr prior to inducing DNA damage . The cells were incubated with DNA damage inducing agents for the indicated time . After incubation , the cells were immediately lysed and assayed for ATPlite luminescence as described in the manufacturer's instructions . For the clonogenic assay , 1 . 0 x 105 cells were plated in 60 mm culture plates and incubated for 24 hr prior to DNA damage induction . Groups of plates were exposed to indicated doses of mitomycin C for 1 hr . After making a dilution series for each group , cells were returned to the incubator until colonies could be detected in the samples ( 7 to 14 days ) , and then were fixed , stained , and scored for survival . Cells were treated with 40 ng/mL of mitomycin C for 48 hr . At forty-four hr after mitomycin C treatment , cells were treated with 0 . 03μg/mL colcemid solution ( Sigma ) for 4 hr . The cells were then trypsinized and exposed to 0 . 075M KCl for 15 min at 37°C , and were fixed in 3:1 methanol:glacial acetic acid . The cells were spread on glass slides , Giemsa stained and metaphases were analyzed using a BX41 Olympus microscope , with 60X or 100X oil objectives . Photographs were taken with the 60X oil objective on a Spot Idea 5 color digital camera . 100 metaphases per sample were analyzed to identify cell population with radial chromosome .
|
The work described here fills a current gap in the study of the 16 known DNA polymerases in vertebrate genomes . Until now , experiments with genetically disrupted mice have been reported for all but pol ν , encoded by the POLN gene . To intensively analyze the role of mammalian pol ν we generated multiple Poln-deficient murine models . We discovered that Poln is uniquely upregulated during testicular development and that it is enriched in spermatocytes . This , and phylogenetic analysis indicate a testis-specific function . We observed a modest reduction in meiotic recombination at a recombination hotspot in Poln-deficient mice . Pol ν has been suggested to function in DNA crosslink repair . However , we found no increased DNA crosslink sensitivity in Poln-deficient mice or POLN-depleted human cells . This is a major difference from some previous findings , and we support our conclusion by multiple experimental approaches , and by the very low or absent expression of functional pol ν in mammalian somatic cells . The present work represents the first description and comprehensive analysis of mice deficient in pol ν , and the first thorough phenotypic analysis in human cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"gene",
"regulation",
"dna-binding",
"proteins",
"animal",
"models",
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"damage",
"model",
"organisms",
"polymerases",
"experimental",
"organism",
"systems",
"sequence",
"motif",
"analysis",
"dna",
"molecular",
"biology",
"techniques",
"mammalian",
"genomics",
"homologous",
"recombination",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"small",
"interfering",
"rnas",
"genomics",
"artificial",
"gene",
"amplification",
"and",
"extension",
"bioinformatics",
"proteins",
"gene",
"expression",
"mouse",
"models",
"molecular",
"biology",
"animal",
"genomics",
"biochemistry",
"rna",
"dna",
"polymerase",
"nucleic",
"acids",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"dna",
"recombination",
"non-coding",
"rna",
"polymerase",
"chain",
"reaction"
] |
2017
|
Analysis of DNA polymerase ν function in meiotic recombination, immunoglobulin class-switching, and DNA damage tolerance
|
A single-tube one-step real-time reverse transcription loop-mediated isothermal amplification ( RT-LAMP ) assay for rapid detection of chikungunya virus ( CHIKV ) targeting the conserved 6K-E1 target region was developed . The assay was validated with sera collected from a CHIKV outbreak in Senegal in 2015 . A novel design approach by combining Principal Component Analysis and phylogenetic analysis of 110 available CHIKV sequences and the LAMP oligonucleotide design software LAVA was used . The assay was evaluated with an External Quality Assessment panel from the European Network for Diagnostics of "Imported" Viral Diseases and was shown to be sensitive and specific and did not cross-detect other arboviruses . The limit of detection as determined by probit analysis , was 163 molecules , and 100% reproducibility in the assays was obtained for 103 molecules ( 7/8 repetitions were positive for 102 molecules ) . The assay was validated using 35 RNA samples extracted from sera , and results were compared with those obtained by quantitative RT-PCR carried out at the Institut Pasteur Dakar , demonstrating that the RT-LAMP is 100% sensitive and 80% specific , with a positive predictive value of 97% and negative predictive value of 100% . The RT-LAMP appeared to show superior performance with material stored for months compared to qRT-PCR and can be therefore recommended for use in infrastructures with poor settings .
Chikungunya virus ( CHIKV ) , a single-stranded positive-sense enveloped RNA virus belonging to the genus Alphavirus , family Togaviridae , was originally isolated in Tanzania in 1953 [1] . The RNA genome ( approximately 12 kb ) , capped at the 5’ and with a poly ( A ) tail at 3’ end , comprises two open reading frames ( ORFs ) interrupted by an untranslated region , the junction region ( J ) . The ORF at the 5’ end encodes four non-structural proteins ( nsP1 , nsP2 , nsP3 and nsP4 ) and the other ORF encodes five structural proteins , including the capsid ( C ) , envelope 3 ( E3 ) , E2 , 6K and E1 [2] . This virus is clustered into three major distinct genotypes based on phylogenetic analysis of the E1 gene sequence: Asian , East/Central/South African and West African [3–5] , and these clusters were also obtained when using full-genome sequences [6] , dividing the East/Central/South African clade into three subgroups ( I-II-III ) . CHIKV is the causative agent of Chikungunya fever , an arthropod-borne viral disease transmitted by the mosquitoes Aedes aegypti and A . albopictus , characterised by a sudden onset of fever , headache , fatigue , nausea , vomiting , rash , myalgia and severe arthralgia , with polyarthralgia as the typical clinical sign of the disease which can persist for several months [7] . Recently , another mosquito belonging to the genus Aedes , A . hensilli Farner , was described as the most important vector of CHIKV during the outbreak on Yap Island , Federal States of Micronesia in 2013 [8] . Clinical symptoms are similar to those observed in other diseases , such as malaria and dengue fever [9 , 10] , but the prognoses of these infections are greatly different and a number of Chikungunya fever cases are commonly misdiagnosed as dengue virus ( DENV ) infections . In addition , dual infections with CHIKV and DENV have been reported [11] . Because of the lack of antiviral treatments for Chikungunya fever and the malaria- and dengue-like symptoms , accurate , specific and sensitive methodologies are needed in order to provide a definite diagnosis . Although virus isolation from blood of viraemic patients , infected tissues or blood-feeding arthropods is considered the gold standard for CHIKV detection , it is time-consuming , needing at least 7 days . Immunofluorescence assays for CHIKV detection require materials that may not be easily available in diagnostic laboratories worldwide and the performance of laboratories shows great variability [12 , 13] . Other serological techniques are based on enzyme-linked immunosorbent assay ( ELISA ) and immunochromatography for rapid detection , with better sensitivity and specificity achieved by the ELISA , although there are some issues with false-negative and false-positive results [14] . Detection of viral RNA is a sensitive method for diagnosis in acute stages when antibody levels are not high [15] . Several reverse transcription ( RT ) PCR-based methods have been developed [15–23] . However , these methodologies need sophisticated and expensive equipment that may not be present in laboratories with limited resources . Currently , cost-effective techniques based on the RT loop-mediated isothermal amplification ( LAMP ) have emerged to substitute PCR because of its simplicity , rapidity , specificity and sensitivity , showing that only a heating block or water bath capable to maintain a constant temperature ( 60°C to 65°C ) is needed [24–26] . Furthermore , reactions could be visualized by monitoring either the turbidity in a photometer or the fluorescence in a fluorimeter , by naked eye under a UV lamp when using an intercalating dye and by colour change [25–29] . As a matter of fact , RT-LAMP assays amplifying the CHIKV structural E1 gene have been previously developed [27 , 29] . However , these previous RT-LAMP assays date from 2007 and 2012 , and the information about the primer design is limited . In this study , we downloaded 110 CHIKV sequences from the NCBI database , and used LAMP Assay Versatile Analysis ( LAVA ) algorithm [30] to derive LAMP primers for an already known conserved CHIKV genome region ( 6K-E1 ) to cover all possible circulating CHIKV strains . This methodology allowed designing a highly sensitive and specific single-tube one-step real-time RT-LAMP for the detection of CHIKV RNA .
An RNA standard was transcribed from the CHIKV 6K-E1 region , ligated into pCRII and evaluated as previously published [31] . These serially 10-fold dilutions were used as templates for absolute one-step quantitative RT-PCR ( qRT-PCR ) using the Light Cycler 480 Master Hydrolisis Probes ( Roche , Mannheim , Germany ) in 20 μL reaction volume containing 1x LightCycler 480 RNA Master Hydrolysis Probes , 3 . 25 mM activator Mn ( OAc ) 2 , 500 nM primers CHIKMW FP1 ( 5’-YGAYCAYGCMGWCACAG-3’ ) and CHIKMW RP1 ( 5’-AARGGYGGGTAGTCCATGTT-3’ ) , 200 nM probe TaqMan probe CHIK P2 ( 5’-6FAM-CCAATGTCYTCMGCCTGGACRCCKTT—TMR-3’ ) [32] , and 1 μL RNA as template . This assay was repeated 8 times . The qRT-PCR reactions were run in the LightCycler 2 . 0 ( Roche ) , as follows: reverse transcription for 3 min at 63°C , activation for 30 s at 95°C , followed by 45 cycles consisting of amplification for 5 s at 95°C and 15 s at 60°C . Finally , a cooling step was added of 40 s at 40°C . Analysis of the reactions was conducted using LightCycler software version 4 . 1 . 1 . 21 ( Roche ) . The specificity was evaluated using the ENIVD External Quality Assessment ( EQA ) panel provided by the Robert Koch Institute ( Berlin , Germany ) ( Table 1 ) , that includes 12 serum samples ( volume per sample , 100 μL ) consisting of 3 CHIKV strains ( 1 of them with different concentrations ) , 1 DENV2 strain , 2 related alphaviruses ( O’nyong-nyong virus—ONNV—and Sindbis virus—SINDV - ) and 2 samples of human plasma as negative controls [33] . Viral RNA was extracted from these serum samples using the QIAamp Viral RNA mini kit ( QIAGEN , Courtaboeuf , France ) and the analysis of these RNA samples was repeated twice . A total number of 110 sequences available of the Chikungunya virus ( S1 File ) were downloaded from the NCBI database . BLAST ( v . 2 . 2 . 28+ ) [34] was used to identify the 6K-E1 target region within these sequences . For the following analysis steps an alignment of these target regions was calculated by GramAlign v3 . 0 [35] . The sequences were split into different subgroups with the help of Principal Component Analysis ( PCA ) of R/adegenet v2 . 0 . 0 [36] and phylogenetic tree ( Neighbor-Joining tree using the R/ape 3 . 2 package ) as the generation of LAMP primers for all sequences at once was not possible . LAMP DNA signatures for each subset were designed by a modified version [https://github . com/pseudogene/lava-dna] of LAVA version 0 . 1 [30] applying loose parameters . It was checked for all combinations of the subgroups , if a combined primer generation was possible . All the designed sets of primers were additionally checked for primer dimerisation with the VisualOMP version 7 . 8 . 42 . 0 ( DNA Software , Ann Arbor , MI ) . In addition , primer combinations were tested for primer dimerisation by comparing amplification signals in positive and negative controls . Table 2 includes the final list of primers for the RT-LAMP CHIKV assay , consisting of 17 unique primers ( 3 amplicons ) . RT-LAMP reactions were run at 64°C for 60 min using either an ESE-Quant TubeScanner ( QIAGEN Lake Constance GmbH , Stockach , Germany ) or Genie II ( Optigene , Horsham , UK ) , in a final reaction volume of 25 μL . The Genie II device allows to create an annealing curve for confirmation of amplification specificity by an additional heating and cooling step from 98°C to 80°C ( 0 . 05°C/s ) for 6 min to allow the re-annealing of the amplified product . Each reaction consisted of 1x RM Trehalose , 6 mM MgSO4 , 5% polyethylene glycol ( PEG ) , 1 μL fluorochrome dye ( FD ) , 0 . 1 μM F3 , 0 . 1 μM B3 , 0 . 8 μM FIP , 0 . 8 μM BIP , 0 . 4 μM FLOOP , 0 . 4 μM BLOOP ( final concentration for each set of primers ) , 8 U Bst 2 . 0 DNA Polymerase , 10 U Transcriptor Reverse Transcriptase and 1 μL template ( RNA or H2O as negative control ) . Before adding Bst 2 . 0 DNA Polymerase , Transcriptor Reverse Transcriptase and template , mixes were incubated at 95°C for 5 min to melt any primer multimers and cooled immediately on ice for 5 min . RM Trehalose , MgSO4 , PEG and FD were supplied by MAST Diagnostica GmbH ( Reinfeld , Germany ) . Bst 2 . 0 DNA Polymerase and Transcriptor Reverse Transcriptase were obtained from New England BioLabs ( Hitchin , Herts , UK ) and Roche , respectively . The analytical specificity of the RT-LAMP assay was evaluated using the EQA panel detailed above [33] , repeating the assay in both the Genie II and ESE-Quant TubeScanner . In order to evaluate the specificity of the RT-LAMP protocol developed to detect CHIKV , other RNA viruses were tested ( Table 1 ) including flaviviruses such as , dengue virus ( DENV , serotypes 1 to 4 ) , yellow fever virus ( YFV ) , West Nile virus ( WNV ) and Ntaya virus ( NTAV ) , and other alphaviruses such as salmonid alphavirus ( SAV ) . DENV strains were provided by ENIVD / Robert Koch Institute . Flavivirus ( YFV , WNV and NTAV ) were provided by M . Weidmann , whilst SAV was provided by B . Lopez-Jimena . The analytical sensitivity of the CHIKV RT-LAMP assay was tested using the ESE-Quant TubeScanner and the CHIKV RNA standard ranging from105 to 10 molecules/μL , in 8 independent runs . The values obtained were subjected to probit analysis ( Statgraphics plus v5 . 1 , Statistical Graphics Corp . , Princeton , NJ ) and the limit of detection at 95% probability was obtained . Thirty-three qRT-PCR positive and 2 qRT-PCR negative serum samples from a recent CHIKV outbreak in Senegal ( 2015 ) were collected and analysed in triplicates at the Institute Pasteur Dakar ( IPD ) ( S1 Table ) . The IPD has ethical approval for use of these anonymized samples for retrospective studies by the Ministry of Health of Senegal . RNA extractions were performed with the QIAamp Viral RNA mini kit and RNA samples were stored at -80°C . The RNA samples were analysed by qRT-PCR , as previously described [17] . In addition , CHIKV RT-LAMP reactions were run at 64°C for 60 min in an ABI7500 Fast Real-time PCR system ( Applied Biosystems , Foster City , CA ) . Sensitivity , specificity , positive predictive value ( PPV ) and negative predictive value ( NPV ) were obtained for the developed RT-LAMP when compared against the results obtained by qRT-PCR .
The molecular RNA standard ( 107−10 molecules/μL ) was evaluated using a one-step qRT-PCR , and this assay was repeated 8 times . Fig 1 shows the mean CP value ± standard deviation ( SD ) . This qRT-PCR showed 100% reproducibility , as positive results were obtained for all the dilutions tested and for all the 8 independent runs . It detected 6 samples ( numbers 2 , 4 , 6 , 7 , 9 and 12 ) ( Table 1 ) of the ENIVD EQA panel . Sample number 9 , was detected in only 1 of 2 cases . The sequences retrieved from GenBank were split into 4 groups , 2 of which consisted of only 1 viral strain , namely CHIKV strain HD 180760 ( HM045817|2005|Senegal , group 2 ) and CHIKV isolate CHIKV STMWG01 ( KJ679577|2011|India , group 3 ) ( Fig 2A and 2B ) . LAVA , the program for LAMP signature design , was executed for each group separately as well as for all possible combinations of the groups . A combined design of LAMP primers was possible for the groups 1 and 3 and groups 3 and 4 . The final selection of primer sets is included in Fig 2C and Table 2 . The RT-LAMP protocol developed was specific to detect CHIKV RNA , and no signal was detected when using RNA of other viruses ( Fig 3A ) . In addition , the annealing curve showed a single temperature peak at 86 . 3°C ( Fig 3B ) . All serum samples of the ENIVD EQA panel detected by qRT-PCR were also detected by RT-LAMP on the 2 LAMP devices , except for RNA sample number 12 which was only detected with the ESE-Quant TubeScanner ( Table 1 ) . In addition , RNA sample number 10 , negative by qRT-PCR , was also detected by RT-LAMP . The developed RT-LAMP detected up to 10 molecules per reaction , although this was only achieved in 1 of 8 repetitions at 45 min . The lowest number of RNA molecules detected in the 8 reactions , showing 100% reproducibility , was 103 ( 36 . 2 ± 3 . 5 min ) , whilst 102 molecules were detected in 7 of 8 repetitions ( mean time , 42 . 7 ± 4 . 1 min ) ( Fig 4 ) . Probit analysis of the results of 8 runs revealed that the limit of detection at 95% probability was 163 molecules . S1 Table summarises the results obtained after the analyses of the RNA samples by qRT-PCR ( CT values ) and RT-LAMP ( TT values , min ) . RNA samples used in this study were initially analysed by qRT-PCR in 2015 resulting in 33/35 positives ( “initial CT values” ) . On repetition of the qRT-PCR after storage at -80°C for 7 months only 19/35 samples remained positive ( “current CT values” ) . When RT-LAMP was used 24/35 samples showed amplification in the 3 replicates tested , with TT values below 30 min in 22 out of 35 samples . Four samples with negative current CT values ( IPD 277599 , 277530 , 274843 and 264781 ) were negative in RT-LAMP , while qRT-PCR negative sample IPD 264842 was positive by RT-LAMP ( TT = 29–42 min ) . Samples IPD 274461 , 274688 , 274464 , 277604 , positive in initial qRT-PCR but with no current CT values , showed positive results by RT-LAMP in the 3 replicates analysed . In addition , 7 RNA samples negative in the 2016 by qRT-PCR amplified by RT-LAMP but not in all 3 replicates ( IPD numbers 277586 , 264998 , 274443 , 277593 , 277551 , 277545 and 264779 ) . The evaluation of the RT-LAMP assay demonstrated a 100% sensitivity ( 95% confidence interval -CI- , 88–100% ) as all the RNA samples were positive by qRT-PCR and RT-LAMP and 80% specificity ( 95% CI , 28–99% ) , due to 1 sample negative by qRT-PCR but positive by RT-LAMP . The calculated predictive values were 97% PPV ( 95% CI , 83–99% ) and 100% NPV ( 95% CI , 39–100% ) .
Rapid diagnostic methods are emerging as cost-effective , specific and sensitive techniques for laboratories with limited resources . The use of isothermal amplification methods avoids expensive equipment as results of the reactions can be visualised by different ways , ranging from naked eye ( colorimetric detection ) to quantitative results ( spectrophotometer and fluorimeter ) or dedicated devices , such as ESE-Quant TubeScanner , T8 ( Axxin , Fairfield , Australia ) and Genie II , within a short period of time . Current CHIKV outbreaks in different parts of the world [8 , 37–39] highlight early detection ( before the onset of clinical symptoms ) is crucial to prevent virus spread , to control outbreaks and to initiate appropriate symptomatic therapy as specific treatment or vaccines are currently not available . In addition , affordable and economic techniques are required for remote rural locations in which CHIKV is currently mainly diagnosed based on clinical signs and symptoms [40] , which is not very reliable . EQA panels have been developed in order to evaluate and verify the performance and reliability of current diagnostic assays in laboratories worldwide , by using different samples ( both negative and positive samples in different concentrations ) which provide information about the specificity and sensitivity of the assays [33 , 41] . The EQA panel used in this study [33] comprises 2 of the 3 CHIKV genotypes , as well as different concentrations of 1 of the CHIKV strains , and the specificity is analysed against other arboviruses and negative samples used as negative controls . The analysis showed that the RT-LAMP developed is specific and sensitive , allowed the detection of all the CHIKV samples included in the EQA panel and no false positives were detected . In contrast , the qRT-PCR used to develop the molecular RNA standard did not detect one of the EQA panel samples ( Table 1 ) . Use of RT-LAMP is spreading and similar specificity and sensitivity levels compared to real-time RT-PCR methods are being reported [22 , 42 , 43] . Related to CHIKV detection , 2 RT-LAMP methods for detection of CHIKV have been described in 2007 and 2012 [27 , 29] , although information about the number of sequences considered for the primer design is limited . The accelerating determination of RNA virus genome sequences limits the traditional design approach in which conserved regions are identified as target regions for molecular assays in alignments of available sequences . Whereas in a recent design of a DENV RT-LAMP assay more than 2 , 000 whole genome sequences were used to design LAMP amplicons distributed across the DENV genome sequence ( DENV LAMP paper accepted to be published at PLoS Neglected Tropical Diseases ) , we here chose an already known conserved target region ( 6K-E1 ) of the CHIKV genome , which was extracted from an initial alignment of all the 110 sequences of the target region deposited in GenBank . A PCA on Single Nucleotide Polymorphism and sequence variation of the 6K-E1 region was carried out using R/adegenet . This assessment allows to discriminate the sequences according to sequence diversity and similarity . Four distinct groups ( Fig 2A ) were identified and the LAMP primer-design was carried on each individual group and all potential combinations in order to minimise the number of primer sets designed . Previously published primers [29] covered all the sequences used in this study and no mismatches were observed in the primers when the sequences were located in the viral genome . However , the other existing assay [27] covered all the sequences but only groups 1 and 3 without any mismatches . Two mismatches located in the F3 and F1c regions were observed when the primers were aligned with sequences belonging to groups 2 and 4 ( data non-shown ) . In addition , these RT-LAMP assays were validated using serum samples , including healthy samples as negative controls . All the healthy samples showed no amplification . Thirty-eight out of 69 positive samples ( acute-phase serum samples ) and 3 out of 42 positives in unknown samples were detected [27 , 29] . CHIKV RT-LAMP detection limits observed in previous studies were 20 and 27 RNA molecules detected per reaction in 30 and 77 min , respectively [27 , 29] , but these authors did not mention if the detection limits were calculated based on a probit analysis or a determined number of repetitions . Therefore , it is hard to compare the analytical sensitivity of our assay with those previously published . As a matter of fact , our RT-LAMP was capable to detect 10 RNA molecules if the run time was extended to 45 min . However , the probit analysis ( calculated after 8 independent data sets ) determined that the limit of detection was 163 molecules detected at 40 . 2 min . The specificity as shown by testing the EQA samples showed concordant results to qRT-PCR results , including sample 10 which was qRT-PCR negative , but RT-LAMP positive . The methodology was validated using RNA samples collected during a CHIKV outbreak in Senegal ( 2015 ) . The results showed a 100% concordance between the positive results obtained with the qRT-PCR used by IPD and the RT-LAMP protocol developed . In addition , the RT-LAMP seemed to be more sensitive as 11 samples out of 35 samples that initially were positive in 2015 ( “initial CT values” ) but negative in 2016 ( “current CT values ) by qRT-PCR were all detected with the RT-LAMP developed . This appears to indicate that LAMP is less affected by the storage conditions of RNA extracts than qRT-PCR and could explain why sample IPD 264842 negative by qRT-PCR was detected in 3 out of 3 LAMP replicates ( TT values ranging from 29 to 42 min ) . Indeed LAMP shows a higher robustness in terms of pH change , temperature stability and the use of untreated fluids , such as stool , blood cultures , and plant extracts that commonly inhibit PCR reactions [44 , 45] . A LAMP assay for Salmonella enterica serovar Typhi was developed and compared with an in-house qPCR , and demonstrated that LAMP reactions were specific and sensitive at pH 7 . 3–9 . 3 , temperatures between 57–67°C , and even when using samples without an extensive DNA purification , that did not yield to amplification by qPCR [44] . The optimization of another LAMP assay allowed direct testing of crude homogenates in grapevine samples without the need for DNA extraction [45] . Our observation that stored RNA extracts can be more reliably detected by RT-LAMP than by qRT-PCR after months of storage could be good news for laboratories with unstable electricity supplies causing temperature fluctuation in their freezers which affects RNA stability . The determination of clinical sensitivity , specificity , PPV and NPV allows interpretation of diagnostic results for clinical decisions [46 , 47] . The RT-LAMP developed scored a sensitivity of 100% and specificity of 80% in reference to the qRT-PCR used by IPD , which means that all samples detected as positive by the LAMP assay are truly positive but the sample IPD 264842 , negative by qRT-PCR and 3 out of 3 positives by RT-LAMP , may actually be positive , as this sample comes from a CHIKV outbreak and , as discussed before , our results demonstrated the higher sensitivity of our RT-LAMP assay , with 11 out of 35 samples initially positive in 2015 ( “initial CT values” ) , negative in 2016 ( “current CT values ) by qRT-PCR , but positive by RT-LAMP . We tested for qPCR inhibitors in sample IPD 264842 by spiking CHIKV into this sample and confirmed that no inhibition was observed by qRT-PCR ( data non-shown ) . The scores obtained for PPV and NPV estimate the probability that the disease is present or absent depending if the result is positive or negative . Since the samples were collected in an outbreak , the results obtained with the RT-LAMP ( PPV = 97% and NPV = 100% ) highlight a good performance of the method in determining true positive cases while excluding negative cases . It has to be cautioned that only 35 samples were analysed and a greater number of positive and negative samples would allow to obtain more accurate results . To conclude , a single-tube one-step real-time RT-LAMP assay was successfully designed using combined PCA and the LAVA software from 110 GenBank sequences for the conserved 6K-E1 target region . The assay was evaluated with an EQA panel from ENIVD and validated using viral RNA extracted from 35 serum samples collected during a recent CHIKV outbreak in Senegal . In comparison to qRT-PCR , the RT-LAMP appeared to show superior performance with material stored for months and can be therefore recommended for use in infrastructure poor settings .
|
Current chikungunya virus ( CHIKV ) outbreaks highlight the necessity of sensitive techniques to allow the virus detection even at an early stage ( before the onset of clinical symptoms ) . In addition , CHIKV sometimes is misdiagnosed with other pathogens ( i . e . , dengue virus or malaria ) , which implies that specific methods have to be developed . Apart from specificity and sensitivity , these techniques have to be affordable for laboratories with very limited resources , and reactions should be easily performed without the need of experienced researchers and expensive equipment . Finally , because of the increase in number of publicly available sequences , the assay should cover all the possible variations observed in those sequences . We have considered all these premises , and we were able to develop a reverse transcription loop-mediated isothermal amplification ( RT-LAMP ) by designing primers using a combination of Principal Component Analysis , phylogenetic analysis and LAVA algorithm . Our assay is specific and does not cross-react with other arboviruses tested , sensitive and has been validated at the Institut Pasteur Dakar , showing very good performance parameters .
|
[
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"Introduction",
"Materials",
"and",
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"Results",
"Discussion"
] |
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] |
2018
|
Development of a single-tube one-step RT-LAMP assay to detect the Chikungunya virus genome
|
The multidimensional computations performed by many biological systems are often characterized with limited information about the correlations between inputs and outputs . Given this limitation , our approach is to construct the maximum noise entropy response function of the system , leading to a closed-form and minimally biased model consistent with a given set of constraints on the input/output moments; the result is equivalent to conditional random field models from machine learning . For systems with binary outputs , such as neurons encoding sensory stimuli , the maximum noise entropy models are logistic functions whose arguments depend on the constraints . A constraint on the average output turns the binary maximum noise entropy models into minimum mutual information models , allowing for the calculation of the information content of the constraints and an information theoretic characterization of the system's computations . We use this approach to analyze the nonlinear input/output functions in macaque retina and thalamus; although these systems have been previously shown to be responsive to two input dimensions , the functional form of the response function in this reduced space had not been unambiguously identified . A second order model based on the logistic function is found to be both necessary and sufficient to accurately describe the neural responses to naturalistic stimuli , accounting for an average of 93% of the mutual information with a small number of parameters . Thus , despite the fact that the stimulus is highly non-Gaussian , the vast majority of the information in the neural responses is related to first and second order correlations . Our results suggest a principled and unbiased way to model multidimensional computations and determine the statistics of the inputs that are being encoded in the outputs .
There is an emerging view that the primary function of many biological systems , from the molecular level to ecosystems , is to process information [1]–[4] . The nature of the computations these systems perform can be quite complex [5] , often due to large numbers of components interacting over wide spatial and temporal scales , and to the amount of data necessary to fully characterize those interactions . Constructing a model of the system using limited knowledge of the correlations between inputs and outputs can impose implicit assumptions and biases leading to a mischaracterization of the computations . To minimize this type of bias , we maximize the noise entropy of the system subject to constraints on the input/output moments , resulting in the response function that agrees with our limited knowledge and is maximally uncommitted toward everything else . An equivalent approach in machine learning is known as conditional random fields [6] . We apply this idea to study neural coding , showing that logistic functions not only maximize the noise entropy for binary outputs , but are also special closed-form cases of the minimum mutual information ( MinMI ) solutions [7] when the average firing rate of a neuron is fixed . Recently , MinMI was used to assess the information content in constraints on the interactions between neurons in a network [8] . We use this idea to study single neuron coding to discover what statistics of the inputs are encoded in the outputs . In macaque retina and lateral geniculate nucleus , we find that the single neuron responses to naturalistic stimuli are well described with only first and second order moments constrained . Thus , the vast majority of the information encoded in the spiking of these cells is related only to the first and second order statistics of the inputs . To begin , consider a system which at each moment in time receives a -dimensional input from a known distribution , such as a neuron receiving a sensory stimulus or post-synaptic potentials . The system then performs some computation to determine the output according to its response function . The complete input/output correlation structure , i . e . all moments involving and , can be calculated from this function through the joint distribution , e . g . . Alternatively , the full list of such moments contains the same information about the computation as the response function itself , although such a list is infinite and experimentally impossible to obtain . However , a partial list is usually obtainable , and as a first step we can force the input/output correlations from the model to match those which are known from the data . The problem is then choosing from the infinite number of models that agree with those constraints . Following the argument of Jaynes [9] , [10] , we seek the model which avails the most uncertainty about how the system will respond . Information about the identity of the input can be obtained by observing the output , or vice versa , quantified by the mutual information [11] , [12] . The first term is the response entropy , , which captures the overall uncertainty in the output . The second term is the so-called noise entropy [13] , ( 1 ) representing the uncertainty in that remains if is known . If the inputs completely determine the outputs , there is no noise and the mutual information reaches its highest possible value , . In many realistic situations however , repeated presentations of the same inputs produce variable outputs producing a nonzero noise entropy [14] and lowering the information transmitted . By maximizing the noise entropy , the model is forced to be consistent with the known stimulus/response relationships but is as uncertain as possible with respect to everything else . We show that this maximum noise entropy ( MNE ) response function for binary output systems with fixed average outputs is also a minimally informative one . This approach is a special closed-form case of the mutual information minimization technique [8] , which has been used to address the information content of constraints on the interactions between neurons . Here we use the minimization of the mutual information to characterize the computations of single neurons and discover what about the stimulus is being encoded in their spiking behavior .
The starting point for constructing any maximum noise entropy model is the specification of a set of constraints , where indicates an average over the joint distribution . These constraints reflect what is known about the system from experimental measurements , or a hypothesis about what is relevant for the information processing of the system . For neural coding , the constraints could be quantities such as the spike-triggered average [15]–[18] or covariance [19]–[22] , equivalent to and , respectively . With each additional constraint , our knowledge of the true input/output relationship increases and the correlation structure of the model becomes more similar to that of the actual system . Given the constraints , the general MNE response function is given by ( see Methods ) ( 2 ) where the -dependent partition function ensures that the MNE response function is consistent with normalization , i . e . . The MNE response function in Eq . ( 2 ) has the form of a Boltzmann distribution [23] with a Lagrange multiplier for each constraint . The values of these parameters are found by matching the experimentally observed averages with the analytical averages obtained by from derivatives of [23] . Many systems in biological settings produce binary outputs . For instance , the neural state can be thought of as binary , with for the silent state and for the “spiking” state , during which an action potential is fired [13] . The inputs themselves could be a sensory stimulus or all of the synaptic activity impinging upon a neuron , both of which are typically high-dimensional [24] . Another example is gene regulation [25] , where the inputs could be the concentrations of transcription factors and the binary output represents an on/off transcription state of the gene . For these systems , the constraints of interest are proportional to . This is because any moments independent of will cancel due to the partition function and any moments with higher powers are redundant , e . g . if or 1 . In this case , the set of constraints may be written more specifically as and the MNE response function becomes the well-known logistic function ( 3 ) with . Thus for all binary MNE models , the effect of the constraints is to perform a nonlinear transformation of the input variables , , to a space where the spike probability is given by the logistic function ( inset , Fig . 1 ) . For neural coding , one of the most fundamental and easily measured quantities is the total number of spikes produced by a neuron over the course of an experiment , equivalent to the mean firing rate . By constraining this quantity , or more specifically its normalized version , the MNE model is turned into a minimum information model . This holds because the response entropy is completely determined by the distribution , which is in turn constrained by if the response is binary . With the response entropy constrained to match the experimentally observed system , maximizing the noise entropy is equivalent to minimizing information . Therefore , as was proposed in [7] , any model that satisfies a given set of constraints will convey the information that is due only to those constraints . With each additional constraint our knowledge of the correlation structure increases along with the minimum possible information given that knowledge , which approaches the true value as illustrated schematically in Fig . 1 . The simplest choice is a first order model ( ) where the spikes are correlated with each input dimension separately . This model requires knowledge of the set of moments , the spike-triggered average stimulus . For , the transformation on the inputs is linear , , where the constant is the Lagrange multiplier for the spike probability constraint . With knowledge of only first order correlations , we see that the model neuron is effectively one-dimensional , choosing a single dimension in the -dimensional input space and disregarding all information about any other directions . With higher order constraints , the transformation is nonlinear and the model neuron is truly multidimensional . For instance , the next level of complexity is a second order model ( ) , in which spikes may also interact with pairs of inputs . This model is obtained by constraining , equivalent to knowing the spike-triggered covariance of the stimulus , resulting in the input transformation . Any other MNE model can be constructed in the same fashion by choosing a different set of constraints , reflecting different amounts of knowledge . The mutual information of the MNE model is the information content of the constraints . The ratio of to the empirical estimate of the true mutual information of the system is the percent of the information captured by the constraints . This quantity is always less than or equal to one , with equality being reached if and only if all of the relevant moments have been constrained . This suggests a procedure to identify the relevant constraints , described in Fig . 2A . First , a hypothesis is made about which constraints are important . Then the corresponding MNE model is constructed and the information calculated . If the information captured is too small , the constraints are modified until a sufficiently large percentage is reached . Any constraints beyond that are relatively unimportant for describing the computation of the neuron . As an illustrative example of the MNE method , consider a binary neuron which itself receives binary inputs ( i . e . a logic gate ) . If the neuron in question receives binary inputs , we are guaranteed to capture 100% of the information with -order statistics because all moments involving powers greater than one of either or any are redundant . However , different coding schemes may encode different statistics of the inputs . For instance , if the neuron receives only two inputs ( Fig . 2B ) , the well-known AND and OR logic gate behaviors are completely described with only first order moments [26] . Correspondingly , the first order model captures 100% of the information . Such a neuron can be said to encode only first order statistics of the inputs , and the spike-triggered average stimulus contains all of the information necessary to fully understand the computation . On the other hand , the XOR gate ( Fig . 2C , left ) requires second order interactions . This is reflected by and accounting for 0% and 100% of the information , respectively . More complicated coding schemes may involve both first and second order interactions , such as for the gate shown in the right panel of Fig . 2C . Here , and account for 10% and 100% of the information , respectively , and correctly quantify the degree to which each order of interaction is relevant to this neuron . Similar situations show up for neurons that receive three binary inputs . The top panel of Fig . 2D shows an example of a neuron which only requires second order interactions . The parameters of are exactly the same as , with the third order coefficient . The bottom panel shows an example of a situation in which third order interactions are necessary . Correspondingly , increases the information explained over from 71% to 100% . These simulations demonstrate that despite the different coding schemes used by neurons , the information content of each order of interaction can be correctly identified using logistic MNE models . In their natural environment , neurons commonly encode high-dimensional analog inputs , such as a visual or auditory stimulus as a function of time . It is important to note that the non-binary nature of the inputs means that the ability to capture 100% of the information between and the inputs with -order statistics is not guaranteed anymore . Often , the dimensionality of the inputs may be reduced because the neurons are driven by a smaller subspace of relevant dimensions ( e . g . [27]–[33] ) . However , even in those cases we are often forced to use qualitative terms such as ‘ring’ or ‘crescent’ to describe the experimentally observed response functions . With no principled way of fitting empirical response functions , the details of the interactions between neural responses and reduced inputs have been difficult to quantify . The MNE method provides a quantitative framework for characterizing neural response functions , which we now apply to 9 retinal ganglion cells ( RGCs ) and 9 cells in the lateral geniculate nucleus ( LGN ) of macaque monkeys , recorded in vivo ( see Methods ) . The visual input was a time dependent sequence of luminance values synthesized to mimic the non-Gaussian statistics of light intensity fluctuations in the natural visual environment [34]–[36] . A 1s segment of the normalized light intensity is shown in Fig . 3A . A previous study has shown that the responses of these neurons are correlated with the stimulus over an approximately 200 ms window preceding the response . When binned at 4 ms resolution , which ensures binary responses , the input is a vector in a 50 dimensional space . However , spikes are well predicted by using a 2 dimensional subspace [29] identified through the Maximally Informative Dimensions ( MID ) technique [37] . These two relevant dimensions , shown for a RGC in Fig . 3B , form a two dimensional receptive field which preserves the most information about the spikes in going from 50 to 2 dimensions . The two linear filters are convolved with the stimulus to produce reduced inputs and , shown in Fig . 3A . The resulting input probability distribution in the reduced space is shown in Fig . 3C . The measured responses of the neuron then form a two-dimensional response function shown in Fig . 3D , where the color scale indicates the probability of a spike as a function of the two relevant input components . To gain insight into the nature of this neuron's computational function and find the important interactions , we apply the MNE method starting with the first order MNE model shown in Fig . 3E . The first order model produces a response function which bears little resemblance to the empirical one and accounts for only of the information . The next step is a second order MNE model ( Fig . 3F ) , which produces a response function quite similar to the empirical one in both shape and amplitude , while accounting for of the information . Thus , for this neuron , knowledge of second order moments is both necessary and sufficient to generate a highly accurate model of the neural responses . This result was typical across the population of cells , as illustrated in Fig . 4A by comparing the information captured by the first order versus second order models . The majority of the cells were well described by the second order model , accounting for over of the information . When averaged across the population , the first order model captured and the second order model captured of . These results suggest that the inclusion of second order interactions are both necessary and sufficient to describe the responses of these neurons to naturalistic stimuli . Since the MNE response function is a distribution of outputs given inputs , another way to check the effectiveness of any MNE model is to compare its moments with those obtained from experiments . The moments constrained to obtain the model will be identical to the experimental values by construction; it is the higher order moments , left unconstrained , that should be compared . In Fig . 4B we show two such comparisons for the correlation functions and , which involve moments unconstrained in the and models . In both cases , the first order model predictions show more scatter than those of the second order model; the latter does a reasonable job of predicting the experimentally observed correlations . This result broadly demonstrates the sufficiency of second order interactions to model these neural responses , and shows that higher-order moments carry little to no additional information . The two-dimensional second order MNE response functions have contours of constant probability which are conic sections . The parameter which governs the interaction between the two input dimensions , , is related to the degree to which the axes of symmetry of the conic sections are aligned with the two-dimensional basis . For example , if the contours are ellipses , then if the semi-major and semi-minor axes are parallel to the axes chosen to describe the input space , and otherwise ( see inset , Fig . 5 ) . To assess the importance of this cross term , we compared the performance of second order MNE models with and without . This additional term can only improve the performance of the model; however , as shown in Fig . 5 , the improvements across the population are small . Thus , the dimensions found using the MID method are naturally parallel to the axes of symmetry of the response functions; however , this does not imply that the response function is separable due to the dependence of the normalization term .
For neural coding of naturalistic visual stimuli in early visual processing , we see that the bulk of what is being encoded is first order stimulus statistics . While the information gained by measuring the spike-triggered average is substantial , it is insufficient to accurately describe the neural responses . A second order model , which takes into account the spike-triggered input covariance , adds a sufficient amount of information . Thus the firing rates of these neurons have encoded the first and second order statistics of the inputs . Due to the fact that the natural inputs are non-binary and non-Gaussian , there exists a potential for very high-order interactions to be represented in the neural firing rate . It is known that higher order parameters of textures are perceptually salient [38]–[40] , but it is unknown whether high order temporal statistics are also perceptually salient . Our results suggest that such temporal statistics are not encoded in the time-dependent firing rate , although they could be represented through populations of neurons or specific temporal sequences of spikes [41] , [42] . Jaynes' principle of maximum entropy [9] , [10] has a long and diverse history , with example applications in image restoration in astrophysics [43] , extension of Wiener analysis to nonlinear stochastic transducers [44] and more recently in neuroscience [45]–[47] . In the latter studies , was maximized subject to constraints on the first and second order moments of the neural states and for a set of neurons in a network . The resulting pairwise Ising model was shown to accurately describe the distribution of network states of real neurons under various conditions . Since then the application of the Ising model to neuroscience has received much attention [48] , [49] , and it is still a subject of debate if and how these results extrapolate to larger populations of neurons [50] . Temporal correlations have also been shown to be important in both cortical slices and networks of cultured neurons [47] . In contrast to maximum entropy models that deal with stationary or averaged distributions of states , the goal of maximizing the noise entropy is to find unbiased response functions . This approach is equivalent to conditional random field ( CRF ) models [6] in machine learning . The parameters of a CRF are fit by maximizing the likelihood using iterative or gradient ascent algorithms [51] and have been used , for example , in classification and segmentation tasks [52] . The parameters of MNE models may also be found using maximum likelihood , or as was done here , by solving a set of simultaneous constraint equations numerically . Another example of a maximum noise entropy distribution is the Fermi-Dirac distribution [23] from statistical physics , which is a logistic function governing the binary occupation of fermion energy levels . Thus , in the same way that the Boltzmann distribution was interpreted by Jaynes as the most random one consistent with measurements of the energy , the Fermi-Dirac distribution can be interpreted as the least biased binary response function consistent with an average energy . However , to our knowledge , this method has never been used in the context of neural coding to determine the input statistics which are being encoded by a neuron and create the corresponding unbiased models . Previous work has applied the principle of minimum mutual information ( MinMI ) [7] to neural coding , thus identifying the relevant interactions between neurons [8] . We have shown that the closed-form MNE solutions for binary neurons constitute a special case of MinMI , since the response entropy is fixed if the average firing rate is constrained . In general , the MinMI principle results in a self-consistent solution that must be solved iteratively to obtain the response function . The reason why MNE models are closed-form is that the constraints are formulated in terms of moments of the output distribution instead of the output distribution itself . In addition to the case of binary responses , MNE models can become closed-form MinMI models for any input/output systems where the response entropy can be fixed in terms of the moments of the output variable . Examples include Poisson processes with fixed average response rate or Gaussian processes with fixed mean and variance of the response rate . The framework for analyzing the interactions between inputs and outputs that we present here can thus be extended to a broad and diverse set of computational systems . Our approach can be compared to other optimization techniques commonly used to study information processing . For example , rate-distortion theory [11] , [12] , [53] , [54] seeks minimum information transmission rate over a channel with a fixed level of signal distortion , e . g . lossy image or video compression . In that case , the best solution is the one which transmits minimal information because this determines the average length of the codewords . In our method , we also obtain minimally informative solutions , not because they are optimal for signal transmission , but because they are the most unbiased guess at a solution given limited knowledge of a complex system . At the other end of the optimization spectrum is maximization of information [1] , [13] , [55] . The goal in that case is to study not how the neuron does compute , but how it should compute to get the most information , perhaps with limited resources . This strategy has been used to find neural response functions for single neurons [56] , [57] , as well as networks [58] , [59] . When confronted with incomplete knowledge of the correlation structure , a maximum information approach would choose the values of the unconstrained moments such that they convey the most information possible , whereas the minimum information approach provides a lower bound to the true mutual information , and allows us to investigate how this lower bound increases as more moments are included . If the goal is to study the limits of neural coding , then maximizing the information may be the best procedure . If , however , the goal is to dissect the computational function of an observed neuron , we argue that the more agnostic approaches of maximizing the noise entropy or minimizing the mutual information are better-suited .
Experimental data were collected as part of the previous study using procedures approved by the UCSF Institutional Animal Care and Use Committee , and in accordance with National Institutes of Health guidelines . A maximum noise entropy model is a response function which agrees with a set of constraints and is maximally unbiased toward everything else . The constraints are experimentally observed moments involving the response and stimulus , , where , which must be reproduced by the model . The set of constraints , including the normalization of , are then added to the noise entropy to form the functional ( 4 ) with a Lagrange multiplier for each constraint . Setting and enforcing normalization yields Eq . 1 . For a binary system , or 1 , all the constraints take the form , and the partition function is , where The values of the Lagrange multipliers are found such that the set of equations ( 5 ) is satisfied , with the analytical averages on the right-hand side obtained from derivatives of the free energy [23] . Simultaneously solving this set of equations has previously been shown to be equivalent to maximizing the log-likelihood [51] . The neural data analyzed here were collected in a previous study [29] and the details are found therein . Briefly , the stimulus was a spot of light covering a cell's receptive field center , flickering with non-Gaussian statistics that mimic those of light intensity fluctuations found in natural environments [35] , [36] . The values of light intensities were updated every ( update rate ) . The spikes were recorded extracellularly in the LGN with high signal-to-noise , allowing for excitatory post-synaptic potentials generated by the RGC inputs to be recorded . From such data , the complete spike trains of both RGCs and LGN neurons could be reconstructed [60] . The neural spike trains were binned at 4 ms resolution , ensuring that the response was binary . The stimulus was re-binned at 250 Hz to match the bin size of the spike analysis . The neurons were uncorrelated with light fluctuations beyond 200 ms before a spike , and the stimulus vector was taken to be the 200 ms window ( 50 time points ) of the stimulus preceding . Just two projections of this 50-dimensional input are sufficient to capture a large fraction of the information between the light intensity fluctuations and the neural responses ( for the example neuron mn122R4_3_RGC , and on average across the population ) . The two most relevant features of each neuron were found by searching the space of all linear combinations of two input dimensions for those which accounted for maximal information in the measured neural responses [37] , subject to cross-validation to avoid overfitting . Each of the two features , and , is a 50-dimensional vector which converts the input into a reduced input , calculated by taking the dot product , i . e . . The algorithm for searching for maximally informative dimensions is available online at http://cnl-t . salk . edu .
|
Biological systems across many scales , from molecules to ecosystems , can all be considered information processors , detecting important events in their environment and transforming them into actions . Detecting events of interest in the presence of noise and other overlapping events often necessitates the use of nonlinear transformations of inputs . The nonlinear nature of the relationships between inputs and outputs makes it difficult to characterize them experimentally given the limitations imposed by data collection . Here we discuss how minimal models of the nonlinear input/output relationships of information processing systems can be constructed by maximizing a quantity called the noise entropy . The proposed approach can be used to “focus” the available data by determining which input/output correlations are important and creating the least-biased model consistent with those correlations . We hope that this method will aid the exploration of the computations carried out by complex biological systems and expand our understanding of basic phenomena in the biological world .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"physics",
"neuroscience/sensory",
"systems",
"computational",
"biology/computational",
"neuroscience",
"computational",
"biology",
"neuroscience/theoretical",
"neuroscience"
] |
2011
|
Minimal Models of Multidimensional Computations
|
Increased pain sensitivity is a comorbidity associated with many clinical diseases , though the underlying causes are poorly understood . Recently , chronic pain hypersensitivity in rodents treated to induce chronic inflammation in peripheral tissues was linked to enhanced tryptophan catabolism in brain mediated by indoleamine 2 , 3 dioxygenase ( IDO ) . Here we show that acute influenza A virus ( IAV ) and chronic murine leukemia retrovirus ( MuLV ) infections , which stimulate robust IDO expression in lungs and lymphoid tissues , induced acute or chronic pain hypersensitivity , respectively . In contrast , virus-induced pain hypersensitivity did not manifest in mice lacking intact IDO1 genes . Spleen IDO activity increased markedly as MuLV infections progressed , while IDO1 expression was not elevated significantly in brain or spinal cord ( CNS ) tissues . Moreover , kynurenine ( Kyn ) , a tryptophan catabolite made by cells expressing IDO , incited pain hypersensitivity in uninfected IDO1-deficient mice and Kyn potentiated pain hypersensitivity due to MuLV infection . MuLV infection stimulated selective IDO expression by a discreet population of spleen cells expressing both B cell ( CD19 ) and dendritic cell ( CD11c ) markers ( CD19+ DCs ) . CD19+ DCs were more susceptible to MuLV infection than B cells or conventional ( CD19neg ) DCs , proliferated faster than B cells from early stages of MuLV infection and exhibited mature antigen presenting cell ( APC ) phenotypes , unlike conventional ( CD19neg ) DCs . Moreover , interactions with CD4 T cells were necessary to sustain functional IDO expression by CD19+ DCs in vitro and in vivo . Splenocytes from MuLV-infected IDO1-sufficient mice induced pain hypersensitivity in uninfected IDO1-deficient recipient mice , while selective in vivo depletion of DCs alleviated pain hypersensitivity in MuLV-infected IDO1-sufficient mice and led to rapid reduction in splenomegaly , a hallmark of MuLV immune pathogenesis . These findings reveal critical roles for CD19+ DCs expressing IDO in host responses to MuLV infection that enhance pain hypersensitivity and cause immune pathology . Collectively , our findings support the hypothesis elevated IDO activity in non-CNS due to virus infections causes pain hypersensitivity mediated by Kyn . Previously unappreciated links between host immune responses to virus infections and pain sensitivity suggest that IDO inhibitors may alleviate heightened pain sensitivity during infections .
Enhanced pain sensitivity is a hallmark of inflammation and is a debilitating feature of many clinical diseases , including chronic Human Immunodeficiency Virus-1 ( HIV-1 ) infections [1 , 2] . However the underlying causes of chronic pain remain poorly defined [3] . Pain hypersensitivity in rats with inflamed joints correlated with elevated IDO expression in brain , and pain hypersensitivity induced following treatments to induce chronic inflammation did not manifest in mice lacking intact IDO1 genes [4] . These findings were interpreted as evidence that sustained inflammation in tissues outside the central nervous system ( CNS ) induced IDO activity in brain that was the underlying cause of pain hypersensitivity . However , it is not known how local inflammation in peripheral ( non-CNS ) tissues induces IDO expression in brain , nor is it clear if IDO mediates pain hypersensitivity in other inflammatory syndromes . We tested the hypothesis that virus infections enhance pain sensitivity by stimulating IDO using murine models of acute or chronic virus infection in which IDO enzyme activity is elevated . Acute influenza A virus ( IAV ) infection stimulates robust increase in lung IDO activity which wanes after virus clearance [5] . Murine Leukemia retrovirus ( MuLV , LP-BM5 strain ) is a natural mouse pathogen that causes persistent infections and pathologies that resemble aspects of human immunodeficiency virus-1 ( HIV-1 ) infections , including sustained IDO activity in lymphoid tissues [6–8] . The role of IDO in MuLV pathogenesis is controversial . A previous study indicated that genetic and pharmacologic IDO ablation led to enhanced interferon type 1 production and increased resistance to MuLV infection [6] . In contrast , a later report found no differences in viral loads and immune pathologies between IDO-sufficient ( WT ) mice and mice lacking intact IDO1 genes [7] . As MuLV infection also induces peripheral neuropathy and pain hypersensitivity [9 , 10] we hypothesized that induced host IDO activity mediates pain hypersensitivity during MuLV infection . We show that IAV and MuLV infections increased pain hypersensitivity via an IDO-dependent mechanism and that a distinctive splenic DC subset expressing the B cell marker CD19 enhanced pain sensitivity in MuLV-infected mice .
IAV respiratory infections in mice stimulate IDO enzyme activity in lungs and lung-draining ( mediastinal ) lymph nodes [5] . To test if IAV infection incited pain hypersensitivity we assessed mechanical nociception ( pain ) thresholds by applying mechanical stimuli ( von Frey filaments ) of increasing force to hind paws until mice responded as described in Methods . As soon as one day post infection ( dpi ) paw withdrawal thresholds ( PWT ) were reduced significantly , relative to baseline thresholds in the same mice before infection ( Fig 1A ) . Increased pain sensitivity persisted during IAV infection and returned to basal levels 2–3 days after IAV clearance at 7-8dpi [5] . In contrast , no significant change in pain sensitivity manifested during IAV infections in IDO1-deficient ( IDO1-KO ) mice ( Fig 1A ) . Thus IDO1 genes were necessary to incite pain hypersensitivity during respiratory IAV infections . MuLV ( LP-BM5 strain ) causes persistent infections and progressive pathologies , including polyneuropathy and pain hypersensitivity [9–11] . Some features of MuLV infections resemble aspects of clinical HIV-1 infections , including elevated IDO activity in lymphoid tissues [6 , 7 , 12] . Consistent with previous studies [9 , 10] , MuLV-infection in B6 ( WT ) mice bred locally caused progressive increase in pain sensitivity until 18dpi , and levels remained elevated thereafter ( Fig 1B and S1A Fig ) . Increased pain sensitivity also manifested in B6 mice purchased from a commercial supplier ( Taconic ) , though pain sensitivity was slightly less severe and took longer to develop , relative to outcomes in B6 mice bred locally ( S1B Fig ) . Pain sensitivity increased in MuLV-infected IDO1-KO mice relative to naïve mice ( Fig 1B and S1B Fig ) , though responses in IDO1-KO mice were significantly less intense than pain hypersensitivity induced in WT mice . Profound reduction in MuLV-induced pain hypersensitivity did not correlate with major changes in virus titers or host immunopathogenesis ( splenomegaly , immunosuppression , cytokine induction ) since these parameters were comparable in WT and IDO1-KO mice ( S1C–S1F Fig ) , as reported previously [7] . Thus transient and sustained increase in pain sensitivity manifested during acute IAV or chronic MuLV infections , respectively , and these responses to virus infection depended on IDO1 gene expression but were not linked to changes in virus infection kinetics or host immunopathogenesis . To test if pharmacologic IDO inhibition alleviated pain hypersensitivity the IDO inhibitor 1-methyl-[D]-tryptophan ( D-1MT , 2mg/ml ) was administered continuously in drinking water to B6 mice with established MuLV infections ( 60dpi ) . Oral D-1MT treatment for 20 days led to significant reduction in pain sensitivity , relative to controls given vehicle only ( Fig 1C ) . It is unclear why oral D-1MT treatment did not alleviate pain hypersensitivity more robustly . As spleen IDO activity increased markedly during MuLV infections ( Fig 1E ) D-1MT may reduce but not abolish spleen IDO activity in this model , though potential off-target effects of D-1MT cannot be excluded . Exposing naïve and MuLV-infected IDO1-KO mice to the natural tryptophan catabolite kynurenine ( Kyn , 200μg/mouse , i/v ) led to rapid increase in pain sensitivity; this effect was more severe in MuLV-infected than in uninfected ( naïve ) IDO1-KO mice ( Fig 2D ) , suggesting that Kyn may synergize with cytokines co-induced by MuLV infection to enhance pain sensitivity . Thus cells expressing IDO1 cause pain hypersensitivity in MuLV-infected mice and Kyn released by cells expressing IDO may mediate this response . Consistent with a previous study [6] , spleen IDO activity increased progressively during MuLV infection until IDO activity was >100-fold higher at 120dpi ( Fig 1E ) . However IDO activity was undetectable in spleens of MuLV-infected IDO1-KO mice ( Fig 1E ) , indicating that IDO1 genes exclusively encoded MuLV-induced IDO enzyme activity , not IDO2 and tryptophan dioxygenase ( TDO ) genes encoding enzymes with similar functions . In contrast , at early or late stages of MuLV-infection IDO enzyme activity ( Fig 1F ) and IDO1 gene transcription in CNS tissues ( S2B Fig ) were not elevated significantly over basal levels . To identify cells expressing IDO and MuLV ( GAG ) genes discrete cell populations were purified by flow cytometry from spleens of MuLV-infected B6 mice and gene transcription was assessed using quantitative RT-PCR . Splenocytes were stained with CD11c and CD19 mAbs since melanoma growth and other inflammatory insults induce selective IDO expression by a discrete subset of dendritic cells ( DCs ) expressing the B cell marker CD19 [13–16] . At early ( 28dpi , Fig 2A ) and later ( 56dpi , Fig 2B ) stages in MuLV infection when immune pathology partially or fully manifests IDO1 transcripts were detected exclusively in sorted spleen cells expressing CD11c and CD19 ( CD19+ DCs ) . Increased IDO1 transcription was not detected in any other spleen cells , including sorted B cells and conventional ( CD19neg ) DCs . At early times in MuLV infection ( 28dpi ) , ecotropic helper ( GAGEco ) and pathogenic ( GAGDef ) MuLV retrovirus genes were transcribed at high levels in sorted cells expressing neither CD19 or CD11c ( CD11cnegCD19neg ) and to lesser extents in CD19+ DCs , relative to sorted conventional DCs and B cells ( Fig 2A ) . At later stages in infection ( 56dpi ) , GAGDef transcription was still relatively high in CD11cnegCD19neg and CD19+ DCs , though GAGEco transcription was relatively high in conventional and CD19+ DCs but not in CD11cnegCD19neg cells ( Fig 2B ) . However , GAGDef and GAGEco transcription remained relatively low in B cells ( CD19+CD11cneg ) at later stages of MuLV . Given previous reports of B cell-specific LP-BM5 expression [17] , these findings suggest that LP-BM5 replicates in cells not expressing CD19 or CD11c and in DCs , as well as in B cells . Alternatively , co-selection of ( or contamination by ) DCs expressing CD19 may explain previous reports of selective MuLV infection in B cells since CD19 and CD11c are commonly used to discriminate between B cells and DCs . Thus CD19+ DCs were highly susceptible to MuLV infection and were the only spleen cells induced to express IDO1 during MuLV infection . To test if increased IDO1 transcription by CD19+ DCs led to increased IDO enzyme activity FACS-sorted CD19+ and conventional ( CD19neg ) DCs from MuLV-infected Act-mOVA transgenic mice expressing ovalbumin ( OVA ) in all cells [18] were cultured alone or with splenocytes from ( OVA ) -specific OT1 ( CD8 ) or OT2 ( CD4 ) TCR transgenic mice and Kyn production was assessed after 3 days . Kyn levels increased significantly in cultures containing sorted CD19+ DCs and OT-2 T cells but Kyn was not detected in cultures containing sorted DCs alone , or sorted DCs cultured with OT-1 T cells ( Fig 2C ) . Thus interactions with OT2 T cells were essential for IDO activity to manifest in CD19+ DCs from MuLV-infected mice . Consistent with these findings , anti-CD4 mAb treatments to deplete CD4 cells in vivo reduced IDO activity ( Fig 2D ) and IDO1 transcription ( Fig 2E ) in spleen to basal levels observed in naïve mice . Collectively , these data show that interactions with CD4 T cells is essential for CD19+ DCs to express functional IDO in MuLV-infected mice and during culture . CD19+ DCs are a minor DC population in spleens of naïve mice ( Fig 3A , <1% of splenocytes and ~10% of splenic DCs ) . CD19+ DCs expanded substantially after MuLV infection , accounting for ~10% of splenocytes and ~50% of splenic DCs at 35dpi ( Fig 3B and 3C ) . Because MuLV infection induces splenomegaly absolute numbers of CD19+ DCs expanded >100-fold relative to numbers in spleens of naïve mice . Conventional ( CD19neg ) DCs also expanded in this period , while relative proportions of splenic B cells were reduced substantially ( Fig 3A and 3B ) . Phenotypic analyses revealed striking differences in maturation between CD19+ DCs and conventional DCs in MuLV-infected mice . At 35dpi , CD19+ DCs expressed uniformly high levels of MHC class II ( MHC II ) and CD80 ( Fig 3D ) characteristic of mature antigen presenting cells ( APCs ) . In contrast , conventional DCs expressed lower and more variable levels of MHC II and CD80/86 ( Fig 3D ) comparable with levels on immature DCs in naïve mice . Thus CD19+ DCs expanded and matured as APCs while conventional DCs also expanded but remained immature during MuLV infection . Higher proportions of splenic DCs stabilized ~28dpi after MuLV infection and in vivo labeling with 5-ethynyl-2-deoxyuridine ( EdU ) at 14-21dpi revealed larger cohorts of dividing ( EdU+ ) splenic CD19+ ( ~30% ) and conventional ( ~16% ) DCs than B cells ( <5% ) from MuLV-infected B6 mice ( Fig 3E and 3F ) . In vivo treatment with depleting anti-CD4 mAbs reduced EdU incorporation by CD19+ and conventional DCs significantly ( S3 Fig ) . Thus MuLV infection induced selective CD19+ DC maturation and DC proliferation , as well as selective IDO expression by CD19+ DCs dependent on interactions with CD4 T cells . We tested if adoptive transfer of splenocytes from B6 mice with fully established MuLV infections ( 56-70dpi ) enhanced pain hypersensitivity in naïve IDO1-KO recipients . Recipients were sublethally irradiated ( 3 . 5Gy ) to facilitate donor cell chimerism and six days after transfer of splenocytes from MuLV-infected B6 ( WT ) donors pain sensitivity was assessed in IDO1-KO recipients . Adoptive transfer of splenocytes from MuLV-infected WT donors caused pain hypersensitivity ( Fig 4A ) , which was sustained until experimental endpoints 32 days after transfer . Splenocyte induced pain hypersensitivity was only slightly less than pain hypersensitivity due to MuLV-infection ( Fig 1B ) . Though splenocytes from MuLV-infected IDO1-KO donors also induced significant increase in pain sensitivity ( Fig 4A ) these responses were significantly less pronounced than responses to splenocytes from MuLV-infected WT donors; moreover , sublethal irradiation may drive some increase in pain sensitivity . Thus IDO was the major driver of pain sensitivity following splenocyte transfer . To complement this approach , we tested if in vivo DC ablation alleviated pain hypersensitivity using transgenic B6 mice expressing human diphtheria toxin receptor ( DTR ) under control of DC-specific CD11c gene promoters ( CD11cDTR mice ) . MuLV-infected CD11cDTR mice ( 56-70dpi ) were treated with diphtheria toxin ( DT , 10ug/kg , i/p , x2 ) to ablate DCs and pain thresholds were monitored . DT treatment reduced pain sensitivity rapidly and significantly in MuLV-infected CD11cDTR mice relative to MuLV-infected CD11cDTR mice not exposed to DT and to control naïve WT mice given DT ( Fig 4B ) . As expected , at experimental endpoints ( 6 days post DT treatment ) the proportions of splenic CD19+ DCs were reduced significantly in DT-treated , MuLV-infected CD11cDTR mice but DT treatment had no effects on CD19+ DCs in MuLV-infected WT ( B6 ) mice ( Fig 4C ) . DT treatment also reduced spleen IDO enzyme activity ( Fig 4D ) and IDO1 gene transcription ( Fig 4F ) in MuLV-infected CD11cDTR mice significantly , as levels were comparable to basal levels in naïve ( uninfected ) mice 6 days after DT treatment . Remarkably , spleen weights were also reduced rapidly and significantly following DT treatment ( Fig 4E ) . In contrast , DT treatment had no significant effects on IDO activity , IDO1 transcription or splenomegaly in MuLV-infected WT ( B6 ) mice ( Fig 4D–4F ) , indicating that the effects of DT treatment were due to ablation of cells expressing DTR . Thus in vivo depletion of splenic DCs alleviated pain hypersensitivity in MuLV-infected IDO-sufficient mice and this response was not caused by DT treatment per se . Collectively , these data reveal that splenic DCs expressing IDO mediate sustained pain hypersensitivity in MuLV-infected B6 mice .
IDO1 mediated pain and depression in rodents with chronic limb joint inflammation and elevated IDO1 expression in brain hippocampus correlated with these responses [4] . Using the spared nerve injury ( SNI ) model , Zhou et al . reported that IDO1 expressed in liver mediated depression but did not enhance mechanical pain sensitivity in this model [19] . In the current study we show that acute influenza ( IAV ) and chronic retroviral ( MuLV ) infections enhanced mechanical pain sensitivity and that IDO1 ablation alleviated these responses to virus infection . IAV and MuLV infections stimulate IDO activity in lungs and peripheral lymphoid tissues , respectively . IAV infections induced rapid increase in IDO activity in lung epithelial cells and in DCs located in lung-associated lymph nodes , and IDO activity at these sites returned to basal levels a few days after virus clearance [5] . Pain sensitivity correlated with changed IDO activity during and after IAV infection , consistent with a causative link between IAV-induced IDO activity and pain sensitivity . Similarly , spleen IDO activity correlated with progressive increase in pain sensitivity that peaked before immunopathologies associated with chronic MuLV infections manifested fully . Pain hypersensitivity peaked faster in mice bred locally than in previous reports [9 , 10] but onset of peak pain hypersensitivity was slower and comparable with previous studies when mice from a commercial supplier were used , suggesting that mouse husbandry factors influence the kinetics of pain hypersensitivity induced by MuLV infection . This point notwithstanding , IDO1 ablation alleviated pain hypersensitivity almost completely during MuLV infection . Thus IDO activity encoded by IDO1 genes caused acute pain hypersensitivity during IAV infections and was the major factor driving progressive pain hypersensitivity during persistent MuLV infections . Furthermore , IDO2 and tryptophan dioxygenase ( TDO ) genes encoding enzymes with identical tryptophan catabolizing activities did not compensate for loss of IDO1 genes to enhance pain sensitivity during IAV or MuLV infections . Pro-inflammatory cytokines such as IL-6 , TNFα and IL-1β have been reported to enhance pain sensitivity [20] . IDO may mediate or synergize with these effects since IDO is co-induced with pro-inflammatory cytokines in many settings of inflammation because interferons are potent IDO inducers . However , IDO1 ablation was sufficient to block induction of pain hypersensitivity during IAV and MuLV infections , which stimulate production pro-inflammatory cytokine responses . CD19+ DCs were the only cell type induced to express IDO in spleen during MuLV infections and IDO1 expression and enzyme activity were not elevated above basal levels ( in naïve mice ) in CNS tissues from mice with established MuLV infections . These findings suggested that sustained IDO activity in peripheral lymphoid tissues was sufficient to incite pain hypersensitivity in MuLV-infected mice . Consistent with this interpretation , adoptive transfer of splenocytes from MuLV-infected IDO1-sufficient mice caused pain hypersensitivity in IDO1-deficient recipients , while selective DC depletion in vivo alleviated pain hypersensitivity in MuLV-infected IDO1-sufficient mice . While the possibility that IAV and MuLV infections induce IDO activity in CNS tissues to incite pain sensitivity cannot be excluded fully , our findings that splenic DCs from MuLV-infected IDO1-sufficient mice and Kyn enhanced pain sensitivity in IDO1-deficient mice suggest that increased IDO activity in peripheral tissues is sufficient to enhance pain sensitivity . Cells expressing IDO may activate local sensory neurons in peripheral tissues directly or Kyn produced by IDO-expressing cells may act on peripheral or CNS neurons to enhance pain sensitivity . Collectively , our findings support the hypothesis that sustained IDO expression by cells in lungs and splenic CD19+ DCs of mice infected with IAV and MuLV , respectively , mediate pain hypersensitivity during infection . Previously , we reported that splenic CD19+ DCs expressed IDO in response to melanoma growth and inflammatory insults that induce interferon type I production , including B7 and TLR9 ligands , DNA nanoparticles and apoptotic cells [13–16 , 21 , 22] . Moreover , CD19+ DCs resemble ‘age-associated B cells ( ABCs ) ’ that accumulate in spleens of aged female mice , in Nba2 mice prone to lupus-like syndromes , and in patients with rheumatoid arthritis [23 , 24] . ABC accumulation in aged female mice was TLR7-dependent , suggesting that endogenous retroviral RNA sensing may promote ABC expansion . Similar considerations may explain why CD19+ DCs expanded as mature APCs and expressed IDO selectively during MuLV infection . CD19+ DCs exhibited potent T cell regulatory phenotypes dependent on IDO and interactions between CD4 T cells and DCs were essential to sustain IDO activity in DCs and to promote Foxp3-lineage regulatory CD4 T cell ( Treg ) differentiation and activation [25 , 26] . Likewise , interactions between CD4 T cells and CD19+ DCs were necessary to sustain IDO activity in CD19+ DCs from MuLV-infected mice . It is unclear if MuLV antigen-specific interactions between CD19+ DCs and CD4 T cells induce IDO activity early in MuLV infection and if these interactions promote Treg differentiation and activation . However exogenous antigens were not required to induce splenic CD19+ DCs to express IDO following systemic B7 or TLR9 ligands and DNA nanoparticle treatments [13–15] , suggesting that self antigens or antigen-independent pathways induced CD19+ DCs to express IDO and activate Tregs in vivo . For example , CD4 T cells may produce IFNγ or stimulate innate immune cells to express IFNαβ to induce IDO during MuLV infection . Previously , critical roles for B cells and CD4 T cells in MuLV-induced immune pathogenesis were described [27 , 28] . Our findings suggest that CD19+ DCs , not conventional B cells , play key roles in MuLV pathogenesis . CD19+ DCs are closely related to B cells and express many B cell markers but are a distinct cell lineage with DC attributes [21] . During MuLV-infection CD19+ DCs were distinguished from conventional B cells by exhibiting mature APC phenotypes , higher susceptibility to MuLV infection , enhanced proliferation and IDO expression dependent on CD4 T cell interactions . A previous report described that IDO ablation led to increased plasmacytoid DCs and interferon type I production in response to MuLV infection and to enhanced survival of mice infected with MuLV alone or with MuLV and Toxoplasma gondii [6] . In contrast , we found no significant effects of genetic or pharmacologic IDO ablation on the course of MuLV infection or immunopathogenesis , consistent with a previous study by O’Connor and Green using IDO1-KO mice , which also revealed that IDO is not critical , or has a redundant role in regulating host immune responses to MuLV infection [7] . The reason for these disparate outcomes is unclear . Nevertheless , DC depletion led to rapid reduction in splenomegaly , suggesting that DCs may play pivotal and previously unappreciated roles in immunopathologies associated with chronic MuLV infection . However IDO ablation also had little impact on the course of acute IAV infections in mice , though primary CD8 responses were more robust and IAV-specific memory CD8 T cell repertoires differed in the absence of IDO [5] . IDO activity is also elevated in patients with persistent HIV-1 or HTLV1 retrovirus infections , indicating that increased IDO activity is a common response to retroviral infection in mice and humans [12 , 29] . It is unclear if IDO contributes to virus control or immunopathogenesis in these clinical syndromes , though previous reports have described human pDC subsets that can express IDO and acquire T cell regulatory phenotypes as a consequence [30 , 31] . Despite the lack of evidence supporting a role for IDO in host-virus immune control and immunopathogenesis , the current study reveals that IDO is a major contributory factor driving pain hypersensitivity during acute IAV and chronic MuLV infections . Thus sustained , elevated IDO activity may also contribute to chronic pain associated with acute and persistent clinical infections in humans . If so , inhibition of IDO may help alleviate heightened pain sensitivity induced as a common comorbidity associated with virus infections . The mechanism by which cells induced to express IDO enhance pain sensitivity during IAV and MuLV infections is unclear . A previous study by Kim and colleagues concluded that increased brain IDO activity mediated pain hypersensitivity in rodent models of experimentally induced arthritis [4] . This conclusion was based on findings that microinjecting IDO inhibitor directly into rat brain hippocampus abolished pain hypersensitivity , while microinjecting IL-6 stimulated brain IDO expression in this rodent arthritis model . Findings from the current study suggest that elevated brain IDO activity may not be necessary to induce pain hypersensitivity during IAV and MuLV infections in mice since lung and lymphoid tissues were the primary sites of elevated IDO activity during IAV and MuLV infections , respectively . Furthermore , increased IDO expression and activity was not detected in CNS tissues of MuLV-infected mice and adoptive transfer of splenocytes from IDO1-sufficient mice with chronic MuLV infections or injection of Kyn heightened pain sensitivity in IDO1-deficient mice . Thus elevated IDO activity in non-CNS tissues was necessary and sufficient to induce pain hypersensitivity during IAV and MuLV infections . It is also unclear how increased IDO activity in lungs or lymphoid tissues leads to elevated sensitivity to mechanical stimulation in hind paws . Though unlikely that limb extremities are impacted directly by IAV and MuLV infection , inflammatory cells expressing IDO or Kyn produced by distal tissues may enter limb extremities or CNS tissues during viral infections and heighten pain sensitivity via direct affects on local nervous tissues . How increased IDO activity mediates increased pain sensitivity is not understood . IDO catabolizes both serotonin and its precursor tryptophan to generate neuroactive quinolinic ( QA ) and kynurenic ( KA ) acids , which mediate diametric neuropathologic and neurodegenerative effects via N-methyl-D-aspartate ( NMDA ) receptors expressed by neuronal tissues to induce pain and behavioral responses [32] . Unlike QA and KA , which can only cross the blood brain barrier via passive diffusion , Kyn is transported efficiently across the blood brain barrier via large neutral amino acid L-system transporters [33] , and may be converted QA and KA to drive neurological responses that enhance pain sensitivity via NMDA receptors expressed in the CNS . Alternatively Kyn generated in non-CNS tissues during IAV or MuLV infections may be converted to QA and KA in non-CNS tissues to promote peripheral neuropathies that heighten pain sensitivity . Kyn is also a weak ligand for aryl hydrocarbon receptors ( AhR ) expressed by multiple cell types in CNS and non-CNS tissues and modulation of AhR signaling during IAV and MuLV infections may also contribute to peripheral neuropathies that drive increased pain sensitivity . Thus Kyn generated by IDO activity anywhere in the body may potentiate pain sensitivity by interactions of Kyn catabolites with nerve cells in CNS or non-CNS tissues . Interestingly , muscular exercise reduced stress-induced depression by promoting Kyn uptake and catabolism into KA by muscle tissues , thus decreasing the potential of Kyn and its catabolites to cause neurologic effects [34] . This finding suggests that muscular exercise may also to alleviate pain hypersensitivity during infections by reducing Kyn availability . In summary , acute IAV and chronic MuLV virus infections enhanced pain sensitivity by elevating IDO activity to increase Kyn availability . Links between host inflammatory responses to infection , elevated tryptophan catabolism and increased pain sensitivity suggest that the common co-morbidities of pain and behavioral disorders associated with many progressive inflammatory diseases of clinical significance may arise due to under-appreciated metabolic responses to inflammatory insults such as tumor growth and autoimmunity , as well as infections .
B6 mice were purchased from Taconic ( Hudson , NY ) or bred in a barrier ( SPF ) facility at GRU . IDO1-KO mice , CD11cDTR , OT1 and OT2 TCR transgenic mice were described previously [15 , 22] . IAV A/PR/8/34 ( PR8 ) propagated in embryonated chicken eggs was kindly provided by Ralph Tripp ( University of Georgia Athens , GA ) . Mice were infected with a non-lethal IAV dose ( 30pfu , 30% of LD50 ) as described [5] . SC1/G6 cells infected with MuLV ( LP-BM5 ) were obtained from the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH [8] . SC1 and XC cells were kind gifts from William Green ( Dartmouth ) . 400ul of SC1/G6 culture supernatant were injected ( i/v ) to infect mice . Ecotropic ( Eco ) retrovirus titers in supernatants were determined by XC plaque assay [35] . 1-5x104 pfu Eco retrovirus was injected per mouse [9] . Adoptive transfer of splenocytes from MuLV-infected mice was accomplished by exposing naïve IDO1-KO recipients to sub-lethal radiation ( 3 . 5Gy ) to create space in hematologic niches . Mechanical nociception was assessed using von Frey filaments ( North Coast Medical Inc , Gilroy , CA ) to determine paw withdrawal threshold ( PWT ) as described [36 , 37] . In brief , mice were stimulated on both hind paws using a series of von Frey filaments ranging in force from 0 . 008g to 2g , starting with the 0 . 008g filament . Positive responses were scored as paw withdrawal occurring two or more times in response to ten successive stimulations . In the event of negative responses , mice were then stimulated with monofilaments of stepwise increasing force . The monofilament that first evoked a positive response was designated the threshold ( in grams ) and no further monofilaments were applied . Cells were analyzed on a LSRII flow cytometer ( Becton-Dickinson ) . Data were analyzed using FACS DIVA ( BD Bioscience ) or FlowJo ( Tree Star , Ashland , OR ) software . An Aria flow sorter ( Becton-Dickinson ) was used for sort spleen cells from MuLV-infected mice under BSL2 conditions . Spleen cells from MuLV-infected mice were stained with PE-conjugated rat anti-mouse CD19 ( clone 1D3 , BD Biosciences ) and APC or PECy7-conjugated hamster anti-mouse CD11c ( clone N418 , eBioScience ) . Cells were sorted into chilled polypropylene collection tubes ( RPMI , 10% FCS ) for culture or resuspended ( RPMI , 5% FCS ) and cell lysis solution was added ( Omega Bio-Tek , Norcross , GA ) to prepare RNA for analysis . In vivo Ethynyl deoxyluridine ( EdU ) labeling and staining were performed using Click-iT Plus EdU flow cytometry assay kits ( Life Technologies ) following manufacturer’s instructions with minor modifications . Briefly , 1mg of EdU was injected into each mouse ( i/p ) and tissues are harvested four hours later . Spleen cells were surface stained with antibodies then fixed and permeablized followed by incubation with fluorophore conjugated azide . Cells are then washed and analyzed on a BD FACS LSRII flow cytometer . 1-methyl-[D]-tryptophan ( D-1MT , Indoximod ) was kindly provided by NewLink Genetics Inc . D-1MT was administered in sweetened drinking water ( 2mg/ml ) as described [15] . IDO enzyme activity was measured by assessing Kyn produced by cell-free tissue homogenates or present in cell cultures using HPLC as described [5] . RNA was purified using HP total RNA kits ( Omega Bio-Tek , Norcross , GA ) , reverse-transcribed using a random hexamer cDNA RT kit ( Clontech , Mountain View , CA ) , and quantitative RT-PCR was performed using an iQ5 or CFX system with SsoFast EvaGreen supermix ( Bio-Rad , Hercules , CA ) . Primers for murine β-actin were ( forward ) 5′-TACGGATGTCAACGTCACAC-3′ and ( reverse ) 5-AAGAGCTATGAGCTGCCTGA-3′ . Validated primers for murine IDO1 were purchased ( realtimeprimers . com ) . Relative expression of GAGEco and GAGDef were evaluated as described [38] . Threshold cycle ( Ct ) values were set in the early linear amplification phase; relative expression was calculated as 2Ct ( β-actin ) − Ct ( target gene ) . Time courses of mechanical nociception ( PWT ) were analyzed by two-way ANOVA with multiple comparisons . Unpaired Student t tests were used to analyze data generated in all other experiments . Two-tailed p values <0 . 05 were considered significant . GraphPad Prism was used to perform all data analyses . 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 protocols were reviewed and approved by the Animal Care and Use Committee at the Georgia Regents University ( AUP#2011–0330 ) . Gene ID: 15930; Ensembl: ENSMUSG00000031551; Vega: OTTMUSG00000020648
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Chronic pain is a factor in diseases that afflict many people , yet the underlying causes of pain are poorly understood . Here we assess the effects of virus infections on pain sensitivity in mice . Infecting mice with two different viruses , influenza and mouse leukemia virus ( MuLV ) increased pain sensitivity . Influenza infection caused transient increase in pain sensitivity , which returned to normal levels after infections were cleared . However persistent MuLV infections caused sustained increase in pain sensitivity . Virus-induced pain sensitivity was reduced substantially in mice lacking the enzyme indoleamine 2 , 3 dioxygenase ( IDO ) , which degrades the amino acid tryptophan . Moreover a natural compound produced by cells expressing IDO enhanced pain sensitivity when administered to mice lacking IDO genes . Thus cells expressing IDO caused increased pain sensitivity in infected mice . A distinctive cell type expressed IDO selectively and accumulated in spleens of MuLV-infected mice . Transfer of spleen cells from MuLV-infected mice caused increased pain sensitivity in uninfected mice while eliminating specific cells in MuLV-infected mice abolished enhanced pain sensitivity . Our findings show that host immune responses to virus infections cause increased pain sensitivity and suggest novel ways to alleviate pain during infections .
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2016
|
Virus Infections Incite Pain Hypersensitivity by Inducing Indoleamine 2,3 Dioxygenase
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The importance of host-specialization to speciation processes in obligate host-associated bacteria is well known , as is also the ability of recombination to generate cohesion in bacterial populations . However , whether divergent strains of highly recombining intracellular bacteria , such as Wolbachia , can maintain their genetic distinctness when infecting the same host is not known . We first developed a protocol for the genome sequencing of uncultivable endosymbionts . Using this method , we have sequenced the complete genomes of the Wolbachia strains wHa and wNo , which occur as natural double infections in Drosophila simulans populations on the Seychelles and in New Caledonia . Taxonomically , wHa belong to supergroup A and wNo to supergroup B . A comparative genomics study including additional strains supported the supergroup classification scheme and revealed 24 and 33 group-specific genes , putatively involved in host-adaptation processes . Recombination frequencies were high for strains of the same supergroup despite different host-preference patterns , leading to genomic cohesion . The inferred recombination fragments for strains of different supergroups were of short sizes , and the genomes of the co-infecting Wolbachia strains wHa and wNo were not more similar to each other and did not share more genes than other A- and B-group strains that infect different hosts . We conclude that Wolbachia strains of supergroup A and B represent genetically distinct clades , and that strains of different supergroups can co-exist in the same arthropod host without converging into the same species . This suggests that the supergroups are irreversibly separated and that barriers other than host-specialization are able to maintain distinct clades in recombining endosymbiont populations . Acquiring a good knowledge of the barriers to genetic exchange in Wolbachia will advance our understanding of how endosymbiont communities are constructed from vertically and horizontally transmitted genes .
The increasing availability of genomic data for closely related strains and species enables bacterial population sizes and structures to be explored in far greater detail than was possible until now . A major question is whether asexually reproducing bacterial cells are organized into “clusters” that contain genetic diversity , yet are distinguishable from each other [1]–[4] . Such clusters can arise through geographic isolation or extreme habitat specialization [5] . Whether bacteria that are not separated by any physical or geographic barriers can evolve into distinct groups is less clear , but studies of free-living bacteria such as Vibrio , Synechococcus and Bacillus have suggested that the formation of sequence clusters correlate with ecological specialization [6]–[8] . Likewise , a recent study of thermophilic archaea indicated ongoing speciation and suggested that these species are maintained by ecological differentiation within hot springs [9] . Studying the mechanisms and selective forces that influence the organization of genetic diversity in unicellular organisms is important for our understanding of speciation processes . In bacteria , recombination between incipient species can potentially be an important factor affecting speciation . In a speciation model whereby populations diverge mainly through neutral processes alone , sequence divergence depends on the ratio of recombination to mutation [10]–[12] . In an ecological model of speciation , adaptive and ecological divergence of incipient species instead depends on the ratio of the selection intensity against recombined , niche-determining genes from the other population to the recombination rate between these populations [13]–[16] . If however , the populations are geographically isolated they may diverge regardless of their potential to recombine . In any of these models , the distinctness of sequences of incipient species can be enhanced by periodic selection , the success of which depends on the rate of recombination within populations . Finally , recombination can be a source of adaptation whereby one species can acquire an adaptive gene from another species . The rate at which substitutions are introduced into a genome by recombination relative to mutation events ( r/m ) varies by more than two orders of magnitude in bacteria [17] . The highest r/m ratios ( >50 ) have been observed for oceanic bacteria of the SAR11 clade [18] , which are the most abundant bacteria in the upper surface waters of the oceans and have been shown to lack the mismatch repair system [19] . The lowest r/m ratios ( <0 . 5 ) have been associated with obligate host-associated bacteria , such as Buchnera aphidicola [20] and other endosymbionts , that have co-evolved with their hosts for hundreds of millions of years . Such long-term co-evolution serves as a strong physical barrier to gene exchange between bacteria adapted to different hosts . In effect , these highly specialized endosymbiont populations are perhaps best described as distinct taxonomic units , or species . Wolbachia is an obligate intracellular symbiont infecting various species of arthropods and filarial nematodes , where it is maternally inherited through the germ line cells [21] . In arthropods , Wolbachia is most known for the ability to manipulate the reproduction of their hosts in various ways , which include induction of parthenogenesis , feminization , male killing , and cytoplasmic incompatibility ( CI ) [21] . In filarial nematodes Wolbachia is mutualistic and necessary for normal development and fertility [22] . In addition to these roles , several studies have emerged in recent years indicating that Wolbachia may also have other functions , such as providing ATP for the host [23] , improving longevity [23] or fecundity [24] , protection against viruses [25] , [26] and uptake of iron [27] . Unlike maternally inherited mutualistic endosymbionts that have been co-evolving with their hosts , arthropod Wolbachia can be lost and gained from the host population and they show high recombination frequencies [17] . Wolbachia is currently defined as a single species , which is further classified into a number of divergent supergroups ( A–N ) . The most well studied supergroups are A and B that infect arthropods and C and D that infect filarial nematodes [28] , [29] . The supergroup classification scheme was originally proposed based on single-gene phylogenies [30] , and more recently supported by multi-locus sequence typing [31] . Since these analyses suggested that Wolbachia supergroups represent genetically distinct clades , it is debated whether some or all of these groups should be designated different species [32] . However , due to high levels of recombination between super-groups in a few marker genes such as the surface protein wsp [33] and frequent exchange of phage DNA [34] , it is unclear whether the super-group classification scheme is representative of the genomes overall . Moreover , no phenotypic traits have been identified that correlate with the separation of arthropod-infecting strains into different supergroups . On the contrary , strains of different supergroup affiliation may display similar phenotypic traits and host ranges . For example , double infections with super-group A and B strains have been found in many insects , and the induction of cytoplasmic incompatibility is common in both supergroups . The distribution of other phenotypic traits is less well investigated [23]–[27] . In the absence of strong host-specialization patterns , niche partitioning within hosts provides an alternative mechanism of speciation . To evaluate the extent of recombination and identify the ecological and physiological features that may explain the separation into supergroups , genome data is required . However , the sequencing of obligate endosymbionts such as Wolbachia is not trivial , since these bacteria are often present in low abundance in their hosts and cannot be cultivated outside of their hosts . Some protocols specifically designed to extract DNA from Wolbachia have been developed in recent years [35] , [36] but the preparation of enough DNA for sequencing is still very time-consuming for obligate host-associated bacteria with low infection densities . Because of these challenges , genomic data is currently only available for a few Wolbachia strains . These are the two supergroup A strains , wMel infecting Drosophila melanogaster [37] and wRi infecting Drosophila simulans [38] and the genomes of one supergroup B strain , wPip , from the mosquito Culex quinquefasciatus [39] , and one supergroup D strain wBm isolated from the nematode Brugia malayi [40] . Early draft genomes have also been presented for two other supergroup B strains , namely wAlbB infecting the mosquito Aedes albopictus [41] and wVitB infecting the parasitic wasp Nasonia vitripennis [42] . Genome sizes are small , in the range of 1 . 5 Mb . Recombination has been shown to be prevalent between strains that belong to supergroup A , suggesting that Wolbachia is a highly recombining intracellular community [38] . Genomes in the A and B-supergroups contain between 20 to 60 ankyrin repeat genes . Although it is generally thought that these genes play a key role in host-interaction processes and may be involved in the reproductive phenotypes , it has been difficult to pinpoint the particular functions of these genes . Wolbachia strains wHa and wNo are especially interesting in the context of this discussion since they share several phenotypic traits , but belong to different supergroups . Importantly , both strains cause CI in their host Drosophila simulans , where they occur as natural double infections in populations on the Seychelles and in New Caledonia [43] . Strain wHa has also been found as a single infection on Hawaii and in French Polynesia , but natural populations of D . simulans infected only with wNo are very rare [44] . Several studies support the hypothesis that the double infection originated on the Seychelles and spread east to the Indo-Pacific islands , where after wNo was lost from some populations [45]–[47] . Notably , a double-infection very similar to the one found for D . simulans is also found in the sister species Drosophila sechellia , which is endemic to the Seychelles . Furthermore , D . simulans and D . sechellia have very similar mitochondrial genomes , despite significant divergence in the nuclear genome . It therefore seems likely that the Wolbachia double-infection preceded the speciation event between D . simulans and D . sechellia . A recent study has estimated the time to a common ancestor of the D . simulans subcomplex ( including D . simulans , D . sechellia and D . mauritiana ) to be ∼242 . 000 years ago [48] , suggesting that the co-infection originated at least a few hundred thousand years ago . In this study , we present a new method for the preparation of DNA from Wolbachia , based on multiple-displacement amplification that enables genome data to be collected for Wolbachia strains with low infection densities . We have applied this protocol to the sequencing of the genomes of Wolbachia strains wHa and wNo that are co-infecting D . simulans . By comparative analysis of these and previously sequenced Wolbachia genomes , we have analyzed whether genomic features such as recombination , genome rearrangements and gene acquisitions could explain the separation of Wolbachia strains into distinct supergroups . The findings are discussed in light of the species concept for bacteria .
We developed a novel procedure for the isolation and amplification of Wolbachia DNA present in low quantity in the insect hosts . In brief , Wolbachia cells were purified from embryos of Drosophila simulans and multiple-displacement amplification ( MDA ) was performed directly on the isolated bacterial cells ( see Materials and Methods ) . Single and 3 kb paired-end sequence reads were collected from the amplified DNA using the 454 sequencing technology and assembled de novo . The sequence coverage obtained from each data set was very large , and we therefore only used 10% to 30% of the data for assembly with Mira ( Table 1 ) . The proportion of single and paired-end reads that assembled was estimated to between 96–97% and 86–88% , respectively ( Table S1 ) . The 454 sequence reads in the assembly had mean and median sizes of 300 to 400 bp , whereas the median length of the 454 sequence reads that did not assemble was less than 100 bp ( Table S1 ) and of lower quality ( Figure S1 ) . Illumina paired-end reads were mapped onto the assembly to correct for frameshift errors generated by the 454 technology . The overall coverage of the wHa and wNo genomes in the final assemblies was about 40 to 80-fold for the 454 data and 100 to 200-fold for the Illumina data ( Table 1 ) . All gaps were closed by PCR on non-amplified DNA , confirming the reliability of the scaffolds obtained from the sequence data of the amplified DNA . In two positions in the wHa genome , located 20 kb apart and containing a long repeat of 7 . 5 kb with 5 genes , the PCR reactions failed from one side . However , single reads and read pairs supported the connection between the repeats and the unique sequences flanking each of the two copies . This is the first demonstration that the MDA method can be applied in order to generate complete genome sequences from a small number of starting cells of uncultivable bacterial endosymbionts . The MDA method is known to produce amplification bias and chimeric reads when applied to single cells , which prevents genome closure . We considered the risk that such artifacts could have influenced the final genome sequence , but found these artifacts to be less dominant when multiple endosymbiont cells were used to start the reaction . Importantly , the entire Wolbachia genomes were represented by the sequence data in the final assemblies although coverage was unevenly distributed across the genome ( Figure S2 ) . The same coverage pattern was observed irrespectively of the method used for sequencing ( Figure S2A–S2C ) , suggesting that the amplification bias is not random , but probably determined by the primer sets included in the amplification kit . The fraction of chimeric reads in the single-end 454 sequence library was about 1% , which is considered normal according to the Newbler manual . As expected , the percentage was higher for the 454 paired-end reads , about 13–14% ( Figure S3 ) , but part of these chimeric read pairs might have been generated during library preparation rather than during the MDA reaction . Even though some regions have a higher amount of chimeric reads , we do not believe that they have had a significant effect on the assembly , since the coverage of these putative chimeric reads closely follow the coverage distribution of non-chimeric reads and hence regions with high amounts of chimeric reads also have high amounts on non-chimeric reads ( Figure S3 ) . We conclude that the overall fraction of chimeric reads was too low to have an effect on the assembly . In retrospect , we mapped the individual sequence reads back to the Wolbachia genome and estimated that more than 97% of all reads represented Wolbachia DNA ( Table 1 ) . The remaining few percent was mostly derived from mitochondrial DNA from Drosophila simulans , with little or no nuclear DNA in the preparation . However , a manual search in the non-assembled sequences produced by the Mira assembly software of the wNo sample revealed the presence of wHa reads in low quantities ( Figure S2D ) . Since most of the wHa genome was covered but no nuclear DNA was detected , it is unlikely that these reads were derived from bacterial sequences integrated into the host nuclear genome . Rather , we believe that there may have been a slight contamination of wHa during sample preparation and sequencing , or that the double-infected line from which wNo was generated was not completely cured of wHa . No wNo reads were found in any of the amplified DNA samples for wHa . In conclusion , the large majority of sequence data generated by the MDA method was of good quality , not chimeric and covered the entire genome with little or no contamination of nuclear DNA . The wNo and wHa genomes are 1 . 3 Mb in size and contain circa 1 , 000 genes , which corresponds to a coding density of about 80% ( Table 2 ) . This is comparable to the fraction of coding DNA in the previously sequenced Wolbachia genomes with the exception of wMel , in which a larger fraction of short open reading frames were identified as genes , resulting in a higher estimated coding density of 94% ( Table 2 ) . As in all previously sequenced genomes of arthropod Wolbachia , several phage-derived fragments were identified . The wHa genome contains two such regions , one of which encodes a nearly complete WO-phage . The wNo genome contains four segments of putative phage origin , of which the two larger fragments together contain all conserved parts of the WO-phage . Pseudogenes were identified in all four phage segments in the wNo genome , making it unlikely that any of them could individually produce phage particles . We observed a similar number of putatively functional IS elements in the two genomes , 12 in wHa and 14 in wNo ( Table S2 ) . Additionally , we identified 58 defective IS elements in wHa , of which 17 were defective IS3 elements . No defective IS3 elements were present in the wNo genome , which only contained a total of 14 defective IS elements . Adding the wHa and wNo genomes to the previously produced draft and complete Wolbachia genome sequences , we tested the robustness of the supergroup classification scheme using three A-group ( wHa , wRi , wMel ) and three B-group ( wNo , wPip , wAlbB ) strains . We identified 660 orthologous core genes present in all six genomes . A phylogenetic analysis with the maximum likelihood method based on a concatenated alignment of the core genes supported the separation of the two supergroups with 100% bootstrap support , and further suggested that wRi and wHa are most closely related within the A-group , and that wPip and wAlbB are sister taxa within the B-group ( Figure 1 ) . Consistently , gene order structures were largely conserved within supergroups , but highly scrambled in all pair-wise comparisons of A- and B-group genomes ( Figure 2 ) . Thus , the classification of these strains into two supergroups is strongly supported by both the sequences and the architectures of the Wolbachia genomes . To test the hypothesis that recombination mediates cohesion within supergroups but is reduced between supergroups , we examined the topologies of single gene trees , studied the spread of sequence divergence estimates , and inferred the relative fraction of intragenic recombination events both within and across the supergroup boundaries . Novel gene acquisitions may confer the ability to inhabit new niches . In the case of endosymbionts , the acquisition of a new gene might potentially broaden the host range , but could also lead to ecological specialization within the existing host . For example , the uptake of a novel gene might contribute to the physical separation of strains with and without the new gene , leading to speciation . To investigate this hypothesis , we examined gene content differences between the two supergroups . In total , we identified 33 and 24 protein clusters that were specific to the A- and B-group genomes , respectively ( Tables S5 , S6 ) . A comparison of the number of protein clusters solely present in the A- or B-supergroup strains to the number of protein clusters found in any other combination of three strains showed that the supergroup specific protein clusters are largely over-represented ( Figure S8 ) . Functional categorization of these clusters identified a few particularly interesting acquisitions in the A-group strains of genes putatively involved in the regulation of arginine transport systems ( argR ) , stress response ( cydAB ) and modulation of host cellular functions ( fic ) . Phylogenetic analyses revealed sequence similarities to several other intracellular bacteria , such as Legionella , Rickettsia and Chlamydia , indicating that these genes may serve a role for the intracellular lifestyle ( Figure 7 , Figure S9 ) . As in Legionella pneumophila , the gene for the arginine repressor ArgR is co-located with three genes for an arginine ABC transporter and phylogenetic reconstruction confirmed the close affiliation between Wolbachia and Legionella of the entire cluster of four genes ( Figure 7 , data not shown ) . Previous studies of other pathogenic bacteria have shown that arginine may be associated with virulence . Additionally , arginine can be converted to nitric oxide by the host as part of the innate immune response . In Legionella , the expression of the genes for the arginine repressor and transporter is sensitive to the presence of L-arginine and derepression is observed during intracellular growth [50] . In analogy , we infer that the Wolbachia ABC-transporters are expressed when the concentration of arginine is low , stimulating uptake of arginine through the ABC-transporters . The Fic domain proteins solely present in the A-group strains are particularly interesting since their homologs in other bacteria have been shown to be secreted into the host cell cytoplasm to modify host regulatory GTPases [51] , thereby causing the disruption of the host actin cytoskeleton [52] , [53] or host cellular rearrangements [54] . The ability to manipulate host GTPases is most likely a general feature of all proteins containing this protein domain since it has also been reported in the distantly related Fic-domain containing human HYPE protein [53] . The comparison of gene contents also indicates possible differences in cell division and lipid II biosynthesis due to gene loss in the B-group strains . For example , the ftsWIBL genes , which are involved in these processes in E . coli are present in the A-group strains , but absent from the B-group strains and present only as pseudogenes in wBm . Additionally , the murC gene , which catalyses the attachment of the first amino acid to the glycan , has been split into two genes located distantly from each other in the genomes of all B-group strains , including wVitB . One of the two genes encodes the N-terminal domain and the other encodes the C-terminal domain fused to a recombinase zinc beta ribbon domain ( PFAM: PF13408 ) ( Figure S10 ) . Interestingly , experimental evidence has shown that a lipid-II-like molecule is synthesized in the supergroup B Wolbachia strain wAlbB [55] , suggesting that the murC gene function is present despite the separation of the sequences encoding the functional domains into two genes . Uniquely present in the B-group strains is a cluster of genes encoding outer membrane proteins which are found in two to three copies in each of the supergroup B genomes , including wVitB . Located at the corresponding genomic position in the A-group strains is a non-coding region of approximately 1 kb , which does not show any significant sequence similarity to genes in the B-group strains ( Figure S11A ) . Eight of the nine proteins in this cluster contain PFAM domains annotated as outer-surface proteins , including the family to which the Wolbachia surface protein ( wsp ) belongs ( PF01617 ) . A phylogenetic analysis revealed a clustering of genes between the strains , rather than within the genome of one strain , suggesting that gene duplication occurred before divergence of these B-group strains ( Figure S11B ) . Short sequence fragments with significant similarity to these surface proteins were identified in the wBm genome , indicating loss from supergroup A . However , since no homologs outside Wolbachia supergroup B could be identified , the origin and function of this outer membrane protein family remain to be determined .
High recombination frequencies were previously estimated for strains belonging to super-group A [38] , and confirmed in this study in both supergroup A and B . Single-gene phylogenies showed all possible divergence patterns for strains within each supergroup in nearly equal proportions , and the spread of the relative dS values for individual genes within supergroups was very high ( 0 . 3–0 . 4 ) , which is in the range of the naturally competent and highly recombinogenic bacterial pathogen Neisseria meningitidis ( Spread = 0 . 34 ) [38] . Thus , there is a very strong bias for substitutions caused by recombination within Wolbachia supergroups , which suggests that there is very little selection against recombination within Wolbachia supergroups , consistent with the species concept . For endosymbionts , co-evolution with hosts is thought to generate a physical barrier that leads to the evolution of ecologically distinct species . This is exemplified by a strong congruence of Wolbachia and host phylogenies for nematode-infecting strains [60] , [61] . However , for Wolbachia strains infecting insects , host and endosymbiont phylogenies are generally not congruent [30] , [31] , [62] . Strains of different supergroups can infect the same host species , as exemplified by wHa and wNo in this study , just as strains of the same supergroup , such as wHa and wMel , can infect different host species . Furthermore , there is no simple association between supergroup affiliation and reproductive disorders , since strains of both supergroups are capable of inducing for example cytoplasmic incompatibility . Yet , our analysis shows that there are differences in gene content between supergroup A and B , which are likely to influence the interactions with the host and the surrounding environment . Notable among the A-group specific functions are genes for uptake of arginine , tolerance to stress and secretion of proteins involved in the modulation of host cellular functions , whereas the B-group specific gene set included genes for outer surface structures . Thus , our data raise the possibility that the supergroups might have evolved into distinct ecotypes within the same host species , potentially avoiding competition through niche partitioning and thereby achieving a stable co-existence . Although niche partitioning has not yet been investigated for hosts infected with multiple Wolbachia strains , Veneti et al . [63] demonstrated that Wolbachia strains of different supergroups show distinct localization patterns within the host embryo . The A-group strains ( with the exception of wRi ) were localized to the posterior part of the embryo , whereas the B-group strains were observed in the anterior part during the syncytial blastoderm stage . However , only a few highly similar strains of each supergroup were included in the analysis , and it remains to be determined whether the observed patterns are characteristic of a broader selection of strains from the two supergroups . Physical separation of endosymbionts within hosts does not necessarily have to be absolute to allow for speciation , since quantitative differences in associations with different habitats might also generate ecologically distinct species [6] . In analogy , differences in abundances and/or compartmentalization within the host could potentially lead to ecologically distinct species . Indeed , distinct localization patterns of endosymbionts within hosts have for example been observed for different genera of whiteflies [64] . While the current overlapping host ranges of supergroup A and B and the occurrence of multiple infections with strains from both groups appears to contradict the possibility of host specialization , several studies have provided some evidence for specialization to hosts and/or habitats [65]–[68] . However , these studies were based small gene datasets , such as the wsp gene that code for a hypervariable surface protein and/or core genes used in multi locus sequence typing . If these genes are as recombinogenic in all Wolbachia strains as reported here , sequence similarity measures within supergroups will reflect gene recombination histories rather than strain relationships . Correlations between genotypes and host-association patterns within supergroups will thus mostly depend more on the gene sets selected for the analyses . In conclusion , both experimental evidence and additional genome data is needed in order to evaluate the ecological distinctness of Wolbachia strains both within and between supergroups . Now that a supergroup specific gene repertoire has been identified , it should be possible to investigate both the ecological roles of these genes , as well as the strain localization at various stages of host development . All evidence gathered in this study indicates that strains from different supergroups represent distinct clusters , but are they irreversibly separated or do they still exchange genetic material ? Importantly , our analyses have shown that recombination events between supergroup A and B have occurred , but that the fragments are of shorter sizes and have had a much lower impact on the genomes than recombination events within the groups . Recombination events that span over all or most of a gene are very rare since only 8 of the 660 gene trees did not provide support for the supergroup division . Consistently , we only identified a few long recombination tracts between the supergroups . These few transfers of co-located genes might thus exemplify how one organism can acquire another population's adaptation while the integrity of its own niche-defining characteristics is still preserved . The wHa and wNo genomes are thought to have co-infected D . simulans for at least 200 , 000 years , which is a relatively short time period compared to at least a few million years since the divergence of the A and B-groups ( as inferred from a few % difference in their 16S rRNA genes ) . Hence , even though we do not find more recombination between wHa and wNo than between other strains belonging to different supergroups , we cannot exclude the possibility that the exchange of genetic material between them would increase given longer time . Alternatively , there is some form of barrier to genetic exchange between strains of supergroup A and B . The simplest form of barrier to gene transfer is the presence of incompatible mobile elements . However , we do not think that this is the case in Wolbachia since the gene phylogenies indicated transfer of phage genes across the supergroup boundary . Moreover , transfer of a complete bacteriophage genome between strains of different supergroups was recently discovered in Nasonia vitripennis [42] . Even so , there is no concrete evidence that these phages regularly transfer genetic material other than their own genomes , and thus there could still be differences in the frequencies at which genetic material is transferred between the two supergroups . Another form of barrier is that the sequence divergence per se limits recombination . The mismatch repair system has been seen to prevent homologous recombination between divergent sequences and loss of the mutSL genes for the mismatch repair system is known to cause dramatic increases in both mutation and homologous recombination frequencies . However , even though we found that the mutS gene is full-length and probably functional in both the A and B-group Wolbachia strains , and that all genomes except the wRi genome have two copies of the mutL gene , we found no inverse correlation between sequence divergence levels and recombination frequencies for individual genes . In natural populations , the mutS gene recombines and is gained and lost in a cyclic manner in response to environmental changes , leading to altered mutation and recombination rates . The resulting mutator phenotypes are selected during periods of environmental fluctuations and then restored by recombination with a functional copy from another strain [69] . We saw that in Wolbachia , one of the mutL genes is associated with a prophage element in wPip and wHa and located near to a previously detected insertion in wMel that might stem from a phage , indicative of horizontal gene transfer . Additionally , we detected intra-genic recombination in both the mutS and mutL genes . Thus , the presence of a seemingly functional mismatch repair system all strains analyzed does not preclude that recombination frequencies could have fluctuated in the past due to gains and losses of these genes . A recent model suggests that almost identical sequences between the donor and recipient are required at one or both ends of a recombination fragment in order for recombination to occur and that the imported fragments are digested until a good enough match is obtained [70] . Consistent with our data , this model predicts that shorter recombination tracts will be found between more divergent sequences , since more cuts are required in order for the ends to match . Essentially , if true , this implies that when two genomes have diverged enough only short fragments can recombine between them . As a consequence , it is unlikely that recombination events are sufficient to invoke convergence between the supergroups even though they share the same habitat for a long period of time , as is the case with the Wolbachia strains wHa and wNo . Although we did not see a correlation between sequence diversity and intragenic recombination , this model cannot be ruled out since the end points of each recombination fragment were not investigated . Genome rearrangements present yet another barrier to recombination and is thereby an important factor in speciation processes in eukaryotic organisms , mainly because of suppressed recombination at rearranged sites during meiosis in heterozygous individuals [71] . Although bacteria do not evolve by sexual reproduction , homologous recombination could be suppressed in chromosomal regions that are not co-linear because of rearrangements or insertions of genes in one of the two genomes . Indeed , a recent study showed that recombination frequencies are suppressed close to lineage-specific genes , which might lead to higher divergence levels in their vicinity [72] . Furthermore , long recombination events can only occur if the target genome has a similar gene order . Since a single long recombination event can override several shorter intra-genic recombination fragments , extensive rearrangements could contribute to the separation of the lineages . The genomes of Wolbachia strains that belong to the same supergroup show much higher colinearity than strains of different supergroups , potentially contributing to the observed lower frequency of recombination events between the A and B supergroups . In summary , a number of different explanations could account for the observed reduced level of recombination between supergroup A and B . Although we do not know whether there has been selection against recombination between supergroups or if the reduced levels of recombination was driven by neutral processes alone , our results strongly suggest that the A and B supergroups have now become irreversibly separated . The acquisition of advantageous novel genes or mutations is hypothesized to trigger speciation events according to the ecotype model of speciation , which has so far only been evaluated for free-living bacteria [73] . In this context , it is notable that we have identified supergroup-specific genes sets that appear to be the result of horizontal gene transfers . Although it is too early to speculate about the functions of these group-specific genes , it is quite possible that their acquisitions induced significant phenotypic changes . Selective advantages associated with any of these phenotypes could have purged diversity within the groups , thereby contributing to the genetic separation of the two lineages . Another scenario could be that the loss or gain of genes in one strain of Wolbachia resulted in reproductive isolation between infected hosts , for example through CI [74] , [75] . However , it is difficult to evaluate the likelihood for such a scenario , since multiple infections and recent horizontal transmission of Wolbachia strains between different host-species have blurred the ancestral patterns of infections . Alternatively , the speciation event may have been triggered or enhanced by extensive rearrangements , due to a burst in the activity of IS-elements . All Wolbachia genomes from supergroup A and B sequenced to date contain an unusually high level of IS-elements . For example , 11% of the genome of Wolbachia strain wRi was estimated to consist of IS-elements , and 17 of the 35 identified breakpoints between the genomes of wMel and wRi are located at IS-elements [38] . Additionally , many of the IS elements in Wolbachia genomes carry mutations that are likely to have rendered these elements non-functional , which is an unusual feature of bacterial IS-elements since they are commonly believed to have a rapid turnover rate within genomes [76] . Making use of the presence of these degraded IS-elements , a recently published simulation study aiming to explain the distribution of IS copies in the modern Wolbachia genomes suggested two major periods of intense transpositional activity , a very recent burst and an ancient expansion of the most divergent IS copies [77] . Such an expansion could have induced major changes in gene order structures , leading to suppressed recombination close to the breakpoints . Since two rearranged genomes can never converge to the same gene orders again , an ancestral expansion of IS-elements followed by genome rearrangements could have irreversibly separated the two groups . However , since the age of the ancestral expansion is not known , it is difficult to test this hypothesis . The recent expansion of IS-elements in Wolbachia could potentially have lead to similar diversifications in more closely related strains , a hypothesis that could be tested by investigating diverse lineages within the same supergroups . It is obvious that no single speciation hypothesis will be applicable to all bacteria . Although Wolbachia is an obligate intracellular bacterium , it is atypical in that it is a generalist with a high prevalence and a broad host range in a diverse group of insects . The most remarkable aspect of its evolution is the expansion of the host range , which might have occurred independently in both supergroups after their separation . The acquisition of genes to manipulate the host combined with high recombination frequencies to shuffle beneficial alleles among all members in the group could help explain much of this ability . Many questions remain to be solved , such as for example if there is adaptive selection for ecological divergence within supergroups , and if strains from different supergroups inhabit different niches within their broad range of host species . To further investigate speciation processes in Wolbachia , we need to study the global distribution patterns and population structures of hosts and endosymbionts . The methods developed in this paper offer the possibility to perform such large-scale , whole-genome surveys of Wolbachia and other endosymbionts .
The wNo-infected fly line was generated by a series of backcrosses on a double-infected fly line collected on Noumea in 1989 [78] . The wHa-infected fly line was collected on Hawaii in 1990 , as a natural single-infection [79] . Both Wolbachia-infected fly lines have been kept at the laboratory of Prof . Kostas Bourtzis for over fifteen years and have extensively been used in Wolbachia-related experimental work . The purification of Wolbachia cells was carried out as in [39] , with some modifications . Flies were allowed to oviposit on apple-juice agar for two hours , and 15–30 embryos were collected for the purification . The embryos were dechorionated in bleach , rinsed with water , and homogenized in phosphate-buffered saline ( PBS ) buffer with a sterile micropestle . The homogenate was centrifuged at 400 x g for 5 min to pellet large debris , including host nuclei . The supernatant was centrifuged at 5 , 400 x g for 5 min to pellet Wolbachia cells . The pellet was re-suspended in PBS , and another slow centrifugation was carried out ( 400 x g for 5 min ) to remove remaining debris . The supernatant was passed first through a 5 µm pore size filter ( Millipore , Bedford , MA ) , and then through a 2 . 7 µm pore size filter ( Whatman , USA ) . The filtrate was centrifuged at 6 , 900 x g for 15 min to pellet the Wolbachia cells . Most of the supernatant was removed , leaving a bacterial pellet in approximately 3–5 µl PBS . A multiple-displacement amplification ( MDA ) was carried out directly on the bacterial pellet , using Repli-g midi kit ( Qiagen ) according to manufacturer's instructions ( protocol for Amplification of Genomic DNA from Blood or Cells ) . The amplified samples were cleaned prior to sequencing with QIAamp DNA mini kit , according to manufacturer's instructions ( Qiagen , supplementary protocol for Purification of REPLI-g amplified DNA ) . Since MDA is known to be extremely sensitive , precautions were taken to avoid contamination during the purification of Wolbachia cells , including sterile-filtering of all solutions , and autoclaving/UV-treatment of plastic utensils . Three independently amplified samples for each Wolbachia strain were used for library construction and sequencing , so that each genome was sequenced by ½ plate of single-end and 3 kb paired-end 454 and 1/12 lane paired-end Illumina . 454 sequencing was done at SciLifeLab Stockholm on a 454 Roche FLX machine using Titanium chemistry and standard preparations for single-end and 3 kb paired-end libraries . Illumina sequencing was done on a HiSeq2000 instrument at the Uppsala SNP & SEQ platform , using standard Illumina protocols for preparation of paired-end libraries , generating 2×100 bp sequences from each fragment . The 454 datasets were assembled de novo with both Newbler ( 454 Life Sciences Corp . , Roche , Branford , CT 06405 , US ) and Mira [80] . Assemblies were compared with Mauve [81] and ACT [82] and the discrepancies between the best assemblies and all sequence gaps were resolved with PCR amplification from total fly DNA extractions ( DNeasy Blood and Tissue kit , Qiagen ) and subsequent direct sequencing of the PCR products . Since the Newbler assembly proved to be generally more correct it was used as a reference to order the contig sequences from MIRA into scaffolds and close the remaining gaps , resulting in two circular Wolbachia genomes . In two positions on the wHa genome PCR-products could not be obtained , but read-pairs that go in and out of the repeat sequence associated with these genome positions support the current arrangement . Gap closure and manual sequence editing of PCR products was done using Consed [83] . Consed was also used to map the Illumina sequences onto the contigs generated using 454 data , in order to correct errors in homopolymer tracts . To evaluate the purity and quality of the DNA samples used for sequencing , the sequence reads were mapped onto the completed genomes . The Illumina reads were filtered using Trimmomatic [84] , and mapped using bwa [85] . The sam-formatted output file from bwa was converted to bam , sorted in coordinates and duplicated reads were marked using Picard tools ( http://picard . sourceforge . net ) . Proper and non-proper read pairs ( as set in the sam-file flag by bwa ) were extracted with samtools [86] . The single and paired-end 454 reads were mapped separately using the Newbler mapper . For the paired-end 454 reads , true and false pairs ( as defined in the output file 454PairStatus . txt by Newbler ) were extracted and mapped separately . Coverage was calculated from the bam-files using the depth command in samtools and subsequently plotted using R ( R development core team 2011 ) . . The mean quality of assembled and non-assembled 454 reads was plotted with Prinseq [87] . An annotation pipeline was developed using the Diya framework [88] . Prodigal was used for gene prediction [89] , GenePrimp for identifying suspicious start/stop codons and pseudogenes [90] , and hmmsearch as implemented in pfam_scan . pl was used for domain prediction with the PFAM database [91] . All annotations were manually edited using Artemis [92] . Overview figures of similarity between complete genomes and local genome regions were generated with GenoPlotR [93] . IS-elements were identified based on open-reading frames and a manual search of all repeats . All IS-elements were assigned to an IS family by TBlastX searches against IS-finder [94] . Functional IS-elements were defined as alignments that could be extended to contain the complete annotated IS-element . IS-elements that were truncated compared to their best hit in IS-finder or contained frameshifts were considered non-functional . Homologous genes between six Wolbachia strains ( wHa , wNo , wRi , wMel , wPip and wAlbB ) were determined using reciprocal protein blast searches between all the protein sequences from the genomes and subsequent clustering with the MCL algorithm [95] . In order for genes to be considered homologous , the shortest protein in a pair needed to be at least 60% of the length of the longer gene and be aligned over at least 80% of its length . Ortholog clusters containing a single gene from all 6 Wolbachia genomes were aligned on the protein level using mafft [96] and backtranslated to nucleotides . The alignments were pruned to remove gap sites present in 50% or more of the aligned sequences . A strain phylogeny was inferred on a concatenate alignment of the single gene orthologs in RAxML using the GTRGAMMA model and constructing 1 slow best maximum likelihood tree and 1000 rapid bootstrap replicates . Additionally , phylogenetic trees were inferred independently for each ortholog cluster by RAxML [97] using the GTRCAT model , and constructing 1 slow best maximum likelihood tree and 100 rapid bootstrap replicates . Pairwise Robinson-Fould ( R-F ) distances were calculated using RAxML by inputting a concatenated file with the 660 individual gene trees . The weighted R-F distances were used to cluster the trees with hclust ( method complete and height cutoff of 1 ) in R . Phylogenetic trees of clusters with members of all strains , but containing paralogous copies and located in prophage regions were inferred by the same method as the single gene orthologs . However , since the current assembly of the wAlbB genome does not contain complete genes for most of the prophage , this strain was excluded from the analysis . The same 660 single-gene ortholog clusters were used to calculate synonymous substitution rates ( dS ) between all pairs of genes in the alignment using codeml from the PAML package with the codon-based model of substitutions described in [98] and nucleotide distances with RAxML using the GTR model . The pair-wise dS-values obtained were used to quantify the amount of recombination within supergroup A and B by plotting relative dS- values in a ternary plot and calculating the spread of the values from the mean relative dS-values by using R , as described in [38] . The alignments of the 660 single-gene ortholog clusters were used for recombination detection within genes with PhiPack [99] ( which calculates the p-values for three individual methods , Neighbour similarity score ( NSS ) , Maxchi and Phi ) and GENECONV [100] . Recombination was inferred for p-values less than 0 . 01 . For counting recombination between vs . within supergroups with geneconv , only global inner fragments with a Bonferroni corrected KA p-value less than 0 . 05 was used . Additionally , to calculate the r/m parameter , two independent ClonalFrame [101] runs were performed on a concatenated alignment of all the single orthologs as individual blocks using 100 . 000 iterations , with a burn-in of 50 . 000 iterations and recording the parameters every 100th iteration . Convergence between the clonal-frame runs was tested using the ClonalFrame graphical user interface . r/m for each node of the tree was calculated from the output file of the two separate ClonalFrame runs . The probability of a substitution generated by mutation was calculated as ( 1-R ) *S and the probability of a substitution being generated by recombination was calculated as R*S , where R is the posterior probability of recombination and S is the posterior probability of substitution . Only positions where the probability of substitution via mutation or recombination was higher or equal to 0 . 95 were counted . The number of recombination events was calculated by looking at continuous stretches of sites were the posterior probability of recombination was never lower than 0 . 5 and contained at least one site with a probability of 0 . 95 . Geneconv was run using three different levels of mismatch penalty , in order to account for differences in divergence between the strains and differences in age of the transferred fragments . The mismatch penalty is inversely proportional to the total number of site differences between two sequences , and directly proportional to the gscale parameter ( except when no mismatches are allowed , gscale = 0 ) according to the formula; mismatch penalty = ( number of total polymorphisms in the alignment ) * gscale/ ( number of site differences between each pair of sequences ) . This means that sequences with a lower number of total differences , will get a higher penalty for a mismatch with the same gscale setting . MCL clusters that contained genes from only super-group A or B were further analyzed by taking the protein sequences from either wHa ( representing the A supergroup ) and wNo ( representing the B supergroup ) and blasting ( tblastn ) them against the complete genomes from the other supergroups , including the supergroup D genome of Wolbachia wBm . Clusters that did not have a match in any of the other genomes with either an e-vale less than e-5 and 60% of the protein aligned or an e-value less than e-20 and 30% of the protein aligned and identity of minimum 35% , were considered supergroup specific . Additionally , if the matches from tblastn contained stop codon or frame-shifts , the hit was called a pseudogene even if the above criteria were met . The protein sequences of fic domain proteins with known function ( FiDo family ) as listed in [102] were downloaded from Genbank . Additionally , the protein sequences for the 10 best non-overlapping blastp hits against the nr database when using the three Wolbachia fic genes were downloaded . Similarly , for cydA , cydB , argR and the arginine ABC transporter genes , the top 50 blastp hits against the nr database were downloaded . For the outer membrane proteins specifically found in the B-supergroup , no additional species were found in the database , but the homologous protein sequences from wVitB were included . In all cases , the protein sequences were aligned with mafft and pruned to remove gap sites that were present in 50% or more of the aligned sequences . The phylogenetic trees were inferred with RAxML using the PROTCATWAG model , and constructing 1 slow best maximum likelihood tree and 1000 rapid bootstrap replicates . The complete sequences of Wolbachia wNo and wHa genomes are deposited in Genbank under accession numbers CP003883 and CP003884 , respectively .
|
Speciation in sexual organisms is defined as the inability of two populations to get viable offspring . Speciation in asexual , obligate endosymbionts is thought to be an indirect consequence of host-specialization . An important question is if divergent endosymbionts would start blending if the host barrier isolating them were removed . Here , we have studied Wolbachia , an abundant group of bacteria in the insect world . Wolbachia is classified into supergroups based on multi-locus sequence typing . We have sequenced the genomes from the Wolbachia strains wNo and wHa . These are particularly interesting since they belong to different supergroups yet co-occur as a double-infection in natural populations of Drosophila simulans . A comparative genomics study showed that wHa and wNo contain no uniquely shared genes . Instead , each strain shares unique gene functions with members of the same supergroup that infect other hosts . This unexpected finding suggests an alternative means of ecological speciation , indicating that speciation is not restricted to host-specialization but rather that related endosymbionts can coexist as separate species in the same host . Our study sheds light on the genomic divergence between different partners inhabiting the intracellular niche of the same host organism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genome",
"sequencing",
"genome",
"evolution",
"microbial",
"evolution",
"comparative",
"genomics",
"biology",
"genomics",
"evolutionary",
"biology",
"genomic",
"evolution",
"microbiology",
"evolutionary",
"processes",
"host-pathogen",
"interaction"
] |
2013
|
Comparative Genomics of Wolbachia and the Bacterial Species Concept
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Replicative DNA helicases expose the two strands of the double helix to the replication apparatus , but accessory helicases are often needed to help forks move past naturally occurring hard-to-replicate sites , such as tightly bound proteins , RNA/DNA hybrids , and DNA secondary structures . Although the Schizosaccharomyces pombe 5’-to-3’ DNA helicase Pfh1 is known to promote fork progression , its genomic targets , dynamics , and mechanisms of action are largely unknown . Here we address these questions by integrating genome-wide identification of Pfh1 binding sites , comprehensive analysis of the effects of Pfh1 depletion on replication and DNA damage , and proteomic analysis of Pfh1 interaction partners by immunoaffinity purification mass spectrometry . Of the 621 high confidence Pfh1-binding sites in wild type cells , about 40% were sites of fork slowing ( as marked by high DNA polymerase occupancy ) and/or DNA damage ( as marked by high levels of phosphorylated H2A ) . The replication and integrity of tRNA and 5S rRNA genes , highly transcribed RNA polymerase II genes , and nucleosome depleted regions were particularly Pfh1-dependent . The association of Pfh1 with genomic integrity at highly transcribed genes was S phase dependent , and thus unlikely to be an artifact of high transcription rates . Although Pfh1 affected replication and suppressed DNA damage at discrete sites throughout the genome , Pfh1 and the replicative DNA polymerase bound to similar extents to both Pfh1-dependent and independent sites , suggesting that Pfh1 is proximal to the replication machinery during S phase . Consistent with this interpretation , Pfh1 co-purified with many key replisome components , including the hexameric MCM helicase , replicative DNA polymerases , RPA , and the processivity clamp PCNA in an S phase dependent manner . Thus , we conclude that Pfh1 is an accessory DNA helicase that interacts with the replisome and promotes replication and suppresses DNA damage at hard-to-replicate sites . These data provide insight into mechanisms by which this evolutionarily conserved helicase helps preserve genome integrity .
Faithful and efficient replication of the genome is essential in every cell cycle , yet there are many naturally occurring obstacles that impede fork progression . These sites include stable protein complexes , DNA secondary structures , and ongoing transcription , each of which can challenge replication fork progression . Failure to circumvent these obstacles can cause DNA double strand breaks ( DSBs ) that impair genome integrity and increase the risk of cancer and other disorders that are associated with genome instability . As many of the proteins involved in DNA replication are highly conserved , model organisms such as S . pombe provide genetically tractable systems to identify and characterize genes with important roles in genome preservation whose human orthologs might have similar functions . DNA replication is accomplished by the multi-subunit replisome , a complex that is assembled at and moves bi-directionally away from replication origins . Replicative helicases , such as the Escherichia coli DnaB and the eukaryotic hexameric MCM complex , are required to unwind the double helix to allow DNA polymerases access to the replication template . In addition , accessory helicases , such as the E . coli rep , dinG and UvrD proteins help the polymerase maneuver past protein complexes , RNA transcripts , and other naturally occurring impediments [1–5] . In Bacillus subtilis the essential accessory DNA helicase PcrA promotes fork movement through transcribed genes [6 , 7] . E . coli rep physically interacts with the replicative DnaB helicase to bypass protein complexes on DNA [8] . The best-studied eukaryotic accessory DNA helicases are the two budding yeast enzymes , ScRrm3 and ScPif1 , which are both members of the Pif1 family [9] . Although these two helicases have largely non-overlapping functions , they both promote progression past naturally occurring hard-to-replicate sites . ScRrm3 acts at over 1000 sites , including RNA polymerase III transcribed genes , the replication fork barrier ( RFB ) within ribosomal DNA ( rDNA ) , inactive replication origins , silencers , telomeres , centromeres , and converged replication forks [10–14] . The diverse ScRrm3-sensitive sites have the common feature of being assembled into stable protein complexes . Removal of these proteins in trans or mutation of their binding sites in cis relieves the requirement for ScRrm3 at the affected site . In its absence , forks tend to stall and break at ScRrm3-sensitive sites . Rather than being recruited to its sites of action , ScRrm3 moves with the replisome through both ScRrm3-sensitive and insensitive sites [15] and interacts with leading strand DNA polymerase ε and PCNA [15 , 16] , suggesting that it is a replisome component . ScPif1 also promotes fork progression , but so far this activity has been observed only at putative G-quadruplex ( G4 ) structures [17–21] . G4 DNA is a stable , four-stranded secondary structure held together by non-canonical G-G base pairs [reviewed in 22] . In cells lacking ScPif1 , DNA replication slows and DNA damage is detected at many G4 motifs . Current evidence suggests that ScPif1 is recruited to G4 motifs after their replication [18] , and the abundance of ScPif1 , unlike that of ScRrm3 , is cell cycle regulated , peaking in late S phase [15 , 23] . Thus , unlike ScRrm3 , ScPif1 probably does not move with the leading strand DNA polymerase . ScRrm3 is a backup helicase for ScPif1 at G4 motifs [17] . As ScRrm3 and ScPif1 are both associated with stalled replication forks [24] , ScPif1 might be a backup for ScRrm3 at some of its targets , such as RNA polymerase III transcribed genes . ScPif1 actions are not limited to its being an accessory DNA helicase , as it also inhibits telomerase by displacing it from DNA ends [25 , 26] , promotes formation of long flap Okazaki fragments [27 , 28] , and is needed for the stable maintenance of mitochondrial DNA [29] and break-induced replication [30–33] . Unlike budding yeast , most eukaryotes , including fission yeast and humans , encode a single Pif1 family helicase . While neither ScRrm3 nor ScPif1 is essential , the fission yeast Pfh1 DNA helicase is required for maintenance of both the mitochondrial and nuclear genomes [34] . Pfh1 also affects nuclear DNA repair: it localizes to DNA damage foci upon exogenous DNA damage , and its absence results in spontaneous DNA damage foci [34] . In earlier work , we and others used two-dimensional gels to show that Pfh1 , like ScRrm3 promotes fork progression through specific stable protein complexes , including RNA polymerase II and III transcribed genes , silencers , converged replication forks [35 , 36] , and telomeres [37] . In addition , microscopic studies show that Pfh1 suppresses formation of ultrafine anaphase bridges that arise at incompletely replicated regions , such as Lac repressor bound LacO arrays [38] , supporting the idea that it is needed to complete DNA replication . Like ScPif1 , Pfh1 binds to and suppresses DNA damage at G4 motifs [39] . Also , ScPif1 and Pfh1 both unwind G4 structures in vitro [17 , 40–43] . In this paper , we address two fundamental questions about Pfh1 function: where does Pfh1 act along the genome and how is it recruited to its sites of action ? Earlier studies focused on Pfh1’s role in replication of a few examples of hard-to-replicate sites [35 , 37 , 39] . Here we used chromatin immunoprecipitation combined with genome-wide deep sequencing ( ChIP-seq ) to study the full range of Pfh1-sensitive sites . This analysis revealed hundreds of sites of Pfh1 binding where replication slows and DNA damage occurs , especially in the absence of Pfh1 . These Pfh1-sensitive sites included all previously identified hard-to-replicate sites as well as novel sites , such as nucleosome depleted regions ( NDR ) . Second , we assayed binding and fork progression to determine if Pfh1 is nearby the replisome during S phase or , if it is recruited to its sites of action . These analyses revealed that Pfh1 and the leading strand DNA polymerase bind both Pfh1-sensitive and Pfh1-insensitive sites to a similar extent . Likewise , mass spectrometry ( MS ) found that Pfh1 interacts with many key replisome components . Together these data argue that Pfh1 is not just recruited to its sites of action , but that it is in proximity to the replisome during DNA synthesis and functions as an accessory DNA helicase at all known classes of hard-to-replicate sites . These results inform our understanding of Pif1 helicases in higher eukaryotes , such as humans , which like S . pombe , encode only one Pif1 family helicase . Given that the helicase domains of Pfh1 and hPIF1 are related ( 36% sequence identity ) [25] , hPIF1 may have similar functions in preserving genome integrity .
We analyzed 621 previously identified Pfh1 binding sites across the S . pombe genome from ChIP-seq on cycling WT cells ( S1 Table ) [39] . Given the low coverage of rDNA repeats and telomeres in the S . pombe annotated genome , the rest of this paper considers only non-telomeric , non-rDNA Pfh1 binding sites . We determined if the peaks of Pfh1 binding correlated with any of sixteen annotated genomic features ( Table 1; Methods ) . Because Pfh1 bound preferentially to GC-rich sites , we determined the significance of its association with features after accounting for GC content using random GC-matched controls ( Methods ) . Pfh1 peaks were significantly associated with many known hard-to-replicate sites , such as tRNA genes , 5S ribosomal RNA ( rRNA ) , and highly active RNA polymerase II transcribed genes ( Table 1 ) . For example , Pfh1 binding occurred ≤ 300 base pairs ( bp ) , the shearing size of the ChIP DNA , from 80 out of 171 ( 47% ) tRNA genes , 18 of 33 ( 55% ) 5S rRNA genes , and 302 of the top 500 ( 60% ) most highly transcribed RNA polymerase II genes ( as defined in Rhind , Chen ( 44 ) ) . In addition , Pfh1 binding sites were significantly associated with meiotic double strand break ( DSB ) hotspots , nucleosome depleted regions ( NDRs ) , 3’ untranslated regions ( UTRs ) , and mating type loci ( Table 1 ) . In all following association analyses , associations with p-values less than the Bonferroni multiple testing adjusted threshold of 0 . 003 will be interpreted as significant . A recent study reported that highly transcribed S . cerevisiae genes are over-represented in ChIP experiments carried out with diverse nuclear proteins , suggesting that their presence might be a technical artifact caused by their high transcription rate [45] . The specific cause of the “hyper-ChIPability” of these regions has not been resolved . It has been proposed that , because highly transcribed genes are more accessible during the pull-down , they may be more likely to interact with beads or antibodies during the IP , and therefore be subject to nonspecific precipitation by the antibody . To ensure that the association with highly transcribed RNA polymerase II genes was not due to this artifact , we used ChIP combined with quantitative PCR ( qPCR ) to examine Pfh1 association to four highly transcribed genes , hsp90+ , tdh1+ , adh1+ , and hta1+ , which are among the top 500 most highly transcribed genes and were all Pfh1-associated sites in the genome-wide analysis . Transcription of hsp90+ , tdh1+ , adh1+ occurs throughout the cell cycle , while hta1+ transcription peaks in S phase [46] . In fission yeast , the G2 phase comprises about 75% of the cell cycle , so the majority of cells in an asynchronous culture are in G2 phase , and most genes are transcribed in this interval [47] . Thus , if the association of Pfh1 with highly transcribed genes was non-specific , it should occur to a similar extent in asynchronous and G2-arrested cells for hsp90+ , tdh1+ , adh1+ , but not hta1+ . We performed ChIP-qPCR experiments in both asynchronous and G2 arrested cells in a temperature sensitive cdc25-22 strain that expressed Pfh1-13Myc . Pfh1 was significantly associated to all four genes in asynchronous cells grown at 25°C , compared to an untagged control strain ( S1 Fig ) , which validated the peaks found in the ChIP-seq data . To arrest the cells in G2 phase , cells growing logarithmically at 25°C were shifted to 37°C for 4h . Consistent with the expectation in the absence of bias , high Pfh1 binding to all four of the highly transcribed genes varied across the cell cycle; it was approximately four times higher in asynchronous compared to G2 arrested cells in the ChIP-qPCR assay ( p ≤ 0 . 016; Fig 1 ) . In contrast , Pfh1 binding to the much less frequently transcribed ade6+ gene was not significantly different in asynchronous versus G2 arrested cells ( p = 0 . 14 , Fig 1 ) . Thus , the ChIP-qPCR experiment confirmed the high Pfh1 binding to these highly transcribed genes seen by ChIP-seq and established that this binding is not an artifact of the ChIP . To identify genomic sites that require Pfh1 for their timely replication , we analyzed genome-wide occupancy of Cdc20 , the catalytic subunit of the leading strand DNA polymerase ε [48] in WT and Pfh1-depleted cells . As previously reported [39] , there are 485 peaks of high Cdc20 occupancy in WT cells and 517 in Pfh1-depleted cells ( S1 Table ) . Although all genomic sites are Cdc20-associated when they are being replicated , high DNA polymerase occupancy correlates with replication fork slowing in both S . pombe and S . cerevisiae [39 , 49] . Most of the high Cdc20 occupancy sites in WT cells were also found in Pfh1-depleted cells ( 390/485 , 80% ) and vice versa ( 389/517 , 75% ) ( Fig 2A ) . In an earlier study , we used these data to demonstrate that many G4 motifs bind Pfh1 and that fork slowing and DNA breakage is more frequent at G4 motifs than at other G-rich regions in Pfh1-depleted cells ( S2 Fig ) [39] . Here we extend this analysis from G4 motifs to the rest of the genome . This analysis showed that in addition to G4 motifs , tRNA genes , 5S rRNA genes , NDRs , replication origins , RNA polymerase II promoters , RNA polymerase II transcribed genes , and meiotic DSB hotspots were significantly enriched among high Cdc20 occupancy sites in both WT and Pfh1-depleted cells ( Table 2 and S2 Table; p < 0 . 001 ) . The sets of high Cdc20 occupancy were identical in the two conditions except that dubious genes were enriched in Pfh1-depleted but not in WT cells . Despite this similarity , the evidence for elevated Cdc20 occupancy was significantly greater in Pfh1-depleted cells ( p ≈ 0 , Wilcoxon signed-rank test , p-values < 10−50 are reported as ≈0; Fig 2B ) . This pattern held for all genomic features tested ( Fig 2C–2F; S3 Table ) . For example , 69% of Cdc20 peaks near NDRs ( p = 2 . 4x10-5; Fig 2C ) and 62% of peaks near highly transcribed RNA polymerase II genes ( p ≈ 0; Fig 2D ) were significantly stronger in Pfh1-depleted cells . This effect was particularly striking at tRNA ( p ≈ 0; Fig 2E ) and 5S rRNA ( p = 1 . 4x10-6; Fig 2F ) genes , where over 97% of the peaks showed evidence of significantly elevated Cdc20 occupancy when cells were Pfh1-depleted compared to WT cells . These findings argue that these genomic features , all of which had significant Pfh1 occupancy in WT cells ( Table 1 ) , were particularly dependent on Pfh1 for timely replication . To compare the Pfh1-dependent effects in more detail , we analyzed the genomic features associated with the 95 peaks of high Cdc20 binding unique to WT cells versus those associated with the 128 peaks of high Cdc20 binding that were found only in Pfh1-depleted cells . No features were enriched among the unique WT peaks . In sharp contrast , 5S rRNA and tRNA genes , meiotic DSB hotspots , NDRs , and dubious genes were all significantly enriched amongst the Cdc20 peaks unique to Pfh1-depleted cells ( S2 Table ) . Taken together , our results show that several classes of genomic features , especially RNA polymerase III transcribed genes , depend on Pfh1 for normal fork progression . In cases where sites were hard to replicate even in WT cells , fork pausing at these sites was significantly more pronounced in Pfh1-depleted cells . In virtually all eukaryotes , including S . pombe , phosphorylation of H2A ( γ-H2A ) marks sites of DNA damage , typically DSBs [50] . To determine if the site-specific increases in replication pausing detected in Pfh1-depleted cells were associated with DNA damage , we analyzed peaks from previous ChIP-seq experiments using anti-γ-H2A antibodies in WT or Pfh1-depleted cells ( S1 Table ) [39] . We quantified the genomic distribution and Pfh1-dependence of the γ-H2A peaks with the same methods used for Cdc20 . As demonstrated in previous work , WT cells had 179 γ-H2A peaks and Pfh1-depleted cells had 582 peaks ( Fig 3A ) [39] . Only two genomic features , the mating type loci and origins of replication , were significantly enriched near high occupancy γ-H2A sites in WT cells ( p < 0 . 001 for both; S5 Table ) . However , in Pfh1-depleted cells , 5S rRNA and tRNA genes , meiotic DSB hotspots , NDRs , the mating type loci , and origins of replication all overlapped significantly with γ-H2A peaks ( p < 0 . 001; Table 2 and S5 Table ) . We also determined the degree of overlap between Cdc20 and γ-H2A peaks in Pfh1-depleted cells . Of the 582 γ-H2A peaks in Pfh1-depleted cells , 71% ( 411 ) also had high Cdc20 occupancy; this number is significantly more than expected by chance ( p < 0 . 001 ) . γ-H2A sites that were high occupancy for both γ-H2A and Cdc20 were enriched for multiple genomic features including 5S rRNA and tRNA genes , origins of replication , meiotic DSB hotspots , 3’ and 5’ UTRs , and promoters ( p < 0 . 001 for all; S6 Table ) . These features include most of those with significant Pfh1 association in WT cells . In contrast , γ-H2A peaks without corresponding Cdc20 peaks were enriched only in 3’ and 5’ UTRs and promoters ( p < 0 . 001 for both ) . The significant overlap between peaks of Cdc20 and γ-H2A binding supports the connection between Pfh1-dependent fork slowing ( as marked by Cdc20 peaks ) and DNA damage ( as marked by nearby γ-H2A ) at several classes of hard-to-replicate sites . These patterns are illustrated for two specific sites , a tRNA gene ( Fig 4A ) and a 5S rRNA gene ( Fig 4B ) . The strengths of the Pfh1 , Cdc20 , and γ-H2A binding are shown relative to input for the different strains in a 10 kilobase ( kb ) window around each gene . At both sites , a Pfh1 peak overlapped the gene ( grey vertical lines mark centers of genes ) . A Cdc20 peak showed a similar overlap with the gene in both WT ( dashed blue line ) and Pfh1-depleted cells ( solid blue line ) , but the peak was stronger in Pfh1-depleted cells . Broad peaks of γ-H2A flanked both genes in Pfh1-depleted cells , consistent with the 40 kb domains of γ-H2A that flank DSB sites [50] . Plots for all tRNA and 5S rRNA genes are shown in S3 and S4 Figs . When the strengths of all the γ-H2A peaks between WT and Pfh1-depleted cells were compared , 88% of all peaks were higher in Pfh1-depleted cells ( p ≈ 0 , Wilcoxon signed-rank test; Fig 3B ) . This pattern held for the subsets of γ-H2A peaks associated with nearly all classes of genomic features ( Fig 3C–3F; S3 Table ) . However , as seen for Cdc20 , the magnitude of the difference was strongest for the RNA polymerase III transcribed genes: 190 of 192 γ-H2A peaks near tRNA genes ( p ≈ 0 , Fig 3E ) and all 67 γ-H2A 5S rRNA peaks ( p ≈ 0 , Fig 3F ) were greater in Pfh1-depleted cells . Our data indicate that Pfh1 promotes replication and suppresses DNA damage at many discrete sites in the genome . We considered two models to explain this pattern of Pfh1 action . First , Pfh1 could act by binding nearby the replisome and mitigating replication obstacles as the replisome moves past Pfh1-sensitive sites . Alternatively , Pfh1 could be recruited only to sites that are hard to replicate or to stalled replication forks . In that case , Pfh1 would have low or no binding to sites that are Pfh1-insensitive . To distinguish between the two possibilities , we used ChIP-qPCR to monitor association of Pfh1 and Cdc20 in synchronized cells . For these experiments , we used a cdc25-22 strain that expressed Pfh1-13Myc inserted at the leu1+ locus under the control of the pfh1+ promoter ( the endogenous pfh1+ locus was unaltered ) . This strain also expressed Cdc20-3HA from its endogenous location . Cells were arrested in G2 phase by incubation at the non-permissive temperature ( 37°C ) and then released at permissive temperature ( 25°C ) . The position in the cell cycle and the quality of the synchrony were determined by FACS analysis ( Fig 5A ) . To determine association and movement of the replication fork , samples were taken throughout one synchronous cell cycle . At each time point , we examined association of Pfh1-13Myc and Cdc20-3HA to three origins of replication and their adjacent regions . We examined binding to the efficient ars3002 and a region18 kb away from ars3002 ( ars3002_18kb ) , ars2004 and a region 30 kb from ars2004 ( ars2004_30kb ) , and ars3005 and a region 26 kb from ars3005 ( ars3005_26kb ) ( Fig 5B ) . If Pfh1 were nearby or associated with the replisome , Pfh1 and Cdc20 would have similar temporal patterns of binding to the three origins and their adjacent sites . If Pfh1 were recruited only to hard-to-replicate sites , then Cdc20 and Pfh1 would have different binding patterns . In fact , under the second model , Pfh1 should not bind to any of these sites , as none of them were Pfh1-dependent in the whole genome analysis . Consistent with the first model , Cdc20 and Pfh1 displayed similar association patterns at all three origins and their adjacent regions ( Fig 5C–5E ) . We first examined the binding to ars3002 and its adjacent region ars3002_18kb ( Fig 5C ) . Although Cdc20 bound in early S phase to ars3002 , earlier than Pfh1 , the peak binding for Cdc20 and Pfh1 was reached at 95 min ( Fig 5C ) . Both proteins had their start of binding to ars3002_18kb at 80 min after release from G2 phase and their peak binding at 95 min ( Fig 5C ) . Thus , Pfh1 and Cdc20 bound to the Pfh1-insensitive site located 18 kb downstream of ars3002 similarly . However , while clear movement of Cdc20 was detected in this region , movement from this origin to the downstream regions was not visible for Pfh1 . Next , we examined the binding of Pfh1 and Cdc20 to the four other Pfh1-insensitive sites ars2004 , ars2004_30kb , ars3005 , and ars3005_26kb . Pfh1 bound to all these four regions , similarly to Cdc20 ( Fig 5D and 5E ) . However , movement of neither Cdc20 nor Pfh1 was detected at any of these origins to their adjacent sites . Because the dynamics of Pfh1 and the replisome were not clear from the above experiments , we further investigated the binding pattern of Pfh1 and Cdc20 at five other regions , including both Pfh1-sensitive and Pfh1-insensitve sites ( Fig 5F ) . We tested two regions that were not origins of replication on chromosome II: one Pfh1-sensitive ( Chr II_nonars_1236154 ) , and a Pfh1-insensitive site 36 kb away ( Chr II_nonars_1272741 ) . Finally , we examined two Pfh1-sensitive tRNAs ( tRNAGLU . 05 and tRNAASN . 05 ) and one Pfh1-insensitive site 6 kb away from tRNAASN . 05 ( tRNAASN . 05_6kb ) . At all five regions , both Pfh1 and Cdc20 had peak binding at 95 min after release from the G2 arrest , which by FACS analysis is mid-S phase ( Fig 5 ) . To determine if Pfh1 was recruited only to Pfh1-sensitive sites , we calculated the ratio of Pfh1 to Cdc20 binding at Pfh1-sensitive and -insensitive sites ( IP/input of Pfh1 divided by IP/input for Cdc20 ) at the peak of replication for all sites ( Fig 5F ) . If Pfh1 were recruited solely to Pfh1-sensitive sites , the ratio of Pfh1/Cdc20 would be higher at Pfh1-sensitive sites compared to Pfh1-insensitive sites . If Pfh1 were in proximity to the replisome , the ratios should be similar at the two classes of sites . Indeed , the Pfh1/Cdc20 ratio was on average 1 . 1 and 0 . 9 for Pfh1-sensitive and Pfh1-insensitive sites , respectively ( Fig 5F ) . Thus , Pfh1 binds similarly to both Pfh1-sensitive and -insensitive sites . These data suggest that Pfh1 is near the replisome during S phase , rather than being recruited to its sites of action at Pfh1-sensitive sites . However , we cannot rule out the possibility that additional Pfh1 molecules are recruited to some or even all Pfh1-sensitive sites upon replisome pausing . If Pfh1 maintains proximity to the replisome , as suggested by its pattern of binding to chromosomal DNA ( Fig 5 ) , then Pfh1 should be associated in vivo with known replisome subunits . To address this possibility , we used immunoaffinity purification mass spectrometry ( IP-MS ) to identify the Pfh1 protein interactions in S phase cells ( Fig 6A ) . In these experiments , Pfh1 was expressed under its endogenous promoter as a GFP fusion ( Pfh1-GFP ) , allowing immunoaffinity isolation of Pfh1 and its associated proteins through the GFP tag [51] . Cells expressing a SV40 nuclear localization signal-GFP fusion ( NLS-GFP ) were used as a negative control for non-specific association of proteins to GFP . Two biological replicates of both Pfh1-GFP and NLS-GFP were isolated in parallel from S phase cells ( Fig 6A ) using anti-GFP antibodies ( Fig 6B ) . Following mass spectrometry analysis of Pfh1-GFP and NLS-GFP immunoisolates , the interaction specificity of individual co-isolating proteins was assessed using the SAINT ( significance analysis of interactome ) algorithm [52] . SAINT determines confidence scores ( ranging from 0 to 1 ) for protein-protein interactions based on the spectrum count distributions obtained from bait ( Pfh1-GFP ) isolations relative to the negative control ( NLS-GFP ) . High confidence Pfh1 interactions were defined as proteins having a SAINT score ≥ 0 . 80 , a threshold used previously to identify functional protein interactions [53 , 54] . By this metric , there were 50 high confidence Pfh1 protein associations that comprise the Pfh1 S phase interactome ( Table 3 and S7 Table ) . Although five of the Pfh1 interacting proteins are uncharacterized , there is functional data for 45 of the 50 proteins . Table 3 lists these proteins . We also assessed the relative abundance of individual Pfh1 interacting proteins within the interaction network by calculating the normalized spectrum abundance factor ( NSAF ) for each protein relative to its proteome abundance value ( PAX ) [55] . NSAF values provide a measure of protein abundance by accounting for factors such as protein length and sample complexity that can influence the number of spectra acquired for a given protein within a sample . Normalizing NSAF values to PAX values , as described in [56] , provides insight into proteins and functional protein classes that are enriched in the Pfh1 isolation relative to their abundances in the cellular proteome . These data are presented in Fig 6C , which also categorizes interacting proteins by function . The replisome is the multi-protein complex that is present at the replication fork as it moves through the chromosome . Multiple replisome components interacted with Pfh1 with high specificity ( SAINT score ≥ 0 . 80; Fig 6C and Table 3 ) . These proteins were: ( 1 ) five of the six subunits of the replicative helicase , the MCM complex ( Mcm2 and Mcm4-7 ) ; ( 2 ) catalytic subunits of two of the three replicative polymerases ( DNA Pol1 from DNA polymerase α; DNA Pol2/Cdc20 , from DNA polymerase ε ) ; ( 3 ) Pol12 , the β subunit of DNA Pol1; ( 4 ) proliferating cell nuclear antigen ( PCNA , Pcn1 ) , a processivity factor that encircles and slides along the DNA; ( 5 ) the three subunits of the single-strand binding replication factor A ( RPA , Ssb1 , 2 and 3 ) ; ( 6 ) the Dna2 helicase-nuclease that is required for Okazaki fragment maturation; and ( 7 ) the two subunits of the FACT complex ( facilitates chromatin transcription ) , Pob3 and Spt1 , which facilitates nucleosome remodeling during both transcription and DNA replication . The association of FACT subunits with Pfh1 suggests that FACT and Pfh1 might act synergistically to promote replication through hard-to-replicate sites . Four mismatch repair ( MMR ) proteins , Msh1 , 2 , 3 and 6 , were also Pfh1-associated . The Msh2/6 and Msh2/3 heterodimers interact directly with DNA for the recognition of base pair mismatches . Because MMR and DNA replication are strongly coupled in budding yeast , MMR proteins are proposed to track with the replisome and hence can also be considered replisome components [57 , 58] . Additional replisome components were present in the Pfh1-GFP isolations but did not meet our SAINT score criterion . These proteins were: ( 1 ) the sixth Mcm subunit ( Mcm3; SAINT score , 0 . 68 ) ; ( 2 ) the catalytic subunit of the lagging strand DNA polymerase; Pol3 ( SAINT score , 0 . 15 ) ; ( 3 ) Dpb2 , the second largest subunit of Pol ε ( SAINT score 0 . 31 ) ; ( 4 ) Pri1 and Pri2 , the primase subunits that function together with DNA polymerase α to synthesize the primers on the leading and lagging strand ( SAINT scores of 0 . 65 and 0 . 33 , respectively ) ; and ( 5 ) Mcl1 , the S . cerevisiae Ctf4 homologue that interacts with DNA polymerase α ( SAINT score 0 . 70 ) . While SAINT scores point to high confidence interactions , being based on detected protein spectral counts , they are influenced by sample complexity and the dynamic range of the co-isolated proteins , and thereby weighted towards large and abundant proteins , and stable interactions . Pfh1-associated replisome components with lower SAINT scores may be smaller proteins , have lower cellular abundances , and/or form transient interactions [59] . We performed two additional experiments to confirm the association of Pfh1 with the replisome . First , we isolated Pfh1 from asynchronous cells both in the presence and absence of DNase ( S7 Table and S5 and S6 Figs ) . Because this experiment was performed with an asynchronous population , only a subset of the proteins that interacted with Pfh1 in S phase was detected ( S6 Fig ) , even without DNase treatment . Of the 19 replisome/replication proteins that passed our stringent SAINT score threshold , ten were found in the DNase untreated sample , and nine of these retained their Pfh1 association after DNase treatment: Ssb1 and 2 , Msh2 , Mcm4 , 5 , and 6 , Cdc20 ( Pol2 ) , Pol12 , and Spt16 . Because these interactions were not DNA-dependent , they are likely due to protein-protein interactions . Second , we isolated Pfh1 and its associated proteins from G2 arrested cells . We detected eight Pfh1-associated replication/replisome proteins in G2 , and all eight were detected with fewer spectral counts in G2 extracts than in S phase extracts . The remaining eleven were not detected at all as Pfh1-interacting proteins in G2 phase ( Table 3; S7 Table ) . Thus , as expected for a replisome component , Pfh1 association with known replisome subunits was either lost or diminished in G2 phase . Together , these results show that Pfh1 associated in vivo with numerous replisome proteins , and that replisome and replication-related proteins represent a substantial subset of specific Pfh1 interactions ( 19 out of 50 proteins ( 38% ) with SAINT score of ≥0 . 8 ) ( Table 3; in bold; Fig 6C ) . Almost all of these associations were S phase-limited or S phase-enriched as well as DNA independent . Pfh1 is a multi-functional protein: in addition to its role in nuclear DNA replication , it promotes DNA repair and is essential for maintenance of mitochondrial ( mt ) DNA [34] . Consistent with Pfh1 having mt function , 8 of the 50 high confidence Pfh1 interaction proteins have mt annotations ( Fig 6C; mt proteins are underlined in Table 3 ) . This subset includes several proteins implicated in mtDNA replication , such as ( 1 ) Rim1 , the mt single-strand DNA binding protein ( MS analyses reveal that ScPif1 is also ScRim1-associated; [60] ) , ( 2 ) Rpo1 , the mtRNA polymerase that is thought to prime mtDNA replication , and ( 3 ) Mgm101 , which is required for maintenance of mtDNA by an unknown mechanism . Consistent with the reported DNA repair functions of Pfh1 [34] , we observed multiple repair proteins among the high confidence Pfh1 interactions ( Table 3 , italics ) , including Rad22 , the S . pombe homolog of budding yeast Rad52 , which is required for homologous recombination [61] , Rad3 , the ATR-like checkpoint kinase [62] , and both subunits of the non-homologous end joining Ku complex , pKu70 and 80 . In addition , Rqh1 ( homolog of human BLM ) DNA helicase and its two interacting partners , the topoisomerase Top3 and Rmi1 , were Pfh1-associated . This highly conserved heterotrimeric complex has multiple functions , but is best known for suppressing DNA damage at hard-to-replicate sites , such as converged forks [63] and/or collapsed replication forks—functions relevant to those of Pfh1 . Finally , six subunits of the 26 subunit RNA polymerase III complex were Pfh1-associated with high confidence ( Rpc1 , 2 , 3 , 4 , 25 and 37 ) , while four others were Pfh1-associated but had SAINT scores <0 . 8 ( Rpc6 , 0 . 71; Rpc9 and 10 , 0 . 30; Rpc19 , 0 . 76 ) [64] . This finding is probably related to RNA polymerase III transcribed genes being among the most potent replication impediments in Pfh1-depleted cells ( Figs 2–4; see Discussion ) .
We used genome-wide assays to determine sites where replication and genome integrity are Pfh1-dependent . The most striking aspect of these data is the strong dependence of RNA polymerase III transcribed genes on Pfh1 . 2D gel analyses showed previously that replication of five of five tested tRNA genes is Pfh1-dependent , and this dependence is seen regardless of whether replication is co-directional or opposite to the direction of transcription through the gene [35] . Here we show that close to 50% ( 80/171 ) of the tRNA genes bound Pfh1 ( Table 1 ) . Moreover , nearly all of the tRNA genes that bound Pfh1 were sites of fork pausing and DNA damage in both WT and Pfh1-depleted cells ( Table 2; high Cdc20 binding: WT: 74/80 , Pfh1-depleted: 80/80: high γ-H2A: WT: 25/80 , Pfh1-depleted: 69/80 ) . However , both Cdc20 ( Fig 2E ) and γ-H2A binding ( Fig 3E ) were significantly higher at virtually all ( 99% ) tRNA genes in Pfh1-depleted compared to WT cells . Likewise , genome-wide analyses revealed that 55% ( 18/32 ) of 5S rRNA genes bound Pfh1 ( Table 1 ) , and again the majority of these genes were sites of fork pausing ( Table 2; WT: 16/18 , Pfh1-depleted: 18/18 ) and/or DNA damage ( WT: 5/18 , Pfh1-depleted: 14/18 ) , and both features were significantly higher in Pfh1-depleted compared to WT cells ( Figs 2F and 3F ) . The independent MS analysis also supports the importance of Pfh1 at RNA polymerase III transcribed genes , as Pfh1 interacted with multiple subunits of RNA polymerase III ( Fig 6C; Table 3 ) . The association of these subunits with Pfh1 is consistent with a model where Pfh1 promotes replication past genes by displacing these proteins from DNA . In addition to RNA polymerase III genes , 60% of the 500 most highly expressed protein-coding genes over a range of growth conditions were bound by Pfh1 ( Table 1 ) , and 48% were sites of fork slowing and/or DNA damage in Pfh1-depeleted cells ( Figs 2D and 3D; S2 and S3 Tables ) . The likelihood of Pfh1 association was significantly greater for highly expressed genes than expected from other RNA polymerase II expressed genes ( p≈0; hypergeometric test ) as only 23% of all genes were Pfh1 associated . A recent paper suggested that association of proteins with highly expressed genes in S . cerevisiae might be an artifact of the ChIP assay [45] . This interpretation is unlikely for our S . pombe analyses as a non-ChIP method , 2D gel analysis of replication intermediates , also showed that replication of three of three tested highly expressed RNA polymerase II transcribed genes is Pfh1-dependent [35] . Also , the peak strength at the highly transcribed genes was elevated in Pfh1-depleted cells compared to WT cells , suggesting that the association is not a ChIP artifact . Moreover , the genome-wide approach in this paper showed that high association of Pfh1 to highly transcribed genes was S phase specific ( Fig 1 ) , even though transcription of the genes is not S phase-limited . In addition , Pfh1 , Cdc20 , and γ-H2A all associate with ribosomal DNA ( rDNA ) , probably the most highly transcribed region in all organisms , but high binding of all three does not occur over the 18 or 28S coding regions [39] . Also , the patterns of γ-H2A occupancy—low at the gene site and highest in flanking regions—are inconsistent with transcription artifacts ( Fig 4 ) , because the artifactual enrichment was observed to be high across the gene body . Thus , high binding of anti-Myc ( used for Pfh1-13Myc ) , anti-HA ( used for Cdc20-3HA ) , and anti-γ-H2A to highly transcribed genes is unlikely an artifact of their high transcription . Fork pausing at highly transcribed genes as marked by high Pol2 occupancy is also demonstrated in budding yeast [49] . In contrast to S . pombe , where Pfh1 depletion increased fork pausing , pausing at these sites is not affected in the absence of ScRrm3 [49] or ScPif1 [18] . However , cells with a double deletion have not been tested , so it is possible that ScRrm3 and ScPif1 have overlapping functions in promoting replication past these genes . Replication of multiple classes of genomic features is Pfh1-dependent . In addition to highly transcribed RNA polymerase II and III genes , this study identified several novel sites of Pfh1 association , such as NDRs , 3’ UTRs , and preferred meiotic DSB sites . In previous work , we showed that Pfh1 promotes fork movement past G4 motifs [39] . We analyzed whether the novel Pfh1 associations could be explained by overlap with G4 motifs or other Pfh1-associated features ( S7 Fig ) . For example , NDRs often overlapped other significantly Pfh1-associated features , such as highly transcribed genes ( 32% ) , but none were sufficient to explain the association completely . Given their open state , it is likely that nucleosome-free regions are enriched for tightly bound proteins , other interactions , or formation of stable secondary structures that pause replication . The association with meiotic DSB hotspots in mitotically growing cells is largely driven by overlap with other elements that challenge replication; 92% ( 265 of 289 DSB ) overlap another hard-to-replicate site identified in this study , most notably highly expressed genes and 3’ UTRs . As noted above , several lines of evidence point to RNA polymerase III genes as the most Pfh1-dependent set of sites . Significant , but relatively smaller , fractions of the other Pfh1-associated features ultimately produce fork stalling and DNA damage in the absence of Pfh1 ( Table 2 ) . However , given the greater length of RNA polymerase II transcribed genes , if some cause fork stalling throughout their entire length , they may have a greater impact on fork progression genome-wide than the shorter tRNA genes . The comprehensive analysis of sites of Pfh1 activity provided here demonstrates that , in addition to G4 motifs [39] , there are many classes of hard-to-replicate sites that depend on Pfh1 for their proper replication . Many of these other sites , in particular RNA polymerase III transcribed genes , exhibit even stronger Pfh1-dependent effects than G4 motifs ( Tables 1 and 2 and S3 Table ) . Altogether , Pfh1 supports DNA replication at thousands of diverse sites across the genome . Although accessory helicases are well studied in bacteria , it is not clear for any of the bacterial enzymes if they are recruited to their sites of action or if they move with the replisome through the genome . Here , we present several lines of evidence that support the association of Pfh1 with the replisome in S . pombe . First , using a strain in which Pfh1 and Cdc20 were both epitope tagged , Pfh1 had strong binding to three different origins during S phase ( Fig 5 ) , even though these origins were not a peak of Pfh1 binding in asynchronous cells . Pfh1 was also bound to adjacent regions of the origins , although neither of these sites were Pfh1-dependent sites . The temporal patterns of binding to the sites adjacent to the origins were similar to those of Cdc20 in the same cells; however , we could not detect a movement of Pfh1 from an origin to its adjacent region , as we detected for Cdc20 at ars3002 . This may be due to the role of DNA polymerase ε in replication initiation [65] , technical challenges , and/or stochastic origin usage in different cells in the synchronized population [66] . If Pfh1 were recruited only to Pfh1-dependent sites , we would have expected to see low binding to these Pfh1-independent sites compared to Cdc20 . Second , the levels of Pfh1 and Cdc20 binding to five other sites were similar , regardless of whether the site was Pfh1-dependent or independent . This again argues against a model in which Pfh1 is only recruited and associated with sites that require it for their normal replication . Third , genome wide analyses show that many sites of high Cdc20 binding overlap with Pfh1 peaks ( 230 of the 485 high Cdc20 binding sites are also sites of high Pfh1 binding ) . These data are most consistent with Pfh1 being in close proximity to the replisome throughout the genome . Our MS analyses also provide independent evidence in support of the hypothesis that Pfh1 maintains proximity to the replisome and is likely a replisome component . During S phase , Pfh1 was associated with many replisomal proteins , including the MCM replicative helicase , subunits of the replicative polymerases , the processivity clamp PCNA , RPA , and four MMR proteins ( Fig 6C ) . The association of Pfh1 with replisome proteins was either S phase limited or S phase enhanced and almost all of these associations were DNase-resistant . While it is possible that Pfh1 would have interactions with replisome subunits if it were recruited after replisome pausing , in the context of the evidence presented above , we conclude that Pfh1’s strong associations with replication proteins are likely due to protein-protein interactions , as expected if it is a replisome subunit . Pfh1 also strongly associated with Spt16 and Pob3 ( homologs of human SPT16 and SSRP1 ) , two subunits of the heterodimeric evolutionarily conserved chromatin remodeling FACT complex ( Table 3 ) . In budding yeast , FACT promotes replication at replication conflicts past transcribed regions , and when FACT is depleted , ScRrm3 occupancy increases at highly transcribed RNA polymerase II and III genes [67] . Also , R-loop formation is elevated in FACT depleted cells , suggesting that the FACT complex resolves R-loop-mediated transcription-replication conflicts . The S phase association of FACT with Pfh1 suggests that the two cooperate to promote replication through these genes in S . pombe as they do in budding yeast . Pfh1 may facilitate replication at these genes by removing R-loops , as does budding yeast ScPif1 [27 , 41] . In combination with its biochemical activities , the enrichment of Pfh1 at 3’ UTRs may also reflect a role in resolving R-loop-mediated transcription-replication conflicts . The genomes of all organisms are littered with hard-to-replicate sites . Accessory helicases promote the movement of the replisome past these natural impediments . Although multiple bacterial accessory helicases have been characterized , much less is known about accessory helicases in eukaryotes . Our genomic and proteomic analyses , in combination with previous work , show that Pfh1 promotes replication and suppresses DNA damage at hundreds of diverse , naturally occurring hard-to-replicate sites . The pattern of binding of Pfh1 through the genome combined with its association with many replisome components argues that it is in close proximity with the replisome to help it maneuver past these obstacles . For many of the Pfh1-sensitive sites ( e . g . , tRNA and other highly transcribed genes ) , replication slowed at these sites even in WT cells , but usually did not result in significant DNA damage . However , when Pfh1 was depleted , fork slowing intensified and DNA damage increased dramatically at hard-to-replicate sites . In budding yeast , the two Pif1 family helicases , ScPif1 and ScRrm3 , collaborate to promote fork progression past replication impediments . Our results establish the importance of this helicase family in S . pombe , a eukaryotic organism that is deeply diverged from S . cerevisiae and shares many genomic characteristics in common with higher eukaryotes . As the replication-impeding obstacles found in budding and fission yeasts are ubiquitous across genomes of other organisms , accessory helicases are likely to be required in all organisms , even though helicases that act at most of these sites have not been identified in higher eukaryotes . Thus , we propose that Pif1 family helicases present in multicellular eukaryotes also act as accessory helicases to promote fork progression and preserve genome stability .
ChIP-qPCR were performed in cdc25-22 leu1-32::PJK148-Pfh1-13MYC-kanmx6 cdc20::cdc20-3HA-kanmx6 cells ( S8 Table ) . Asynchronous samples were grown at 25°C . The G2 phase cells were arrested at 37°C and collected after 4h arrest . To perform ChIP-qPCR in synchronized cells , cells were arrested at 37°C for 4h and released at permissive temperature at 25°C . Samples from the cell synchrony were collected for FACS analysis and ChIP-qPCR at 0 , 20 , 40 , 60 , 65 , 70 , 75 , 80 , 85 , 90 , 95 , 115 , 140 , and 165 min . The ChIP experiments were performed as described previously [35] . Briefly , cells were cross-linked in 1% formaldehyde at 25°C for 5 min . The chromatin was sheared to an average of ~300 bp with a Covaris E220 system . G2 phase and asynchronous cells were immunoprecipitated with anti-Myc antibody ( Clontech Cat . nr 631206 ) . The synchronous cells were divided ( 3/4 of sample for Pfh1-Myc and 1/4 of sample for Cdc20-HA ) and immunoprecipitated with either anti-Myc antibody or anti-HA antibody ( Santa Cruz Biotechnology Cat . nr Sc7392x ) . Both immunoprecipitated DNA and the corresponding input amount for each sample were purified and quantified by real-time PCR , using the primer pairs described in S9 Table . At least two biological replicates were performed for each synchronous ChIP analysis . To calculate the average ratio of the peak binding time in Fig 5F , the IP/Input of two biological replicates were used . The time for peak binding ( highest binding ) in replicate 1 was 95 min and 70 min for replicate 2 . The difference between the highest peak binding for the two synchronies was due to different start times of S phase after G2 release , which was detected by FACS analysis . S . pombe cells were collected in 165 mM EDTA , 0 . 1% sodium azide 70% EtOH . Cells were pelleted , washed in 100% EtOH , and stored at 4°C . In preparation for FACS analysis , approximately 2 x 106 cells were washed in 3 ml of 50 mM Na citrate , pH 7 . 2 , and incubated overnight at 37°C in 0 . 5 ml 50 mM Na citrate plus 0 . 1 mg/ml RNaseA . Following sonication , cells were incubated in 1 μM Sytox Green ( Molecular Probes ) at room temperature for 30 minutes . Cells were analyzed using a FACScan single laser fixed-alignment benchtop analyzer . We tested for enrichment between the genomic locations of the ChIP-seq peaks with sixteen sets of genomic annotations ( Table 1 ) . The ChIP-seq data for Pfh1 , Cdc20 , and γ-H2A are available in GEO data set GSE59178 [39] . All peaks analyzed are available in S1 Table . We took the location of genes , coding sequences , essential genes , dubious genes , 3’ and 5’ UTRs , promoters , tRNAs , centromeres , 5S rRNAs , long terminal repeats , and the mating type loci from PomBase [68] . We also considered the locations of meiotic DSB hotspots [69] , NDRs [70] using podbat [71] , origins of replication [72] , the 500 most highly expressed genes from expression data collected across several growth conditions [44] , and G4 motifs [39] . For Pfh1 and Cdc20 sites , regions within 300 bp were considered associated , and for γ-H2A peaks , we considered a window of 5 kb , since DNA damage results in elevated phosphorylation in a window of roughly this size around the break [50] . Overlaps between sets of genomic locations were calculated using BEDTools [73] . To determine if the observed association between two sets of genomic features , such as Pfh1 binding peaks and tRNA genes , was significantly greater than expected , we followed our previously described procedure for generating GC-content-aware empirical p-values [18 , 39 , 74] . In brief , we compared the observed number of overlaps to the number obtained when scrambling the peak locations 1 , 000 times in a manner that preserved the length , chromosome , and GC content of the regions . The number of randomized sets of peaks that obtain as many or more overlaps with the annotation ( e . g . , tRNA ) as the actual peaks is the empirical p-value . If no random sets meet the level of overlap with the actual peaks , then the p-value is reported as < 0 . 001 . We accounted for the testing of each set of ChIP-seq peaks with multiple genomic features using the Bonferroni correction . Strains expressing Pfh1-GFP were previously described ( S8 Table ) [34] . Briefly , the pJK148-integrating vector was used to express Pfh1-GFP from the leu1 locus using the endogenous Pfh1 promoter . The control IP strain expressed GFP-NLS from the leu1 locus under the control of the P3nmt promoter . The GFP-NLS construct was generated in pJK148 using the plasmid pFA6a-kanMX6-P3nmt1-GFP tagging construct as a PCR template with the addition of two SV40 nuclear localization signals introduced by PCR primers . For cell synchronization , cdc25-22 strains were grown to early mid-log ( 0 . 5 x 107 cells/ml ) at the permissive temperature of 25°C . The cells were collected by filtration and shifted to 37°C for G2 arrest . After 4 hours of incubation at 37°C , the media was quickly cooled ( 2 minutes by swirling in an ice bath ) to 25°C for synchronized growth . Cells harvested at the G2 time point were collected at the end of the 4 hour 37°C incubation . Cells harvested at the S phase time point were collected at 84 minutes , corresponding to the start of replication . Cell cycle progression and the timing of DNA replication were confirmed by FACS analysis . Strains expressing either Pfh1-GFP ( yKM333 ) or GFP alone ( yKM346 ) from the S . pombe leu1-32 locus in a strain background containing the cdc25-22 mutant were confirmed to progress normally through the cell cycle by FACS analysis . Two liters of S . pombe cells were either synchronized or grown to mid-log and harvested by centrifugation at 4°C for 10 minutes at 4 , 000 rpm ( 2 , 704 x g ) . Cell pellets were resuspended in freezing buffer ( 20 mM Na-HEPES , 1 . 2% polyvinylpyrrolidone ( W/V ) , pH 7 . 4 containing a protease inhibitor cocktail ( v/v 1/100 ) ( Sigma ) and frozen as cell droplets in liquid nitrogen [75] . Frozen cell droplets were cryogenically ground using a Retsch MM301 Mixer Mill ( 20 cycles x 2 . 5 min at 30 Hz ) ( Retsch , Newtown , PA ) to achieve greater than 85% cell lysis , as assessed using light microscopy . Approximately 12 grams of ground , frozen cells were resuspended in lysis buffer ( 100mM Hepes KOH , pH 7 . 9 , 300mM potassium acetate , 10mM magnesium acetate , 10% glycerol , 0 . 1% NP-40 , 2mM EDTA , 2mM B-glycerophosphate , 50mM NaF , 10mM NaVO4 , 1mM DTT , protease inhibitor cocktail ( Roche ) in a ratio of 5ml of lysis buffer per 1 gram of cells . Cells were gradually added to the lysis buffer with continuous mixing to avoid cell clumps . Lysis buffer conditions of varying salt concentrations ( 50–900 mM potassium acetate ) were optimized for efficiency of Pfh1-GFP purification . Cell lysate was homogenized using a PT 10–35 Poyltron ( Kinematica ) for 3 sets of 10 seconds ( 30 seconds total ) with 1 minute on ice in between each set . Insoluble material from the cell lysate was removed by centrifugation at 8000 rpm ( 9265 x g ) for 10 minutes at 4°C . For immunoaffinity purification of Pfh1-GFP , the cell lysate supernatant was incubated for 30 minutes at 4°C with approximately 20 mg of M-270 epoxy magnetic beads ( Invitrogen Dynal ) conjugated with 50 μg of in-house developed rabbit polyclonal anti-GFP [51] . Following incubation , the beads were collected and washed six times with 1ml of lysis buffer . Proteins were eluted from the beads by incubation with 40 μl of 1x LDS sample buffer ( Life Technologies ) by shaking for 10 minutes at room temperature , followed by 10 minutes at 70°C . Eluted proteins were alkylated with 50 mM chloroacetamide for 30 minutes at room temperature in the dark . To determine if DNA mediates the interactions of Pfh1-GFP , chromosomal DNA of the cell lysate was incubated with an excess of DNaseI ( 640 U/g of cell or ~70 ug/ml , 30 min . at 4°C ) during the IP experiment immediately before the addition of conjugated beads ( S6 Fig ) . DNaseI digestion was assessed by precipitation of DNA from the cell lysate before and after DNaseI treatment and was visualized on an agarose gel by ethidium bromide staining ( S6B Fig ) . Low molecular weight DNA was observed in samples of cell lysate taken before and after DNaseI treatment ( S6B Fig , lanes 1 and 2 ) , suggesting that chromosomal DNA was degraded during earlier steps of the IP experiment before the addition of DNaseI . Enzymatic activity of DNaseI was not affected in the cell lysate , as demonstrated by the digestion of plasmid DNA ( pDNA ) that was added to a sample of the cell lysate prior to DNaseI digestion ( S6B Fig , lanes 3 and 4 ) . Following immunoaffinity purifications ( n = 2 ) of Pfh1-GFP or NLS-GFP from S phase cells , the isolated protein complexes were separated by gel electrophoresis . Samples were digested in-gel with trypsin and peptides were extracted from gel pieces using 0 . 5% formic acid . Peptides were concentrated by vacuum centrifugation to approximately 12 μl . 4uL of the sample was injected for nanoscale liquid chromatography tandem mass spectrometry ( nLC-MS/MS ) analysis on a Dionex Ultimate 3000 RSLC directly coupled to an LTQ-Orbitrap Velos with ( ETD ( ThermoFisher Scientific ) instrument . Data were automatically acquired with MS2 fragmentation of the top 20 most intense precursor ions by collision-induced dissociation ( CID ) . Parameters for data processing were also followed as described previously [56] . Briefly , raw files containing MS2 data were extracted by Proteome Discoverer ( version 1 . 3; Thermo Scientific ) and uploaded to SEQUEST ( version 1 . 20 ) and searched against a compiled database of the yeast protein sequences from S . cervisiae and S . pombe . Post-search validation of the SEQUEST data was conducted by an X ! Tandem algorithm in Scaffold ( version Scaffold_3_00_04; Proteome Software ) using the following filter selections to reduce peptide and protein global false discovery rate to < 1%: 99% protein confidence , 95% peptide confidence , and a minimum of two unique peptides per protein . Protein interactions were accessed for specificity and enrichment ( Pfh1-GFP vs . NLS-GFP control ) using the SAINT ( significance analysis of interactome ) algorithm [52] . A SAINT confidence score cutoff of 0 . 80 was selected to retain high confidence Pfh1 interactions . The protein interaction partners of Pfh1 were placed in the context of known protein interaction data from STRING ( v9 . 1 , medium confidence level , text mining = OFF ) [76] and visualized using Cytoscape [77] . Within Cytoscape , nodes represent proteins that interact with Pfh1 , and edges represent previously reported interactions among proteins in the network . To determine enrichment of protein interactions within the network relative to the background proteome , NSAF ( normalized spectrum abundance factor ) values were calculated to take into account protein length and the total number of spectra present in each individual IP experiment . NSAF values were normalized to proteome abundance ( PAX ) values for S phase S . pombe ( pax-db . org ) , and the NSAF/PAX ratio was mapped onto each node ( node color ) .
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Progression of the DNA replication machinery is challenged in every S phase by active transcription , tightly bound protein complexes , and formation of stable DNA secondary structures . Using genome-wide analyses , we show that the evolutionarily conserved fission yeast Pfh1 DNA helicase promotes fork progression and suppresses DNA damage at natural sites of fork pausing , which occur at “hard-to-replicate” sites . Our data suggest that Pfh1 interacts with the replication apparatus . First , mass spectrometry revealed that Pfh1 interacts with many components of the replication machinery . Second , Pfh1 and the leading strand DNA polymerase occupy many common regions genome-wide , not only hard-to-replicate sites , but also sites whose replication is not Pfh1-dependent . The human genome encodes a Pfh1 homolog , hPIF1 , and contains all of the same hard-to-replicate features that make fission yeast DNA replication dependent upon Pfh1 . Thus , human cells likely also require replicative accessory DNA helicases to facilitate replication at hard-to-replicate sites , and hPIF1 is a good candidate for this role .
|
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"and",
"life",
"sciences",
"yeast",
"and",
"fungal",
"models",
"non-coding",
"rna",
"organisms"
] |
2016
|
Pfh1 Is an Accessory Replicative Helicase that Interacts with the Replisome to Facilitate Fork Progression and Preserve Genome Integrity
|
Mycetoma is a neglected tropical disease , endemic in many tropical and subtropical regions , characterised by massive deformity and disability and can be fatal if untreated early and appropriately . Interleukins ( IL ) -35 and IL-37 are newly discovered cytokines that play an important role in suppressing the immune system . However , the expression of these interleukins in patients with Madurella mycetomatis ( M . mycetomatis ) induced eumycetoma has not yet been explored . The aim of this study is to determine the levels of IL-1 family ( IL-1β , IL-37 ) and IL-12 family ( IL-12 , IL-35 ) in a group of these patients and the association between these cytokines levels and the patients’ demographic characteristics . The present , case-control study was conducted at the Mycetoma Research Centre , Soba University Hospital , University of Khartoum , Sudan and it included 140 individuals . They were divided into two groups; group I: healthy controls [n = 70; median age 25 years ( range 12 to 70 years ) ] . Group II: mycetoma patients [n = 70 patients; median age 25 ( range 13 to 70 years ) ] . Cytokines levels were measured in sera using enzyme linked immunosorbent assay ( ELISA ) . There was a significant negative correlation between IL-1β and IL-12 levels and lesion size and disease duration , while IL-37 and IL-35 levels were significantly positively correlated with both lesion size and disease duration . The analysis of the risk factors of higher circulatory levels of IL-37 in patients of mycetoma showed a negative significant association with IL-1β cytokine , where a unit increment in IL-1β will decrease the levels of IL-37 by 35 . 28 pg/ml . The levels of IL-37 among the patients with a duration of mycetoma infection ≤ 1 year were significantly low by an average of 18 . 45 pg/ml compared to patients with a mycetoma infection’s duration of ≥ 5years ( reference group ) . Furthermore , the risk factors of higher levels of IL-35 in mycetoma patients revealed a negative significant association with IL-12 , as a unit increment in IL-12 decreases the levels of IL-35 by 8 . 99 pg/ml ( p < 0 . 001 ) . Levels of IL-35 among the patients with duration of mycetoma infection ≤ one year were significantly low on average by 41 . 82 pg/ml ( p value = 0 . 002 ) compared to patients with a duration of mycetoma infection ≥ 5 years ( reference group ) . In conclusion , this study indicates that both IL-35 and IL-37 are negatively associated with the levels of IL-1β and IL-12 in eumycetoma mycetoma infection; and high levels of IL-37 and IL-35 may have a negative impact on disease progression .
Mycetoma is a chronic granulomatous subcutaneous inflammatory disease , caused by certain bacteria ( actinomycetoma ) or fungi ( eumycetoma ) . This infection progresses to affect the deep structures and bones leading to massive destruction , deformities and disabilities [1] . It constitutes a major health problem in many tropical and subtropical countries and it is highly endemic in Sudan , Mexico , and India . In Sudan , more than 8500 patients were managed at the Mycetoma Research Centre in Khartoum , of whom 70% were infected with the fungus M . mycetomatis . The disease affects all age groups , but it occurs most commonly in young men at the age group 20 to 40 years [2] . The disease is usually painless and the clinical diagnosis is commonly based on the presence of subcutaneous mass , multiple sinuses and seropurulent discharge with grains . Treatment opportunities comprise of various chemotherapeutics agents and wide surgical excision of the infected tissues and may possibly end up with limb amputation [3] . Although most individuals in endemic areas have antibodies against the causative agent of mycetoma; only few develop the disease [4] . Both innate and adaptive immunity play a role in host resistance to causative agents and in the development of disease . Therefore , T-cell responses seem to be important in the progress of mycetoma [5 , 6] . A T-helper ( Th ) 2-like response was reported in primary lesions and in draining lymph nodes in patients with Streptomyces somaliensis infection and after stimulation of peripheral blood mononuclear cells by M . mycetomatis antigens [5 , 7] , whilst Th1 response was reported in the acute phase of infection and in healthy endemic controls [8 , 9] . Macrophages stimulated with live conidia of Pseudallescheria boydii also induced a Th2 response , whereas hyphae induced a Th1 response [10] . Experimental infection of nude athymic rats and mice with Nocardia ( N . ) asteroids let to fatal disease dissemination [11 , 12] . In addition , T lymphocytes from previously immunised animals directly killed N . asteroides [12 , 13] . Moreover , Trevino-Villarreal and associates [14] reported that N . brasiliensis cell wall-associated lipids are implicated in the development of experimental actinomycetoma and act principally by inhibiting several microbicidal effects of macrophages , including the inhibition of TNF-α production , phagocytosis , production of nitric oxide ( NO ) , and bacterial killing . In addition , they demonstrated that the N . brasiliensis wall-associated lipids suppressed the expression of major histocompatibility complex class II ( MHC II ) , CD80 , and CD40 by dendritic cells ( DCs ) and strongly induced the production of TGF-β by these cells . It has been suggested that pre-existing Th2 environment caused by schistosomiasis promotes the development of mycetoma as patients with mycetoma were significantly more positive for schistosoma antibodies than healthy endemic controls [9] . These findings suggested that Th2 like response and anti-inflammatory/immunosuppressive cytokines could have a negative impact on mycetoma development and disease progression . IL-1 is a polypeptide which has two forms; IL-1α and IL-1β . It is involved in the acute-phase response and is accountable for several alterations that are related to the onset of various medical disorders [15 , 16] . It is demonstrated recently that higher levels of IL-1β cytokine are strongly associated with surgically treated mycetoma patients , in comparison to those treated without surgery [17] . It is known that IL-1β is a pro-inflammatory cytokine that is involved in cell death coordination [18] . IL-1β cytokine is cleaved into the mature , active form primarily by inflammasome dependent caspase activity [18] . It is possibly that mature IL-1β secretion by macrophages activates IL-1 receptor type 1 ( IL-1R1 ) on macrophages , fibroblasts and epithelial cells , inducing production of the CXC chemokine CXCL1/KC , which binds to CXCR2 on neutrophils and mediates recruitment of neutrophils from peripheral blood to stimulate inflammation at the site of mycetoma invasion . Therefore , these higher levels of IL-1β cytokine advocate a crucial role in M . mycetomatis pathogenesis . IL-37 , which is a member of the IL-1 family , has emerged as a potent anti-inflammatory cytokine that suppresses both innate and adaptive immune responses [19] . Its role in human diseases is not completely understood yet [20] . However , the anti-inflammatory properties of IL-37 have been associated with inflammatory diseases , such as systemic lupus erythematosus ( SLE ) [21] , and inflammatory bowel disease [22] . It has been reported that IL-37 is negatively associated with pro-inflammatory cytokines such as IL-1β , IL-6 , IL-17 , TNF-α and IFN-γ in peripheral blood mononuclear cells ( PBMCs ) of patients with degenerative intervertebral discs [23] and Graves’ disease ( GD ) [24] . IL-37 protein level in PBMCs and DCs is up-regulated when stimulated by Toll-like receptor ( TLR ) ligands or pro-inflammatory cytokines [25] . In vitro , over expression of IL-37 in macrophages or epithelial cells greatly inhibits the production of major pro-inflammatory cytokines such as TNF-α , IL-1α , IL-1β , IL-6 , IFN-γ and macrophage inflammatory protein 2 [25 , 26] . In vivo , IL-37 transgene protects mice from lipopolysaccharides-induced shock and chemical-induced colitis [19 , 27] . IL-37 interferes with the innate protective anti-Candida host response by reducing the production of pro-inflammatory cytokines and suppressing neutrophil recruitment in response to Candida infection , resulting in an increased susceptibility to disseminated candidiasis [28] . Moreover , IL-37 markedly reduced inflammasome activation and disease severity in murine aspergillosis [29] . In addition to its role in innate immunity; IL-37 plays a pivotal role in regulating adaptive immunity by inducing T regulatory ( Treg ) cells and impairing activation of effector T-cell responses [30] . To our knowledge , there has been no study to report the relationship between IL-37 and mycetoma pathogenesis so far . Interleukin-12 ( IL-12 ) is frequently denoted as a B cell cytokine , although it is mainly formed by innate immune cells , comprising epithelial cells , DCs , and macrophages [31] . IL-12 is a multimer that plays a fundamental role in immune regulation and is extensively involved in infections . It binds to the heterodimeric IL-12 receptor , which is principally present in T cells and on natural killer ( NK ) cells [31] . IL-12 induces Th1 responses [32] , which consequently increase the cytotoxic cytokines , in addition to IFN-γ by T cells [31 , 32] . IL-35 is a recently identified heterodimeric cytokine which belongs to the IL-12 cytokine family , composed of the subunits of IL-27; β chain Epstein-Barr-virus ( EBV ) -induced gene 3 ( Ebi3 ) and IL-12α chain p35 [33 , 34] . IL-35 is a potent immunosuppressive cytokine produced by B regulatory cells ( Breg ) [35] , DCs [36] , and to a lesser extent , by endothelial cells , smooth muscle cells , and monocytes [37] . The biological effect of IL-35 is poorly understood , however IL-35 is recognised as a typical anti-inflammatory cytokine , and the predominant mechanism of suppression is associated with its ability to suppress T cell proliferation and effector functions [33 , 38] . Given the direct immunosuppressive effect of IL-35 , many studies have been conducted to evaluate its role in the development of several diseases . IL-35 can suppress several types of chronic inflammatory diseases such as inflammatory bowel disease [26] , and decreased the severity of collagen induced arthritis in animals via enhancement of IL-10 production [39] and suppression of Th17 cells [40] . In an asthma model , intra-tracheal instillation of IL-35 decreased disease severity by diminishing the Th2 cell counts [41] and by reducing the production of IL-17 [42] . In bacterial infections , Shen and associates [35] found that mice without IL-35 expression demonstrated an obviously improved resistance to infection with the intracellular bacterial pathogen Salmonella typhimurium . In addition , IL-35 has been increased in the serum of adults and children with sepsis , and administration of anti-IL-35 p35 antibodies diminished dissemination of the bacteria in septic animals [43] . Similarly , tuberculous patients exhibited an increase in serum IL-35 and in mRNA expression of both subunits of IL-35 ( p35 and EBI3 ) in white blood cells and peripheral blood mononuclear cells [44] . However , the role of IL-35 in mycetoma pathogenesis has not been highlighted yet . With this background this study was set to determine the IL-1 cytokine family ( IL-1β , IL-37 ) and IL-12 cytokine family ( IL-12 , IL-35 ) circulating levels of in patients infected with M . mycetomatis , and to explore the association between the pro-inflammatory/anti-inflammatory cytokine levels and the patients’ demographic characteristics . This is correlation may help to understand the pathogenesis of mycetoma disease .
This case-control study was conducted at the Mycetoma Research Centre , Soba University Hospital , University of Khartoum , Sudan . After a written informed consent , blood samples were taken from patients and a matched control population living in the mycetoma endemic areas of Sudan between 2015 and 2016 . Samples collection was previously described in details by Nasr and associates [17] . In this study 140 individuals were enrolled; 49 ( 35% ) were females and 91 ( 65% ) were males with an overall median age of 25 years ( range 12–70 years ) . Seventy patients were infected with M . mycetomatis . The study population was divided into two groups; group I: healthy controls [n = 70; median age 25 years ( range 12 to 70 years ) ] . Group II: mycetoma patients [n = 70 patients; median age 25 ( range 13 to 70 years ) ] . Both groups had a similar gender distribution , 54 ( 80% ) were males in each group . The diagnosis of eumycetoma was established by various techniques and that included imaging , molecular and histopathological techniques , and grain culture [1 , 45] . Surgical biopsies are obtained by a wide local incision under anaesthesia and appropriate surgical conditions as part of the routine patients’ treatment protocol [45] . After medical examination , healthy controls were selected from blood bank donors or healthy volunteers to match the patient’s birthplace geographically . All healthy controls were questioned for acute or chronic infectious diseases , autoimmune family history and genetic disorders . Then all study participants gave their informed written consent and the study was approved by the Ethics Committee of the Faculty of Medicine , University of Khartoum , Sudan . One hundred μl of blood were collected on Whatman qualitative filter paper , Grade 1 , circles , diam . 42 . 5 mm ( Sigma-Aldrich Chemical Co . , St . Louis , MO , USA ) for determination of cytokines . The use of filter paper dried whole blood spots ( DBS ) for specimen collection was preferred to facilitate collection , storage and transportation of specimens and it is in line with the World Health Organization recommendations and also used in several previous studies [46–48] . Sera were extracted from filter-paper samples as described previously in details [17] . IL-1β , IL-37 , and IL-12 were measured in the sera using commercially available enzyme linked immunosorbent assay ( ELISA ) kits ( Abcam , Cambridge , UK ) . Serum levels of IL-35 were estimated using a sandwich ELISA commercial kit ( Colorful Gene Biological Technology , Wuhan , China ) . Cytokine assays were performed in duplicates according to the manufacturers’ protocols . The sensitivity of Human ELISA kits for IL-1β , IL-37 , IL-12 and IL-35 cytokines was 0 . 5 pg/ml . The data were managed by SPSS version 24 . 0 statistical software for Windows ( IBM SPSS statistics ) and appropriate statistical tests were used . The results are expressed as mean ± standard deviation ( SD ) or median with interquartile range ( IQR ) . Spearman correlation test was used to evaluate the associations between serum IL-37 and IL-35 levels and laboratory values as well as serum cytokine levels . For non-parametric data , comparisons between the groups were performed using the Kruskal–Wallis test . One-way ANOVA was used for parametric data . General linear models were used to assess the risk factors for circulating IL-37 and IL-35 among mycetoma patients with different disease duration and lesions size of mycetoma infection adjusted with other variables . A test with a p value <0 . 05 was considered statistically significant . This study was approved by the Ethics Committee of Soba University Hospital , Khartoum , Sudan ( SUH , EC-17-029 ) . Written informed consent was taken from all the participants before enrolment in the study . The work described here was performed in accordance with the Declaration of Helsinki [49] .
The levels of cytokines ( IL-1β , IL-37 , IL-12 and IL-35 ) were constitutively correlated among mycetoma patients with different lesions diameters . The levels of IL-1β were constitutively positively correlated with IL-12 and lesions diameter ( Table 2 ) . On the other hand , the levels of cytokine IL-1β were constitutively negatively correlated with IL-37 and IL-35 ( Table 2 ) . Furthermore , the levels of cytokine IL-37 were constitutively positively correlated with IL-35 ( Table 2 ) . However , the levels of cytokine IL-37 and IL-35 were constitutively negatively correlated with IL-12 ( Table 2 ) . In the patients group , the levels of cytokines ( IL-1β , IL-37 , IL-12 and IL-35 ) were constitutively correlated with the duration of mycetoma infection . Levels of IL-1β showed a consistent positive correlation with IL-12 and negative correlation with IL-37 and IL-35 ( Table 3 ) . Whereas , levels of IL-37 were constitutively positively correlated with IL-35 ( Table 3 ) . However , the levels of IL-37 and IL-35 were constitutively negatively correlated with IL-12 ( Table 3 ) . Circulating serum cytokine levels were determined in all mycetoma patients and were compared between the different lesion diameters among mycetoma patients and healthy controls . Overall , there was a significant difference in the levels of all studied cytokines between the four groups ( Three levels of lesion diameter and the healthy controls ) , ( Table 4 ) . Distribution of IL-1β levels has decreased dramatically with lesion diameter [for lesion diameter ≤ 5 cm: the mean ± SD ( 3 . 39 ± 1 . 07 ) ; for 5–10 cm: ( 2 . 32 ± 0 . 05 ) ; for ≥ 10 cm: ( 2 . 08 ± 0 . 11 ) , p value < 0 . 001] . However , the circulating serum levels of IL-37 were significantly increased with lesion diameter [for lesion diameter ≤ 5 cm: the mean ± SD ( 107 . 92 ± 5 . 96 ) ; for 5–10 cm: ( 141 . 45 ± 12 . 96 ) and for ≥ 10 cm: ( 193 . 20 ± 15 . 01 ) , p value < 0 . 001] , ( Table 4 ) . Our results showed a significant reduction of circulating IL-12 levels versus lesion diameter [for lesion diameter ≤ 5 cm: the mean ± SD ( 25 . 22±3 . 34 ) ; for 5–10 cm: ( 14 . 45± 3 . 32 ) ; for ≥ 10 cm: ( 9 . 65 ± 0 . 36 ) , p value < 0 . 001] , ( Table 4 ) . Circulating levels of IL-35 were significantly increased with increasing lesions’ diameter [for lesion diameter ≤ 5 cm: the mean ± SD ( 255 . 15 ± 1 . 72 ) ; for 5–10 cm: ( 263 . 23 ± 3 . 26 ) ; ≥ 10 cm: ( 449 . 71 ± 22 . 2 ) , p value < 0 . 001] , ( Table 4 ) . Circulating levels of IL-1β had significantly decreased with increasing disease duration [ ( ≤ 1 year; median = 2 . 3 pg/ml ) , ( 2–4 years; median = 2 . 2 pg/ml ) and ( ≥ 5 years; median = 2 . 2 pg/ml ) ] , p value = 0 . 017 ( Table 5 ) . Serum levels of IL-12 dramatically decreased with the increase in disease duration [ ( ≤ 1 year; median = 12 . 5 pg/ml ) , ( 2–4 years; median = 10 . 2 pg/ml ) and ( ≥ 5 years; median = 9 . 8 pg/ml ) ] and p value < 0 . 001 ( Table 5 ) . However , circulating levels of IL-37 were positively increased with different durations of mycetoma infection [ ( ≤ 1 year; median = 145 pg/ml ) , ( 2–4 years; median = 178 pg/ml ) and ( ≥ 5 years; median = 185 . 2 pg/ml ) ] , p value <0 . 001 ( Table 5 ) . Similarly , serum levels of IL-35 were also significantly increased with increasing duration of mycetoma infection [ ( ≤ 1 year; median = 262 . 5 pg/ml ) , ( 2–4 years; median = 423 . 5 pg/ml ) and ( ≥ 5 years; median = 436 pg/ml ) ] , p value < 0 . 001 ( Table 5 ) . The analysis of the risk factors of higher levels of IL-37 in patients of mycetoma showed a significant negative association with IL-1β , where a unit increment in IL-1β decreases the levels of IL-37 by 9 . 1 pg/ml , p value = 0 . 008 ( Table 6 ) . Serum levels of IL-37 among the patients with lesion diameter ≤ 5 cm and 5–10 cm have significantly lower on average by 75 . 4% and 52 . 6% , respectively , compared to patients with lesion diameter ≥ 10 cm ( reference group ) . Serum levels of IL-37 among the patients of mycetoma showed no significant difference between males and females , p value = 0 . 176 ( Table 6 ) . Circulating levels of IL-37 significantly decreased with increasing age groups [ ( 19–24 years ) ; p value = 0 . 010 and ( 30–39 ) years; p value = 0 . 029] , ( Table 6 ) . The analysis of the risk factors of higher serum levels of IL-35 in mycetoma patients showed no significant association with IL-12 , p value = 0 . 182 ( Table 7 ) . Circulating levels of IL-35 among the patients with lesions’ diameter ≤ 5 cm and 5–10 cm were significantly decreased by 174 . 4% and 176 . 5 , respectively; compared to patients with lesion diameter ≥ 10 cm ( reference group ) , p value < 0 . 001 ( Table 7 ) . Serum levels of IL-35 among mycetoma patients showed no significant difference between males and female , p value = 0 . 575 ( Table 7 ) . Circulating levels of IL-35 showed no significant association with the different age groups and different types of antifungal medication given ( Table 7 ) . The analysis of the risk factors of higher circulatory levels of IL-37 in patients of mycetoma showed a negative significant association with IL-1β , where a unit increment in IL-1β decreases the levels of IL-37 by 35 . 28 pg/ml , p < 0 . 001 ( Table 8 ) . Levels of IL-37 among patients with a disease duration ≤ 1 year had significantly decreased on average by 18 . 45 pg/ml compared to patients with a disease duration ≥ 5years ( reference group ) . However , there was no significant difference in levels of IL-37 between patients with infection duration 2–4 years and ≥ 5 years , p value = 0 . 793 ( Table 8 ) . In addition , serum levels of IL-37 among the mycetoma patients showed no significant difference between males and females , p value = 0 . 627 ( Table 8 ) . Furthermore , the circulating levels of IL-37 had significantly decreased with increasing age groups [ ( 19–24 years; p value = 0 . 022 ) , 25–29 years; p value = 0 . 030 and ( 30–39 ) years; p value = 0 . 022] , ( Table 8 ) . Interestingly , levels of IL-37 among mycetoma patients showed a statistically significant difference between itraconazole compared to ketoconazole , p < 0 . 001 ( Table 8 ) . The analysis of the risk factors of higher levels of IL-35 in patients of mycetoma revealed a negative significant association with IL-12; as a unit increment in IL-12 decreases the levels of IL-35 by 8 . 99 pg/ml , p value < 0 . 001 ( Table 9 ) . Levels of IL-35 among the patients with a mycetoma with a disease duration of ≤ 1 year had significantly decreased , p value = 0 . 002 , on average by 41 . 82 pg/ml compared to patients with a disease duration ≥ 5years ( reference group ) , ( Table 9 ) . However , patients with an infection duration of 2–4 years and ≥ 5 years showed no significant difference in IL-35 levels , p value = 0 . 391 . Furthermore , there was no significant difference ( p value = 0 . 49 ) in IL-35 levels between male and female mycetoma sufferers ( Table 9 ) . Additionally , the circulating levels of IL-35 showed no significant association with the different age groups ( Table 9 ) . Interestingly , mycetoma patients who were treated with itraconazole showed significant increased levels of IL-35 compared to the patients treated with ketoconazole , p value <0 . 001 ( Table 9 ) .
Although mycetoma represents a major health problem in many tropical and subtropical areas , there is no prevention or control measures for this neglected disease [22 , 23] . In mycetoma endemic areas , most individuals have antibodies against the causative agents , however only few develop disease [4] . Few researchers believed that patients who develop mycetoma seem to be deficient in their cell-mediated immunity [6] . Hence , we aimed to investigate the profiles of the pro-inflammatory ( IL-1β and IL-12 ) and the anti-inflammatory immunosuppressive ( IL-37 and IL-35 ) cytokines among mycetoma patients and their association with disease characteristics . As far as we know , this is the first study addressing the relation between immunosuppressive cytokines ( IL-37 and IL-35 ) and mycetoma infection . Our data showed that eumycetoma patients presented higher circulating levels of IL-1β , IL-12 , IL-37 and IL-35 compared to controls . In addition , serum levels of IL-1β and IL-12 were significantly decreased with increasing lesions’ diameter and disease duration , whereas levels of IL-37 and IL-35 were significantly higher with increasing lesions’ diameter and disease duration . These findings indicate that immunosuppressive cytokines like IL-37 and IL-35 , which could suppress cell-mediated immune responses , may exacerbate the disease progression . Data of the current study clearly showed the serum levels of IL-1β and IL-12 in eumycetoma patients with different lesion size and disease duration were positively correlated with each other , and negatively correlated with IL-35 and IL-37 . It has been demonstrated that the first line of innate immune response against mycetoma infection is by phagocytes , from which macrophages represent the major phagocytic cells [50] . In general , protective immunity to fungal infections [51] involves activation of TLRs generating inflammatory cytokines through pattern-recognition receptors and pathogen associated molecular patterns [52 , 53] . IL-1β and other pro-inflammatory cytokines are produced early in response to fungal infections and promote phagocytosis and other means of innate immune response [54 , 55] . Following inflammatory stimuli , several cell types including immune and non-immune cells produce IL-37 , as a protective mechanism to prevent runaway inflammation and excessive tissue damage [56] . IL-37 directly inhibits generation of pro-inflammatory cytokines and down-regulates macrophage cytokine release , and therefore innate immunity [57 , 58] . Moreover , IL-37 induces macrophages towards an M2-like phenotype [59] . M1 macrophages are the most critical effector cells in the innate immune defense system and are characterised by high expression levels of iNOS , subsequent NO production and secretion of pro-inflammatory cytokines , such as IL-1β and IL-12 [60] . However , M2 macrophages secrete anti-inflammatory cytokines , such as IL-10 [61] and express arginase 1 , which inhibits NO production , thus rendering these cells ineffective in killing infectious agents including fungal agents [61 , 62] . Furthermore , DCs expressing IL-37 secreted higher levels of IL-10 and reduced levels of IL-1β and IL-12 . Therefore , the presence of IL-37 in DCs impairs their function in prime T cells and promotes their ability to induce Treg cells that produce IL-10 , which is also a potent anti-inflammatory cytokine [30] . Our results have consistently shown higher circulatory levels of IL-37 in patients of mycetoma which is negatively associated with IL-1β , as a unit increment in IL-1β decreases the levels of IL-37 by 35 . 28 pg/ml . Based on the aforementioned data , we can speculate that IL-37 could play a role in damping inflammatory response in mycetoma infection which leads to disease progression and this is not in the patient’s favor . In the current work , the circulating levels of IL-1β and IL-12 in eumycetoma patients with different lesion size and disease duration were negatively correlated with IL-35; whereas serum levels of IL-35 were increased with increasing lesion size and disease duration , and levels of IL-1β and IL-12 simultaneously decreased . This may probably be an attempt to dampen ongoing inflammation . Both IL-1β and IL-12 have a pivotal role in inflammatory and cell-mediated immune responses . Macrophages , Th1 and cytotoxic T-cells ( CTLs ) , which constitute the main component of cell-mediated immunity , play an important role in the protective immunity against mycetoma infection [2] . As fatal dissemination of N . asteroids infection occurs in nude athymic rats and mice [11 , 12] , T cells from previously immunized animals are able to kill N . asteroids in new infections [12 , 13] . IL-35 could suppress Th1 and macrophage responses [63] , whereas deficiency in IL-35 increases macrophage’s activation and induces Th1 responses [35 , 63] . The increased immunity found in mice lacking IL-35 is associated with a higher activation of macrophages and inflammatory T cells , as well as enhancing function of antigen-presenting cells [35] . In another infection model , Cao and his co-workers reported higher serum levels of IL-35 in septic patients compared to controls , and IL-35 gradually increased with increased sepsis severity . Moreover , administration of anti-IL-35 antibodies diminished dissemination of the bacteria in septic animals and enhanced local neutrophil recruitment with increasing in inflammatory cytokines and chemokines production [43] . Furthermore , IL-35 suppressed the proliferation of antigen-specific CTLs and IFN-γ production [64] . Our data revealed that higher levels of IL-35 in patients with mycetoma is negatively associated with IL-12 , where a unit increment in IL-12 decreases the levels of IL-35 by 8 . 99 pg/ml . This finding indicates that IL-35 may be a risk factor for mycetoma infection and have a negative role in the clinical presentation of the disease . Prevalence of mycetoma infection may vary with age . Data from this study showed variation of mycetoma prevalence with age; 74 . 3% of the patients’ age were 19–39 years . This finding is consistent with previous studies which reported that mycetoma mostly affects ages between 20 and 40 years-old , In addition , our data demonstrated that mycetoma infection was predominant in males , as the male to female ratio in patient’s group was 4:1 . This finding is running parallel with the results of a previous study which demonstrated that in a tertiary facility in Khartoum , Sudan , the male to female ratio is 4:1 , whereas at the primary care level in White Nile State , Sudan , the reported male to female ratio was 1 . 6:1 . Another studies reported that male to female ratios in mycetoma infections were in the range of 1 . 6–6 . 6:1 [2] . The predominance of mycetoma in males may be attributed to increased exposure in men who engage in different manual labors including agricultural work . Moreover , influence of sex hormones might have a role in susceptibility to mycetoma infections and disease progression [4 , 65] . Our data showed that about , 50% of the patients have lesion diameter more than 10 cm . This result reflected that most mycetoma patients tend to present late with massive lesions . This finding could be attributed to the nature of mycetoma which is usually painless and slowly progressive . In addition , the lack of health facilities in endemic areas , the low socio-economic status of the affected patients and their poor health education [1 , 4 , 66] are amongst the reasons why the current treatment of mycetoma is suboptimal , characterised by low cure rates and frequent recurrence often leading to amputation [67 , 68] . However , clinical experience shows that early and small mycetoma lesions are associated with good outcome and prevent severe complications of the disease . One of the remarkable findings of the current study is the significant increase of IL-37 and IL-35 levels with itraconazole treatment compared to the ketoconazole . A previous study by Friccius and colleagues suggested that the dose of 10 μg/ml itraconazole leads to strong inhibition of the cytokines IL-2 , IL-4 , IL-9 and IFN-γ and slight inhibition of TNF-α cytokine production in PBMC after 6 and 24 hours of incubation . These results demonstrate that IL-35 and IL-37 can be one of the underline factors associated with inhibition of the cytokines related to Itraconazole [69] . In conclusion , our study revealed that the levels of IL-37 and IL-35 were consistently positively correlated with different diameters of mycetoma lesions as well as its duration . However , the levels of IL-1β and IL-12 were consistently negatively correlated with different diameters of lesions and the duration of mycetoma infection . The analysis of the risk factors of higher circulatory levels of IL-37 in patients of mycetoma showed a negative significant association with IL-1β cytokine , where a unit increment in IL-1β will decrease the levels of IL-37 by 35 . 28 pg/ml . Levels of IL-37 among the patients with a mycetoma infection duration ≤ 1 year had significantly decreased on average by 18 . 45 pg/ml compared to patients with a mycetoma infection duration ≥ 5years ( reference group ) . Furthermore , the risk factors of higher levels of IL-35 in patients of mycetoma revealed a negative significant association with IL-12 , as a unit increment in IL-12 decreases the levels of IL-35 decrease by 8 . 99 pg/ml , p < 0 . 001 . Levels of IL-35 among the patients with a mycetoma infection duration ≤ 1 year had significantly decreased ( p value = 0 . 002 ) on average by 41 . 82 pg/ml compared to patients with a mycetoma infection duration ≥ 5years ( reference group ) . More investigations are needed to explore the mechanism by which IL-35 and IL-37 contribute in the mycetoma infection outcomes . This will help in understanding the role of these cytokines IL-35 and IL-37 in the pathogenesis of mycetoma , and may exploit it as a potential therapeutic target to prevent mycetoma diseases recurrence .
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Mycetoma is a progressive chronic granulomatous fungal or bacterial infection that may result in massive destruction of subcutaneous tissues , muscles and bones . Mycetoma is a neglected disease which is endemic in many tropical and subtropical areas . If the disease is not treated properly , eventually it ends up with amputation and adverse medical , health and socioeconomic effects on patients and the community . Previous data suggested a crucial role of adaptive immunity in host resistance to causative agents and in the disease progress . The recently identified IL-35 and IL-37 cytokines revealed an important role in immune suppression . Nevertheless , the expression of these interleukins in patients with mycetoma has not yet been investigated . Therefore , the present case-control study aimed to determine the levels of IL-1 family ( IL-1β , IL-37 ) and IL-12 family ( IL-12 , IL-35 ) in these patients and the association between these cytokines levels and the patients’ demographic characteristics . The results of this study showed that , the levels of IL-37 and IL-35 were consistently positively correlated with different diameters of mycetoma lesions as well as its duration . However , the levels of IL-1β and IL-12 were consistently negatively correlated with different diameters of lesions and the duration of mycetoma infection . The analysis of the risk factors of higher circulatory levels of IL-37 in patients of mycetoma showed a negative significant association with IL-1β cytokine Furthermore , the risk factors of higher levels of IL-35 in patients of mycetoma revealed a negative significant association with IL-12 . These findings uncover a possible the role of IL-35 and IL-37 in the pathogenesis of mycetoma , and may declare their potential value in treatment of mycetoma .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[] |
2019
|
The Role of Interleukin-1 cytokine family (IL-1β, IL-37) and interleukin-12 cytokine family (IL-12, IL-35) in eumycetoma infection pathogenesis
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How do adapting populations navigate the tensions between the costs of gene expression and the benefits of gene products to optimize the levels of many genes at once ? Here we combined independently-arising beneficial mutations that altered enzyme levels in the central metabolism of Methylobacterium extorquens to uncover the fitness landscape defined by gene expression levels . We found strong antagonism and sign epistasis between these beneficial mutations . Mutations with the largest individual benefit interacted the most antagonistically with other mutations , a trend we also uncovered through analyses of datasets from other model systems . However , these beneficial mutations interacted multiplicatively ( i . e . , no epistasis ) at the level of enzyme expression . By generating a model that predicts fitness from enzyme levels we could explain the observed sign epistasis as a result of overshooting the optimum defined by a balance between enzyme catalysis benefits and fitness costs . Knowledge of the phenotypic landscape also illuminated that , although the fitness peak was phenotypically far from the ancestral state , it was not genetically distant . Single beneficial mutations jumped straight toward the global optimum rather than being constrained to change the expression phenotypes in the correlated fashion expected by the genetic architecture . Given that adaptation in nature often results from optimizing gene expression , these conclusions can be widely applicable to other organisms and selective conditions . Poor interactions between individually beneficial alleles affecting gene expression may thus compromise the benefit of sex during adaptation and promote genetic differentiation .
The concept of a fitness landscape unites the three levels of evolutionary change – genotype , phenotype , and fitness – into a mathematical picture of the potential for , and constraints upon , adaptive evolution . By mapping genotypes to a measure of fitness , fitness landscapes guide our understanding of how epistasis – nonlinear interactions between the fitness effects of mutations – shapes evolution . Strong epistasis implies that landscapes are rugged , with many peaks , or locally optimally genotypes [1] , [2] . The magnitude and form of epistasis is predicted to determine the number of evolutionary trajectories [3] , [4] , the rate and repeatability of adaptation [5]–[7] , and the benefit of sex [8] . Recent experimental work with a wide variety of model organisms has revealed diminishing returns as a general trend of adaptation [9]–[13] , with relatively few cases of synergy [11] , [14] or sign epistasis [15] ( i . e . , the same mutation being beneficial or deleterious in different contexts [16] ) . Antagonism between adaptive mutations might imply that these populations are summiting peaks in their fitness landscapes with just a handful of genetic changes . This explanation might lead to further trends , such as a negative relationship between the initial selective coefficient of a mutation and its epistatic interactions that could prove to be a useful predictor of a saturating process of adaptation [17] . In order to definitely link diminishing returns to the ascent of local peaks , as well as to understand the existence of the peaks themselves , we must understand the phenotypes that link genotype and fitness in the adaptive landscape . Mathematically convenient formulations such as Fisher's geometric model for adaptation near a single peak [18] have been used to interpret the trend toward antagonism [19] . This approach assumes stabilizing selection a priori . What remains unclear is what types of physiological interactions give rise to fitness landscapes of varying shape and form , as well the constraints upon mutational changes to underlying phenotypes . Models of metabolic pathways have been amongst the most successful in translating underlying biochemical phenotypes to fitness . The contribution of enzyme activities upon metabolic flux has been formalized via Metabolic Control Analysis ( MCA ) [20] , [21] . The ability of this approach to predict the fitness consequences of changes in enzyme properties has been verified in experimental systems that vary from Escherichia coli in lactose-limited chemostats to the flight properties of butterflies ( reviewed in [22] ) . Turning to multiple enzymes , MCA theory has suggested a general trend toward synergistic interactions between activity-increasing mutations in a metabolic pathway [23] , [24] . A major limitation , however , has been that the costs of enzyme expression [25] have not been included in classical MCA . Whereas the dependence of flux through a metabolic pathway saturates with increasing levels of a given enzyme , the costs will continue to accumulate . The balance of these two selective factors will generate an intermediate optimum , and thus stabilizing selection . Inclusion of expression costs to MCA has enabled predictions of the optimum levels of a single enzyme [26] , and was used to compare the differential utility of alternate , degenerate pathways [27] . An open question , however , is how the balance between catalytic benefits and expression costs plays out to optimize enzyme expression across many enzymes simultaneously . In order to study how evolution would simultaneously optimize expression of multiple genes , we have developed a model system of an engineered Methylobacterium extorquens AM1 ( EM ) in which we altered its central metabolism to be dependent upon a foreign pathway ( Figure 1A for details ) . M . extorquens grows on methanol by oxidizing it first to formaldehyde , and then through a series of steps to formate , which is either fully oxidized to CO2 or incorporated into biomass [28]–[32] . In the EM strain we removed the endogenous pathway for formaldehyde oxidation in wild-type ( WT ) [28] and replaced it with two genes encoding a foreign pathway that oxidizes formaldehyde via glutathione ( GSH ) derivatives [29] . Eight populations dependent upon this introduced metabolic pathway evolved in methanol-containing medium via serial transfers for 900 generations [10] , [33] . Adaptation of the unfit EM strain to grow on methanol consistently involved beneficial mutations that altered expression of the foreign GSH pathway ( Figure 1B ) . When the GSH pathway was introduced , the two enzymes were cloned together on a single mRNA transcript behind a strong native promoter present on a medium copy plasmid ( ∼9 cell−1 ) [10] , [29] , [34] , [35] . As such , the costs of expression outweighed the catalytic benefits , and among the targets of adaptation we identified by resequencing strains evolved in separate populations , we universally obtained beneficial mutations that decreased expression of these enzymes [10] , [34] . These mutations reduced expression of the GSH pathway through three classes of underlying mechanisms: Class A decreased expression per gene copy , Class B reduced gene dosage by lowering plasmid copy number , and Class C integrated the introduced pathway into the host genome , which also reduced plasmid copy number [33] , [34] ( Figure 1B ) . In terms of epistasis , mutations in multiple genes along a single adaptive trajectory – including one mutation ( here ‘A1’ ) reducing expression of the GSH pathway – have been shown to exhibit a general trend of diminishing returns that was devoid of sign epistasis [10] . However , here we are interested in uncovering the trends and mechanisms underlying epistatic interactions between mutations that arose in separate adapting lineages and affect expression of the same metabolic pathway . We combined independently-arising beneficial mutations affecting gene expression of this two-enzyme metabolic pathway and report strong antagonism and sign epistasis for fitness . These interactions were increasingly antagonistic for larger benefit mutations . Such strong antagonism did not stem from the effects of mutational combinations upon enzyme levels , but rather from the nonlinear mapping between enzyme expression and organismal fitness . By developing a quantitative model that relates expression cost and catalytic benefit to fitness , we characterized the overall shape of this fitness landscape and revealed that some of these single mutations can optimize multiple phenotypes simultaneously , leading to a big jump toward the single , global optimum .
To explore the pattern of epistatic interactions between beneficial mutations affecting expression of the GSH-dependent pathway , we combined beneficial plasmid mutations that emerged during experimental evolution and affected distinct traits [34] . We focused upon Class A ( decreased expression per copy ) and B ( reduced gene dosage ) mutations because of their genetic tractability , and the prediction that these represent orthogonal mechanisms to achieve lower expression . We hypothesized that mutational combinations between these classes would result in enzyme levels that would be the product of the individual perturbations ( Figure 1C ) . Three class A mutations , A1–A3 , and one class B mutation , B5 , occurred independently , whereas B2 and B3 were isolated together from the same plasmid . We generated 12 plasmids that paired each Class A mutation with each one from Class B , as well as with the B2–B3 pair , and measured their relative fitness via competitions with a fluorescently labeled ancestor [34] ( Tables S1 , S2 ) . The observed fitness values for the mutational combinations were substantially less than expected based upon a simple multiplicative null model incorporating the single mutant effects ( i . e . , Wij = Wi×Wj; R2 = 0 . 53 , adj-R2 = 0 . 32; Figure 2A ) . The increasingly strong antagonism for higher expected fitness values suggested a potential negative relationship between the selective coefficients observed for each mutation and the average epistasis that mutation exhibited with other mutations . We observed that the individually most beneficial mutations ( large s ) engendered the greatest antagonism ( ε<0 ) when combined with other mutations , including several examples of sign epistasis ( Figure 2B ) . Several theoretical arguments suggest that the geometry of fitness landscapes might induce correlations between the size of a mutation and the strength and direction of epistasis . Epistasis has been observed to be coupled to the mean fitness effect of mutations [36] , [37] . A single beneficial mutation of large effect may appreciably change both the mean fitness of subsequent mutations and the remaining distance to the optimum , potentially skewing its own epistatic coefficients . Given the emerging empirical consensus and theoretical arguments for antagonistic epistatic interactions among beneficial mutations , we analyzed several other datasets to ask whether the strength and form of the relationship between selective effect and average epistatic effect held for intragenic and intergenic datasets . For this comparison we analyzed the relationship between s and ε for previous datasets from M . extorquens and E . coli where the beneficial mutations occurred consecutively in a variety of genes across the genome of a single adapting lineage [10] , [11] , combinations of mutations from two genes of the bacteriophage ID11 [12] , and two datasets of within-protein interactions for β-lactamase [17] , [38] . These datasets also displayed signs of a correlation between s and increasingly negative ε ( as noted in [37] ) , with the exception of the intragenic data for β-lactamase ( Figure S1 ) . Negative trends in the relationship between initial selective coefficient and epistasis may seem like obvious evidence for diminishing returns . However , recent theoretical work has shown that in models where mutations have random effects and no tendency to be either synergistic or antagonistic , a pattern of diminishing returns occurs between mutations if they are selected conditioned on being beneficial in the ancestral background [39] . In Supplementary Text S1 we show this behavior in a simple model of evolution on fitness landscapes with no mean epistatic tendency and show how it leads to a pattern of diminishing returns between beneficial mutations as a form of regression to the mean . This analysis suggests that genotype-fitness data alone , without knowledge of the phenotypic effects of mutations or the physiological causes for trade-offs , might be insufficient to infer the mechanism underlying a pattern of epistasis . What physiological factors underlie the strong antagonism observed between mutations affecting expression of the foreign GSH pathway ? A first possibility is that mutational combinations lead to smaller changes in protein expression than expected from the single mutants and that such antagonistic behavior at the level of expression phenotypes merely propagated through as observed antagonism at the level of fitness . Because we used combinations that largely derived from pairing mutations that reduced expression per copy ( Class A ) with those that decreased plasmid copy number ( Class B ) , our null hypothesis was that these mechanisms should act independently to alter expression , such that the expression level of an A+B mutant pair would simply be the product of these two values . Consistent with this prediction , enzyme levels were well described by the null model of multiplicative independence between paired perturbations ( Figure 3A , Table S1 ) . A simple linear model of log-transformed changes in enzyme levels as a function of the presence of the single mutations with no interaction terms explains much of the variation for both FlhA and FghA ( adjusted-R2 = 0 . 85 ( FlhA ) and 0 . 86 ( FghA ) ) . This predictability can be seen in the high correlation between observed expression phenotypes for paired perturbations and those expected based upon the single changes . Since mutational combinations did not introduce epistatic interactions at the level of gene expression , we built a model of the fitness landscape based upon enzyme levels in order to ask how its shape would contribute to antagonism . Building upon earlier work on single enzymes [26] ( Supplementary Text S2 ) , we generated a model of the fitness landscape that calculates fitness as flux through the pathway above a threshold , minus the sum of two costs :The hyperbolic expression for catalysis has been used before [40] to effectively describe the dependence of steady-state flux to the levels of a single enzyme and incorporates a “Vmax” term for the pathway , and an Eh half-maximal enzyme level term . We only model FlhA concentration as beneficial to fitness even though FghA is absolutely required for growth on methanol [34] . This is because , over the parameter range of our perturbations , fitness appeared to rise monotonically with decreasing levels of FghA . This suggests FghA is a typical enzyme that has a low metabolic “control coefficient” [20] , [21] and that it only limits catalysis at exceptionally low levels . None of our perturbations pushed FghA levels below 2% , and for comparison β-galactosidase levels in lactose-limited chemostats only impacted fitness significantly if they decreased activity to ≤1% [41] . The threshold flux term was added to the model to capture an unusual right-shift of the typical relationship between enzyme concentration and fitness observed with FlhA in these data , such that fitness approached zero even in the presence of measurable concentrations of functioning enzyme . We have observed similar behavior when manipulating levels of the analogous enzyme in the endogenous , tetrahydromethanopterin-dependent pathway for formaldehyde oxidation in WT ( SM Carroll , CJM , unpublished ) . As both of these enzymes occur directly downstream of formaldehyde production , this threshold phenomenon may be explained by toxic effects of elevated steady-state formaldehyde concentrations at low enzyme levels . Finally , there are two cost terms for FlhA and FghA . The cost per molecule for each enzyme was treated as a linear function , consistent with prior work [26] , [42] . The six parameters of this benefit - costs model were fit using the data from the EM ancestor , single mutants , as well as strains with inducible promoter plasmids ( 27 data points; Table S3 ) . The inducible promoter plasmids contained a cumate-responsive repressor to modulate the levels of flhA-fghA from ancestral levels to lower values ( Table S1 ) . These data were critical for capturing the steep decline of the fitness landscape at low values of FlhA . The resulting benefit - costs model captured the curvature of the fitness landscape ( Figure 4 ) and , unlike the simple multiplicative model , it was able to predict the 17 combinations of mutations that were not used for model fitting with high precision ( R2 = 0 . 98 ) ( Figure 3B ) . From the perspective of the ancestral genotype , in the model fitness rises gently with decreased expression of either enzyme , but then declines rapidly upon reaching catalytically-limiting levels of FlhA . A similar cliff exists for low values of FghA [34] , but at enzyme levels beyond the range of our dataset and below the detection threshold of our enzyme assay method ( see Methods ) . Our fitness landscape model that precisely maps phenotypes to fitness allowed us to explore how much local topography may have influenced the direction of phenotypic change during evolution by de novo mutations ( Figure 5 ) . We compared the changes in enzyme expression caused by single beneficial mutations relative to three factors: 1 ) the local gradient in the fitness landscape for the ancestor ( greater decreases of FlhA versus FghA because the former is more costly ) , 2 ) the direct vector pointing to the global fitness optimum and 3 ) equal proportional changes between the enzymes which might be expected due to the physical constraint of their being expression from a single transcript . All mutations moved toward the global optimum rather than ascend in the phenotypically steepest direction on the local fitness landscape . Mutations B2 and B5 affected copy number but , through mechanisms we do not currently understand , led to greater decreases in FghA than FlhA . In contrast , B3 was directly along the line of equivalent change in both enzymes . This mutation was identified along with B2 as a plasmid haplotype , and this B2–B3 combination allowed this lineage to accomplish a similar phenotypic ( and fitness ) change as the other mutants .
We found that combining adaptive mutations that optimize expression of a two-enzyme pathway exhibited strong antagonistic interactions and sign epistasis . Fitness values of mutational combinations were generally less than expected relative to a null model of independent , multiplicative effects upon fitness . We further observed a negative relationship between s and ε for individual mutations . Other datasets of intragenic epistasis revealed similar trends between s and ε; however , this overall trend of epistasis ( e . g . , antagonism ) does not imply a specific connection with properties of the individual mutations , such as s . For example , this trend will also arise as a consequence of regression to the mean when the beneficial mutations assayed are conditioned to be beneficial in the ancestral background and the effect of a mutation has a component that is independently distributed on each possible genetic background . Therefore , to extract biological insight from the quantitative relationship between s and ε , we must interrogate the mechanisms that lead to antagonistic epistasis . The first possible explanation for antagonism in our data would be non-linearities in the way mutations combined to affect enzyme expression . However , as expected from having chosen combinations that combined class A mutations with those from class B , these orthogonal mechanistic effects resulted in independent effects on enzyme expression that were jointly well predicted with a simple , multiplicative model . As in this system , many ecologically relevant genes are encoded on plasmids whose regulation and gene dosage may both be effected by separate sets of mutations . More broadly , mutations that influence different traits that make joint contribution to a higher phenotype such as fitness are common . At the level of individual genes , for example , catalytic improvement of an enzyme often results from the joint contribution of mutations that improve protein stability and those that enhance kinetic parameters [43]–[45] . The second factor that could generate antagonism is the curvature of the underlying fitness landscape for gene expression . Recent theory has shown that almost any formulation of fitness based upon multiple underlying phenotypes will generate epistasis at the level of fitness , even when the mutations – as we observed here – do not interact epistatically on the underlying trait phenotypes [46] . Previous models have formulated fitness as a function of gene expression correctly predicted the evolution of optimal levels of gene expression [26] . Here we extended this model framework to multiple enzymes and used it for the first time to interpret beneficial mutational effects from phenotype to fitness , which we characterized both individually and in combination . Our fitness landscape model was able to predict the fitness values of the mutation combinations with high precision ( R2 = 0 . 98 ) . The asymmetry in the curvature of this fitness landscape results from the relatively gentle effects of expression costs relative to the sharp transition in fitness effects due to rate limitation upon catalysis [41] . This observed selection to maintain an intermediate optimum of enzyme levels is distinct from the selective neutrality on a catalytic plateau that was predicted by classical MCA analyses that did not incorporate expression costs [40] . Knowledge of the underlying fitness landscape allows us to understand aspects of the epistatic interactions not evident from fitness values alone . For example , we observed that the A3 and B5 mutations had fairly comparable individual fitness values ( 1 . 420±0 . 032 vs . 1 . 457±0 . 033; mean and 95% CI ) , but the former had three-fold more antagonistic epistasis than the latter ( average = −0 . 46 vs . −1 . 34; t-test , p = 0 . 025; Figure 6 ) . The modeled fitness landscape illuminates the underlying reason for this difference . Both mutations rest near the peak value of enzyme expression , but on opposite sides ( B5 has 70% the level of FghA as A3 ) . This poises B5 such that it is much more sensitive to further reductions in expression than A3 . Thus , although these two mutations are essentially equivalent if one only considers their fitness values , their locations in phenotypic space change their likelihood for antagonism and sign epistasis . One consequence of sign epistasis between mutations affecting the phenotypes like gene expression is a reduction of the benefit caused by recombination bringing beneficial mutations together into the same genome ( i . e . , Fisher-Muller model ) . This tradeoff between benefits and costs is inherent to gene expression , and thus results in stabilizing selection . These patterns of epistasis are likely very common , given the apparent ubiquity of stabilizing selection upon gene expression from microbes to primates [47]–[50] . With a complete fitness landscape defined by biochemical phenotypes , we can now interpret the genetic landscape in terms of what was accessible to individual mutations . In contrast to how selection acts upon standing genetic variation , the de novo mutations fixed in experimental populations ignored the phenotypically-local best direction of change and “jumped” towards the global optimum . This highlights that the classic quantitative genetics intuition of climbing in the direction of the steepest selection gradient [51] , which is appropriate for small populations containing standing genetic variation , fails to capture the phenotypic potential of a sizeable pool of de novo mutations arising from large populations . In our case , it was not the large magnitude of expression change that was surprising , per se , but change in the ratio of their expression . Given that flhA and fghA are encoded on the same transcript , it was notable that all but one of the single mutations down-regulated FghA to a large extent while only cutting FlhA levels in half . Whereas this system allowed individual mutations to reach near-optimality , a multi-step trajectory was required for the directed evolution of the LacI repressor to reverse its regulatory logic [52] . In that system , the first round mutation simply broke the old logic to become constitutive , which in combination with two latter mutations allowed the “anti-LacI” phenotype to emerge and locate the fitness peak they predicted from a computational model . Our results suggest that relatively large moves in multi-phenotype space can emerge as winners , provided the genetic architecture at least allows rare mutations to achieve this possibility . Finally , the near optimal expression levels of these beneficial mutations becomes even more remarkable when considering that this optimization did not happen in isolation , but in adapting populations that contained many beneficial mutations simultaneously [33] , [34] , [53] . In varying environments , such diversity in large microbial populations may lead to genetically complex adaptation such as stable polymorphisms [54] . In a stable environment , this diversity leads to ‘clonal interference’ [55] , a type of serial fixation that effectively sorts for mutations of the greatest effect amongst what was possible . This would have impacted the mutations that affected GSH pathway expression in two ways . Firstly , there were many different genetic solutions to reducing expression of these enzymes [34] . One type in particular - the Class C mutations that resulted from integration of the introduced plasmid into the host chromosome – occur at very high rates and emerged to detectable levels repeatedly , up to 17 times per population [33] . These mutations confer ∼⅔ the benefit of the Class A and B mutations [34] , however , and were only found to rise to fixation in three of eight populations despite more than 100 observed occurrences [33] . In this regard , clonal interference aids finding optimal solutions by allowing only the best individual mutations to fix . Recently , however , it has been shown that fixation probability of contending mutations is only partly dependent upon their individual effect because they commonly hitchhike with other beneficial mutations present [56]–[58] . This leads to a second effect of clonal interference , which is competition between lineages carrying beneficial mutations affecting distinct phenotypic processes . Indeed , the ancestral genotype faced a variety of phenotypic challenges besides just optimizing expression of the GSH pathway [14] , [59] , [60] . Some of these mutations in other loci had beneficial effects up to 3× larger than those described here [10] and were segregating at the same time as mutations affecting expression of the GSH pathway [33] , [53] . Even with so much turmoil in the populations , the eventual winners discovered nearly optimal solutions to this local , two-enzyme expression optimization in order to win the battle for fixation . Population size thus contributed to the fixation of optimal solutions by both increasing the number of mutations occurring and escaping drift in the first place , and by facilitating competition between multiple potential solutions . These factors conspired to allow selection to reward – when mutationally possible – lineages that made long-range , lucky jumps to distant peaks on the phenotypic landscape .
The EM strain was generated previously by deleting the mptG gene of M . extorquens AM1 in the white strain WT CM502 [61] lacking carotenoid pigments due to an unmarked mutation in crtI ( encoding phytoene desaturase ) [62] , followed by introduction of pCM410 [10] . Eight replicate populations seeded by the EM strain were grown in 9 . 6 ml methanol ( 15 mM ) minimal media incubated in a 30°C shaking incubator at 225 rpm . Populations were transferred to fresh media at a 1/64 dilution rate ( thus six generations per growth cycle , Nfinal≈109 ) and propagated for 600 generations . One liter of minimal media consists of 100 ml of phosphate buffer ( 25 . 3 g of K2HPO4 and 22 . 5 g of NaH2PO4 in 1 liter of deionized water ) , 100 ml of sulfate solution ( 5 g of ( NH4 ) 2SO4 and 0 . 98 g of MgSO4 in 1 liter of deionized water ) , 799 ml of deionized water , and 1 ml of trace metal solution . One liter of the trace metal solution consists of 100 ml of 179 . 5 mM FeSO4 , 800 ml of premixed metal mix ( 12 . 738 g of EDTA disodium salt dihydrate , 4 . 4 g of ZnSO4·7H2O , 1 . 466 g of CaCl2·2H2O , 1 . 012 g of MnCl2·4H2O , 0 . 22 g of ( NH4 ) 6Mo7O24·4H2O , 0 . 314 g of CuSO4·5H2O , and 0 . 322 g of CoCl2·6H2O in 1 liter of deionized water , pH 5 ) , and 100 ml of deionized water [14] . All strains and plasmids used are indicated in Table S2 . All plasmids constructed in this study were maintained in E . coli 10-beta strain ( New England Biolabs ) and were transferred to M . extorquens via electroporation [63] or tri-parental mating with the helper strain pRK2073 [64] . Plasmid DNA in E . coli was extracted using the QIAprep Spin MiniPrep Kit ( Qiagen ) . The PmxaF expression vector pCM160 [65] , its variant pCM410 in the EM strain expressing the flhA-fghA cassette [10] , and the cumate-inducible vector pHC112 expressing the flhA-fghA cassette [34] have been described previously . In order to combine Class A and B mutations which accumulated on separate pCM410 derivatives during experimental evolution of the EM strain ( Figure 1B , Table S1 ) , Class B mutations ( from pCM410B2B3 , pCM410B2 , pCM410B3 , and pCM410B5 ) were moved to pCM410 derivatives bearing Class A mutations ( pCM410A1 , pCM410A2 , and pCM410A3 ) through the procedures delineated below . Fragments containing B2–B3 , B2 , and B3 mutations were first obtained by digesting pCM410B2B3 , pCM410B2 , or pCM410B3 with SfiI and NheI . These were then ligated into the plasmid backbone of pCM410A1 , pCM410A2 , or pCM410A3 cut with the same enzymes . A fragment containing the B5 mutation was obtained by digesting of pCM410B5 with SfiI and SexAI and then ligated into the plasmid backbone pCM410A1 , pCM410A2 , or pCM410A3 cut by the same enzymes . The above procedures were also applied to introduce B2–B3 , B2 , B3 , and B5 mutations into pHC112 in order to generate pHC112 derivatives that vary in their plasmid copy number . Fitness assays were performed by a previously described procedure [34] . Strains were first physiologically acclimated through one 4-day growth cycle in 9 . 6 ml of minimal media supplemented with 15 mM methanol . In addition , for strains bearing cumate-inducible promoter plasmids ( pHC112 derivatives ) , different concentrations of cumate ( Table S1 ) were added to growth media to modulate the expression of FlhA and FghA enzymes . After this acclimation phase , each of these strains was mixed with a fluorescent variant ( CM1232 ) of the EM ancestor [10] by a 1∶1 volume ratio , diluted 1/64 into 9 . 6 ml of fresh growth media , and incubated in a 30°C shaking incubator at 225 rpm . The ratios of the two populations before ( R0 ) and after ( R1 ) competitive growth were quantified by a LSR II flow cytometer ( BD Biosciences ) for at least 50000 cell counts per sample . The forward scatter threshold of LSRII was adjusted to 300 to ensure unbiased detection of the test and reference strains despite their potential differences in cell size . Fitness values ( W ) relative to the reference strain were calculated by a previously described equation assuming an average of 64-fold size expansion of mixed populations during competitive growth [35]:In order to convert to absolute differences in growth rate , the EM ancestor grows under these conditions with a growth rate of 0 . 0654+/−0 . 0016 h−1 [10] . The activities of FlhA [66] and FghA [67] were assayed in three replicates as described using cells harvested from mid-exponential phase cultures . Cells were collected through centrifugation at 10 , 000× g for 10 min , frozen at −80°C , and used for enzyme assays within a week . Right before assays frozen cell pellets were suspended in 50 mM Tris-HCl buffer ( pH 7 . 5 ) and physically disrupted in tubes containing Lysing Matrix B and shaken at speed 6 . 0 m/s on a FastPrep®-24 bead beater ( MP Biomedicals ) for 40 seconds . Insoluble debris in the cell lysate was removed by centrifugation at 13 , 000× g , 4°C for 15 min . The total protein concentration of the cell lysate was quantified using the Bradford method [68] . Kinetic analysis of FlhA and FghA activities over 10 min at 30°C was performed in 200 µl reaction mixtures using a SpectraMax M5 Plate Reader ( Molecular Devices ) . The copy number of pCM410 derivatives in M . extorquens was quantified by a real-time PCR approach described previously [34] . Briefly genomic DNA of M . extorquens from mid-exponential phase cultures was extracted by an alkaline lysis method [69] . Detection of plasmid DNA was targeted at the kan gene using primers HC410p18 ( 5′-GAAAACTCACCGAGGCAGTTCCATAG-3′ ) and HC410p19 ( 5′-TCAGTCGTCACTCATGGTGATTTCTCA-3′ ) . Detection of chromosomal DNA was targeted at the rpsB gene ( encoding the 30S ribosomal protein S2 ) in the chromosome META1 using primers HCAM111 ( 5′-TGACCAACTGGAAGACCATCTCC-3′ ) and HCAM113 ( 5′-TTGGTGTCGATCACGAACAGCAG-3′ ) . Real-time PCR experiments were performed in three replicates with the PerfeCTa SYBR Green SuperMix ( Quanta Biosciences ) on a DNA Engine Opticon2 ( MJ Research ) , and the average threshold cycle ( Ct ) of each PCR reaction was determined using the Opticon Monitor v . 2 . 02 software ( MJ Research ) . Each real-time PCR reaction contained 25 ng of genomic DNA extracted from various strains and kan- or rpsB-specific primers . To establish a standard curve ( SC ) of plasmid copy numbers , 1 , 0 . 1 , 0 . 01 , and 0 . 001 ng of pCM410 ( equivalent to 9 . 09×107 , 9 . 09×106 , 9 . 09×105 , and 9 . 09×104 plasmid molecules , respectively ) were mixed with 25 ng of genomic DNA ( equivalent to 3 . 03×106 genome copies ) of the plasmid-less , white WT M . extorquens ( CM502 ) [61] . The standard curve is a plot of ΔCt ( i . e . Ctkan–CtrpsB ) versus plasmid molecules on a log2 scale . For each strain , by interpolating its ΔCt value against the SC the absolute quantity of plasmid DNA can be estimated using the following equation: Multiplicative models predicting fitness or gene expression were fit and assessed as standard linear models following a log transformation of the response variable . The model for the fitness landscape was fit using a non-linear routine in Matlab . The raw data as well as commented code in Matlab and R that completely recreates the analysis and figures has been deposited at www . datadryad . org ( doi:10 . 5061/dryad . 8hb23 ) .
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The pace and outcome of a series of adaptive steps in an evolving lineage depends upon how well different beneficial mutations stack on top of each other . We found that independent beneficial mutations that affected gene expression for a metabolic pathway did not work well together , and were often jointly deleterious . The most beneficial mutations interacted the most poorly with others , which was a trend we found common in other biological systems . Through generating a model that accounted for enzymatic benefits and expression costs , we uncovered that this antagonism was caused by a phenotype to fitness mapping that had an intermediate peak . This allowed us to predict the fitness effect of double mutants and to uncover that the single winning mutations tended to move straight to the peak in a single step . These findings demonstrate the importance of considering the phenotypic changes that cause nonlinear interactions between mutations upon fitness , and thus influence how populations evolve .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"microbial",
"metabolism",
"microbial",
"mutation",
"population",
"genetics",
"metabolic",
"networks",
"microbiology",
"bacterial",
"biochemistry",
"microbial",
"evolution",
"molecular",
"genetics",
"forms",
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"evolution",
"microbial",
"physiology",
"metabolic",
"pathways",
"gene",
"expression",
"biology",
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"bacterial",
"evolution"
] |
2014
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Mapping the Fitness Landscape of Gene Expression Uncovers the Cause of Antagonism and Sign Epistasis between Adaptive Mutations
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Experimental leishmaniasis is an excellent model system for analyzing Th1/Th2 differentiation . Resistance to Leishmania ( L . ) major depends on the development of a L . major specific Th1 response , while Th2 differentiation results in susceptibility . There is growing evidence that the microenvironment of the early affected tissue delivers the initial triggers for Th-cell differentiation . To analyze this we studied differential gene expression in infected skin of resistant and susceptible mice 16h after parasite inoculation . Employing microarray technology , bioinformatics , laser-microdissection and in-situ-hybridization we found that the epidermis was the major source of immunomodulatory mediators . This epidermal gene induction was significantly stronger in resistant mice especially for several genes known to promote Th1 differentiation ( IL-12 , IL-1β , osteopontin , IL-4 ) and for IL-6 . Expression of these cytokines was temporally restricted to the crucial time of Th1/2 differentiation . Moreover , we revealed a stronger epidermal up-regulation of IL-6 in the epidermis of resistant mice . Accordingly , early local neutralization of IL-4 in resistant mice resulted in a Th2 switch and mice with a selective IL-6 deficiency in non-hematopoietic cells showed a Th2 switch and dramatic deterioration of disease . Thus , our data indicate for the first time that epidermal cytokine expression is a decisive factor in the generation of protective Th1 immunity and contributes to the outcome of infection with this important human pathogen .
Experimental leishmaniasis has been the first model to directly demonstrate the relevance of the T helper 1/T helper 2 ( Th1/Th2 ) dichotomy for the outcome of an infection or disease in vivo . Upon cutaneous infection with L . major , C57BL/6 mice generate a Th1 response and subsequently control infection , mainly due to activation of macrophages by IFN-γ . BALB/c mice develop a Th2 response and succumb to progressive disease ( for a review see [1] ) . The decisive events for the development of a Th1 or Th2 response take place early after infection - most likely during the first two days - since an influence on Th1/2 differentiation can only be achieved by pharmacological manipulation in this critical time frame [1] . The regulatory mechanisms responsible for this differentiation have long been supposed to primarily occur in the lymph node [1] . However , the molecular and cellular mechanisms preceding the immunological mechanism in the lymph nodes are not identified so far . During the last years it became increasingly clear that the skin as the site of primary infection also influences the direction of the immune response . We and others have demonstrated that already 2 days after infection there is a higher percentage of granulocytes in the infiltrate of BALB/c compared to C57BL/6 mice and that antibody-mediated elimination of these cells in susceptible mice results in a Th1 response and restoration of resistance [2] , [3] . These differences in granulocyte infiltration point to important differences in the microenvironment of the infected tissue within the first hours of infection , which can be decisive for the direction of the T cell response . Accordingly , a change in the route or site of infection , e . g . via blood stream or nasal mucosa instead of skin , results in a Th2 response and non-healing disease in originally resistant mice [4] , [5] confirming a relevant influence of the local environment of the skin on T-cell priming in experimental leishmaniasis . The infected tissue has been suggested to generate signals within the first hours after inoculation of parasites which are then integrated and transferred via dendritic cells ( DCs ) to T-cells in draining lymph nodes where they induce Th1 or Th2 differentiation . These signals have been termed “tissue” or “danger” signals [6]–[8] . The types of such signals at the site of infection are as manifold as their potential cellular sources since they could derive from either resident or infiltrating cells . Therefore our aim was to perform a global search for such early tissue signals at the site of infection which have the potential to influence the specific immune response . Using microarray technology and bioinformatics we confirmed the existence of tissue signals which appear within the first hours at the site of infection and identified for the first time the epidermis as an important source for these signals . Among these signals we identified chemokines for macrophage recruitment as well as several cytokines with the potential to induce Th1 response ( e . g . via influencing DC ) which showed a stronger expression in the epidermis of resistant mice . Such cytokines included IL-4 and IL-6 . While early neutralization of IL-4 resulted in a Th2 switch in originally resistant mice , also early production of IL-6 in the skin was revealed as a novel decisive factor in the generation of protective Th1 immunity . Thus , we present strong evidence that early activation of epidermal cells influences the resulting T-cell response against L . major .
To test our hypothesis that immunomodulatory mediators relevant for defense against infection with L . major and the generation of a Th1/Th2 response are expressed early at the site of initial infection , we screened for gene expression patterns using both global microarray analysis as well as the more sensitive real time PCR technique . Employing microarray technology we detected significant up-regulation of 189 genes and down-regulation of 16 genes in both mouse strains 16 hours after infection ( Table S1 , S2 ) . The total number of regulated genes was greater in resistant than in susceptible mice ( 205 vs . 146 genes ) . While only 4 genes were regulated significantly stronger in susceptible BALB/c mice , 59 genes were regulated significantly stronger in resistant C57BL/6 mice ( Table 1 and Table S3 , S4 ) and encompassed genes with a well known function in Th1/Th2 differentiation ( such as IL-1β and osteopontin ( opn ) ) [9] , [10] . Considering the fact that whole tissue samples were analyzed one has to take into account that some genes could be highly regulated in a fraction of cells , while their absolute expression levels could still remain under the detection limit of the microarray analysis . Thus , the much more sensitive real-time PCR was applied i ) to confirm the microarray data for selected genes ii ) to extend the gene expression analysis especially to immunomodulatory mediators with known or suspected influence on Th1/2 differentiation whose expression was not detected by the less sensitive microarray analysis ( Figure 1A and Table S5 ) . PCR not only confirmed the induction of genes revealed by microarray analysis , but due to the higher sensitivity of the PCR analysis an up regulation of several chemokines and of the cytokines IL-12 , TNFα , IL-4 , and IL-6 could also be detected . All these genes were more strongly induced in C57BL/6 than in BALB/c . For selected genes , among them those with suspected influence on Th1/2 differentiation ( such as opn , TNFα , IL-6 ) , we confirmed both expression on protein level and higher up-regulation in C57BL/6 mice by immunoprecipitation as well as cytometric bead assay ( Figure 1B ) . Besides cytokines many other genes were regulated in the skin in response to L . major . To get some functional insight into the pattern of regulated genes we applied an automated unbiased functional clustering using GENMAPP software [11] , [12] and gene ontology annotations to determined which functional clusters among the regulated genes were statistically overrepresented . Accordingly , we found a statistically significant overrepresentation of genes involved in “inflammatory response” , “chemotaxis” and “cytokines” in the group of genes up-regulated in both mouse strains ( Table S6 , S7 ) . In agreement with the PCR-data we detected a significant over-representation of genes involved in “chemotaxis” and in “cell-mediated immune response” among those genes which were significantly stronger regulated in resistant mice . Importantly , genes involved in keratinocyte differentiation were also overrepresented in this group of genes ( Table 2 ) . These data indicate that gene expression of keratinocytes is markedly influenced early after infection with L . major . Functional clustering and detection of overrepresented transcription factor binding sites both indicated involvement of keratinocytes in the early immune response . This prompted us to analyze more closely the role of the epidermis in early cutaneous gene expression during L . major infection in vivo by laser microdissection of epidermal keratinocytes from skin 16 hours after infection . For further verification , we applied in-situ-hybridization as it additionally allows spatial allocation of gene induction also in other cells outside the epidermis ( e . g . infiltrating leukocytes ) . After laser capturing of microdissected keratinocytes ( Figure 2 , left panel ) , we found induction of many immune mediators by RT-PCR , among them IL-4 , opn , TNFα , IL-1β , IL-12 and IL-6 ( Figure 2 , right panel , Table S8 ) . This way we showed that the stronger induction of cytokines applied not only for the whole skin , but in particular also for keratinocytes of resistant C57BL/6 mice . Similarly , when we analyzed the cellular cutaneous expression pattern by RISH ( Figure 3A ) or by immunohistochemistry for opn ( Figure 3B ) in C57BL/6 mice we found major expression of opn , CXCL10 , CXCL2 and TNFα by the epidermis . These results do not exclude that other cells present in the infected dermis also contribute to the observed induction of gene expression . However , they do reveal that keratinocytes are a hitherto unknown , yet important source of the early and distinctly expressed genes in infected footpads . Functional clustering had revealed an over-representation of chemotactic factors among the more strongly induced genes in resistant mice . Opn was the chemokine with the most prominent differential expression . It is known as a cytokine able to induce a Th1 response and as a chemoattractant for macrophages , but has not been linked to leishmaniasis yet . We therefore investigated , whether epidermal expression of macrophage chemokines ( opn , CCL5 , CCL2 ) correlated with a more rapid infiltration of mature macrophages in resistant mice [2] . Time-course experiments with real-time PCR revealed that the stronger peak in expression of macrophage chemokines after 8–24 h of infection in C57BL/6 mice was indeed followed by a stronger expression of Emr1 , the gene encoding F4/80 , a well established marker for mature macrophages ( Figure 4 ) . Thus , our global search for early tissue signals at the site of infection revealed the epidermal expression of chemokines which help to explain the known differences in leukocyte recruitment during experimental leishmaniasis . Our experiments had revealed that 16 hours after infection , the epidermis in resistant C57BL/6 mice is a major and stronger source not only for chemokines , but also for cytokines known to have the potential to induce Th1 cells ( IL-12 , IL-1β , TNFα and opn ) . Interestingly , keratinocytes in resistant C57BL/6 mice had also shown a higher induction of IL-4 and thus of a cytokine characteristically released by Th2 cells and counteracting Th1 cytokines . However , IL-4 has previously been reported to be necessary for Th1 differentiation and resistance to L . major in vivo , but only when administered exclusively during the first 8 hours of infection [13] . This accentuates the relevance of the early crucial time frame for Th1/Th2 differentiation and demonstrates that not only expression , but also duration of expression can be crucial for the effect of cytokines . When we performed time course analyses for several cytokines , we detected an early peak of cytokine gene expression for IL-4 and IL-6 at 3-6 hours followed by a peak of IL1-β and IL-12 expression at 21 hours which was stronger in resistant mice and declined to baseline levels within one or two days ( Figure 4 ) . This temporal expression pattern thus correlated perfectly with the described crucial time frame for Th1/Th2 differentiation . For IL-4 it was particularly consistent with its previously shown pharmacological effects on Th1 , but not Th2 cell differentiation . Our results would thus unravel the so far undetected source for IL-4 as a Th1-inducing cytokine in vivo . Of note , there were no strain specific differences in the expression of other Th2 cytokines such as IL-13 . We did not succeed in the - notoriously difficult - demonstration of IL-4 protein ( due to the often low but yet efficacious concentrations in the tissue ) . However , as an indirect sign for the biological activity of IL-4 we demonstrated that Ym-1 ( Figure 4 ) , a gene specifically regulated by IL-4 and IL-13 in macrophages , was expressed in infiltrating leucocytes ( by RISH , Figure 3A ) and was more strongly induced in resistant mice following the induction of IL-4 ( Figure 4 ) . There was a striking resemblance between IL-4 and IL-6 with regard to their epidermal and temporal expression pattern ( Figure 4 ) . Expression of IL-6 peaked even more rapidly . While it had been demonstrated that early treatment of susceptible mice with IL-4 resulted in a Th1 shift [13] , our data now pointed to a corresponding relevance of endogenous IL-4 in resistant mice for induction of the Th1 immune response . To prove this hypothesis we injected 1µg of neutralizing anti-IL-4 antibody in the infected footpads of resistant mice at the time of parasite inoculation and 4h later . When we measured L . major specific cytokine secretion by CD4+-T-cells one week later we detected increased levels of IL-4 and IL-13 and simultaneously decreased levels of IFNγ ( Figure 5 ) . Thus , neutralization of endogenous IL-4 in infected footpads at the time of infection resulted in a clear Th2 switch . Due to this time course and its likewise dual role in Th1/Th2-differentiation [14]–[18] , we hypothesized that IL-6 could act in a similar way as IL-4 and support Th1-immunity during a narrow , early time frame of L . major infection . To analyze whether locally produced IL-6 affects adaptive immunity and resistance in L . major infection we generated mice with a selective IL-6 deficiency in non-hematopoietic cells ( e . g . keratinocytes ) . To this end we reconstituted lethally irradiated , IL-6-deficient mice with IL-6 competent wild type bone marrow ( wt→IL-6−/− chimeric mice ) . As control group , irradiated C57BL/6 wild type mice were reconstituted with wild type bone marrow ( wt→wt mice ) and irradiated IL-6−/− mice were reconstituted with wild type bone marrow from IL-6−/− mice ( IL-6−/−→IL-6−/− chimeric mice ) . After infection with L . major , the deficiency of IL-6 in non-hematopoietic cells and keratinocytes ( wt→IL-6−/− chimeric mice ) resulted in a marked deterioration of disease ( Figure 6A ) , shown by increased footpad swelling as well as by significantly increased numbers of living parasites in footpads ( Figure 6B ) and lymph-nodes ( data not shown ) compared to the control groups of mice . This aggravation correlated with a significant reduction in secretion of IFNγ by L . major –specific CD4+ T-cells ( restimulated in vitro with L . major antigen presented by DC ) and a corresponding increase in the antigen-specific secretion of IL13 by L . major-specific CD4+ T-cells ( Figure 6C ) . These data indicate that there is a switch from a Th-1 response towards a Th-2 response in wt→IL-6−/− chimeric mice . The switched Th-cell cytokine pattern proofed to be biologically highly relevant as it directly correlated with the course of disease in wt→IL-6−/− chimeric mice , i . e . a highly significant increase in footpad swelling and a more than 1000 fold higher local parasite titer .
Resistance in experimental leishmaniasis depends on the development of a L . major specific Th1 response , while Th2 differentiation in BALB/c mice results in susceptibility . The decisive events for the development of a Th1 or Th2 response take place during the first 3 days of infection [9] , [19]–[22] . There is growing evidence that the microenvironment of the infected tissue delivers the initial triggers that affect Th-cell differentiation . The nature or source of such triggers , however , is still enigmatic . Using a combination of gene array analysis , functional clustering , microdissection , RISH and Real-time PCR , we were able to confirm the hypothesis that molecules relevant for defense against infection with L . major and for the generation of a Th1/Th2 response not only are early induced at the site of infection , but also at great part by the cell-rich epidermis and with a significantly stronger expression in resistant mice . While the relevance of opn , IL-12 IL-1β , and TNFα for the crucial recruitment of macrophages and the induction of a Th1 response are well known [1] , [9] , [10] , [23] , the crucial relevance of IL-4 and IL-6 for resistance and Th1 response had to be especially elaborated . Although an early expression of some chemokines and cytokines in infiltrating leukocytes and in the skin has been found previously [24]–[28] , such a pronounced and differential expression of immune-modulatory mediators in the skin within the first hours of L . major infection was hitherto not described . Strain specific differences were so far only reported for CXCL10 [24] . Moreover , the major source for cytokine production in the first hours of infection was not known . The results of functional clustering pointed to a relevant , so far unrevealed involvement of keratinocytes in the early immune response . Our spatial analysis of gene expression by microdissection , in-situ hybridization and immunohistochemistry confirmed that subcutaneous injection of L . major induces several genes and especially cytokines in keratinocytes during the first hours of infection . Moreover , detection of proteins in supernatants of minced footpad tissue strongly suggests that induction of genes results in expression and efficient secretion of epidermally produced immune-mediators , which affect the parasite-induced immune response . The mechanism by which L . major induces gene expression in keratinocytes early after infection remains enigmatic . A direct interaction between L . major and keratinocytes seems unlikely because of the subcutaneous infection and because keratinocytes do not take up L . major in vitro [29]–[32] . It is possible that epidermal gene induction is caused by cytokines ( e . g . CCL4 , IL-8 and CCL2 ) released from resident macrophages and early infiltrating granulocytes [2] , [24] , [33]–[38] , after their initial contact with the parasites . By such a crosstalk , epidermal keratinocytes would be involved in an amplification of the L . major-related inflammatory tissue signal , since they are capable of synthesizing and secreting large amounts of cytokines [39] . Such an amplification due to high numbers of cells could be essential since only few resident macrophages are present early after infection and infiltrating granulocytes are not equipped for sustained transcription of inflammatory mediators . Most importantly , the induction of gene expression in keratinocytes was significantly different between susceptible and resistant mice , indicating an influence of epidermal cells on the direction of the ensuing immune response . One mechanism by which this influence is executed could be the differential recruitment of leukocytes . A higher percentage of granulocytes in BALB/c mice is relevant for susceptibility , while a higher percentage of mature macrophages in C57BL/6 mice is associated with resistance towards L . major [2] , [3] , [40] . Here we demonstrate the temporal correlation between a significantly stronger and transient expression of macrophage chemoattractants ( CCL5 , CCL2 and opn ) with a peak after 8h post infection and an ensuing significantly more pronounced macrophage infiltration 3 days after infection . Opn was also described as an early Th1-inducing cytokine acting on DC [10] , [41] . Together with the significantly stronger expression of the Th1-inducing cytokines IL-12 , TNFα and IL-1β in the epidermis of resistant mice , this points to a direct effect of epidermal gene expression on Th1/2 differentiation . IL-12 is the best characterized Th1-inducing cytokine so far [19]–[21] , although a stronger early expression of IL-12 in vivo in resistant mice has so far never been demonstrated , neither for the skin nor for the lymph nodes . Similarly , IL-1β has so far not been shown to be differentially expressed in skin , whereas we had previously described a more prominent expression of IL-1α in lymph nodes of resistant mice [9] . The time course of transient IL-12 and IL-1β expression from 8 h to 72 h after infection correlates perfectly with the critical time frame for Th1/Th2 differentiation [9] , [19]–[21] . In confirmation of this relevance , we and others previously showed that application of either IL-12 or IL-1β at the site of infection during the first three days after injection with L . major was able to promote Th1 differentiation and to inhibit disease progression in BALB/c mice [9] , [42] . This study now shows that keratinocytes are an essential natural source to provide early significant amounts of IL-12 or IL-1β in C57BL/6 mice . In addition , our finding of a transient IL-4 expression in the skin of resistant mice may explain a paradoxon in experimental leishmaniasis: IL-4 on one hand is the best characterized Th2 cytokine; correspondingly , IL-4 production in the draining lymph nodes of BALB/c has been associated with induction of a Th2 response [43]–[45] . On the other hand , IL-4 is able to induce production of IL-12p70 in murine and human DC in vitro [46] , [47] and to promote a protective Th1 immune response in susceptible BALB/c mice but only when administered during the first 8 h of infection , and not if given for a prolonged period of time [13] . Thus , IL-4 induces a Th1 response earlier in infection , most likely via increasing IL-12 secretion from DCs in a defined early time frame . However , it remained unclear whether endogenous IL-4 is involved in Th1 immunity . We now demonstrated that the epidermis could provide IL-4 whose expression is temporally restricted to exactly the crucial early time frame . While it is technically problematic to detect the low local levels of IL-4 protein , we found that expression of Ym-1 , a gene specifically regulated by IL-4 in macrophages ( and IL-13 which did not shown differential expression ) , exactly followed the differential IL-4 expression with a stronger peak in C57BL/6 mice . This could indicate a temporally defined and quantitatively distinct local presence of biological active IL-4 . More important , we could clearly demonstrate that neutralizing early endogenous IL-4 in resistant mice locally by anti-IL-4 antibody resulted in a switch to a Th2 cytokine secretion pattern of CD4+ T-cells from draining lymph nodes one week later ( Figure 5 ) . This clearly indicates that early endogenous IL-4 , most likely produced by keratinocytes , is mandatory for the induction of a Th1 response against L . major . Since IL-4 is induced in the epidermis - and thus in direct vicinity to DCs - , we suggest that under physiological conditions epidermal IL-4 could act in a paracrine way on DCs so that they are prepared to release IL-12 when migrating to the lymph node . Similar to IL-4 we found a very rapid , but also temporally restricted induction of IL-6 ( mRNA as well as protein ) in the skin which likewise was significantly stronger in resistant mice . Microdissection revealed that keratinocytes are an important source of local IL-6 expression . Like IL-4 , IL-6 has been demonstrated to induce Th2 differentiation via direct action on Th cells [14] , [15] , while it was also required for the development of Th1 immunity in murine tuberculosis , colitis and experimental autoimmune encephalomyelitis [16]–[18] . Besides , IL-6 appears to be involved in differentiation of Th17 cells and in inhibition of regulatory T-cells [48] while it also inhibited differentiation , maturation and activation of DC [49]–[52] . In the light of these results it is reasonable to conclude that the effect of IL-6 on either Th1 or Th2 differentiation in an emerging immune response crucially depends on timing as well as site of action . In case of its complete genetic deletion , these effects may set each other off , which may explain why it resulted in only minimal net effects on the course of experimental leishmaniasis [53] , [54] . In order to assess whether lack of cutaneous IL-6 production would impair development of Th1 cells , we demonstrated that IL-6 −/− mice with IL-6 competent bone marrow and thus with a constitutional lack of IL-6 at the site of infection , became markedly more susceptible to L . major ( more than 1000 fold more parasites compared to control animals ) and showed a shift from a Th1 towards a Th2 response . Thus , IL-6 is a new tissue signal whose early local presence promotes Th1 cells . The mechanism by which IL-6 affects the Th1 response is currently unclear . It may not primarily instruct DC for Th1 priming [49]–[52] , but could inhibit conversion of naïve T-cells into Foxp3+ regulatory T-cells ( Treg ) [48] so that its local absence leads to increased influence of Treg on Th1 cells . However , the function of Treg in experimental leishmaniasis is not completely clear and both inhibitory as well as exaggerating effects of Treg on the course of L major infection have been described [55]–[59] . Thus , it is also tempting to speculate that the immediate induction of IL-6 after contact with the parasites may prompt resident and early infiltrating cells to generate a Th1 promoting local micromilieu , characterized e . g . by ensuing expression of IL-4 , IL1β , IL-12 and opn . In summary , our approach directed at a global view of gene expression and thus reflecting the biology of systems revealed a stronger expression of immunomodulatory mediators in the infected skin and epidermis of resistant compared to susceptible mice . Our approach for the first time defines the global pattern of the early “tissue signal” and moreover identifies keratinocytes as a critical modulator of the microenvironment in L . major infected skin . Furthermore , our data indicate for the first time that epidermal cytokine expression , e . g . of IL-4 and IL-6 is a decisive factor in the generation of protective Th1 immunity and contributes actively to the outcome of inflammatory reactions .
All experiments with mice were performed with the approval of the State Review Board of Nordrhein-Westfalen ( Germany ) according to the German law for animal welfare ( Tierschutzgesetz ) § 8; reference number 8 . 87–50 . 10 . 36 . 08 . 009 . Specific pathogen-free , female C57BL/6 and BALB/c mice were purchased from Charles River , Germany , and were housed in microisolator cages and given mouse chow and water ad libitum . Mice were 8–12 wk of age when used in experiments . All experiments with mice were performed with the approval of the State Review Board of Munster ( Germany ) . L . major ( MHOM/IL/81/FE/BNI ) parasites were cultivated in Schneider's Drosophila Medium supplemented with 10% FCS , 2% human urine , 2% glutamine , and 1% Penicillin/Streptomycin . Soluble Leishmania antigen ( SLA ) was prepared by 5 repeated freeze and thaw cycles in phosphate buffered saline ( PBS ) . Mice were infected subcutaneously by application of 2×107 promastigotes ( stationary phase ) of L . major in 50 µl PBS into the left hind footpad . The right footpad was injected with 50 µl PBS alone and served as internal control to ensure that gene expression was not caused by the injection stimulus . After sacrifice , footpads from mice were harvested 1 to 72 hours after infection for gene expression analysis . In the experiments neutralizing local IL-4 1 µg of neutralizing rat anti-mouse IL-4-antibody or irrelevant rat IgG ( Biolegend , Uithoorn , Netherlands ) was used . Mice were injected at the time of parasite inoculation and 4h later . In three independent experiments , total RNA from 16 h L . major infected C57BL/6 or BALB/c mice and PBS injected control animals was isolated and subsequently processed for microarray hybridization using Affymetrix Murine Genome MG_U74Av2 arrays according to the manufacturer's instructions ( Affymetrix ) . Microarray data were analyzed using MicroArray Suite Software 5 . 0 ( Affymetrix ) using data from corresponding control samples as baseline and further studied applying the Expressionist Suite software package ( GeneData ) , which allows identification of genes that are significantly regulated in multiple independent experiments as described previously [60] . We retained only genes which were significantly regulated in every single experiment ( change p-value <0 . 05 , fold-change ≥2 , expression over background ) as well as in the complete set of experiments ( fold-change of ≥3 . 0 , p-value of <0 . 05 , paired t-test ) . To compare L . major induced alterations of cutaneous expression patterns between resistant and susceptible mouse strains , signal log ratios of infected versus uninfected control samples in both mice strains were evaluate by paired t-test . We retained only genes with a p-value <0 . 05 and a differential fold-change regulation of ≥1 . 75 . Principal component analysis ( PCA ) was applied to mathematically reduce the dimensionality of the entire spectrum of gene expression values of a microarray experiment to three components as described previously [61] and revealed that individual experimental groups were well reproducible and clearly separated . To analyze the microarray data in the context of biological functions , we used information available from the Gene Ontology ( GO ) consortium ( http://www . geneontology . org ) [11] , [12] . The GO terms represent a defined vocabulary describing the biological process , cellular components , and molecular functions of genes in a hierarchical directed acyclic graph structure . Statistical analysis was performed for groups of >10 genes using GenMAPP software [11] , [12] . For each of the existing GO terms , the cumulative number of genes meeting our criteria ( e . g . up- or down-regulated ) and of all genes represented on the microarray was calculated . The Z score is calculated for every GO term by subtracting the expected number of genes meeting the criterion from the actual number , and division of this value by the standard deviation of the actual number of genes:with N as the total number of genes measured , R as the total number of genes meeting the criterion , n as the total number of genes in the specific GO term , and r as the number of genes meeting the criterion in the specific GO term . A positive Z score indicates that there are more genes meeting the criterion in the specific GO term than expected by chance . The Z-score is transferred to p-values under the assumption of a hypergeometric distribution . For RISH , the complete coding regions of the genes analyzed were amplified by PCR and cloned into pBluecript ( Stratagene , La Jolla , CA ) ( Table S9 ) . For antisense and sense probes , the plasmid was linearized by appropriate restriction enzymes and T7 or T3 RNA polymerase was used to synthesize digoxigenin labeled probes . The RISH was performed on 4 µm frozen sections according to an protocol described earlier [62] . Immunohistochemical staining was performed [2] using a rabbit anti-mouse opn antibody ( Assay Designs , Ann Arbor , MI , USA ) . Tissue-Tec O . C . T . ( Sakura , Germany ) -embedded hind feet were cut in 12 µm sections on a microtome , transferred onto PEN-covered glass slides ( PALM , Germany ) and immediately stored at −80°C . Sections were stained with cresyl violet ( 1% in aqua bidest ) followed by rehydration in 100 , 96 , 70% ethanol and dried at 37°C for half an hour . Collection of keratinocytes by LMPC was performed as described [63] , utilizing the PALM Laser Microbeam Microdissection System with Laser Pressure Catapulting and Robocut Software ( PALM , Germany ) . A minimum of 200 cell equivalents ( about 1000 visible cells ) were isolated and collected into a reaction tube cap , which was filled with TRIzol Reagent ( Invitrogen , Germany ) . For total skin RNA extraction , skin from the hind footpad was excised and pulverized in liquid nitrogen and Potter homogenized in TRIzol Reagent . Subsequent RNA isolation was performed using TRIzol Reagent ( Invitrogen , Germany ) and subsequently RNeasy Minikit ( Qiagen , Germany ) cleanup preparation including DNAse digestion . The RNA from laser microdissected keratinocytes was TRIzol extracted , following the manufacturer's protocol for small quantities of cells . The quality and approximate quantity of the resulting RNA was determined using the microfluidics system ( Agilent 2100 Bioanalyzer , Agilent Technologies ) . Whole skin material was transcribed into cDNA utilizing H-minus reverse transcriptase ( MBI Fermentas , Germany ) . cDNA synthesis and amplification of laser microdissected samples were performed with the Microarray Target Amplification Kit ( Roche Diagnostics , Germany ) , following the manufacturer's instructions . Total RNA extracted from 1000 keratinocytes was used as template for first-strand synthesis . Expression of selected genes was confirmed by real-time RT-PCR as described previously [60] , see Table S10 for primer sequence information . To determine secreted proteins in infected footpad tissue , dissected feet were flushed repeatedly with a total of 500 µl PBS and adjusted to equal protein concentrations . Cytokine concentrations were determined using the cytometric bead assay ( BD Bioscience , San Jose , CA , USA ) . Osteopontin was immunoprecipitated using monoclonal goat anti-opn antibody ( RD Systems , Minneapolis , MN , USA ) , following Protein A sepharose , elution of protein and subsequent analysis using polyacrylamid gel electrophoresis and western blotting . Samples were adjusted to equal protein concentrations and separated by SDS/PAGE ( 12% acrylamide ) . Subsequently , proteins were transferred by blotting onto membrane ( Pall GmbH , Dreieich , Germany ) , incubated for 1 h in NaCl/Tris/0 . 1% ( v/v ) Tween 20/5% ( w/v ) nonfat dried milk , and then for 1 h in NaCl/Tris/5% ( w/v ) nonfat dried milk containing goat anti-opn antibody . The membrane was washed three times in NaCl/Tris/0 . 1% Tween-20 and incubated with a phosphatase-conjugated secondary anti-goat-IgG to visualize opn using the standard phosphatase reaction . The bands were scanned densitometrically . C57BL/6 mice or IL-6 gene deficient mice were lethally irradiated ( 5 . 0 Gy on 2 days resulting in a cumulative dose of 10 Gy ) and reconstituted with bone marrow cells ( 107 cells/mouse ) obtained from wt C57BL/6 or from IL6−/− mice . For experimental leishmaniasis chimeric mice were used 6 weeks after bone marrow reconstitution . Cutaneous leishmaniasis was initiated by subcutaneous application of 2×107 promastigotes ( stationary phase ) of L . major in 50 µl PBS into the left hind footpad . Footpad thickness was assessed weekly using a metric caliper . Specific swelling of the infected footpad was assessed by subtracting the diameter of the infected footpad from that of the non-infected footpad . Footpads , lymph nodes and spleen from each mice were harvested for limiting dilution assay ( LDA ) and determination of cytokine profile . The experiments were repeated 2–3 times . Parasite numbers in bone marrow and liver as a parameter for systemic spread were determined 8–12 weeks after infection by a limiting dilution assay ( LDA ) modified by using leishmania medium as specified above instead of slant blood agar [64] . For cytokine assay mice were euthanized and draining lymph nodes were aseptically removed . A single cell suspension was prepared and CD4+ T cells were collected using biomagnetic enrichment procedures ( Miltenyi Biotec , Bergisch Gladbach , Germany ) according to the manufacturer's recommendations . Bone marrow derived dendritic cells ( DC ) were generated as previously described [65] . Shortly , the femur bone was aseptically removed from euthanized C57BL/6 mice and the bone marrow was flushed out . Bone marrow DCs were expanded with IL-4 and GM-CSF for 6 days . DCs ( 1×106 cells/ml ) were incubated with SLA equivalent to 5×106 L . major for 48 h . For assessment of cytokine secretion DC and CD4+ T cells ( 5×104/100 µl ) were mixed in a ratio 1∶5 and cultured in RPMI1640 plus 2 mM glutamine , 50 µM mercaptoethanol and 10% FCS for 48 hours . Culture supernatants were assayed by cytometric bead assay BD Bioscience , San Jose , CA , USA ) according to the manufacturer's instructions . To determine whether differences were statistically significant , Student's t test was performed , using a two-tailed distribution . Indication of p-values are as follows * <0 . 05; ** <0 . 01; *** <0 . 001 . CCL2 GeneID: 20296 , CCL3 GeneID: 20302 , CCL4 GeneID: 20303 , CCL5 GeneID: 20304 , CCL7 GeneID: 20306 , CCL9 GeneID: 20308 , CCR1 GeneID: 12768 , CCR2 GeneID: 12772 , CCR5 GeneID: 12774 , CD14 GeneID: 12475 , Chi3l3 GeneID: 12655 , CXCL10 GeneID: 15945 , CXCL1 GeneID: 14825 , CXCL2 GeneID: 20310 , CXCL9 GeneID: 17329 , Emr1 GeneID: 13733 , Ifi202b GeneID: 26388 , IFN-g GeneID: 15978 , IL-10 GeneID: 16153 , IL-12p35 GeneID: 16159 , IL-12p40 GeneID: 16160 , IL-13 GeneID: 16163 , IL-1a GeneID: 16175 , IL-1b GeneID: 16176 , IL-4 GeneID: 16189 , IL-6 GeneID: 16193 , Ost GeneID: 20750 , Ptx3 GeneID: 19288 , S100a8 GeneID: 20201 , S100a9 GeneID: 20202 , Saa3 GeneID: 20210 , Slpi GeneID: 20568 , Sprr2a GeneID: 20755 , Sprr2B GeneID: 20756 , Sprr2h GeneID: 20756 , Temt GeneID: 21743 , Tgfb1 GeneID: 21803 , TNFa GeneID: 21926 , Tnfrsf1b GeneID: 21938 .
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To clear skin infections with the parasite Leishmania major , a specific T-helper ( Th ) -cell immune response has to be generated . The type of Th-cell response is determined early after infection by yet unknown mechanisms . In resistant mice a Th1-pattern is generated . A Th2-pattern in BALB/c mice , however , results in susceptibility . An analysis of these mechanisms is important for a better understanding of both host-parasite interactions and non-infectious Th-cell driven inflammatory skin disorders ( e . g . atopic dermatitis ) . We analyzed how the infected skin influenced the Th-cell response . Therefore , we compared gene-expression early after infection in the skin of resistant and susceptible mice . Several cytokines ( like IL-1β , IL-12 , osteopontin , IL-4 and IL-6 ) were more strongly produced in the skin of resistant mice and therefore could be important for Th1-differentiation . We demonstrated that they were expressed by epidermal keratinocytes . Using mice with a deficiency for IL-6 in keratinocytes but not in immune cells and by inhibiting the action of early produced IL-4 we revealed that keratinocyte-derived IL-6 and IL-4 are important for resistance against Leishmania . Thus , our results indicate that the epidermis controls Th1-differentiation and may be a new pharmacological target for modification of Th-differentiation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/immune",
"response",
"immunology/innate",
"immunity",
"dermatology/skin",
"infections",
"infectious",
"diseases/skin",
"infections",
"immunology/immunity",
"to",
"infections"
] |
2010
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Keratinocytes Determine Th1 Immunity during Early Experimental Leishmaniasis
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The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets . Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however , variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes . To illustrate the scope of these challenges , we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets , revealing substantial differences between the screens . We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens . Genome-scale metabolic network reconstructions also enable a high-throughput , quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes . Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes .
With the rise of antibiotic resistance , there is a growing need to discover new therapeutic targets to treat bacterial infections . One attractive strategy is to target genes that are essential for growth and survival [1–4] . Discovery of such genes has been a long-standing interest , and advances in transposon mutagenesis combined with high-throughput sequencing have enabled their identification on a genome-scale . Transposon mutagenesis screens have been used to discriminate between in vivo and in vitro essential genes [1 , 5] , discover genes uniquely required at different infection sites [6] , and assess the impact of co-infection on gene essentiality status [7] . However , nuanced differences in experimental methods and data analysis can lead to variable essentiality calls between screens and hamper the identification of essential genes with high-confidence [8 , 9] . Additionally , a central challenge of these screens is in interpreting why a gene is or is not essential in a given condition , hindering the identification of promising drug targets . These data are often used to validate and curate genome-scale metabolic network reconstructions ( GENREs ) [10 , 11] . GENREs are knowledgebases that capture the genotype-to-phenotype relationship by accounting for all the known metabolic genes and associated reactions within an organism of interest . These reconstructions can be converted into mathematical models and subsequently used to probe the metabolic capabilities of an organism or cell type in a wide range of conditions . GENREs of human pathogens have been used to discover novel drug targets [12] , determine metabolic constraints on the development of antibiotic resistance [13] , and identify metabolic determinants of virulence [14] . Importantly , GENREs can be used to assess gene essentiality by simulating gene knockouts . Through in silico gene essentiality analysis , GENREs can be useful in the systematic comparison of gene essentiality datasets . Here , we perform the first large-scale , comprehensive comparison and reconciliation of multiple gene essentiality screens and contextualize these datasets using genome-scale metabolic network reconstructions . We apply this framework to the Gram-negative , multi-drug resistant pathogen Pseudomonas aeruginosa , using several published transposon mutagenesis screens performed in various media conditions and the recently published GENREs for strains PAO1 and PA14 . We demonstrate the utility of interpreting transposon mutagenesis screens with GENREs by providing functional explanations for essentiality , resolving differences between the screens , and highlighting gaps in our knowledge of P . aeruginosa metabolism . Finally , we perform a high-throughput , quantitative analysis to assess the impact of media conditions on identification of core essential genes . This work demonstrates how genome-scale metabolic network reconstructions can help interpret gene essentiality data and guide future experiments to further enable the identification of essential genes with high-confidence .
We obtained data from several published transposon mutagenesis screens for P . aeruginosa reference strains PAO1 and PA14 in various media conditions [15–19] . PAO1 is the most widely used laboratory strain and was originally collected from the wound of a patient in Australia [20] . PA14 is a highly virulent reference strain , originally collected from a burn wound [21] . The genomes of the two strains are highly similar; however , the slightly larger genome of PA14 contains pathogenicity islands not found in PAO1 [22] . For each collected screen , we determined candidate essential genes as described in Methods ( S1 Table ) . Briefly , where available , we used the published essential gene lists identified by the authors of the screen . Otherwise , we defined genes as essential in a particular screen if the corresponding mutant did not appear in that screen , suggesting that a mutation in the corresponding gene resulted in a non-viable mutant . Candidate essential gene lists ranged in size from 179 to 913 for PAO1 and from 510 to 1544 for PA14 , suggesting substantial variability between the screens ( Table 1 , S1 Dataset , S2 Dataset ) . To investigate the similarity between the different candidate essential gene lists for the two strains , we performed hierarchical clustering with complete linkage on the dissimilarity between the candidate essential gene lists , as measured by Jaccard distance ( Fig 1A and 1C ) . Interestingly , the screens clustered by publication rather than by media condition for both strains . As an example from the PAO1 screens , rather than clustering by lysogeny broth ( LB ) media , sputum media , pyruvate minimal media , and succinate minimal media , all three of the screens from the Lee et al . publication clustered together , all three of the screens analyzed in the Turner et al . publication clustered together , and the Jacobs et al . transposon mutant library clustered independently . This result suggests that experimental technique and downstream data analysis play a large role in determining essential gene calls , motivating the importance of comparing several screens to identify consensus essential gene lists , or genes identified as essential across multiple screens . We then measured the overlap of the candidate essential gene lists to calculate how many genes were shared across all the screens as well as those unique to particular sets of screens , defined as intersections ( Fig 1B and 1D ) . This analysis revealed substantial differences in the overlap of the candidate essential genes across the screens . Using the number of intersections as an indicator of variability , comparison of the PAO1 screens resulted in more than 30 intersections and comparison of the PA14 screens resulted in seven . Furthermore , we found 192 genes were shared by all PA14 screens and 17 genes were shared by all PAO1 screens . These numbers of core essential genes are lower than expected , particularly for strain PAO1 . Typically , essential genes average a few hundred for a bacterial genome [23] . We reasoned that the low number of observed core essential genes as well as the number of observed intersections might be due to the variety of media conditions tested across the PAO1 screens . We repeated our analysis focusing only on the LB media screens for both PA14 and PAO1 ( Fig 2 ) . As anticipated , the number of observed intersections for both strains decreased , indicating that the considered media conditions impact the essentiality status of a gene . Interestingly , the trends for the number of observed core essential genes remained unchanged , with 434 genes shared across both PA14 LB media screens and only 44 genes shared across all PAO1 LB media screens . This differential between the PA14 and PAO1 results could be due to comparing three screens for PAO1 versus comparing only two screens for PA14 . The heterogeneity observed from the PAO1 comparison could be attributed to a number of factors such as screening approach ( e . g . , individually mapped mutants versus transposon sequencing ) , library complexity , metrics of essentiality , data analysis , and the media conditions tested . Overall , the PA14 screens had higher numbers of essential genes compared to those for PAO1 , with all the PA14 screens having at least 400 essential genes . There were four PAO1 screens with less than 350 essential genes . Strain-specific differences in essentiality have been reported previously but are underappreciated [24] . This result adds to the growing literature emphasizing how the genetic background of the strain analyzed may impact the identification of essential genes . Both the clustering and the overlap analysis revealed discordant essentiality calls between the screens . These discrepancies could be due to differences in both experimental technique and data analysis . To investigate the possibility that the heterogeneity was due to data analysis alone , we re-analyzed the sequencing data for PAO1 transposon sequencing screens performed on LB using the same analytical pipeline ( Fig 3 ) [18 , 25] . We limited our re-analysis efforts to screens with publicly available sequencing data . As expected , when the same analysis pipeline was applied to the two screens , there was an increase in the number of commonly essential genes compared to those between the published results . These results indicate that data analysis accounts for some of the variability between the datasets . However , there were still genes that were identified as uniquely essential to each screen . These results suggest that experimental differences , such as differences in library complexity , the number of replicates , and read depth , likely also contribute to variability between the datasets . Taken together , results from this comparison revealed vast differences between the candidate essential gene lists across screens , even for those from the same media condition . These differences may be due to numerous experimental and data analysis factors . Ultimately , this variability complicates the discovery of essential genes with high-confidence . A central challenge of transposon mutagenesis screens lies in the interpretation of why a gene is or is not essential in a given condition . Here , we demonstrate the utility of genome-scale metabolic network reconstructions to contextualize gene essentiality and provide mechanistic explanations for the essentiality status of metabolic genes . To do this , we compared the in vitro candidate essential gene lists to predicted essential genes from the PAO1 and PA14 GENREs [26] . These GENREs were previously shown to predict gene essentiality with an accuracy of 91% [26] . For both models , we simulated in silico gene knockouts under media conditions that approximated those used in the in vitro screens and assessed the resulting impact on biomass synthesis as an approximation for growth ( S3 Dataset , S4 Dataset ) . Genes were predicted to be essential if biomass production for the associated mutant model was below a standard threshold . Predicted essential gene lists for both the PAO1 and PA14 models under the different media conditions were compared to the candidate essential gene lists for each of the experimental screens and the matching accuracy between model predictions and the in vitro screens was assessed ( Fig 4A , S2 Table ) . As expected , most genes were identified as nonessential by both the screens and the models . Several of these nonessential genes encode redundant features in the metabolic network , such as isozymes or alternate pathways . For example , thioredoxin ( Trx ) reductase is an essential enzyme critical for DNA synthesis and protection against oxidative stress [27] . However , because there are two isozymes for Trx reductase in P . aeruginosa , neither of the Trx reductase encoding genes , trxB1 and trxB2 , were identified as individually essential in the single-gene deletion screens . Additionally , several of the nonessential genes are involved in accessory metabolism , such as the production of small molecule virulence factors . For instance , genes involved in the synthesis of pyoverdine , a metabolite involved in iron scavenging , were non-essential ( e . g . , pvdA , pvdE , and pvdF ) . Interestingly , the number of screen-essential genes predicted as nonessential was significantly larger than the number of screen-nonessential genes predicted as essential ( p = 0 . 0195 , as measured by Wilcoxon signed-rank test ) . We hypothesize that the reason for this difference is due to the increased likelihood of an in vitro screen missing a gene , potentially due to gene length or transposition cold spots [16] , and subsequently incorrectly identifying it as essential . This analysis can help to provide specific functional explanations for essentiality . Where there is an agreement between the model predictions and in vitro screens , we can use the network to explain why a gene is or is not essential . Similarly , we can analyze the network to explain why a gene may be essential in one media condition versus another . A mismatch denotes some discrepancy between the model predictions and the experimental results . These mismatches may point to a gap in the model , indicating that it is missing some relevant biological information . Alternatively , the mismatches may be due to experimental variability such as differences in environmental conditions or technique . To begin contextualizing the gene essentiality datasets using the GENREs , we focused on metabolic genes that were identified as essential or as nonessential in all LB screens for either PAO1 or PA14 ( which we termed “consensus essential genes” and “consensus nonessential genes” , respectively ) ( S3 Table , S5 Dataset , S6 Dataset ) . Consensus essential genes have a greater likelihood of being truly essential rather than experimental artifacts since they were identified as such in multiple independent screens . We then compared these lists of consensus essential genes and consensus nonessential genes to the model predictions of essentiality in LB media . From this comparison , we found 45 of 113 consensus essential genes predicted to be essential by the PA14 model and 777 of 800 consensus nonessential genes predicted to be nonessential by the PA14 model . For PAO1 , we found seven of 15 consensus essential genes predicted to be essential by the PAO1 model and 843 of 863 consensus nonessential genes predicted as nonessential by the PAO1 model ( S3 Table ) . The low number of consensus essential genes for PAO1 reflects the high variability between screens , as highlighted in Figs 1 and S1 . Several of the model-predicted consensus essential genes are involved in pathways known to be critical for bacterial cell survival . For example , both the model and the screens identified the gene folA as essential in PA14 . The gene product of folA , dihydrofolate reductase , is necessary for purine and pyrimidine synthesis and is targeted by the antibiotic trimethoprim [28] . Additionally , both the model and the screens identified the gene fabZ as essential in PA14 . The gene product of fabZ encodes for ( 3R ) -hydroxymyristoyl-AC dehydratase and is involved in the synthesis of type II fatty acids . Given the critical role of type II fatty acids in bacterial membrane formation , enzymes involved in their synthesis are attractive antimicrobial targets [29] . Given that these genes were found to be essential by both the screens and the model , there is higher confidence in their essentiality status . Next , we used the models to delineate subsystem assignments for the model-predicted consensus essential and nonessential genes ( Fig 4B for PA14 and S1 Fig for PAO1 ) . As expected , the consensus nonessential genes spanned most subsystems within the network , likely due to redundancy in the network as well as the presence of accessory metabolic functions that are not critical for biomass production . In contrast , for PA14 , the consensus essential genes were limited to seven of the 14 subsystems within the network ( note that this trend does not hold for PAO1 because there were very few consensus essential genes to consider ) . These seven subsystems capture metabolic pathways that are critical for bacterial growth and survival . For instance , lipid metabolism is essential for building and maintaining cell membranes , while carbohydrate metabolism is critical for ATP generation . None of the genes involved in transport were consensus essential genes . Because we only considered screens performed in LB media , transport of individual important metabolites , such as a specific carbon sources , was not a limiting factor given the abundant availability of such compounds in rich media conditions . However , we would expect that if we considered screens performed under minimal media conditions , relevant transport genes would be essential for bacterial growth . Because these consensus essential genes were also predicted to be essential by the model , we can use the network to provide functional reasons for essentiality . For example , both the model and screens identified the gene adk , encoding adenylate kinase , as essential . Using the model , we determined that when adk is not functional , the conversion of deoxyadenosine diphosphate ( dADP ) to deoxyadenosine monophosphate ( dAMP ) cannot proceed , impacting the cell’s ability to synthesize DNA and ultimately produce biomass ( Fig 4C ) . The model can also tease out less obvious relationships . For instance , both the model and the screens identified glmS , encoding glucosamine-fructose-6-phosphate aminotransferase , as essential . Using the model , we found that when glmS is not functional , the conversion of L-Glutamine to D-Glucosamine phosphate cannot proceed . D-Glucosamine phosphate is an essential precursor to both Lipid A , a component of the endotoxin lipopolysaccharide , and peptidoglycan , which forms the cell wall ( Fig 4D ) . For each of the model-predicted consensus essential genes , we identified which biomass components could not be synthesized when the gene was removed from the model ( S7 Dataset and S8 Dataset ) . Further analysis is necessary to tease out the metabolic pathways that prevent synthesis of these biomass metabolites; however , from the examples above it is evident that GENREs can provide both obvious and non-obvious functional explanations for essentiality , streamlining the interpretation of transposon mutagenesis screens . In addition to identifying consensus essential and nonessential genes that were in agreement with the models , we also uncovered discrepancies between model predictions and experimental results . For PAO1 and PA14 , respectively , there were 8 and 68 consensus essential genes that the models predicted to be nonessential and 20 and 23 consensus nonessential genes that the models predicted to be essential . These mismatches between model predictions and experimental results provide insight into gaps in our understanding of P . aeruginosa metabolism . In the case where a consensus essential gene was predicted to be non-essential by the model , this result suggests that the model has some additional functionality that is not available in vitro . This result could be an inaccuracy of the network reconstruction or it could be a result of using a non-condition-specific network where the model has access to all possible reactions in the network . Because cells undergo varying states of regulation , gene essentiality can be modulated as a result . Thus , profiling data such as transcriptomics could be integrated into the network reconstruction to generate a condition-specific model to improve model predictions under specified conditions [30 , 31] . Alternatively , the discrepancy between the model predictions and screen results could be due to differing levels of enzyme efficiency , which is not captured by the P . aeruginosa GENREs . For example , if a major isozyme is disrupted , minor isozymes with the same functionality may not be efficient enough to overcome the disruption and allow growth of the mutant , resulting in the gene for major enzyme being essential experimentally . However , because the P . aeruginosa GENREs do not account for enzyme efficiency , the isozymes are able to overcome the disruption , resulting in the gene for the major enzyme predicted as non-essential . To investigate the possibility that isozymes contribute to the discrepancy between the model predictions and experimental results , we found isozymes associated with 19 of the 68 PA14 consensus essential genes predicted to be non-essential by the model . These 19 genes warrant further testing to fully tease out their essentiality status . In contrast , in the case where a consensus nonessential gene was predicted to be essential , this result indicates that the model is missing key functionality , pointing to areas of potential model curation . Using this list of discrepancies to guide curation ( Table 2 ) , we performed an extensive literature review and found several suggested changes to the metabolic network reconstruction ( S9 Dataset ) . For instance , we incorrectly predicted as essential the gene fabI ( PA1806 ) , which is linked to triclosan resistance; however , a recent study discovered an isozyme of fabI in PAO1 called fabV ( PA2950 ) [32] . To account for this new information , we suggest changing the gene-protein-reaction ( GPR ) relationship for the 28 reactions governed by fabI to be “fabI OR fabV” , making fabI no longer essential in the model . Additionally , our model incorrectly predicted the genes ygiH ( PA0581 ) and plsX ( PA2969 ) to be essential due to a GPR formulation of “ygiH AND plsX” for several reactions in glycerolipid metabolism . Literature evidence suggests that the gene-product of plsB ( PA3673 ) is also able to catalyze these reactions . Specifically , the gene-products of both plsB and the ygiH/plsX system are able to carry out the acylation of glycerol-3-phosphate from an acyl carrier protein whereas only the gene-product of plsB is able to carry out this reaction for acyl-CoA thioesters [33 , 34] . This experimental evidence motivates changing the GPRs for 16 reactions in glycerolipid metabolism . In addition to changes in the GPR formulation for specific reactions , we also identified a potential change to the biomass reaction . Two PAO1 genes , glgA ( PA2165 ) and algC ( PA5322 ) , are incorrectly predicted as essential for the synthesis of glycogen , a biomass component . Glycogen is not an essential metabolite for P . aeruginosa growth; however , it is very important for energy storage , which is why it was initially included in the biomass reaction [35] . Removal of glycogen from the biomass equation would make glgA and algC accurate predictions as nonessential genes in PAO1 . Implementing these proposed changes in the PAO1 and PA14 GENREs resulted in enhanced predictive capability of the models ( S10 Dataset , S11 Dataset , S3 Table ) . The updated PAO1 model predicted consensus gene essentiality status in LB media with an accuracy of 97 . 4% compared to 96 . 8% for the original model . Meanwhile , the updated PA14 model predicted consensus gene essentiality status in LB media with an accuracy of 90 . 5% compared to 90 . 0% for the original mode . It is worth noting that , although these changes to the reconstructions were made to address essentiality discrepancies in LB media conditions , they also improved the PAO1 model predictive capabilities for consensus genes in sputum media , increasing accuracy from 92 . 6% to 93 . 0% . While we identified several changes to the model to improve predictions , there were several genes for which we could find no literature evidence to change their predicted essentiality status . These genes highlight gaps in our current knowledge and understanding of Pseudomonas metabolism and indicate areas of future research . Identification of these knowledge gaps is not possible without the reconciliation of experimental data with model predictions . Ultimately , this analysis demonstrates the utility of integrating data with GENREs to improve gene annotation and suggest areas of future research . In addition to contextualizing essentiality for a given media condition , we also used the model to explain why certain metabolic genes are essential in one media-type versus another . We compared consensus LB essential genes to consensus sputum essential genes for PAO1 and identified the essential genes that were either shared by both conditions or unique to one condition versus the other . Overall , 18 genes were commonly essential , while 92 genes were uniquely essential in sputum and 26 genes were uniquely essential in LB , indicating the presence of condition-dependent essential genes . We then focused our analysis just on those genes that were also present in the PAO1 model and compared these lists to model predictions . We found four genes that both the model and the screens indicated as uniquely essential in sputum but not in LB . Interestingly , all four of these genes ( pyrB , pyrC , pyrD , and pyrF ) are involved in pyrimidine metabolism . Applying flux sampling [36] to the PAO1 metabolic network model , we investigated why these four genes were uniquely essential in sputum but not in LB ( Fig 2E ) . The pyrimidine metabolic pathway directly leads to the synthesis of several key biomass precursors ( UMP , CMP , dCMP and dTMP ) , making it an essential subsystem within the network . Under LB media conditions , there are two inputs into the pathway , one through L-Glutamine and the other through Cytosine . However , in sputum media conditions , L-Glutamine is the only input into the pathway . Because of this reduction in the number of available substrates in sputum media , the steps for L-Glutamine breakdown must be active to synthesize the biomass precursors . Thus , the genes responsible for catalyzing this breakdown are essential in sputum media conditions . In contrast , because there are two LB substrates that feed into pyrimidine metabolism , if a gene involved in the breakdown of one of the substrates is not functional the other substrate is still accessible , thus making the deletion of that gene nonessential . As stated above , further constraining the model with profiling data from both media conditions would help to further contextualize differences in the essentiality results by modulating the availability of certain reactions . Nevertheless , as demonstrated here , the metabolic network reconstruction can be a useful tool for providing functional explanations for why certain genes are essential in one condition versus another . Given the variability in the number of candidate essential genes across the screens , we were interested in using the models to quantitatively evaluate the impact of media conditions on essentiality . We first focused our analysis on how the number of considered minimal media conditions impacts the number of condition-independent essential genes identified , or the number of genes found as essential in every condition . To do this , we simulated growth of the PA14 model on 42 different minimal media and performed in silico gene knockouts , identifying the genes essential for biomass production on each media condition ( Fig 5A ) . We then randomly selected groups of minimal media conditions and compared their essential gene lists to determine the commonly essential genes , defined as the overlap . We performed this random selection of minimal media conditions for group sizes ranging from two to 40 minimal media conditions considered . For each group size , we randomly selected minimal media conditions 500 times . As expected , the more media conditions considered , the smaller the overlap of essential genes ( Fig 5B ) . This relationship between the number of media conditions considered and the size of the overlap is best characterized by an exponential decay , with the size of the overlap eventually converging on 131 genes as 40 conditions are considered . This result suggests that to identify a core set of condition-independent essential genes , dozens of minimal media screens need to be compared . However , variability between the screens , as indicated by the error bars , could still confound interpretation , necessitating the comparison of replicates and potentially even more screens to truly identify condition-independent essential genes with high confidence . We next assessed how modifications to a rich media , like LB , impact gene essentiality . LB is a complex media with known batch-to-batch variability [37 , 38] . For instance , tryptophan can degrade over time due to light exposure and autoclaving can affect the deamidation of l-asparagine and l-glutamine [38] . These alterations can result in very low concentrations of usable metabolite , which may impact the ability of mutants to grow . Given the challenge of modeling concentration , here the simulations focus on the presence or absence of metabolites in LB media . Specifically , we randomly selected carbon source components from LB media in sets of varying sizes , ranging from two to 21 LB media components considered . We then used these sets as the model media conditions and performed in silico gene knockouts to identify essential genes for biomass production on each LB media formulation ( Fig 6A ) . For each set size , we randomly selected LB components 100 times and calculated the average number of essential genes identified as well as the number of shared essential genes across all 100 sets . As the number of LB media components increases , we found that the size of the essential gene lists decreases linearly ( Fig 6B ) . If we were to consider even more media components beyond the scope of LB , we predict that this linear relationship would eventually plateau due to limitations in the metabolic network . This result suggests that a media richer than LB may be necessary to identify a core set of condition-independent essential genes . Interestingly , we found that as more complex LB media formulations are considered , the number of shared essential genes across 100 simulations quickly converges on 111 . Indeed , only three LB media components were needed to achieve this overlap . Thus , even though the average size of essential gene lists is larger for less complex media formulations , the overlap of these larger essential gene lists still results in the same overlap as more complex media formulations , suggesting that changes in complex media formulation have minimal impact on determining a core set of essential genes . However , for this analysis , we had compared 100 random media formulations for each set size , potentially masking the impact of media changes on essentiality . To identify how many LB media formulations need to be compared to converge on this overlap value , we re-ran this analysis 10 times and , for each iteration , determined the number of samples , or replicates , needed to recapture the 111 overlapping genes ( Fig 6C ) . In more complex media formulations , relatively few comparisons are needed to identify the 111 overlapping essential genes . However , as fewer LB media components are considered , more comparisons need to be made . For example , in the case of formulations consisting of only three LB media components , nearly 60 comparisons are needed to converge on the 111 overlap essential genes . Thus , as the media formulation diverges from true LB due to batch-to-batch variability , more comparisons are necessary to converge on a core set of essential genes . Taken together , these computational analyses define the scope that is needed to identify condition-independent essential genes . These results suggest that both the number of media conditions and the number of replicates analyzed can impact our ability to determine condition-independent essential genes .
The identification of both condition-dependent and condition-independent essential genes has been a long-standing interest [39 , 40] . Determination of these essential processes can aid in the discovery of novel antibacterial targets as well as the discovery of minimal genomes required to sustain life [7 , 41] . In this study , we performed a large-scale comparison of multiple gene essentiality datasets and contextualized essential genes using genome-scale metabolic network reconstructions . We applied this approach to several P . aeruginosa transposon mutagenesis screens performed on multiple media conditions and demonstrated the utility of GENREs in providing functional explanations for essentiality and resolving differences between screens . Finally , using the P . aeruginosa GENRE , we performed a high-throughput , quantitative analysis to determine how media conditions impact the identification of condition-independent essential genes . The resulting insights would be challenging to develop without the use of a computational model of P . aeruginosa metabolism . Our work enables the elucidation of mechanistic explanations for essentiality , which is challenging to determine experimentally . Ultimately , this approach serves as a framework for future contextualization of gene essentiality data and can be applied to any cell type for which such data is available . Additionally , by quantifying the impact of media conditions on the identification of condition-independent essential genes , we contribute novel insights for design of future gene essentiality screens and identification of core metabolic processes . Recent advances in deep-sequencing technologies combined with transposon mutagenesis have enabled high-throughput determination of candidate essential genes for a variety of bacterial species in a wide range of environmental conditions [42] . While researchers have demonstrated reasonable reproducibility within a given study [43] , variability across studies has been suggested but not assessed on a large-scale [1 , 44] . Our comparison of multiple P . aeruginosa transposon mutagenesis screens revealed substantial variability in candidate essential genes within and across media conditions , particularly for strain PAO1 . This finding adds to the growing body of literature highlighting discrepancies between purported essential gene lists . For example , one study compared the putative essential genes identified from a transposon-directed insertion site sequencing ( TraDIS ) generated mutant library of Escherichia coli to the established Keio collection of single gene mutants and found numerous discrepancies [45] . Another study screened transposon sequencing ( Tn-seq ) generated mutant libraries for Streptococcus pneumoniae in 17 in vitro conditions to identify core essential and conditionally essential genes [5] . As part of their analysis , they compared their results to previous studies and found both agreement and disagreement between the putative essential gene lists . Numerous factors may contribute to this lack of overlap between the screens , such as differences in experimental methodology , differences in data analysis and statistical determination of essentiality , as well as environmental variability between the screens [8 , 9 , 45] . Using a mathematical model , one study identified duration of mutant outgrowth , sensitivity of the mutant detection method , and initial concentration of the mutant in the culture as having a large impact on essentiality calls [9] . Thus , experimental and data analysis differences lead to discrepancies between screens and complicate our ability to identify high-confidence sets of condition-dependent and condition-independent essential genes . Focusing on one of these factors , we used the metabolic model of P . aeruginosa strain PA14 to quantitatively assess how media formulation impacts the identification of condition-independent essential genes . While previous in vitro studies have surveyed conditional essentiality in numerous environmental conditions , these screens used an already established mutant library for each media-type [46] . In this work , we computationally generated de novo mutant libraries for individual media conditions , eliminating any bias from starting with an established mutant library . Ultimately , we found that to determine a high-confidence set of core essential genes for minimal media conditions , more than 40 minimal media formulations need to be compared . We extended this analysis to consider how differences in rich media formulations impact gene essentiality and found that as rich media formulations diverge , as many as 60 replicates are needed to identify condition-independent essential genes with high-confidence . Taken together , these computational results suggest a rich opportunity for a large-scale experimental effort to identify with high confidence condition-independent essential genes . These insights would be impossible to garner without computational modeling due to the sheer number of comparisons made . While our analysis of rich media formulations investigated how the presence or absence of media components impact essentiality calls , future work could extend this analysis to determine how subtle variations in component concentration alter the essentiality status of a mutant . In addition to variability between datasets , a central difficulty of performing gene essentiality screens lies in the interpretation of why a gene is essential in a given condition . Oftentimes , laborious follow-up experiments are necessary to investigate the role of a gene in a given condition using lower-throughput approaches [42] . Here , we presented a strategy for contextualizing gene essentiality data using genome-scale metabolic network reconstructions . We demonstrated the utility of this approach by providing functional reasons for essentiality for consensus LB media essential genes . For these genes , we determined which specific components of biomass could not be synthesized when the gene was knocked out . Additionally , by analyzing the network structure and flux patterns , we used the model to explain why certain genes are essential in one condition versus another . Our computational approach provides testable hypotheses regarding the functional role of a gene in synthesizing biomass in a given environmental condition , streamlining downstream follow-up experiments . In future work , profiling data could be integrated with the metabolic networks to further enhance the utility of these models in contextualizing gene essentiality [30] . Additionally , integration of transcriptional regulatory networks with the GENREs would further expand the number of genes considered [47] . In summary , genome-scale metabolic network reconstructions can guide the design of gene essentiality screens and help to interpret their results . The identification of both condition-independent and condition-dependent essential genes is vital for the discovery of novel therapeutic strategies and mechanistic modeling streamlines the ability to identify these genes . This framework can be applied to numerous other organisms of both clinical and industrial relevance .
Transposon insertion library datasets were downloaded from the original publication for each screen where available . Screens were renamed following this pattern: Strain . Media . NumEssentials , where Strain indicated whether the screen was for strain PAO1 or PA14 , Media indicated which media condition the screen was performed on , and NumEssentials indicated the number of essential genes identified for the given strain on the given media condition . Specifically , for the PAO1 . LB . 201 , PAO1 . Sputum . 224 , and PAO1 . Pyruvate . 179 datasets , Dataset_S01 was downloaded from [19] . For the PAO1 . LB . 335 , PAO1 . Sputum . 405 , and PAO1 . Succinate . 640 datasets , Dataset_S01 was downloaded from [18] . For the PA14 . LB . 634 dataset , S1 Table was downloaded from [17] . For the PA14 . Sputum . 510 dataset , Dataset_S04 was downloaded from [18] . For the PAO1 . LB . 913 dataset , PA_two_allele_library5 . xlsx was downloaded from the Manoil Laboratory website ( http://www . gs . washington . edu/labs/manoil/libraryindex . htm ) . For the PA14 . LB . 1544 dataset , NRSetFile_v5_061004 . xls was downloaded from the PA14 Transposon Insertion Mutant Library website ( http://pa14 . mgh . harvard . edu/cgi-bin/pa14/downloads . cgi ) . The PAO1 and PA14 genome-scale metabolic network reconstructions were downloaded from the Papin Laboratory website ( http://www . bme . virginia . edu/csbl/Downloads1-pseudomonas . html ) . Candidate essential genes were determined for each screen as follows . For PAO1 . LB . 201 , we considered genes to be essential if they were not disrupted in all six of the Tn-seq runs on LB in the original dataset . For PAO1 . Sputum . 224 , we considered genes to be essential if they were not disrupted in all four of the Tn-seq runs on sputum in the original dataset . For PAO1 . Pyruvate . 179 , we considered genes to be essential if they were not disrupted in all three of the Tn-seq screens on Pyruvate minimal media in the original dataset . For PAO1 . LB . 335 , PAO1 . Sputum . 405 , and PAO1 . Succinate . 640 , we used the genes that were labeled as essential in the original dataset . For PAO1 . LB . 913 , the mutants listed in the transposon insertion library were compared to a list of all known genes in the PAO1 genome . Genes in the PAO1 genome that were not in the mutant library list were considered to be essential . For PA14 . LB . 634 , we used the genes listed as essential in the original dataset . For PA14 . BHI . 424 and PA14 . Sputum . 510 , we used the genes that were labeled as essential in the original dataset . For PA14 . LB . 1544 , the mutants listed in the transposon insertion library were compared to a list of all known genes in the PA14 genome . Genes in the PA14 genome that were not in the mutant library list were considered to be essential . Hierarchical clustering with complete linkage was performed on the candidate essential gene lists for the PA14 and PAO1 screens and visualized with a dendrogram . The overlap between the datasets was visualized using the R-package , UpsetR [48] . PAO1 . LB . 335 sequencing data were downloaded from NCBI SRA under the accession number SRX031647 . PAO1 . LB . 201 sequencing data were downloaded from NCBI SRA under the accession number PRJNA273663 . Data were analyzed using methods adapted from [18 , 25] . Briefly , reads were mapped to the PAO1 reference genome ( GCA_000006765 . 1 ASM676v1 assembly downloaded from NCBI ) using bowtie2 v . 2 . 3 . 4 . 1 . Open reading frame assignments were modified where 10% of the 3’ end of every gene was removed in order to disregard insertions that may not interrupt gene function . Aligned reads were mapped to genes and we removed the 50 most abundant sites to account for potential PCR amplification bias . We applied weighted LOESS smoothing to correct for genome position-dependent effects . One-hundred random datasets were generated by randomizing insertion locations . Previous analysis showed that results begin to converge after 50 random datasets [18] . We compared the random datasets to the experimental datasets with a negative binomial test in DESeq2 . We corrected for multiple testing by adjusting the p-value with the Benjamini-Hochberg method . We used the mclust package in R to test whether a gene was ‘reduced’ or ‘unchanged’ . Genes were called ‘essential’ if they were assigned to the ‘reduced’ category by mclust with an adjusted p-value <0 . 05 and uncertainty <0 . 1 . In silico gene essentiality screens were performed in relevant media conditions using the PAO1 and PA14 genome-scale metabolic network reconstructions [26] . Specifically , media formulations were computationally approximated for LB , sputum , pyruvate minimal media , and succinate minimal media for the PAO1 simulations and LB and sputum for the PA14 simulations . Systematically , genes were deleted from the models one-by-one and the resulting impact on biomass production was assessed . If biomass production for the associated mutant model was below 0 . 0001 h-1 , a standard threshold , the knocked-out gene was predicted to be essential [26] . For each in silico predicted essential gene , we determined which biomass components specifically could not be synthesized using the COBRA toolbox function , biomassPrecursorCheck ( ) [49] . Statistical significance for the comparison of the “mismatch: model nonessential , screen essential” category and the “mismatch: model essential , screen nonessential” category was assessed using the Wilcoxon signed-rank test . For each of the consensus essential and nonessential genes that were also present in the PAO1 and PA14 models , we determined which subsystems they participated in using an in-house script ( see Supplementary Information ) . Briefly , we first converted model subsystems to broad subsystems based on KEGG functional categories [50] . We then identified the reactions associated with the gene of interest and used the broad subsystem of this reaction to indicate the subsystem assignment for the gene of interest . Where there was more than one reaction connected to a gene , we used the reaction associated with the first instance of the gene in the network for subsystem assignment . The impact of media conditions on flux through pyrimidine metabolism in the PAO1 metabolic network reconstruction was assessed using the flux sampling algorithm optGpSampler [36] . Briefly , optGpSampler samples the solution space of genome-scale metabolic networks using the Artificial Centering Hit-and-Run algorithm and returns a distribution of possible flux values for reactions of interest . Three-thousand flux samples were collected for each simulation , using one thread and a step-size of one . Maximization of biomass synthesis was set as the objective function . Flux sampling simulations were performed for PAO1 grown in LB media and sputum media . The median flux values for every reaction in pyrimidine metabolism were compared between the LB and sputum simulations to determine whether flux was higher , lower , or unchanged in sputum versus LB . The impact of media formulation on gene essentiality predictions was assessed using the PA14 genome-scale metabolic network reconstruction . For the minimal media analysis , the PA14 model was grown on 42 different minimal media and in silico essential genes were identified as described above . We then randomly selected groups of minimal media conditions of varying sizes , ranging from two to 41 minimal media conditions considered , and found the intersection of the group’s predicted essential gene lists , or the genes that were identified as essential in every condition considered within that group . For each group size , we randomly selected minimal media conditions 500 times . For the LB media analysis , we randomly selected components from LB media in sets of varying sizes , ranging from two to 21 LB media components considered , used these sets as the model media conditions , and identified in silico essential genes as above . For each set size , we randomly selected LB components 100 times and calculated the average total number of essential genes identified and the intersection of the essential genes across all 100 sets . To determine how many LB media formulations needed to be compared to converge on this intersection , we re-ran this LB media formulation analysis 10 times and , for each iteration , determined the number of samples needed to achieve the size of the overlap if all 100 samples were considered at each set size . Code and files necessary to recreate figures and data can be found here: https://github . com/ablazier/gene-essentiality The COBRA Toolbox 2 . 0 . 5 [49] , the Gurobi 6 . 5 solver , and MATLAB R2016a were used for model simulations . optGPSampler1 . 1 was used for flux sampling simulations [36] . Bowtie2 v . 2 . 3 . 4 . 1 [51] and Samtools v . 1 . 3 . 1 [52] were used for transposon sequencing analysis . R 3 . 3 . 3 was used for all other analyses and figure generation .
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With the rise of antibiotic resistance , there is a growing need to discover new therapeutic targets to treat bacterial infections . One attractive strategy is to target genes that are essential for growth and survival . Essential genes can be identified with transposon mutagenesis approaches; however , variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes . We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa . We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens . The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2019
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Reconciling high-throughput gene essentiality data with metabolic network reconstructions
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Respiratory syncytial virus ( RSV ) is the most frequent cause of lower respiratory disease in infants , but no vaccine or effective therapy is available . The initiation of RSV infection of immortalized cells is largely dependent on cell surface heparan sulfate ( HS ) , a receptor for the RSV attachment ( G ) glycoprotein in immortalized cells . However , RSV infects the ciliated cells in primary well differentiated human airway epithelial ( HAE ) cultures via the apical surface , but HS is not detectable on this surface . Here we show that soluble HS inhibits infection of immortalized cells , but not HAE cultures , confirming that HS is not the receptor on HAE cultures . Conversely , a “non-neutralizing” monoclonal antibody against the G protein that does not block RSV infection of immortalized cells , does inhibit infection of HAE cultures . This antibody was previously shown to block the interaction between the G protein and the chemokine receptor CX3CR1 and we have mapped the binding site for this antibody to the CX3C motif and its surrounding region in the G protein . We show that CX3CR1 is present on the apical surface of ciliated cells in HAE cultures and especially on the cilia . RSV infection of HAE cultures is reduced by an antibody against CX3CR1 and by mutations in the G protein CX3C motif . Additionally , mice lacking CX3CR1 are less susceptible to RSV infection . These findings demonstrate that RSV uses CX3CR1 as a cellular receptor on HAE cultures and highlight the importance of using a physiologically relevant model to study virus entry and antibody neutralization .
Respiratory syncytial virus ( RSV ) infects nearly every child by the age of 2 [1] . It causes severe lower respiratory disease in ~2% of these infants , making RSV infection the most frequent cause of hospitalization of infants and children in the developed world [2–4] . While supportive care successfully treats nearly all of these infants , in the developing world RSV infection causes the death of an estimated 66 , 000 to 199 , 000 children under five years of age annually [5 , 6] . The elderly are also susceptible to RSV disease and RSV is the second most frequent cause of ‘excess deaths’ during the winter months in this population , behind influenza virus [7 , 8] . Despite this great clinical impact , there are currently no approved vaccines or therapeutic antiviral drugs against RSV . RSV infection has been studied mainly in immortalized cell lines , where the virion G glycoprotein uses cell-surface heparan sulfate as a receptor ( HS ) [9–11] . However , immortalized cell lines may not be the best model for the study of RSV entry as they differ in many aspects from the human airway epithelium in vivo . Primary , well differentiated human airway epithelial ( HAE ) cultures have been shown to accurately represent the human airway epithelium , both in appearance and function [12] . HAE cultures have served as a model for numerous respiratory viruses , including RSV , parainfluenza viruses , human/avian influenza viruses , and coronaviruses , and are considered to be an ideal in vitro model of viral interaction with the respiratory epithelium in vivo [13–18] . We previously found that RSV infects HAE cultures via the apical surface and nearly exclusively infects ciliated cells [19] . However , HAE cultures do not express detectable HS on their apical surface [13] , leading us to hypothesize that a different viral receptor is responsible for RSV attachment to these cells and likely to human airways . CX3CR1 , surfactant protein A , and annexin II have also been shown to bind the G protein and proposed to act as cellular receptors for RSV [20–23] . Recombinant RSV lacking its G gene is able to infect HAE cultures [24] , albeit poorly , suggesting that the RSV F protein also has attachment activity . ICAM-1 , TLR4 , and nucleolin have been proposed to function as F protein receptors [25–27] , but most of this work has been performed in immortalized cells and needs to be reexamined in primary cultures . Here we compared the abilities of soluble HS and two anti-G monoclonal antibodies ( mAbs ) to inhibit RSV infection , finding that HS neutralized infection of HeLa cells but not HAE cultures and that the mAbs neutralized infection of HAE cultures much better than HeLa cells , indicating the use of different receptors on these different cells . One of the mAbs , 131-2g , previously characterized as “non-neutralizing” in immortalized cells , did neutralize RSV on HAE cells . This mAb had been shown to block G protein binding to CX3CR1 [23] . Here we find that CX3CR1 is detectable on ciliated cells in HAE cultures . Both a mAb against CX3CR1 and mutations in the G protein CX3C motif reduced RSV infection of these cells . These results suggest that RSV uses CX3CR1 as a cellular receptor on physiologically relevant HAE cultures and , likely , in the airways of the human lung .
Recombinant green fluorescence protein ( GFP ) -expressing RSV ( rgRSV ) ( derived from D53 , strain A2 with a variant F protein ) [28] and a mutant of this virus lacking the genes encoding the G and SH proteins ( rgRSV-F ) which grows to lower titers than the parent virus [29] , were previously described . The following reagent was obtained through BEI Resources , NIAID , NIH: Human Respiratory Syncytial Virus , A2001/2-20 ( 2–20 ) , Purified from HEp-2 Cells , NR-43938 . RSV strain B1 ( subgroup B ) was purchased from the American Type Culture Collection ( Manassas , VA ) . MAb L9-resistant mutants RSV2 ( F165L , F170L , I175T and C186R ) and RSV6a ( F168S , F170P , C186R and V225A ) were selected as previously described from the WT ( Long strain ) of RSV [30] . Recombinant RSV D53 with a cysteine to serine mutation at amino acid 186 in the G protein ( C186S RSV ) was generated by reverse genetics [31] , as was the RSV A2 CX3C-mutant strain with an alanine insertion at position 286 ( CX4C RSV ) [32] . All viruses were propagated and titered in HeLa cells ( American Type Culture Collection ) . HeLa cells and 293A cells ( American Type Culture Collection ) were grown in DMEM containing 10% heat inactivated fetal bovine serum ( FBS ) . Chinese hamster ovary ( CHO ) mutant A745 cells ( gift from Dr . J . D . Esko , University of California , San Diego , San Diego , CA ) which are severely deficient in the production of all glycosaminoglycans [33] were grown in RPMI 10% FBS . Primary , well-differentiated human airway epithelial ( HAE ) cultures were grown on collagen coated Transwell inserts ( Corning Incorporated , Corning , NY ) as previously described [34] . Upon reaching confluency and forming tight junctions , the apical medium was removed and cultures were maintained at the air-liquid interface for 4 to 6 weeks to form well-differentiated , polarized cultures . For neutralization experiments , rgRSV was incubated with increasing concentrations of heparan sulfate ( Sigma-Aldrich , St . Louis , MO ) , protein A-purified mAb L9 [35] or mAb 131-2g [36] ( gift from Dr . L . J . Anderson , Emory University School of Medicine , Atlanta , GA ) , compared to the appropriate mouse isotype controls , IgG2a or IgG1 ( R&D Systems , Minneapolis , MN ) . RSV isolate 2–20 or B1 were incubated with 5 μg/ml mAb 131-2g . All mAbs were diluted in DMEM 10% FBS and incubated with virus for 30 min at room temperature prior to inoculation of HeLa and HAE cultures . For anti-CX3CR1 blockade experiments , cells were incubated with 25 μg/ml CX3CR1 specific rat mAb ( clone 2A9-1 ) ( MBL International Corporation , Woburn , MA ) or rat IgG2b ( Becton Dickinson , East Rutherford , NJ ) for 30 min prior to inoculation . HeLa and HAE cultures were inoculated with ~200 PFU at 37°C . The inoculum was removed 2 hr later and cultures rinsed 3 times with PBS . At 24 hr post inoculation , cultures were fixed , permeabilized and incubated with an FITC-labeled anti-RSV polyclonal antibody ( Virostat , Portland , ME ) . Infected ( green from GFP or FITC ) cells were detected and counted on an EVOS FL Cell Imaging System ( Life Technologies , Carlsbad , CA ) . A codon-optimized version of the RSV A2 G protein gene ( MP341 ) was modified to express the F120A mutant by inserting a synthetic gBlock Gene Fragment ( Integrated DNA Technologies , Coralville , IA ) into restriction digested parent plasmid . 293A cells were transfected with pcDNA3 . 1 , MP341 or F170A using Lipofectamine 2000 ( Life Technologies ) , or infected with rgRSV , WT ( Long strain ) , RSV2 , RSV6a or C186S viruses ( MOI of 0 . 1 ) . 24 hr post transfection/infection , cells were disrupted with Triton-X100 , boiled , separated under reducing conditions by SDS-PAGE and transferred to nitrocellulose . The A2 strain RSV G protein was detected by immunoblot with 130-2g ( gift from Dr . L . J . Anderson ) , L9 , or 131-2g and anti-mouse antibody-horseradish peroxidase ( HRP ) ( Kirkegaard & Perry Laboratories , Gaithersburg , MD ) . The RSV G protein from Long strain derived viruses was detected by immunoblot with rabbit antiserum against RSV ( gift from Dr . P . L . Collins , NIAID , NIH , Bethesda , MD ) and anti-rabbit antibody-HRP ( Kirkegaard & Perry Laboratories ) or L9 or 131-2g and anti-mouse antibody-HRP . Cross-sections of formalin fixed and paraffin embedded HAE cultures were subjected to antigen retrieval ( BioGenex , Fremont , CA ) prior to incubation with polyclonal rabbit CX3CR1-specific IgG antibody ab8020 ( Abcam , Cambridge , UK ) or isotype control ab27478 ( Abcam ) followed by Texas Red labeled anti-rabbit IgG ( Vector Laboratories , Burlingame , CA ) and ProLong Gold anti-fade reagent with DAPI ( Life Technologies , Carsbad , CA ) . Images were taken on an Olympus BX61 microscope ( Olympus Corporation , Tokyo , Japan ) . CHO A745 cells were transfected with a plasmid encoding human CX3CR1 ( gift from Dr . P . M . Murphy , National Institute of Allergy and Infectious Disease ) or pcDNA3 . 1 using Lipofectamine LTX ( Life Technologies ) . 24 hr post transfection , cells were stained with a PE-labeled polyclonal anti-CX3CR1 antibody ( BioLegend , San Diego , CA ) and imaged on an EVOS FL Cell Imaging System ( Life Technologies ) to determine cell surface expression . At 24 hr post transfection , parallel wells of cells were inoculated as above with rgRSV at an MOI of 0 . 1 . 48 hr post inoculation , infected ( green fluorescent ) cells were visualized and counted . C57BL/6 CX3CR1GFP mice , which lack functional CX3CR1 through replacement of the murine CX3CR1 gene with that for enhanced green fluorescent protein [37] , and C57BL/6 mice ( gift from Dr . S . Partida-Sanchez , Nationwide Children’s Hospital , Columbus , OH ) were maintained in biosafety level 2 ( BL2 ) containment under pathogen-free conditions . Male mice ( age , 6 to 8 wk ) were lightly anesthetized and inoculated intranasally ( i . n . ) with 106 PFU of rgRSV or 105 PFU of rgRSV-F in a 30 μl volume . No animals were excluded from analyses . No blinding was done . At 5 days post inoculation , 3 mice per group were examined . Lungs were harvested and homogenized and viral titers were assayed on HeLa cells . All animal experiments were carried out in strict accordance with the accredited conditions in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Institutional Animal Care and Use Committee ( Welfare Assurance Number A3544-01 ) at The Research Institute at Nationwide Children's Hospital , AR09-00012 . All experimental procedures were performed under isoflurane anesthesia , and all efforts were made to minimize suffering . All data presented for in vitro experiments are a single experiment representative of three . Data from three replicates of each experimental condition are expressed as mean ± standard deviation . Data for in vivo experiments with rgRSV are expressed as the mean ± standard deviation of three combined experiments ( each with three animals per group ) . All experiments were repeated ≥ 3 times . A 2-tailed student’s t-test was employed to determine the significance of the differences between experimental conditions . A P-value of <0 . 05 was considered to be statistically significant .
HS is not detectable on the apical surface of well-differentiated HAE cultures [13] , suggesting that RSV uses a receptor other than HS to attach to these physiologically relevant cells . If HS is not the virus receptor on HAE cells , soluble HS should not reduce RSV infection of these cells . To test this possibility , recombinant green fluorescent protein expressing RSV ( rgRSV ) was incubated with soluble HS prior to inoculation of HeLa and HAE cultures . Soluble HS ( 20 μg/ml ) neutralized infection of HeLa cells without reducing infection of HAE cultures ( Fig 1A ) . This result is consistent with HS acting as a virus receptor on HeLa cells , but not on HAE cultures . At a 5-fold higher concentration of soluble HS ( 100 μg/ml ) RSV infection of HeLa cultures was reduced by over 100-fold , but infection of HAE cultures was reduced by only 2-fold . The partial sensitivity of RSV to this higher concentration may be the result of HS binding to lower affinity sites on the virions . If RSV uses a receptor other than HS to attach to HAE cultures , monoclonal antibodies ( mAbs ) against the G protein may differ in their ability to block virus attachment and entry into immortalized versus HAE cultures depending on where on the G protein they bind . MAb 131-2g binds the G protein and has been characterized as “non-neutralizing” because it does not reduce RSV infection of HEp-2 cells [36] and we confirmed this result for HeLa cells ( Fig 1B ) . However , mAb 131-2g did neutralize rgRSV infection of HAE cultures . The ability of mAb 131-2g to reduce infection of HAE , but not HeLa cultures suggests that mAb 131-2g binds near and interferes with the function of a domain in the G protein that is important for attachment to HAE cultures but not for attachment to HeLa cells . This result demonstrates that these two domains on the G protein are distinct and confirms our hypothesis that RSV uses a different receptor to infect HAE cells compared to HeLa cells . Furthermore , these results indicate the importance of using physiologically relevant HAE cultures for determining the neutralizing abilities of antibodies against the G protein . We also tested G protein mAb L9 [35] for its ability to neutralize rgRSV . MAb L9 was previously shown to neutralize RSV infection of immortalized cells [35 , 38] . However , we found that mAb L9 also inhibited rgRSV infection of HAE cells , but the inhibition of HAE infection was approximately 10-fold greater than the inhibition of HeLa infection ( Fig 1C ) . The ability of mAb L9 to neutralize rgRSV on HeLa and HAE cultures may be because the mAb blocks G protein binding to both HS and the unidentified HAE receptor , respectively . We have previously found that recombinant RSV lacking the G gene is much less infectious for HAE cultures than for HeLa cells [24] , indicating that the G protein is more important for infection of HAE cultures . The greater neutralizing ability of mAb L9 on HAE cultures is likely due to the fact that the G protein is more critical for efficient infection of these cells . Therefore , the ability of mAb L9 to neutralize rgRSV infection of both HeLa and HAE cells suggests that the attachment sites on the G protein for each cell type are close to each other . For other paramyxoviruses , receptor binding differs between laboratory adapted strains and clinical isolates [39 , 40] . To confirm that mAb 131-2g neutralization of rgRSV ( subgroup A ) in HAE cultures is not an artifact of using a laboratory adapted virus , we examined the neutralizing ability of this mAb for a low-passage subgroup A clinical isolate , RSV A2001/2-20 ( 2–20 ) [41] . MAb 131-2g efficiently neutralized 2–20 infection of HAE cultures , but not of HeLa cells ( Fig 1D ) , similar to rgRSV . We also examined the neutralizing ability of mAb 131-2g for B1 , an member of the RSV subgroup B . B1 infection of HAE cultures was also reduced , while infection of HeLa cells was not , similar to rgRSV ( Fig 1E ) . These results demonstrate that regardless of the source or subgroup of RSV , mAb 131-2g neutralizes infection of HAE cultures , but not HeLa cells . The RSV G protein is an N-terminally anchored type II transmembrane protein [42] ( Fig 2A ) . It has a highly conserved central domain that is flanked by two highly O-glycosylated , mucin-like regions that also contain several N-linked glycans [42 , 43] . Partially overlapping this central conserved region are 4 conserved cysteines linked by two disulfide bonds to form the neck of a ‘noose’ [44] . The last 2 cysteines are part of a CX3C motif [23] . Immediately C-terminal to this motif is the highly basic heparin-binding domain that binds to HS [45] . MAb L9 has been used to select neutralization-resistant viruses in HEp-2 cells . These resistant mutants each have multiple amino acid substitutions clustered in and around the central conserved domain of the G protein indicating that L9 binds to this region [30] . MAb 131-2g binds a fragment of the G protein that includes amino acids 1 to 173 and reacts with both RSV subtypes , A and B [46] . The amino acid sequence of this fragment is less than 44% conserved between the strains with the exception of the completely conserved 164–176 region . These observations suggest that mAb 131-2g , like mAb L9 , binds within the central conserved domain of the G protein . Because mAb 131-2g is non-neutralizing in immortalized cells , no neutralization resistant virus variants have been isolated . To determine if mAb 131-2g binds at or near the same region as mAb L9 , we used immunoblotting of the G protein in parental Long strain RSV ( WT ) and two L9-selected RSV mutants derived from it , RSV2 and RSV6a . Partially purified virions from these mutants contain the G protein , detectable with rabbit anti-RSV serum , as does WT ( Fig 2B ) . As expected , mAb L9 detected the WT RSV G protein , but not the G protein from the two L9 neutralization-resistant RSV mutants . MAb 131-2g was also able to detect WT RSV , but not the two L9 neutralization-resistant RSV mutants , suggesting that one or more of the mutated amino acids in RSV2 and RSV6a are also important for mAb 131-2g binding . Both mAb L9-resistant RSV mutants contain multiple mutations in their G proteins: F165L , F170L , I175T and C186R in RSV2; and F168S , F170P , C186R and V225A in RSV6a [30] . Both have mutations in F170 and C186 . To determine if F170 is required for mAb L9 and 131-2g binding , we generated a plasmid encoding a G protein with an F170A mutation and tested the reactivity of each mAb to this mutant G protein by immunoblot . Neither mAb was able to detect the F170A mutant G protein ( Fig 2C ) . Mutant G protein was present in the virions as demonstrated by another mAb , 130-2g , which binds within the C-terminal region of the G protein . To determine if C186 is required for mAb L9 and 131-2g binding we generated a recombinant RSV whose only mutation is C186S in its G protein and tested the ability of these mAbs to bind . Virions from the C186S mutant did contain G protein as detected by mAb 130-2g , but neither mAb L9 nor 131-2g were able to detect the mutant G protein ( Fig 2D ) . These results indicate that both F170 and C186 are critical for both mAb L9 and 131-2g binding to the G protein and , therefore , that the epitopes of these mAbs overlap . However , as shown above , mAb L9 neutralizes RSV infection of HAE cultures more efficiently than mAb 131-2g and neutralizes RSV in HeLa cells , unlike 131-2g ( Fig 1B and 1C ) , indicating that while the epitopes of these mAbs overlap , they are not identical . C186 is the second cysteine of the CX3C motif in the G protein . The cysteine spacing of this motif is similar to the only CX3C chemokine , fractalkine [23] . The G protein has been shown to bind to the fractalkine receptor , CX3CR1 , and can use it to initiate infection when it is transiently expressed in immortalized cells [23] . MAb 131-2g blocks G protein binding to CX3CR1 [23] . Here we found that mAb 131-2g efficiently reduces RSV infection of HAE cultures ( Fig 1b–1e ) , leading us to hypothesize that RSV uses CX3CR1 as a receptor on these cells . To determine if CX3CR1 is expressed on HAE cells in general and on ciliated cells , the target cells for RSV infection , we stained fixed cross-sections of HAE cultures with antibodies against CX3CR1 . These antibodies stained the apical surface of HAE cultures , primarily and robustly staining the cilia on ciliated cells ( Fig 3A ) . This finding indicates that CX3CR1 is located on the correct cell type to be an RSV receptor on HAE cultures . Its expression in this location could be responsible for the ciliated cell tropism of RSV . To validate the functional role of CX3CR1 on HAE cultures , we incubated HAE and HeLa cultures with CX3CR1-specific mAb or isotype control prior to inoculation with rgRSV . Anti-CX3CR1 reduced RSV infection of HAE cultures significantly compared to isotype control , while neither antibody reduced RSV infection of HeLa cells ( Fig 3B ) . Incubation of HAE cultures with anti-CX3CR1 before , during and after inoculation inhibited RSV infection to a greater extent ( Fig 3C ) , but again did not reduced infection of HeLa cultures . These results indicate that RSV interaction with CX3CR1 is important for efficient infection of HAE cultures , but not HeLa cells . If CX3CR1 is a receptor for the RSV G protein on HAE cultures , the G protein CX3C motif would likely be important for infection of these cells . To test this possibility , we inoculated HeLa and HAE cultures with rgRSV or the RSV mutant described above with a mutation in the last cysteine of the CX3C motif ( C186S RSV ) . rgRSV and C186S RSV were comparably infectious for HeLa cells ( Fig 3D ) . In contrast , while rgRSV readily infected HAE cultures , C186S RSV was poorly infectious for these cells , similar to RSV completely lacking the G protein [24] . This finding indicates that C186 in the G protein is critical for RSV infection of HAE cultures and may be involved in RSV attachment to these cells . Next , we determined the infectivity of recombinant RSV with an additional amino acid ( alanine ) inserted between the two cysteines in the CX3C motif , CX4C RSV . rgRSV with an intact CX3C motif was comparably infectious for HeLa and HAE cultures ( Fig 3E ) . However , CX4C RSV was poorly infectious for HAE cultures . These results with both of these mutant viruses indicate that the CX3C motif is important for efficient infection of HAE cultures , supporting the hypothesis that CX3CR1 is a receptor on these cells . The CHO A745 cell line is defective in xylosyl transferase , the enzyme that initiates glycosaminoglycan synthesis on a protein by linking xylose to a serine or threonine in the proper context [33] . The result of this defect is a severe deficiency in total glycosaminoglycan expression , including expression of HS . RSV is poorly infectious for CHO A745 cells , infecting them 17-fold less efficiently than CHO K1 cells [24] . To demonstrate that CX3CR1 is capable of acting as a receptor in the absence of HS , we transiently expressed CX3CR1 in CHO A745 cells , a cell line deficient in HS expression , and determined RSV infectivity for these cells . At the time of inoculation roughly 10% of cells expressed cell surface CX3CR1 ( Fig 4A ) , while control cells did not ( Fig 4B ) . Expression of CX3CR1 resulted in a 4-fold increase in infection as compared to the same cells transfected with empty vector ( Fig 4C ) . A mAb against CX3CR1 significantly reduced RSV infection compared to an isotype control ( Fig 4D ) , indicating that the increase in infection was due to RSV interaction with the receptor . Further , mAbs 131-2g and L9 significantly reduced RSV infection of CX3CR1 expressing cells , supporting the hypothesis that these mAbs neutralize RSV infection by blocking the interaction of the G protein with CX3CR1 . Together , these data suggest that CX3CR1 is a receptor on HAE cultures and that the neutralizing ability of antibodies against the G protein can differ when assayed in cells expressing CX3CR1 rather than HS . To examine the importance of CX3CR1 as a receptor in vivo , we intranasally inoculated WT and CX3CR1-/- mice with rgRSV . Five days post inoculation , at the peak of RSV replication in the mouse lung , we found that CX3CR1-/- mice had significantly lower titers of RSV ( Fig 5A ) relative to WT mice . This result supports the hypothesis that murine CX3CR1 acts as an RSV receptor in the mouse lung . In this experiment , rgRSV retained some of its infectivity for CX3CR1-/- mice , suggesting that RSV also uses an alternate , less efficient mechanism for initiating infection in the absence of the G protein-CX3CR1 interaction . To examine this possibility , we inoculated mice with rgRSV lacking the G and SH genes , such that the F protein is the only glycoprotein expressed , and found that this virus infected both WT and CX3CR1-/- mice comparably , albeit poorly ( Fig 5B ) . This finding suggests that G protein interaction with CX3CR1 is important for efficient infection of mice , but that the F protein may also have some attachment activity .
RSV efficiently infects many immortalized cell lines , but it is not clear how closely this infectious process represents RSV infection of the human airway . The results in this report indicate that the initial , receptor-mediated step of infection in a primary cell culture model for the human airway epithelium , well-differentiated HAE cultures , differs from the initial step of infection in HeLa cells . The cellular receptor in immortalized cells is HS [9 , 10] . However , HS is not detectable on the apical surface of HAE cultures [13] and , as shown here , soluble HS does not inhibit infection of HAE cultures , indicating that a different receptor is used in these cultures and , therefore , in vivo . CX3CR1 , surfactant protein A , and annexin II have been shown to bind the G protein and proposed to act as cellular receptors for RSV [20–23] , but the functional in vivo receptor ( s ) for RSV had not been identified . Here we present evidence for CX3CR1 as a cellular receptor on physiologically relevant HAE cultures and , therefore , in the human lung . Tripp et al . found that the RSV G protein mimics the chemokine CX3CL1 ( fractalkine ) in its ability to bind to its receptor , CX3CR1 [23] . CX3CR1 is expressed in epithelial cells , smooth muscle cells , microglia , neurons , T cells , monocytes , dendritic cells , and NK cells [47–53] . RSV is able to infect nearly all immortalized cell lines and infects primary epithelial cells , smooth muscle cells , neuronal cells , eosinophils , and dendritic cells [19 , 40 , 54 , 55] . Here we found that CX3CR1 is detectable on the cilia of ciliated cells in HAE cultures , the cell targeted by RSV [19] . In two publications that appeared while the present report was under review , Jeong et al . confirmed this location [56] and Chirkova et al . found CX3CR1 on the majority of RSV infected cells in primary human bronchial epithelial cultures [57] . MAb 131-2g has been shown to block G protein binding to CX3CR1 [23] . MAb 131-2g also reduces leukocyte migration in vitro [23] and inflammation in the lungs of RSV infected mice [58] , suggesting that the G protein modulates the host inflammatory response via its interaction with CX3CR1 . In RSV infected mice , treatment with mAb 131-2g mediates viral clearance and reduces RSV pathogenesis [59] . Because mAb 131-2g is non-neutralizing in immortalized cells , it was thought that this reduction in virus load could not be due to virus neutralization , but might instead be due to antibody dependent cell mediated cytotoxicity . Furthermore , it was suggested that the reduction in pathogenesis was due to prevention of G protein-mediated leukocyte chemotaxis . Here we demonstrate that mAb 131-2g is strongly neutralizing on HAE cultures and that CX3CR1 is likely an important receptor on these cells . As our CX3CR1-/- mouse infection data suggest that RSV uses CX3CR1 as a receptor on the murine airway epithelium , RSV neutralization by mAb 131-2g could have directly caused the decrease in viral load , resulting in decreased pathogenesis . A previous report did not find a difference in RSV yield from CX3CR1-/- and WT mice [60] . However , infectious virus in the lung was not quantified in that study which relied instead on real-time PCR . Indeed , several studies have utilized the mouse model and mAb 131-2g to examine the effects of the RSV G protein-CX3CR1 interaction on the immune response to RSV , but its use of CX3CR1 as a receptor on epithelial cells in the murine lung has not yet been directly examined . Differences in measles virus ( MV ) receptor usage between clinical isolates and laboratory strains have been well documented to be due to selection during growth in immortalized cells . SLAM ( CD150 ) is used as a receptor by clinical isolates [40] , but not by the laboratory adapted Edmonston strain of MV which uses CD46 [56] . Because mAb 131-2g blocks G protein binding to CX3CR1 , our finding that mAb 131-2g neutralizes infection of clinical isolates for HAE cultures ( Fig 1D ) indicates that the use of CX3CR1 is not an artifact of using a laboratory strain of RSV . RSV infection of cells lacking CX3CR1 suggests that RSV interacts with an additional receptor on these cells . Recombinant RSV lacking the G gene is able to infect HAE cultures [24] , albeit poorly , suggesting that there may also be an F protein receptor on these cultures . It is possible that more than one receptor may be involved in RSV infection of airway cells . Others have presented evidence that ICAM-1 , TLR4 , and nucleolin can function as F protein receptors [25–27] . These proposed RSV receptors have been studied mainly in immortalized cells and further investigation is needed to determine their role , if any , in RSV infection of HAE cultures . Here we have localized a critical element of the binding site of 131-2g , a mAb that neutralizes RSV infection in HAE cultures , but not in HeLa cultures . We found that both F170 and C186 of the G protein are required for binding by mAbs L9 and 131-2g on an immunoblot , indicating that the epitopes of these mAbs overlap . Furthermore , since both mAbs bound the G protein that had been treated with a reducing agent , mAb binding was not dependent on the structure provided by the disulfide bonds , but rather on the primary sequence . Sullender previously found that mAb 131-2g does not bind to cells expressing a fragment of the G protein that includes amino acids 1 to 173 in an immunoblot [46] , but does bind this fragment in a cell immunofluorescence assay . It is possible that the site on the G protein that is recognized by mAb 131-2g has two components , one that includes amino acids F170 and C186 and another that is conformational . Further characterization is required and may provide further insights into this HAE-only neutralizing epitope on the G protein . Titers of serum neutralizing antibodies correlate with protection from RSV [61 , 62] . The findings presented here demonstrate that some antibodies to the G protein , one of only two neutralizing antigens in RSV , can neutralize RSV infection only when assessed on physiologically relevant HAE cultures . For this reason , some of the in vivo neutralizing antibodies to the G protein may be missed when assessing neutralizing activity on immortalized cells . Conversely , it is possible that other antibodies that neutralize RSV on immortalized cells may not have neutralizing activity in vivo . Moreover , it is possible that the use of HAE cultures may be important for more accurately quantifying neutralizing antibodies against other respiratory viruses , particularly those that use HS as a surrogate receptor for infection in immortalized cells . The importance of using a physiologically relevant model to study paramyxovirus entry has been previously demonstrated . Palmer et al . found that fusion inhibitors against human parainfluenza virus ( HPIV ) display similar inhibition in vivo and in HAE cultures , but not in immortalized cell culture [39] . These findings demonstrate , and ours confirm , the importance of evaluating antivirals in natural host tissue . Efficient neutralization of RSV infection of HAE cultures by mAbs against the G protein bolsters the suggestion that the G protein should be considered for inclusion in vaccine candidates . Indeed , vaccination of mice or cotton rats with a G protein fragment ( amino acids 131–230 ) induces neutralizing antibodies and protects against RSV challenge [63 , 64] . Also , mice immunized with a shorter peptide ( amino acids 148 to 198 ) generate antibodies that neutralize both A and B strains of RSV [65] . However , the neutralizing activity of these antibodies was determined using immortalized cells . Neutralizing activity determined on HAE cultures may well be greater and should more accurately reflect neutralizing activity in vivo .
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Respiratory syncytial virus ( RSV ) is the second most common infectious cause of infant death worldwide . Despite this great clinical impact , no effective antivirals or vaccines against RSV are available . Here we find that the RSV attachment ( G ) glycoprotein uses CX3CR1 as a receptor on primary human airway epithelial ( HAE ) cultures , an excellent model of RSV infection of the human lung . The G protein contains a CX3C motif and we find that this region is critical for its role in infection of HAE cultures , but not of immortalized cells . Furthermore , we find that antibodies against the G protein neutralize RSV infection of HAE cultures differently from immortalized cells . These insights suggest that HAE cultures should be used to quantify neutralizing antibodies , including during vaccine development , that the CX3CR1 interaction with the RSV G protein could be a target for antiviral drug development , and that the G protein should be considered for inclusion in vaccines .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Respiratory Syncytial Virus Uses CX3CR1 as a Receptor on Primary Human Airway Epithelial Cultures
|
Understanding the relationship between external stimuli and the spiking activity of cortical populations is a central problem in neuroscience . Dense recurrent connectivity in local cortical circuits can lead to counterintuitive response properties , raising the question of whether there are simple arithmetical rules for relating circuits’ connectivity structure to their response properties . One such arithmetic is provided by the mean field theory of balanced networks , which is derived in a limit where excitatory and inhibitory synaptic currents precisely balance on average . However , balanced network theory is not applicable to some biologically relevant connectivity structures . We show that cortical circuits with such structure are susceptible to an amplification mechanism arising when excitatory-inhibitory balance is broken at the level of local subpopulations , but maintained at a global level . This amplification , which can be quantified by a linear correction to the classical mean field theory of balanced networks , explains several response properties observed in cortical recordings and provides fundamental insights into the relationship between connectivity structure and neural responses in cortical circuits .
Information about a sensory stimulus is passed along a hierarchy of neural populations , from subcortical areas to the cerebral cortex where it propagates through multiple cortical areas and layers . Within each layer , lateral synaptic connectivity shapes the response to synaptic input from upstream layers and populations . In a similar manner , lateral connectivity shapes the response of cortical populations to artificial , optogenetic stimuli . The densely recurrent structure of local cortical circuits can lead to counter-intuitive response properties [1–5] , making it difficult to predict or interpret a population’s response to natural or artificial stimuli . This raises the question of whether there are underlying arithmetical principles through which one can understand the relationship between a local circuit’s connectivity structure and its response properties . In principle this relationship could be deduced from detailed computer simulations of the neurons and synapses that comprise the circuit . In practice , such detailed simulations can be computationally expensive , depend on a large number of unconstrained physiological parameters , and their complexity can make it difficult to pinpoint mechanisms underlying observed phenomena . In many cases , however , one is not interested in how the response of each neuron is related to the detailed connectivity between every pair of neurons . Relevant questions are often more macroscopic in nature , e . g . “How does increased excitation to population A affect the average firing rate of neurons in population B ? ” For such questions , it is sufficient to establish a relationship between macroscopic connectivity structure and macroscopic response properties . One such approach is provided by the mean-field theory of balanced networks [6–10] , which is derived in the limit of a large number of neurons and a resulting precise balance of strong excitation with strong inhibition . This notion of precise balance implies a simple relationship between the macroscopic structure of connectivity and firing rates , and balanced network models naturally produce the asynchronous , irregular spiking activity that is characteristic of cortical recordings [6 , 7 , 11 , 12] . However , classical balanced network theory has some critical limitations . While cortical circuits do appear to balance excitation with inhibition , this balance is not always as precise and spike trains are not as asynchronous as the theory predicts [13–19] . Moreover , precise balance is mathematically impossible under some biologically relevant connectivity structures [8–10] , implying that the classical theory of balanced networks is limited in its ability to model the complexity of real cortical circuits . We show that cortical circuits with structure that is incompatible with balance are susceptible to an amplification mechanism arising when excitatory-inhibitory balance is broken at the level of local subpopulations , but maintained at a global level . This mechanism of “imbalanced amplification” can be quantified by a linear , finite-size correction to the classical mean field theory of balanced networks that accounts for imperfect balance and local imbalance . Through several examples , we show that imbalanced amplification explains several experimentally observed cortical responses to natural and artificial stimuli .
We begin by reviewing and demonstrating the classical mean-field theory of balanced networks and a linear correction to the large network limit that the theory depends on . A typical cortical neuron receives synaptic projections from thousands of neurons in other cortical layers , cortical areas or thalamus . These long range projections are largely excitatory and provide enough excitation for the postsynaptic neuron to spike at a much higher rate than the sparse spiking typically observed in cortex . The notion that excitation to cortical populations can be excessively strong has been posed in numerous studies and is typically resolved by accounting for local , lateral synaptic input that is net-inhibitory and partially cancels the strong , net-excitatory external synaptic input [2 , 6 , 20–23] . Balanced network theory takes this cancellation to its extreme by considering the limit of large external , feedforward synaptic input that is canceled by similarly large local , recurrent synaptic input . In this limit , a linear mean-field analysis determines population-averaged firing rates in terms of the macroscopic connectivity structure of the network [6 , 7] . To demonstrate these notions , we first simulated a recurrent network of NE = 4000 excitatory ( population E ) and NI = 1000 inhibitory spiking neurons ( population I ) receiving synaptic connections from an “external” population ( X ) of NX = 4000 excitatory neurons modeled as Poisson processes . Cortical circuits are often probed using optogenetic methods to stimulate or suppress targeted neuronal sub-populations [24 , 25] . As a simple model of optogenetic stimulation of cortical pyramidal neurons , we added an extra inward current to all neurons in population E halfway through the simulation ( Fig 1a ) . Neurons in the local population ( E and I ) were modeled using the adaptive exponential integrate-and-fire ( AdEx ) model , which accurately captures the responses of real cortical neurons [26–28] . Connectivity was random with each neuron receiving 800 synaptic inputs on average and postsynaptic potential amplitudes between 0 . 19 and 1 . 0 mV in amplitude . The recurrent network produced asynchronous , irregular spiking ( Fig 1b ) , similar to that observed in cortical recordings [11 , 21 , 29 , 30] . Firing rates in populations E and I were similar in magnitude to those in population X and were increased by optogenetic stimulation ( Fig 1c ) . As predicted by balanced network theory , local synaptic input ( from E and I combined ) was net-inhibitory and approximately canceled the external input from population X and artificial stimulation combined ( Fig 1d ) . We next show that a more realistic model of optogenetic stimulation breaks the classical balanced state , providing a demonstrative and experimentally relevant example of imbalanced amplification and suppression that explains phenomena observed in recordings from mouse somatosensory cortex . So far we considered networks with discrete subpopulations . Connectivity in many cortical circuits depends on continuous quantities like distance in physical or tuning space . To understand how the amplification and suppression mechanisms discussed above extend to such connectivity structures , we next considered a model of a visual cortical circuit . We arranged 2 × 105 AdEx model neurons ( 80% excitatory and 20% inhibitory ) on a square domain , modeling a patch of L2/3 in mouse primary visual cortex ( V1 ) . Neurons received external input from a similarly arranged layer of 1 . 6 × 105 Poisson-spiking neurons , modeling a parallel patch of L4 ( Fig 5a ) . We additionally assigned a random orientation preference to each neuron , modeling the “salt-and-pepper” distribution of orientation preferences in mouse V1 . Connectivity was probabilistic and , as in cortex [40–42] , inter- and intralaminar connections were more numerous between nearby and similarly tuned neurons . Specifically , connection probability decayed like a Gaussian as a function of distance in physical and orientation space ( Fig 5b ) , where distance in both spaces was measured using periodic boundaries , i . e . wrapped Gaussians were used in place of regular Gaussians .
We described a theory of amplification in cortical circuits arising from a local imbalance that occurs when recurrent connectivity structure cannot cancel feedforward input . We showed that this imbalanced amplification is evoked by optogenetic stimuli in somatosensory cortex and sensory stimuli in visual cortex , since these stimuli cannot be canceled by the connectivity structure in those areas . Our theoretical analysis of imbalanced amplification explains several observations from cortical recordings in those areas . Even though firing rates in balanced networks in the large N limit do not depend on neurons’ f-I curves ( see Eq ( 3 ) ) , quantifying firing rates under imbalanced amplification relies on a finite size correction that requires an assumption on how firing rates depend on neurons’ input . For simplicity , we used an approximation that assumes populations’ mean firing rates depend linearly on their average input currents , giving rise to Eqs ( 4 ) and ( 17 ) . In reality , neurons’ firing rates depend nonlinearly on their mean input currents , and also depend on higher moments of their input currents . However , the salient effects of imbalanced amplification are not sensitive to our assumption of linearity . For instance , Eq ( 5 ) , which quantifies the strong synaptic currents evoked under imbalanced amplification , does not depend on any assumption about neurons’ f-I curves . However , the precise value of the firing rates elicited by this strong input does depend on neurons’ f-I curves . We found that the linear approximation to f-I curves in Eqs ( 4 ) and ( 17 ) performed well at approximating firing rates in our spiking network simulations and also explained several observations from cortical recordings . This may be partly explained due to the fact that our spiking network simulations used neuron models that exhibit spike frequency adaptation , which is known to linearize f-I curves [50 , 51] and help networks maintain balance [10] . However , the linear approximation we used cannot explain some phenomena that rely on thresholding and other nonlinear transfer properties [47 , 52] . The notion of imbalanced amplification extends naturally to models with nonlinear transfer functions and future work will consider the implications of nonlinearities . Balanced networks are related to , but distinct from , inhibitory stabilized networks ( ISNs ) [2 , 47 , 53] and stabilized supralinear networks that can transition between ISN and non-ISN regimes [47] . The primary distinction is that ISNs are defined by moderately strong recurrent excitation ( strong E → E ) whereas balanced networks are defined by very strong external , feedforward excitation ( strong X → E ) canceled by similarly strong net-inhibitory recurrent connectivity . Classical balanced networks are necessarily inhibitory stabilized at sufficiently large N ( small ϵ ) unless wEE = 0 . However , strongly coupled ( approximately balanced ) networks can be non-ISN at moderately large N ( small ϵ ) if wEE is small . Cat V1 is believed to be inhibitory stabilized , which can be used to explain its surround suppression dynamic [2] . However , evidence from optogenetic and electrophysiological studies , suggests that mouse L2/3 V1 might not be inhibitory stabilized: Lateral connection probability is small between pyramidal neurons ( small wEE ) [49] , stimulation of PV neurons does not produce the paradoxical effects that characterize ISNs [54] , and modulating pyramidal neuron firing rates only weakly modulates excitatory synaptic currents in local pyramidal neurons [48 , 54] . Nonetheless , pyramidal neurons and PV neurons in mouse V1 exhibit surround suppression [48] , which we showed is explained by imbalanced amplification . Despite the similarity in their names , the mechanism of imbalanced amplification studied here is fundamentally different from the mechanism of balanced amplification [55] . First , imbalanced amplification is related to steady-state firing rates , while balanced amplification is a dynamical phenomenon . Moreover , balanced amplification is intrinsic to the local , recurrent circuit: It produces large firing rate transients when local , recurrent inhibition is inefficient at canceling local , recurrent excitation . Imbalanced amplification , on the other hand , produces large steady state firing rates when local , recurrent input is unable to effectively cancel feedforward , external excitation . The analysis of our spatially extended network model relied on an assumption of periodic boundaries in space , which are not biologically realistic , but approximate networks with more realistic boundary conditions [8] . Without periodic boundary conditions , the integral eqs ( 10 ) , ( 11 ) and ( 16 ) are equally valid , but the integrals are defined by regular convolutions in space instead of circular convolutions . As a result , the spatial Fourier modes do not de-couple , so Eqs ( 12 ) , ( 13 ) and ( 17 ) are no longer valid , though they should still offer a good approximation when connectivity is much narrower than the the spatial domain [8] . In addition , anisotropic connectivity statistics , arising for example from tuning dependent connectivity in visual cortical circuits with coherent orientation maps [56] , would prevent the integral operator in Eqs ( 10 ) , ( 11 ) and ( 16 ) from being a convolution operator , and therefore preclude the use of Fourier series for the solution . Future work will consider the effects of non-periodic boundaries and non-convolutional connectivity kernels on spatially extended balanced networks . We only considered neuron models with current-based synaptic input . In S2 Text , we show that our numerical results and analysis extend naturally to models with more realistic conductance-based synapses . The analysis makes use of an approximation that relates a conductance-based model to a current-based model with similar membrane potential statistics [57–59] . We focused on firing rates , but sensory coding also depends on variability and correlations in neurons’ spike trains . Our previous work derived the structure of correlated variability in heterogeneous and spatially extended balanced networks when connectivity structure prevents positive and negative correlations from cancelling , effectively providing an analogous theory of imbalanced amplification of correlated variability [12] . Combining those findings with the theory of steady-state firing rates presented here could yield a more complete theory of neural coding in cortical circuits and the effects of imbalanced amplification on coding .
We modeled recurrently connected networks with N neurons , composed of NE = 0 . 8N excitatory and NI = 0 . 2N inhibitory neurons . The recurrent network receives external input from a network of NX neurons that drive the recurrent network . The membrane potential of neuron j from the excitatory ( a = E ) or inhibitory ( a = I ) population has Adaptive Exponential integrate-and-fire dynamics , C m d V j a d t = - g L ( V - E L ) + g L Δ T exp [ ( V - V T ) / Δ T ] + I j a ( t ) - w τ w d w d t = - w . Whenever V j a ( t ) > V th , a spike is recorded , the membrane potential is held for a refractory period τref then reset to a fixed value Vre , and w is incremented by B . Neuron model parameters for all simulations were τm = Cm/gL = 15ms , EL = −72mV , VT = −60mV , Vth = −15mV , ΔT = 1 . 5mV , Vre = −72mV , τref = 1ms , τw = 150ms and B = Cm0 . 267 mV/ms . We write all currents in terms of Cm , so Cm can be any constant . Membrane potentials were also bounded below by Vlb = −100mV . Synaptic input currents were defined by I j a ( t ) = [ X j a ( t ) + R j a ( t ) ] C m ( 18 ) where X j a ( t ) is the feedforward input and R j a ( t ) the recurrent input to neuron j in population a = E , I . The recurrent input was defined by R j a ( t ) = ∑ b = E , I ∑ k = 1 N b J j k a b ∑ n η b ( t - t n b , k ) where t n b , k is the nth spike time of neuron k in population b = E , I . The external input to the recurrent network is defined similarly by X j a ( t ) = ∑ k = 1 N X J j k a X ∑ n η X ( t - t n X , k ) . ( 19 ) where t n X , k is the nth spike time of neuron k = 1 , … , NX in population X . Each coefficient , J j k a b , represents the synaptic weight from presynaptic neuron k in population b to postsynaptic neuron j in population a . For all simulations , we modeled synaptic kinetics using ηb ( t ) = exp ( −t/τb ) /τb for t > 0 where τE = 8ms , τI = 4ms , and τX = 10ms . Note that the integral of ηb ( t ) over time is equal to 1 for all three kernels , so the choice of time constant , τb , does not effect time-averaged synaptic currents . We used τI < τE < τX to prevent excessive synchronous events that break the balanced state . While inhibition may be faster than excitation in many cortical circuits , excitatory neurons are more likely to contact distal dendrites and inhibitory neurons are more likely to contact the soma [60 , 61] , which could make inhibition functionally faster than excitation . In any case , using fast inhibition is common practice in spiking network simulations with strong or dense connectivity [11 , 12 , 47 , 62 , 63] and a complete resolution of this issue is outside the scope of this study . In Figs 1 , 2 and 3 an extra term , S = 2 mV/ms , was added to X j E ( t ) for stimulated neurons during the second half of the simulation to model optogenetic stimulation . We used NE = 4000 , NI = 1000 and NX = 4000 ( so N = 5000 ) except for Fig 1f where all Nb values were scaled . Connections were drawn randomly with connection probabilities pEE = pIE = pIX = 0 . 1 , pEI = pII = pEX = 0 . 2 . Specifically , for each neuron in presynaptic population b = E , I , X , we sampled pab Na postsynaptic targets from population a = E , I randomly and uniformly with replacement . Since outgoing connections were sampled with replacement , some neurons connected multiple times to other neurons . Synaptic weights were then defined by J j k a b = ( # of contacts ) × J a b where JEE = 0 . 4mV , JIE = 0 . 83 mV , JII = JEI = −1 . 67 mV , JEX = JIX = 0 . 47 mV . This gives postsynaptic potential amplitudes between 0 . 19 and 1 . 0 mV . For Figs 1f and 4 , the values of Jab and the values of pab were each multiplied by ( 5000/N ) 1/4 so that they were unchanged at N = 5000 and so that ϵ ∼ 1 / N . This is slightly different from the more common practice of fixing small connection probabilities and scaling Jab like 1 / N . We instead fixed a relatively dense connectivity at N = 5000 and the network became increasingly sparse and weakly connected at increased N . Both approaches have the same mean-field ( since the mean-field only depends on the product of pab and Jab ) , but our approach prevents excessively small synaptic weights at large N and prevents dense connectivity at large N , which is computationally expensive and susceptible to oscillatory and synchronous spiking . Spike times in the external population were modeled as independent Poisson processes with rX = 5 Hz . In Fig 3 , external input to the L5 population was created using the spike times of excitatory neurons from the simulations in Fig 2 . Simulations for Fig 4 were identical to those in Figs 2 and 3 except there were N = 2 × 104 neurons in the L2/3 model , synaptic weights to neurons in that population were multiplied by 1 / 2 , and connections probabilities were also multiplied by 1 / 2 . Hence , in relation to Fig 2 , N was increased by a factor of four and ϵ was halved . Simulations for Fig 5 used algorithms adapted from previous work [12] . The recurrent network ( L2/3 ) contained N = 2 × 105 AdEx model neurons , NE = 1 . 6 × 105 of which were excitatory and NI = 4 × 104 inhibitory . Excitatory and inhibitory neurons in L2/3 were arranged on a uniform grid covering the unit square [0 , 1] × [0 , 1] ( arbitrary spatial units ) . The external population ( L4 ) contained NX = 1 . 6 × 105 neurons arranged on an identical , parallel square . Each neuron in each population was assigned a preferred orientation chosen randomly and uniformly from 0 to 180° . Connections were chosen randomly as above , but connection probabilities depended on the neurons’ distances in physical and orientation tuning space . Specifically , the connection probability from a neuron in population b = E , I , X at coordinates x = ( x1 , x2 ) to a neuron in population a = E , I at coordinates y = ( y1 , y2 ) was p a b ( x - y , d θ ) = p ¯ a b G ( x - y ; α b ) g ( d θ / 180 ° ; α b , θ ) where dθ is the difference between neurons’ preferred orientation , g ( u ; α ) = 1 2 π α ∑ k = - ∞ ∞ e - u 2 / ( 2 α 2 ) is a one-dimensional wrapped Gaussian and G ( u; α ) = g ( u1; α ) g ( u2; α ) is a two dimensional wrapped Gaussian . The connection probability averaged over all distances is p ¯ a b , which were chosen to be the same as in previous figures , p ¯ E E = p ¯ I E = p ¯ I X = 0 . 1 and p ¯ E I = p ¯ I I = p ¯ E X = 0 . 2 . As above , outgoing connections were chosen with replacement , so some neurons made multiple contacts onto other neurons . Connection widths in physical space were αE = 0 . 15 and αI = αX = 0 . 04 ( as measured on the unit square ) . Connection widths in orientation space were αE , θ = αE , θ = 0 . 1 and αX , θ = 0 . 125 ( corresponding to widths of 18° and 22 . 5° when measured in degrees ) . Connection strengths , Jab , were the same as in Figs 1 , 2 and 3 except multiplied by a factor of 1 . 2 . Each neuron in L4 was modeled as a Poisson process with rate given by r X ( x , θ ) = r ¯ X r X , x ( x ) r X , θ ( θ ) where x is the location of the neuron , θ is its preferred orientation , r X , x ( x ) = c + ( 1 - c ) G ( x - x 0 ; σ X ) and r X , θ ( θ ) = c θ + ( 1 - c θ ) g ( [ θ - θ 0 ] / 180 ° ; σ X , θ ) . This models a stimulus with orientation θ0 = 0 . 5 ( representing 90° ) and centered at spatial coordinates x0 = ( 0 . 5 , 0 . 5 ) . The parameters σX and σX , θ quantify the width of L4 firing rates in physical and orientation space . For all panels in Fig 5 , we used σX , θ = 0 . 1 ( width 18° ) and cθ = 0 . 75 . We used σX = 0 . 2 for Fig 5d–5i and σX = 0 . 06 for Fig 5j–5o . In both cases , we chose r ¯ X and c so that the minimum and maximum of rX , x ( x ) were 10 and 20 Hz respectively . For the spatially extended network , the connectivity kernels , W and W X , are defined in Results where wab ( x , θ ) = JabNbpab ( x , θ ) / ( JEXpEXNX ) . The Fourier series in physical and orientation tuning space is defined by u ˜ ( n , k ) = ∫ ∫ u ( x , θ ) e - 2 π i ( x · n + k θ ) d x d θ where the triple integral is over the two dimensions of physical space and one dimensional orientation space . The Fourier series of the convolution kernels defined above turns convolution into multiplication in the Fourier domain , from which Eq ( 10 ) gives I ˜ = ( 1 / ϵ ) [ W ˜ r ˜ + X ˜ ] where X ˜ , W ˜ , and W ˜ X are defined in Results with w ˜ a b ( n , k ) = w ¯ a b exp [ - 2 π 2 ( | n | 2 α b 2 + k 2 α b , θ 2 ) ] , w ¯ a b = w ˜ a b ( 0 , 0 ) = J a b p a b N b / ( J E X p E X N X ) , and ∥ n ∥ 2 = n 1 2 + n 2 2 . Using the linear approximation , r = gI then gives Eq ( 17 ) . Firing rates for dashed curves in Fig 5 and all firing rates in Figs 6 and 7 were obtained by first computing Eq ( 17 ) , then inverting the Fourier transform numerically using an inverse fast Fourier transform . Solid curves in Fig 5 were computed similarly , except using Eq ( 13 ) in place of Eq ( 17 ) . All simulations and numerical computations were performed on a MacBook Pro running OS X 10 . 9 . 5 with a 2 . 3 GHz Intel Core i7 processor . All simulations were written in a combination of C and Matlab ( Matlab R 2015b , MathWorks ) . The differential equations defining the neuron model were solved using a forward Euler method with time step 0 . 1 ms .
|
Understanding how the brain represents and processes stimuli requires a quantitative understanding of how signals propagate through networks of neurons . Developing such an understanding is made difficult by the dense interconnectivity of neurons , especially in the cerebral cortex . One approach to quantifying neural processing in the cortex is derived from observations that excitatory ( positive ) and inhibitory ( negative ) interactions between neurons tend to balance each other in many brain areas . This balance is achieved under a class of computational models called “balanced networks . ” However , previous approaches to the mathematical analysis of balanced network models is not possible under some biologically relevant connectivity structures . We show that , under these structures , balance between excitation and inhibition is necessarily broken and the resulting imbalance causes some stimulus features to be amplified . This “imbalanced amplification” of stimuli can explain several observations from recordings in mouse somatosensory and visual cortical circuits and provides fundamental insights into the relationship between connectivity structure and neural responses in cortical circuits .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"action",
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"medicine",
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"optogenetics",
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"electrophysiology",
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"neurophysiology"
] |
2018
|
Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits
|
A few studies investigated the relationship between toxoplasmosis and mental disorders , such as obsessive compulsive disorder ( OCD ) . However , the specific nature of the association between Toxoplasma gondii ( T . gondii ) infection and OCD is not yet clear . The aim of this study was to collect information on the relationship between OCD and toxoplasmosis and assess whether patients with toxoplasmosis are prone to OCD . For the purpose of this study , 6 major electronic databases and the Internet search engine Google Scholar were searched for the published articles up to July 30th , 2018 with no restriction of language . The inverse variance method and the random effect model were used to combine the data . The values of odds ratio ( OR ) were estimated at 95% confidence interval ( CI ) . A total of 9 case-control and 3 cross-sectional studies were included in our systematic review . However , 11 of these 12 articles were entered into the meta-analysis containing 9873 participants , out of whom 389 were with OCD ( 25 . 96% positive for toxoplasmosis ) and 9484 were without OCD ( 17 . 12% positive for toxoplasmosis ) . The estimation of the random effect model indicated a significant common OR of 1 . 96 [95% CI: 1 . 32–2 . 90] . This systematic review and meta-analysis revealed that toxoplasmosis could be as an associated factor for OCD ( OR = 1 . 96 ) . However , further prospective investigations are highly recommended to illuminate the underlying pathophysiological mechanisms of T . gondii infection in OCD and to better investigate the relationship between OCD and T . gondii infection .
The T . gondii is a neurotropic apicomplexan protozoan that infects one-third of the world’s human population by affecting some tissues , including brain , eyes , and testes in warm-blooded mammals [1] . Infection with this parasite is due to the consumption of raw or undercooked meat containing tissue cysts or consumption of food or drinking water contaminated with oocysts shed by cats . Moreover , organ transplantation , blood transfusion , and vertical transmission during pregnancy from mother to fetus are other causes of T . gondii transmission [2] . The T . gondii infection is generally asymptomatic in immunocompetent individuals . However , immunocompromised patients may experience severe clinical complications , such as chorioretinitis , encephalitis , and pneumonitis . Toxoplasmosis also leads to psychotic symptoms and changes in the personality of individuals [3] . The T . gondii has a specific tropism for brain tissue , where tachyzoites can invade to microglia , astrocytes , and neurons and create cysts in these cells . The considerable production of neurotransmitters , such as dopamine by T . gondii , induces the increased production of bradyzoites and destruction of cyst walls that may be responsible for behavioral changes [4 , 5] . Recently published systematic review and meta-analysis studies have examined the relationship between T . gondii infection and various psychiatric disorders; such as bipolar disorder [3 , 6] , schizophrenia [6 , 7] , epilepsy [8] , and depression [6 , 9] . The results of these studies showed that toxoplasmosis is an associated factor for bipolar disorder , schizophrenia , epilepsy , but not for depression . The OCD is a common , chronic , and debilitating psychiatric condition that affects about 3% of the general population [10 , 11] . This disorder is identified by unwanted and recurrent thoughts , which cause marked distress . Individuals with OCD are struggling to reduce their anxiety by mental acts and repetitive behaviors [12] . According to the World Health Organization , OCD is one of the top ten disorders which affect people’s income and quality of life although it has the least effect [13] . Some of the available data indicate the possibility of an association between toxoplasmosis and OCD [14 , 15] although there are some contradictory results [16] . Therefore , the main purpose of this systematic review and meta-analysis was to evaluate the relationship between T . gondii and OCD .
This study was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis ( PRISMA ) guidelines [17] . The protocol was registered in the PROSPERO with the registration number of CRD42018106354 [18] . To identify the published studies on the association between toxoplasmosis and OCD , the researchers performed a systematic search in 6 databases , namely PubMed , Scopus , ScienceDirect , Web of Science , EMBASE , ProQuest , and the Internet search engine Google Scholar . This systematic review was conducted through gathering the articles published up to July 30th , 2018 with no restriction of language . The search process was accomplished using the following keywords “Toxoplasma” OR “toxoplasmosis” AND “Obsessive-Compulsive Disorder” OR “OCD” . The inclusion criteria included: ( 1 ) studies published until July 30th , 2018 , ( 2 ) case-control and cross-sectional studies about the relationship between toxoplasmosis and OCD , ( 3 ) original research papers , ( 4 ) studies with available full texts , and ( 5 ) studies with information on the exact total sample size and positive samples in the case and control groups . The exclusion criteria were: ( 1 ) studies with no exact information about the sample size in the case and control groups , ( 2 ) review articles , and ( 3 ) non-human studies . All the retrieved articles from the search strategy were imported to EndNote ( version X7 ) . After the removal of duplicated papers , the titles and abstracts were independently reviewed by two researchers . In the next step , eligible articles were selected for full-text download ( Fig 1 ) . Data from relevant studies were extracted into a Microsoft Excel datasheet . The extracted variables included the name of first author , year of publication , location of the study , diagnostic method , OR , number of seropositive cases and control , as well as the age and gender of the participants in the case and control groups . The researchers of the current study were very careful about extracting the correct information . In this regard , the authors of the three selected articles were contacted for more detailed information [19–21] . Two researchers independently assessed the quality of the included papers using standard strengthening the Reporting of Observational Studies in Epidemiology checklist ( STROBE ) . This scale includes 22 items that are related to the title , abstract , introduction , methods , results , and discussion sections of the articles . This checklist included items assessing objectives , different components of the methodology ( e . g . , study design , study size , study population , bias , statistical methods ) , key results , limitations , generalizability , and funding of the studies . The assigned scores were within the range of 0–44 . Based on the STROBE checklist assessment , articles were categorized into 3 groups ( low quality: less than 15 . 5 , moderate quality: 15 . 5–29 . 5 , and high quality: 30 . 0–44 . 0 ) . The S1 Checklist indicates the quality of the included studies [22] . The data entered into Microsoft Excel were exported to Stata version 14 ( Stata Corp , College Station , TX , USA ) for the analysis [23] . The common OR were estimated using inverse variance and random-effects model for each included study . Furthermore , the heterogeneity index was determined using Cochran’s Q and I squared statistics . I squared values less than 25% , 25–50% , and greater than 50% were defined as low , moderate , and high heterogeneity , respectively [23] . The publication bias was examined by the Egger test . A sensitivity analysis was performed using Stata version 14 ( Stata Corp , College Station , TX , USA ) to identify the possible effect of each study on the overall results by removing each study .
Out of 2500 identified articles , 392 articles were excluded due to the duplication , and 2056 articles were also eliminated on the basis of their titles and abstracts . After reading the full text of the articles , 12 papers were included in our systematic review [14–16 , 19–21 , 24–29] . Eventually , 11 of these 12 articles [14–16 , 19–21 , 24 , 25 , 27–29] were entered into this meta-analysis with respect to the inclusion/exclusion criteria ( Fig 1 ) . One of the papers was excluded due to the lack of detailed information about the number of patients with OCD [26] . Information and characteristics about the investigated publications are presented in Table 1 and Table 2 . Studies were published from 2006 to 2018 . Accordingly , 9 out of the 12 studies had a case-control design , and 3 of them were cross-sectional studies ( Table 1 ) . One of the articles was not analyzed due to the unclear data about the exact number of patients with OCD [26] . The total number of participants involved in the 11 included studies in the meta-analysis was 9873 , including 389 OCD patients and 9484 controls . Studies were conducted in Turkey [14 , 16 , 25] , Czech Republic [15 , 19 , 21] , China [27 , 29] , USA [20] , Mexico [28] , Saudi Arabia [24] , and Iran [26] . Anti-Toxoplasma antibodies ( IgG and IgM ) were determined using enzyme-linked immunosorbent assay [14–16 , 19 , 20 , 24–29] , indirect immunofluorescence assay [14] , complement fixation test [15 , 19] , and enzyme immunoassays [26] . One of the studies did not address the method through which Toxoplasma is diagnosed [21] . Meta-analysis results showed that the OR of the chance of toxoplasmosis in OCD patients compared to control groups was 1 . 96 ( 95% CI: 1 . 32–2 . 90 ) ( Fig 2 ) . The test of heterogeneity showed a moderate heterogeneity among the studies included in the meta-analysis ( chi2 = 15 . 37 , P = 0 . 119 , I2 = 34 . 9% ) . Publication bias was assessed by Egger’s test and the results showed no publication bias ( P = 0 . 540 ) . Sensitivity analysis using the “one study removed at a time” technique demonstrated that the impact of each study on meta-analysis was not significant on the overall estimates ( Fig 3 ) .
One of the limitations of the included studies in the present research was that the individuals were invited to participate in some of these studies through snowball sampling technique using Facebook , fliers , and electronic media [15 , 19 , 21] . In this regard , the researcher ( s ) posted a Facebook announcement to invite people to take part in diverse psychological , ethological , and psychopathological experiments . However , the samples recruited in the mentioned studies cannot be representative of the general population since all people do not have access to Facebook . Moreover , the provided information were not based on the medical records; therefore , there were possibilities of wrong or at least obsolete data . To clarify , some patients may be infected with Toxoplasma after being tested for the presence of anti-Toxoplasma antibodies using serological methods . This could result in positively biased incidence rates of particular disorders . Accordingly , the obtained results cannot be generalized to the whole population . In one of these studies , the questionnaire contained many questions related to sexual behaviors and sexual preferences [21] . As a result , the participants were composed of those who were interested in these topics . Another limitation was that some studies were conducted only on children and adolescents , which made it difficult to generalize the findings to the society as a whole [25 , 29] . There were also , some limitations in our research , including ( 1 ) few numbers of studies that investigated the relationship between T . gondii infection and OCD , ( 2 ) small sample size in the included studies , ( 3 ) reports with various quality , ( 4 ) available studies with no sufficient information on disease status/severity , ( 5 ) lack of the published articles in many parts of the world regarding the seroprevalence of toxoplasmosis among patients with OCD , ( 6 ) lack of the evaluation of various associated factors , such as familial history and Rh phenotype . Based on the currently available data , T . gondii infection was more frequent in OCD patients than the control group . The results of this study were indicative of a probability of positive association between the prevalence rate of toxoplasmosis and OCD . However , many questions remained to be answered in future studies . Therefore , further research should be performed to evaluate the reduction rate regarding the prevalence of OCD following the treatment of toxoplasmosis and the recognition of the physiopathological mechanisms involved in T . gondii infection in OCD . Also , it is highly desirable to obtain empirical data from other parts of the world .
|
Toxoplasma gondii ( T . gondii ) is an obligate neurotropic parasite that infected about 25–30% of the total human population in the developed and developing countries . The obsessive compulsive disorder ( OCD ) is a psychiatric disease that affects the income and quality of life . Some studies confirmed an association between infectious agents as the associated or protective factors specifying the development of psychiatry diseases . Among various pathogens associated with psychological disorders , most of the attention is on T . gondii , which has a life-long asymptomatic latent phase after a short acute stage in healthy individuals . The detrimental effect of T . gondii on immunocompromised people and pregnant women is an important concern for public health . The correlation between toxoplasmosis and OCD is still relatively understudied with a paucity of documented findings . The previous meta-analysis reviewed only two studies and reported a 3 . 4-fold greater chance of OCD . The results of our study presented stronger evidence of a positive relationship between toxoplasmosis and OCD . Eventually , our research team hopes to present an overview of what is known and encourage more intensive research to determine the real impact of this parasite on the occurrence of OCD that may contribute to the prevention of OCD worldwide .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"neuropsychiatric",
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"statistics",
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] |
2019
|
Relationship between toxoplasmosis and obsessive compulsive disorder: A systematic review and meta-analysis
|
Maximum growth rate per individual ( r ) and carrying capacity ( K ) are key life-history traits that together characterize the density-dependent population growth and therefore are crucial parameters of many ecological and evolutionary theories such as r/K selection . Although r and K are generally thought to correlate inversely , both r/K tradeoffs and trade-ups have been observed . Nonetheless , neither the conditions under which each of these relationships occur nor the causes of these relationships are fully understood . Here , we address these questions using yeast as a model system . We estimated r and K using the growth curves of over 7 , 000 yeast recombinants in nine environments and found that the r–K correlation among genotypes changes from 0 . 53 to −0 . 52 with the rise of environment quality , measured by the mean r of all genotypes in the environment . We respectively mapped quantitative trait loci ( QTLs ) for r and K in each environment . Many QTLs simultaneously influence r and K , but the directions of their effects are environment dependent such that QTLs tend to show concordant effects on the two traits in poor environments but antagonistic effects in rich environments . We propose that these contrasting trends are generated by the relative impacts of two factors—the tradeoff between the speed and efficiency of ATP production and the energetic cost of cell maintenance relative to reproduction—and demonstrate an agreement between model predictions and empirical observations . These results reveal and explain the complex environment dependency of the r–K relationship , which bears on many ecological and evolutionary phenomena and has biomedical implications .
Density-dependent population growth is commonly described by a logistic curve with two parameters: r and K . The carrying capacity K is the maximum population size that can be supported by the available resource in a local environment , whereas the maximum growth rate r is the number of individuals produced per individual per unit time when the population size is much smaller than K . Evolutionary biologists typically treat r as a measure of fitness , whereas ecologists often regard K as a fitness proxy [1] . Because of such biological importance of r and K , their relationship has been studied for over half a century , most often in the context of r/K tradeoffs and r/K selection [1] . Specifically , it has been argued that in fluctuating environments , population sizes are usually much lower than K , so increasing K has little effect on population growth; selection is thus focused on r as a means to expanding the population . Under this condition , organisms are said to be under r selection to become r strategists , which are characterized by a relatively high fecundity but low probability of surviving to adulthood , along with other traits such as small body size , early maturity onset , short generation time , and the ability to disperse offspring widely . By contrast , when the environment is more or less stable or predictable , populations often approach the carrying capacity , making raising r irrelevant; hence , selection is centered on K to increase the population size . Under this condition , organisms are said to be subject to K selection to become K strategists , which are characterized by a relatively low fecundity but high survivorship , along with a large body size , long life expectancy , and the production of fewer offspring , which often require extensive parental care until they mature [2–4] . Comparing r-selected and K-selected organisms revealed an apparent r/K tradeoff , possibly because investing energy/resources in improving r compromises the investment in improving K and vice versa [4] , but it could also be because r-selected organisms have relatively unimpressive K and vice versa . The r/K selection and r/K tradeoff were once highly fashionable topics in ecology , but they lost popularity in the 1990s when empirical studies obtained more complex results than theoretical predictions [5] . Nonetheless , the essence of r/K selection was later blended into other life-history models [6] . Studying r/K selection and r/K tradeoff with evolutionary ecology approaches can be difficult because ( i ) the mechanistic basis of the tradeoff is unclear , ( ii ) the initial environment where the relevant traits evolved is usually unknown , ( iii ) the natural environment is hard to manipulate , and ( iv ) the number of replicates/species is insufficient most of the time [5] . The topic of r/K tradeoffs was , however , revived in microbial studies in the last decade [7–11] . Although these studies have the benefits of manipulated environments and sufficient replicates , they are small in terms of genotype and environment numbers , and the r/K tradeoff is not consistently observed across experiments [7–11] . For example , a recent study reported positive correlations between r and K in bacteria and fungi across environments [11] . However , it is generally unknown under what conditions r/K tradeoffs and trade-ups , respectively , are expected . Related to this question is a lack of clear understanding of the mechanistic basis of various r–K relationships . The compromise between ATP production rate ( i . e . , number of ATPs generated per unit time ) and efficiency ( i . e . , number of ATPs generated per unit resource ) is commonly used to explain the r/K tradeoff [12–14] , but this cannot be the whole story because it cannot explain the r/K trade-up . Given the long history of studying the r–K relationship , it is surprising that this relationship at the mutational level is rarely researched [11] . In fact , Charlesworth showed almost 30 years ago that pure phenotypic correlations among life-history variables are unlikely to provide useful information on tradeoffs because selection and environmental effects may generate positive correlations between traits even when they have negative underlying correlations , and he suggested that studying genetic correlations can help understand evolutionarily relevant tradeoffs and predict evolutionary responses to new selective pressures [15] . In this study , we take advantage of a recently released dataset of >7 , 000 yeast genotypes with known genome sequences and growth curves under multiple environments to address the following suite of questions . First , do mutations simultaneously influence r and K ? Second , when a mutation simultaneously influences r and K , are the effects concordant or antagonistic ? Third , are the answers to the above two questions influenced by the environment , and how ? Fourth , what is the mechanistic basis of the potentially varying r–K relationship ? We report that the pleiotropic effects of mutations on r and K tend to be concordant under poor environments but antagonistic under favorable environments and demonstrate that these general trends are explainable by the relative impacts of two factors: the tradeoff between the speed and efficiency of ATP production and the energetic cost of cell maintenance relative to reproduction .
Illingworth and colleagues sequenced the genomes of 85 MATa and 86 MATα haploid Saccharomyces cerevisiae strains derived from a 12th-generation two-parent intercross pool constructed from a North American strain and a West African strain that diverged from each other at 0 . 53% of genomic nucleotide positions [16] . Hallin and colleagues then mated each of the MATa strains with each of the MATα strains to obtain 7 , 310 diploids with known genotypes [17] . They grew these strains in nine different solid media ( S1 Table ) with four replicates and measured the cell number in each replicate by colony scan-o-matic [18] from 0 and 72 h of growth at 20 min intervals . We first developed a method to simultaneously estimate the maximum growth rate r ( number of cells produced per cell per h ) and the carrying capacity K ( number of cells ) by fitting growth data to logistic curves ( see Materials and methods ) . For each genotype under each environment , we used this method to estimate r and K for each replicate ( see Fig 1A for an example ) and then averaged among replicates that pass our quality standard ( S1 Data ) ; we similarly averaged the coefficient of determination ( Rg2; the subscript g refers to growth ) of the fitted logistic curve among qualified replicates ( see Materials and methods ) . We found that yeast growths tightly follow logistic curves . Across the nine environments , the median Rg2 among genotypes is in the range of 0 . 979–1 . 000 ( Fig 1B ) . Except for one environment ( phleomycin ) , at least 75% of genotypes have Rg2 > 0 . 98 ( Fig 1B ) . Under each environment , we measured Spearman's rank correlation ( ρrK ) between the estimated r and K among all genotypes . In six of the nine environments , ρrK is significantly negative ( all P < 10−11 ) , revealing r/K tradeoffs ( S1 Fig ) . But in the other three environments ( NaCl , caffeine , and galactose ) , ρrK is significantly positive ( all P < 10−166 ) , showing r/K trade-ups ( S1 Fig ) . For example , ρrK = −0 . 52 in the allantoin medium ( Fig 2A ) but 0 . 32 in the caffeine medium ( Fig 2B ) . Because the same genotypes were used in all environments , the above results indicate that the environment affects the relationship between r and K . To exclude the possibility that these results are caused by biased estimations of r and K , we conducted a computer simulation in which the specified r and K are uncorrelated . The simulated data resembled the empirical data in all other aspects such as numbers of replicates , genotypes , environments , time points measured during growth , the range of r , the range of K , and Rg2 ( see Materials and methods ) . The simulated data were analyzed as the actual data , but in none of the environments did we find ρrK to differ significantly from 0 . Furthermore , the estimated r and K are sufficiently accurate when compared with the values specified in the simulation ( see Materials and methods ) . We also confirmed by computer simulation that growth need not reach saturation for reliable estimations of r and K ( see Materials and methods ) . These results support that the observed varying ρrK across environments is genuine . The type of stress does not seem to determine whether ρrK is positive or negative because the three environments with a positive ρrK belong to three different types of stress ( S1 Table ) . To investigate what environmental factors impact the sign and magnitude of ρrK , we considered environment quality Q , which is the mean r of all genotypes in the environment [19] . We found that Q and ρrK are strongly negatively correlated ( ρ = −0 . 88 , P = 3 . 1 × 10−3; Fig 2C ) , suggesting that reducing the environment quality turns r/K tradeoffs into trade-ups . As a comparison , we also calculated the mean K of all genotypes in an environment but found it uncorrelated with ρrK ( ρ = 0 . 18 , P = 0 . 64; Fig 2D ) . This is probably because the total amount of carbon and nitrogen provided varies among the media ( S1 Table ) , making the mean K not directly comparable among the nine environments . To understand the genetic basis of the r/K tradeoffs and trade-ups , we respectively mapped quantitative trait loci ( QTLs ) for r ( rQTLs ) and K ( KQTLs ) in each environment and identified 93–96 QTLs per trait per environment ( see Materials and methods ) . Through a series of steps that maximize the difference between the total phenotypic variance of a trait explained by QTLs and that explained by the same number of random single-nucleotide polymorphisms ( SNPs ) , we retained the 36 most significant QTLs per trait in each environment ( S2 Data ) for further analysis ( see Materials and methods ) . In each environment , the 36 top rQTLs together explain 65%–81% of the total variance of r ( Fig 3A ) as well as 21%–60% of the total variance of K in the same environment ( Fig 3B ) . Similarly , the 36 top KQTLs together explain 53%–77% of the total variance of K ( Fig 3B ) as well as 27%–66% of the total variance of r in the same environment ( Fig 3A ) . That rQTLs partially explain the K variance and vice versa has two possible explanations . First , some rQTLs and KQTLs share the same underlying causal mutations . In other words , some mutations are pleiotropic , affecting both r and K . Second , rQTLs and KQTLs have distinct causal mutations and are independently distributed in the genome , but rQTLs explain the K variance and vice versa owing to the linkage disequilibrium between rQTLs and KQTLs in the mapping population , which could still exist after 12 generations of crosses . Under this explanation , 36 randomly picked SNPs should explain the r variance as much as the 36 KQTLs do . But what we found is that in each of the nine environments , the 36 KQTLs explain the r variance much better than the 36 randomly chosen SNPs do ( P < 0 . 001 based on 1 , 000 random samplings of 36 SNPs ) ( Fig 3A ) . The same is true when comparing rQTLs and random SNPs in explaining the K variance ( Fig 3B ) . These observations refute the second potential explanation , suggesting that some rQTLs and KQTLs share causal mutations , which is supported by the recent finding that altering the ribosomal RNA gene copy number in Escherichia coli simultaneously alters r and K [11] . That the sign of ρrK turns from positive into negative as the environment quality Q rises ( Fig 2C ) and that r and K share underlying genetic components ( Fig 3A and 3B ) predict that the fraction of QTLs with antagonistic effects on r and K rises with Q . To confirm this prediction , in each environment , we estimated the effects of each rQTL on r and K by regression ( see Materials and methods ) . If the two effects are of the same direction , the QTL has concordant effects; otherwise , it has antagonistic effects . In seven of the nine environments , most rQTLs show antagonistic effects; in one other environment ( the NaCl medium ) , most rQTLs show concordant effects . In the remaining environment ( the caffeine medium ) , equal numbers of rQTLs show concordant and antagonistic effects . The fraction of antagonistic rQTLs indeed rises with Q ( ρ = 0 . 94 , P = 4 . 9 × 10−4; Fig 3C ) . We similarly analyzed the effects of KQTLs on r and K in each environment . In seven of the nine environments , most KQTLs exhibit antagonistic effects . The opposite is true in the remaining two environments ( the NaCl and caffeine media ) . Again , the fraction of antagonistic KQTLs rises with Q ( ρ = 0 . 74 , P = 0 . 018; Fig 3C ) . Not unexpectedly , neither the fraction of antagonistic rQTLs nor the fraction of antagonistic KQTLs in an environment correlates significantly with the mean K of all genotypes in the environment ( P > 0 . 5 in both cases ) . The above results strongly suggest that the phenotypic effects of a given QTL on r and K may be antagonistic in one environment but concordant in another . In other words , the environment modulates the type of pleiotropy of the QTL , which we refer to as pleiotropy by environment interaction , a form of genotype by environment interaction [20] . To our knowledge , QTL pleiotropy by environment interaction has not been reported beyond one case in plants [21] . To explore this phenomenon in our data , we examined each 3 kb genomic segment—which harbors 1 . 5 genes and 3 . 0 mapping SNPs on average—and counted the number of times that an rQTL or KQTL identified in an environment resides in this segment . This treatment is necessary because ( i ) the causal genetic variant of a QTL cannot be traced to the nucleotide resolution despite much of the linkage in the original parental strains being broken in 12 generations of crosses and ( ii ) each mapping SNP may represent multiple SNPs that are in complete linkage disequilibrium ( see Materials and methods ) . We considered only the 36 top rQTLs and 36 top KQTLs per environment . We referred to a segment as an enriched segment if four or more QTLs were found in the segment among the nine environments . A total of 21 enriched segments were detected . By contrast , our simulation showed that only 0 . 83 segments are expected to have ≥4 QTLs if all 36 × 2 × 9 = 648 QTLs are randomly distributed in the yeast genome . Among the 21 segments , 18 harbor at least one rQTL and at least one KQTL . Because one segment is expected to have only 0 . 144 QTLs if all QTLs have distinct causal mutations , the ≥4 QTLs in each of these 18 segments likely have the same causal mutation . Because the causal mutation is unknown , an SNP representing the causal mutation was chosen ( see Materials and methods ) , and its effects on r and K in each environment were estimated . Fig 4 shows the effects of these 18 representative SNPs on r and K in each of the nine environments , and they clearly demonstrate pleiotropy by environment interactions . For example , SNP #66 has significant concordant effects on r and K in the NaCl and galactose media but significant antagonistic effects in the rapamycin , allantoin , and isoleucine media ( Fig 4A ) . The prevailing explanation of the r/K tradeoff is the compromise between ATP production rate and efficiency , which states that increasing the rate of ATP production per unit time improves the growth rate but reduces the efficiency of resource utilization by lowering the total amount of ATP produced , causing K to decrease [12–14] . This model , however , cannot explain why lowering Q turns r/K tradeoffs into trade-ups , as observed in our study . One deficiency of the model is the implicit assumption that the amount of ATP used per generation is independent of the growth rate . Population growth requires energy for producing new cells as well as energy for maintaining existing cells . While the per-generation cost for the former is probably independent of the growth rate , the cost for the latter should be proportional to the generation time T , which equals ln2/rN , where rN ( ≤ r ) is the growth rate when the population size is N . Indeed , as early as 50 years ago , Prit showed in multiple organisms that the extra substrates ( glucose or glycerol ) needed to produce the same amount of dry weight increases linearly with the inverse of the growth rate [22] . Hence , it is possible that when r is low , increasing r raises K because the per-generation cell-maintenance cost is reduced in spite of a lowered efficiency in resource utilization [23] . Below , we examine this model quantitatively . Let a be the per-cell maintenance cost of energy per h . Hence , the per-cell maintenance cost per generation is aT = aln2/rN , where T is the generation time in h . Let b be the energy cost to produce a new cell . Thus , the total energy cost per cell per generation is aln2/rN + b , and the corresponding cost per cell per h is a + brN/ln2 . The above result indicates that as rN increases , the energy used and produced per h , or ATP production rate , must increase . The tradeoff between ATP production rate and efficiency dictates that the efficiency of resource usage , f ( rN ) , must then decline . Hence , f ( rN ) , which is between 0 and 1 , is a decreasing function of rN . Let the amount of resource usage per cell per generation be CN when the population size is N . Following a recent study [24] , we have CNf ( rN ) =aln2/rN+b . ( 1 ) Let us now consider the situation of N << K , under which rN = r and CN = C . So , Eq 1 can be written as C= ( aln2/r+b ) /f ( r ) . ( 2 ) It is difficult to derive an analytical formula relating r and K from Eqs 1 or 2 because the exact form of f ( rN ) is unknown and because CN changes with population growth as a result of changes of rN and f ( rN ) . Nevertheless , when the total amount of resource is fixed , the larger the C or CN , the fewer generations the population can grow for , and hence , the smaller the K . Taking derivatives on both sides of Eq 2 , we get dCdr=−aln2f ( r ) r2−f′ ( r ) ( aln2r+b ) [f ( r ) ]2=[−aln2f ( r ) ]+[−f′ ( r ) ( arln2+br2 ) ][f ( r ) ]2r2 . ( 3 ) On the right-hand side of Eq 3 , the denominator is positive , the first term of the numerator is negative , and the second term of the numerator is positive . Hence , dC/dr may be positive or negative , depending on the values of a , b , and r and the function f ( r ) . Now let us consider the scenario when r approaches 0 . Given that f ( r ) will approach 1 , f ′ ( r ) cannot be infinity . Hence , the second term of the numerator in the right-hand side of Eq 3 approaches 0 , while the first term of the numerator remains considerably negative . Consequently , dC/dr < 0 , meaning that C decreases with r , which results in r/K trade-ups . Let us turn to the scenario when r is very large . Now , the first term of the numerator is negligible relative to the second term , leading to dC/dr > 0 and r/K tradeoffs . In other words , regardless of a , b , and f ( r ) , the model creates r/K trade-ups at very low r and tradeoffs at very high r . To analyze the behavior of the model further , especially when r is not too small nor too large , we assume that f ( r ) = 1 − ( r/rMAX ) w , where rMAX is the maximum possible r of any genotype in any environment and w > 0 . Based on the finding that the cost of maintenance per h is about 1% of the cost of reproduction in yeast [24] , we assume a = 0 . 01 and b = 1 . We drew the numerical relationship between C and r when rMAX = 0 . 5 and w = 3 ( S2 Fig ) . One can see that C declines as r increases to an intermediate value ( approximately 0 . 13 ) and then rises as r further increases . Hence , K should rise and then decline as r increases , creating r/K trade-ups when r is small but tradeoffs when r is large . The above finding is made without specifying how r is altered . Hence , it applies when r is altered by an environmental shift , a mutation , or a combination of the two , as long as the parameters of the model stay more or less unchanged and K is measured under a fixed amount of resource when different environments are compared . Because Q is defined by the average r across all genotypes , it follows that an overall r/K trade-up among genotypes is observed in low-Q environments , while an overall tradeoff is observed in high-Q environments ( Fig 2C ) . Lipson proposed verbally that when maintenance cost is considered , r/K trade-ups should be observed in slow-growth environments and tradeoffs should be observed in fast-growth environments [23] . His proposal is supported by observations from our model and empirical data . Furthermore , our model predicts that , under a given environment , ρrK for a subgroup of genotypes could be positive or negative depending on the range of r for this subgroup of genotypes . In other words , it is possible to observe a positive ρrK for a subgroup of low-r genotypes and a negative ρrK for another subgroup of high-r genotypes in the same environment , provided that the range of r among genotypes in the environment is large enough . In addition , our model predicts that the critical r value at which r/K tradeoffs turn into r/K trade-ups should be more or less the same in different environments if a , b , and f ( r ) are similar among different environments . To verify these predictions , in each environment , we divided all genotypes into bins of 500 genotypes based on their r values in the environment . We then computed the mean K and mean r of each bin . In each environment , we identified the bin with the highest mean K and then averaged the mean r of this bin across the nine environments , which arrived at rtp = 0 . 1076 ( the subscript "tp" stands for turning point; see black vertical line in Fig 5 ) . We found that in most but not all environments , K tends to increase with r when r < rtp but decrease with r when r > rtp , even when the r range spans rtp in an environment ( Fig 5 ) . Thus , the r/K tradeoffs and trade-ups can simultaneously appear in one environment , and the turning point between tradeoffs to trade-ups is similar among the nine environments . Admittedly , there may be other models that could explain the r–K trade-up . For example , a mutation that renders some strains more efficient in using a nutrient than other strains can result in an r–K trade-up . But this hypothesis cannot explain why the r–K trade-up turns into tradeoff when Q increases because the ability to better use a nutrient could occur in both high- and low-Q environments . By contrast , the maintenance cost coupled with the conflict between the speed and efficiency of ATP production can explain trade-ups , tradeoffs , and the turn from trade-ups into tradeoffs when Q rises . Although we cannot prove that our model is the only possibility , it appears to be the simplest and probably the most general explanation .
Using the growth data of over 7 , 000 yeast strains in nine environments , we conducted the largest-ever investigation of the relationship between r and K . We showed an overall r/K tradeoff in high-quality environments but an overall r/K trade-up in low-quality environments , where the quality of an environment is measured by the average maximal growth rate ( r ) of all genotypes in the environment . By mapping rQTLs and KQTLs , we found that at least some mutations simultaneously influence r and K . Interestingly , the effects of the same mutation on these two traits can be concordant in one environment but antagonistic in another . In general , concordant mutational effects on r and K are more common in low-quality environments , while the opposite is true in high-quality environments . Finally , we proposed a model involving a compromise between the speed and efficiency of ATP production and the relative costs of cell maintenance and division that satisfactorily explains our observations . Our model predicts that r/K tradeoffs and trade-ups can even coexist in a single environment in different ranges of r values , which is subsequently confirmed by the empirical data . Warringer and colleagues measured the growth rate and efficiency of 39 S . cerevisiae strains , 39 S . paradoxus strains , and a few strains from other yeasts in a large number of liquid media [25] . But they reported rate-efficiency tradeoffs across all strains examined in only two media . Nevertheless , we found that more media in their data show tradeoffs if only intraspecific variations are considered . For instance , we found rate-efficiency tradeoffs in 12 of the 196 media among S . cerevisiae strains and in 39 of the 196 media among S . paradoxus strains . Interestingly , the rate-efficiency correlation turns from positive into negative values as the average growth rate in a medium increases ( Spearman’s ρ = −0 . 199 , P = 0 . 005 in S . cerevisiae; ρ = −0 . 130 , P = 0 . 070 in S . paradoxus; S3 Fig ) . Thus , our primary finding appears to hold in liquid media as well . Recently , Reding-Roman and colleagues observed both r/K tradeoffs and trade-ups when examining microbial growths of multiple genotypes in multiple media that differ in the glucose concentration [11] . However , their findings are distinct from ours in that they observed a maximal r when K is intermediate , while we observed a maximal K when r is intermediate . Furthermore , their explanatory model is based on the Monod function [26] , which neglects the cell-maintenance cost [22] . Because their observation was based on a relatively small number of genotypes and their environments varied in the concentration of only one component ( glucose ) , the generality of their findings is unclear . At any rate , their observations differ from ours and their model cannot explain our observations . While the classic r/K-selection theory predicts that selecting for r leads to a reduction in K and vice versa , our findings paint a more complex picture . In a constant environment , adaptation will likely improve r and K concordantly if the initial r is low . But when r reaches a certain level , further adaptation will cause antagonistic changes of r and K . Whether r or K will further increase while the other trait will decrease depends on which of the two traits is the main target of selection . These predictions can be tested using Lenski's long-term experimental evolution of 12 populations of E . coli in a constant low-glucose medium . Novak and colleagues examined the relationship between r and K in the first 20 , 000 generations of evolution of these E . coli populations [7] . Their results are broadly consistent with our predictions . For instance , they reported that both r and K increased quickly in the first 2 , 000 generations , after which r continued to improve slowly , but K stopped rising and even declined in some populations . Apparently , r is the main target of selection in this case . While Novak and colleagues reported no clear correlation between r and K among the 12 populations at the end of 20 , 000 generations , their Fig 3 showed a positive r–K correlation among populations with relatively low r values and a negative correlation among populations with relatively high r values [7] . Similarly , previous mixed reports of r/K tradeoffs and trade-ups [7–10] are actually expected rather than surprising . Hence , considering the varying intrinsic relationship between r and K is critical to predicting how r and K respond to natural selection and largely explains why Pianka’s r/K-selection–based prediction of life-history traits [4] does not always work [5] . Our findings may also have implications in medicine . For instance , our results suggest that , in applying antibiotics to control microbial infection , it is important to apply a sufficiently high dose such that r is below the turning point rtp . Only in this range will reducing r also lower K; otherwise , reducing r will increase K . The same principle may apply in the treatment of cancer , which is intimately related to the growth of the tumor cell population [27 , 28] . Nevertheless , we caution that , because our discovery is made in a unicellular organism , its generality , especially among multicellular organisms , awaits future exploration . We identified a number of QTLs with concordant effects on r and K in one environment but antagonistic effects in another . Such pleiotropy by environment interaction means that a mutation that cannot be fixed in one environment because of antagonistic effects on two traits may be easily fixed in another environment when its effects become concordant and hence has evolutionary implications . But how common pleiotropy by environment interaction is remains unknown , although both pleiotropy [29] and genotype by environment interaction [20 , 30 , 31] appear prevalent . Our findings illustrate the necessity and power of discerning the relationship between phenotypic traits at the mutational level for understanding the cause of their positive or negative correlation among individuals , populations , or species . With the rapid progress in genomic technology and high-throughput phenotyping , this approach promises to offer deeper and broader insights into phenotypic variation and evolution .
We acquired from Hallin and colleagues the unsmoothed growth data of 7 , 310 diploids produced from all pairwise crosses between 85 MATa and 86 MATα haploid strains of S . cerevisiae [17] . The haploids were randomly drawn from a 12th-generation two-parent intercross pool derived from a North American wild strain and a West African wild strain [17] . The colony size for each diploid genotype was measured and cell number inferred at 217 time points from 0 to 72 h at 20 min intervals with four replicates by scan-o-matic , a high-resolution automatic microbial growth phenotyping approach [18] . The four replicates were initiated from different precultures and run in different instruments and plate positions in the scanner to minimize bias . Because the cell number estimation is based on colony scan , the estimated K reflects the total volume of the cell population and is robust to cell size . The diploids were grown in nine different solid agar media , which are synthetic complete media with additional stressors or alternative carbon or nitrogen source ( allantoin , caffeine , galactose , glycine , hydroxyurea , isoleucine , NaCl , phleomycin , and rapamycin ) ( S1 Table ) . Because the genomes of all 171 haploids were sequenced [16] , all 7 , 310 diploids have known genome sequences [17] . Note that the original experiment contained 86 MATa and 86 MATα haploids , but all crosses involving one MATa strain were contaminated and removed . There are two potential biases in measuring growth from Hallin and colleagues' experiment [17] . First , the growth of a colony could be affected by its neighbors on the plate; this is referred to as the positional effect . Second , some regions on the plate may have systematically higher or lower growths because of differential lighting and evaporation of water; this is referred to as the spatial effect . Hallin and colleagues used grid reference correction [17] because the grid reference was shown to be useful in correcting the spatial effect in the original development of scan-o-matic by Zackrisson and colleagues [18] . Nevertheless , there is one distinction between Hallin and colleagues' data [17] and Zackrisson and colleagues' data [18] that could make the helpful correction in Zackrisson and colleagues' work detrimental in Hallin and colleagues' study . Specifically , the grid reference correction was verified in a plate of 1 , 536 colonies of the same genotype [18]; there was no positional effect on this plate because all positions had the same neighbors . In Hallin and colleagues' experiment , 384 controls of the same genotype were placed on each plate . A control colony in Hallin and colleagues was potentially subject to both the spatial effect and positional effect because different controls no longer shared the same neighbors . If a control colony grew rapidly because its neighbors grew slowly and were outcompeted by the control , this rapid growth was due to the positional effect . If one attempts to correct it by the grid reference , one is mistakenly assuming that the rapid growth is due to the spatial effect , and the correction introduces a bias , making the corrected neighboring genotypes’ growth rates even lower than the true values . Therefore , performing the grid reference correction can bias the estimation of genetic effects for the sake of correcting nongenetic effects . In addition , it is possible that r and K are differentially influenced by neighbors because r is determined mostly by earlier sections of a growth curve when competition among neighbors are not strong , while K , a feature determined mostly by later sections of a growth curve , is more likely influenced by neighbors . However , because each genotype had four replicates at different plate positions in the scanner , the spatial effect is mostly randomized and uncorrelated with the genotype . There is therefore little need to correct for the spatial effect . This said , we performed the normalization as in Hallin and colleagues and confirmed that our primary finding that r–K trade-ups turn into tradeoffs when Q rises still holds ( Spearman’s ρ = −0 . 75 , P = 0 . 026 ) . Similarly , because different strains were placed randomly on plates , the positional effect on each strain is random so is not expected to create general trends as discovered in our analysis . Indeed , as mentioned in the Discussion , the above turn from tradeoffs into trade-ups is also present for yeast growth in liquid media , which has no spatial or positional effect . The logistic equation was used to describe density-dependent population growth [32] , and it was popularized by Raymond Pearl and Lowell Reed when they substituted r and K into the Verhulst model [33] . As early as 1913 , the logistic growth of yeast was demonstrated by Carlson [34] . Our estimation of r and K from growth data is based on the following logistic equation . dNdt=rN ( 1−NK ) ( 4 ) Integrating Eq 4 leads to N=K1+ ( KN0−1 ) e−rt , ( 5 ) where N0 is the initial population size and t is the growth time . The r estimated here is also known as r0 in the literature and is the maximum cell growth rate . It should not be confused with the maximum population growth rate , often written as rmax and estimated from the mid-log phase of a growth curve . We first estimated r and K for each replicate of each genotype in each environment by fitting Eq 5 to the data of cell number N and time t using the NonLinearModel . fit function in Matlab . We then removed low-quality replicates in the following manner . We assumed that r and K estimates that are far from the nearest neighbors are outliers and set cutoffs based on the fold difference between outliers and medians . Because K has a wider range than r , different cutoffs for r and K were used . In practice , we removed all replicates whose estimated r is larger than 200% or smaller than 50% of the median r from all r estimates from all genotypes in the same environment . We similarly removed all replicates whose estimated K is larger than 400% or smaller than 25% of the median K estimate from all genotypes in the same environment . The majority of removed replicates were extreme outliers , with r or K estimates being negative or hundreds of times bigger than nonoutliers . Changing the lower r cutoff to 33% , higher r cutoff to 300% , lower K cutoff to 20% , and higher K cutoff to 500% impacts <1% of the number of retained replicates . After the quality control , in each environment , 93 . 2%–100% of genotypes have at least three retained replicates . The r and K estimates of a genotype in an environment are the average values of all remaining replicates . For each remaining replicate , we computed the fraction of variance in the growth data explained by the logistic regression ( Rg2 ) and then computed the average Rg2 across the remaining replicates . We found no correlation between mean Rg2 across genotypes in an environment and the mean r or K of all genotypes in the environment . We calculated the standard error ( SE ) of the r and K estimates from replicates . The median SE of r among all genotypes varies from 0 . 0034 to 0 . 013 in the nine environments , while the median SE of K among all genotypes varies from 1 . 2 × 105 to 2 . 6 × 105 in the nine environments . The median SE of r ( or K ) is uncorrelated with mean r ( or K ) among environments . We also calculated the standard deviations of r and K among genotypes under each environment to be used in simulations ( see below ) . To exclude the possibility that the observed correlation between r and K is an artifact of our r and K estimation , we performed a computer simulation . We simulated the growth of 7 , 000 genotypes in nine environments to best mimic the real data . In each environment , the r and K of all genotypes used in the simulation followed normal distributions with the same means and standard deviations as estimated from the actual data . We then computed the cell number using the logistic curve from 0 to 72 h at 20 min intervals . We added a random noise to each computed cell number at each time point; the noise follows a normal distribution with mean = 0 and variance = median ( 1 − Rg2 ) in each environment × SST ( i . e . , the total sum of squares of cell numbers for each replicate ) . By doing so , our median fitted Rg2 from simulated data equals the empirical median Rg2 . Four independent replicate growth datasets were simulated per genotype per environment . Using the simulated data , we estimated r and K for each replicate of each genotype as in the estimation using the actual data . As expected , the r and K estimates from the simulated data have similar ranges as those from the actual data . In each simulated environment , 95 . 1%–99 . 9% of simulated genotypes have r and K estimated . Among them , 71 . 6%–74 . 6% of genotypes have estimated r and K that , respectively , deviate from the simulated value by <1% , and 93 . 0%–97 . 6% of the genotypes have estimated r and K that , respectively , deviate from the simulated value by <20% . Hence , our estimation of r and K is accurate under logistic growth . Of the nine simulated environments , none showed a significant correlation between r and K upon multiple-testing corrections . We also confirmed by computer simulation that growth need not reach saturation for reliable estimations of r and K . Specifically , we simulated growth using low r and high K to avoid saturation , resulting in a median last-hour growth rate that was 23 . 3% of the initial growth rate . Yet , estimates of r and K were generally accurate and unbiased . Before QTL mapping , we first coded the genotype at each SNP as 0 , 1 , or 2 if it was homozygous for the West African allele , heterozygous , or homozygous for the North American allele , respectively . We then filtered the SNPs that contain redundant information such that only the middle SNP is maintained when several neighboring SNPs are in complete linkage disequilibrium . This resulted in 13 , 350 remaining SNPs for QTL mapping . We mapped rQTLs and KQTLs in each environment following a recent QTL study [35] , using a false discovery rate ( FDR ) of 0 . 05 . Briefly , this approach performs multiple rounds of mapping . In each round , at most one most significant SNP in each chromosome will be mapped as a QTL , and the residuals from fitting all mapped QTLs from all previous rounds will be used for the next round of mapping . FDR is calculated by a permutation test . We stopped the mapping after six rounds , resulting in 93–96 QTLs per trait . We calculated the total phenotypic variance explained by all mapped QTLs ( R2 ) . We then removed the QTL that has the smallest effect on total R2 and recalculated the total R2 explained using all remaining QTLs . We repeated this process and removed small-effect QTLs one by one until we retained 48 , 36 , 24 , or 18 QTLs per trait . By doing so , we acquired equal numbers of rQTLs and KQTLs in each environment . We also calculated the total fraction of phenotypic variance explained ( R2SNPs ) by 96 , 48 , 36 , 24 , or 18 randomly picked SNPs , respectively . When we retained 48 QTLs , the averaged fraction of R2 explained for all traits is R2QTLs = 0 . 738 . This value reduced to 0 . 703 when we retained 36 QTLs . The averaged R2QTLs dropped quickly when fewer than 36 QTLs were considered . We found that the difference between R2QTLs and R2SNPs is maximized when 36 QTLs were compared with 36 random SNPs . Focusing on these 36 large-effect QTLs instead of 93–96 total QTLs per trait allowed us to study how environment affects mutational pleiotropy with increased confidence . We performed a linear regression using the genotypes of the 36 rQTLs in one environment to predict the r in that environment . The regression coefficient for each rQTL was used as a measure of the effect of this rQTL on r . Similarly , a regression using the 36 rQTLs to predict K in that environment gave the effect of each rQTL on K . The same method was used to estimate the effects of each KQTLs on r and on K . In addition to the r–K relationship , the rate–yield relationship is frequently discussed in the literature [7 , 36] , in which rate refers to the growth rate and yield refers to the dry weight produced per mole of substrate . The r–K relationship is equivalent to the rate–yield relationship when K is measured under a fixed amount of resource , because K = yield × amount of resource in moles .
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Two parameters are widely used to describe density-dependent population growth: the maximum growth rate per individual ( r ) and the maximum population size or carrying capacity ( K ) . The relationship between these parameters is the subject of many fundamental theories and debates in ecology and evolutionary biology . Interestingly , although only r/K tradeoffs are expected and explained thus far , r/K trade-ups have also been reported . The present study surveyed the relationship between r and K using the growth curves of over 7 , 000 yeast strains in nine environments , discovering that the r–K correlation changes from trade-ups to tradeoffs as the environment quality improves . Analysis of the genetic underpinnings of variations in r and K confirms that the same mutation tends to have concordant influences on r and K in poor environments but antagonistic influences in rich environments . It is proposed that the environment-dependent pleiotropic effects of mutations on r and K are a result of the tradeoff between the speed and efficiency of energy production and the energetic cost of cell maintenance relative to reproduction . The varied relation between r and K may have biomedical implications for the antibiotic control of microbial infections and the population growth of tumor cells .
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2019
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Environment-dependent pleiotropic effects of mutations on the maximum growth rate r and carrying capacity K of population growth
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The centrosome is a non–membrane-bound cellular compartment consisting of 2 centrioles surrounded by a protein coat termed the pericentriolar material ( PCM ) . Centrioles generally remain physically associated together ( a phenomenon called centrosome cohesion ) , yet how this occurs in the absence of a bounding lipid membrane is unclear . One model posits that pericentriolar fibres formed from rootletin protein directly link centrioles , yet little is known about the structure , biophysical properties , or assembly kinetics of such fibres . Here , I combine live-cell imaging of endogenously tagged rootletin with cell fusion and find previously unrecognised plasticity in centrosome cohesion . Rootletin forms large , diffusionally stable bifurcating fibres , which amass slowly on mature centrioles over many hours from anaphase . Nascent centrioles ( procentrioles ) , in contrast , do not form roots and must be licensed to do so through polo-like kinase 1 ( PLK1 ) activity . Transient separation of roots accompanies centriolar repositioning during the interphase , suggesting that centrioles organize as independent units , each containing discrete roots . Indeed , forced induction of duplicate centriole pairs allows independent reshuffling of individual centrioles between the pairs . Therefore collectively , these findings suggest that progressively nucleated polymers mediate the dynamic association of centrioles as either 1 or 2 interphase centrosomes , with implications for the understanding of how non–membrane-bound organelles self-organise .
The centrosome is a major microtubule organising centre , with critical roles in cell migration , division , shape maintenance , and cilia function . Animal interphase cells are generally thought to have 1 centrosome , consisting of 2 mature microtubule-based structures , called centrioles . Centriole pairs are proteomically and functionally distinct [1] yet apparently remain physically associated , a phenomenon called centrosome cohesion [2–7] . Experimental changes to intercentriolar distance during interphase result in defects in cell migration , ciliary function , and mitosis [8–12] , underscoring the functional importance of centrosome cohesion . How 2 centrioles coordinately assemble into a single centrosome yet maintain distinct functions is largely unexplored . Centrioles are not bounded by a lipid membrane but instead by 2 distinct structures , termed the pericentriolar material ( PCM ) and pericentriolar fibres [2 , 13] . Current models of PCM assembly emphasise high dynamics of constituent proteins , potentially as a liquid-like , toroidal structure [14 , 15] . In contrast , comparatively little is known about either the structure or assembly of pericentriolar fibres . Rootletin , or ciliary rootlet coiled-coil protein ( gene symbol CROCC ) , localises to pericentriolar filaments , and rootletin knockout or knockdown results in both loss of filaments and centrosome cohesion [2 , 8 , 16 , 17] . CNAP1 , a paralogous gene , interacts with rootletin , likely at centriole proximal ends [2 , 18] . One model posits that rootletin pericentriolar fibres directly connect centriole pairs to keep them spatially restricted [2 , 5 , 16 , 18] . Consistent with this proposal , rootletin is not found on mitotic centrosomes [5 , 18–20] . The kinetics of pericentriolar fibre dissolution , when they reform , and the principles governing their replication are poorly understood , however . To address these questions , this study uses fluorescence imaging , genome editing , and cell fusion to obtain unprecedented spatiotemporal information about the morphology , dynamics , and assembly properties of rootletin fibres , which are referred to as roots . Roots are bifurcating adhesive structures that are licensed to form on centrioles by polo-like kinase 1 ( PLK1 ) enzymatic activity . Both mother and daughter centrioles form independent roots that do not remain connected in response to organelle movement in vivo . Thus , they adopt a structure and function that allow centriole pairs to independently position during interphase , providing new insight into centrosome self-organisation .
Pericentriolar filaments near centrosomes have been described for many decades [21] , but their ubiquity in different cell types is unknown . The prevalence of rootletin fibres was systematically documented by immunofluorescent staining and enhanced confocal airyscan imaging in a range of human cell types , whether cancerous , immortalised , or primary . Thorough antibody validation , obtained by multiple independent lines of evidence , ensured specific recognition of rootletin ( S1 Fig and summarised in Materials and methods ) . Endogenous rootletin almost ubiquitously formed bifurcating fibres at the centrosome , henceforth referred to as roots ( Fig 1A ) . Costaining and segmentation of a range of markers of either centrioles or the PCM showed limited overlap with roots by either three-dimensional ( 3D ) structured illumination microscopy ( SIM ) super-resolved imaging ( Fig 1B ) or confocal airyscan ( S1E Fig ) , indicating that roots occupy a different locale , adjacent to the PCM and centrioles . Segmentation of both roots and centrioles , as marked by a stable green fluorescent protein–Centrin1 fusion ( GFP-Centrin1 ) , showed that roots are large relative to centrioles , at approximately 10-fold the size of a centriole on average in retinal pigment epithelium ( RPE ) cells ( Fig 1C ) . Roots were much shorter than ciliary rootlets—the prominent rootletin fibres found in specialised ciliated cell types , including photoreceptor cells—however ( [16 , 17]; S1F and S1G Fig ) . Centriole replication normally proceeds through the appearance of a nascent procentriole from the base of an existing centriole during S and G2 phase [22 , 23] . To examine whether procentriole formation influences root structure , centrosomes containing either 2 or 4 GFP-Centrin1 marked centrioles were classified , corresponding to either unreplicated centrioles or diplosomes , respectively . No difference in rootletin intensity or size was detected ( Fig 1D ) , suggesting that procentriole growth does not influence root structure . Procentrioles mature into centrioles during mitosis , dependent on PLK1 activity , becoming replication competent after physically moving away from a centriole ( a process termed disengagement ) [24] . Therefore , the effects of PLK1 kinase inhibition on root formation were investigated ( Fig 1E ) . Cells arrested in mitosis through PLK1 blockade contained monopolar spindles [25] , which were devoid of roots ( Fig 1F ) , consistent with previous work [5 , 16 , 18–20] . Because the inhibition of PLK1 results in cell cycle arrest , mitotic exit was forced into an ensuing interphase without cell division—by addition of the cyclin-dependent kinase 1 ( CDK1 ) inhibitor RO-3306 [26]—to understand subsequent effects on root structure in interphase . Control cells were also arrested in mitosis , but instead using the Eg5 kinesin motor inhibitor S-trityl-L-cysteine ( STLC ) followed by RO-3306 . Cells forced into interphase in this manner reformed roots despite unsuccessful mitotic genome segregation ( Fig 1F; right-hand panel ) . However , forced mitotic exit after PLK1 blockade resulted in partial root reformation relative to STLC control ( Fig 1G ) . These results suggest that centrioles are capable of root reformation in G1 regardless of PLK1 activity in the previous mitosis . In contrast , procentrioles must be modified by PLK1-dependent processes before they are competent to form roots in the next cell cycle . Furthermore , because PLK1 promotes centrosomal PCM expansion during mitosis [14] , mitotic centrosomes disassemble roots even in the absence of centrosome maturation . Taken together , roots are large bifurcating fibres , found commonly in a range of cell types on mature PLK1-modified centrioles during the interphase . The dynamics and biophysical properties of enhanced GFP ( eGFP ) -tagged rootletin were examined in living cells by utilising both cDNA transgene overexpression and tagging of endogenous alleles . Consistent with previous work [2 , 16] , overexpression of eGFP-rootletin resulted in fibres and bifurcating fork structures that were longer than endogenous rootletin ( e . g . , compare S2A Fig with Fig 1 ) . Time-lapse imaging of eGFP-rootletin fibre formation following transfection showed that eGFP-rootletin first appeared focally in a single location , prior to the emergence of a larger network over many hours , eventually filling the cytoplasm ( Fig 2A and S2A Fig ) . Fibres increased in size not only by extension in length outwards from a single point but additionally by the coalescence of multiple fibres to form larger aggregates , frequently through end-on fusions ( compare arrows in Fig 2A; S1 Video ) . Cell cycle–dependent changes in the centrosomal intensity of meGFP-tagged rootletin were followed by 3D confocal time-lapse imaging . Because overexpressed rootletin fibres were larger than endogenous antibody stained roots , and overexpression can influence quantitative measures of protein function in vivo [27] , CRISPR Cas9 was used to insert an in-frame fusion of meGFP into the endogenous rootletin ( CROCC ) locus and therefore study rootletin behaviour with live-cell microscopy at endogenous levels for the first time ( S3 Fig ) . Homozygous tagging in the diploid breast cancer cell line Cal51 resulted in fluorescent signal closely resembling antibody staining ( S3E Fig ) . Rootletin-meGFP was barely detectable at the centrosome during mitosis , consistent with immunofluorescent staining ( Fig 1 ) , and consequently , a stably coexpressed NEDD1-mRuby3 fluorescent fusion was used to mark the PCM and allow tracking of centrosomes throughout the cell cycle and independently of rootletin levels , in a spectrally distinct fluorescent channel ( Fig 2B; S2 Video ) . Additionally , fluorescently labelled chromatin was monitored to visualise mitotic substages . Rootletin began to be released from the centrosome >2 hours prior to anaphase ( Fig 2C ) . By anaphase , centrosomal rootletin could not be detected above cytoplasmic levels , suggesting disassembly of all centrosomal roots . Rootletin centrosomal levels increased from anaphase but , unexpectedly , continued to increase at a slow rate for approximately 9 hours and thus significantly into G1 phase . Staging of rootletin intensity relative to centrosome separation revealed that its release from the centrosome began prior to centrosome separation and continued after it , with low levels of rootletin still present during centrosome separation , which could be ripped apart during poleward centrosome migration ( Fig 2D ) . Because roots were disassembled in mitosis , it was investigated whether cytoplasmic mitotic rootletin levels were decreased , by western blotting of synchronised cells ( Fig 2E ) . Rootletin cytoplasmic levels were high in mitosis by western blot despite the absence of roots , indicating that cytoplasmic and centrosomal rootletin levels do not always correlate . Together , these results suggest that the removal of rootletin from centrosomes begins early in mitosis or in late G2 phase of the cell cycle , prior to both chromatin condensation and centrosome separation , and then continues during these processes . Rootletin assembly at the centrosome begins from anaphase and occurs slowly for approximately 9 hours into G1 phase . Previous work has implicated the centriole proximal factor CNAP1 in rootletin centrosomal localisation [2 , 12 , 18] , and in agreement , small interfering RNA ( siRNA ) -mediated knockdown of CNAP1 resulted in reduced centrosomal rootletin localisation ( S4A Fig ) . It was investigated whether ectopic CNAP1 plasma membrane localisation via a C-terminal CAAX domain fusion [28] was sufficient to induce root polymerisation outside of the centrosome ( S4 Fig ) . However , neither plasma membrane–localised mScarlet-CNAP1-CAAX , nor the CNAP1-binding partner CEP135 ( membrane localised as CEP135-mScarlet-CAAX [29] ) induced the formation of ectopic roots . Some PCM components show dynamic exchange of subunits on the seconds timescale—a property that is thought to be important for centrosome assembly [13] . Fluorescence recovery after photobleaching ( FRAP ) was therefore used to ask whether rootletin forms steady state polymers . FRAP of extended eGFP-rootletin fibres showed almost no movement of eGFP-rootletin over a time period of 10 minutes , however—even after a relatively rapid bleach ( S2B Fig; approximately 1 second ) . Lack of recovery was not due to image bleaching or fibre movement out of the field of view , because adjacent unbleached ends of the fibre remained unchanged . To investigate very slow dynamic exchange of endogenous centrosomal rootletin-meGFP , on the hours timescale , cells were arrested at the G1/S phase boundary of the cell cycle using thymidine , to circumvent the effects of cell cycle progression on root morphology ( Fig 2C ) . Total centrosomal rootletin-meGFP fluorescent signal was then bleached , and recovery followed by tracking of NEDD1-mRuby3 marked centrosomes during time-lapse imaging ( Fig 2F ) . Recovery of rootletin-meGFP fluorescence on this long timescale was limited to approximately 30% . Together , it can be surmised that eGFP-rootletin fibres are predominantly diffusionally stable structures that are progressively assembled slowly over hours following anaphase . How a single interphase cell coordinately organises 2 disengaged centrioles is unclear . The prevalence of centrosomal cohesion was systematically documented in a range of human tissue culture cell types by automated fluorescence imaging and analysis of centrosome position ( Fig 3A ) . Quantification of the percentage of cells with split centrosomes—defined as 2 PCM foci >1 . 5 μm apart—showed that it was low at approximately 10% , dependent on cell type ( see Materials and methods for further discussion of the definition of split centrosome ) . Thus , in most cell types , centrioles remain cohered in close proximity during interphase , consistent with previous work [6 , 30–34] . It was investigated whether the minority of split centrioles remain stably separated over time , perhaps due to a permanent failure of centrosome cohesion . However , single-cell 3D confocal live imaging of centriole pairs marked by GFP-Centrin1 showed transient splitting . Therefore , a single mother–daughter centriole pair would split into 2 and then rejoin , often repeatedly ( Fig 3B; S3 Video ) . Transient centriole splitting was manifest in live-cell imaging of several different cell types , including Cal51 , HeLa , RPE , and U2OS cells ( Fig 3B–3E; S4 Video and S5 Video ) . In agreement with a published report [31] , HeLa Kyoto cells had high levels of centrosome separation , with approximately 50% of cells showing split centrioles in a fixed asynchronous population ( Fig 3A ) . Because low levels of rootletin expression accompanied short roots in HeLa ( Fig 1A ) and previous work has shown that rootletin knockdown results in the loss of centrosome cohesion [2 , 35] , the effect of increasing root length by rootletin overexpression on centrosome position was investigated in HeLa cells . This increase in fibre length significantly increased centrosome cohesion in interphase HeLa cells , as measured by automated imaging and analysis of immunofluorescently stained samples ( Fig 3F , P < 0 . 001 , Fischer’s exact test ) . Together , these results show that , although mother and daughter centrioles generally remain cohered into a single focal location , they are able to transiently split apart in interphase in a manner that is antagonised by eGFP-rootletin overexpression . How might rootletin fibres respond to transient centriole splitting ? Two opposing models for root behaviour after centriole splitting were postulated ( Fig 3G ) . The first was maintenance of a stable root contact between centrioles as they move apart , for example , due to stretching . The second was loss of physical connection and disentanglement ( ‘Stable contact’ versus ‘Disentangle’ , respectively ) . Surprisingly and in contrast to cohered centrosomes , rootletin fibres were not detected between split centrioles ( Fig 3H , Fig 3I and S5 Fig ) . Instead , roots from each centriole were generally only detected as linked together at a distance of less than approximately 1 . 5 μm ( Fig 3J ) , thus supporting the disentanglement model . Simultaneous 2-colour airyscan microscopy of root disentanglement in living cells revealed that roots occupy markedly heterogenous orientations that change in response to in vivo centriole movement ( Fig 3K; S6 Video ) . The centrosome distal ends of roots have the capacity to pivot relative to centrosome proximal ends , suggesting a common more stable attachment point at the proximal end . Pivoting of centrosome distal tips was not just observed in centrosomes with split centrioles but also in cohered centrosomes , with roots maintained stably at the centriole–centriole interface ( Fig 3L; S7 Video ) . As centrosomes remerged after a split , roots did not necessarily join but could alternatively contact the PCM of the opposing centriole . Together , these observations indicate that although roots can be maintained stably at the interface between mother–daughter centrioles , their orientation is heterogeneous , and notably , in response to centriole movement , a continuous direct rootletin linkage is not detected . Because disengaged centrioles can transiently split ( Fig 3 ) , the comparative structure of roots and PCM on mother and daughter centrioles during splitting was investigated further . Root area was approximately halved in split versus cohered centrioles ( Fig 4A ) , suggestive of equal partitioning of 2 independent roots . Indeed , discrimination of the mother and daughter using CEP164 immunostaining showed that roots are nucleated symmetrically on both mother and daughter ( Fig 4B ) . A similar comparison of PCM structure with the PCM resident Pericentrin ( PCNT ) showed similarly that both mother and daughter centrioles individually nucleate PCM when split ( Fig 4C ) , something also evident in the live-cell imaging of NEDD1-mRuby3 ( Fig 3H–3K ) and previous work [24 , 32 , 34] . These observations imply that both mature centrioles independently maintain roots and PCM during centrosome splitting in interphase . Given the dynamic nature of centrosome cohesion ( Fig 3 ) and root disentanglement , it was of interest to investigate whether mother–daughter centriole pairs would be maintained in cells with 4 centrioles ( Fig 4D ) . Centriole position in cells forced into interphase after a failed mitosis by STLC treatment ( Fig 1E ) showed all possible centrosome cohesion configurations . Most commonly , all 4 centrioles grouped as 1 ( Fig 4E ) , but notably , other spatial arrangements were equally as likely as 2 pairs . Thus , 2 mother–daughter centriole pairs are not maintained separately but will cohere together , even in a grouping such as a single centriole and 3 cohered . Overexpression of eGFP-rootletin promoted centrosome cohesion in interphase cells with 4 mature centrioles created by sequential STLC/RO-3306 treatment ( S6A–S6C Fig ) , a similar effect to that seen in cells with normal centrosome numbers ( Fig 3F ) . Some cells with supernumerary centrosomes are able to cluster them to form a bipolar spindle during mitosis [36] . It was therefore investigated whether , in contrast to cells with 2 centrosomes ( Fig 2 ) , cells with supernumerary centrosomes retained centrosomal rootletin during mitosis . Cells clustering supernumerary centrosomes at spindle poles during mitosis did not contain roots , however ( S7 Fig ) , consistent with a model wherein rootletin does not promote mitotic centriole clustering . Centrosome cohesion was further examined using polyethylene glycol–mediated cell fusion of 2 different cell lines , 1 expressing endogenously tagged rootletin-meGFP and the other expressing endogenously tagged rootletin-mScarlet ( Fig 4F , S3F Fig and see Materials and methods for details of fusion ) . Fused cells contained centrosomes of 2 fluorescent colours , 1 from each different cell line , as well as 2 nuclei . Because rootletin shows very slow diffusional exchange ( Fig 2 ) , this allowed the origin of centriole pairs in fused cells to be distinguished based on the emitted fluorescence . As per after mitotic failure ( Fig 4E ) , mother–daughter centriole pairs were not exclusively maintained after cell fusion , but instead , fluorescent roots of different colours engaged each other ( Fig 4G and S6D Fig ) . Fusion of cell lines expressing rootletin-meGFP or NEDD1-mRuby3 similarly showed that fluorescent roots from 1 cell could embrace all 4 centrioles once fused ( S6E Fig ) . Therefore , by 2 independent methods , mother–daughter centriole pairs are not stably maintained in cells with 4 centrioles .
Cells must carefully regulate centrosome number and position , coordinating 2 centrioles that are capable of distinct functions [23 , 32 , 37] . The data here provoke an interesting hypothesis: that interphase cells always have 2 centrosomes that are generally held together by stable fibres that reach outward into the cytoplasm . Three key pieces of evidence are provided . Firstly , both mature interphase centrioles in a pair independently nucleate roots , as well as PCM . Secondly , these units—consisting of a centriole/root/PCM—have the capability to transiently spatially separate during interphase , accompanied by root disentanglement . Thirdly , cells engineered with 2 centriole pairs do not maintain them separately but instead dynamically make new groupings . Thus , there is remarkable pliability in the maintenance of centrosome cohesion , with individual centrioles able to rearrange between pairs , through dynamic splitting of roots . These conclusions are consistent with previous observations of split centrioles [6 , 30–34] . It is possible that centriole independence may aid plasticity of centrosome function such that the 2 centrioles can either act as one or separately . Thus , the data explain previous observations that centrioles may have either different or coordinated functions [23 , 32 , 37] . It cannot be totally excluded that fine rootletin fibres exist , below the detection limit of imaging , which thus keeps centrioles continuously linked . However , there is no evidence for this , either in the motion of roots in living cells or in fixed-cell analyses ( Fig 3 and Fig 4 ) , nor is it consistent with previous electron microscopy [2] . How non–membrane-bound organelles regulate their position , size , and number within the cellular interior is still not understood . Recent work has postulated that organelles such as centrosomes and P granules phase-separate as liquid-like compartments [38] . This model is characterised by high internal turnover of components parts , spherical shape , and the ability of multiple organelles to fuse [15] . In contrast , roots are diffusionally stable , remain separate through multiple cycles of merging and splitting , and are not spherical , instead potentially engendering polarity to the centrosome as a branched organelle . Therefore , roots have surprisingly different organisational principles in comparison to the PCM . Further work will be needed to understand whether this has implications for how centrosomal position is regulated . Rootletin loss in mice results in mechanical fragility in ciliated tissues such as photoreceptors , apparently due to the loss of ciliary rootlets [17] . Whether roots contribute to cellular mechanics , in either specialised cell types or nonciliated cycling cells through the maintenance of diffusionally stable contacts , will be an interesting future topic . In conclusion , root-mediated splitting of 2 centrosomes might allow plasticity of cytoskeletal function , thus explaining how 2 non–membrane-bound organelles coordinately function in either 1 or 2 locations during the interphase [30 , 34 , 39] . It is tempting to suggest that progressively nucleated , diffusionally stable polymers might also regulate the subcellular position and number of other organelles .
Multiple lines of evidence were obtained to indicate that a commercially available anti-rootletin antibody ( Novus Biologicals NBP1-80820 ) specifically recognises the product of the CROCC gene . siRNA depletion of rootletin ( CROCC ) using RNA interference removed signal by both immunofluorescence and western blot in multiple cell types ( S1A , S1B and S1D Fig ) . An antibody-independent method—GFP tagging—showed similar protein abundances to measurements made by immunofluorescence , both in time and space ( this is apparent throughout Figs 1–3 ) . For example , centrosomal rootletin signal was virtually undetectable in metaphase by either antibody or GFP tagging . Anti-rootletin antibody also stained eGFP-rootletin when overexpressed as a transgene ( S1C Fig ) , and ciliary rootlets in mouse photoreceptor cells ( S1F and S1G Fig ) . Cal51 ( German Collection of Microorganisms and Cell Cultures ACC303 ) , U2OS ( American Type Culture Collection ATCC HTB-96 ) , HeLa Kyoto , PANC-1 , and IMR-90 cell lines were grown in Dulbecco's modified Eagle's Medium ( DMEM ) supplemented with 10% fetal calf serum , Glutamax , and 100 μg/ml penicillin/streptomycin . hTERT RPE1 cells were cultured in DMEM/F12 with 10% Fetal Bovine Serum ( FBS ) , penicillin/streptomycin , and 4 . 2% sodium bicarbonate . h-TERT BJ-5ta ( ATCC CRL-4001 ) were grown in a 4:1 mixture of DMEM to M199 . h-TERT HPNE ( ATCC CRL-4023 ) were grown in a 3:1 mixture of DMEM to M3:BaseF medium , with 5% fetal calf serum , 10 ng/ml EGF , 2 mM glutamine , and 750 ng/ml puromycin . All tissue culture reagents were purchased from Sigma-Aldrich . DNA transfection was with lipofectamine 3000 ( Invitrogen ) according to the manufacturer's instructions . SiR-Hoechst ( Tebu Bio ) was incubated for 30 minutes at 200 nM before replacing with fresh medium for imaging . siRNA transfection was with RNAiMax transfection reagent ( ThermoFisher Scientific ) . siRNAs used against rootletin ( CROCC ) , CEP250/CNAP1 , and nontargeting were Dharmacon ON-TARGET plus SMARTpools . siRNA against ODF2 was silencer select from ThermoFisher Scientific ( #4427037 ) . NEDD1-mRuby3 contained 5 glycine residues as a linker between the gene and fluorescent protein and was expressed from the vector pcDNA 3 . 1 ( + ) . Plasma membrane targeting was with a C-terminal fusion of the CAAX motif of KRAS4b , consisting of the amino acid sequence KMSKDGKKKKKKSKTKCVIM . mScarlet-cNAP1-CAAX was constructed with HD In-fusion cloning ( Clontech ) , according to the manufacturer’s instructions . CEP135-mScarlet-CAAX was synthesised by GeneArt ( ThermoFisher Scientific ) . Five glycine residues were often , but not always , used as a linker between fusion proteins . A Zeiss Elyra S . 1 equipped with a 63x NA 1 . 4 lens was used to acquire 16-bit 3D SIM images with 3 rotations and 5 phases . Double-colour labelling was with various combinations of either Alexa 488 or ATTO 488 , and either Alexa 594 , Alexa 568 , or ATTO 565 , on cells seeded on high-precision 170-nm glass coverslips ( Ibidi μ-slide , 80827 ) . Reconstruction was using Zen Blue software , using automatic parameters . The median ( ± median absolute deviation ) lateral and axial resolution of the system using these settings was measured at 114 ± 4 nm and 352 ± 15 nm , respectively ( full-width at half-maximum ) . Channel alignment was performed in Zen Blue , using a double-colour bead calibration standard . Stable cell lines expressing cDNA constructs were produced by transfection followed by culture for at least 4 days , either with or without antibiotic selection , followed by fluorescence-activated cell sorting . CRISPR clones were produced essentially as described in [27] , with some modifications . Guide RNA was expressed from pSpCas9 ( BB ) -2A-GFP ( PX458 ) ( Addgene plasmid #48138 ) . Guide RNA sequences all overlapped the CROCC STOP codon and against the +ve strand were as follows ( 5'—3' ) : CCAGCAGGAGCTCATTTCTC , CCAGAGAAATGAGCTCCTGC , and CAGGAGCTCATTTCTCTGGG . Donor plasmids were constructed in the vector pUC19 by HD In-fusion cloning ( Clontech ) . They consisted of 800 base pair homology arms from the C-terminus of the CROCC genomic reference sequence , surrounding the meGFP or mScarlet coding sequence . Five glycine residues linked the gene and fluorescent protein . This insert was cloned into the BamH1 site of the vector pUC19 . Insertion of meGFP into the endogenous CROCC locus was detected by extraction of genomic DNA using QuickExtract DNA extraction solution ( epicentre ) according to the manufacturer’s instructions , followed by junction PCR with the following primers: forward: GGCTGGCCTTACCTTCCCTT; reverse: CTGGAAGGCCTGTCACTGTC . Tissue culture cells were fixed in 4% paraformaldehyde or ice-cold 100% methanol for 10 minutes , permeabilised in 0 . 1% Triton , and blocked in 3% bovine serum albumen ( ThermoFisher Scientific ) . Mouse photoreceptor cells were isolated from retina by gentle dissection before fixation and staining as for tissue culture cells . Antibodies used were as follows: rabbit anti-CROCC ( 1:250–1:750 , Novus Biologicals NBP1-80820 ) , mouse anti-NEDD1 ( 1:500 , Abcam ab57336 ) , rabbit anti-PCNT ( 1:1000; Abcam ab4448 ) , mouse anti-SAS6 ( 1:300 , Santa Cruz Biotechnology sc-81431 ) , mouse anti-CENPJ ( 1:100 , Santa Cruz Biotechnology sc-81432 ) , mouse anti-gamma Tubulin ( 1:1000 , ice-cold methanol; GTU-88 ) , mouse anti-CETN1 ( 1:4000 , EMD Millipore 20H5 ) , mouse anti-CEP164 ( 1:200 , Santa Cruz Biotechnology sc-515403 ) , rabbit anti-CEP350 ( 1:500 , Atlas Antibodies HPA030845 ) , rabbit anti-CNAP1 ( Proteintech , 14498-1-AP ) , rabbit anti-CDK5RAP2 ( Atlas Antibodies , HPA046529 ) , alpaca anti-GFP nanobody ( 1:400 , Chromotek gba-488 ) , and FluoTag-X2 anti-mScarlet ( 1:500 , NanoTag Biotechnologies , N1302-At565 ) . Images are presented as maximum-intensity projections from 3D data unless otherwise stated . Image brightness and contrast settings were changed linearly and consistently between samples for display purposes of representative images , but not for quantitation . The intensity of centrosomal rootletin-meGFP in cycling cells was determined by automated centrosome tracking after movie acquisition . Centrosomes were segmented and tracked using the Trackmate plugin in ImageJ/Fiji [40] , using LAP Tracker , and confirmed as successful by manual analysis of tracking . NEDD1-mRuby3 was tracked , a marker of the PCM that was present throughout the cell cycle . Individual cell tracks were aligned in time relative to anaphase , or the nearest frame to anaphase , based on both bright-field and SiR-hoechst fluorescent DNA labelling . Segmentation from fixed images was in Cell Profiler software , with data analysis in Knime software . For calculation of per-cell centriole splitting , nuclei were detected based on hoechst staining and cytoplasm by using a watershed algorithm outwards from nuclei based on gamma-tubulin staining . Mitotic cells were excluded based on hoechst staining . Centrosomes were detected with PCNT staining and defined as split if a cell contained 2 PCNT foci centroids >1 . 5 μm apart by Euclidean straight-line distance . A length of 1 . 5 μm was chosen as the definition of split centrioles because this distance was the threshold above which roots rarely linked centrioles in imaging ( Fig 3J ) , thus providing an unbiased definition of split centrioles . For segmentation of roots , various thresholding strategies were used in CellProfiler , including propagation outwards from a GFP-Centrin1 seed region , or direct thresholding . Spacing of PCM staining was measured by adaptive thresholding followed by calculation of 2D Euclidean distance between centroids . Roots were segmented using propagation from PCM and then manually classified as linked if 1 pixel overlap occurred between a root from each PCM . Segmentation of centriole and PCM markers in Fig 1B was in cell profiler using automatic threshold and declumping of adjacent objects based on shape . Antibodies used were rabbit anti-CROCC ( 1:250–1:750 overnight; Novus Biologicals , 80820 ) , rabbit anti-CROCC ( 1:250–1:750 overnight; Novus Biologicals , 80821 ) , and mouse monoclonal beta-Actin ( 1:10000 1 hour at room temperature; Sigma-Aldrich ) . Cells were lysed for 20 minutes on ice in RIPA buffer ( 50 mM Tris HCl , pH 8 , 150 mM NaCl , 1% NP40 , 0 . 5 M sodium deoxycholate , 0 . 1% SDS , complete protease inhibitor cocktail , PhosSTOP [Roche] ) . Protein concentration was quantitated using the bicinchoninic acid method ( Sigma-Aldrich ) . Whole-cell extracts were separated by electrophoresis on a 3% to 8% Tris-Acetate gel and transferred to PVDF membrane using the iBlot2 system ( ThermoFisher Scientific ) according to the manufacturer’s instructions . Membranes were blocked in 5% milk dissolved in 0 . 1% Tween/TBS . Cells were imaged without phenol red in either L15 CO2-indepdendent medium or in Fluorobrite Imaging medium with 5% CO2 at 37°C , in Ibidi u-slide 8-well dishes . Imaging was with a Carl Zeiss 880 airyscan , either in airyscan or standard confocal mode , using either a 63x NA 1 . 4 or 100x NA 1 . 4 oil immersion lens . Airyscan processing was performed with automatic settings in Zen Black . The median ( ± median absolute deviation ) lateral and axial resolution of the system was measured at 198 ± 7 . 5 nm and 913 ± 50 nm ( full-width at half-maximum ) , respectively . FRAP was performed essentially as described in [14] , bleaching using a 488 argon laser at 100% for the minimum time required to cause approximately 50% fluorescence loss ( keeping the same duration in all samples ) . Cells were fused using Hybri-Max 50% 1450 polyethylene glycol solution ( Sigma-Aldrich ) . Briefly , cells were trypsinised , resuspended in PBS , and mixed at a 1:1 ratio . After spinning , the PBS was aspirated , and PEG was added dropwise over 30 seconds to the cell pellet and left for an additional 3 . 5 minutes at room temperature . Serum-free medium was then added dropwise for 1 minute before 10-minute incubation at 37°C with normal medium , followed by exchange for fresh medium . Fused cells constituted approximately 1% of the population and consequently were enriched by fluorescence-activated cell sorting . The fluorescence intensity from endogenously tagged rootletin ( either meGFP or mScarlet ) was dim as detected by flow cytometry , and so cells were labelled with either CellTrace Violet or CellTrace Far Red dye ( ThermoFisher Scientific ) to enable efficient sorting . Labelling was for 1 minute at room temperature in PBS , at 500 nM or 20 nM for CellTrace Violet or CellTrace Far Red , respectively . Cells were FACS sorted by gating for either CellTrace Violet , CellTrace Far Red , or NEDD1-mRuby3 positivity relative to negative controls , directly into imaging dishes . The majority of these cells were aneuploid relative to the single-colour lines as expected . Cells with centrosomes marked by NEDD1-mRuby3 fluorescence contained up to 4 foci , due to turnover of this marker at the centrosome . Cells were arrested for 12 hours in either 200 nM BI2536 ( Sigma-Aldrich ) , 10 μM STLC , or 50 ng/ml Nocodazole . Only mitotically arrested cells were analysed further , by mitotic shake-off . Mitotic exit was forced with RO-3306 ( 10 μM ) for 6 hours , or cells were released from mitotic blockade using 2 washes in warm medium . Dihydrocytochalasin B ( DCB ) treatment was at 4 μM for 18 hours , followed by 3 washes in fresh medium . Cells were transfected with eGFP-rootletin for 24 hours before overnight arrest in STLC . Mitotic shake-off was performed into RO-3306 , allowing a 6 . 5-hour release . Imaging was by tile-scanning confocal z-stacks . Transfected cells were identified in CellProfiler through segmentation of eGFP-rootletin filaments by global Robust Background threshold . Centrosome cohesion was measured by segmentation of PCNT foci without declumping , thus grouping cohered centrosomes as 1 focus . Split centrosomes were identified in this case as cells with 2 , 3 , or 4 PCNT foci by Robust Threshold segmentation . Cells either without any detected centrosomes or with greater than 4 foci constituted around 10% of cells , and these were discarded from further analysis .
|
Many subcellular organelles are not enclosed by a lipid membrane but instead exist freely in the cellular interior . How the number and position of such non–membrane-bound organelles are maintained is a general unanswered question . Here , I study this problem by focusing on the centrosome—an organelle that nucleates microtubules in fundamental cellular processes . Despite much work characterising the properties of centrosomes , little is known about centrosomal fibres , called roots , which have been suggested to maintain a single centrosome . Using a combination of genome editing , cell fusion , and live-cell imaging , I show that roots are diffusionally stable polymers that assemble slowly over many hours and are able to disentangle as centrosomes transiently split apart into 2 separate units held by the fibres . I propose a model of centrosome number and position regulation using stable fibres , which project outwards into the cytoplasm to form an interface with the surrounding cellular milieu . This model could help our understanding of how organelle position and number are maintained in cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"centrosomes",
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] |
2018
|
Stable centrosomal roots disentangle to allow interphase centriole independence
|
HIV-1 infection begins with the binding of trimeric viral envelope glycoproteins ( Env ) to CD4 and a co-receptor on target T-cells . Understanding how these ligands influence the structure of Env is of fundamental interest for HIV vaccine development . Using cryo-electron microscopy , we describe the contrasting structural outcomes of trimeric Env binding to soluble CD4 , to the broadly neutralizing , CD4-binding site antibodies VRC01 , VRC03 and b12 , or to the monoclonal antibody 17b , a co-receptor mimic . Binding of trimeric HIV-1 BaL Env to either soluble CD4 or 17b alone , is sufficient to trigger formation of the open quaternary conformation of Env . In contrast , VRC01 locks Env in the closed state , while b12 binding requires a partial opening in the quaternary structure of trimeric Env . Our results show that , despite general similarities in regions of the HIV-1 gp120 polypeptide that contact CD4 , VRC01 , VRC03 and b12 , there are important differences in quaternary structures of the complexes these ligands form on native trimeric Env , and potentially explain differences in the neutralizing breadth and potency of antibodies with similar specificities . From cryo-electron microscopic analysis at ∼9 Å resolution of a cleaved , soluble version of trimeric Env , we show that a structural signature of the open Env conformation is a three-helix motif composed of α-helical segments derived from highly conserved , non-glycosylated N-terminal regions of the gp41 trimer . The three N-terminal gp41 helices in this novel , activated Env conformation are held apart by their interactions with the rest of Env , and are less compactly packed than in the post-fusion , six-helix bundle state . These findings suggest a new structural template for designing immunogens that can elicit antibodies targeting HIV at a vulnerable , pre-entry stage .
HIV/AIDS is a global health epidemic . Over 2 million people die annually from the disease , which is caused by human immunodeficiency virus ( HIV ) infection . HIV-1 entry into target cells is initiated by the interaction of the surface envelope glycoproteins ( Env ) with CD4 and a co-receptor ( typically CCR5 or CXCR4 ) on target cells [1] . Env is a heterodimer of a transmembrane glycoprotein ( gp41 ) and a surface glycoprotein ( gp120 ) ; these dimers are organized as trimers on the surface of the viral membrane . The gp120 portion of Env is recognized by target cell receptors , including CD4 and a co-receptor molecule , while gp41 promotes fusion of viral and cellular membranes , resulting in viral infection of the cell [2] . Upon binding to the CD4 receptor , gp120 undergoes a conformational change [3] , [4] , resulting in exposure of epitopes that can be bound by co-receptor molecules [5] and the eventual formation of the transient “pre-hairpin intermediate” conformation . In the “pre-hairpin intermediate” , the gp41 molecules rearrange so that the N-terminal peptides form a trimer of helices that present the fusion peptide to the target cell , while the C-terminal helices remain attached to the viral membrane [2] . This stage is vulnerable to a number of neutralizing antibodies [6] and peptides [7] , [8] , [9] capable of binding either the N- or C-terminal peptides . Upon fusion with the target cell membrane , further gp41 rearrangement results in the pairing of N- and C-terminal peptides to create a six-helix post-fusion bundle [10] . However , a detailed structural understanding of these molecular rearrangements in the context of native trimeric Env remains elusive . A number of X-ray crystallographic structures have been reported for selected monomeric gp120 constructs [11] , [12] , [13] , [14] , [15] , [16] , [17] , while structural information on gp41 has come from crystallographic and NMR spectroscopic structures of the post-fusion six-helix bundle conformation [18] , [19] , [20] . Cryo-electron tomographic studies at ∼20 Å resolution have begun to bridge the gap between structures of monomeric gp120 and the structure of native trimeric Env , as displayed on the surface of intact HIV-1 and SIV , and in soluble versions of trimeric Env [21] , [22] , [23] , [24] . These analyses have provided insights into the structures of trimeric Env complexed with neutralizing antibodies and have suggested a working model for structural changes in trimeric Env that occur upon engagement of the CD4 receptor on a target cell [21] , [23] . Binding of Env to the target cell CD4 receptor and co-receptor is prevented by antibodies that target the viral CD4 and co-receptor binding sites . Some of the most potent and broadly neutralizing antibodies isolated to date target the CD4 binding site , underscoring the importance of this step in the HIV entry process . The recently described CD4 binding site antibodies VRC01 , VRC02 , and VRC03 [25] represent a growing family of antibodies , including b12 , PG9 , PG16 , HJ16 and the PGT antibodies , that have the ability to block infection in vitro by a broad spectrum of HIV-1 strains [26] , [27] , [28] , [29] . Crystallographic studies of the complexes formed between monomeric HIV-1 gp120 and Fab fragments from antibodies b12 [17] , VRC01 [16] or VRC03 ( PDB ID:3SE8 ) have provided important insights into key interactions that underlie antibody recognition of the CD4-binding region of HIV-1 gp120 . These antibodies vary in effectiveness: some , such as VRC01 , can efficiently neutralize a large proportion ( ∼91% ) of HIV-1 isolates tested , while others , such as b12 , neutralize a smaller set ( ∼41% ) of viruses in the same study [25] . A structural understanding of changes in the conformation of trimeric Env at different stages of viral entry and the mechanisms by which antibodies and other reagents block viral entry is critical for the development of effective vaccines and therapeutic agents against HIV/AIDS . Here , we extend our previous cryo-electron tomographic studies [21] , [22] , [23] , [24] and present a more complete picture of the HIV entry process by showing that HIV-1 Env binding to either soluble CD4 ( sCD4 ) or the co-receptor mimic 17b ( each representing a distinct step in the HIV entry process ) , leads to the same structural opening , or activation , of the Env spike . We also demonstrate structurally that the broadly neutralizing antibodies VRC01 , VRC02 , VRC03 are able to block this activation , locking Env in a state that resembles closed , native Env . Lastly , we present , at ∼9 Å resolution , the cryo-electron microscopic structure of soluble trimeric Env in the 17b-bound state , revealing it as a novel , activated intermediate conformation of trimeric Env that could serve as a new template for immunogen design .
HIV-1 BaL virus treated with 2 , 2′-dithiodipyridine ( AT-2 ) , a gift from Julian Bess and Dr . Jeffrey Lifson , was prepared as described previously [30] . Antibodies b12 and 17b were provided by the NIH AIDS Research and Reference Reagent Program ( Germantown , MD ) . Antibodies VRC01 , VRC02 and VRC03 were provided by Dr . John Mascola ( Vaccine Research Center , NIH , Bethesda , MD ) . VRC02 , 17b and b12 Fab fragments were produced by papain digestion ( Southern Biotech , Birmingham , AL ) . Soluble KNH1144 Env trimers were provided by Drs . Kenneth Kang and William Olson ( Progenics Inc . ) and were from the same lot used in previously reported cryo-electron tomographic studies [23] . Purified AT-2-treated HIV-1 BaL viruses , with either antibodies , Fab fragments or 2-domain ( 1–183 ) soluble CD4 ( sCD4 ) , were deposited on Quantifoil MultiA carbon grids and plunge-frozen in liquid ethane maintained at about −180°C to prepare vitrified specimens for cryo-electron tomography . For each experiment , equal volumes of antibody in 1× PBS ( ∼1 mg/ml ) or sCD4 ( ∼8 mg/ml ) and virus in 1× TNE ( ∼500–1000 mg/ml p24CA ) were incubated together for 1 hr at 4°C ( approximate ratio of 3 . 3–6 . 6 mg p24CA to 6 mg of antibody ) . Immediately before grid preparation , 1 . 5–2 ml of 10 nm protein A gold was added to the mixture . For each grid , 2 µl was placed on a plasma-cleaned 200-mesh Quantifoil Multi-A grid ( Quantifoil , Jena , Germany ) . Excess buffer was blotted onto filter paper using a Vitrobot Mark III ( FEI ) for six seconds at an offset of −2 mm . Blotting was done at room temperature in 100% humidity . Grids were immediately plunged into liquid ethane and stored in liquid N2 before imaging . Specimens for tomography were imaged in a Polara Tecnai G2 transmission electron microscope ( FEI ) operated at 200 kV and equipped with an energy filter ( Gatan ) , with the specimen maintained at liquid nitrogen temperatures . Typically , ∼61 images were taken for each tilt series in 2° increments between ±60° at an average dose of ∼2 . 5 electrons/Å2 for each image . Tilt series were imaged at 34 , 000× with a defocus value of ∼2 . 5 µm using a camera with a pixel size of 4 . 1 Å . For single particle analysis , data were collected in an FEI Titan Krios microscope ( FEI Company , Hillsboro , OR ) operated at 80 kV . Images were recorded using a Gatan US4000 4K×4K CCD camera ( Gatan Inc . Pleasanton , CA ) , at a nominal magnification of 75 , 000× , corresponding to a pixel size of 1 . 08 A . A total of 3 , 299 micrographs ( 4096×4096 pixel ) were collected at underfocus values spanning the range from 1 . 5 to 5 µm . Fiducial-based reconstruction of tomograms was carried out using weighted back-projection techniques [31] . The 3D reconstructions presented were derived typically from about 60 tilt series ( range 34–95 ) with spikes from about 300 individual virions ( range 177–408 ) contributing to the final map . Automatic fiducial-based reconstruction of tomograms was performed using IMOD software . Individual virions were segmented and subjected to automated spike-picking procedures . Alignment , classification and 3D averaging of the extracted spike volumes was done as previously described [32] for experiments with bound VRC01 , VRC02 or VRC03 antibodies , and final density maps were obtained after five to seven 3D classification and alignment iterations beginning with the raw spike images . Iterative 3D classification and alignment runs were executed until no further changes were observed in the final density maps . For experiments in which sCD4 or 17b were present , where the data appeared to be more heterogeneous , we used a more advanced collaborative alignment and clustering algorithm based on the concept of minimizing matrix rank and its convex surrogate , the nuclear norm [33] , to derive the structures of the liganded complexes . Coordinates ( PDB IDs: 3DNN , 3NGB , 3SE8 , 2NY7 & 1GC1 ) were fitted into density maps using the software package UCSF Chimera [34] . A list of all complexes and coordinates used in tomographic studies is provided in Table 1 . A total of 3 , 299 micrographs , with defocus values spanning a range from 1–10 µm , were collected , and of these , a subset of 1 , 639 non-astigmatic , contamination-free , and artifact-free micrographs were selected based on visual inspection of each binned image , its 2D-Fourier transform and the output of two independent whole image CTF determinations using the programs tomoctffind [35] and ctffind3 [36] . Single particle analysis was carried out using EMAN2 [31] , [37] . Micrographs were band-pass filtered and subject to semi-automated particle selection using a box width of 168 Å . A few representative particles were manually selected from the first micrograph and seeded to initiate the automated particle picking routine . Automatically picked boxes were edited manually to remove entries that could be clearly discarded as junk , and the procedure was repeated for an initial set of 193 micrographs to yield 20 , 541 2D projection images of individual Env complexes . The number of selected boxes per micrograph varied from 21 to 257 . Particles were extracted with a box size of 288×288 pixels , CTF parameters for each micrograph were determined from individual particle stacks using the program e2ctf . py in the EMAN2 suite [37] , and the results were compared with the previously obtained values as criterion for inclusion in the dataset ( defocus range was 1 . 952 to 5 . 012 for the final set of particles used ) . A set of 19 , 080 particles was then subject to eight iterations of 2D classification . Inspection of the resulting 128 class averages confirmed the heterogeneous nature of the sample , which separated into unliganded ( 13 , 337 particles ) and 17b-complexed ( 5 , 451 particles ) images . Separate sets of eight initial models for soluble Env and the complex with 17b Fab were built from the segregated particle sets . Initial models were selected based on visual similarity with lower resolution models of the complexes obtained by cryo-electron tomography and subvolume averaging [23] . Selected models were subject to five independent iterations of refinement against the corresponding particle datasets . A second set of 102 , 700 individual projection images was then extracted from the entire micrograph data set , CTF-corrected as above for the smaller data set , and subjected to five iterations of refinement using the two reference models generated in the previous step . The re-projections from this final model are presented in Figure S6 .
Biochemical analyses have established that sCD4 binding to monomeric gp120 induces local changes in conformation [38] , [39] , [40] , [41] . In previous cryo-electron tomographic studies [21] , we determined that native trimeric Env undergoes large quaternary changes upon simultaneous binding of sCD4 and the co-receptor mimic , 17b [42] , to gp120 on intact HIV-1 BaL virions . Each gp120 protomer is rotated away from the central 3-fold axis to create an opening at the apex of the trimer . By fitting three copies of the atomic coordinates of the monomeric gp120/sCD4/17b complex into the density map , we derived a molecular model for this complex [21] . To test whether the binding of sCD4 alone or 17b alone results in measurable conformational changes , we carried out cryo-electron tomography ( see Figures S1 , S2 , S3 for more information ) of HIV-1 BaL virions after incubating them individually with soluble sCD4 or 17b . Envelope glycoprotein spikes are clearly visible on the membrane surface for viruses incubated with either 17b or sCD4 ( Figure S1c , S1d ) . Analysis of density maps derived from these complexes of trimeric Env shows that binding of sCD4 alone , or 17b alone , results in a quaternary structural change in trimeric Env density similar to that observed when both sCD4 and 17b are bound ( Figure 1 ) . The quaternary structural change observed with 17b alone is surprising because HIV-1 BaL is a CD4-dependent strain and not expected to bind co-receptor molecules or a co-receptor mimic in the absence of CD4 . Since no crystallographic structures have been previously reported for the complex of monomeric gp120 with sCD4 alone or with 17b Fab , we fitted the relevant subsets of the gp120/sCD4/17b molecular coordinates into the density maps to obtain a molecular interpretation of the sCD4 and 17b-bound states . In each case , there is a rotation of each gp120 protomer that repositions the V1V2 loops relative to that in the unliganded state ( Figures 1a , 1b ) , as depicted by the movement of the stumps of the V1V2 loops in the fitted coordinates . The fits reveal that the gp120 portions of the sCD4-bound ( Figures 1c , 1d ) or 17b-bound trimers ( Figures 1e , 1f ) display molecular orientations in these separate binary complexes similar to the gp120 portion in the sCD4-17b-bound ternary complex ( Figures 1g , 1h ) . This result establishes that 17b , which is referred to as a CD4i ( CD4-“induced” ) antibody because CD4 binding enhances its binding to Env , can bind and generate an open quaternary conformation of the Env spike in both the presence and absence of sCD4 . We conclude that , while CD4 binding is sufficient for formation of the open quaternary Env conformation in HIV-1 BaL and in soluble Env trimers , binding of an antibody targeted to the co-receptor binding site can also generate a similar conformational change ( at ∼20 Å resolution ) , in the absence of CD4 . Structural models for trimeric Env in which the V1V2 loops are located at the base of the Env trimer [13] or mechanisms for CD4-mediated structural changes that invoke major rearrangements in the inner core of gp120 [43] are not consistent with the results we present here . Thus , while the binding efficiency of CD4i antibodies , such as 17b , is increased by pre-binding of CD4 [5] , 17b binding to HIV-1 BaL Env is able to trigger formation of the open quaternary Env conformation even in the absence of CD4 . The binding interfaces between gp120 and sCD4 and between gp120 and the broadly neutralizing antibody VRC01 are very similar , and share many of the same residues on gp120 [16] . Despite these similarities , the functional consequences of trimeric Env binding to VRC01 or sCD4 are profoundly different [25] . We therefore carried out cryo-electron tomographic experiments to determine if the quaternary structure of VRC01-bound Env is different from the open Env structure observed upon sCD4 binding . Starting from cryo-electron tomograms of VRC01- , VRC02- or VRC03-bound viruses ( Figures 2a–2d ) , we then determined the 3D structures of these trimeric Env complexes by classification and 3D averaging . Density maps of trimeric Env bound to VRC01 show the expected additional density from the bound antibodies , which project upwards from the apex of the spike towards the target cell membrane ( Figures 2e , 2f ) . By fitting three copies of the X-ray structure of monomeric gp120 with VRC01 Fab ( PDB ID: 3NGB ) into the map , we obtained a molecular model for the VRC01-bound Env trimer . Similar tomographic experiments carried out with VRC03-bound viruses resulted in a density map for the complex with Env . We fitted three copies of the gp120-VRC03 coordinates ( PDB ID:3SE8 ) into the density map of trimeric Env bound to VRC03 to derive a molecular model for this complex on the surface of the virus ( Figures 2g , 2h ) . The quaternary structures of trimeric gp120 complexed with VRC01 and VRC03 are similar , with the bound antibodies projecting at an angle of ∼30° from the apex of the spike towards the direction of the target cell membrane . X-ray structures for the gp120-VRC02 complex have not been reported , but the density maps of trimeric Env complexed to VRC02 ( Figure S4 ) are very similar to those obtained with VRC01 and VRC03 , consistent with the expectation that all three antibodies recognize similar binding sites on gp120 [25] . The density maps for the Env-VRC02 complex were obtained using whole IgG , as in the case of the maps with VRC01 , VRC03 and 17b , but only the density corresponding to one Fab fragment is visible on each gp120 monomer in each of these density maps . Because of the inherent flexibility of the hinge regions in antibody molecules , portions ( i . e . the other Fab and Fc segments ) not in direct contact with gp120 have random orientations relative to Env , and the density from these portions is therefore smeared out in the overall density map . We used the complex of trimeric Env with purified Fab fragments to validate this point . Direct comparison of density maps obtained for Fab-bound and IgG-bound trimeric Env complexes ( Figures S4a–S4d ) are similar and superimposable ( Figures S4e , S4f ) . In addition , both of these VRC02-bound complexes are similar to the complexes formed by VRC01 and VRC03 as shown by the superposition of all three IgG-bound density maps ( Figure S5 ) . The molecular structures of trimeric Env bound to VRC01 or VRC03 antibodies ( Figure 3 ) is very close to that obtained previously for unliganded trimeric Env [21] ( PDB ID: 3DNN ) . Thus , despite the remarkable similarities in sCD4 and VRC01 interaction with gp120 ( Figure 4 ) , VRC01 ( and also VRC02 , VRC03 ) binding retains the closed , unliganded trimer conformation , while sCD4 binding results in transition of Env to the open trimer conformation . Consistent with these dramatic differences in the structural consequences of CD4 or VRC01 binding , spike shedding , which is known to occur in the presence of CD4 [44] , [45] , [46] is not induced by VRC01 [47] . Comparison of the density map and fitted coordinates of the gp120-VRC01 trimer with that of the gp120-b12 trimer provides a new insight into the different conformations observed when different neutralizing antibodies are bound to Env ( Figure 5 ) . As viewed from the apex of the spike , the direction of the Fab densities extending outward from the spike is roughly similar for both antibodies ( Figure 5a ) . However , a side view reveals that the b12 Fab fragment is attached to gp120 slightly higher relative to the viral membrane as compared to VRC01 , and is oriented roughly parallel to the membrane in contrast to VRC01 , which is oriented upwards ( Figure 5b ) . Despite general similarities in their overall architectures , there are dramatic differences in the quaternary structure of trimeric gp120 between these gp120-antibody complexes . Inspection of the fits shows that , in order to accommodate b12 , each gp120 protomer in the trimer is rotated outward by an in-plane rotation of ∼20° relative to the orientations in the VRC01 complex ( Figure 6a ) . If b12 binding were to occur without any changes in conformation from that observed in the complex with VRC01 , the orientation of the bound antibody would be sterically restricted because of contacts with the rest of the trimer ( Figure 6b ) . These results elucidate a key difference in the binding between these neutralizing antibodies to trimeric Env on the native viral surface: while VRC01 , 02 and 03 are capable of binding trimeric Env in its native state without significant rearrangement in the quaternary conformation , an outward rotation of each gp120 protomer by ∼20° is essential for b12 binding . Some level of conformational flexibility in the binding site on gp120 may be required to accommodate initial contact with b12; subsequent rearrangement by an “induced-fit” type of mechanism [48] could then result in the partially open quaternary state observed in the density maps . The requirement for a rearrangement of the quaternary structure may limit neutralization ability of b12 to only strains in which packing constraints in trimeric Env are loose enough to allow these gp120 rotations . These findings explain the results from biochemical analyses which have shown that b12 binding results in a greater conformational change in gp120 as compared to that observed with VRC01 [47] , that b12 and 17b cannot simultaneously bind gp120 [25] and that mutations in HIV-1 gp120 can be identified which result in escape from neutralization by b12 , but not by VRC01 [49] . Biochemical experiments have shown that binding of either sCD4 or VRC01 can enhance the binding of 17b to monomeric gp120 [25] , [40] . This contrasts with the differing effects of sCD4 and VRC01 on the quaternary conformation of trimeric Env ( Figures 1 , 2 ) . To test whether VRC01 binding influences 17b binding in the context of native trimeric Env , we performed experiments in which 17b and VRC01 were both added to HIV-1 BaL ( Figure 7 ) . 3D classification of trimeric Env subpopulations revealed the presence of two distinct antibody-bound populations . In the first , only VRC01 was bound , and Env was in the closed conformation . In the second , both 17b and VRC01 were bound , and Env had an open conformation ( Figures 7b , 7c ) . Importantly , in these latter complexes , the bound VRC01 is angled downwards towards the viral membrane , as expected from the out-of-plane rotation of gp120 that occurs with transition from the closed to the open conformation . No closed , 17b-bound state was detected . These experiments suggest that envelope glycoprotein spikes that were bound by VRC01 first retain the closed conformation and block 17b binding , while those that were bound by 17b first and transitioned to the open state allow subsequent binding of VRC01 . To test this hypothesis , we pre-incubated virus with VRC01 for an hour before adding 17b , and determined the distribution of antibody-bound Env complexes . Under these conditions , all detectable Env displayed the closed , VRC01-bound conformation with no evident 17b or VRC01/17b-bound populations ( Figure 7d ) . We conclude that there are structural constraints that prevent binding of 17b in the VRC01-bound closed conformation; these constraints are not present in the closed , unliganded state , which can bind 17b and transition to the open state . The potent neutralization ability of VRC01 may therefore be attributed to its ability to block the CD4- or co-receptor- triggered transition of Env to the open state . The structural studies we have presented show that binding of CD4 and/or 17b , a co-receptor mimic , can lead to a large quaternary change in the arrangement of gp120 in trimeric Env . To provide further insight into epitopes exposed by activation of Env , we used single particle cryo-electron microscopic methods to determine structural changes that occur upon 17b binding at resolutions higher than that achieved by tomography . For this purpose , we used SOSIP Env trimers from the Clade A strain KNH1144 [50] . These trimers are soluble , proteolytically cleaved trimers that are stabilized by the presence of an engineered intermolecular disulfide bond between gp120 and gp41 ( SOS ) , combined with a single residue change , I559P , within gp41 [51] . Importantly , we have shown previously that these trimers , which contain the complete ectodomain of trimeric Env , display the same 17b-induced change in quaternary structure we report here for native trimeric Env [23] . Cryo-electron microscopic images of soluble trimeric Env incubated with 17b Fab fragments show the presence of well-separated molecular complexes ( Figure 8a ) . Classification of the projection images allows separation of the unliganded trimeric gp140 and other partially bound species ( Figure 8b ) from 17b-bound complexes ( Figure 8c ) . Initial model building allowed visualization of the 3D structure and verification that the re-projections matched the projection image classes obtained from the raw data ( Figure S6a ) . Progressive refinement ( see Methods for details ) results in progressively higher amount of detail emerging in the map , as evidenced by comparing the re-projections of the final map ( Figure S6b ) as compared with the initial map ( Figure S6a ) . The structure of the soluble Env-17b Fab complex at ∼9 Å resolution shows the extensive rearrangement in the N-terminal region of gp41 as Env transitions from the closed to open conformation ( Figure 9a ) . From fitting three copies of the gp120 and 17b subset of 1GC1 coordinates of the gp120-sCD4-17b complex [12] we obtained a molecular model for the gp120 and 17b components of this complex . The densities for the three gp120 protomers as well as the Fv portion of 17b are well-resolved and match the coordinate fits for the ternary complex . At the early stages of the refinement , density for the entire Fab is visible , as in the case of the maps derived by tomography of 17b-bound trimeric Env complexes [21] , [23] . With progressive refinement and improved alignment of the gp41 and gp120 components of the images , the alignment of the Fv portion improves at the expense of the rest of the Fab because of the flexibility of the hinge regions at the center of the Fab fragment . A prominent feature of the density map is the appearance of three long densities , which we attribute to α-helical segments , resolved at the center of the complex ( Figure 9a ) . These helices span a region that is essentially a solvent-filled cavity in the unliganded state ( Figure 9b ) . These helices were not resolved in the lower resolution tomographic density maps of sCD4/17b-bound native Env trimers [21] , 17b-bound native trimers ( Figures 1e , 1f ) or in 17b-bound SOSIP gp140 trimers [23] , where they appear as unresolved blobs of density at the center of the spike ( see Figure 1h ) . Since we know that this additional density in the middle can only come from gp41 , there are only two possible assignments for the long rods of density: either they represent the N-terminal gp41 helices or they represent the C-terminal gp41 helices . If the density is assigned to the C-terminal helix , it would result in placement of the free end of the helix corresponding to the MPER ( membrane proximal extended region ) at the apex of the spike facing the target cell membrane . This is , however , not a plausible assignment , as the MPER peptide is the region immediately adjacent to the transmembrane region of gp41 , and must reside at the base of the spike . We conclude that the rods of density in the middle must therefore arise from the N-terminal helix . There is no ambiguity about the orientation of the N-terminal helix in the structure because we know from the sequence of gp41 that the fusion peptide resides at the free end of the N-terminal helix , and that the C-terminal end is connected to the rest of the polypeptide . By fitting coordinates for the N-terminal gp41 helix ( N34; PDB ID: 1AIK ) into the density map , we arrive at an assignment in which the fusion peptide is at the free end facing the target cell membrane and is connected to the rest of the polypeptide at the bottom on the side of the viral membrane . The N-terminal end of this helix is located ∼15 Å–20 Å below the top of the spike and forms an arrangement of three tilted helices arranged around the central 3-fold axis of the trimer ( Figure 10 ) . The entire length of the 34-amino acid long peptide fits into the density , and we presume that the less-ordered 26-amino acid fusion peptide of gp41 or the polypeptide segment connecting the fusion peptide to the N-terminal helix are not visible in our map . The fits to the density map show that the three N-terminal helices are held apart by close interactions with the rest of the Env ectodomain , and are not in the closely packed coiled-coil structure observed in the post-fusion state [10] , [18] , [19] . The looser packing of the three gp41 N-terminal helices likely arises from the additional disulfide bind present in SOSIP gp140 trimers between the gp120 and gp41 polypeptides . Comparison of the packing arrangement of the gp41 N-terminal helices in the open quaternary conformation ( Figure 11a ) with that determined by X-ray crystallography and NMR spectroscopy for the post-fusion state ( Figure 11b ) reveals the structural change that must occur during this transition ( Figure 11c ) . The transition from the open quaternary conformation to the post-fusion state requires a change in angle by ∼15° of each of the three N-terminal helices . In addition , the helices become more compactly packed as a result of their interaction with each other and the C-terminal portion of gp41 . Previous models for the mechanism of HIV entry have postulated that activation of Env by CD4 and co-receptor binding should lead to formation of a transient “pre-hairpin” intermediate [2] , [19] , [52] , in which the N-terminal helices of gp41 become exposed and accessible to binding by entry inhibitors [53] . No 3D structure is available for this intermediate , which by definition is a transient intermediate that is formed when the exposed fusion peptide makes contact with the target cell membrane . There is a large body of evidence indicating that the pre-hairpin intermediate contains a tightly packed three-stranded coiled-coil core and that this state is inhibited by drugs such as enfuvirtide [7] . In the structure we describe here , the N-terminal helices are not closely packed , and there is no target membrane for insertion of the fusion peptide . We therefore favor the view that we have captured an activated intermediate whose formation precedes that of the pre-hairpin intermediate . The structure of our activated intermediate nevertheless provides an explanation for many of the biochemical properties attributed to the pre-hairpin intermediate . Thus , the absence of close packing of the N-terminal gp41 helices in the pre-fusion , open state , and their close association with the rest of Env explains the biochemical observation that they are unable to form stable trimers on their own unless they are artificially stabilized [54] . Further , in our structure , the gp41 N-terminal helices show a larger opening at the side of the target cell and greater burial on the side of the viral membrane . This gp41 arrangement accounts for the large differences observed in the accessibility of larger peptides to the different ends of the gp41 N-terminal helix [8] . This “steric restriction” of access to gp41 by the asymmetric environment of the N-terminal helix may also explain why pre-hairpin intermediate reactive antibodies such as D5 [6] , which bind the viral-membrane side of the gp41 N-terminal helix , are much more effective as scFv fragments than as whole antibodies [8] . Whether the N-terminal helix is accessible to gp41-reactive antibodies such as 8066 [55] , D5 [56] or HK20 [57] in the intermediate stage we have trapped , and whether structural changes occur in gp41 and or gp120 as a result of binding are important questions that can now be addressed . It is also possible that some of the properties attributed to the pre-hairpin intermediate may be attributable to the activated intermediate , and this also remains to be tested . From our results , we propose that the open quaternary conformation of trimeric Env is an activated intermediate that is a precursor to the pre-hairpin intermediate ( Figure 12 ) . In the activated intermediate , the N-terminal helix trimer is not physically separated from the rest of the Env trimer , as it is in schematic models for the pre-hairpin intermediate [2] , [52] . Instead , we postulate that the pre-hairpin intermediate is formed by release of the constraints that hold the three gp41 N-terminal helices apart . This event is likely to occur with dissociation of gp120 upon insertion of the fusion peptide into the target membrane . This transient pre-hairpin intermediate decays on a time scale of many minutes , with the N-terminal helices undergoing further rearrangement to be part of a six-helix bundle that is composed of three inner ( N-terminal ) and three outer ( C-terminal ) helices . We anticipate that both the activated state ( where gp120 has not yet dissociated ) , and the pre-hairpin intermediate state , where the N-terminal gp41 helices span the region between viral and cell membranes , are targets for antibodies such as D5 . The structure we present here for the activated intermediate is such that the N-terminal helices are partially buried at the center of the open spike , and not in the extended state schematically envisioned for the pre-hairpin intermediate [2] , but closer to the “committed” intermediate conformation suggested from entry kinetics studies [58] . Further , this assignment renders the arrangement of the fusion peptide in HIV entirely consistent with that seen for other viruses , such as influenza and Ebola , that use this mechanism of fusion to enter target cells .
In this work , we present a number of structures of trimeric HIV-1 Env and antibody complexes obtained by cryo-electron microscopy , including a 9 Å map of a novel , previously unknown activated intermediate that precedes formation of the pre-hairpin intermediate . The discovery ( Figure 1 ) that binding of the co-receptor site on gp120 can induce the same quaternary conformational changes in trimeric Env as those that occur with CD4 binding is surprising . It is conceivable that the ability of 17b to induce a conformational change similar to that observed with CD4 is strain-specific , and that strains other than HIV-1 BaL may have a lesser propensity to undergo this 17b-induced change in the absence of added sCD4 . In this context , we reported previously that , while soluble KNH1144 Env trimers were capable of displaying an open , 17b-bound conformation , similar trimers from JR-FL displayed the 17b-bound conformation only in the presence of bound sCD4 [23] . The differences in the quaternary structures of trimeric Env bound to either VRC01 or b12 were also unexpected; these findings provide a molecular basis for understanding the differences in neutralization efficiency of these two antibodies . A CD4 mimic may be expected to induce similar structural changes as sCD4 itself . However , we show now that this is not the case , based on determination of the quaternary structures of native trimeric Env bound to VRC01 , VRC02 and VRC03 antibodies . Thus , despite the remarkable similarities in the interactions of sCD4 and VRC01 with gp120 ( Figure 4 ) , VRC01 ( and also VRC02 , VRC03 ) binding retains the unliganded ( closed ) trimer conformation , while sCD4 binding results in transition of Env to the open trimer conformation . Initial studies of VRC01 binding [25] suggested that VRC01 binding enhances binding of 17b to gp120 monomers . Our results now establish that this does not hold true for trimeric gp120 , highlighting the importance of analyzing antibody binding to gp120 trimers in the context of the native virus . Furthermore , the evidence that 17b binding in intact viruses is actually blocked by VRC01 binding suggests a more general structural mechanism for neutralization by antibodies such as VRC01 . Our results show that this “CD4 mimic” not only blocks CD4 binding by binding a similar region on Env , but that it actually prevents the opening of the spike necessary for exposure of the pre-hairpin intermediate , and by extension , infection . Thus , a central finding from our comparative structural analyses is that binding of ligands , including antibodies with very similar footprints on gp120 , as judged by crystallographic and mutagenesis studies , can have profoundly different outcomes for the conformation of trimeric gp120 . Strains that are less susceptible to the type of conformational rearrangements in gp120 and gp41 that are required to accommodate b12 binding may therefore be less likely to be neutralized by antibodies such as b12 , offering a possible explanation for the lower neutralization breadth of b12 . The reason for poorer neutralization breadth of VRC03 relative to VRC01 may lie in lower affinity [25] for gp120 , since there appear to be no significant differences either in the X-ray structures of their respective complexes with gp120 monomers [16] ( PDB ID: 3ES8 ) or in the complexes formed with native trimeric Env ( Figure 2 ) . Whether the formation of the open quaternary state of Env is always sufficient to expose gp41 and lead to fusion between viral and target cell membranes remains an unanswered question . It also is unclear whether there are subtle variations in the quaternary structures , such as differential exposure of the gp41 fusion peptide in the open states populated by distinct ligand binding combinations . Our description of the structure at ∼9 Å resolution suggests a new structural template for designing immunogens that can elicit antibodies targeting HIV at a vulnerable , pre-entry stage . Previous models for the mechanism of HIV entry have postulated that activation of Env by CD4 and co-receptor binding should lead to formation of a transient “pre-hairpin” intermediate , in which the N-terminal helices of gp41 become exposed and accessible to binding by entry inhibitors . No structure is available for this intermediate , but many biochemical experiments have been used to deduce its likely structural properties . In our work , we show that activation of Env by CD4/co-receptor binding leads to the formation of an activated intermediate where three N-terminal helices are nestled at the center of the complex , surrounded by trimeric gp120 that has moved outwards . It is likely that the presence of the additional disulfide bond and possible the Ile to Pro mutation in the SOSIP gp140 constructs stabilizes the activated intermediate . We further show that the N-terminal helices in this activated intermediate are not in the same compact structure that is observed for the post-fusion state . The differences are significant: the transition from the open quaternary conformation to the post-fusion state requires a change in angle by ∼15° of each of the three N-terminal helices . In addition , the helices become more compactly packed as a result of their interaction with each other and the C-terminal portion of gp41 . Our discovery , therefore , suggests a new template for immunogens; antibodies elicited using this model could potentially have greater potency than those currently elicited using peptide mimics based on the compact post-fusion structure . Conversion of the activated three-helix structure to the post-fusion six-helix bundle is a key event common to the entry mechanisms of many viruses , such as HIV , influenza , Ebola and Moloney murine leukemia virus [19] . Determination of the spatial arrangement of the gp41 N-terminal helices thus provides a structural template for vaccine design that is based on an experimentally observed intermediate state . This intermediate state could prove to be more immunogenic than templates derived from the structure of the post-fusion six-helix bundle state which occur at a late stage in the entry process [10] . The N-terminal helix of gp41 is one of the most conserved regions in HIV-1 Env . Because the open Env conformation occurs prior to membrane fusion and the N-terminal helices are more exposed than in the compact organization of the post-fusion state , immunogens based on this conformation are likely to elicit antibodies that could be effective in blocking entry across a broad spectrum of HIV-strains . Our observations are also consistent with the finding that , in HIV-1 , dissociation of gp120 from gp41 exposes an epitope located in the loop between N- and C-terminal segments of gp41 [3] , which is recognized by the murine monoclonal antibody KK20 . Overall , our findings are consistent with a model in which native trimeric HIV-1 is in a closed , but metastable conformation . Binding of ligands , such as sCD4 , and co-receptor mimics , such as 17b , result in the formation of a dramatically different , activated intermediate conformation . In this conformation , gp120 and gp41 undergo coordinated structural changes , resulting in exposure of the gp41 fusion peptide through an opening at the apex of the spike . The structural analyses presented here demonstrate that different neutralizing antibodies block viral entry by distinct structural mechanisms . Antibodies such as VRC01 appear to neutralize HIV by binding the CD4 site and blocking activation of Env , thereby preventing the opening of the spike necessary for exposure of the fusogenic components of gp41 and subsequent viral entry . Antibodies such as b12 also appear to hold trimeric Env in the closed state , but with subtle rearrangements in the quaternary packing of gp120 in the trimer ( Figures 5 , 6 ) . Therefore , the neutralization ability of b12 may be limited to strains in which packing constraints in trimeric Env are loose enough to allow these gp120 rotations , potentially explaining its lower neutralization efficacy compared to VRC01 . In contrast to VRC01 and b12 , 17b binding blocks entry by capturing a conformation in which the fusion peptide is physically prevented from contact with the target cell membrane by the bound antibody . In a recent study , Scheid et al [59] found that broadly neutralizing activity in serum from HIV-infected patients arose not from one highly potent neutralizing antibody , but from the aggregate activity of several antibody specificities . It is clear , therefore , that multiple antibodies can bind the same Env trimer simultaneously , and that the same antibody can be accommodated on the gp120 surface in very different quaternary states of trimeric Env . Effective strategies for HIV neutralization may thus require elicitation of a variety of antibodies , including those like VRC01 that lock gp120 and gp41 in the unliganded conformation , antibodies like 17b that block access of the fusion peptide to the target cell by binding at the apex of Env , and antibodies such as 8066 [55] , D5 [56] or HK20 [57] that bind Env following its activation , but before fusion between viral and target cell membranes .
|
HIV infection occurs following the binding of viral envelope glycoproteins ( Env ) to receptors on target cell surfaces . Binding to these molecules induces conformational changes in Env , ultimately leading to the exposure of a viral fusion peptide and fusion of viral and cellular membranes . Understanding the structure of Env at each step during HIV entry is of fundamental importance in the design of compounds that can combat infection . Here , we use cryo-electron tomography to characterize the conformational changes that occur in Env at individual steps in the entry process , revealing the unexpected finding that binding to a co-receptor mimic alone induces the same conformational changes as binding to CD4 . Furthermore , using single particle cryo-electron microscopy , we show structural evidence , at sub-nanometer resolution , of a novel , activated intermediate state of HIV where highly conserved , interior components of the viral spike are exposed . We show that transition to this state can be blocked by addition of a highly neutralizing antibody , VRC01 , revealing a possible mechanism for its potent neutralizing ability . Discovery of the structure of this new Env intermediate provides a template for the design of immunogens aimed at eliciting antibodies that could block HIV entry .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"hiv",
"viral",
"diseases"
] |
2012
|
Structural Mechanism of Trimeric HIV-1 Envelope Glycoprotein Activation
|
In 1980 , human diploid cell vaccine ( HDCV , Imovax Rabies , Sanofi Pasteur ) , was licensed for use in the United States . To assess adverse events ( AEs ) after HDCV reported to the US Vaccine Adverse Event Reporting System ( VAERS ) , a spontaneous reporting surveillance system . We searched VAERS for US reports after HDCV among persons vaccinated from January 1 , 1990–July 31 , 2015 . Medical records were requested for reports classified as serious ( death , hospitalization , prolonged hospitalization , disability , life-threatening-illness ) , and those suggesting anaphylaxis and Guillain-Barré syndrome ( GBS ) . Physicians reviewed available information and assigned a primary clinical category to each report using MedDRA system organ classes . Empirical Bayesian ( EB ) data mining was used to identify disproportional AE reporting after HDCV . VAERS received 1 , 611 reports after HDCV; 93 ( 5 . 8% ) were serious . Among all reports , the three most common AEs included pyrexia ( 18 . 2% ) , headache ( 17 . 9% ) , and nausea ( 16 . 5% ) . Among serious reports , four deaths appeared to be unrelated to vaccination . This 25-year review of VAERS did not identify new or unexpected AEs after HDCV . The vast majority of AEs were non-serious . Injection site reactions , hypersensitivity reactions , and non-specific constitutional symptoms were most frequently reported , similar to findings in pre-licensure studies .
Three cell culture rabies vaccines are licensed in the United States: human diploid cell vaccine ( HDCV , Imovax Rabies , Sanofi Pasteur ) , purified chick embryo cell vaccine ( PCECV , RabAvert , Novartis Vaccines and Diagnostics ) , and rabies vaccine adsorbed ( RVA , Bioport Corporation ) . Only HDCV and PCECV are available for use in the United States [1] . These vaccines are indicated for post- and pre-exposure prophylaxis to prevent human rabies [1 , 2 , 3 , 5] . Rabies post-exposure prophylaxis ( PEP ) involves prompt and thorough wound cleansing followed by passive immunization with human rabies immunoglobulins ( HRIG ) and vaccination with four doses of HDCV or PCECV ( given in a series separated by several days ) for persons previously unvaccinated with rabies vaccine ( five doses in persons with altered immunocompetence ) . Persons who previously received a complete vaccination series ( pre-exposure or postexposure ) should receive only two doses of vaccine . Pre-exposure vaccination , with three doses of either vaccine given for a primary course , is recommended for persons in high-risk groups such as veterinarians and their staff , animal handlers , rabies researchers , and certain laboratory workers . Pre-exposure vaccination should also be considered in persons ( e . g . , international travelers ) who are likely to come in contact with rabid animals in areas or countries where dog or other animal rabies is enzootic and immediate access to appropriate medical care , including rabies vaccine and immune globulin , might be limited [1 , 2] . Serologic monitoring of vaccinated persons in the highest risk groups is recommended with a single booster dose of vaccine given if the serum titer falls below the recommended cut off . PCECV became available in 1997 [3] and a safety assessment of VAERS reports during 1997–2005 was conducted in 2006 [4] . HDCV , which was licensed on June 9 , 1980 , is prepared from the Pitman-Moore strain of rabies virus grown on MRC-5 human diploid cell culture [5] . In pre-licensure studies of HDCV , local reactions ( e . g . , pain at the injection site , redness , swelling , and induration ) were the most common adverse events ( AEs ) following vaccination [1] , affecting approximately 25% of recipients [5] . Mild constitutional symptoms ( e . g . , fever , headache , dizziness , and gastrointestinal symptoms ) were observed in 20–56% of recipients [1 , 5] . In one study , up to 6% of persons presented with systemic hypersensitivity reactions after receiving booster vaccination with HDCV following primary rabies prophylaxis , 3% occurring within 1 day of receiving boosters , and 3% occurring 6–14 days after boosters [1 , 7] . Post-marketing reports of hypersensitivity reactions after HDCV have previously been reviewed [6] , but this is the first summary of all AEs reported to VAERS since product licensure more than 30 years ago . In the present study , we assess the safety of HDCV from the inception of VAERS through July 31 , 2015 .
Established in 1990 , VAERS is a national vaccine safety surveillance system , co-administered by the Centers for Disease Control and Prevention ( CDC ) and Food and Drug Administration ( FDA ) that receives spontaneous reports ( also known as passive surveillance ) of AEs following immunization [8] . Anyone can report and adverse event ( AE ) including healthcare providers , vaccine recipients , vaccine manufacturers , and other reporters . Reports are submitted voluntarily either directly from individual reporters , who may be reporting for themselves or others , or secondarily from vaccine manufacturers , that also receive spontaneous reports and in turn submit them to VAERS . Reporting is encouraged for any clinically important or unexpected AE , even if the reporter is not sure if a vaccine caused the event [8] . VAERS accepts all reports without rendering judgment on clinical importance or whether vaccine ( s ) might have caused the AE . The VAERS report form collects information on age , sex , vaccine ( s ) administered , AE ( s ) experienced , medical conditions at the time of vaccination , and medical history . Signs and symptoms of AEs are coded by trained personnel and entered into a database using the Medical Dictionary for Regulatory Activities ( MedDRA ) , a clinically validated , internationally standardized medical terminology [9] . A VAERS report may be assigned one or more MedDRA preferred terms ( PT ) . A PT is a distinct descriptor for a symptom , sign , disease , diagnosis , therapeutic indication , investigation , surgical , or medical procedure , and medical , social , or family history characteristic [10] . MedDRA PTs are not confirmed diagnoses . Reports are classified as serious based on the Code of Federal Regulations if one of the following is reported: death , life-threatening illness , hospitalization or prolongation of hospitalization , permanent disability , or a congenital anomaly [11] . For serious reports from sources other than vaccine manufacturers , medical records are routinely requested and made available to VAERS personnel . Reports of medication errors ( e . g . , drug administered to patient of inappropriate age ) may also be reported and are assigned MedDRA PTs , even if there is no AE per se . We analyzed US VAERS reports received by July 31 , 2015 for persons vaccinated with HDCV during January 1 , 1990 through July 31 , 2015 . We excluded non-US reports and duplicate reports . All patient medical data was anonymized . Because VAERS is a routine surveillance program conducted for public health , it does not meet the definition of research and is not subject to Institutional Review Board approval and informed consent requirements . Physicians reviewed all reports and all available medical records for serious reports and the following conditions ( regardless of seriousness ) : anaphylaxis , Guillain-Barré Syndrome ( GBS ) , and pregnancy . The main diagnosis was categorized into a MedDRA System Organ Class ( SOC ) group . Reports suggestive of anaphylaxis or GBS were verified using the Brighton Collaboration criteria or a physician’s diagnosis [12 , 13] . We used Empirical Bayesian ( EB ) data mining [14] to identify AEs reported more frequently than expected following HDCV in the VAERS database . EB data mining screens for vaccine-event pairs that are reported more frequently than expected , i . e . disproportional reporting . Furthermore , EB data mining can minimize false-positive signals resulting from the algorithm’s shrinkage towards the null when observed and/or expected counts are low . EB05 is defined as the lower 90% CI limit of the adjusted ratios of the observed counts over expected counts [15] . Through data-mining analysis , HDCV reports were compared with all other vaccines in the VAERS database . We used published criteria [15 , 16] to identify , with a high degree of confidence , HDCV vaccine-event pairs reported at least twice as frequently as would be expected ( i . e . , lower bound of the 90% confidence interval surrounding the EB geometric mean [EB05] ≥2 ) . We clinically reviewed those HDCV reports containing PTs which exceeded the data mining threshold noted above .
There were five death reports after HDCV and the causes of death are shown in Table 2 . The causes of death in four reports were unrelated to vaccination . In one report in which the cause of death was acute disseminated encephalomyelitis ( ADEM ) , the possibility that HDCV could have contributed to the condition could not be ruled out . This report involved a 34 year-old female who received two doses of HDCV , 7 days apart . She had received rabies vaccines in the past without problems . Two days after the second dose of HDCV , she presented with hemiparesis , fever , headache , neck pain , photophobia , speech impairment , right leg tremors , nausea and vomiting . She was hospitalized and was treated with steroids and intravenous antibiotics , but her condition worsened and she died 9 days after vaccination . The autopsy found the cause of death as acute disseminated encephalomyelitis ( ADEM ) following rabies vaccine . Pathological examination of the patient’s brain tissue found pathological changes compatible with ADEM . PCR testing of the same lot of Imovax administered to the patient was negative for rabies virus . The most frequent AE diagnostic category noted among non-death serious reports was immune system disorders , which accounted for 23 ( 26 . 1% ) of 88 reports ( Table 4 ) . Sixteen of these reports were hypersensitivity or non-anaphylactic allergic reactions , and seven were reports of anaphylaxis . General disorders and administration site conditions , comprised mainly of constitutional signs and symptoms ( e . g . , headache , fever ) , accounted for 21 ( 23 . 9% ) reports . Nervous system disorders were noted in 18 ( 20 . 4% ) reports , including GBS ( four reports ) and seizures ( three reports ) . Through July 20 , 2015 , data mining analysis revealed disproportional reporting for the PTs angioedema and urticaria for HDCV . Because these allergic-type signs and symptoms had been reported in the past , no further review was needed . No disproportional reporting was observed for other PTs , including GBS or other neurological conditions .
During the period of the study , a total of 3 . 8 million doses of HDCV were distributed in the US ( data shown with permission of Sanofi Pasteur ) . In our review of 25 years’ of AE reports to VAERS following HDCV we did not observe any new or unexpected AEs . The vast majority of AEs were non-serious and already known to be associated with HDCV . We noted a marked decrease in the number of reports during 2005 and 2006 which coincides with the recall of several vaccine lots of HDCV during 2004 [17] . The most common AEs reported were consistent with injection site reactions observed during pre-licensure trials and with hypersensitivity reactions that were previously described [1 , 6] . We also noted that constitutional symptoms ( e . g . , headache , nausea , fever ) were among the most frequently reported AEs , which is consistent with findings from pre-licensure studies [1 , 5] . During the first four years of post-marketing experience , systemic allergic reactions were observed at a rate of 11 cases per 10 , 000 vaccinees [6] . Most of these reactions were associated with booster immunizations . Consistent with this earlier finding , we noted ( through automated analysis of reports , clinical review of serious reports , and Empirical Bayesian data mining ) that hypersensitivity reactions ( e . g . , urticaria , pruritus ) were frequently reported . Anaphylactic reactions were more likely to occur among individuals who had experienced symptoms or signs compatible with a hypersensitivity reaction after HDCV . Although these anaphylactic reactions after HDCV are rare , they pose a serious dilemma for the patient and the attending physician . A patient’s risk for acquiring rabies must be carefully considered before deciding to discontinue vaccination [1 , 2] . Recommendations from the Advisory Committee on Immunization Practices state that once initiated , rabies prophylaxis should not be interrupted or discontinued because of local or mild systemic adverse reactions to rabies vaccine . Usually such reactions can be successfully managed with anti-inflammatory , antihistaminic , and antipyretic agents . When a person with a history of hypersensitivity to rabies vaccine must be revaccinated , empiric intervention such as pretreatment with antihistamines might be considered . Epinephrine should be readily available to counteract anaphylactic reactions , and the patient should be observed carefully immediately after vaccination [1] . GBS is an acute , immune-mediated paralytic disorder of the peripheral nervous system [18] . GBS is most commonly associated with Campylobacter jejuni and other infectious agents , and it is believed to be an autoimmune process triggered by antigenic stimulation leading to demyelination and destruction of peripheral nerves [18] . An increased risk of GBS after influenza vaccination was first observed with the 1976–1977 A/New Jersey ( ‘‘swine influenza” ) vaccine [19] . Although questions about GBS following non-influenza vaccines have been raised , no study has confirmed a causal association between GBS and other vaccinations [20] . Acute disseminated encephalomyelitis ( ADEM ) was noted as the cause of death in one report . ADEM is a severe and sudden demyelinating disease which may occur following viral or bacterial infections , and less often , following vaccinations [21] . However , the association between ADEM and vaccination , including rabies vaccines , are mostly from case reports [22–24] . The Institute of Medicine reviewed the available evidence for a limited number of vaccines ( not including rabies vaccines ) and found there was no evidence to accept or reject a causal association between ADEM and vaccination [25] . VAERS is a national system which is useful for identifying rare AEs and events that were not observed during pre-licensure trials . Any safety concern or ‘signal’ should be studied in other systems or through the design of epidemiological studies [8] . VAERS is a spontaneous reporting system that has important limitations which include over- or under-reporting , biased reporting , and inconsistency in quality and completeness of reports [8] . With few exceptions , such as injection site reactions , VAERS generally cannot assess causality between an AE and receipt of a vaccine . Rabies is a life-threatening disease , and the benefits of vaccination far outweigh the risks in persons exposed or potentially exposed to the virus . Our findings are reassuring . Most AEs were non-serious and have previously been described .
|
In 1980 , human diploid cell rabies vaccine ( HDCV , Imovax Rabies , Sanofi Pasteur ) , was licensed for use in the United States . To assess adverse events ( AEs ) after HDCV reported to the US Vaccine Adverse Event Reporting System ( VAERS ) , a spontaneous reporting surveillance system . We searched VAERS for US reports after HDCV among persons vaccinated from January 1 , 1990–July 31 , 2015 . Medical records were requested for reports classified as serious ( death , hospitalization , prolonged hospitalization , disability , life-threatening-illness ) , and those suggesting anaphylaxis and Guillain-Barré syndrome ( GBS ) . Physicians reviewed available information and assigned a primary clinical category to each report using the Medical Dictionary for Regulatory Activities ( MedDRA ) system organ classes . We used a special type of analysis , Empirical Bayesian data mining , to identify AEs reported more frequently after HDCV than the same AE after other vaccines . VAERS received 1 , 611 reports after HDCV;93 ( 5 . 8% ) were serious . Among all reports , the three most common AEs included pyrexia ( 18 . 2% ) , headache ( 17 . 9% ) , and nausea ( 16 . 5% ) . Among serious reports , four deaths appeared to be unrelated to vaccination . This 25-year review of VAERS did not identify new or unexpected AEs after HDCV . The vast majority of AEs were non-serious . Injection site reactions , hypersensitivity reactions , and non-specific constitutional symptoms were most frequently reported , similar to findings in pre-licensure studies .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"clinical",
"research",
"design",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"data",
"mining",
"vaccines",
"preventive",
"medicine",
"research",
"design",
"viruses",
"rabies",
"clinical",
"medicine",
"rna",
"viruses",
"neglected",
"tropical",
"diseases",
"vaccination",
"and",
"immunization",
"information",
"technology",
"research",
"and",
"analysis",
"methods",
"rabies",
"virus",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"computer",
"and",
"information",
"sciences",
"zoonoses",
"medical",
"microbiology",
"microbial",
"pathogens",
"adverse",
"events",
"guillain-barre",
"syndrome",
"lyssavirus",
"allergies",
"anaphylaxis",
"clinical",
"immunology",
"viral",
"pathogens",
"autoimmune",
"diseases",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"organisms"
] |
2016
|
Post-Marketing Surveillance of Human Rabies Diploid Cell Vaccine (Imovax) in the Vaccine Adverse Event Reporting System (VAERS) in the United States, 1990‒2015
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Circadian oscillator networks rely on a transcriptional activator called CLOCK/CYCLE ( CLK/CYC ) in insects and CLOCK/BMAL1 or NPAS2/BMAL1 in mammals . Identifying the targets of this heterodimeric basic-helix-loop-helix ( bHLH ) transcription factor poses challenges and it has been difficult to decipher its specific sequence affinity beyond a canonical E-box motif , except perhaps for some flanking bases contributing weakly to the binding energy . Thus , no good computational model presently exists for predicting CLK/CYC , CLOCK/BMAL1 , or NPAS2/BMAL1 targets . Here , we use a comparative genomics approach and first study the conservation properties of the best-known circadian enhancer: a 69-bp element upstream of the Drosophila melanogaster period gene . This fragment shows a signal involving the presence of two closely spaced E-box–like motifs , a configuration that we can also detect in the other four prominent CLK/CYC target genes in flies: timeless , vrille , Pdp1 , and cwo . This allows for the training of a probabilistic sequence model that we test using functional genomics datasets . We find that the predicted sequences are overrepresented in promoters of genes induced in a recent study by a glucocorticoid receptor-CLK fusion protein . We then scanned the mouse genome with the fly model and found that many known CLOCK/BMAL1 targets harbor sequences matching our consensus . Moreover , the phase of predicted cyclers in liver agreed with known CLOCK/BMAL1 regulation . Taken together , we built a predictive model for CLK/CYC or CLOCK/BMAL1-bound cis-enhancers through the integration of comparative and functional genomics data . Finally , a deeper phylogenetic analysis reveals that the link between the CLOCK/BMAL1 complex and the circadian cis-element dates back to before insects and vertebrates diverged .
In flies and mammals , circadian timing is controlled via interlocked transcriptional feedback loops that rely on basic helix-loop-helix ( bHLH ) , PAS domain transcription factors [1 , 2] . In both fly and mammalian systems an evolutionary conserved bHLH heterodimer acts as the central transcriptional activator . The pair is called CLOCK [3] and CYCLE [4] in Drosophila , while the mammalian orthologues are CLOCK [5] and BMAL1 [6] . In mammals the CLOCK paralog NPAS2 can substitute for CLOCK function in the suprachiasmatic nucleus [7 , 8] . Like most transcription regulators of the bHLH family members , DNA binding of the CLK/CYC or CLOCK/BMAL1 pairs has been shown to involve canonical CANNTG E-box sequences [9–11] both in flies and mammals [6 , 12] . However , the low information content of this motif does not provide a sufficient explanation for the specificity of gene induction by the CLOCK transcription factor , nor does it allow to build a model that can predict clock regulated transcripts on a genome-wide scale . Both the possibility of informative nucleotides flanking the E-boxes or the possibility that a combination of closely spaced partner signals could contribute cooperatively to the specificity was considered in flies and mammals [13 , 14] . Either mechanism can in theory significantly increase binding affinity of CLK/CYC to DNA , e . g . an increase in total ΔG0 of 1 kcal/mol from one additional good hydrogen bond raises binding affinity by a factor of 5 . In Drosophila , the best-studied enhancer is that of the period ( per ) gene where a 69-bp fragment upstream of the transcription start site ( TSS ) drives circadian gene expression [9] . This enhancer depends on a canonical E-box , but it was also shown that its immediate 3' flank contributes to drive large amplitudes and tissue specific expression [15] . Interestingly , the fly enhancer can also be activated by the murine CLOCK/BMAL1 complex [6] . The next best studied enhancer is that of the timeless ( tim ) gene [11] which harbors closely spaced E and TER boxes , the latter being a variant of the consensus E-box which coincides with the mammalian E'-box [16] . In the mouse , well-studied CLOCK/BMAL1 elements include the Per1 [6] , Per2 [17] , Avp [14] and Dbp [18] genes . A study of the Avp promoter suggested that CLOCK/BMAL1 enhancers use a combination of a canonical E-box and a second more degenerate version thereof [14] . More recently a pyrimidine-rich 22 nucleotides sequence was found to cooperate with the core E-box in the Avp promoter [19] . So far , however , it was not possible to compile this information to build a predictive algorithm for CLK/CYC or CLOCK/BMAL1-activated enhancers . Computational strategies for the optimal discovery of cis-elements from genomic sequence pose formidable algorithmic challenges [20] . Among the many ways to model transcription factor binding sites , position weight matrices ( PWMs ) reflect most closely the biophysics of protein-DNA interactions [21–23] . Recent algorithms that exploit phylogeny to infer PWMs apply probabilistic ( Gibbs ) sampling to evolutionary models [24–26] , or implement expectation maximization to optimize scoring schemes that incorporate phylogeny [27–30] . Most of these methods allow for relatively simple model architectures , mostly single block motifs or symmetric structures [31] . Hidden Markov Models ( HMMs ) [32] and their phylogenetic extensions [33 , 34] are best suited for more complex model structures like the one we use . The phylogenetic HMMs currently focus on optimizing trees [33] rather than motif identification; the latter would require optimizing the state dependent equilibrium frequencies . However conventional HMMs , for which motif training is well established , can be supplemented with a weighting scheme approximating the phylogenetic dependencies [35 , 36] , which is what we will use here . Our analysis starts with the five known CLK/CYC targets among the clock genes in Drosophila , per [9 , 10 , 37] , tim [38] , vrille ( vri ) [39] , Par-domain protein 1 ( Pdp1 ) [40] , and clockwork orange ( cwo ) ( formerly CG17100 ) [38 , 41 , 42] . Starting from the 69-bp enhancer in the period gene , we found a cis-element that is both common to all five genes and highly conserved among Drosophila species . This enhancer , which we validate using functional data , not only refines the core circadian E-box ( E1 ) , but also incorporates a flanking partner element ( E2 ) that resembles the more degenerate E-box discussed above , and which is found at a very specific distance of the core E-box with an uncertainty of one nucleotide . While such structures are not implemented in common motif discovery programs , they are conveniently modeled with hidden Markov models ( HMMs ) [32] . We thus trained such an HMM model from the available fly sequences . Remarkably , the Drosophila model was able to predict many known mammalian CLOCK/BMAL1 targets without modification and with high specificity . A deeper phylogenetic analysis revealed the presence of the cis-element throughout insects and vertebrates . This shows that despite important differences in the organism's clock architectures , e . g . , rhythmic mRNA accumulation of Clock in flies versus Bmal1 in mammals , an ancient element in the circadian cis-regulatory code has been maintained since their common ancestor 500 million years ago .
The 69-bp enhancer upstream of the per promoter in D . melanogaster was discovered and dissected in great detail [9] . Using genome sequences from 12 Drosophila species [43 , 44] , we searched for presence of this enhancer in this clade ( Figure 1 ) . Although not immediate to find ( in current UCSC alignment the enhancer is absent in half of the species ) , we identified sequences in all species that show remarkable conservation in a ∼25 bp subfragment tightly collocated around the central canonical E-box motif ( Figure 1A ) . The subfragment harbors a half E-box ( GTG ) located 9 bp to the right of the central E-box in the species close to D . melanogaster , and 10 bp for more remote clade members , e . g . D . grimshawi . Moreover the subfragment contains the 18 bp E-box [10] and the 3' flanking regions showing the strongest attenuation in activity upon deletion [15] . We then searched for similar flanking signals in the vicinity of other conserved E-boxes near promoters of validated CLK/CYC targets . We noticed that all five known target genes contain such dimeric signals that can be aligned with the per enhancer ( Figure 1B ) , and also that this particular signal is conserved in all species considered . To make this more systematic we focus on the vicinity of all conserved E-boxes that can be found around the TSSs of the circadian transcripts per-RA , tim-RA , Pdp1-RD , vri-RA , and cwo-RA . We used multiple alignments from the UCSC browser ( http://genome . ucsc . edu/ ) and considered all islands of ± 30 bp around degenerate CANNGT sequences that were present at least in the subclade consisting of D . melanogaster , D . yakuba , D . simulans , D . sechellia , and D . erecta ( in total about 660 nucleotides per gene for each species , available at http://circaclock . epfl . ch/training_seqs . fa ) . While conservation often extends to all 12 species , sub-optimal alignments required that we apply this milder criterion ( cf . alignment of per , Figure S3A ) . A preliminary motif finding analysis of this restricted set of sequences based on the MEME algorithm [45] ( using motifs length of 7 ) confirmed the presence of E-box-like dimers in these sequences ( Figure S1 ) . These were spaced with an accuracy of plus or minus one base pair as in the per enhancer ( Figure 1A ) . To model this configuration we implement a HMM reflecting the dimer structure ( Figure 2A ) , and train the emission probabilities from the example sequences using the Baum-Welsh algorithm [32] . The model is cyclic so that several instances of the motifs can occur per sequence , we also allow to by-pass E2 in the case that it would not be sufficiently supported by the training sequences . We seeded the model only with one E-box ( Figure 2B , left ) flanked by a weak T nucleotide to break the palindrome symmetry of the bare E-box , while the putative partner site ( E2 ) is initialized with a fully uninformative model . Only the emissions are trained while the transition probabilities p1 from background to E1 , and p2 form E1 to E2 are held fixed ( Methods ) . These transitions tune the stringency of the E1 and E2 parts , and reflect the chemical potential of the regulators that would bind to the E1 and E2 boxes [23] . We varied p1 and p2 over a wide range and retained the combination that maximizes the enrichment of hits among genes that show induction by CLK in functional genomics assays ( Figure 3 ) . Importantly , despite the uninformative seed and large search space , converged models do reflect the right flank described above for a wide range of transitions , the combination retained ( p1=2−11 , p2=2−4 ) show a AACGTG right consensus . Apart from details in the emission probabilities , this model is quite stable for a range of p1 and p2 values ( Figure S2 ) . Inspection of the converged model indicates that effectively 15 high scoring instances of E1 box were used , and 6 for the E2 box . The latter were from vri ( 2–3 instances ) , per ( 1–2 ) , tim ( 1 ) , Pdp1 ( 1 ) and cwo ( 1 ) . In these five genes it is noticeable that multiple E1-E2 copies are found , and that E1 also often occurs alone ( Figure S3 ) . For instance , the second conserved site in the per intron ( Figure S3A ) could provide an explanation for the promoterless per allele found to cycle in a restricted part of the nervous system [46] . Thus , the converged model is consistent with the attenuated CLK/CYC activation in mutated 69-bp enhancers with deletions that are immediately 3' of the right central E-box [15] . Furthermore the model captures the mammalian architecture in which a canonical and a fuzzier E-box are juxtaposed [14] . Training a model on five genes raises the question about its generalization to further putative CLK/CYC targets . To address this we used several microarray datasets that measure ‘CLK targetness' [38] ( Methods ) and assessed correlation with sequence match from our model . Windows of ±2500 bp around all annotated TSSs were scanned with our HMM model , in which the five training genes were found among the first 13 highest scores ( Figure 3B ) . Recently a glucocorticoid receptor-CLK fusion protein ( GR-CLK ) was used in S2 cells and cultured fly heads to induce CLK targets under cycloheximide treatment [38] . In this assay new protein synthesis is blocked to minimize indirect effects . Even though it is not formally excluded that the fusion protein could interfere with partner complexes , this experiment is best suited to test the specificity of the sequence model . We show that highly induced genes in the GR-CLK experiment are significantly enriched in high scoring hits from the sequence model , so that we can identify a set of ∼30 genes among the top 57 induced genes which show highly significant 2- to 6-fold enrichment in sequence specificity ( Figure 3A and Table S1 ) . Importantly , the five training genes are excluded from the set of positives in this analysis . When testing how much E2 contributes to the observed enrichment , we found that it contributes only marginally: it reduces specificity for low sensitivities and increases specificity at higher sensitivities ( Figure S4A ) . Nonetheless , several of the highly induced genes in the GR-CLK experiment , e . g . , CG13624 , show presence of E1-E2 . Moreover , these sites show highly increased conservation profiles specifically at the predicted locations including the E2 site ( Figure S3F and S3G ) . Below we show that increased specificity from E2 is most important in mammals . We also considered expression levels in ClkJrk flies [47 , 48] since CLK/CYC targets are predicted to be down-regulated in this mutant . Moreover we tested cycling transcripts in light-dark ( LD ) and dark-dark ( DD ) conditions with phases that are compatible with known CLK/CYC targets , i . e . , peak time accumulations in windows ZT6–20 ( Methods ) . No signature of enriched E1-E2 motifs was detected in either the ClkJrk or cycler datasets ( Figure S4 ) . This can be expected since both differential expression in ClkJrk mutants , or rhythmic mRNA accumulation , also reflect indirect mechanisms downstream of the CLK/CYC transcription factor . We extensively searched whether other p1 and p2 parameters would detect enrichment without success . Consistently , we do not detect enrichment of the motif in mouse transcripts showing differential expression in a recent mRNA profiling of Clock mutants [49] ( Figure S5 ) . Similarly , in an early study of rhythmic transcript profiles in fly heads , we did not detect enrichment of consensus E-boxes in the vicinity of periodic transcripts [50] . Further annotating the list of 57 GR-CLK induced genes with the sequence score from the E1-E2 model , the 24-hour periodicity and phase of the transcripts in LD and DD , or with the differential regulation in ClkJrk flies show that some genes qualify as CLK/CYC regulated genes according to several independent criteria ( Table S1 ) . Among those , the C2H2 zinc finger transcription factor cbt , CG3348 , CG11050 , CG8008 are the most noticeable . From the purely genomic side , conserved E1-E2 sites are enriched in D . melanogaster when compared to permuted E1-E2 matrices ( Figure S6A and S6B ) . From the likelihood scores of known examples , we estimate about one hundred genes to be potentially controlled by medium to high affinity E1-E2 sites ( Figure S6C , gene lists in at http://circaclock . epfl . ch/fly_conserved_16 . txt ) . Even though the model was derived from fly sequences , the core E-box shows similarities to the brain-specific in vitro measured NPAS2/BMAL1 binding consensus GGGTCACGTGT[TC]C[AC] ( underlined bases are consistent with our model ) [51] . Scanning the mouse genome with the full E1-E2 model taken straight from the flies revealed that many common circadian transcripts show instances of this signal that are highly conserved in mammals ( Figure S7 ) . Several of these genes also contain multiple instances of the motif , as in the flies . With few exceptions , sites are found in the vicinity of the core promoter ( e . g . , Per2 , Tef ) or in the introns ( Dbp , Cry2 , RevErbα ) . Given the much greater complexity of mammalian genomes as compared to insects , it comes as a great surprise that the fly model predicts known circadian genes in mouse with highly enriched specificity ( Figure 4 ) . Among the 13 common circadian genes used as a test set , we find 7 among the top 1% of predictions when we would expect none ( p < 10−12 , binomial distribution ) . In addition the restriction to sites that are highly conserved in mammals ( measured using PhastCons [52] ) increases the specificity ( compare Figures 4 and S8 ) . From the scores of known examples , we thus estimate in the order of hundred CLOCK/BMAL1 binding sites in mouse ( Figure S6D ) . Finally , the two spacer lengths were about equally represented among the conserved hits with scores above 15 bits ( given at http://circaclock . epfl . ch/bedFiles ) . Importantly , while the E2 sequence played a marginal role in the specificity analysis of the GR-CLK data in flies , it plays a much more prominent role in mouse . For example , the Dbp site ranks only at position 804 and that of Per2 at position 3021 when E2 is not used in the prediction ( Figure S8 , right ) ; overall the 13 test genes are clearly shifted to the bulk of scores . The conservation pattern of many of these hits shows tight increase around the E1-E2 sequences ( Figure S7 ) , which further supports the functional role of the predicted loci . Moreover , several of these predictions coincide with known CLOCK/BMAL1 functional circadian enhancers , e . g . , those in the Per1 [6] , Per2 [17] or Dbp [18] genes . As with the Drosophila ClkJrk data , putative CLOCK/BMAL1-induced genes identified from a Clock mutant array experiments in mice [49] did not show enriched E1-E2 boxes presumably due to indirect effects , except perhaps for a weak tendency in the liver ( Figure S5 ) . Consistent with our model , recent circadian band shift assays with mouse liver extracts indicate that a sequence closely related to the E1-E2 site is able to shift the CLOCK/BMAL1 complex more specifically than single E-boxes [53] . Finally , the phase distribution among the conserved hits that cycle in liver [54] shows a clear phase preference around ZT12 , as expected for CLOCK/BMAL1 targets ( Figure 4B ) . We first provide a phylogenic analysis of the activators CLOCK/BMAL1 binding E1 in mammals , birds , frogs , fishes , flies , mosquito and honey bee . Beyond these species , notably in the nematodes , no orthologues can be found . Both CLOCK and BMAL1 harbor two conserved PAS domains , in addition to the preserved DNA binding bHLH domain ( Figure 5A; full-length protein alignments are given at http://circaclock . epfl . ch/jarFiles ) , whose conservation exceeds by far the bHLH consensus motif [55 , 56] . As the complex is expected to bind the E1 site , its conservation is consistent with the high information content ( 11 . 0 bits ) of the E1 motif . To track the presence of the E1-E2 motif in a broader set of species , we consider two gene families among the best conserved circadian CLK/CYC or CLOCK/BMAL1 targets . First , the Period genes are primary targets of CLOCK/BMAL1 whose genes products function as repressors of CLOCK/BMAL1 , hence closing a negative feedback loop at the core of the circadian oscillator . While flies have a single period gene , vertebrates have multiple copies , e . g . , three in mammals . The presence of E1-E2 signals near promoters of period genes generalizes beyond flies and mammals to a broad set of species including birds , frogs , fishes , flies , mosquito and honey bee ( Figure 5B ) . While the mammalian site is at the TSS and that of fly is around −500 bp , the fish promoter is unannotated and the site is at 2 . 6 kbp upstream of the annotated PER3 protein . Interestingly the mammalian E2 motif shares many similar bases with the fish . Even though nematodes have a putative period homologue ( lin-42 ) , we could not detect presence a proximal E1-E2 in C . elegans and C . briggsae , which is both consistent with the absence of CLOCK/BMAL1 and the still uncertain existence of circadian rhythms in nematodes [57] . Second , the PAR-domain basic leucine zipper ( PAR bZip ) transcription factors Tef/Hlf/Dbp ( mouse ) are homologues of the fly circadian gene Pdp1 ( PAR domain protein 1 ) and are prominent clock output genes directly regulated by E-box motifs [12 , 18] . Their function is to mediate rhythmic physiology in organs such as the liver and kidney , where they induce , e . g . , the cytochrome P450 enzymes [58] . Among the three murine paralogues , Tef is the most ancient representative with putative orthologues in most vertebrates and insects . In few species , e . g . , in zebrafish and Xenopus tropicalis , full-length mRNA are available for Tef , elsewhere we relied on annotations inferred from a combination of ESTs and proteins ( from other species ) to genome alignments provided in the UCSC web browser . We could find E1-E2 elements in the vicinity of the Tef promoter in most of the vertebrates and insects , some harboring several copies ( Figure 5C ) . Interestingly , the locations of the instances of the E1-E2 motif shows a typical conservation structure ( in the PhastCons scores ) in subgroups where non-coding sequences can be multiply aligned , i . e . , the mammals , the fishes , and the flies . Even if the exact position of the TSS is poorly documented in many of these species , we find that more than 85% of the shown sequences for both the Period and Tef genes occur within 1 . 5 kbp of an annotated start . Furthermore , 75% ( respectively 25% ) of the likelihood scores are above 15 . 1 bits ( respectively 19 . 5 bits ) and the median score is at 17 . 1 bits . Using background statistics for the E1-E2 likelihood score computed as in [23] ( Figure S9 ) , we estimate that the probability per position to find a motif having a likelihood score greater than 17 bits is 5 × 10−7 , or 2 × 10−6 for scores of 15 bits . Assuming independent positions , we estimate that the probability p to find conserved hits ( PhastCons > 0 . 5 ) in regions of 1 . 5 kbp around the mammalian , fish and insect promoters is p = 2 × 10−9 for 17 bits hits and p = 10−7 for 15 bits . Here we used that the genomic fraction of conserved sites ( PhastCons > 0 . 5 ) is 10% in mammals ( UCSC mm8 assembly , PhastCons score based on 18 species ) , 23% in fish ( fr2 assembly , 4 species ) , and 40% flies ( dm3 , 15 species ) . This simple calculation thus suggests that the conserved configurations found for the Period and Tef genes are highly unlikely due to chance .
Even though novel post-transcriptional mechanisms regulating the circadian clockworks are regularly uncovered [59] , transcriptional control remains an essential ingredient of molecular clocks that is particularly relevant for relaying circadian output functions [2] . Output genes can be induced by the transcription factors of the core oscillator , or via tissue specific effectors such as Dbp , Hlf and Tef in mouse , which are themselves direct CLOCK/BMAL1 targets [58] . This layered design complicates the interpretation of experiments such as mRNA steady state time courses , particularly if one is interested in deciphering new direct targets of the core regulators . This task can be greatly facilitated using functional experiments like the glucocorticoid-CLK fusion experiments , which have improved specificity compared with the profiling of mutants , and accurate models for the cis-regulatory sequences bound by the regulators . Presently the mechanisms that facilitate the recruitment to DNA and subsequent trans-activating activity of the main circadian regulator CLK/CYC or CLOCK/BMAL1 are not fully understood . Likely though , this situation will evolve rapidly , helped by approaches such as large-scale chromatin immuno-precipitation analyses or comparative genomics . We used the latter to derive a probabilistic model for CLK/CYC-regulated circadian enhancers consisting of two partner signals , E1 and E2 , linked by a spacer that can tolerate a variability of one nucleotide . E1 has an E-box core flanked by informative T's ( or A on the reverse strand ) , while the second half is more degenerate and resembles previously reported TER boxes [11] or E' boxes [16] . The close proximity of the two sites suggests a cooperative binding of two partner complexes , one of which is the CLK/CYC heterodimer , while the second possibly identical factor needs to be identified . To validate the predictive power of the model in Drosophila , we analyzed a recent study in which a GR-CLK fusion was used to induce CLK/CYC targets in S2 cells . We found an unusual number of high sequence scores among the highest induced genes , even though the E2 part did not contribute a large improvement in this case . This could reflect two scenarios: either the fusion protein interferes with a putative E2 binding complex , or it could simply be that the list of highest affinity CLK/CYC targets does not extend much beyond the list of known five , even though we identified several strong candidates that harbor the expected cis-element ( Figure S3 and Table S1 ) . Consistent with the first functional study of the period enhancer [9] we find no preferential orientation of the E1-E2 elements . Anecdotally , it is interesting that the double E1-E2 site around −2 . 5 kb in the vrille promoter ( Figure S3C ) is located on a fragment that is inverted in D . grimshawi only ( Figure S10 ) . Having built the model from Drosophila sequences only , it was quite remarkable that the unchanged E1-E2 model identified high scoring hits in the majority of known CLOCK/BMAL1 targets in mouse . Among genes with putative E1-E2 elements , many instances of the motif are highly conserved , and the conservation patterns are often concentrated just on top of the identified elements while rapidly decreasing outside of it . Unlike in flies , the E2 element appears to be a determinant for specificity in mouse . Given that tissue-specific expression analyses [60 , 61] revealed largely non-overlapping circadian regulation programs , it is not excluded that future analyses will reveal enhancer elements permitting tissue specific predictions . We showed that our model predicted peak expression phases in mouse liver that were preferentially centered around ZT12 ( Figure 4B ) , which is consistent with an induction by CLOCK/BMAL1 . It might be possible to find subclasses in the E1-E2 model that drive expression with more specific phases , e . g . , by modifying the binding affinity of the E2 element . There should nevertheless be limits to this undertaking as mRNA accumulation is also influenced by processes downstream of transcription . Noticeably , many of our predicted CLOCK/BMAL1 targets show non-cycling steady state mRNA abundances , at least when assessed in liver [54] . It is likely that some will cycle in other tissues , however , long mRNA half-lives can easily mask rhythmic transcription rates as has been reported for the albumin gene [62] . In conclusion we built a probabilistic sequence model , termed E1-E2 , that predicts enhancers driven by the bHLH proteins CLK/CYC in insects and CLOCK/BMAL1 in mammals . This model not only refines the circadian E-box beyond its core nucleotides but also emphasizes the role of a flanking partner motif that may involve binding of a novel co-regulator complex . A deeper phylogenetic analysis showed that conserved instances of E1-E2 are found both in promoters of core circadian clock genes , and in genes mediating circadian output . E1-E2 seems to occur in vertebrates and insects but not in nematodes . This is perhaps not surprising as the existence of circadian behavior in nematodes is still controversial [57] . Absence of E1-E2 could also reflect the Coelomata hypothesis that groups arthropodes with chordates in a monophyletic clade [63] . In this perspective our findings would suggest that the CLOCK/BMAL1 based oscillator evolved after the nematodes separated from a common ancestor . Alternatively , the nematodes could have lost some oscillator components as a result of their live style in the soil , which largely shields them from daily light cues . Our report is not the first example of an ancient linkage between bHLH regulators and companion cis-elements . An even deeper conservation of a cis-regulatory element has been reported in proneural genes controlled by bHLH factors of the Hes family [64] . Several reasons , e . g . , the necessity to maintain highly stable key developmental programs , were proposed to explain such unusually high conservation . Here , it is interesting that the BMAL1 protein , unlike genes in the Period or Crytochromes families , stands out as the only circadian component in the murine clock with no functionally redundant paralogues . The high degree of conservation in its target sites is thus consistent with the unique function of BMAL1 ( CYC ) as the master activator in the circadian network . We surely expect that comparative genomics combined with functional datasets will allow further dissecting the circadian and other cis-regulatory codes .
MultiZ [65] Multiple alignments were downloaded from the UCSC table browser ( Multiple alignments of 14 insects with D . melanogaster , dm3 , April 2006 , but we restricted these to Drosophila species ) . We used the Drosophila melanogaster genome and annotations version r5 . 1 to analyze windows of ±2 , 500 bases around all annotated transcripts . These sequences were used to identify flanking sequences around conserved CANNGT motifs in the five training genes; for the period gene we added the 69-bp enhancer from the species missed in the multiple alignment ( Figure 1A and Figure S3A ) . Model training . The sequences used for the model training are given at http://circaclock . epfl . ch/training_seqs . fa . We implemented a standard Baum-Welsh optimization in which each sequence is independent ( no explicit use of the multiple alignments is made ) . We took into account phylogenetic relationships by attributing a geometric weighting reminiscent of [35] reflecting the Drosophila species tree ( Figure 1C ) : droGri2: weight = 1/8 , dp4: 1/8 , droYak2: 1/16 , droEre2: 1/16 , droPer1: 1/8 , droWil1: 1/4 , droSim1: 1/32 , dm3: 1/16 , droAna3: 1/8 , droSec1: 1/32 , droMoj3: 1/16 , droVir3: 1/16 . Thus each gene is counted as one and we used fixed pseudo-count of 0 . 3 for each nucleotide . Species identifiers are those used in the UCSC alignments . Training is done on both strands simultaneously with tied ( reverse complemented ) emission probabilities using a custom HMM implementation following [32] . We scanned ( decoded ) windows of ±2 , 500 bp for all annotated transcripts ( r5 . 1 ) with the cyclic E1-E2 model . The converged HMM model is provided at http://circaclock . epfl . ch/Models/M_11_4_0 . 3_3_2_13_0_1 . mod , while the seed model is http://circaclock . epfl . ch/Models/seed . M_11_4_3_2_13_0_1 . mod . We used posterior decoding to compute the posterior state probabilities Psi for state s at position i ( Figure S3 ) , and the expected likelihood ( EL ) for a sequence is computed as ( es ( Oi ) ) minus the likelihood of the background ( Figure 3 ) . Here , es ( Oi ) is the ( emission ) probability to observe nucleotide Oi at position i in the state s . In the case of multiple transcripts , the highest score was used as the gene score . Correspondence between Affymetrix oligos and genes was done with the Annotations provided at NetAffx . com for the DrosGenome1 and Drosophila_2 arrays ( July 2007 versions ) . To scan the full mm8 mouse genome ( from the UCSC genome browser ) we extracted the two weight matrices from the Drosophila HMM ( given at http://circaclock . epfl . ch/Models/M_11_4_0 . 3_3_2_13_0_1 . p1 . mat and http://circaclock . epfl . ch/Models/M_11_4_0 . 3_3_2_13_0_1 . p2 . mat ) , and computed the standard likelihood ( LL ) ( wi ( Oi ) /b ( Oi ) ) for the chained matrices at each genomic position . Here wi ( Oi ) is the probability to observe nucleotide Oi at position i and b ( Oi ) is the background probability for nucleotide Oi . As in flies we allow for a zero or one nucleotide spacer and consider the maximum of the two scores . We used a single nucleotide background ( 0-th order ) with 29% of A and T's , and 21% of C or G's . To filter for conservation ( Figures 4 and S8 ) , we average PhastCons scores [52] ( from alignments with 17 vertebrates , UCSC genome browser ) at the positions of the hit ( 25 or 26 bases depending on spacer ) . Hits are mapped to genes when they occur in windows of ±2 kb of the transcription units from the affyMOE430 table at UCSC . The latter was used for easy comparison with expression data . A set of 15 known circadian genes was used to test the specificity of prediction in mouse: Cry1 , Cry2 , Per1 , Per2 , Per3 , Dbp , Tef , Hlf , Wee1 , Bhlhb2 ( Dec1 ) , Bhlhb3 ( Dec2 ) , Nr1d1 ( RevErbα ) , Nr1d2 ( RevErbβ ) , Bmal1 ( Arntl ) , and Clock , of which the latter two are not expected to be self-induced . Two ClkJrk mutant time series of 12 time points each [47 , 48] were used to quantify differential regulation induced by the mutation , we applied a one-sample t-test to the 24 merged log2-expression ratios at each time point . GR-CLK induction data was from [38]; replicated conditions were averaged and the fold induction between stimulated and un-stimulated cells was computed separately for the S2 cells and the cultured fly heads . The two were then summed to make a single score for each gene . The obtained rankings correlate tightly with the original analysis . DD and LD cycling scores ( amplitude and phases ) were compiled from a comprehensive collection of previously described time courses [66] using established methods [67] . Information regarding genes induced or repressed in Clock mutant mice is taken from [49] . Mouse liver data used for Figure 3B is from [54] , rhythmicity was assessed via the 24 hour Fourier component ( F24 ) as in [67] . The protein sequences for the CLOCK and BMAL1 homologues of insects and vertebrates , was taken from NCBI when available , and if not , we used the tBlastn table and the predicted protein from the UCSC database . The protein alignments were produced using ClustalW [68] and visualized using Jalview [69] . To identify instances of the E1-E2 motifs in the Period and Tef promoters , we used the UCSC browser to find the genomic regions around bona fide ( when supported by full length mRNA in the specie ) or putative ( inferred from aligning mRNA , ESTs or protein from other species ) homologues of these genes . We then scanned these sequences for instances of E1-E2 and the highest scoring instances near putative promoters were retained . Additional data and model files are given at http://circaclock . epfl . ch . Genes used for Figure 4B are listed in the file http://circaclock . epfl . ch/cyclers_mouse_fig4B . txt . Predictions ( . bed file format ) for flies and mouse can be uploaded to the UCSC Genome browser as custom tracks .
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Life on earth is subject to daily light/dark and temperature cycles that reflect the earth rotation about its own axis . Under such conditions , organisms ranging from bacteria to human have evolved molecularly geared circadian clocks that resonate with the environmental cycles . These clocks serve as internal timing devices to coordinate physiological and behavioral processes as diverse as detoxification , activity and rest cycles , or blood pressure . In insects and vertebrates , the clock circuitry uses interlocked negative feedback loops which are implemented by transcription factors , among which the heterodimeric activators CLOCK and CYCLE play a key role . The specific DNA elements recognized by this factor are known to involve E-box motifs , but the low information content of this sequence makes it a poor predictor of the targets of CLOCK/CYCLE on a genome-wide scale . Here , we use comparative genomics to build a more specific model for a CLOCK-controlled cis-element that extends the canonical E-boxes to a more complex dimeric element . We use functional data from Drosophila and mouse circadian experiments to test the validity and assess the performance of the model . Finally , we provide a phylogenetic analysis of the cis-elements across insect and vertebrates that emphasizes the ancient link between CLOCK/CYCLE and the modeled enhancer . These results indicate that comparative genomics provides powerful means to decipher the complexity of the circadian cis-regulatory code .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biochemistry",
"fish",
"computational",
"biology",
"drosophila",
"mammals"
] |
2008
|
Modeling an Evolutionary Conserved Circadian Cis-Element
|
Sequencing projects have identified large numbers of rare stop-gain and frameshift variants in the human genome . As most of these are observed in the heterozygous state , they test a gene’s tolerance to haploinsufficiency and dominant loss of function . We analyzed the distribution of truncating variants across 16 , 260 autosomal protein coding genes in 11 , 546 individuals . We observed 39 , 893 truncating variants affecting 12 , 062 genes , which significantly differed from an expectation of 12 , 916 genes under a model of neutral de novo mutation ( p<10−4 ) . Extrapolating this to increasing numbers of sequenced individuals , we estimate that 10 . 8% of human genes do not tolerate heterozygous truncating variants . An additional 10 to 15% of truncated genes may be rescued by incomplete penetrance or compensatory mutations , or because the truncating variants are of limited functional impact . The study of protein truncating variants delineates the essential genome and , more generally , identifies rare heterozygous variants as an unexplored source of diversity of phenotypic traits and diseases .
Recent population expansion and limited purifying selection have led to an abundance of rare human genetic variation [1–3] including stop-gain and frameshift mutations . Thus , there is increasing interest in the identification of natural human knockouts [3–8] through the cataloguing of homozygous truncations . However , heterozygous truncation can also lead to deleterious functional consequences through haploinsufficiency due to decreased gene dosage , or through a dominant-negative effect [9 , 10] . In order to quantify the importance of heterozygous protein truncating variation , we characterized genes showing fewer de novo truncations in the general population than expected under a neutral model . We hypothesized that there is a set of genes that cannot tolerate heterozygous protein truncating variants ( PTVs ) because of early life lethality .
We used stop-gain ( nonsense ) single nucleotide variants and frameshift ( insertions/deletions ) variants to assess tolerance to heterozygous PTVs across the human genome . We considered transcripts from 16 , 260 autosomal protein coding genes annotated by the consensus coding sequence ( CCDS ) project [11] , for which de novo mutation rate estimates were recently calculated [12] , and where the number of synonymous variants in sequenced individuals followed expectation ( Methods ) . The study dataset included 11 , 546 exomes in which we observed 39 , 893 rare PTVs ( allele frequency < 1% ) , affecting 12 , 062 ( 74 . 1% ) genes . To test whether there is a subset of genes that are intolerant to heterozygous truncation , we simulated a model of generation of neutral de novo PTVs for all genes ( i . e . assuming viability of affected individuals ) . By randomly assigning 39 , 893 hypothetical stop-gain and frameshift variants to genes according to their de novo mutation rate [12] , we observed that 12 , 916 out of 16 , 260 genes ( 95% CI , 12 , 805–12 , 991 ) would be expected to carry at least one stop-gain or frameshift variant . The expected number of genes is significantly greater than the 12 , 062 truncated genes observed in the study dataset for the same number of PTVs ( 6 . 6% depletion , empirical p-value computed by Monte Carlo simulation < 10-4; Fig 1A ) . The depletion in number of observed truncated genes was greater when severe PTVs , i . e . those predicted to have the greatest functional impact [13] , were considered ( n = 10 , 340 vs . a neutral expectation of 11 , 821–11 , 978; 13 . 1% depletion p < 10-4 ) . This suggests that a measurable fraction of de novo heterozygous stop-gain and frameshift variants are highly deleterious and hence under strong purifying selection . Hereafter we denote that fraction as the haploinsufficient genome ( fhi ) . We assessed the functional properties of the subset of genes that were not observed to carry PTVs ( n = 4 , 198 ) ( Table 1 ) . These genes were highly conserved , had fewer paralogs , were more likely to be part of protein complexes and were more connected in protein-protein interaction networks than the rest of the genes . Furthermore , they had characteristics of essentiality and haploinsufficiency , and a higher probability of CRISPR-Cas9 editing compromising cell viability [14] . The set of genes not carrying PTVs was enriched in OMIM genes annotated with ‘haploinsufficient’ or ‘dominant negative’ keywords [15] , and was enriched in genes associated with increased mortality in mouse models [16] ( Table 1 ) . Non truncated genes were overrepresented in functional categories such as transcription regulation , developmental processes , cell cycle , and nucleic acid metabolism ( S1 Table ) , in line with earlier characterization of haploinsufficient genes [17] . Together , these results indicate that a number of basic cellular functions depend on the integrity of coding and expression of both alleles of component genes . The enrichment pattern was the opposite for the set of 2347 genes with homozygous PTVs . In particular genes with homozygous PTVs have more paralogs , are less likely to be part of protein complexes , have a smaller posterior probability of haploinsufficiency , are depleted in genes which affect cell viability in CRISPR-Cas9 , have higher dN/dS values , are less likely to be essential , have lower connectivity indices , are depleted in ClinVar and OMIM , and are depleted in genes associated with increased mortality in mice . All these observations are significant and details are listed in S4 Table . Genes without PTVs in our analysis may be truly part of the haploinsufficient genome or the result of insufficient sample size to detect rare events . Thus , we next sought to estimate the total haploinsufficient fraction ( fhi ) of the genome in the full population by a modeling approach . Assuming that a fraction fhi of genes do not carry de novo PTVs while the remaining genes do so according to their neutral mutation rates [12] , fhi can be estimated by fitting a model to the observed relative distribution of PTVs ( relative to the rest of genes; Methods ) . This analysis estimates a fraction of the haploinsufficient genome of fhi = 10 . 8% ( 95% CI = 9 . 5–11 . 7% ) of protein coding genes ( Fig 1A ) . Some genes may tolerate PTVs because their functional effects are masked by incomplete penetrance [18] , by compensatory variants [19] , or because of a low functional impact of the truncation [13] . In addition , false positive errors in sequencing and variant calling procedures contribute to the distribution of observed variants [20–22] . We collectively treated these factors as noise , because they can lead to the observation of a truncated gene in a viable individual without truly probing the general viability of carrying only one functional allele in a given gene . Therefore , we extended our model to allow for the possibility of observing PTVs in the haploinsufficient fraction of the genome by introducing a second parameter representing the number of variants originating either from biological ( incomplete penetrance , compensatory variants and low impact truncation ) or technical noise ( false positive sequencing or variant calling errors ) ( Methods , model A ) . Using this extension , the estimated fraction of genes intolerant to PTVs increased to 24 . 4% ( 95% CI , 18 . 3–32 . 1% , Fig 1B ) . An important consequence of biological and technical noise is that the fraction of genes bearing PTVs does not saturate as a function of the number of observed PTVs , but keeps rising . Our model predicts that after having sequenced 40 , 000 exomes ( representing a sample of approximately 90 , 000 PTVs ) more than 50% of newly identified truncated genes will result from biological and technical noise ( S1 Fig ) —an important consideration for ongoing sequencing programs and interpretation of resources , such as that of the Exome Aggregation Consortium ( ExAC , http://exac . broadinstitute . org ) . At the sample size of 40 , 000 exomes , and with 2 to 6% of all observed truncations due to technical errors [5 , 6 , 8] , 400 to 1025 genes intolerant to PTVs will exhibit truncations due to sequencing and variant calling errors . For the same sample size , 2345 to 2549 genes intolerant to PTVs will exhibit truncations due to incomplete penetrance , compensatory variants or low impact truncation . We next assessed the robustness of these estimates using an alternative approach that models the expected number of PTVs as a function of the observed synonymous coding variants ( Methods , model B ) . This model assumes that , in the absence of deleterious consequences , the number of heterozygous PTVs correlates with the number of synonymous variants observed in a gene . This approach resulted in highly similar estimates of fhi ( 26 . 1% , 95% CI 19 . 7–34 . 1% ) compared to the previous model . The posterior probabilities from model B are highly correlated with two published scores for haploinsufficiency[10 , 23] ( Spearman R > 0 . 31 , p-value<2 . 2e-16 in both cases ) . All three approaches showed a similar predictive power for 175 known haploinsufficient genes causing Mendelian disorders[15] ( S2 Fig ) . Model B also underscores that there is a continuum of tolerance to heterozygous truncation , with a large number of genes harboring fewer heterozygous PTVs than expected under a neutral model ( Fig 2 ) . It is however important to indicate that long genes have a high number of expected PTVs , thus the observation of a small number of PTVs in these genes still reflects a strong depletion and high posterior probability of being intolerant to heterozygous truncations . Indeed , of the 282 genes with a posterior probability of being intolerant to heterozygous truncation higher than 0 . 99 , 155 have observed PTVs ( Fig 2 and S2 Table ) . As expected , genes highly depleted of PTVs show similar properties to the genes without any PTVs ( S3 Table ) . In particular , they are enriched for known haploinsufficient genes associated with Mendelian diseases . The comparison between the observed and expected number of PTVs in a gene is key to evaluating its functional tolerance to truncation .
This work identifies a substantial proportion of genes that do not tolerate loss of one of the two gene copies , and by the evidence for a gradient of haploinsufficiency across a large proportion of the coding genome . Heterozygous PTVs are rarely compensated at the gene expression level , as shown in our previous work [13] and in recent analyses [7] . Despite the absence of dosage compensation , Rivas et al . suggest that homeostatic mechanisms at the cellular level maintain biological function [7] . However , we show clear evidence that over 10% of the genes cannot be compensated , while an additional 10 to 15% of truncated genes may be rescued by incomplete penetrance or compensatory mutations , or because the truncating variants are of limited functional impact . The importance of these variants has also been observed in model organisms . Studies in mice show that when homozygous knockout mutants are not viable , up to 71 . 7% of heterozygous PTVs have phenotypic consequences [24] . The systematic phenotyping of knockout mice also demonstrates that haploinsufficiency might be more common than generally suspected [25] . However , a practical limitation of the above approaches , in particular in animal studies , is that observation of phenotypes resulting from damaging mutations may require exposure to specific triggers or environmental interactions [6 , 25] . In contrast , in humans , life-long exposures may eventually reveal a phenotypic trait or disease associated with heterozygous gene truncations [8] . Here , clinical symptoms could be observed later in life , and present sporadically–not necessarily within a pedigree . This is illustrated by a recent report on the consequences of haploinsufficiency of cytotoxic T-lymphocyte-associated protein 4 gene ( CTLA-4 ) presenting as undiagnosed or misdiagnosed sporadic autoimmune disorder in the second to fifth decades of life [26] . Despite the prevalence of rare heterozygous PTVs , there has been more attention to the occurrence of homozygous truncations ( human knockouts ) . However , the genes that are observed with biallelic PTVs have , as a set , characteristics of dispensability: less conservation , greater redundancy , less biological and cellular centrality , and limited essentiality in mice and cellular models . Thus , we argue that homozygous truncations result from high allele frequency variants that are less likely to carry functional consequences ( the exception being recessive disorders in a population ) . There are a number of possible limitations to the present study . In the modeling work , we analyzed rare variants ( less than <1% allele frequency ) to focus on de novo events and for consistency with the de novo mutation rates estimated by Samocha et al . [12] . Nevertheless , our estimates held true when the whole analysis was repeated with the smaller subset of singleton variants—singletons possibly reflect false positive sequencing and alignment calls . It was also repeated with all variants irrespective of allele frequency ( instead of analyzing variants of less than 1% allele frequency ) ( S3 Fig ) . Initially we omitted splice-site variants because of less predictability of the functional consequences . Extending the analysis to include splice-site variants did not change the results and conclusions ( S3 Fig and S5 Table ) . This demonstrates that the results do not originate from a specific subset of variants . We did not have primary control on sequencing coverage for some of the exome sequence datasets that could result in ascertainment errors . To correct for this potential bias , we discarded genes where the observed number of synonymous mutations deviated from expectation . The intolerance of genes to de novo truncation was assessed across combined human populations . Therefore , estimations of the haploinsufficient genome account for the fraction of haploinsufficient genes common to all humans . Intolerance to heterozygous PTVs should be regarded as a different concept than gene sequence conservation . PTVs in a conserved gene might have a recessive mode of inheritance and are thus potentially observable in a viable individual . On the other extreme , positively selected genes could be haploinsufficient upon heterozygous truncation . These considerations notwithstanding , we consistently identified a quantifiable fraction of the human genome that is intolerant to heterozygous PTVs , with an estimated lower bound of 9 . 5% . The prevalent nature of rare heterozygous PTVs suggests that a map of “essentiality” on the basis of dominant loss of function is within reach . The concept of the essential genome has been explored in analyses of minimal bacterial genomes [27] , mouse knockout studies [28] , studies of transposon or chemical mutagenesis [29] , and in studies that used CRISPR-Cas9 genome-editing technology [14 , 30] . Here , we propose that mapping the haploinsufficient genome will improve the understanding of the genetic architecture of diseases . In agreement with the recent work of Li et al . , [6] we argue that the burden of rare human heterozygous variation is an unexplored source of diversity of phenotypic traits and diseases .
We collected exome data from public and non-public sources [31–38] ( S6 Table ) . With the exception of the Swiss HIV Cohort Study these sources are not disease-specific cohorts . Variants were filtered based on Hardy-Weinberg equilibrium ( discarded if p <1x10-8 ) . For public data sets , variants were called at the data source with their respective pipelines . For non-public data sets , sequence reads were aligned using BWA , and called with Haplotypecaller using GATK 3 . 1 . Variants were annotated with SnpEff 3 . 1 and filtered as described in [39–41] . Only transcripts from autosomal protein coding genes reliably annotated by the Consensus Coding Sequence ( CCDS , Release 12 04/40/2013 ) project[11] that underwent the full process of CCDS curation ( 'Public' status in CCDS terminology , n = 17 , 756 ) were considered . As a reference background throughout all analyses , a total number of 16 , 521 autosomal protein coding genes was obtained by considering genes with available de novo mutation rate from Samocha et al . [12] and with at least one synonymous , missense , stop-gain or frameshift variant detected in the exome data . We discarded genes where the observed number of synonymous mutations deviated from expectation ( see below ) . For consistency with [12] , we only retained variants mapping within the limits of the reference transcript used to assess the de novo mutation rate per gene . Furthermore , only rare stop-gain and frameshift variants ( allele frequency <1% ) were considered to assess the deviation from neutral expectations . Throughout the study we considered each rare variant as a single de novo event of mutation , irrespective of the number of individuals in which it was observed . Under a neutral model , the expected number of de novo PTVs ( stop-gain or frameshift ) in a gene is determined by its probability of de novo mutation ( assessed from the sequence context and gene length ) [12] and the number of sequenced individuals . However , potential intolerance to heterozygous truncation would decrease the expected number of de novo PTVs as a consequence of embryonic or early life lethality . To model the expected number of variants in a gene accounting for potential deleterious effects , we used two approaches . First we evaluated the relative distribution of PTVs across genes ( hereafter the model A ) . This model assumes that genes tolerating heterozygous truncation will be found truncated in the population according to their relative probability of de novo mutation ( relative to the rest of genes ) , while a fraction of genes will not be observed as truncated due to early lethality . Alternatively , we assessed a second model ( hereafter the model B ) in which the absolute number of de novo PTVs in a gene is estimated from the probability of de novo PTVs and the absolute number of observed de novo synonymous coding variants in that gene . Model A is formulated as follows . The total number V of observed PTVs is composed of a fraction V e of false positive variants ( including sequencing errors and incomplete penetrance ) and the complementary fraction V ( 1 − e ) . We assume that the total set of genes G can be divided in two classes of genes , named HI for the haploinsufficient class and HS for non-haploinsufficient class of relative sizes fhi and ( 1 − fhi ) respectively . We assume that the fraction of variants V e is distributed across all genes proportionally to their relative de novo neutral mutation rates . However , the V ( 1 − e ) fraction of variants should only be observed in the ( 1 − fhi ) fraction of HS genes . Therefore , in model A the expected number of variants in a HI gene g is Eg|HItrunc=Vepgtrunc∑i∈Gpitrunc while the expected number of variants in a HS gene g is Eg|HStrunc=Vepgtrunc∑i∈Gpitrunc+V ( 1−e ) pgtrunc∑i∈HSpitrunc Assuming that 1−fhi=∑i∈HSpitrunc∑i∈Gpitrunc , then Eg|HStrunc=Vepgtrunc∑i∈Gpitrunc+V ( 1−e ) pgtrunc∑i∈Gpitrunc11−fhi We note that model A is based on observed variants and therefore false negative errors are not considered . To formulate model B , we assume that the expected number of de novo synonymous mutations in a gene g is Egsyn=Mpgsyn , where pgsyn is the de novo rate of synonymous mutations in a gene g and M is a constant . Following [12] we estimate M from the regression of the observed number of synonymous mutations ( Ogsyn ) in a gene on pgsyn: Ogsyn=Mpgsyn+e . To avoid genes with low coverage , we disregarded from the analysis those genes whose residual in the above regression is higher than 3 times the standard deviation of all residuals . We note that , in contrast to [12] we omit the intercept term in this regression , because we expect no variants in a gene for which pgsyn equals zero . Having estimated M , the expected number of PTVs in a gene g is given by: Egtrunc=Mpgtrunc . Introducing gene specific deviations from the neutral expectation as well as for systematic errors , the number of observed PTVs can be written as: Egtrunc=Mpgtruncsg , where sg accounts for gene specific differences . We do not estimate sg for each gene , but assume that genes can be classified into two groups ( haploinsufficient and non-haploinsufficient ) , each having a distinct class specific value ( sHI and sHS ) : Eg|HStrunc=MpgtruncsHS Eg|HItrunc=MpgtruncsHI sHI and sHS include the sum effect of systematic false positive and negative errors , as well as class specific differences in the penetrance mutations , however it is not possible to separate these individual components . Both in model A , or in model B , to estimate the fraction of genes intolerant to heterozygous PTVs we use the following mixture model . We define a random variable xg as the number of PTVs in gene g . A latent random variable zg can take two values: HI or HS and has the probability density distribution: P ( zg=HI ) :=fhi P ( zg=HS ) :=1−fhi where the parameter fhi represents the fraction of genes intolerant to heterozygous PTVs . The conditional probability distribution of xg given zg is defined as: P ( xg=k|zg=HI ) =Poisson ( k , λHI ) P ( xg=k|zg=HS ) =Poisson ( k , λHS ) λHS=Eg|HStrunc λHI=Eg|HItrunc . where Eg|HStrunc and Eg|HItrunc are the expected number of PTVs in a gene g from the HS and the HI classes respectively as formulated in either model A or model B . Marginalizing over the values of the latent variable zg yields the probability density distribution of xg as: P ( xg=k ) =fhiPoisson ( k , λHI ) + ( 1−fhi ) Poisson ( k , λHS ) . The probability that any gene acquires k variants is: P ( X=k ) =∑gP ( xg=k ) ngenes , wherengenesis the total number of genes . The model’s parameters ( e and fhi in model A , and fhi , sHI and sHS in model B ) are estimated by fitting the cumulative density distribution of X to the empirical cumulative density distribution of the data by least-squares fitting using the Nelder-Mead simplex numerical optimization algorithm ( as implemented in the Apache Commons Math library ) . This method provided better estimates for reproducing the distribution of variant counts per gene compared to other alternatives considered ( S4 Fig ) . In order to estimate the variability of the inferred model parameters we repeated the parameter estimation on 500 bootstrap replicates . Each bootstrap replicate was generated by resampling the list of genes with replacement . Using the estimated parameters we calculate the posterior probability of haploinsufficiency for gene g as: P ( zg=HI|xg=og ) =P ( zg=HI ) P ( xg=og|zg=HI ) P ( xg=og ) , where og is the observed number of PTVs in the gene g . Gene sets were obtained from the Reactome pathway database version 40 ( http://www . reactome . org/ ) . dN/dS values were assessed as described in [13] . Degree of connectivity in the protein-protein interaction network was obtained from the OGEE database ( http://ogeedb . embl . de/ ) . Paralogs were counted using Ensembl Biomart’s 'Human Paralog Ensembl Gene ID' attribute . Genes in protein complexes were obtained from Gene Ontology term GO:0043234 ( named “protein complex” ) . Genes affecting cell viability in CRISPR-Cas9 experiments were collected from [14 , 30] . Severity of protein truncation was assessed by the NutVar score ( http://nutvar . labtelenti . org ) [13] . Phenotypic consequences in mouse models were downloaded from ftp://ftp . informatics . jax . org/pub/reports/HMD_HumanPhenotype . rpt and filtered for the Mammalian Phenotype Ontology term “Mortality/Aging” ( MP:0010768 , MP:0005374 , MP:0005373 , MP:0005372 ) . For the assessment of depletion or enrichment of functional gene sets we used one tailed hypergeometric test . We adjusted the p-values by the Benjamini- Hochberg method to correct for multiple testing . We tested pathways with at least 100 elements only . We estimated the number of exomes required for a certain number of sampled PTVs using the jackknife projection as in [42] .
|
Genome sequencing provides evidence for large numbers of putative protein truncating variants in humans . Most truncating variants are only observed in few individuals but are collectively prevalent and widely distributed across the coding genome . Most of the truncating variants are so rare that they are only observed in heterozygosis . The current study identifies 10% of genes where heterozygous truncations are not observed and describes their biological characteristics . In addition , for genes where rare truncations are observed , we argue that these are an unexplored source of diversity of phenotypic traits and diseases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
The Characteristics of Heterozygous Protein Truncating Variants in the Human Genome
|
Malaria infection begins when a female Anopheles mosquito injects Plasmodium sporozoites into the skin of its host during blood feeding . Skin-deposited sporozoites may enter the bloodstream and infect the liver , reside and develop in the skin , or migrate to the draining lymph nodes ( DLNs ) . Importantly , the DLN is where protective CD8+ T cell responses against malaria liver stages are induced after a dermal route of infection . However , the significance of parasites in the skin and DLN to CD8+ T cell activation is largely unknown . In this study , we used genetically modified parasites , as well as antibody-mediated immobilization of sporozoites , to determine that active sporozoite migration to the DLNs is required for robust CD8+ T cell responses . Through dynamic in vivo and static imaging , we show the direct uptake of parasites by lymph-node resident DCs followed by CD8+ T cell-DC cluster formation , a surrogate for antigen presentation , in the DLNs . A few hours after sporozoite arrival to the DLNs , CD8+ T cells are primed by resident CD8α+ DCs with no apparent role for skin-derived DCs . Together , these results establish a critical role for lymph node resident CD8α+ DCs in CD8+ T cell priming to sporozoite antigens while emphasizing a requirement for motile sporozoites in the induction of CD8+ T cell-mediated immunity .
Sterile immunity against live sporozoite challenge is elicited by immunization with radiation-attenuated sporozoites [1] and is , in part , mediated by CD8+ T cells specific for the Plasmodium circumsporozoite ( CS ) antigen [2 , 3] . Using a model mimicking natural exposure to sporozoite-infected mosquitoes , we previously demonstrated that CS-specific CD8+ T cell responses are primed by DCs in the skin-draining lymph nodes ( DLNs ) of mice [4] . Following activation in the DLNs , CS-specific CD8+ T cells migrate to the liver where they eliminate parasite-infected hepatocytes [4 , 5] . Subsequently , others have shown that immune responses generated in the DLNs are sufficient for sterile protection against live sporozoites [6] . These findings challenged the prevalent idea that CD8+ T cell responses against malaria liver stages originate exclusively in hepatic tissues . How do skin-deposited sporozoites elicit cell-mediated immune responses in the DLNs ? The induction of malaria-specific CD8+ T cells is critically dependent on dendritic cells ( DCs ) [4 , 7–11] , a diverse population of specialized antigen-presenting cells ( APCs ) . The phenotypic diversity of DCs is exemplified in murine skin-DLNs which contain lymphoid-tissue resident DCs ( composed of CD8α+ and CD11b+ subsets ) , B220+ plasmacytoid DCs , and three distinct subsets of skin-derived migratory DCs [12] . In addition , DCs differ in their ability to present antigen to CD4+ and CD8+ T cells [12 , 13] and are located within different compartments in the DLN [14 , 15] . For any cutaneously-deposited pathogen or vaccine , this phenotypic and spatial heterogeneity raises the question of how antigen is transported to the secondary lymphoid tissue and which DCs are responsible for priming CD8+ T cells . This issue is especially important for malaria given that immunization with sporozoites represents the gold-standard for malaria vaccination and understanding the factors that contribute to efficient antigen presentation may aid vaccine design [16] . Several studies have examined T cell , APC , and parasite interactions in infections other than malaria [17 , 18]; however , the role of different DC subsets in the transport and presentation of parasite antigens is not well understood or , in the case of Leishmania infection , is controversial [19 , 20] . In contrast , these questions have been well studied in viral models . In infections with tissue-tropic viruses , such as influenza virus and Herpes simplex virus ( HSV ) , tissue-derived DCs play prominent roles in either the transport of antigen to lymph node ( LN ) -resident DCs or the direct presentation of antigen to CD8+ T cells [21 , 22] . In other infections such as vaccinia virus in which the virus can infect dendritic cells , direct presentation of antigen to CD8+ T cells has been observed just beneath the subcapsular sinus [23] and within the LN parenchyma [24 , 25] . Based on these viral paradigms , there are several potential routes by which antigen might be presented to malaria-specific CD8+ T cells after skin delivery of sporozoites . One possibility is that sporozoite antigen is acquired in the dermis by skin-resident migratory DCs , trafficked to the DLNs , and presented directly to CD8+ T cells . Alternatively , skin-emigrant DCs may transfer antigens to LN-resident DCs for presentation and CTL activation . These models are supported by the fact that sporozoites are injected and , in some cases , develop in the skin after mosquito inoculation ( reviewed in [26] ) . However , these models fail to take into account the sporozoites’ exquisite motility and the superior immunogenicity of live , irradiated sporozoites versus dead sporozoites . Therefore , we investigated a third possibility: that CD8+ T cell priming does not require skin-derived DCs , but instead , depends on antigen delivery to lymphoid tissues by migratory parasites . To acquire insight into these issues , we used genetically manipulated parasites , advanced imaging technologies , and transgenic mice with constitutive or conditional loss of distinct APC subsets . We found that skin-derived DCs are not needed for CD8+ T cell priming; rather the ability of parasites to actively traverse out of the skin is critical for the induction of CD8+ T cell responses by LN-resident DCs . Our data provide the most complete picture to date of the events required for the development of an adaptive cell-mediated immune response against skin-deposited malaria liver stages .
Given the prolonged residence and development of parasites in the skin after inoculation [27 , 28] , we hypothesized that migratory skin DCs may be critically involved in CD8+ T cell priming , either by direct presentation of sporozoite antigens or via transfer of such antigens to LN-resident DCs . To evaluate the contribution of skin-derived migratory DCs we used a knock-in mouse model in which the diphtheria toxin receptor ( DTR ) is expressed under the control of the murine langerin promoter [15] . Administration of diphtheria toxin ( DT ) to these animals rapidly depletes Langerhans cells in the epidermis ( which only recover after ~2 weeks ) , langerin+ CD103+ DCs in the dermis ( which begin to recover after ~ 5 days ) , and langerin-expressing skin-emigrant DCs in the DLN [15 , 29–31] and S1A-C Fig . Though langerin is expressed by a population of LN-resident CD8α+ DCs [15 , 30] , this subset is missing on the C57BL/6 background [32] . Therefore , MuLangerin-DTR/EGFP mice on the C57BL/6 background allow us to examine the role of Langerhans cells and langerin+ dermal DCs in CD8+ T cell priming without affecting LN-resident CD8α+ DCs . Following depletion of these subsets with DT , we transferred OT-1 TCR transgenic T cells specific for the H-2Kb-SIINFEKL ligand into treated animals and immunized these mice with P . berghei CS5M sporozoites , a transgenic parasite expressing SIINFEKL within the CS protein [7] . To mimic the natural situation as closely as possible , mice were immunized through the bites of P . berghei CS5M-infected mosquitoes and the magnitude of the OT-1 response was examined in the DLNs , spleens , and livers 10 days later . There was no difference in the expansion nor antigen-specific IFN-γ production of OT-1 cells recovered from WT and MuLangerin-DTR/EGFP mice treated with DT ( Fig . 1 and S1D Fig . ) . These data demonstrate that Langerhans cells and langerin+ dermal DCs are dispensable for the transport and/or presentation of sporozoite antigens to CD8+ T cells in the DLN . Having established a nonessential role for Langerhans cells and langerin+ dermal DCs in sporozoite antigen presentation to CD8+ T cells , we hypothesized that the parasites themselves may deliver antigens to LN-resident DCs . To investigate the contribution of direct parasite access to the DLN for T cell activation , we applied two approaches . First , we limited parasite motility by antibody-mediated immobilization [33–35] . To this end , we pre-treated sporozoites with a CS-specific mAb before intradermal ( ID ) injection of the parasites . Additionally , we used monovalent Fab fragments in these experiments to avoid potential opsonization of the organism . We then assessed the parasite burden in the DLNs at 2 and 5 hours post-inoculation , as this is when we have previously detected the highest numbers of sporozoites in this organ [4] . Strikingly , both full-length CS-specific mAb ( 3D11 ) and monovalent Fab fragments ( 3D11:Fab ) significantly reduced parasite burden in the DLN as compared to parasites incubated with a control mAb ( Fig . 2A ) . Such a reduction in direct parasite access to the DLN was accompanied by a dramatic reduction in the clonal expansion of antigen-specific CD8+ T cells in the DLN as well as the spleen and liver ( Fig . 2B ) , two sites of effector T cell migration after initial priming in the DLN [4] . As a second approach , we generated mutant parasites that expressed the SIINFEKL epitope in a CS-restricted manner and were unable to migrate out of the skin due to a deletion in the N-terminal third of the CS protein [36] , P . berghei CS5MΔN ( S2A-C Fig . ) . In further agreement with the hypothesis that sporozoite motility is required for parasite entry into the lymphatics , P . berghei CS5MΔN sporozoites exhibited a severe impairment in their migration to the DLN ( S2D Fig . ) . To evaluate the effect of these changes in DLN access on the development of cell-mediated immunity , mice were given CFSE-labeled OT-1 cells , injected ID with P . berghei CS5M or P . berghei CS5MΔN sporozoites , and OT-1 proliferation was examined 3 days later in the DLN . OT-1 proliferation was significantly reduced in mice injected with P . berghei CS5MΔN sporozoites ( S2E and F Fig . ) . To determine whether the diminished CD8+ T cell response observed at day 3 was due to a delay in sporozoite migration to the DLN , rather than an absolute reduction , we measured the expansion of OT-1 cells in the DLN , spleen , and liver 10 days after ID inoculation of P . berghei CS5M or P . berghei CS5MΔN sporozoites . In line with our previous findings , we found that ID immunization with P . berghei CS5MΔN sporozoites led to drastically reduced OT-1 responses 10 days after immunization as compared to P . berghei CS5M sporozoites ( Fig . 2C ) . Importantly , when injected IV , P . berghei CS5MΔN sporozoites were fully infectious ( S2G Fig . ) and induced similar OT-1 responses in the spleen and liver as compared to control parasites ( P . berghei CS5M ) ( Fig . 2D ) . These findings demonstrate that the SIINFEKL epitope is expressed by the mutant parasite and is efficiently presented by APCs upon sporozoite access to lymphoid organs . Together , our studies with Fab-treated and transgenic parasites establish a critical role for sporozoite migration to the DLN in CD8+ T cell priming after ID inoculation . The requirement for sporozoite-mediated delivery of antigen to the DLN was unexpected and led us to further characterize the location and behavior of parasites in this organ . P . berghei CS5M sporozoites were injected ID into footpads and the popliteal LNs were harvested at various times after sporozoite inoculation , fixed , sectioned , and stained for confocal analysis . We detected sporozoites in the DLN as early as 30 minutes after sporozoite inoculation with the majority of sporozoites in close association with CD169+ macrophages populating the subcapsular sinus ( SCS ) ( Fig . 3A ) . By 1 hour after ID inoculation , sporozoites were still present in , or immediately adjacent to , the subcapsular or medullary sinuses and displayed the characteristic crescent shape of intact sporozoites ( Fig . 3B ) . Higher magnification revealed the presence of particulate CS staining around sporozoites in the DLN ( Fig . 3B ) , reminiscent of the CS vesicles first detected by transmission electron microscopy [37] . The presence of intact sporozoites peaked at around 2 hours post-inoculation but declined over the next few hours ( Fig . 3C ) . The remaining intact sporozoites were enriched in deeper interfollicular LN areas and could be found in association with DCs ( Fig . 3C and D ) . In contrast to intact parasites , particulate CS antigen was frequently observed in the LN parenchyma and underneath the B cell follicles ( Fig . 3E ) , with the proportion of CS-positive events associated with DCs increasing with time ( Fig . 3F ) . To observe the behavior and fate of sporozoites in the DLN in vivo , we injected GFP-expressing P . berghei CS5M sporozoites into the footpads of CD11c-EYFP reporter mice [38] . Using multiphoton intravital microscopy ( MP-IVM ) we were able to observe motile P . berghei CS5M GFP sporozoites in the superficial 100 μm of the DLN 5 hours after ID injection ( Fig . 3G and S1 Movie ) . The sporozoites we observed in the DLN did not display gliding motility and moved more slowly than what has been reported in the skin [39] . The reduced motility we observed in the DLN is likely a constraint of the tissue microenvironment because sporozoites in the salivary glands of mosquitoes also exhibit decreased speeds as compared to sporozoites in the skin [39] . In agreement with our static imaging results showing DC-associated sporozoites , dynamic imaging revealed internalization of a live sporozoite by a DC after inoculation by infectious mosquito bites ( Fig . 3H and S2 Movie ) . Together , these findings provide direct evidence for the migration of viable sporozoites to the DLN , their access to the parenchymal region after ID deposition , and direct uptake by LN-resident DCs . Our observations demonstrating the early acquisition of sporozoite-derived antigens by LN-resident DCs prompted us to evaluate the kinetics of CD8+ T cell priming in the DLN . To study antigen presentation in the DLN , we examined CD8+ T cell cluster formation around CD11c+ antigen-presenting cells ( APCs ) in situ , an established surrogate for antigen presentation [24 , 25 , 40–42] . In these studies , a large number of precursor cells ( 2 . 5–10×106 cells ) was required to visualize antigen presentation by microscopy [24 , 25 , 40–42] . Accordingly , we fluorescently labeled and transferred 2×106 OT-1 cells to recipient mice before ID inoculation of sporozoites into the footpads . DLNs were fixed , sectioned , and stained for confocal analysis at 8 , 16 , 24 , and 48 hours after ID inoculation ( Fig . 4A ) . We detected OT-1 clusters in the paracortex and the boundary between the paracortex and follicles known as the cortical ridge [43] as early as 8 hours after parasite injection and these clusters increased in size and number by 16 hours . By 24 and 48 hours , the fluorescence intensity of the labeled OT-1 cells was substantially reduced , an effect likely due to dilution of the cytoplasmic label following OT-1 proliferation . Importantly , we did not observe cluster formation , nor reduced fluorescence of the labeled and transferred OT-1 cells , in mice injected with control parasites lacking the SIINFEKL epitope in CS ( S3 Fig . ) . To examine directly the DC subset ( s ) involved in the presentation of sporozoite antigens , we employed histo-cytometry , an analytical microscopy method that provides quantitatively similar results to flow cytometry but additionally gathers spatial information , allowing for the quantification of cellular interactions in situ [14] . Because our results indicated a critical role for LN-resident DCs in CD8+ T cell responses against malaria sporozoites , we designed a 6-color panel to discriminate between CD8α+ and CD11b+ LN-resident DCs on stained LN sections ( S4A and B Fig . ) . As before , we relied on OT-1 cluster formation as a surrogate for antigen presentation and quantified DCs in direct association with OT-1 clusters ( S4B Fig . ) . Histo-cytometric analysis revealed the presence of CD8α+ DCs in direct physical contact with OT-1 clusters at 8 and 16 hours after ID inoculation of sporozoites ( Fig . 4B-D ) . At these early time points , the great majority of OT-1 clusters were associated with CD8α+ DCs but not CD11b+ DCs , indicating CD8+ T cell activation by the LN-resident CD8α+ DC subset ( Fig . 4C and D ) . By 24 hours , OT-1 clusters were smaller but still enriched for the presence of CD8α+ DCs ( S4C-E Fig . ) , whereas OT-1 clusters were nearly absent at 48 hours ( S4F Fig . and Fig . 4A ) . These results demonstrate activation of naïve antigen-specific CD8+ T cells by CD8α+ DCs in the DLN and are fully consistent with the accepted model of temporally distinct phases of T cell priming [41] . It is well established that effector formation requires long-lasting , stable interactions between T cells and DCs bearing cognate antigen [41 , 44] . We therefore utilized dynamic MP-IVM to examine the duration of CD8+ T clusters in the DLN . OT-1 cells and polyclonal CD8+ T cells expressing distinct fluorescent proteins were purified and transferred to recipient mice 1–4 days prior to sporozoite injection and imaging . The superficial 200 μm of the popliteal LN accessible to 2P imaging was imaged 7–12 hours after ID inoculation of P . berghei CS5M sporozoites ( S3 Movie ) . The dynamic behavior of OT-1 cells differed from polyclonal CD8+ T cells in the same DLN 12 hours after ID inoculation ( Fig . 5A ) , with OT-1 cells exhibiting a significantly reduced mean speed ( Fig . 5B ) and confinement ratio ( Fig . 5C ) as compared to polyclonal CD8+ T cells . To ascertain whether these physical interactions were correlated with OT-1 activation , we transferred OT-1 cells to recipient mice and injected mice ID with P . berghei CS5M sporozoites or P . berghei ANKA ( WT ) sporozoites that do not carry the SIINFEKL epitope in CS . Following sporozoite inoculation , DLNs were harvested and the up-regulation of CD69 , an early activation marker , and the production of IFN-γ were examined on OT-1 cells by flow cytometry . By 8 hours post-inoculation with P . berghei CS5M sporozoites , the majority of OT-1 cells were CD69+ . The proportion of CD69+ OT-1 cells steadily increased by 16 hours and decreased 24 hours after sporozoite inoculation ( Fig . 5D ) . Antigen-stimulated OT-1 cells produced detectable levels of IFN-γ 16 hours after P . berghei CS5M injection with the proportion of IFN-γ-producing cells increasing modestly with time ( Fig . 5E ) . In contrast , we did not observe up-regulation of CD69 or IFN-γ production by OT-1 cells in mice injected with parasites lacking the SIINFEKL epitope , P . berghei ANKA ( WT ) ( Fig . 5D and E ) . These studies indicate that the early-sustained interactions between sporozoite antigen-bearing DCs and CD8+ T cells occurs under the conditions in which we observe robust T cell priming and activation . Given the enrichment of CD8α+ DCs in OT-1 clusters at time points corresponding to CD8+ T cell activation , we next examined the contribution of these DCs to sporozoite-specific T cell activation in Batf3−/− mice that possess substantial defects in the numbers of CD8α+ DCs [45] , [46] . CFSE-labeled OT-1 cells were transferred to WT and Batf3−/− C57BL/6 mice 1 day before ID inoculation with P . berghei CS5M sporozoites . The number of divided OT-1 cells was diminished by 50% in Batf3−/− C57BL/6 mice at day 3 in the DLN ( Fig . 6A and B ) . To further investigate the requirement for CD8α+ DCs in the presentation of sporozoite antigens , we examined the OT-1 response in Batf3−/− C57BL/6 mice at several time points after exposure to P . berghei CS5M-infected mosquito bites . A significant reduction in OT-1 expansion was observed in the DLN , spleen , and liver of Batf3−/− C57BL/6 mice at day 8 , 14 , and 17 ( Fig . 6C and S5A Fig . ) . Following a report demonstrating the potential presence of CD8α+ DCs in the DLNs of Batf3−/− C57BL/6 mice at steady state and in additional organs after administration of IL-12 [47] , we repeated the priming experiments in Batf3−/− mice on the 129/SvEV background in which CD8α+ DCs and langerin+ dermal DCs are largely absent and observed a 75% reduction in OT-1 expansion ( S5B and C Fig . ) . Besides the important role for CD8α+ DCs in the activation of CS-specific CD8+ T cells , we also found a reduction in CD8+ T cell priming in the absence of CD169+ macrophages ( S6 Fig . ) . Despite a reduction in OT-1 expansion in DT-treated CD169-DTR mice , we did not observe OT-1 cluster formation around CD169+ macrophages lining the subcapsular or medullary sinuses ( Fig . 4 ) and therefore conclude that CD169+ sinus-associated macrophages may play an indirect role in the generation of optimal CD8+ T cell responses .
Plasmodium sporozoites are introduced into the dermis of their mammalian host by infectious mosquito bites ( reviewed in [26] ) , and a proportion of these sporozoites enter the lymphatics early after inoculation [4 , 39 , 48] . Following this natural route of parasite delivery , CS-specific CD8+ T cells are primed in the LNs draining the site of sporozoite inoculation [4] . Here , we present clear evidence indicating that direct parasite access to the DLN is required for the induction of CD8+ T cells directed against P . berghei CS ( Fig . 7 ) . Two independent experimental approaches were used to establish the immunological significance of parasites in the DLN . First , immobilization of sporozoites through pre-treatment with a CS-specific mAb , or monovalent Fab fragments derived from this antibody , resulted in reduced parasite burdens in the DLN and severely diminished CD8+ T cell responses directed against an antigenic determinant in CS . Second , mutant parasites with a defect in their ability to exit the skin and enter the DLNs also generated poor CD8+ T cell responses when injected ID . The relationship between live , motile sporozoites and the induction of robust CD8+ T cell responses is further supported by the observation that when dead sporozoites are injected ID they do not reach the DLN [39] and do not induce CD8+ T cell responses [4] . Reduced CS-specific CD8+ T cell responses were also observed in the spleen following IV injection of dead sporozoites [49] . This was surprising because IV injected sporozoites have direct access to the spleen , the site of CTL activation after IV immunization [50] . Therefore , the importance of live sporozoites in the development of CD8+ T cells likely extends beyond efficient drainage to lymphoid organs and may indicate a requirement for sporozoite motility within these organs . Sporozoites are renowned for their ability to migrate through many different cell types [51–54] . As sporozoites glide they release trails of CS comprised of 25–90 nm beadlike particles [37] . Our confocal analysis revealed the accumulation of particulate CS around intact parasites 1 to 2 hours after sporozoite injection . Based on the nature of CS staining and the timing of our in vivo observations , we hypothesize that particulate CS is actively shed by motile sporozoites in the DLN and may represent a source of antigen for cross-presentation by LN-resident DCs . In addition , we frequently observed sporozoites underneath the B cell follicles and immediately adjacent to the cortical ridge of the DLN . Therefore , live sporozoites may provide a source of CS that can be directly sampled and presented by DCs in the cortical ridge and paracortex of the DLN , the location of CD8+ T cell priming in our model . In further support of this idea , we visualized abundant stores of DC-associated CS in the DLN , which could derive from internalization of shed CS or direct uptake of live sporozoites as shown in S2 Movie . The observation of CD8+ T cell cluster formation and IFN-γ production as early as 8 and 16 hours post-immunization , respectively , is in agreement with a limited role for migratory skin-DCs since these cells require anywhere from 16 hours to 5 days to migrate to the DLN [15 , 55] . In addition , we observed robust CD8+ T cell priming in the absence of langerin+ dermal DCs and Langerhans cells using the MuLangerin-DTR/EGFP mouse model . However , it should be noted that migratory langerin- dermal DCs remain in the skin of DT-treated MuLangerin-DTR/EGFP mice [15] and two vaccine models have demonstrated the involvement of these cells in the presentation of skin-derived antigens to CD8+ T cells [56 , 57] . Nonetheless , the location of CD8+ T cell cluster formation complements the diminished priming we observed in Batf3-deficient mice because CD8α+ DCs are known to populate the cortical ridge and paracortex of the DLN [14 , 43] . Importantly , histo-cytometric analysis of LN sections revealed a significant enrichment of CD8α+ DCs in association with OT-1 clusters at time points corresponding to T cell activation . Although sporozoites can invade and develop within LN-resident CD11b+ myeloid cells [58] , our studies demonstrate a pivotal role for CD8α+ DCs in the presentation of sporozoite antigens to CD8+ T cells . The reduced CD8+ priming we observed in mice lacking CD169+ macrophages further emphasizes the importance of LN-resident APCs in liver stage immunity . Because OT-1 clusters were not directly associated with CD169+ macrophages , we conclude that these cells play an indirect role in CD8+ T cell priming . CD169+ macrophages may facilitate CD8+ T cell priming via the capture and transfer of antigen to cross-presenting DCs , a phenomenon shown to take place in the spleen following delivery of blood-borne antigens [59] , or in a cytokine-dependent manner as demonstrated for LN-resident innate lymphoid cells [60] . Regardless of the mechanism , elucidating the contribution of CD169+ macrophages to liver stage immunity remains a key direction for future research . Others have shown a requirement for CD8α+ DCs in the presentation of Plasmodium blood and liver stage antigens [8 , 9 , 61–62] . Our study now documents the presentation of sporozoite antigens by LN-resident CD8α+ DCs in situ while providing a potential explanation—direct parasite access to these DC subsets—for the superior immunogenicity of live versus dead parasites . We have based our analysis on the immunodominant CS protein [63]; however , it is possible that CD8α+ DCs present additional liver stage antigens in the liver draining lymph nodes or the liver site of infection , as shown previously [9] . Because liver stage antigens elicit broad , cross-stage protection [64–65] , an important future direction will be to evaluate the contribution of CD8α+ DCs to the generation of CD8+ T cells against these antigens . The recent success of the IV administered live , attenuated P . falciparum sporozoite vaccine ( PfSPZ ) [16] , along with the discovery of a human homologue of murine CD8α+ DCs [66–68] , suggests that antigen delivery to this DC subset may also be critical for CTL responses in humans . Because large-scale vaccination with the IV administered PfSPZ vaccine is likely infeasible , there is a growing need to improve the efficacy of non-IV routes , which have been shown to be inefficient in mice [6 , 69] , and suboptimal in humans [70] . One promising strategy is to use adjuvants to reduce the number of irradiated sporozoites required to confer sterile immunity after ID immunization [69] . Given the vital role for CD8α+ DCs in the presentation of malaria antigens , and the unique expression of pattern recognition receptors among different DC subsets [71–72] , it will be interesting to determine whether adjuvant activation of CD8α+ DCs can provide better protection after ID immunization . The antagonistic effect of anti-sporozoite antibodies on CD8+ T cell priming reported here and elsewhere [7] suggests that humoral immunity may be generated at the expense of cellular immunity . Therefore , it may be difficult to generate robust CD8+ T cell responses in individuals with high-titer anti-CS antibodies . This assertion is based on the following observations: ( 1 ) antibody-treated parasites are immobilized in the skin [35]; ( 2 ) immobilized parasites are cleared by CD11b+ leukocytes in the skin [51]; and ( 3 ) skin-resident CD11b+ leukocytes are largely dispensable for CS-specific CD8+ T cell responses ( reported here ) . Thus , a major challenge for the malaria vaccine effort will be to incorporate the very behaviors required for successful parasite infection—cell traversal and cell invasion—into a vaccine that elicits long-lasting , protective immunity . A critical question remains: Why do malaria parasites go to the DLN ? This question gains additional significance when one considers that the LN is dispensable for parasite development but imperative for host immune responses . As there is no evidence for sporozoite chemotaxis to lymphatic vessels , parasite entry into the lymphatic vessels in the skin appears to be an accidental but migration-dependent process . Importantly , this phenomenon is not unique to rodent malaria models but was also observed in early human studies [73] . Together , these results underscore the importance of live , motile sporozoites in the induction of CD8+ T cell responses and thus , have implications for whole parasite vaccine efforts .
All animal procedures were approved by the Institutional Animal Care and Use Committee of Johns Hopkins University ( Protocol number: MO13H123 ) following the NIH guidelines for animal housing and care or performed according to protocols approved by the NIAID and NIH Animal Care and Use Committee . A detailed list of mouse strains can be found in the S1 Methods section . P . berghei CS5M parasites [7] and P . berghei ANKA parasites expressing GFP under the HSP70 promoter ( P . berghei-ConF ) have been described previously [51] . P . berghei CS5MΔN parasites were created by transfection of P . berghei ANKA with a linearized pR-CSRepΔN5M plasmid . The pR-CSRepΔN5M plasmid was generated by ligating an EagI-PacI digestion product of the pCSRep5M plasmid into the pR-CSRepΔN plasmid containing the N-terminal deletion of the CSP locus and a drug selection cassette [36] . Mutant parasites were selected by pyrimethamine and cloned by limiting dilution . Sporozoites were dissected by hand from Anopheles stephensi salivary glands , radiation-attenuated in a cesium radiator at 20 , 000 rad . , and injected ID with a nanofil syringe equipped with a 33 gauge needle ( World Precision Instruments , Sarasota , FL ) . Alternatively , mice were injected by the bites of 20–30 irradiated day 21 P . berghei CS5M-infected mosquitoes . P . berghei CS5M-GFP parasites were generated by crossing P . berghei CS5M parasites with P . berghei ConF parasites , followed by selection as described previously [74] . At the indicated time points , LNs were harvested in TRIzol Reagent ( Life Technologies ) and parasite RNA was extracted according to the manufacturer’s instructions . Parasite burden was measured by RT-PCR using primers that recognize P . berghei specific sequences within the 18S rRNA and SYBR Green ( Applied Biosystems ) as outlined previously [75] . Parasite burdens were normalized with GAPDH expression . Single cell suspensions were prepared by grinding the spleen and DLNs between the rough sides of 2 microscope slides and by filtering with nylon mesh . Liver homogenates were passed over a 35% percoll gradient and filtered through nylon mesh . Lymphocytes were resuspended in DMEM supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , 50 mM sodium bicarbonate , 2mM glutamine , 100 U/ml penicillin , 100 μg/ml streptomycin , 25 mM HEPES . OT-1 T cells or polyclonal control CD8+ T cells were sorted with a MACS CD8-negative selection kit ( Miltenyi ) . Prior to cell transfer , the quality of purification was verified by flow cytometry using antibodies against Vα2 and CD8 . Cells of approximately 90% purity were stained and transferred 1 day before sporozoite inoculation . 5×103 OT-1 ( CD45 . 1+ ) cells were transferred IV to recipient ( CD45 . 2+ ) mice 1 day before needle or mosquito bite inoculation of irradiated sporozoites . In some experiments , expansion of OT-1 cells was measured by flow cytometry on days 8 , 10 , 14 , or 17 . In other experiments , 1–2×106 OT-1 cells were labeled with 10 μM CFSE ( Invitrogen ) or 100 μM CellTracker Blue CMF2HC ( Invitrogen ) and transferred IV to recipient mice . OT-1 cluster formation and proliferation were evaluated 8–72 hours after sporozoite inoculation . Detailed methods for sample preparation , imaging , and data analysis are available in the S1 Methods section . Mice were anesthetized and popliteal LNs were exposed . MP-IVM was performed by a protocol modified from a previous report [76] . Flow cytometric data was analyzed with FlowJo software ( TreeStar ) . Raw imaging data were processed and analyzed with Imaris software ( Bitplane ) . Differences between two groups were compared using a Student’s t test ( two-tailed ) for normal distributions or Mann-Whitney test for non-normal distributions . One-way analysis of variance with Tukey post-test was used to compare differences between more than two groups . ( ns = not significant , * = P < 0 . 05 , ** = P < 0 . 01 , *** = P < 0 . 001 )
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Malaria is responsible for the deaths of 0 . 5–2 million people each year . A safe and effective vaccine is likely needed for the control or eradication of malaria . Immunization with irradiated sporozoites , the infectious stage of the parasite transmitted by mosquitoes , protects people against malaria through the activation of specialized effector cells called CD8+ T cells , which can eliminate live parasites . The induction of such malaria-specific CD8+ T cells is critically dependent on dendritic cells , a diverse population of antigen-presenting cells . It was previously unclear how dendritic cells acquire sporozoite antigens to induce the protective CD8+ T cell response . Using a combination of functional studies and high-resolution imaging , we report here that live sporozoites access skin-draining lymph nodes after infection and directly provide antigens to resident dendritic cells that in turn activate CD8+ T cells . These results underscore the importance of live , motile sporozoites in the induction of protective CD8+ T cell responses and provide a mechanistic understanding for the superior immunogenicity of whole parasite vaccines .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Lymph-Node Resident CD8α+ Dendritic Cells Capture Antigens from Migratory Malaria Sporozoites and Induce CD8+ T Cell Responses
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The human immune system depends on a highly diverse collection of antibody-making B cells . B cell receptor sequence diversity is generated by a random recombination process called “rearrangement” forming progenitor B cells , then a Darwinian process of lineage diversification and selection called “affinity maturation . ” The resulting receptors can be sequenced in high throughput for research and diagnostics . Such a collection of sequences contains a mixture of various lineages , each of which may be quite numerous , or may consist of only a single member . As a step to understanding the process and result of this diversification , one may wish to reconstruct lineage membership , i . e . to cluster sampled sequences according to which came from the same rearrangement events . We call this clustering problem “clonal family inference . ” In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model ( multi-HMM ) framework for B cell receptor sequences . We describe an agglomerative algorithm to find a maximum likelihood clustering , two approximate algorithms with various trade-offs of speed versus accuracy , and a third , fast algorithm for finding specific lineages . We show that under simulation these algorithms greatly improve upon existing clonal family inference methods , and that they also give significantly different clusters than previous methods when applied to two real data sets .
B cells effect the antibody-mediated component of the adaptive immune system . The antigen-binding properties of B cells are defined by their B cell receptor , or BCR . BCRs bind a wide variety of antigens , and this flexibility arises from their developmental pathway . B cells begin life as hematopoietic stem cells . After a number of differentiation steps the cells perform somatic recombination , or rearrangement . For the heavy chain locus , a V gene , D gene , and J gene are randomly selected , trimmed some random amount by an exonuclease , and then joined together with random nucleotides ( forming so-called N-regions ) . The light chain process is slightly simpler , in that only a V and J recombine , but proceeds via similar trimming and joining processes . These processes form the third complementarity determining region ( CDR3 ) in each of the heavy and the light chain , which are important determinants of antibody binding properties . Then a series of checkpoints on the BCRs ensure that the resulting immunoglobulin is functional and not self-reactive through negative selection ( reviewed in [1] ) . This process results in naive B cells with fully functioning receptors . When stimulated by binding to antigen in a germinal center , naive cells reproduce and mutate by via the process of somatic hypermutation , and then are selected on the basis of antigen binding and presentation to T follicular helper cells [2] . This process is called affinity maturation . It is now possible to sequence B cell receptors in high throughput , which in principle describes not only the collections of antigens to which the immune system is ready to react , but also implicitly narrates how they came to be . It is of great practical interest for researchers to be able to reconstruct events of this development process using BCR sequence data . Such reconstruction would shed light on the process of B cell receptor maturation , a subject of continual study since the landmark work of Eisen and Siskind in 1964 [3 , 4] . Furthermore , there are specific maturation pathways of great importance , such as the B cell lineages leading to broadly neutralizing antibodies to HIV [5 , 6] . Being able to reconstruct the structure and history of these lineages allows investigation of the binding properties of these intermediates , which could be helpful to design effective vaccination strategies to elicit high-affinity antibodies [7] . For example , recent studies have shown the promise of a sequential immunization program for eliciting these antibodies [8]; lineage reconstruction will aid in identifying desirable intermediate BCRs . The clonal family inference problem is an intermediate step to such lineage reconstruction ( Fig 1 ) . Rather than trying to reconstruct the full lineage history of the set of sequences , the goal is only to reconstruct which sequences came from the same rearrangement event . Full lineage reconstruction would also require building phylogenetic trees for each of the clonal families . However , these clonal families can be an object of interest themselves [9] . The motivation behind our approach to the clonal family inference problem , like many before us , is to use the special structure of BCR sequences ( which for simplicity we describe for the heavy chain; the same concepts and approaches can be applied to the light chain ) . This structure follows from VDJ recombination and affinity maturation: for example , by definition the identity of the germline genes cannot change through affinity maturation . Thus , if the per-read germline gene identity could be inferred without error , then any pair of sequences from a clonal family must have the same inferred germline gene identity . If one also assumes that sequences evolve only through point mutation , then sequences must have identical-length CDR3s if they are to be in the same clonal family . Most current methods for B cell clonal family inference make these assumptions , and proceed by first stratifying sequences by inferred V and J germline genes and CDR3 length , then only consider pairs of sequences within a stratum as potential members of the same clonal family . If one assumes further that any clonal families with pairs of highly diverged sequences also contain intermediates between those sequences , one might assume that there is a path between any pair of sequences such that neighboring sequences in a path are similar . This suggests a strategy in which pairs of sequences that are similar at some level ( such as 90% similar in terms of nucleotides ) in the CDR3 are considered to be in the same clonal family , and where membership is transitive , which corresponds to an application of single-linkage clustering . Instead of designing such an algorithm that works only when a set of rigid , predefined assumptions are satisfied , an alternative is to formalize a model of B cell affinity maturation into a generative probabilistic process with a corresponding likelihood function . Once this likelihood function is defined , one can infer clonal families by finding the clustering that maximizes the likelihood of generating the observed sequences . Likelihood methods in the form of a hidden Markov models ( HMM ) have been applied to B cell receptor sequences for a decade [10–13] . This previous work has been to use HMMs to analyze individual sequences . For likelihood-based clustering we are only aware of the work of Laserson [14 , 15] , who uses Markov chain Monte Carlo to infer clusters via a Dirichlet mixture model ( reviewed in [16] ) . Unfortunately the Laserson algorithm is only described in a PhD thesis and does not appear to be publicly available . In related work , Kepler [17 , 18] uses a likelihood-based phylogenetics framework to perform joint reconstruction of annotated ancestor sequence and a phylogenetic tree . In this paper we present a method for inferring clonal families in an HMM-based framework that comfortably scales to tens of thousands of sequences via parallel algorithms , with approximations that scale to hundreds of thousands of sequences . For situations in which specific lineages are of interest , users can specify “seed” sequences and find the clonal family containing that seed in repertoires with one million sequences . Our clustering algorithm is based on a “multi-HMM” framework for BCR sequences that we have previously applied to the annotation problem: to infer the origin of each nucleotide in a BCR ( or TCR ) sequence from the VDJ rearrangement process [19] . We use this framework to define a likelihood ratio comparing two models which differ by the collapse of two clonal families into one , and use it for agglomerative clustering . Because this likelihood ratio comes from an application of the forward algorithm for HMMs , it integrates out all possible VDJ annotations . We find that it outperforms previous algorithms on simulated data , and that it makes a significant difference when applied to real data .
In order to calculate a set of probabilities suitable for use in the clonal family inference problem , we begin with the HMM framework introduced in [19] . In that paper we focused on inferring parameters of an HMM and using it to obtain BCR annotated ancestor sequences , which was primarily based on the most likely path through each HMM , i . e . the Viterbi path . We also described Viterbi annotation with a multi-HMM , i . e . annotation using a collection of sequences that were assumed to form a clonal family . In this application , we will use the forward algorithm for HMMs [20] to obtain the corresponding marginal probability , which is the sum of sequence generation probabilities over all possible paths through the HMM . This is a more appropriate tool for the clonal family inference problem because here we are interested in integrating over annotated ancestor sequences ( that is , paths through the HMM ) to decide whether sequences are related . By using a multi-HMM , we can use this total probability to calculate a likelihood ratio that two clusters derive from the same , or from different , rearrangement events . We perform agglomerative clustering using this likelihood ratio to group sequences for which the probability of a common ancestry is higher than that of separate ancestry ( details in the Methods ) . This approach allows us to calculate the total probability of the partition ( i . e . clustering ) at each stage in the clustering process , which provides both an objective measure of partition quality , and easy access to not only the most likely partition but also to a range of likely partitions of varying degrees of refinement . As in our previous work , the parameters of the HMM can be inferred “on the fly” given a sufficiently large data set or be inferred on some other data set . Briefly , we do a cycle of Viterbi training , which is started with an application of Smith-Waterman alignment , in which the best annotation for each sequence with a current parameter set is used to infer parameters for the next cycle . As described in detail elsewhere [19] , data is aggregated if there are insufficient observations for a given allele for training . In addition to this principled method for full-repertoire reconstruction , we have implemented two more approximate versions which trade some accuracy for substantial increases in speed . In the first , which we call point partis , we forgo integration over all possible annotated ancestor sequences and instead find the most likely naive sequence point estimate for each cluster . Clusters are then compared based on the Hamming fraction ( Hamming distance divided by sequence length ) between their respective naive sequences , and are merged if the distance is smaller than some threshold . This threshold is set dynamically based on the observed mutation rate in the sample at hand . In order to achieve further improvements in speed , we can also avoid both complete all-versus-all comparison of the sequences at each step , and calculation of the joint naive sequence for each merged cluster . For this we find the most likely naive sequence for each individual sequence , and then pass the results , together with a dynamically-set clustering threshold , to the clustering functionality of the vsearch program [21] . We call this vsearch partis . We have also included a method which , using the full likelihood , reconstructs the clonal family containing a given “seed” sequence . Because clonal families are generally significantly smaller than the total repertoire , this option is much faster than the full-repertoire reconstruction methods . We see this option as being useful when specific sequences are identified as interesting through a binding assay or because they are shared between repertoire samples . This is labeled full partis ( seed ) . This clustering has been implemented as part of continued development of partis ( http://github . com/psathyrella/partis ) . As before , the license is GPL v3 , and we have made use of continuous integration and containerization via Docker for ease of use and reproducibility [22] . A Docker image with partis installed is available at https://registry . hub . docker . com/u/psathyrella/partis/ . In the absence of real data sets with many sequences for which the true annotations and lineage structures are known , we compare these new clustering methods against previous methods using simulated sequences generated as described in [19] . These simulations were done for the heavy chain locus only . We performed comparison both on samples , which we call 1× , which mimic mutation frequencies in data ( overall mean frequency of about 10% ) and on samples , which we call 4× , with quadrupled branch lengths ( overall mean frequency of about 25% ) to explore results in a more challenging regime . Per-sequence mutation frequencies are distributed according to the empirical distribution ( see [19] ) . We compare the three partis methods to three methods from the literature . The first , labeled “VJ CDR3 0 . 9” , is representative of annotation- and distance-based methods which have been used in a number of papers [18 , 23–26] . It begins by annotating each individual sequence , and proceeds to group sequences which share the same V and J gene and the same CDR3 length , and have CDR3 sequence similarity above some threshold , which is commonly 0 . 9 [24] . For this comparison we use partis annotation; for a comparison of annotation methods themselves see [19] . We also compare against Change-O’s clustering functionality [27] fed with annotations from IMGT , with IMGT failures ( when it does not return an annotation ) classified as singletons . We perform a partial comparison against MiXCR [28] . Since this method does not currently report which sequences go into which clusters , and instead only reports cluster summary statistics , we cannot perform a detailed evaluation . The authors of MiXCR note in personal communication , however , that they plan to report this information in future versions . We use per-read averages of precision and sensitivity to quantify clustering accuracy . In this context , the precision for a given read is the fraction of sequences in its inferred cluster which are actually in its clonal family , while sensitivity for a given read is the fraction of sequences in its true clonal family that appear in its inferred cluster ( details in Methods ) . We find that partis is much more sensitive than previous methods , at the cost of some loss of precision ( Fig 2 ) . The point partis approximate implementation is less specific than the full implementation , while the even faster vsearch approximation loses some precision and some sensitivity . We investigate these differences in more detail for the first simulation replicate via an intersection matrix with entries equal to the size of the intersection between each of the 40 largest clusters returned by pairs of algorithms ( Figs 3 and 4 , S6 , S7 , S8 and S9 Figs ) . Full partis infers clonal families correctly the majority of the time at typical mutation levels , and in this experiment it incorrectly split a cluster of true size around 45 . These results degraded somewhat with the point partis approximation , and somewhat more with the vsearch approximation . The VJ CDR3 0 . 9 method consistently under-clustered for the largest cluster sizes . The seeded full partis method correctly reconstructed the lineage of interest starting from a randomly sampled sequence , while ignoring all others . In order to understand performance on the many smaller clusters and to get a simpler overall picture , we also compared cluster size distributions for the various methods with the simulated distribution ( Figs 5 and 6 , S1 and S2 Figs ) . Here we can see that partis is able to accurately infer the true cluster size in a variety of regimes , whereas other methods tend to under-merge clusters of all sizes . In order to further understand the source of these differences , we also compare results against two methods of generating incorrect partitions starting from the true partition , which we call synthetic partitions ( S3 and S4 Figs ) . The first , called synthetic 60% singleton is generated from the true partition by splitting 60% of the sequences into singleton clusters . The second , called synthetic neighbor 0 . 03 , merges together true clonal families which have true naive sequences closer than 0 . 03 in Hamming distance divided by sequence length . We find that the performance of synthetic 60% singleton tracks that of the VJ CDR3 method , while the performance of synthetic neighbor 0 . 03 tracks that of partis . Finally , to investigate the performance of the seeded full partis method , we calculate the precision and sensitivity of this method on a number of widely varying sample sizes ( Fig 7 ) . For these simulations we used a Zipf ( power-law ) distribution of cluster sizes with exponent 2 . 3 , and randomly selected one seed sequence from a randomly selected large cluster . We find that seeded partis frequently obtains very high sensitivity , although precision decreases as sample size increases . This precision decrease is from incorrect merges of clusters . We have manually checked these incorrect merges , and found that the true ( i . e . simulated ) naive sequences of clusters which are incorrectly merged with the seeded cluster typically differ by one to six bases . Because these differences occur either within the bounds of the true eroded D segment , or within the true non-templated insertions , it is difficult to distinguish them from somatic hypermutation . This echoes the observation that partis precision is driven by the presence or absence of clusters which stem from different rearrangements , but which are very similar in naive sequence ( compare partis and synthetic neighbor 0 . 03 in S5 Fig ) . In order to handle insertion-deletion ( indel ) mutations which occur during somatic hypermutation , we have implemented a heuristic method in the preliminary Smith-Waterman alignment step in partis . In short , this works by “reversing” inferred indel mutations in germline-encoded regions and proceeding with the clustering algorithm . We find that partis performance is typically unaffected when indels occur in non-CDR3 germline-encoded regions , although performance suffers when indels occur in the CDR3 ( Fig 8 ) . This is because indel mutations in the CDR3 are quite difficult to distinguish from insertions and deletions stemming from the VDJ rearrangement process using indel-handling schemes ( such as ours ) that only take one sequence at a time into account . In order to understand the difference this method makes on real data , we applied partis and the other algorithms to subjects in the Adaptive data set from [29] used in previous publications [19 , 30 , 31] , as well as the data set from [24] , which we will call the “Vollmers” data set . These data sets were Illumina sequenced via amplicons covering the heavy chain CDR3 , and thus do not have complete V or J sequences . Especially in the case of the V region for the Vollmers data , it is not possible to confidently identify the germline V gene for each of the BCR sequences . Thus , these data sets make for an interesting comparison between methods ( such as VJ CDR3 ) which require single germline gene identifications , to our method , which integrates over such identifications . Results are shown for Adaptive subject A ( Fig 9 ) , and for a subject from the Vollmers data set ( Fig 10 ) . The rest may be found on figshare at http://figshare . com/s/9b85e4ac54d011e5bd3e06ec4b8d1f61 . Note that the identifiers shown for the Vollmers data are an obfuscated version of the original identifiers in the data; contact the authors for more details . These results are not presented to make any strong statement about the true cluster size distribution , the correctness of which cannot be be independently evaluated , but rather to show that the partis results are different from those of other methods on real data , as seen under simulation . When we applied the various methods to a randomly chosen set of 20 , 000 sequences from two different sets , we found that the various methods agree that both samples are dominated by singletons , but there is substantial discord at the high end of the distribution , especially in Adaptive subject A ( Figs 9 and 10 ) . These differences in composition are examined in more detail using cluster intersection matrices . The cluster size distribution inferred by partis approximately follows a power-law , with exponent about 2 . 3 . Adaptive subject A ( Fig 9 ) has mutation levels two and a half times higher than Vollmers subject 15-12 ( Fig 10 ) , making inference more challenging for A . Both of these data sets consist of shorter sequences than the simulated sequences , which contain the entire V and J regions . Reads in the Adaptive samples are 130 base pairs ( losing about two thirds of the V and one half of the J ) , while those in the Vollmers data set vary in length , but typically span all of the J but only 20 to 30 bases in the V . Likelihood-based clustering using partis is computationally demanding , though within a range applicable to real questions given appropriate computing power ( Fig 11 ) . On a computing cluster with about 25 8-core machines , full and point partis can cluster ten thousand sequences in 4 to 7 hours , while vsearch partis can cluster one hundred thousand sequences in 4 hours . Our implementation of “VJ CDR3 0 . 9” used partis annotation , but this approach could be made much faster by using a fast method for annotation [28 , 32] . Time required can also vary by an order of magnitude depending on the structure of the sample ( cluster size and mutation level ) . We have developed an algorithm to infer clonal families using a likelihood-based framework . Although the framework does take annotation information into account by using a VDJ-based HMM , the algorithm is distinguished from other clustering methods in that it does not fix a single annotation first and then use that annotation for downstream steps . Instead we find that by integrating over annotated ancestor sequences using an HMM , we are able to obtain better clonal family inference than with the current common practice of rigidly inferring VJ annotation and then clustering on HCDR3 identity for heavy chain sequences . Our simulations show that existing algorithms frequently do not sufficiently cluster sequences which sit in the same clonal family . Our application to real data shows that the partis algorithms using our default clustering thresholds return more large clusters on two real data sets , indicating that this difference in clustering is not simply an artifact of our simulation setup . The performance differences between our various approximate algorithms indicates the sources of the partis’ improved performance . The reasonably good performance of the point partis variant shows the importance of clustering on inferred naive sequences rather than observed sequences and inferring these naive sequences with an accurate probabilistic method . Furthermore , the difference between point and full partis is some measure of the importance of integrating out uncertainty in annotated ancestor sequences . We find that partis’ main weakness is in separating out clusters with highly similar naive sequences . Indeed , its performance tracks a simulated method that merges clonal families with true ( i . e . simulated ) naive sequences that are closer than 3% in nucleotides , in simulations with about 10% divergence from the naive sequence . Although the VDJ rearrangement process generates a very diverse repertoire , biases in gene family use and other rearrangement parameters mean that pairs of highly similar naive sequences are frequently generated . This may indicate an inherent limitation in clonal family inference methods that only use data from heavy chain . Our method builds on previous work for doing likelihood-based analysis of BCR sequences . In particular , we are indebted to Tom Kepler for initiating the use of HMMs in BCR sequence analysis [10] and for developing likelihood-based methods to infer unmutated common ancestor sequences while integrating over rearrangement uncertainty [17 , 18 , 33–35] . We did not compare to several related methods that have been described in the literature . ClonalRelate [36] is an extension to the “VJ CDR3” method that allows some flexibility in requiring V and J calls to be the same by combining various mismatch penalties into a distance that is used for agglomerative clustering . IMSEQ [32] is a recent method which is reported to be quite fast; however the current version appears mainly aimed toward T cell receptors , as it does not handle somatic hypermutation . As it clusters based on V and J genes and 100% CDR3 similarity , it is equivalent to the annotation-based method described above , except with a threshold inappropriate to B cells . Cloanalyst performs joint reconstruction of annotated ancestor sequence and a phylogenetic tree given a collection of sequences assumed to form a clonal family [17] . Immunitree apparently uses a Dirichlet process mixture model for clustering , however , the algorithm is only fully described in a PhD thesis [14] , and does not appear to be publicly available ( note that https://github . com/laserson/vdj performs straightforward single-linkage clustering and is in fact written by a sibling of the Immunitree author ) . IgSCUEAL [37] is a recent method that performs annotation and clustering using a phylogenetic approach . Its clustering algorithm , however , is not part of the public distribution and is apparently undergoing revision . There are several opportunities to improve partis . First , our current approach requires likelihood ratios to exceed a value based on cluster size; these cluster sizes are based on observing distributions of likelihood ratios under simulation . A more principled approach would be preferable . Second , our approach to insertion-deletion mutations in affinity maturation only uses one sequence at a time . Thus it has an inherent difficulty differentiating between mutations in the course of affinity maturation versus insertion-deletion events that are part of VDJ rearrangement . Third , our current code is only for the heavy chain alone or the light chain chain alone . Extending the work to paired heavy and light chain BCR data is conceptually straightforward , although will require additional software engineering . Fourth , HMMs have certain inherent limitations , stemming from the central Markov assumption that the current state is ignorant of all states except for the previous one . As reviewed in [19] , this limits the scope of events that can be modeled using partis , excluding correlation between different segments of the BCR [31 , 38 , 39] , palindromic N-additions [40] , complex strand interaction events [41 , 42] , or the appearance of tandem D segments [43] . Some of these limitations could be avoided by using Conditional Random Fields ( reviewed in [44] ) , and although linear-chain conditional random fields enjoy many of the attractive computational properties of HMMs , this flexibility will come with a computational cost . Fifth , partis does not attempt to infer germline genotype , as do [45] , and so treats genes and alleles on an equal footing . We will treat this as a model-based inference problem in future development . Sixth , we will continue to refine heuristics to provide the accuracy of the full likelihood-ratio calculation with minimal compute time . We note , for instance , that a small decrease in the lower naive Hamming fraction threshold substantially improves performance for the seed partis simulation compared to that shown here ( in Fig 7 ) . In additional future work , we will explore opportunities to combine clonal family inference and phylogenetics to obtain inference of complete B cell lineages . This could potentially take the form of a phylo-HMM [46] , although a more straightforward approach would be to take the product of a phylogenetic likelihood and a rearrangement likelihood [17] . For example , one might use HMM-based clustering as is described here with a high likelihood ratio cutoff to obtain a conservative collection of clusters , and then a phylogenetic criterion to direct further clustering . In addition to these methodological improvements , we will also apply partis to a variety of data sets for validation and to learn about the structure of natural repertoire . For validation , there are some data sets , e . g . [47] , which due to experimental setup have sequences known to make a clonal lineage . Also , new microfluidics technology applied to BCR sequencing also gives heavy and light chain data [48 , 49]; although a single heavy chain clonal lineage can have light chains from independent rearrangement events , this type of data does provide further evidence of clonality for validation of clonal family inference procedures . In addition to this sort of validation , there are now an abundance of data sets that can be used to characterize the size distribution of the clonal families in various immune states , such as health , immunization , and disease . As a final note , partis works to solve a challenging likelihood-based inference problem . We recognize that in contrast to existing heuristic approaches based on sequence identity , our software is quite computationally demanding . In this first paper we have developed the framework and overall approach , as well as many computational optimizations . This optimization work is ongoing , and there remain many avenues for improvement . As a comparison , likelihood-based phylogenetic inference has taken two decades of optimization to scale to tens of thousands of sequences at a time with approximate algorithms [50] . We are continually making improvements to the algorithm to make it scale to larger data sets and are committed to building algorithms that scale to the size of contemporary data sets . Although such algorithms may end up being rather different than this version of partis , we believe that likelihood-based algorithms will provide a solid foundation for large-scale molecular evolution studies of B cell maturation .
To introduce the way in which we use HMMs for BCR clustering , consider the canonical “dishonest casino” HMM [20] . In this introductory example , one imagines that a casino offers a game in which the casino alternates between a fair die and a die that is biased towards a given number , say 6 . Assume the dice are switched with probability p each roll , corresponding to the HMM on two states , with a transition probability of p between the states . One favorite game of bioinformaticians is to infer the maximum likelihood identity of the die for each roll given a sampled sequence of roll outcomes , which is solved by the Viterbi algorithm . The so-called forward algorithm , on the other hand , infers the marginal probability of a sequence of outcomes . The likelihood ratio used in this paper fits into the metaphor with a slight variant of the game ( Fig 12 ) . In this variant , a pair of outcomes ( a . k . a . emissions , in this case integers in the range 1 to 6 ) are sampled at each step . The player knows that either the emissions came from rolling the same die twice and then switching out the die with probability p after each step , or they came from rolling two dice which are independently switched out with probability p . The new game , corresponding to the methods in this paper , is to figure out which of these scenarios is correct , and with what support . The marginal probability of a sequence of emissions under the “double roll” scenario is that of a pair-HMM with transition probability p with identical emission probabilities , while the latter “two dice” scenario is that of two independent HMMs . The ratio of these two marginal probabilities is a likelihood ratio quantifying the strength of evidence for the “double roll” scenario . Now , stepping back into the world of VDJ recombination , we will apply this logic to the HMM structure introduced in [19] . This HMM , building on prior work [10–12] , has one state for each position in every V , D , and J gene , and a state for each of the joining N-regions for heavy chain sequences . Light chain sequences are simpler , in that they have only V , J , and one N-region , and so for the rest of this methods section we will only describe the heavy chain procedure . Continuing with the metaphor , the identity of the die ( of which there are now many ) for each roll corresponds either to an annotation of that nucleotide as being from a given non-templated insertion base , or as being from a specific nucleotide in a specific V , D , or J gene . That is , a path through the HMM corresponds to an annotated ancestor sequence . Our previous paper [19] was focused on inferring these annotated ancestor sequences using the Viterbi algorithm . Here we focus on the question of whether a group of sequences came from the same rearrangement event rather than on the annotated ancestor sequences themselves . However , this distribution of annotated ancestor sequences is highly informative about the clonality of a group of sequences . We would like use these annotated ancestor sequence inferences but avoid putting too much trust in one specific and necessarily uncertain inference , and instead account for the diversity for possible annotations . We do so as follows . Using σ to designate paths and x for a sequence , the marginal probability P ( x ) of generating x via any path is P ( x ) = ∑ σ P ( x ; σ ) , where P ( x ; σ ) designates the probability of generating x with the path σ through the HMM . Now for a pair of sequences x and y , P ( x , y ) = ∑ σ P ( x , y ; σ ) , is the probability of generating both x and y using emissions from a single pass through the HMM . Thus P ( x , y ) / ( P ( x ) P ( y ) ) is a likelihood ratio such that values above 1 support the hypothesis that x and y come from the same rearrangement event and values less than 1 support the hypothesis that they do not . Recall that all of these probabilities can be calculated efficiently via the forward algorithm . More generally , if we would like to evaluate whether sequence sets A and B ( each of which are assumed to descend from single rearrangement events ) actually all came from a single rearrangement event . For that we can calculate P ( A ∪ B ) P ( A ) P ( B ) ( 1 ) where P ( X ) can be calculated by a ( simple ) HMM if X has one element , a pair-HMM if X has two elements , etc . , so in general a multi-HMM . Note that this not a phylogenetic likelihood , but a rather strictly HMM-based likelihood , and so does not attempt to incorporate any tree structure into the computations . We use this likelihood ratio for agglomerative clustering . Specifically , at each step we pick the pair A and B that have the largest likelihood ratio Eq ( 1 ) and merge them by replacing A and B from the list of clusters and adding A∪B ( Fig 13 ) . We stop agglomerating according to a likelihood ratio threshold , as described in the section after next . Agglomerative clustering has been applied before for clonal family inference [36] , however in this case rather than averaging the distances for the merged clusters ( as for average linkage clustering ) we recompute likelihood ratios with the newly merged set of sequences . The HMM architecture we use is the same as that of [19] , which for the most part follows previous work [10–12] by representing each germline base in each V , D , and J allele as an HMM state . All of these states can be combined to create a single HMM for the entire VDJ rearrangement process . In order to allow likelihood contributions from the N-region , we replace the single insert state found in previous work with four states , corresponding to naive-sequence N-addition of A , C , G , and T . The emissions of these four states are then treated as for actual germline states: the A state , for example , has a large probability of emitting an A , and a complementary probability ( equal to the observed mutation probability ) of emitting one of the other three bases . Our application of HMMs also differs from previous work using HMMs for B cell receptor sequence analysis in that we do inference under a model which simultaneously emits an arbitrary number of symbols k . When k = 2 this is typically called a pair HMM [20] , and we call the generalized form a multi-HMM ( k ≥ 2 ) . One can also think of this as doing inference while constraining all of the sequences to come from the same path through the hidden states of the HMM . In our setting , the k sequences resulting from such a multi-HMM model are the various sequences deriving from a single rearrangement event ( which differ only according to point substitution from somatic hypermutation ) . HMM inference is performed by an efficient new HMM compiler , called ham , which we wrote to inference on an arbitrary ( multi- ) HMM specified via a simple text file ( https://github . com/psathyrella/ham/ ) . A straightforward application of hierarchical clustering in this setting , in which the likelihood ratio is computed for every cluster at every stage of the algorithm , would not scale to more than a few hundred sequences . Thus as described above , we also use Hamming fraction ( Hamming distance divided by sequence length ) between inferred naive sequences to avoid expensive likelihood ratio computation . In order to compare unequal-length sequences , we first align the conserved cysteine in every sequence , and then pad all sequences on both ends with ambiguous nucleotides until they are all the same length . In addition to point partis described as an approximate method above , we also use naive Hamming fraction in the full partis method in order to identify sequences that are either very likely or very unlikely to be clones . We assume that clusters which differ by more than 0 . 08 in naive Hamming fraction are not clonal , and therefore avoid calculating the full likelihood for these cases . This threshold is for repertoires with typical mutation levels ( around 5% ) ; we find that increasing the threshold as mutation increases ( to 0 . 15 at 20% mutation ) provides optimal performance . We interpolate and extrapolate linearly for other mutation levels . In addition , we assume that clusters that are closer than 0 . 015 ( regardless of mutation levels ) in naive Hamming fraction are clonal , and merge these without calculating the full likelihood . While the naive Hamming fraction only takes into account the Viterbi path ( i . e . it does not sum over all potential annotated ancestor sequences ) , and it has no probabilistic interpretation , it has the not insignificant virtue of being much faster to calculate . According to standard statistical theory , we should merge an a priori specified pair of clusters A and B when the likelihood ratio Eq ( 1 ) is greater than one . However , in the midst of a series of agglomerations , we are not in the setting of a single decision for clusters that have been presented to us . Instead , at every stage we are comparing a quadratic number of potential merges and asking if the pair of clusters with the largest likelihood ratio deserve to be merged . This effectively presents substantial multiple testing issues: even when no more clusters should be merged , the nonzero-width of the empirical likelihood ratio distribution will typically have points above one . Furthermore , the marginal probability P ( A ) of , say , the kth largest cluster after some number of merges is going to be biased by the fact that the sequences in that cluster were selected to merge . Such issues are not new in computational biology [51] . We also note that we are only calculating this likelihood ratio when pairs of sequences are similar enough in their inferred naive sequences to merit such a likelihood ratio calculation , further taking us from the statistically ideal setting . We have found it useful to use a likelihood ratio threshold greater than 1 , and use a threshold that decreases as the candidate cluster size , i . e . the size of a proposed cluster , increases ( Table 1 ) . These values were selected as a trade-off between accurate reconstruction of large clonal families on the one hand , and accuracy at the low end of the cluster size distribution on the other . Thus if we want to minimize the chance of missing highly-mutated members of a large clonal family we should choose lower thresholds , but if we instead want to avoid mistakenly merging unrelated singletons we should choose higher ones . In light of this , the thresholds can be set on the command line . While it would be straightforward in principle to account for insertions and deletions ( indels ) during somatic hypermutation within the HMM by adding extra transitions for deletions and extra states for insertion , this approach would entail a very substantial computational cost . When restricting to substitution mutations , each germline state can either transition to the next germline state , or it can leave the region . If we allowed indels within the V , D , and J segments , however , each state would also need to investigate the probability to transition to a special insertion state as well as to any subsequent germline state . This would introduce a quadratic dependence on the number of states and the resulting algorithm would not be able to analyze realistically-sized data sets . We thus instead adopt an approach to indel mutations based on the annotation from our preliminary Smith-Waterman step ( implemented with ighutil [30] ) . In cases where ighutil detects an insertion with respect to a germline segment , we “reverse” the insertion by removing it from the query sequence . Similarly , candidate deletions are reversed by inserting the corresponding germline bases from the best germline match when the putative deletion happens in a germline segment . In both cases the original sequences are maintained , but the partis processing of the sequences is done on the modified sequences . As with any Smith-Waterman implementation , this approach depends on several arbitrary parameters: the match and mismatch scores and the gap-opening penalty . In particular , a larger gap-opening penalty relative to the match/mismatch scores decreases sensitivity to indels . On all samples which we have encountered , a good initial set of match:mismatch scores is 5:1 . Sequences with lower mutation rates , for which 5:1 is less optimal , are returned with no D segment match , and then re-run with match:mismatch scores of 5:2 . Sequences which still have no D matches are then rerun with scores of 5:3 . This procedure gives good results in all parameter regimes which we have encountered in the data . Similarly , we find that a gap-opening penalty of 30 provides good sensitivity to indel mutations in simulation . Each of these parameters may also be set with a command line flag . In order to test the effectiveness of this method , we made simulated samples in which each sequence has a 50% chance of having an indel mutation after being generated on a tree . Each indel has equal probability of being an insertion or a deletion , and the indel’s position is chosen from the uniform distribution either on the bulk of the V segment ( between position 10 and the conserved cysteine ) , or on the CDR3 . The length of each indel is drawn from a geometric distribution with mean 5 . These samples are not intended to mimic any particular data set , but are instead designed to provide an extremely stringent test of performance in the presence of indel mutations ( Fig 8 ) . The accuracy of the full likelihood framework which we have described above does not come without some computational cost . As such we have also implemented two other algorithms which make some reasonable trade-offs in accuracy in order to gain some speed . Point partis . One of the biggest contributors to both annotation and partitioning accuracy comes from our multi-HMM framework’s ability to run simultaneously on an arbitrary number of sequences . Since this ability is entirely separate to the summation over all possible rearrangements , it makes sense to decouple the two in order to optimize for speed . We can , in other words , cluster using the single best ( Viterbi ) annotated ancestor sequence for all sequences in a cluster ( inferred simultaneously on the whole cluster with the multi-HMM ) , without summing over all germline genes and all rearrangement boundaries . We call this point partis , to emphasize that it uses the best point ( i . e . single ) annotation inference to do clustering . In order to cluster on these inferred naive sequences , we use the hierarchical agglomeration described above , but with Hamming fraction as the metric ( instead of log likelihood ratio ) . As in the case of the likelihood ratio merging thresholds described above , we perform a simple optimization procedure on a wide variety of simulation samples which span the range of possible lineage structures and mutation levels that we observe in real data . For typical ( low ) mutation levels near 5% , we use a threshold of 0 . 035; the threshold then increases to 0 . 06 as the mutation frequency reaches 20% . Simple linear interpolation ( extrapolation ) is used inside ( outside ) of this range . Note that these thresholds are much tighter than those mentioned above for full partis optimization: while above we were trying to exclude cases where there was any doubt as to their clonality , here we are attempting to accurately divide clonal from non-clonal clusters in the naive Hamming distribution . Comparing to Fig 7 in [19] , we note that this threshold is equivalent to the expected fractional error in the inferred naive sequence . vsearch partis . The point method , however , still performs full all-vs-all comparisons on the entire data set , and recalculates the full Viterbi naive sequence on each cluster each time more sequences are added . While this is a good way to ensure the best accuracy , there exist clustering algorithms with many optimizations which trade some of this accuracy for improved speed . vsearch [21] is one such tool , and we have included a version of partis which infers the Viterbi naive sequence for each single query , and then passes these sequences to vsearch . This sacrifices some accuracy , particularly on larger clonal families , but is extremely fast . We use vsearch version 1 . 1 . 3 in cluster fast mode with the maximum accept and reject thresholds set to zero , and the id threshold set ( again , based on coarse heuristic optimization ) to one-half the threshold described above for point partis . We have added an option to reconstruct the lineage of a user-specified sequence using full partis , for situations in which one is only interested in one specific clonal family . We call such a user-specified sequence a seed sequence . This is shown as “full partis ( seed ) ” ( Figs 3 and 4 ) . Here we chose a seed sequence at random from a randomly-selected “large” cluster , where “large” means with size greater than or equal to the mean N leaves for the sample . It can be seen that this method accurately reconstructs the single lineage of interest while running much more quickly than the other methods ( Fig 11 ) . To benchmark results , we simulate sequences using the procedure described in [19] . This provides a bountiful supply of sequences for which the correct lineage structures are known , and with any desired combination of tree topologies and mutation parameters , but with all other properties mimicking empirical values . Briefly , the simulation proceeds by sampling a set of parameters defining a single rearrangement ( e . g . V exonuclease deletion length , V allele , etc . ) from their empirical joint distribution observed in a data set . Then TreeSim [52] is used to simulate a tree and Bio++ [53] is used to simulate sequences . We emphasize that these sequences are not generated at any stage using partis’ HMM , and no information concerning the simulation is fed to the clustering code other than the simulated sequences . The number of leaves ( BCR sequences per clonal family ) is distributed geometrically with the indicated mean value in all figures except Figs 7 and S1 . In Fig 7 we have used a Zipf ( power law ) distribution . In S1 Fig , on the other hand , we have used a box-shaped distribution to check that our methods do not depend on a monotonically decreasing distribution . In order to simulate a given number of sequences , we simply divide the desired number of sequences by the expected number of sequences per clone and simulate the resulting number of clones . For indel simulations , half of the simulated sequences have a single indel , whose length is drawn from a geometric distribution with mean 5 . In order to emphasize the importance of the indel’s location , we show samples where they are distributed evenly either within the CDR3 , or within the bulk of the V segment ( specifically between position 10 and the conserved cysteine ) . We use per-sequence averages of sensitivity and precision to quantify clustering accuracy . In this context , a true positive ( TP ) statement about a sequence x is the correct identification of another sequence in x’s clonal family , i . e . correctly clustering a sequence with x . A false postive ( FP ) statement is incorrectly clustering a sequence with x , while a false negative ( FN ) statement is not clustering a sequence with x that should be clustered . Sensitivity x = | TP x | | TP x + FN x | Precision x = | TP x | | TP x + FP x | Thus , as described above , the precision for a given read is the fraction of sequences in its inferred cluster to which it is truly clonally related . The sensitivity for a given read is the fraction of sequences in its true cluster that appear in its inferred cluster . We average these two quantities over all sequences ( Figs 2 , S3 and S4 ) . These figures also show the average harmonic mean of this sensitivity and precision ( a . k . a . F1 score ) , as an aggregate measure of the quality of the clustering . We also show intersection matrices: the matrix of intersection sizes between pairs of large clusters in two partitions ( examples in Figs 3 , and 4 , 9 and 10; the full set of plots is available at figshare at http://figshare . com/s/9b85e4ac54d011e5bd3e06ec4b8d1f61 . To make these plots , we first take the 40 largest clusters from each of the two partitions . Each non-white square indicates that there was a non-empty intersection between the two clusters; the square is shaded by the size of the clusters’ intersection divided by their mean size . The position of the square shows the relative sizes of the two clusters . Thus a value of 1 . 0 implies identity , so very similar partitions will show many dark squares near the diagonal , and will also have similar cluster sizes marked on the x and y axes . Performance versus sample size . Given the large size of modern deep sequencing data sets , we have also investigated performance as a function of sample size . This function depends on the clonal lineage structure . At one extreme , a sample with only a few sequences stemming from a few clonal families is generally trivial to partition even just by visual inspection . As the number of clonal families increases , however , each family becomes closer and closer to other families , and it becomes more and more difficult to distinguish between them . At the point where the naive sequences corresponding to each family are separated by only a few bases , accurate overall clustering becomes impossible even in principle , since a difference of only a few bases which stems from rearrangement cannot be distinguished from somatic hypermutation . In order to evaluate this performance we show several performance metrics as a function of sample size ( S5 Fig ) . Here we show the two complementary precision and sensitivity metrics in the top row , and their harmonic mean ( F1 score ) in the bottom row . It can be seen the behavior of the partis with sample size is similar to that of the synthetic partition which joins neighboring true clusters which are closer than some threshold . This is expected , and demonstrates that performance of the partis method decreases as the number of true naive rearrangements in the sample increases , and thus the clonal family inference problem is becoming inherently more difficult . Non-independence of clustering steps poses a challenge for parallelization , and we approach this challenge with a combination of principled probability calculations and reasonable heuristics . The basic strategy is to begin with a large number of processes , each running on a small subset of the data sample . When each of these processes finishes clustering its allotted sequences , it reports back to the parent program , which collects the results from each subprocess and reapportions the resulting clusters among a new , smaller number of processes for the next step . The process then repeats until we arrive at a single process which is comparing all clusters against all other clusters . On the face of it , each step in this scheme would take much longer than the previous one since it is comparing more sequences . However , because each process caches all the likelihoods it calculates , and because both factors in the denominator for each likelihood ratio Eq ( 1 ) is guaranteed to have been calculated in a previous step , we can choose the process number reduction ratio such that each stage of paralellization takes roughly the same time . An important part of this process is the allotment of sequences to processors . At present we apportion them randomly in order to achieve a ( very ) roughly equal number of computations per process . This is far from ideal , however , because we want to merge clonal sequences as soon as possible in order to avoid unnecessary comparisons to non-clonal sequences . This must be balanced , however , by the need to evenly distribute the workload across all processes . In the future we will study in more detail the optimal allotment scheme , and anticipate substantial speed increases .
|
Antibodies must recognize a great diversity of antigens to protect us from infectious disease . The binding properties of antibodies are determined by the DNA sequences of their corresponding B cell receptors ( BCRs ) . These BCR sequences are created in naive form by VDJ recombination , which randomly selects and trims the ends of V , D , and J genes , then joins the resulting segments together with additional random nucleotides . If they pass initial screening and bind an antigen , these sequences then undergo an evolutionary process of reproduction , mutation , and selection , revising the BCR to improve binding to its cognate antigen . It has recently become possible to determine the BCR sequences resulting from this process in high throughput . Although these sequences implicitly contain a wealth of information about both antigen exposure and the process by which we learn to resist pathogens , this information can only be extracted using computer algorithms . In this paper we describe a likelihood-based statistical method to determine , given a collection of BCR sequences , which of them are derived from the same recombination events . It is based on a hidden Markov model ( HMM ) of VDJ rearrangement which is able to calculate likelihoods for many sequences at once .
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2016
|
Likelihood-Based Inference of B Cell Clonal Families
|
Antigenic variation in African trypanosomes requires monoallelic transcription and switching of variant surface glycoprotein ( VSG ) genes . The transcribed VSG , always flanked by ‘70 bp’-repeats and telomeric-repeats , is either replaced through DNA double-strand break ( DSB ) repair or transcriptionally inactivated . However , little is known about the subtelomeric DSBs that naturally trigger antigenic variation in Trypanosoma brucei , the subsequent DNA damage responses , or how these responses determine the mechanism of VSG switching . We found that DSBs naturally accumulate close to both transcribed and non-transcribed telomeres . We then induced high-efficiency meganuclease-mediated DSBs and monitored DSB-responses and DSB-survivors . By inducing breaks at distinct sites within both transcribed and silent VSG transcription units and assessing local DNA resection , histone modification , G2/M-checkpoint activation , and both RAD51-dependent and independent repair , we reveal how breaks at different sites trigger distinct responses and , in ‘active-site’ survivors , different switching mechanisms . At the active site , we find that promoter-adjacent breaks typically failed to trigger switching , 70 bp-repeat-adjacent breaks almost always triggered switching through 70 bp-repeat recombination ( ∼60% RAD51-dependent ) , and telomere-repeat-adjacent breaks triggered switching through loss of the VSG expression site ( 25% of survivors ) . Expression site loss was associated with G2/M-checkpoint bypass , while 70 bp-repeat-recombination was associated with DNA-resection , γH2A-focus assembly and a G2/M-checkpoint . Thus , the probability and mechanism of antigenic switching are highly dependent upon the location of the break . We conclude that 70 bp-repeat-adjacent and telomere-repeat-adjacent breaks trigger distinct checkpoint responses and VSG switching pathways . Our results show how subtelomere fragility can generate the triggers for the major antigenic variation mechanisms in the African trypanosome .
Several important parasites , including those that cause malaria and Human African Trypanosomiasis ( HAT ) , achieve antigenic variation and evasion of the host adaptive immune response through monoallelic expression and clonal phenotypic variation of surface proteins [1] , [2] . The African trypanosomes are flagellated parasitic protists of major medical and veterinary importance . They are the causative agents of HAT , and Nagana in cattle , and they proliferate in the mammalian host bloodstream . In Trypanosoma brucei , antigenic variation requires mono-telomeric expression and switching of variant surface glycoprotein genes ( VSGs ) . It is this continuous process of allelic exclusion , transcription of only one telomeric VSG at a time in each cell , which is essential for the persistence of a chronic infection . T . brucei has long been a paradigm for antigenic variation but the molecular triggers and the mechanisms mediating VSG recombination and switching are not fully understood . Telomeres are specialized structures that cap chromosome ends , consisting of long tracts of T2AG3-repeats in T . brucei and in human cells . T . brucei subtelomeres are the exclusive expression sites ( ESs ) for VSG genes [3] . One among approximately fifteen bloodstream-form ESs ( BESs ) is active in each cell and RNA polymerase I drives transcription at an extra-nucleolar site known as the expression site body ( ESB ) [4] , [5] , [6] . The BESs are polycistronic transcription units with promoters located up to 60 kbp from the telomere-adjacent VSG [7] . Sequencing of multiple BESs revealed a conserved arrangement , with VSGs flanked by repetitive sequences; the telomeric repeats ( up to 15 kbp tracts ) downstream and the 70-bp repeats ( 0 . 2–7 . 1 kbp tracts ) upstream [7] . The minichromosomes , of which there are up to 100 copies per genome , contain additional archival , non-transcribed VSG genes flanked by telomeric repeats and 70-bp repeats . The BESs typically also encode several Expression Site Associated Genes ( ESAGs ) , but these genes are always separated from the VSG by 70-bp repeats [7] . The single active , transcribed VSG accounts for approximately one-tenth of total cell protein , which forms a dense protective coat on each cell [8] , while inactive VSG mRNAs are approximately 10 , 000-fold less abundant than the active VSG mRNA [9] . Antigenic variation appears to be a stochastic process , typically involving duplicative transposition and replacement of the active VSG [10] , [11] . The process can also occur via loss or replacement of the entire active BES [12] , [13] , [14] , [15] or via in situ BES switching , whereby activation of a previously silent BES is coordinated with BES inactivation , typically with no detected DNA rearrangement . The majority of archival VSGs , up to 2 , 000 subtelomeric genes and pseudogenes [16] , [17] , are not associated with BES promoters . Thus , recombination and replacement of the active VSG is required to utilize this archive for long-term immune evasion . 70-bp repeat sequences define the 5′ boundaries for VSG recombination [18] and 70-bp repeats are found upstream of most archival VSGs [17] , serving as potential templates for homologous recombination; this involves gene conversion or , in the case of telomeric VSGs , break-induced replication ( BIR ) , whereby the template is copied to the chromosome end [10] . The long 70-bp repeat tracts found at active BESs are , therefore , recombination substrates that facilitate the translocation of archival VSG genes to the transcribed telomere [19] . It has been proposed that this transcribed 70-bp repeat tract is also fragile , such that the DNA breaks that trigger antigenic variation originate here [10] . The dominant mechanism of chromosomal double-strand break ( DSB ) repair in T . brucei is homologous recombination [20] . RAD51-independent , microhomology-mediated end-joining ( MMEJ ) also operates , while non-homologous end-joining has not been detected [21] . Studies on strains lacking the RAD51 homologous strand-exchange protein [22] , the RAD51-3 paralogue [23] or the RAD51-interacting protein , BRCA2 [24] , indicate that each of these factors promotes VSG switching . In contrast , TOPO3α , a type 1A topoisomerase , functions with RMI1 as an anti-recombinase , suppressing BES crossovers but promoting duplicative VSG transposition through 70-bp repeat recombination [13] , [14] . Despite recent progress , little is known about the subtelomeric DSBs that naturally trigger antigenic variation in T . brucei , the subsequent DNA damage responses , or how these responses determine the mechanism of VSG switching . We show that natural breaks accumulate close to the telomere in both transcribed and non-transcribed BESs . We induced DSBs at different sites within both active and silent BESs and recovered survivors for analysis , those that switch and those that don't . We find that the site of the DSB has a major impact on the DSB response and the probability and mechanism of VSG switching .
Although artificial DNA breaks between the VSG and the 70-bp repeats at the active BES enhance antigenic variation in T . brucei , the presence of natural breaks has only been mapped to the VSG-distal side of these repeats [10] . We , therefore , used ligation-mediated PCR ( LM-PCR ) to investigate the distribution of natural DSBs in the vicinity of the VSG221 gene , in either the active transcribed or silent state; the VSG221 locus on chromosome 6a is single-copy and hemizygous . LM-PCR involves the ligation of a specific oligonucleotide to sites of DSBs followed by amplification of products using primers specific for the ligated oligonucleotide and for the locus of interest . The PCR products , each representing a distinct DSB , are then separated on a gel and detected using an appropriate probe . LM-PCR , therefore , provides a ‘snap-shot’ of DSBs in a population of cells . We used three specific VSG221 BES primer-probe combinations to assay breaks across three distinct regions ( FIG . 1A; see maps to the left-hand side of the blots in FIG . 1B ) . A chromosome-internal primer-probe combination was used as a control . LM-PCR assays revealed DSBs in all three subtelomeric regions and , in contrast to a previous report [10] , transcription status had little impact on the number of DSBs , which were detected at a similar frequency regardless of whether the VSG was transcribed or silent ( FIG . 1B ) . Thus , we suggest that DNA replication rather than transcription generates natural breaks . Following a comparison of the subtelomeric regions examined , we tentatively suggest that breaks could be more frequent closer to the telomere . We detected several VSG221-flanking breaks when only 4 , 000 cells were sampled ( FIG . 1B ) , meaning that the frequency of these potential antigenic variation triggers exceeds the frequency of antigenic variation by two orders of magnitude; variants arise at a rate of approximately 1×10−5 per cell division [13] . We conclude that natural subtelomeric breaks typically fail to trigger antigenic variation . To examine the consequences of DSBs within BESs , a panel of T . brucei strains were established with a tetracycline-inducible I-SceI meganuclease gene [20] and a single I-SceI cleavage site within the active or silent VSG221 BES; I-SceI cleaves a specific 18-bp sequence and produces a single DSB . The three sites selected for integration of the I-SceI site within the active VSG221 BES ( FIG . 2A ) were adjacent to the BES promoter , approximately 60-kbp from the VSG ( VSGpro ) ; adjacent to the 70-bp repeats , upstream of the VSG ( VSGup ) or; adjacent to the T2AG3 repeats , downstream of the VSG ( VSGdown ) . Antigenic variation is not expected following recombination and repair at a silent site , but we did want to assess the impact of transcription on DSB repair . For this purpose , we also analyzed equivalent DSBs in VSGpro and VSGdown strains with a silent VSG221 BES . Immunofluorescence analysis confirmed that >99% of cells expressed VSG221 in the ‘active-VSG221’ strains and that <0 . 1% of cells expressed VSG221 in the ‘silent-VSG221’ strains . We also demonstrated that the latter strains could reactivate the VSG221 BES ( data not shown ) . Using a combination of Southern blotting ( FIG . 2B ) , PCR and drug-sensitivity assays for loss of expression of the break-adjacent selectable marker ( data not shown , see FIG . 2A ) , we confirmed efficient and tightly regulated DSB-induction at the correct locus in all five strains detailed above; at least two independent assays used for each strain . The Southern blot analysis shown in Figure 2B reveals the terminal restriction fragments and the expected in vivo cleaved fragments in the active transcribed and silent VSGdown strains after 6 h of induction . Cleavage is almost complete after 24 h , as indicated by loss of the terminal restriction fragments , and we obtained similar results for the active VSGup strain ( FIG . 2B ) . In contrast , an I-SceI site embedded within T2AG3-repeats was inaccessible ( FIG . S1 ) . We next used a clonogenic assay to assess survival following DSBs in active and silent BESs . Cells were distributed in multi-well plates under DSB-inducing conditions and , after several days , wells with live cells were counted . Cloning efficiency averaged approximately 85% in cells with DSBs in the silent BES but was strikingly lower following DSBs in the active BES ( FIG . 2C ) ; only approximately 5% of VSGup or VSGdown cells survived . The low cloning efficiency indicates that a break at the active BES is typically lethal . This may be because transcription interferes with the DSB response or , since VSG expression is required for cell-cycle progression [25] , because the DSB response interferes with VSG transcription; the DSB response does indeed interfere with transcription in mouse cells [26] . Importantly , failure to tolerate a DSB is consistent with our observation that natural DSBs far exceed instances of antigenic variation ( see above ) . We suggest that these natural DSBs at the active BES are also typically lethal . To explore antigenic variation following DSBs at the active transcribed VSG locus , we generated cloned DSB-survivors from the VSGpro ( 24 clones ) , VSGup ( 22 clones ) and VSGdown ( 32 clones ) strains . As above , the VSG221 BES was maintained in the transcribed state prior to DSB-induction , using antibiotic-selection ( see FIG . 2A ) , which was removed immediately prior to limiting dilution cloning under DSB-inducing conditions . This ensured that each cloned survivor represented an independent DSB-repair event and , unlike previous approaches , did not require any selection for cells that had modified expression of the VSG or a BES-reporter . Using immunofluorescence analysis , we scored for survivors that had undergone antigenic variation ( FIG . 3A; example fluorescence images are shown in FIG . 4A ) . In the VSGpro strain , only two survivors ( 8% ) had inactivated VSG221; in the VSGup strain , all survivors ( 100% ) had inactivated VSG221; and , in the VSGdown strain , nine survivors ( 28% ) had inactivated VSG221 ( FIG . 3A ) . Thus , antigenic variation is efficiently triggered by a DSB adjacent to the 70-bp repeats , is less efficiently triggered by a DSB adjacent to the telomeric repeats and is rarely triggered by a DSB adjacent to the BES promoter . Antigenic variation in every DSB-survivor from the active VSGup strain reflects a massive increase in switch frequency at 5×10−2 switches per DSB-induced cell; this is 5 , 000-fold higher than the natural rate of antigenic variation , estimated at approximately 1×10−5 switches per cell , per generation [13] . As expected , analysis of 24 silent VSGpro ( expressing VSG121 ) and 25 silent VSGdown ( expressing VSGX ) DSB-survivors failed to reveal any activation of the silent VSG221 gene triggered by a break within the silent BES ( data not shown ) . Drug-sensitivity assays confirmed that DSBs were generated in the majority of non-switched survivors from the VSGpro and VSGdown active site strains; 22/22 and 18/23 of these non-switched survivors were drug-sensitive , indicating disruption of RFP:PAC , and NPT expression , respectively ( see FIG . 2A ) . Among non-switched VSGpro survivors , three displayed repair via MMEJ as described previously [21] . Based on a previous analysis [27] , we speculated that a T2AG3-like sequence downstream of VSG221 served as a telomere-seed in the majority of non-switched VSGdown survivors , allowing for repair by de novo telomere addition . This was confirmed using PCR assays ( FIG . S2A–B ) and also explains continued NPT expression in five of these clones . Taken together , our results confirm the generation of DSBs in non-switched survivors and show that these breaks often fail to trigger antigenic variation when adjacent to the BES promoter or the T2AG3-repeats . We also used a series of PCR assays , as above ( see FIG . S2A ) , to confirm that DSBs had been generated in survivors from the silent VSGpro and VSGdown strains . From the VSGpro strain , eight survivors ( 33% ) lost both the promoter-adjacent RFP:PAC gene and the VSG221 gene and nine ( 38% ) lost only RFP:PAC; the remaining seven ( 29% ) repaired within RFP:PAC ( data not shown ) via MMEJ [21] . From the VSGdown strain , 24 survivors ( 96% ) retained a promoter-adjacent RFP:PAC gene , eleven ( 44% ) retained VSG221 and only five ( 20% ) retained NPT ( data not shown ) . These results illustrate , consistent with the cloning-efficiency data shown in Figure 2C , how DSBs at either end of a silent BES are well-tolerated , even if they result in loss or replacement of part or all of the BES . We next used our series of PCR assays ( see FIG . S2A ) to explore the DNA rearrangements associated with antigenic variation . Following a DSB adjacent to the 70-bp repeats ( VSGup strain ) , we found that VSG221 was lost in all but one of the switched survivors ( FIG . 3B , clone 15 ) , while only one of these also lost ESAG1 ( FIG . 3B , clone 9; FIG . 3D ) . Thus , antigenic variation typically occurred through recombination within the 70-bp repeats following a break adjacent to these repeats , as reported previously [10] . The clone that lost ESAG1 may have switched through subtelomere loss or replacement , while the clone that retained VSG221 may have switched through telomere crossover or promoter inactivation . In striking contrast , following a DSB adjacent to the telomeric repeats ( VSGdown strain ) , eight ( 89% ) of the switched survivors lost ESAG1 ( FIG . 3C; FIG . 3D ) ; the only clone that retained ESAG1 had lost VSG221 indicating recombination within the 70-bp repeats ( FIG . 3C ) . We , therefore , asked whether a distal reporter adjacent to the promoter remained intact and active in the ESAG1-negative survivors; we had inserted an RFP:PAC-cassette adjacent to the BES promoter ( see FIG . 2A ) to monitor BES loss in the active VSGdown strain because we had previously observed BES loss following a DSB at the silent VSGdown site [28] . The analysis revealed that all eight ESAG1-negative survivors were also RFP negative by fluorescence microscopy ( see FIG . 4A ) and all but one of these had lost the RFP-PAC gene ( FIG . 4B , FIG . S2C ) . We conclude that , when the DSB was adjacent to the telomeric repeats , seven of nine switched clones lost or replaced the BES; one clone underwent recombination within the 70-bp repeats and retained ESAG1 while another clone underwent recombination elsewhere within the BES and inactivated the promoter , thereby retaining RFP-PAC . In the two survivors that switched following a DSB adjacent to the promoter ( VSGpro strain ) , the RFP:PAC , ESAG1 and VSG221 genes were lost in one while all of these genes were retained in the other ( FIG . S2D ) . This indicated BES loss or replacement in the first clone and promoter inactivation in the second; RFP:PAC sequencing revealed repair by MMEJ [21] in this second clone . Thus , DSBs adjacent to the 70-bp repeats trigger recombination within the 70-bp repeats; DSBs adjacent to the telomeric repeats often fail to do so , resulting in loss or replacement of the entire BES in around 25% of survivors , and DSBs at the promoter only rarely bring about antigenic variation . We also show that a break can occasionally lead to promoter inactivation . Figure 4C shows several examples of switched clones expressing new VSGs . VSG recombination and antigenic variation in T . brucei can occur via RAD51-dependent or RAD51-independent mechanisms [29] . These are most likely based on homologous strand-exchange and MMEJ , respectively [21] . Although T . brucei RAD51 forms sub-nuclear foci following induction of DSBs at a chromosome-internal locus [20] , no significant increase in the proportion of cells with RAD51 foci was observed following induction of DSBs at BESs ( FIG . 5A ) . This may reflect failure to accumulate RAD51 or a reduced dosage of accumulated RAD51 . We therefore used a rad51 gene knockout approach in both the active VSGpro and VSGup backgrounds ( FIG . 5B ) . Clonogenic assays , using rad51 null strains , allowed us to quantify the contribution of RAD51 to subtelomeric DSB repair and antigenic variation . The cloning efficiency of rad51-null strains is only approximately 10% prior to I-SceI induction , indicating a major defect in DNA repair in the absence of RAD51 ( FIG . 5C ) . Following I-SceI induction , cloning efficiency was reduced further by approximately 90% ( VSGpro:rad51 strain ) or 70% ( VSGup:rad51 strain ) . By comparing cloning efficiency in the VSGup-rad51 strain and the VSGup-RAD51 strain ( 2 . 3% v 6 . 2%; compare FIG . 5C and FIG . 2C ) , we see that approximately 40% of VSGup survivors are RAD51-independent . Based on significantly higher DSB-survival in the VSGup:rad51 strain compared to the VSGpro:rad51 strain ( FIG . 5C ) , we tentatively suggest more efficient RAD51-independent repair in the VSGup strain . Among a panel of VSGup:rad51 survivors , twenty ( 91% ) had undergone VSG switching , as determined by VSG221 immunofluorescence assay and , similar to the results in a RAD51 background , all of these had lost VSG221 and only two had lost ESAG1 ( FIG . 5D ) . These results indicated RAD51-independent recombination within the 70-bp repeats . Thus , RAD51-independent ( likely MMEJ-based ) recombination makes an important contribution to antigenic variation and we suggest that it is more efficient within 70-bp repeat sequences than within non-repetitive sequences . A common DSB response is local DNA resection , involving degradation of the 5′ strand of dsDNA to generate ssDNA with a 3′ end . The resulting ssDNA serves as a substrate for the assembly of DNA repair and recombination factors [30] . We used a series of slot-blot assays ( FIG . 6A ) to monitor DNA resection following induced DSBs . In these assays , specific probes are used to detect signals on native DNA and denatured DNA in parallel , revealing the presence of single-stranded regions or the sum of both single-stranded and double-stranded regions , respectively . In all strains analyzed , with breaks at active ( FIG . 6B ) and silent BESs ( FIG . 6C ) , we detected local resection , typically peaking 12 h after meganuclease induction . The signal is reduced for the active VSGdown strain , but this may be due to the greater distance between the DSB and the regions probed for ssDNA , and also complete loss of the VSG221 and NPT genes in some cells ( see reduced signals in the ‘d’ columns ) . Thus , DNA resection is a common response to DSBs within a BES . We did note , however , failure to detect resection on the DSB-distal side of the 70-bp repeats in the active VSGup strain ( FIG . 6B; compare Ψ and VSG221 probes ) . This suggested inefficient resection through the 70-bp repeats , either due to the rapid formation of recombination intermediates or some other property of the repeat-sequence itself . This is consistent with a role for the 70-bp repeats in facilitating VSG diversification by increasing the efficiency of recombination and also in serving as a ‘buffer’ that helps to protect the rest of the BES and the chromosome from the fragile end . We previously reported continued cell cycle progression following T . brucei telomere deletion [28] and , in contrast , activation of a G2/M checkpoint in response to a DSB at a chromosome-internal locus [20] . We speculated that a severed DSB response [31] could explain failure to use the 70-bp repeats for recombination in the VSGdown strain . We used DAPI-stained nuclear and mitochondrial ( kinetoplast ) DNA as cytological markers to define position in the nuclear cell-cycle [32] and to examine cell cycle checkpoint responses; specifically , cells with a single nucleus and two separated kinetoplasts ( 1N2K ) correspond to nuclear G2 . A comparison of cells following DSBs in the silent VSGdown strain or in the active VSGdown or VSGup strains , revealed an increased proportion of G2 cells only in the VSGup strain ( FIG . 7A ) . Thus , T2AG3 repeat-adjacent DSBs , in either silent or active BESs , fail to trigger the G2/M checkpoint . This may be analogous to the anticheckpoint mediated by telomere-repeat sequences in yeast [33] . This analysis also revealed a later accumulation of post-mitotic ( 2N2K ) cells , between 24 and 48 h after I-SceI induction , in all three strains with DBSs at the active BES ( data not shown ) . Since VSG expression is required for progression to cytokinesis [25] , later accumulation of post-mitotic cells supports the view that DSB responses interfere with local transcription [26] rather than transcription interfering with the DSB response . Previously , it has not been possible to observe DNA damage and repair foci associated with BESs ( see FIG . 5A ) . We recently described T . brucei γH2A , a phosphorylated form of histone H2A that accumulates at DNA repair foci in response to DNA damage [34] . Immunofluorescence microscopy was used to explore the subnuclear accumulation of γH2A foci in response to DSBs in the strains described above . Although telomere-adjacent breaks failed to trigger the G2/M checkpoint , we observed robust γH2A responses in all strains examined ( FIG . 7B ) ; I-SceI induction increased the proportion of cells with γH2A foci from approximately 20% , representing naturally occurring DNA-damage , to >50% , representing additional BES-associated breaks . We next assessed the appearance of these γH2A foci during the cell-cycle . In all cases , foci were predominantly associated with the S- and G2-phases ( FIG . 7C ) , as described previously for natural breaks and for chromosome-internal breaks [34] . Representative images are shown in Figure 7D and reveal indistinguishable foci in the three strains presented . Thus , we conclude that γH2A foci that form in response to telomere repeat-adjacent breaks fail to signal the G2/M checkpoint but are still efficiently disassembled prior to progression to mitosis . These results are consistent with a telomere-adjacent DNA damage response that is severed after DNA resection and γH2A focus assembly but prior to the G2/M checkpoint .
Our survey of the VSG221 locus suggests that natural DSBs could be more frequent closer to the telomeric T2AG3-repeats . Indeed , the subtelomeric regions of a number of cell types have been shown to be fragile and prone to frequent breakage [35] . For example , human subtelomeres are recombination hot-spots [36] and mammalian telomeres are fragile sites [37] . Subtelomeres are also unstable in the malaria parasite , P . falciparum , and undergo frequent breakage and repair [38] . Our findings now indicate that subtelomeres are also fragile in African trypanosomes . So why are subtelomeres prone to breaks ? Our results indicate fragility independent of transcription , implicating DNA replication as the source of these breaks . Indeed , replication stress and fork collapse during S-phase is likely a major source of DSBs in all eukaryotes [39] . Subtelomeric DNA , due to secondary structure or local chromatin structure , could be particularly prone to replication stress , making replication forks more likely to stall and collapse . In this regard , it is notable that an I-SceI site embedded within telomeric repeats at the active BES was not cleaved following I-SceI induction in vivo ( FIG . S1 ) , suggesting inaccessible chromatin associated with tracts of T2AG3-repeats . The apparent transition from ( I-SceI ) accessible to inaccessible chromatin at the T2AG3-repeat junction could present a challenge for the replication machinery to negotiate . It has been proposed that short telomeres at the active BES are prone to breaks that increase the rate of antigenic variation [40] , [41] . This cannot explain high numbers of breaks detected in our LM-PCR assays , however , since the active VSG221-associated T2AG3-tracts are in excess of 5-kbp in all of the strains used here [27 , also see FIG . 2B and ]FIG . S1 . The 70-bp repeats have also been proposed to be the source of frequent breaks that trigger antigenic variation [10] . Deletion of the 70-bp repeat tract at the active BES demonstrated a role for these tracts in duplicative transposition [10] , [19] , but these studies did not distinguish between roles in triggering breaks or in subsequent recombination . We suggest that breaks within the 70-bp repeats , or between two blocks of 70-bp repeats [10] , would generate effective substrates for single-strand annealing [42] , a recombination pathway which would generate a ‘repeat’ deletion , rather than lead to VSG replacement . Breaks on the VSG- and telomere-proximal side of the 70-bp repeats , on the other hand , clearly do trigger antigenic variation [10; this study] . We show that the probability of antigenic variation is highly dependent upon the site of the subtelomeric DSB at the active BES . These breaks are not well-tolerated , however , and cell death is a common outcome . Even successful repair within the active BES commonly fails to bring about antigenic variation following breaks at certain sites . These findings are consistent with the high rate of natural DSBs that we observe at the active BES , relative to antigenic variation , and suggest that cells often die or fail to switch following these natural DSBs . Lesions at the active BES are probably typically lethal because VSG expression is compromised , while genes within silent BESs are dispensable and loss of these genes is tolerated . Our results also show that the site of a subtelomeric break has a major impact on the mechanism of antigenic variation . Subtelomeric breaks on either side of the active VSG can trigger antigenic variation but a DSB adjacent to the telomeric repeats is substantially less efficient in this regard . It is notable that a DSB within the BES can also trigger promoter inactivation . One switched survivor from the VSGpro strain underwent MMEJ and inactivated the promoter and another from the VSGdown strain inactivated the promoter and lost part of the BES . These are similar to in-situ switching events and may explain RAD51-dependent in-situ switching as reported previously [22] . Thus , in situ switching can be triggered by DSB-repair that does not substantially alter the sequence of the BES . T . brucei TOPO3α suppresses RAD51-dependent crossovers and recombination beyond the 70-bp repeats within the BES , thereby favoring recombination within these repeats [13] . We find , consistent with previous studies [13] , [22] , that antigenic variation associated with 70-bp repeat-recombination involves both RAD51-dependent and independent pathways . Notably , however , our results suggest a higher rate of RAD51-independent recombination within the 70-bp repeats than observed in the BES promoter region . MMEJ is RAD51-independent and we suggest that this repair mechanism is more efficient within 70-bp repeat sequences , due to the relative abundance of potential ‘microhomologies’ . Thus , recombination followed by Break-Induced Replication to the chromosome end and replacement of the active VSG could be initiated by microhomology . Our data do not reveal differences in the DNA damage response due to BES transcription in T . brucei . Rather , they reveal a different response due to telomere-repeat proximity . We show that subtelomeric breaks trigger γH2A focus formation and DNA resection . The increase in γH2A foci in response to DSBs allowed us , for the first time , to visualize repair sites associated with VSG recombination . Notably , γH2A focus formation is associated with a G2/M cell-cycle checkpoint following DSBs upstream of the active VSG but not following breaks immediately adjacent to the telomeric repeats . These latter cells also failed to use the 70-bp repeats for recombination and , instead , underwent antigenic variation via BES loss or replacement . Failure to trigger this checkpoint following telomere-repeat-adjacent breaks was independent of the transcription status of the BES . Telomere-associated proteins are known to repress the DNA damage response [43] . In Schizosaccharomyces pombe , a telomeric DSB-response is severed due to the absence of epigenetic marks required for cell-cycle arrest [31] , and telomeric repeats also suppress the checkpoint response in Saccharomyces cerevisiae [33] . This anticheckpoint effect is thought to prevent the fusion of linear chromosomes . We propose the operation of a similar anticheckpoint in T . brucei . Our results suggest a checkpoint bypass mechanism when the break is adjacent to the telomeric repeats and the G2/M checkpoint may be required for efficient participation of the 70-bp repeats in recombination . Natural breaks adjacent to the telomeric repeats may similarly explain previous reports of BES loss or replacement [12] , [13] , [14] , [15] . DNA DSBs are triggers for antigenic variation . Here , we probe DSB responses , BES recombination pathways and mechanisms of antigenic variation . First , we show that subtelomeres are fragile; thereby generating the DNA breaks that trigger antigenic variation . We then demonstrate VSG replacement and BES loss in response to distinct subtelomeric breaks , and also provide evidence for in situ switching as a response to subtelomeric DSBs . It is 70-bp repeat recombination that makes the major contribution to antigenic variation because most archival VSGs are flanked by these repeats and use them for gene-conversion . We suggest that breaks between the telomeric and 70-bp repeats trigger this pathway . What follows is a DNA damage response that includes DNA resection , histone modification and , depending upon the site of the break , a G2/M checkpoint . Formation of 70-bp repeat ssDNA then promotes interaction with similar templates elsewhere in the genome; these repeats may be favored substrates for recombination simply because they are highly repetitive . Recombination is then either RAD51-dependent or RAD51-independent; most probably MMEJ-based in this latter case . In conclusion , we provide novel insight into the triggers , associated DNA damage responses and mechanisms of antigenic variation in African trypanosomes . Our findings may also be relevant to subtelomeric gene rearrangements in human cells and to immune evasion mechanisms in other pathogenic protists , fungi and bacteria , such as Plasmodium sp . , Pneumocystis sp . and Borrelia sp . , respectively [44] .
T . brucei Lister 427 cells were grown and genetically manipulated as described [28] . The strain referred to here as VSGdown-silent was described previously [28] . Puromycin or G418 selection ( 2 µg/ml ) were used to ensure that the VSG221 BES remained active prior to I-SceI induction . I-SceI was induced using tetracycline ( Tet ) at 1 µg/ml ( Sigma ) . For clonogenic assays , a mean of 0 . 3 to 50 cells per well were seeded in 96-well plates with or without Tet . Survivors were assessed microscopically after 5–7 days . All clones analyzed were from plates with <30% positive wells . Repaired survivors were scored for puromycin sensitivity at 1 µg/ml . DSB-survivors that displayed >99% VSG221 positive cells , as determined by immunofluorescence analysis , were scored as non-switched , while survivors that displayed >98% VSG221 negative cells were scored as switched . Proportion of 1N2K cells and cells with γH2A repair foci were counted by two of us to generate mean values ± SD . The BES promoter-targeting constructs , pESP-RFP:PAC , pESP-RSP and pESPi-RSP were derived from pESPiRFP:PAC [28] . Briefly , the tetracycline-operator was removed from pESPiRFP:PAC to derive pESP-RFP:PAC and an I-SceI site was added to derive pESP-RSP . To insert an I-SceI site at the NotI site between the RFP and PAC genes , ‘I-SceI’ primers were annealed and ligated to give pESP-RSP . The RSP cassette replaced RFP-PAC in pESPiRFP-PAC to give pESPi-RSP . Transfections with SacI-KpnI digests of pESP-RSP or piESP-RSP were used to generate VSGpro active and silent strains , respectively . The ES-70 cassette was assembled using primers containing the I-SceI site and targeting fragments to amplify the PAC resistance cassette . The PCR product was transfected to generate VSGup strains . pTMF-Sce [28] was digested with SmaI and transfected to generate VSGdown active strains . To generate the pTMFEm construct SceHexU/SceHexL primers were annealed and ligated to SpeI/PstI digested pTelo1 ( pBluescript with sixteen T2AG3-repeats at the MCS ) . pTMFEm was digested with SmaI and transfected to generate VSGtelo strains . RAD51 gene disruption targets were amplified by PCR from T . brucei genomic DNA , using Phusion high-fidelity DNA polymerase ( New England Biolabs ) . The targets were assembled such that they flanked BSD or NPT selectable markers . Both constructs were digested with Acc651 and NotI prior to transfection . Details of primers/oligonucleotides are available on request . Ligation-mediated PCR ( LM-PCR ) was carried out as described [10] . Briefly , DNA DSBs were detected by in-gel blunt-end linker ligation and PCR . The BES locus-specific primers were: LMPCRi ( tagcagaatgcaacgtcga ) , LMPCRii ( ttggcgactataacggctg ) and LMPCRiii ( ggcgttaccaagcttgttga ) . Slot blots for the detection of ssDNA were carried out as described [20] . Southern blotting and sequencing were carried out according to standard protocols [45] . RFP , PAC , ESAG1 [13] , VSG221 and telomere-repeat-specific primers were used for the PCR assays . Other details of oligonucleotides are available on request . Extracts of total cell protein were separated on SDS-polyacrylamide gels and stained with Coomassie-blue or subjected to western blotting using standard protocols [45] . We used rabbit anti-VSG221 , rabbit anti-RAD51 [23] and an ECL+ kit ( GE Healthcare ) . For immunofluorescence microscopy , cells were labeled using a standard protocol with rabbit anti-VSG221 rabbit anti-γH2A [34] or mouse anti-Myc ( Source Bioscience ) , and fluorescein or rhodamine-conjugated goat anti-rabbit or anti-mouse secondary antibodies ( Thermo Scientific Pierce Antibodies ) . RFP was detected directly . Cells were mounted in VectaShield ( Vector Laboratories ) containing 4 , 6-diamidino-2-phenylindole ( DAPI ) . Images were captured on an Eclipse E600 microscope ( Nikon ) using a Coolsnap FX ( Photometrics ) charged coupled device camera and processed in Metamorph 5 . 4 ( Photometrics ) .
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Previous studies on antigenic variation in African trypanosomes relied upon positive or negative selection , yielding only cells that underwent variation . This made it difficult to define individual switched clones as independent , potentially introduced bias in the relative contribution of each switching mechanism and precluded analysis of cells undergoing switching . We show that DNA double-strand breaks ( DSBs ) naturally accumulate close to Trypanosoma brucei telomeres . Using the I-SceI meganuclease , we then established a system to trigger breaks in all cells in a population . The specificity , temporal constraint and efficiency of cleavage facilitated the application of a quantitative approach to dissecting subtelomeric break responses and their consequences . Accordingly , we show that the DSB-site determines probability and mechanism of antigenic switching , that DSBs can trigger switching via recombination or transcription inactivation and that a checkpoint-bypass mechanism can explain switching via VSG expression site deletion . Our results provide major new insights into the mechanisms underlying antigenic variation and provide a new model to explain how the repeats flanking VSG genes serve distinct roles in fragility and recombination . The findings are also relevant to telomeric gene rearrangements that control immune evasion in other protozoal , fungal and bacterial pathogens such as Plasmodium , Pneumocystis and Borrelia species , respectively .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biology",
"microbiology",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
DNA Break Site at Fragile Subtelomeres Determines Probability and Mechanism of Antigenic Variation in African Trypanosomes
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A critical question in biology is the identification of functionally important amino acid sites in proteins . Because functionally important sites are under stronger purifying selection , site-specific substitution rates tend to be lower than usual at these sites . A large number of phylogenetic models have been developed to estimate site-specific substitution rates in proteins and the extraordinarily low substitution rates have been used as evidence of function . Most of the existing tools , e . g . Rate4Site , assume that site-specific substitution rates are independent across sites . However , site-specific substitution rates may be strongly correlated in the protein tertiary structure , since functionally important sites tend to be clustered together to form functional patches . We have developed a new model , GP4Rate , which incorporates the Gaussian process model with the standard phylogenetic model to identify slowly evolved regions in protein tertiary structures . GP4Rate uses the Gaussian process to define a nonparametric prior distribution of site-specific substitution rates , which naturally captures the spatial correlation of substitution rates . Simulations suggest that GP4Rate can potentially estimate site-specific substitution rates with a much higher accuracy than Rate4Site and tends to report slowly evolved regions rather than individual sites . In addition , GP4Rate can estimate the strength of the spatial correlation of substitution rates from the data . By applying GP4Rate to a set of mammalian B7-1 genes , we found a highly conserved region which coincides with experimental evidence . GP4Rate may be a useful tool for the in silico prediction of functionally important regions in the proteins with known structures .
An important question in biology is the identification of functional residues in proteins . This information can help us understand the relationship between protein structures and functions as well as guide us to design new proteins by genetic engineering . However , experimental techniques for identifying functional sites , e . g . mutagenesis , are time consuming and expensive , which prohibits the brute force scanning of functional sites by experiments . Therefore , bioinformatics tools are useful , because they can narrow down the candidate sites for experimental investigation . Evolution operates similar to a high-throughput mutagenesis experiment: spontaneous mutations introduce protein variants in each generation and then the functional effects of the spontaneous mutations are “measured” by natural selection [1] . Therefore , protein sequences contain signatures of natural selection which reflect the functions of amino acid residues . For example , mutations at the functionally important sites tend to disrupt the proteins' normal functions , so these sites usually are more conserved than unimportant ones . If the sequences of a family of homologous proteins can be collected from multiple species , we may compare these sequences to infer which sites are more important than others . A number of bioinformatics tools based on phylogenetics have been developed to infer functional sites by the simple idea that functionally important amino acid sites tend to be more conserved than unimportant ones [2]–[11] . Given the multiple sequence alignment and the phylogenetic tree of a protein family , these phylogenetic methods can infer the amino acid substitution rate at each site in the alignment and an unusually low substitution rate implies that the site is functionally important . It has been shown that the predicted conserved sites coincide with experimental evidence , which confirms that these bioinformatics tools are useful . However , these existing methods are far from flawless . Most of the popular methods , e . g . Rate4Site [7] used in the ConSurf web server [11] , assume that the substitution rates are independent across sites . In statistical terms , this means that the sites in the alignment are independent and identically distributed ( i . i . d . ) . The i . i . d . assumption simplifies the statistical modeling , but it is unrealistic from the viewpoint of biology . The i . i . d . assumption implies that the slowly evolved functional sites are randomly distributed in the protein tertiary structure . In contrast , it is well known that functionally important sites tend to be close to each other in the protein tertiary structure and form functional regions , e . g . ligand binding sites or catalytic active sites . Clearly the i . i . d . assumption is inappropriate if a functional region consists of a number of sites . Several methods have been developed to incorporate the spatial correlation of evolutionary patterns , e . g . substitution rates at the protein level or dN/dS ratios at the codon level , to overcome the drawbacks of the i . i . d . assumption [3] , [5] , [8] , [12]–[16] . Most of these methods use a sliding window framework , in which the amino acid substitution rate or the dN/dS ratio at a focal site is approximated by the average substitution rate in a set of neighbor sites in the protein tertiary structure [3] , [12] , [13] . A site is considered to be a neighbor of the focal site if the Euclidean distance between the two sites is smaller than a predefined window size . Unfortunately , these sliding window methods also have intrinsic drawbacks . Firstly , in most , if not all , of sliding window methods the neighbor sites , including the focal site itself , are weighted equally in the inference of the substitution rate . However , clearly the focal site itself contains more information on its substitution rate than the sites near the boundary of the sliding window . Secondly , it is unclear how to determine the optimal window size [17] , [18] . If the window size is too large , there will be too many distant sites in the window , which could bias the estimation at the focal site . In contrast , if the window size is too small , the sliding window methods will not be able to capture the spatial correlation of substitution rates and may lead to overfitting . Furthermore , there is evidence that the optimal window sizes may vary among different protein families [12] . Very recently , a Bayesian model which combines the Potts model in statistical physics and the phylogenetic model has been proposed by Watabe and Kishino to infer protein patches under positive selection in protein tertiary structures [16] . In Watabe and Kishino's model , the Potts model is used to define a prior distribution of dN/dS ratios over a protein tertiary structure . This model solved many problems of the sliding window framework . However , the prior distribution in Watabe and Kishino's model is unnormalized [16] , which makes it difficult to design efficient algorithms to estimate hyperparameters . An advanced algorithm , thermodynamic integration [19] , was used in Watabe and Kishino's model to infer hyperparameters . However , the algorithm may be very inefficient , especially if there are many hyperparameters in the Potts model . Here we propose to incorporate a Gaussian process with the phylogenetic model to overcome the drawbacks of the existing methods . The Gaussian process has been widely applied in geostatistics and machine learning to capture the spatial correlation of interesting features [20] , [21] . Here we will briefly introduce the basic idea of the Gaussian process . More details of the Gaussian process and its applications can be found in the geostatistics and machine learning literature , e . g . [20] . A Gaussian process defines a probability distribution over functions , namely that a single sample point of the Gaussian process is a function over a space , e . g . a 3D space . Because the sample points of the Gaussian process are “smooth” functions , the Gaussian process encodes an intrinsic spatial correlation . Thus physically closely located points in the space are more likely to have similar function values . Therefore , the Gaussian process is very useful for defining prior distributions over spatially correlated patterns . For example , in this paper we are interested in modeling the spatial correlation of site-specific substitution rates in protein tertiary structures . If we image each residue in a protein tertiary structure as a single point in the 3D space , the Gaussian process can be used to define a prior distribution of site-specific log substitution rates over these points ( residues ) . The “smoothness” property of Gaussian process prior suggests that two physically closely located sites are more likely to have similar site-specific log substitution rates than two distantly located sites . Then , the Gaussian process prior can be combined with standard phylogenetic likelihood functions [22] to infer site-specific substitution rates from real data . We name this kind of hybrid model of Gaussian processes and phylogenetics as a phylogenetic Gaussian process model ( Phylo-GPM ) . In the Phylo-GPM framework , the spatial correlation of substitution rates can be naturally described and the strength of spatial correlation can be learned from the data . Therefore , it overcomes the common drawback of the sliding window methods that the window size must be manually specified . Unlike Watabe and Kishino's model [16] , the phylogenetic Gaussian process model uses a normalized prior , so simple algorithms , i . e . the widely used Metropolis algorithm [23] , [24] , can be used to efficiently infer hyperparameters . We have developed software , GP4Rate , based on the Phylo-GPM framework . In both simulated and real datasets , GP4Rate outperforms Rate4Site , a widely used tool based on the i . i . d . assumption . Therefore , GP4Rate may be a useful tool for the identification of functionally important sites .
Simulations were implemented to evaluate the performance of GP4Rate and to compare it with the widely used software , Rate4Site [7] . In the comparisons , Rate4Site is used as a representative of the classic phylogenetic models which use the discrete Gamma distribution to describe the variation of substitution rates across sites [25] but do not consider the spatial correlation of site-specific substitution rates in the protein tertiary structure . Because the true site-specific substitution rates are known in the simulated alignments , the estimated site-specific substitution rates can be compared with the true rates to evaluate the performance of the two methods . We generated two sets of simulated alignments based on different assumptions . In this and the next section , we will describe the first set of simulations which were based on a 2D toy protein structure . Thereafter we will describe the second set of simulations which were based on more realistic assumptions . To generate simulated alignments , we need a phylogenetic tree to describe the evolutionary relationship between simulated sequences , a protein structure to calculate the pairwise Euclidean distances between sites , a substitution model , and a vector of substitution rates . Note that the following discussions will be mainly based on the substitution rates rather than their log values . A simple phylogenetic tree was used in all simulations ( Figure 1A ) . The tree consisted of four sequences and all the branch lengths were equal to 0 . 2 substitution per site . Because the total branch length was equal to 1 substitution per site , on average an amino acid site only contained a single substitution . Therefore , the accurate estimation of substitution rate at a single site is challenging . The JTT substitution model [26] , [27] was used in all simulations . Note that the protein tertiary structure and the vectors of substitution rates used in the two sets of simulated alignments were different and will be described in detail below . In the 2D toy protein model , the protein tertiary structure was described by a 20 by 20 regular 2D grid , in which each dot corresponds to an amino acid in the toy protein structure ( Figure 1B ) . In addition , we assumed that the distance between adjacent sites in the 2D grid is equal to 5 Å . This distance is comparable to the average distance between carbon atoms of the physically interacting residues in real proteins . Even though the 2D toy protein model is artificial and no real protein has a similar structure , it is useful because the estimated site-specific substitution rates can be easily visualized by a heatmap ( Figure 2 ) . Therefore , we used the 2D toy protein model to check the correctness of the program and to get insights on the performance of GP4Rate . Two different spatial configurations of site-specific substitution rates were used in the 2D toy protein simulations . In the first configuration , the 20 by 20 grid was divided into 4 non-overlapping blocks , each of which was a 10 by 10 grid ( Figure 2A ) . Sites within a block had the same substitution rates but different blocks could have different substitution rates . Two substitution rates , 0 . 2 and 1 . 8 , were used for simulations and the substitution rates of blocks were alternatively arranged in the 2D protein structure ( Figure 2A ) . Therefore , the toy proteins consisted of two conserved blocks with low substitution rates ( 0 . 2 ) and two variable blocks with high substitution rates ( 1 . 8 ) . The second configuration was similar to the first one , but the sizes of non-overlapping blocks were 5 by 5 instead of 10 by 10 ( Figure 2B ) . Twenty simulated alignments were generated for each configuration of site-specific substitution rates . It is easy to notice that the average site-specific substitution rate is equal to 1 in both configurations . A program based on Bio++ [28] , [29] was developed to implement the simulations . For each simulated alignment , we ran two separate MCMC chains using GP4Rate to estimate site-specific substitution rates . For each MCMC chain , iterations were implemented and the trace plots of the MCMC outputs were monitored to ensure the convergence of the MCMC chains . The first of the iterations were discarded as burn-in . Then , the two chains were combined to calculate the average substitution rate at each site . To compare the performance of GP4Rate with that of Rate4Site , we also used Rate4Site to estimate the substitution rates . To make the results of GP4Rate and Rate4Site more comparable , the phylogenetic tree and branch lengths were fixed to the true values in both GP4Rate and Rate4Site . We firstly randomly sampled two simulated alignments , one for each configuration , as examples to get insights on the performances of GP4Rate and Rate4Site . As shown in Figure 2C and 2D , the site-specific substitution rates estimated by GP4Rate are smoothly distributed within the 2D protein structures . In addition , GP4Rate segments the 2D protein structures into blocks which correspond to the true patches with different substitution rates . In contrast , the spatial distributions of substitution rates estimated by Rate4Site are far from smooth . The sites with similar substitution rates are not clustered together and do not form clearly bounded patches ( Figure 2E and 2F ) . Thus , GP4Rate can capture the spatial correlation of substitution rates but Rate4Site cannot . To quantitatively evaluate the performance of GP4Rate and Rate4Site , we used receiver operating characteristic ( ROC ) curves to measure the power of the two methods . ROC curves are widely used to evaluate the accuracy of binary classifiers . The area under a ROC curve is usually used as a measure of the power of the corresponding method . To apply ROC curves to the simulated datasets , we must divide the amino acid sites into two categories , functional sites and nonfunctional sites , before generating simulated alignments . The functional sites are used as true positives while the nonfunctional sites are used as true negatives . In the 2D toy protein simulations , functional sites evolved at the lower rate ( 0 . 2 ) while nonfunctional sites evolved at the higher rate ( 1 . 8 ) . Then , the ROC curves were created by plotting the average true positive rates versus the average false positive rates using the ROCR library in R [30] . As shown in Figure 3A and 3B , the areas under the ROC curves generated by GP4Rate are larger than those generated by Rate4Site , which suggests that GP4Rate outperforms Rate4site . ROC curves measure whether a model can distinguish slowly evolved functional sites from the other sites . If a model can assign relatively low substitution rates to slowly evolved sites and relatively high rates to the other sites , it will perform well in the evaluations based on ROC curves . However , ROC curves cannot capture potential systematic biases of the model . For example , if the model adds a constant bias to the site-specific substitution rates , its ROC curves will be exactly the same regardless of the magnitude of the constant bias . Therefore , we used a simple loss function complementary with the ROC curves to capture any potential systematic biases of the estimated site-specific substitution rates . The loss function is defined by the following formula ( 1 ) in which is the total number of sites in the alignment , while and are the true and estimated log substitution rates at site i , respectively . The log values of site-specific substitution rates are used in the right-hand side of Equation 1 , since we want to emphasize the differences between low substitution rates . It is desirable because both GP4Rate and Rate4Site were designed to detect conserved regions with low substitution rates . Unlike ROC curves , a model which introduces a larger systematic bias will have a higher average loss than a model which introduces a smaller bias . We plotted the losses of both GP4Rate and Rate4Site in the 2D toy protein simulations . As shown in Figure 3C and 3D , GP4Rate outperforms Rate4Site , as evident by the lower losses produced by GP4Rate ( paired Wilcoxon test , for both of the two configurations ) . The improved accuracy originates from GP4Rate's ability to model the spatial correlation of site-specific substitution rates , since the performance gap between GP4Rate and Rate4Site becomes smaller in the second configuration which consists of smaller conserved and variable patches . GP4Rate has two hyperparameters , i . e . the characteristic length scale and the signal standard deviation , which model the strength of spatial correlation of substitution rates and the marginal variation of substitution rate at a single site , respectively . An advantage of GP4Rate over the sliding window methods is that the hyperparameters can be learned from the data . In contrast , the window size of the sliding window methods must be predefined before analyses . To show that GP4Rate can learn the hyperparameters from the data , we plotted the estimated median hyperparameters of the simulated alignments . As shown in Figure 4A , the characteristic length scales estimated in the first configuration are about 3 fold larger than those estimated in the second configuration . Because the patches are much larger in the first configuration , the result suggests that GP4Rate can learn the magnitude of the spatial correlation of substitution rates from the data . The estimated signal standard deviations in the two configurations are similar , which matches the intuition that the two configurations are similar except in the strength of spatial correlations of substitution rates . In summary , when spatial correlation of substitution rates exists in proteins , GP4Rate always outperforms Rate4Site . However , the spatial correlation of site-specific substitution rates may be insignificant in some proteins . Therefore , we also evaluated both GP4Rate and Rate4Site in simulated alignments in which the spatial correlation of site-specific substitution rates is absent . These simulated alignments were generated by randomly shuffling the columns in each alignment in the first spatial configuration of substitution rates ( Figure 2A ) . The permutations of alignments destroyed the spatial patten of site-specific substitution rates . Here we only summarize the performance of GP4Rate and Rate4Site in the permuted alignments and more details can be found in the online Supplementary Material . The absence of spatial correlation results in close-to-zero characteristic length scales in GP4Rate , which confirms that GP4Rate can detect the absence of spatial correlation when there is none . Plots of ROC curves show that GP4Rate and Rate4Site have effectively the same power to distinguish slowly evolved sites from the other sites . In contrast , when we use the loss function ( Equation 1 ) to measure the accuracy of estimated substitution rates , GP4Rate is less accurate than Rate4Site . Nevertheless , GP4Rate and Rate4Site have similar power to find slowly evolved functional sites , since in practice it is the relative rankings of sites instead of their absolute substitution rates tell us which sites may be more likely to be functional . We generated a second set of simulated alignments based on more realistic assumptions . The basic idea is that if we have a large number of highly diverged sequences , a simple method which does not consider the spatial correlation of substitution rates may accurately estimate the site-specific substitution rates because of the rich information in a very large dataset . We may generate simulated alignments based on the real protein tertiary structure and the presumably accurately estimated site-specific substitution rates . These simulated alignments may have similar features as real proteins . In this set of simulations , we used the same phylogenetic tree ( Figure 1A ) and the JTT substitution model [26] , [27] used in the 2D toy protein simulations . The protein tertiary structure and the site-specific substitution rates were based on a real protein , B-cell lymphoma extra large ( Bcl-xL ) . This protein has been studied using Rate4Site and the two predicted conserved patches coincide with the regions with known functions [31] . We downloaded the protein tertiary structure of Bcl-xL from Protein Data Bank ( PDB ID: 1MAZ [32] ) . The site-specific substitution rates estimated by Rate4Site were obtained from the ConSurf-DB database [10] . In ConSurf-DB , 131 unique homologs of Bcl-xL were automatically collected and then Rate4Site was applied to estimate the site-specific substitution rates . Because of the very large number of sequences in the dataset , the estimation of site-specific substitution rates may be relatively accurate . We generated 20 simulated alignments based on the above assumptions and both GP4Rate and Rate4Site were applied to the simulated alignments using the same setting described in the 2D toy protein simulations . To evaluate the performance of GP4Rate and Rate4Site by ROC curves , we divided the sites into two categories before generating simulated alignments: slowly evolved functional sites and others . Based on the site-specific substitution rates reported by ConSurf-DB , the 10 percent most slowly evolved sites were considered to be functional while the others were not . As shown in Figure 5A , GP4Rate is more powerful to distinguish slowly evolved sites from the other sites , since the area under the ROC curve of GP4Rate is larger than that of Rate4Site . In addition , based on the loss function defined by Equation 1 , GP4Rate produces lower losses in 18 out of the 20 simulated alignments ( Figure 5B ) and the median loss of GP4Rate is significantly smaller than that of Rate4Site ( paired Wilcoxon test , value<10−4 ) . Therefore , GP4Rate still outperforms Rate4Site in the realistic simulations . The B7-1 ( CD80 ) family is a member of the immunoglobulin superfamily ( IgSF ) and is critical for the regulation of immune responses [33] . The protein tertiary structure of the human B7-1 protein has been determined [34] , [35] . The human B7-1 protein consists of two IgSF domains ( IgV and IgC ) , each of which shows an anti-parallel sandwich structure [34] . We applied GP4Rate and Rate4Site to 7 mammalian B7-1 sequences downloaded from the NCBI HomoloGene database [36] and compared their performances . The N-terminal and C-terminal sequences were trimmed in the alignment , because the corresponding atoms are absent in the X-ray crystal structure . The resulting alignment consists of 199 amino acid sites . Then the phylogenetic tree was inferred by PhyML with the model [37] . The protein sequences in the alignment are very similar to each other as evident by the lack of gaps in the alignment ( data not shown ) . Therefore , the information in each site in the alignment is very limited and it is hard to infer site-specific substitution rates accurately . We used the human B7-1 protein structure ( PDB ID: 1I8L [35] ) to calculate the pairwise Euclidean distances between the carbon atoms of amino acids . Then , we applied GP4Rate to the B7-1 alignment to infer site-specific substitution rates . We ran two independent MCMC chains for iterations , and the first of the iterations were discarded as burn-in . We first estimated the posterior marginal distributions of hyperparameters based on the MCMC samples . As shown in Figure 6 , the estimated characteristic length scale is significantly higher than 0 , which confirms that the substitution rates are correlated in real proteins . The presence of spatial correlation of substitution rates may facilitate the discovery of slowly evolved functional regions . To test this hypothesis , the mean site-specific substitution rates of the MCMC samples were calculated and the 20 most slowly evolved sites were considered to be functional . Then , the 20 most slowly evolved sites were superimposed onto the protein tertiary structure ( PDB ID: 1I8L [35] ) . As shown in Figure 7A , the slowly evolved sites predicted by GP4Rate are not randomly distributed and instead form a single large region in the IgC domain . A systematic mutagenesis study has suggested that the IgC domains are important for binding CTLA-4 and CD28 [38] , even though the effects of the IgC domain may be indirect [35] . To test whether the predicted slowly evolved sites overlap with the experimentally verified functional sites [38] , the 7 experimentally verified functional sites in the IgC domain were mapped onto the human B7-1 structure ( Figure 7A ) . Clearly 4 experimentally verified functional sites in the IgC domain , i . e . Q157 , D158 , E162 , and L163 , are within the slowly evolved patch predicted by GP4Rate , which highlights the potential usefulness of GP4Rate . To compare GP4Rate with Rate4Site , we also applied Rate4Site to the same dataset . The superimposition of the 20 most slowly evolved sites predicted by Rate4Site is shown in Figure 7B . The sites predicted by Rate4Site are present in both the IgV and IgC domains and do not form clearly bounded regions . Even though 2 experimentally verified functional sites in the IgC domain , i . e . F106 and I113 , overlap with the sites predicted by Rate4Site , the 4 experimentally verified functional sites detected by GP4Rate do not overlap with the sites predicted by Rate4Site . Therefore , GP4Rate and Rate4Site can provide complementary insights to real data . To investigate which model , GP4Rate or Rate4Site , fits the B7-1 dataset better , we performed a Bayesian model comparison . The direct comparison between GP4Rate and Rate4Site is impractical , because Rate4Site is based on the maximum likelihood principle instead of the Bayesian principle . However , it is not very difficult to develop a Bayesian version of Rate4Site by specifying a prior distribution over parameters . Therefore , we developed a Bayesian version of Rate4Site and compared it with GP4Rate . Details of the Bayesian model comparison can be found in the online Supplementary Material and we only summarize the results here . We compared the site-specific substitution rates estimated by the original Rate4Site and its Bayesian version and found that the two programs produced essentially the same result . Therefore , the marginal likelihood estimated by the Bayesian version of Rate4Site may be used to evaluate how good the original Rate4Site fits the B7-1 dataset . The log marginal likelihood of GP4Rate is equal to while the log marginal likelihood of the Bayesian Rate4Site is equal to , which suggests a very large Bayes factor of GP4Rate compared with the Bayesian Rate4Site ( ) . Therefore , GP4Rate fits the B7-1 dataset much better than the Bayesian Rate4Site .
Many phylogenetic methods have been developed to identify slowly evolved amino acid sites which may be functional . However , the most widely used methods , e . g . Rate4Site , ignore the spatial correlation of site-specific substitution rates . Some other methods use the sliding-window framework to capture the spatial correlation of substitution rates , but the statistical method for choosing the optimal window size is largely unknown . Since the strength of the spatial correlation of substitution rates is unknown in most of proteins , the sliding window methods are problematic in real data analyses . In GP4Rate , both of the two issues are solved under a Bayesian statistical framework . By using the Gaussian process to define the prior distribution of the site-specific log substitution rates , GP4Rate can naturally model the spatial clustering of functionally important sites and the hyperparameters which measure the strength of spatial correlation can be inferred from the data instead of being manually specified before the analyses . In simulated datasets , GP4Rate significantly outperforms Rate4Site . The power of GP4Rate is mainly derived from the fact that GP4Rate has the added ability to model the spatial correlation of substitution rates . By borrowing statistical information from neighbor sites with similar substitution rates , GP4Rate can estimate the site-specific substitution rates with a much higher accuracy than Rate4Site . In the case study of B7-1 genes , GP4Rate predicted a slowly evolved functional patch in the protein tertiary structure and 4 sites within the region are well supported by experimental evidence . In contrast , the slowly evolved sites predicted by Rate4Site are scattered and do not form clearly bounded regions . In addition , we have shown that GP4Rate fits the B7-1 dataset much better than Rate4Site based on Bayesian model comparison . The performance gap between GP4Rate and Rate4Site will be maximized when the protein sequences are very similar to each other and the spatial correlation is strong . Therefore , GP4Rate is most suitable to analyze small gene families , e . g . new genes or small gene families derived from recent gene duplication events . When the spatial correlation of substitution rates is weak , GP4Rate and Rate4Site may generate similar results . For example , we applied GP4Rate to 38 RH1 genes [39] and found that the spatial correlation of substitution rates is much weaker in the RH1 dataset than that in the B7-1 dataset ( data not shown ) . In this case , the difference between GP4Rate and Rate4Site is subtle . Therefore , a rigorous model comparison as shown in the case study of B7-1 genes may be important in data analyses . Because GP4Rate is based on MCMC simulations , it is slower than Rate4Site . For example , it took about 1 CPU day for GP4Rate to analyze the B7-1 dataset . However , GP4Rate is still very useful for small scale problems , e . g . guiding mutagenesis experiments , since the experimental time is much longer than the execution time of GP4Rate . The time cost of GP4Rate can be reduced in the future using advanced algorithms , e . g . more efficient MCMC sampling algorithms or sparse approximations of the Gaussian process [40] . The most time consuming step of GP4Rate is the Cholesky decomposition whose time complexity is a cubic function of the number of sites in the alignment . In practice , a simple method to reduce the computational time is to perform the analyses based on a selected subset of amino acid sites . For example , it is well known that surface residues are more likely to be involved in interactions with other proteins or ligands . If these interactions are most interesting to users , a fast analysis based only on the surface residues may be appropriate . In addition to modeling the spatial correlation of site-specific substitution rates , protein tertiary structures have been used to improve phylogenetic models and the estimation of site-specific substitution rates in a few other studies [41]–[46] . These methods can be roughly divided into two categories . The first category of models assumes that the fixation probability of new mutations is determined by how the mutations influence the stability of the protein [41]–[43] . Typically it is assumed that mutations which stabilize the protein structure are more likely to be fixed than mutations which destabilize the protein structure . Unlike this category of models , the Phylo-GPM framework does not provide a mechanistic interpretation for the estimated substitution rates . However , GP4Rate may be more powerful to identify functional regions which are not directly relevant to the stability of proteins . The second category of models assumes that the site-specific substitution rates or dN/dS ratios are influenced by the local environment of the focal site in the protein tertiary structure [44]–[46] . For example , it has been shown that the dN/dS ratio of a site is influenced by its relative solvent accessibility ( RSA ) [44]–[46] . It is relatively straightforward to combine the Phylo-GPM framework with local features of amino acid sites . For example , in this study we assume that the site-specific log substitution rates follow a zero-mean Gaussian distribution . We may replace the zero-mean rate vector by a new one in which the mean of log substitution rate at a site is a linear function of its local features , e . g . RSA . It is very interesting to investigate whether adding local features to the Phylo-GPM framework improves model fitting in the future . The Phylo-GPM framework proposed in this paper may be used as a general tool to model the spatial correlation of patterns in the protein tertiary structure . The phylogenetic hidden Markov model ( Phylo-HMM ) is a popular method which combines the hidden Markov model and statistical phylogenetics [47] . It has been used to model the spatial correlation of evolutionary patterns along primary sequences [17] , [48]–[53] . The Phylo-GPM framework may be viewed as an extension of the Phylo-HMM to the protein tertiary structures . In the future , new methods based on the Phylo-GPM framework may be developed to identify functional divergence or positive selection in proteins .
GP4Rate is an open-source software application written in C++ and its source code is freely available from http://info . mcmaster . ca/yifei/software . html . GP4Rate combines the protein alignment and the protein tertiary structure to infer groups of close-located functional sites evolved at low rate . We assume that the protein alignment , the phylogenetic tree , and the tertiary structure of one protein in the alignment are provided by users . In GP4Rate , both the topology and the branch lengths of the phylogenetic tree are fixed to improve the speed of the program . In addition , we assume that the protein sequences in the alignment belong to the same gene family and have very similar functions , which implies that the functionally important sites do not vary among sequences and the site-specific substitution rates do not change over time . However , we do assume that the substitution rates can vary across different sites . The site-specific rates are used as proxies of functionality: very low substitution rates suggest the corresponding sites are functionally important . In most molecular phylogenetic programs , e . g . Rate4Site [7] , PAML [54] , and PhyML [37] , the site-specific substitution rates are assumed to be i . i . d . and follow a simple discrete distribution , usually the discrete Gamma distribution [25] . Recently , Dirichlet process pirors have been used to model the variable substitution rates over sites to overcome the inflexibility of the simple discrete distributions [55] , but it is still assumed that the site-specific substitution rates are i . i . d . The i . i . d . assumption implies that slowly evolved functional sites are scattered in the protein tertiary structure . The major contribution of this paper is to relax the i . i . d . assumption using the Gaussian process [21] which can naturally capture the spatial correlation of site-specific substitution rates in the protein tertiary structure . In GP4Rate , the parameters are estimated using the Bayesian principle . In Bayesian statistics , the parameters are random variables and the conditional distribution of parameters given data , i . e . the posterior distribution , gives us an estimation of parameters . For simplicity of presentation , first we focus on the vector of site-specific log substitution rates , which is the collection of log values of substitution rates at all amino acid sites , and defer the discussions on the other parameters . The posterior distribution of the vector of log site-specific substitution rates can be defined by the following equation , ( 2 ) In the equation , is the vector of site-specific log substitution rates , is the protein alignment while is its i-th column , and is the phylogenetic tree with the associated branch lengths . is the site-specific likelihood at site i , which is a function of the site-specific log substitution rate at site i . is the fundamentally important prior distribution of site-specific log substitution rates . A realistic should be able to describe the spatial correlation of site-specific substitution rates . In GP4Rate , is specified by a zero-mean Gaussian process . A Gaussian process is a probability measure defined over a function space . In the statistical modeling of site-specific substitution rates , we are only interested in the marginal distribution of the Gaussian process over a finite set of spatial locations which correspond to the locations of residues in the protein tertiary structure . By the definition of Gaussian processes , the marginal distribution of a zero-mean Gaussian process is a zero-mean multivariate Gaussian distribution [21] . Therefore , may be rewritten in the following format , ( 3 ) The correlation of site-specific substitution rates is determined by the covariance matrix , in which is the pairwise distance matrix which measures the Euclidean distance between the carbon atoms of amino acids in the protein tertiary structure . Furthermore , the covariance function is parameterized by two hyperparameters , and , which measure the strength of spatial correlation and the variation of substitution rates across sites , respectively . By plugging and , the prior distribution of the hyperparameters , into Equation 2 , it can be expanded to the following format , ( 4 ) In the following sections , we will provide more details on the specifications of the right-hand side terms of Equation 4 and the MCMC algorithm for the sampling of parameters , i . e . , , and . As mentioned above , follows a zero-mean multivariate Gaussian distribution ( Equation 3 ) . In the multivariate Gaussian distribution , the covariance matrix is specified by a covariance function . By default , GP4Rate uses the Matérn 1 . 5 covariance function , ( 5 ) In the equation , is an element in the covariance matrix while is an element in the distance matrix which measures the Euclidean distance between site i and site j in the protein tertiary structure . is an indicator function which is equal to 1 if site i and site j are the same site and equal to 0 otherwise . The covariance function contains two free parameters , and . is the characteristic length which determines the strength of the spatial correlation of substitution rates . If it is small , the spatial correlation is weak and only nearby sites have similar log substitution rates . Instead , if it is large , the spatial correlation is strong and distant sites can have similar log substitution rates . is the signal standard deviation which measures the marginal variation of log substitution rates at a single site . Small implies that the variation of log substitution rates is small . is a fixed “jitter” term which introduces a small amount of noise to the diagonal elements in . The “jitter” term ensures that the Cholesky decomposition , a critical numerical algorithm in the MCMC simulations , is numerically stable and improves the mixing of the MCMC simulations [56] . The “jitter” term is usually a small positive number ( e . g . ) , so it does not significantly change the behavior of the covariance function [56] . Clearly Equation 5 implies that the covariance of log substitution rates are decreasing with increasing Euclidean distance between two amino acid sites , which is compatible with our intuition that nearby sites tend to have similar substitution rates due to similar functions . In addition to the Matérn 1 . 5 covariance function , GP4Rate has two alternative covariance functions for users to choose . One is the Matérn 2 . 5 covariance function , ( 6 ) The other is the widely used squared-exponential covariance function , ( 7 ) The three covariance functions are all special cases of the general Matérn covariance function [21] . The major difference between them is that the three covariance functions describe different levels of smoothness in the spatial distribution of site-specific log substitution rates [21] . In the squared-exponential covariance function , the site-specific log substitution rates are smoothly distributed in the protein tertiary structure . Therefore , it is most suitable to model proteins with relatively large functional regions . In contrast , the Matérn 1 . 5 covariance function is the least smooth one and is suitable to model proteins with small functional patches . In this paper , we used the Matérn 1 . 5 covariance function in all analyses to allow for proteins that may have relatively small functional patches and could have nearby sites with very different substitution rates . The hyperparameters in the covariance functions , i . e . and , follow a prior distribution . We assume that the characteristic length , , and the signal standard deviation , , are independent and follow exponential distributions . Therefore , the prior distribution is defined by the following probability density function , ( 8 ) We choose and to be large so that the prior distribution has relatively weak information . To fully define the unnormalized posterior distribution ( Equation 4 ) , the likelihood must be specified . We follow the standard phylogenetic model first described by Felsenstein [22] . We assume that the substitution model in the phylogenetic likelihood function is fixed to the JTT model [26] , [27] while the phylogenetic tree is fixed to the one provided by the users . The likelihood can be calculated by the pruning algorithm and the gaps in the alignment may be treated as missing data [22] . However , the calculation of the likelihood function can easily become the most time consuming step in the MCMC sampling , because we need to evaluate the likelihood millions of times . We have applied a simple linear interpolation method to reduce the computational time of the likelihood evaluation [57] . GP4Rate calculates the site-specific log likelihoods at a set of evenly spaced substitution rates and then approximates the site-specific log likelihoods at other rates by interpolation . Note that the linear interpolation is performed based on the site-specific substitution rates while is the vector of their log values , so an exponential transformation , i . e . , must be performed for each site i before the interpolation . By default , GP4Rate calculates and caches the site-specific log likelihoods at 4000 evenly spaced substitution rates , ranging from to 20 . In each step of the likelihood calculation , if is between and 20 , the corresponding site-specific log likelihood is approximated by the following formula , ( 9 ) On the right hand side , and are the two cached substitution rates which are closest to , while and are the site-specific log likelihoods of and , respectively . In practice , is rarely bigger than 20 or smaller than . If it is indeed outside this , the log likelihood at the closest boundary is used as the approximate log likelihood . GP4Rate uses MCMC simulations to sample parameters from their posterior distribution . The algorithm follows previous studies by Neal [56] , [58] . As described in the previous sections , the parameters in GP4Rate have two components . The first one is and the second one consists of and . In each iteration , the two components are sequentially updated by the Metropolis algorithm with symmetric proposals [23] , [24] . To update , GP4Rate uses a proposal distribution suggested by Neal [56] , ( 10 ) In the equation , is the current vector of site-specific log substitution rates while is the new proposal . is the Cholesky decomposition of the covariance matrix and is a vector of independent standard Gaussian variables . The proposal distribution is tuned by the constant , . A large leads to large changes of while small leads to small changes . is chosen to make the acceptance rate of new proposals close to 0 . 25 . Instead of updating and in the original scale , we transform them to the log scale . The use of a log scale removes the boundaries of the two parameters and makes the MCMC sampling of and independent from the scale of the data [56] . The two parameters are updated by a sliding window method with a bivariate Gaussian proposal [58] . The Gaussian proposal is tuned so that the acceptance rate of new proposals is close to 0 . 25 . In practice , the update of is much faster than the update of and , because the update of and requires a Cholesky decomposition whose time complexity is , in which is the total number of sites in the alignment . Therefore , it is reasonable to update more often than and [56] . In each iteration is updated 50 times while the pair of and is updated once . After every 10 iterations , the values of , , and are recorded .
|
To understand how a protein functions , a critical step is to know which regions in its protein tertiary structure may be functionally important . Functionally important protein regions are typically more conserved than other regions because mutations in these regions are more likely to be deleterious . A number of phylogenetic models have been developed to identify conserved sites or regions in proteins by comparing protein sequences from multiple species . However , most of these methods treat amino acid sites independently and do not consider the spatial clustering of conserved sites in the protein tertiary structure . Therefore , their power of identifying functional protein regions is limited . We develop a new statistical model , GP4Rate , which combines the information from the protein sequences and the protein tertiary structure to infer conserved regions . We demonstrate that GP4Rate outperforms Rate4Site , the most widely used phylogenetic software for inferring functional amino acid sites , via simulations with a case study of B7-1 genes . GP4Rate is a potentially useful tool for guiding mutagenesis experiments or providing insights on the relationship between protein structures and functions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models"
] |
[
"evolutionary",
"modeling",
"sequence",
"analysis",
"biology",
"computational",
"biology"
] |
2014
|
Phylogenetic Gaussian Process Model for the Inference of Functionally Important Regions in Protein Tertiary Structures
|
Defective viral genomes of the copy-back type ( cbDVGs ) are the primary initiators of the antiviral immune response during infection with respiratory syncytial virus ( RSV ) both in vitro and in vivo . However , the mechanism governing cbDVG generation remains unknown , thereby limiting our ability to manipulate cbDVG content in order to modulate the host response to infection . Here we report a specific genomic signal that mediates the generation of a subset of RSV cbDVG species . Using a customized bioinformatics tool , we identified regions in the RSV genome frequently used to generate cbDVGs during infection . We then created a minigenome system to validate the function of one of these sequences and to determine if specific nucleotides were essential for cbDVG generation at that position . Further , we created a recombinant virus unable to produce a subset of cbDVGs due to mutations introduced in this sequence . The identified sequence was also found as a site for cbDVG generation during natural RSV infections , and common cbDVGs originated at this sequence were found among samples from various infected patients . These data demonstrate that sequences encoded in the viral genome determine the location of cbDVG formation and , therefore , the generation of cbDVGs is not a stochastic process . These findings open the possibility of genetically manipulating cbDVG formation to modulate infection outcome .
Defective viral genomes ( DVGs ) , which are generated during the replication of most RNA viruses , potentiate the host innate immune response [1–5] and attenuate the infection in vitro and in vivo [4 , 6–9] . Importantly , in naturally infected humans , the presence of DVGs correlates with enhanced antiviral immune responses during RSV infection [6] and reduced disease severity in influenza virus infection [8] . Significant effort is currently invested in harnessing DVGs as antivirals due to their strong immunostimulatory activity and ability to interfere with the replication of the standard virus . However , despite over 50 years of appreciating their critical functions in multiple aspects of viral infections , the molecular mechanisms that drive DVG generation remain largely unknown . This lack of understanding hampers our ability to effectively harness DVGs for therapeutic purposes and limits our capacity to generate tools to elucidate their mechanism of action and impact during specific viral infections . There are two major types of DVGs: deletion and copy-back ( cb ) [10] . Both types are unable to complete a full replication cycle without the help of a co-infecting full-length virus [11 , 12] and can be packaged to become part of the viral population [13] . Deletion DVGs , common in influenza virus and positive strand RNA viruses , retain the 3’ and 5’ ends of the viral genomes but carry an internal deletion [14–16] . These types of DVGs are believed to arise from recombination events [17 , 18] and can strongly interfere with the standard virus [19] . cbDVGs are common products of non-segmented negative sense ( nns ) RNA virus replication , including Sendai virus ( SeV ) , measles virus , and respiratory syncytial virus ( RSV ) , and are the primary stimulators of the innate immune response during nnsRNA virus infection [6 , 7 , 20 , 21] . cbDVGs arise when the viral polymerase detaches from its template at a “break point” and resumes elongation at a downstream “rejoin point” by copying the 5’ end of the nascent daughter strand [12 , 22] . This process results in the formation of a new junction sequence and a truncated genome flanked by reverse complementary ends [23] . cbDVGs have long been thought to result from errors made by the RNA-dependent RNA polymerase ( RdRp ) during replication due to a combination of lack of proofreading activity and the presence of a polymerase with lower replication fidelity [12] . No pattern or specific sequences for the break and rejoin points of cbDVGs have been reported so far . Based on our consistent observations of discrete populations of cbDVG generated during RSV infections in vitro and in vivo [6] , we set out to test the hypothesis that the generation of cbDVGs is not completely stochastic but instead is regulated by a carefully orchestrated process . Upon identification of cbDVG populations generated during infection , we show that specific viral sequences within the viral genome are preferred sites for cbDVG generation and that these sequences are conserved across viral strains . Utilizing this knowledge , we generated a recombinant virus that produced a restricted set of cbDVGs , serving as strong evidence that specific sequences dictate where cbDVGs are generated . In addition , we demonstrate for the first time that common cbDVGs are generated independently in natural infections in humans , further supporting an orchestrated origin for cbDVGs .
To acquire a comprehensive view of the population of cbDVGs generated during infection , we developed an algorithm to identify cbDVG junction regions within RNA-seq datasets with high sensitivity . The principle of this Viral Opensource DVG Key Algorithm ( VODKA ) is illustrated in Fig 1A . In brief , the break point ( T’ ) and the rejoin point ( W ) are far apart in the parental viral genome , but in cbDVGs they become continuous and form the new cbDVG junction sequence when the viral polymerase ( Vp ) is released from the template and rejoins in the nascent strand . Since cbDVG junction sequences are absent in the full-length viral genome , VODKA identifies the sequence reads that capture junction sequences and then filters out false-positives reads that fully align to the reference genome . We corroborated VODKA’s performance by testing for the presence of the highly dominant DVG-546 in samples from infections with SeV Cantell ( Fig 1B ) . Briefly , 98 , 543 out of the 98 , 626 ( 99 . 9% ) cbDVG junction reads identified by VODKA from an RNA-seq data set obtained from SeV Cantell-infected cells mapped exactly to the known junction region of DVG-546 ( Fig 1C , bottom panel ) . By aligning cbDVG junction reads to the SeV full-length antisense genome , we determined the location of the major break and rejoin regions ( blue peaks in the upper panel of Fig 1C ) . Each read that aligned to a break or rejoin region contained two portions , one of which fully aligned to the reference genome . The aligned reads in the break region ( pink box , Fig 1C ) and in the rejoin region ( gray box , Fig 1C ) corresponded to the DVG-546 sequence before the break point and after the rejoin point , respectively . The breakpoint for DVG-546 predicted by VODKA ( 14932±1_15292±1 ) exactly matched the one identified by Sanger sequencing ( ↑ in Fig 1C ) , thereby establishing the efficiency and accuracy of VODKA in identifying cbDVG-specific sequences . We then used VODKA to identify the population of cbDVGs generated during RSV infection . RSV is a virus known to generate immunostimulatory cbDVGs in infected patients [6] , and thus a subject of interest in our laboratory . We analyzed pooled RNA-seq datasets from six RSV-infected cell cultures . These cultures were infected with RSV generated from the same parental stock ( strain A2 ) that was first depleted of DVGs and then passaged independently in different cell lines to generate six different stocks enriched for DVGs . The presence of cbDVGs in these infections was confirmed using a specific RT-PCR followed by Sanger sequencing . By aligning VODKA-identified cbDVG junctions to the RSV A2 reference antigenome , we observed 4 major break hotspots spanning over 1300 nucleotides ( red down-facing arrows in Fig 1D ) . In contrast , only 2 major rejoin hotspots were observed within a narrower region of 223 nucleotides in length at the 3’end of the viral genome ( black down-facing arrows in Fig 1D ) . Remarkably , the rejoin area with the highest peak included counts present in all six virus stocks . We then compared these break and rejoin hotspots to those generated in infections with a different stock of RSV enriched in cbDVGs ( stock7 ) from which the major cbDVGs were identified upon Sanger sequencing of PCR amplicons . We observed that the cbDVG rejoin points from stock7 were located within the strongest rejoin hotspot , whereas its four break points were distributed more broadly across the genome ( Fig 1D ) . These results reveal strong hotspots for the polymerase to rejoin during cbDVG formation and suggest a large degree of conservation of the RSV cbDVGs rejoin positions . To determine if candidate hotspots were involved in cbDVG generation , we selected the region containing the most break points ( Break1 , dark grey in Fig 1D ) , or the most rejoin points ( Rejoin1+Trailer , light grey in Fig 1D ) for further testing using a minigenome system [24] . We constructed an RSV minigenome backbone ( BKB ) that included the reporter gene mKate2 for flow cytometry quantification of transcripts produced by the viral polymerase . In addition , the minigenome BKB included restriction enzyme sites to insert the selected Break1 and/or Rejoin1 regions ( Fig 2A ) . The goal was to use this system to establish whether sequences in the candidate break and rejoin regions altered the polymerase elongation capacity , eventually leading to the generation of cbDVGs . The strategy used for detection of cbDVGs is illustrated in S1 Fig . As illustrated in Fig 2B , in this system mKate2 expression should only occur if the viral polymerase replicates the entire minigenome sequence from the trailer to the leader . Co-transfection of the minigenome construct with the four helper plasmids expressing the polymerase proteins ( L , P , NP , and M2-1 ) , resulted in mKate2 expression in 8–17% of the cells , whereas no mKate2 expression was detected in control transfections that lacked the viral polymerase ( Fig 2C; -Vp ) . Constructs containing only the Rejoin1 sequence led to similar mKate2 expression as the BKB construct , whereas constructs containing Break1 caused a ~30% reduction in mKate2 expression ( Fig 2D and 2E ) . We verified that the difference in mKate2 expression among transfections with different constructs was not due to variable transfection efficiency ( S2A–S2C Fig ) , or cell death ( S2D Fig ) . These results are consistent with the concept that during cbDVG generation , the viral polymerase falls off the template at the break region leading to a reduced amount of newly synthetized template available for mKate2 transcription . To formally assess whether candidate break and rejoin sequences lead to cbDVG formation , we cloned the designated Pair1 composed of Break1/Rejoin1 into the minigenome system . Upon transfection , we observed that the construct containing Pair1 led to a similar degree of mKate2 expression than the construct bearing Break1 alone ( Fig 3A and 3B ) . We also observed two major amplicons ( white arrowheads in Fig 3C ) , both of which were absent in cells transfected with the construct bearing Break1 alone . These two amplicons contained cbDVGs that were confirmed by conventional Sanger sequencing ( S3A Fig ) . The individual break and rejoin points of these minigenome-generated cbDVGs are indicated in Fig 3D . Interestingly , the rejoin points clustered in close proximity to the rejoin points that we identified from in vitro infected cells . Taken together , these data demonstrate that RSV cbDVG rejoin points fall into a discrete region of the viral genome , which is critical for cbDVG generation . Since the late Rejoin1 + early Trailer region of the RSV genome was highly enriched with DVG rejoin points relative to other regions in the RSV genome , we then examined which specific features within this region impacted cbDVG generation . This region is within one of most A and U enriched areas of the RSV genome ( Fig 4A ) , suggesting that nucleotide composition might play a role in directing cbDVG formation . To avoid affecting the L “gene end” signal and the genome trailer region , we chose to mutate six nucleotides at the beginning of this rejoin region ( nucleotide positions 191–186 from the 3’ end of antigenome ) to either all Us ( named GC>Us ) or all GCs ( named AU>GCs ) . We then used RT-PCR ( DI-1/DI-R primer set ) to detect cbDVG-like fragments formed in the cells co-transfected with all U’s or all GCs mutant constructs and polymerase-expressing plasmids , as described earlier . Mutant GC>Us generated a dominant amplicon ( lane2 , Fig 4B ) that was absent in cells transfected with mutant AU>GCs ( lane4 , Fig 4B ) . From sequencing PCR products within the areas marked by asterisks in Fig 4B , we identified five distinct rejoin points from mutant AU>GCs and three from mutant GC>Us ( ↑ in Fig 4C ) . Compared to WT Pair1 , mutant GC>Us did not generate rejoin points proximal to the mutated region ( grey area in Fig 4C ) , whereas mutant AU>GCs still produced cbDVG-like fragments at the mutated area . To rule out bias due to primer location , we designed two additional forward primers to detect cbDVG-like fragments from the same samples . Rejoins detected with DI-F2 primers are identified with red arrows in Fig 4C , while rejoins detected with DI-F3 are indicated with blue arrows . Transfections with mutant GC>Us resulted in one strong amplicon while no predominant amplicon was observed in transfections with mutant AU>GCs ( Fig 4D ) , agreeing with results obtained using the DI-F1 primer set . Sequencing confirmed that the strong amplicons produced by all three different primer sets in transfections with mutant GC>Us were analogous cbDVG-like fragments and shared their break and rejoin points ( sequence in S3 Fig , DVG 303bp ) . To examine if the observed lack of a predominant product resulting from mutant AU>GCs was due to a general reduction of replication ability of the viral polymerase induced by mutations , we introduced the same mutations in the construct with Rejoin1 alone and examined mKate2 expression by flow cytometry . We found no significant differences between Rejoin1 and the two mutants , Rejoin1-GC>Us and Rejoin1-AU>GCs . Neither of these constructs reduced mKate2 expression compared to BKB ( S4 Fig ) , suggesting that the function of the RSV minigenome system remained intact despite of the mutations . Altogether , these data suggest that a minimal content of C nucleotides in the rejoin region determines if cbDVGs are produced at that particular genomic location . To determine if any of the two Cs within the mutated sequence was critical for cbDVG rejoining at this location , we performed a similar analysis using three new constructs: first C at position 188 or second C at position 186 from the 3’ end of antigenome mutated to U ( named C188U or C186U , respectively ) , or both Cs mutated to Us ( named AU ) . Transfection of the C186U , but not the C188U construct , resulted in one major DVG amplicon ( indicated with an asterisk in Fig 4E; sequences in S3 Fig ) . The C186U construct rejoin points skipped the mutation area and concentrated in the early trailer region , similar to GC>Us . This was confirmed by the two other primer sets . A strong band shown in lane C188U at a high molecular weight ( indicated with an arrowhead in Fig 3E ) was determined to not correspond to a cbDVG by Sanger sequencing . The construct bearing the double mutation ( AU ) behaved similar to C186U in terms of the rejoin positions ( Fig 4C ) . Thus , we found the second C at position 15037 ( position 186 from 3’ trailer end of antigenome ) to be critical for cbDVG generation . Next , to establish whether Rejoin1 impacts on cbDVG generation during viral infection , we created a mutant virus harboring mutations identical to the GC>Us minigenome construct . This virus is herein identified as gRSV-FR-GC>Us . The backbone of the recombinant RSV ( Line 19 ) included the mKate2 gene and we used mKate2 expression to estimate its replication . As shown in Fig 5A , cells infected with gRSV-FR-GC>Us expressed the same level of mKate2 protein as cells infected with the WT reporter virus ( gRSV-FR-WT ) at 72 h post infection . Both viruses began to generate cbDVGs at passage 3 ( P3 ) and the pattern of cbDVGs was maintained , and became stronger , by P5 ( Fig 5B ) . We verified that P5 gRSV-FR-GC>Us still carried the mutations we introduced ( Fig 5C ) . Interestingly , gRSV-FR-WT produced 4 major DVGs , whereas gRSV-FR-GC>Us only generated one dominant cbDVG ( asterisks in Fig 5B , confirmed sequence in S3G and S3H Fig ) , which is consistent with results from the minigenome system . The dominant cbDVG generated in cells infected with gRSV-FR-GC>Us rejoined at the early trailer region and skipped the mutation site , similar to what was observed in the minigenome system . Cells infected with gRSV-FR-WT produced one cbDVG that rejoined within the mutation site and three other cbDVGs that rejoined at the same region of the mutant virus ( Fig 4D ) . A population of cbDVGs lacking generation at the mutation site can be repeatedly observed upon independent passages of the mutant virus , albeit the specific species of DVGs varied in different lineages ( S5 Fig ) . These data further support a critical role of Rejoin1 in cbDVG generation . The majority of rejoin points found in infection with gRSV-FR-WT , which derived from RSV Line 19 , located within the early trailer sequence , rather than around the mutation site as found during infection with RSVstocks1-7 derived from RSV line A2 ( Fig 5D ) . Alignment of both sequences revealed one natural mutation in RSV Line 19 that introduced three GCs right at the beginning of the trailer sequence , which are not present in RSV A2 ( sequence indicated with a red horizontal line in Fig 5D ) . The increased GC content in this position in Line 19 likely explains why gRSV-FR-WT generates more cbDVGs at this location than RSV A2 stocks1-7 . Regardless of this natural preference for rejoining in the early trailer , gRSV-FR-GC>Us diminished the rejoin signal at the mutation site as no cbDVGs rejoin points were found at this location resulting in less diversity of cbDVG generation compared to the WT virus . Overall , these data confirm that the common rejoin region sequence tested in the minigenome system determines cbDVG rejoining during RSV infection and that the content of C nucleotides , and possibly G nucleotides , in this region critically determines the site of cbDVG rejoin . To examine whether the Rejoin1 region was utilized during natural infections , we applied VODKA to RNA-seq datasets obtained from RSV-positive pediatric samples . A total of 10 clinical specimens were sequenced; 4 were classified as DVG-low and 6 as DVG-high based on semi-quantification following cbDVG PCR . VODKA outputs were aligned to the reference genome of an RSV strain A isolate ( Reference genome NCBIKJ672447 , 2012 ) and showed that , consistent with previous cbDVG-RT-PCR results , samples from DVG-low patients ( upper panel in Fig 6A ) contained ~8 fold less cbDVG junction reads than DVG-high patients ( lower panel in Fig 6A ) . In addition , coverage mapping showed the presence of multiple break and rejoin regions . Some of them were a mix of both break points and rejoin points ( Fig 6A , read and black arrows ) . The rejoin points were particularly noteworthy because the majority of them clustered within one narrow AU-rich “Rejoin1+ Trailer” region ( red ticks in Fig 6B ) similar to that identified in in vitro infections ( blue ticks in Fig 6B ) . According to the frequency of different cbDVG junction positions , we illustrated the top 6 major cbDVGs ( one of them is a snap-back ) in Fig 6C ( details summarized in Table 1 , Break Rejoin position shown as T’_W ) . All of them were found in multiple patients ( Fig 6C and Table 1 ) . The most abundant cbDVG again had the rejoin point within the “Rejoin1+Trailer” region , despite of a higher diversity of rejoin points compared to in vitro infection . Taken together , these results demonstrate that a conserved rejoin region drives the generation of most cbDVGs during RSV infection in vitro and in vivo and that identical RSV cbDVGs are generated in different naturally infected individuals .
DVGs are critical regulators of viral replication and pathogenesis in multiple RNA virus infections , but the mechanisms modulating their generation are unknown . Historically , DVGs were thought to result from random errors introduced by the viral polymerase during replication . However , mounting evidence indicates that the generation of cbDVGs is not totally stochastic . Specifically , we show that during RSV infection discrete hotspots in the viral genome mark sites for the viral polymerase to release and rejoin during cbDVG formation , both in vitro and during natural RSV infections in humans . Moreover , we show that the content of C nucleotides , and possibly G nucleotides , within the major rejoin hotspot critically impacts the generation of cbDVGs at that position . We also identified specific nucleotides that , when mutated , altered the ability of recombinant viruses to generate diverse species of DVGs . The identification of a specific sequence involved in cbDVG formation opens the unprecedented possibility of genetically manipulating the content of cbDVGs during infection . This possibility may significantly impact our ability to generate tools to further understand the role of these viral products in virus pathogenesis , as well as potentially manipulate the cbDVG content with antiviral and/or therapeutic purposes . In this study , we utilized a custom-designed algorithm , VODKA , to identify cbDVG in infections in vitro or from children naturally infected with RSV . VODKA outputs were consistent with previous results obtained using classic DVG-RT-PCR and demonstrated a higher sensitivity in the detection of cbDVGs both in vitro and in clinical samples . False-positive DVG junction reads were ruled out by screening all reads aligned to the host ( reads from human transcriptome ) using VODKA . This test resulted in a minimal number of hits compared to viral samples , adding to the evidence reported throughout this manuscript to support the specificity of cbDVG detection by VODKA . VODKA can successfully identify cbDVGs in a number of viruses , including SeV ( Fig 1 ) , offering a powerful tool for cbDVG detection in clinical samples . Furthermore , since cbDVGs , compared to other types DVGs , have the most potent immunostimulatory function , VODKA can be used to identify candidates for development of novel cbDVG-based adjuvants . Based on our data , we conclude that the rejoin position significantly influences cbDVG generation . One C nucleotide substitution alone can influence the location of the DVG rejoin point implying that a strong rejoin signal likely needs an optimal number of C , and possibly G nucleotides , in specific locations . However , the total amount of cbDVGs produced and their immunostimulatory activity are not necessarily altered by the single C substitution in one rejoin hotspot , suggesting the redundancy of rejoin hotspots in cbDVG generation . More research needs to be done to investigate whether mutations in other rejoin hotspots or in combination will alter the overall amount of cbDVGs and their function . Interestingly , the same differential distribution of cbDVG rejoin points was observed when we compared cbDVG generation from infections with RSV A2 and Line19 , which differ in their GC content at the beginning of the trailer region . This observation also implies that the preference of usage among different hotspots as cbDVG rejoin points may vary among different RSV subtypes . In addition , our data suggest that rejoin sequences influence the function of break signals when inserted as pairs in the construct . Our data is in agreement with data from in vitro infections with measles virus lacking the C protein , where break points of cbDVGs were widely distributed along the genome , whereas the rejoin points were clustered in a narrow region close to 5’ end of the genome [25] . Further investigation into the molecular details of how the viral polymerase recognizes these signals may lead to important insights about the mechanism involved in RSV virus replication and the generation of cbDVGs . A lower density of nucleocapsid proteins ( NPs ) at certain genomic locations has been shown to result in increased cbDVG formation in SeV infection [26] . However , the mutations described to be responsible for low NP density were absent in our SeV stocks , suggesting that alternative mechanisms are likely involved . The usage of C nucleotides as a signal closely resembles the recognition of “gene end” or “gene start” by the viral polymerase when working on transcription [27 , 28] and it would be intriguing to evaluate if the mechanisms of cbDVG generation and viral RNA transcription are related . Another factor influencing DVG accumulation is their length , which is tightly related to the spatial structure of the viral RNPs . In paramyxoviruses , although it is thought that “only genomes with hexametric or heptametric lengths are efficiently replicated” [29 , 30] , some cbDVGs generated in vitro do not obey this rule [25 , 31 , 32] . For RSV , we observed that a number of cbDVGs do not follow the rule of six or seven . Nonetheless , cbDVGs with certain length may have increased replication efficiency and thus an enhanced fitness advantage . Interestingly , in our minigenome system , although cbDVGs from Pair1 contained the expected rejoin point positions , break points frequently fell into a region further ahead of Break1 , suggesting that the distance between the Break and Rejoin points may also play a role in determining where the break position is . In addition to genomic sequences , other factors , such as viral proteins , likely play an important role in DVG generation . For instance , influenza viruses harboring a high fidelity polymerase generate fewer deletion DVGs [33] . Mutations in non-structural protein 2 of influenza have also been shown to increase the de novo generation of DVGs by altering the fidelity of viral polymerase [34] . Host factors may be essential contributors to DVG generation as well [10] . For example , vesicular stomatitis virus produces a large amount of snap-back DVGs in most cell lines , except human-mouse somatic cell hybrids , and this cellular attribute was mapped to human chromosome 16 [35] . Similarly , infection with measles virus did not show de novo generation of defective interfering particles ( DIPs ) in human WI-38 cells and SeV did not produce cbDVGs in chicken embryo lung cells [36 , 37] . Despite the potential importance of these additional factors on DVG generation , the current work represents a major paradigm shift with the identification of sequences that regulate cbDVG formation . Remarkably , we found various common cbDVGs present in more than one patient and at least one of those cbDVGs was also present in infections in vitro . These observations support a conserved origin for cbDVGs during infection and challenge the idea that DVGs occur as random product of virus replication . To date , all studies on DVG biology have been correlative in nature . This work opens up new areas of investigation and can ultimately allow us to manipulate the ability of viruses to produce DVGs as a powerful tool to study the role of DVGs in viral pathogenesis .
Studies of human samples were approved by University of Pennsylvania Institutional Review Board . The embryonated chicken eggs used in these studies were 10 days old and were obtained from Charles River . A549 cells ( human type II alveolar cells , ATCC , #CRM-CCL185 ) and HEp2 cells ( HeLa-derived human epithelial cells , ATCC , CCL23 ) were cultured at 7% CO2 and 37°C with Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum ( FBS ) , 1 mM sodium pyruvate , 2 mM L-Glutamine , and 50 mg/ml gentamicin . BSR-T7 cells ( Hamster kidney cells , BHK cells constitutively expressing the T7 polymerase , provided by Dr . Christopher Basler’s lab at Icahn School of Medicine ) and were maintained in 10% FBS DMEM with 1 mg/ml Geneticin ( Invitrogen ) . All cell lines were treated with mycoplasma removal agent ( MP Biomedicals ) and routinely tested for mycoplasma before use . Sendai virus Cantell stock ( referred to as SeV HD , containing a high DVG particle content ) was prepared in embryonated chicken eggs as described previously [7 , 38] . The SeV HD stock used in these experiments had a high infectious to total particle ratio of 500:15 , 000 . RSV-HD stocks 1–7 ( stock of RSV derived from strain A2 , ATCC , #VR-1540 with a high content of cbDVGs ) were prepared and characterized as described previously [6 , 39] in MAVS KO ( 3 lineages , stock1-3 ) , STAT1 KO ( 3 lineages , stock4-6 ) , and WT A549 cells ( 1 lineage , stock7 ) , respectively . Briefly , RSV was fixed-volume passaged until stocks accumulated a high content of cbDVGs . The cell lines were kindly provided by Dr . Susan Weiss ( University of Pennsylvania ) . Mammalian expression vectors for RSV N ( NR-36462 ) , P ( NR-36463 ) , M2-1 ( NR-36464 ) , and L ( NR-36461 ) proteins , and the RSV reverse genetic backbone pSynkRSV-line19F ( rRSV-FR , NR-36460 ) were obtained from BEI Resources . Detailed information of the constructs can be found in reference [40] . The backbone plasmid of the RSV minigenome used for testing various DVG junction regions was constructed by cloning two regions of sequences amplified from pSynkRSV-line19F into the pSl1180 vector . The first region included a T7 promoter , a hammerhead ribozyme , RSV leader sequence , and genes encoding monomeric Katushka 2 ( mKate2 ) , while the second region included the RSV trailer sequence , a Hepatitis delta virus ribozyme and a T7 terminator . These regions were sequentially cloned into psl1180 vector using restriction enzyme pairs SpeI/SandI and SandI/EcoRI , respectively . The potential cbDVG break and/or rejoin regions ( positions in S1 Table ) were then inserted between those two regions using restriction enzyme pairs NotI/SandI and SandI/SpaI , respectively . A detailed scheme of the construct can be seen in Fig 2A . Pair1 and Rejoin1 mutations were introduced using the site-directed mutagenesis commercial kit QuickChange II XL ( Agilent , CA ) according to the manufacture’s protocol . All primers used for cloning are listed in S1 Table . Mutations in reverse genetic backbone pSynkRSV-line19F were generated by fusion PCR using primers in S1 Table as previously described [41] . Nasopharyngeal aspirates from pediatric patients were obtained from the Clinical Virology Laboratory of the Children’s Hospital of Philadelphia . All samples used were banked samples obtained as part of standard testing of patients . Samples were de-identified and sent to our lab for RNA extraction and cbDVG detection as indicated below . Total RNA was extracted using TRIzol or TRIzol LS ( Invitrogen ) according to the manufacturer’s specifications . For detection of RSV DVGs in RSV infection , 1–2 μg of isolated total RNA was reverse transcribed with the DI1 primer using the SuperScript III reverse transcriptase ( Invitrogen ) without RNase H activity to avoid self-priming . Recombinant RNase H ( Invitrogen ) was later added to the reverse transcribed samples and incubated for 20 min at 37°C . DVGs were partially amplified using both DI1 primer and DI-R primer . The temperature cycle parameters used for the cbDVG-PCR in a BioRad C1000 Thermal Cycler were: 95°C for 10 min and 33–35 cycles of 95°C for 30 sec , 55°C for 30 sec and 72°C for 90 sec followed by a hold at 72°C for 5 min . Ramp rate of all steps was 3 degree/sec . Detailed method can be found in[6] . For detection of cbDVGs in the RSV minigenome system , extracted RNAs were treated with 2 μl TurboDNaseI ( Invitrogen ) for 15 min at 37°C , followed by reverse transcription . Same procedures as above were utilized , except replacing DI1 primer with DI-F1 , DI-F2 , and DI-F3 primers . These were then all paired with DI-R reverse primer to amplify the different sizes of PCR products . Sequences of all primers are listed in S1 Table . Total RNA ( 1 μg ) was reversed transcribed using the high capacity RNA to cDNA kit from Applied Biosystems . cDNA was diluted to a concentration of 10 μg/μl and amplified with specific primers in the presence of SYBR green ( Applied Biosystems ) . qPCR reactions were performed in triplicate using specific primers and the Power SYBR Green PCR Master Mixture ( Applied Biosystems ) in a Viia7 Applied Biosystems Light-cycler . Gene expression levels of RSV G were normalized to the GAPDH copy number . Sequences of primers used in these studies can be found in S1 Table . RNA-Seq for SeV Cantell and RSV HD stocks 1–6 were performed as previously described [42] . RNA was extracted using TRIzol reagent and was re-purified using the PicoPure RNA isolation kit ( Thermo Fisher Scientific ) . RNA quality was assessed using the RNA Pico 6000 module on an Agilent Tapestation 2100 ( Agilent Technologies ) prior to cDNA library preparation . For SeV RNA-Seq dataset , total cDNA libraries were prepared starting from 75 ng ( SeV Cantell ) and 450 ng ( RSV HD stocks ) of extracted raw RNA using the Illumina TruSeq Stranded Total RNA LT kit with Ribo-Zero Gold , according to the manufacturer’s instructions . Samples were run on Illumina NextSeq 500 to generate 75 bp , single-end reads , resulting in 21–53 million reads per sample , with an average Q30 score ≥ 96 . 8% . For sequencing of samples from RSV-positive patients , including 4 DVG low patients and 6 DVG high patients , 100–450 ng of extracted raw RNA was used for preparation of cDNA library using the same kit as above . Samples were run on Illumina NextSeq 500 to generate 150bp , paired-end reads , resulting in 60–170 million reads/sample with average Q30 score ≥ 84 . 6% . To analyze genomic AU-content relative to DVG break and rejoin points , we calculated the percentage of A or U nucleotides over sliding windows of 40 bases using the Python programming language ( Python Software Foundation , https://www . python . org/ ) . We plotted AU-content and cbDVG rejoin points in R using the ggplot2 package [43] . Based on our in vitro RSV experiments , we made the assumption that most cbDVGs are generated from the viral sequence near the 5’ end region of the genome ( close to the Trailer sequence ) . Therefore , starting with the last 3kb of a reference viral genome , we built an index of potential DVG sequences by taking all possible combinations of two non-overlapping segments of L bases , where L is the read length . The segments are linked by reverse complementing the second segment ( C-D ) and adding the first segment ( A-B ) to it ( S6 Fig ) . Sequenced reads are aligned to the potential DVGs using bowtie2 [44] , and subsequently undergo two filtering steps . First , reads are removed unless they map across a breakpoint ( A_C ) with at least 15bp of mapped segment on each side . Second , the reads that map cleanly to the reference genome are filtered out . This pipeline gives the output read counts for each breakpoint ( A_C ) . To be consistent with the structure of copy-back DVGs in Fig 1A , A is equivalent to break point T’ and C is equivalent to rejoin point W . VODKA output reads were further aligned to reference viral genomes ( RSV A2: NCBI accession number KT992094 . 1; RSV 2012 clinical isolate: NCBI accession number KJ672447 ) or known SeV DVG-546 to identify the potential DVG junction regions using the Geneious 7 . 0 software . BHK cells constitutively expressing the T7 polymerase ( BSR-T7 cells ) were transfected with different minigenome constructs , gRSV-FR-WT , gRSV-FR-GC>Us , or gRSV-FR-AU>GCs as well as the sequence–optimized helper plasmids encoding N , P , M2-1 , and L , all under T7 control as described previously [40] . Cells were incubated with transfection complex ( total plasmid: lipofectamine = 1:3 . 3 ) for 2 h at room temperature and then at 37°C for overnight using Opti-MEM as medium . The following morning , the medium was replaced with antibiotic free tissue culture medium containing 2% FBS . For minigenome experiments , cells were harvested at 48 h post-transfection for either RNA extraction or flow cytometry . For mutant virus production , cells were maintained and split every 2–3 days until cytopathic effects ( CPEs ) were observed . Then viruses were collected and blindly passaged in HEp2 cells three times to obtain P3 . P3 was titrated and passaged two more times at MOI of 10 to generate P4 and P5 . Transfected BSR-T7 cells were trypsinized 48 h post transfection and were either directly diluted in FACS buffer ( PBS containing 2% FBS and 20 mM EDTA ) or stained with aqua LIVE/DEAD . Cells were washed twice in FACS buffer before flow cytometry analysis on an LSRFortessa ( Becton Dickinson ) . Data analysis was performed using Flowjo version Legacy . All statistical analyses were performed with GraphPad Prism version 5 . 0 ( GraphPad Software , San Diego , CA ) and R v3 . 4 . 1 . A statistically significant difference was defined as a p-value <0 . 05 by one-way analysis of variance ( ANOVA ) with a post hoc test to correct for multiple comparisons ( based on specific data sets as indicated in each figure legend ) . The VODKA algorithm is open-source and available at: https://github . com/itmat/VODKA . All data are available upon request to the corresponding author . Raw RNA-Sequencing data of FISH-FACS sorted SeV infected cells and RSV infected samples have been deposited on the Gene Expression Omnibus ( GEO ) database for public access ( SeV: GSE96774; RSV: GSE114948 ) .
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Copy-back defective viral genomes ( cbDVGs ) regulate infection and pathogenesis of Mononegavirales . cbDVGs are believed to arise from random errors that occur during virus replication and the predominant hypothesis is that the viral polymerase is the main driver of cbDVG generation . Here we describe a specific genomic sequence in the RSV genome that is necessary for the generation of a large proportion of the cbDVG population present during infection . We identified specific nucleotides that when modified altered cbDVG generation at this position , and we created a recombinant virus that selectively produced cbDVGs based on mutations in this sequence . These data demonstrate that the generation of RSV cbDVGs is regulated by specific viral sequences and that these sequences can be manipulated to alter the population of cbDVGs generated during infection .
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2019
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A specific sequence in the genome of respiratory syncytial virus regulates the generation of copy-back defective viral genomes
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We seek to characterize the motility of mouse fibroblasts on 2D substrates . Utilizing automated tracking techniques , we find that cell trajectories are super-diffusive , where displacements scale faster than t1/2 in all directions . Two mechanisms have been proposed to explain such statistics in other cell types: run and tumble behavior with Lévy-distributed run times , and ensembles of cells with heterogeneous speed and rotational noise . We develop an automated toolkit that directly compares cell trajectories to the predictions of each model and demonstrate that ensemble-averaged quantities such as the mean-squared displacements and velocity autocorrelation functions are equally well-fit by either model . However , neither model correctly captures the short-timescale behavior quantified by the displacement probability distribution or the turning angle distribution . We develop a hybrid model that includes both run and tumble behavior and heterogeneous noise during the runs , which correctly matches the short-timescale behaviors and indicates that the run times are not Lévy distributed . The analysis tools developed here should be broadly useful for distinguishing between mechanisms for superdiffusivity in other cells types and environments .
Cell motility is an integral part of biological processes such as morphogenesis [1] , wound healing [2] , and cancer invasion [3] . But what are the rules that govern how cells move ? Cell migration involves a multitude of organelles and signaling pathways [4] and therefore a fruitful , bottom-up approach studies correlations between cell motion and sub-cellular processes that govern motility , including surface interactions [5] , integrin signaling pathways [6] , or formation of focal adhesions [7] . An alternate approach with recent successes is to develop simple models at the cellular scale that can help identify a coarse-grained set of rules that govern cell migration in specific cell types . One such class of models , composed of self-propelled ( SPP ) or active Brownian particles [8] has been used to make predictions about the motion of biological cells in many contexts , including density fluctuations [9] , formation of bacterial colonies [10] , and both confined [11] , and expanding monolayers [12] . These SPP models represent each cell as a particle that moves by generating active force on a substrate , which acts along a specified vector θ ^ . Therefore , the parameters for the model specify both the magnitude of the force as well as how the orientation of the force changes with time . Given the ubiquity and usefulness of these models , one would like to have a standard framework for extracting these parameters from experimental data for all trajectories . In the past this has often been accomplished by analyzing ensemble-averaged features of cell trajectories . One such quantity is the time averaged mean-squared displacement ( MSD ) , which is the squared displacement between positions r → ( t ) and r → ( t + d t ) averaged over all starting times t and the ensemble of trajectories . This yields the MSD as a function of timescale , 〈 ( r ( t + dt ) ) − r ( t ) ) 2〉 ∝ dtα . Ballistic motion , which corresponds to a cell moving in a straight line at constant speed , corresponds to α = 2 . Diffusive motion , where a cell executes a random walk with no time correlation in orientation , corresponds to α = 1 . In non-active matter at low densities , thermal fluctuations generically induce diffusive behavior at long timescales . In contrast , many cell types , including T-cells [13] , Hydra cells [14] , breast carcinoma cells [15] , and swarming bacteria [16] display super-diffusive dynamics , defined as trajectories with a MSD exponent between 1 < α < 2 . Several authors have proposed explanations for why super-diffusive migration might be beneficial in biological systems . For example , super-diffusive trajectories are well known for being the optimal search strategy for randomly placed sparse targets [17 , 18] , and have been found in animal foraging and migration patterns in jellyfish [19] , albatross , and bumblebees [20] . In the context of cell biology , superdiffusive migration implies that cells are covering new areas more quickly than they would if they were executing a simple random walk . Although super-diffusive dynamics are commonly observed in in vitro experiments , the fundamental mechanism that generates anomalous diffusion in cell trajectories remains unclear . Pinpointing the mechanism would allow biology researchers to better isolate the signaling pathways that govern these processes . Although one might think that simply including the effects of persistent active forces generated by cells would change the long-time behavior , it turns out that standard self-propelled particle models exhibit a fairly sharp crossover from ballistic to diffusive motion , with no extended superdiffusive regime . Since SPP models are commonly used to model cells and superdiffusive dynamics are commonly observed in experiments , we would like to identify the mechanism generating superdiffusitivity to improve the ability of these models to capture cellular phenomena . Standard SPP models include smoothly varying persistent random walkers and standard run-and-tumble particles ( RTP ) [21] . Persistent random walkers obey the following equations of motion for the cell center of mass ri and the orientation angle θi: ∂ t ri→= v 0 θ ^ i , ( 1 ) ∂ t θ i = η ( t ) , ( 2 ) where η ( t ) is a Gaussian white noise ( 〈η ( t ) η ( t′ ) 〉 = 2Dr δ ( t − t′ ) ) . In a standard persistent random walk , the speed v0 and the rotational diffusion coefficient Dr , which controls the strength of fluctuations in orientation , are constant . In a standard run-and-tumble model , particles are ballistic during runs , ∂tθi = 0 , followed by tumbling events where large changes in orientation occur . Variations of run-and-tumble models are characterized by the distribution of times particles remain in the run state . Two different classes of modifications to SPP models have been highlighted as being able to generate super-diffusive behavior on long timescales . The first modification is a heterogeneous speed model , which draws rotational diffusion coefficients and particle speeds from distributions [15 , 22] . While persistent random walk models transition from ballistic to diffusive behavior at one characteristic timescale , heterogeneous speed models possess a heterogeneous distribution of crossover timescales , which generates an MSD with a broad superdiffusive regime , though the system becomes diffusive on timescales longer than 1 / D r m i n . The second modification is a Lévy walk model , which is a run-and-tumble model where particles have power law distributed run times: P ( τ ) = μ τ o ( 1 + τ / τ o ) 1 + μ , ( 3 ) ⟨ τ ⟩ = τ o μ - 1 , ( 4 ) with P ( τ ) the distribution of run times with mean < τ > for μ > 1 . [23] . In contrast to the heterogeneous SPP model , super-diffusivity generated by Lévy walks is not transient , so that the long-time MSD scaling exponent is constant: MSD ∝ dt3−μ . So which of these models is the “right” one for a given cell type ? By analyzing ensemble-averaged statistics such as the MSD and the velocity autocorrelation function ( VACF ) , one group of researchers was able to show that heterogeneous motility models matched data from breast cancer carcinoma cells [15] . This model , based on an autoregressive process ( AR-1 ) , uses a Bayesian inference method to extract activity and persistence from cell trajectories . However , these quantities do not directly correspond to physical quantities such as cell speed or rotational diffusion . Effects of cell heterogeneity were also explored in human fibrosarcoma cells by Wu et al . , where the authors show that these effects are sufficient to explain non-Gaussian velocity distributions [24] , similar to those we observe in mouse fibroblast cells . The authors also investigate anisotropic contributions , modeling 3D human fibrosarcoma trajectories with a 3D anisotropic persistent random walk . Differentiating between inherently anisotropic behavior and cell response to external cues such as chemotaxis is another difficult problem , investigated in T cells by Banigan et al . using a unique model that features a mix of passive Brownian particles and persistent random walkers [25] . Other efforts evaluated a different ensemble-averaged quantity , the probability displacement distribution , and used that data to suggest that T-cells were undergoing generalized Lévy walks [13] . We would like to better understand whether these ensemble-averaged quantities are in fact a unique identifier of the underlying mechanism for superdiffusivity . Moreover , we also seek to develop a systematic procedure for using experimental data to constrain both the appropriate mechanism and the optimal model parameters for a specific subtype . To this end , we use automated tracking software to analyze over 1000 mouse fibroblast trajectories and , using the work of Metzner and colleagues as inspiration , extract parameters for a generalized model based on persistent random walkers . We demonstrate that some ensemble-averaged statistics , such as the MSD and VACF , can not distinguish between mechanisms for superdiffusivity . In order to better distinguish , we begin with a very general model for cell dynamics . Although standard SPP models have only two fit parameters , average cell speed v0 and average rotational noise Dr , in principal a generalized SPP model could have arbitrary distributions for cell speed P ( v0 ) and rotational diffusion P ( Dr ) with arbitrary correlations between them . The heterogeneity motility model from [15] is the limit of such a model with Gaussian-distributed P ( v0 ) and P ( Dr ) , while a standard Lévy walk is the limit with a constant v0 and a specialized bimodal P ( Dr ) . Generalized Lévy walks such as those studied in [13] have additional parameters . Because this is such a large parameter space , we first constrain the functional form of these distributions using specific features of single cell trajectory statistics . We find that the mouse fibroblast data are consistent with run-and-tumble dynamics but the run times are not power-law distributed , confirming that in mouse fibroblasts the mechanism for superdiffusivity is heterogeneous dynamics and not Lévy walk statistics . The toolkit we have developed here should be useful for pinpointing the origin of superdiffusivity in many other cell types .
Cell motility data was collected from C3H10T1/2 mouse fibroblast cells ( ATCC ) . Although the cells were cultured on gold-coated shape memory polymer substrates , which in principle can be programmed to form anisotropic nanowrinkles [26] , all of the data in this manuscript is from cells cultured on “control” substrates that remain flat throughout the entire experiment , as our goal is to characterize the origin of superdiffusivity in this most simple case . While the data used in this manuscript are from an experimental protocol with a temperature shift , Baker et al . saw very similar superdiffusive trajectories in systems with no temperature changes [27] , indicating that the superdiffusivity we observe here was not generated by or dependent on temperature changes . Future work will analyze behavior on more complicated wrinkled or transitioning substrates . Cell nuclei were labeled with Hoechst dye and cell motility imaged by time-lapse microscopy . The resultant image stacks were analyzed using the ACTIVE image analysis package to track nuclei centers-of-mass [27] . See S1 Text for more information on substrate preparation . Cell motility was characterized using statistical analysis of cell nuclei trajectories , including MSD , VACF and displacement probability distributions . Tumbling events were identified with a one dimensional Canny edge detection algorithm , as shown in Fig 1 . This algorithm takes a time series of changes in orientation and classifies each timestep as either a “run” or “tumble . ” Additional details on cell trajectory analysis can be found in S1 Text . This manuscript focuses on two different models for non-interacting active particles . The first model is a Lévy walk with constant particle speed v0 at all timesteps . Particles execute ballistic runs with zero rotational noise for times τ drawn from the distribution in Eq 3 and a mean run time 〈τ〉 given by Eq 4 . The generalized SPP model has particles which follow the equations of motion seen in Eqs 1 and 2 , however the parameters for each model are not constant in time . A particle is initialized with a random orientation and assigned an initial speed v0 and rotational diffusion Dr drawn from distributions P ( v 0 ) = | v 0 | σ v 2 e - ( v 0 - μ v ) 2 σ v 2 and P ( D r ) = 1 π σ D 2 e - ( D r - μ D ) 2 σ D 2 . To account for possible correlations between the speed and rotational diffusion variables in our model , we utilize a copula modeling method [28] . First , we sample a bivariate normal distribution with a covariance matrix given by Σ = [ 1 p p 1 ] , where p = 0 indicates no correlation and p = ±1 indicates full positive ( negative ) correlation . Then we use the standard method of inverse cumulative distribution functions to transform the marginal distributions into the distributions P ( v0 ) and P ( Dr ) listed above . This results in a set of variables with a correlation between them parameterized by p , and also with the desired marginal distributions . Following sampling v0 and Dr , we evolve the particle position and orientation for a time τ drawn from P ( τ ) = 1 τ 0 e τ / τ 0 , where τ0 is the mean run time determined by experimental data . The particle then undergoes a tumbling event across one time step where Dr = 2π , where the value of rotational diffusion is chosen to approximate an event where the orientation is completely randomized . After the tumble a new v0 and Dr are assigned until the next tumbling event . In contrast to a Lévy walk or standard SPP model , motility parameters are varied in time to replicate the variations and changes in a biological environment . For both models , particle trajectories are constructed by numerically integrating the equations of motion using a simple Euler scheme with a timestep dt = 0 . 1 . For fitting purposes , we choose the natural timescale in our simulations equal to four minutes in experiments , which is the time between frame captures . In addition , we use the averaged goodness-of-fit of model MSD , VACF and displacement probability distributions compared to that of mouse fibroblast trajectories to determine optimal model parameters , shown in Table 1 and discussed later in the text . Finally , we note the VACF for experimental data shows a sharp dropoff across one frame due to errors in reconstructing the nuclei centers caused by imaging noise and fluctuations in dye intensity . To replicate this feature we incorporate positional noise into both models through small Gaussian fluctutations . After particle trajectories are constructed , each position is changed by a vector δ r → = d r ϕ ^ , where dr is drawn from a Gaussian distribution of variable width Δ and the direction ϕ ^ is chosen randomly from the unit circle . This replicates experimental error in reconstructing cell positions , and allows our model trajectories to match the mouse fibroblast data .
Previous reports have compared models to experimental data using ensemble-averaged statistics to confirm model validity such as the MSD and the VACF . Therefore , our first goal is to determine whether one of the existing models for explaining superdiffusive cell trajectories is a better fit to the experimental MSD and VACF data , shown by the red lines in Fig 2 . For comparison , we simulate a Lévy walk model with dynamics given by Eqs 3 and 4 , as well as a generalized SPP with no Lévy-walk behavior , described in more detail below . With the best-fit parameters , we find that both models match the data equally well . As shown in Fig 2 ( B ) , the velocity autocorrelation function exhibits a sharp decrease after the first frame window , due to errors that we make in reconstructing the nuclei center of mass caused by imaging noise and fluctuations in the dye intensity . Therefore , we add an additional term to the model that shifts the particle position by a Gaussian distributed variable with zero-mean and variance Δ2 to account for this effect . While the mean-squared displacement and velocity auto-correlation function are standard metrics for characterizing ensembles of trajectories , they may not be ideal for studying systems with superdiffusion . In an investigation of the Lévy walk properties of T-cells , Harris et al . study a quantity that reveals structures on shorter timescales: the probability for a cell to be at a displacement r ( t ) at time t [13] . They suggest that generalized Levy walks can be distinguished in part by collapsing these probability distributions with rescaled displacements ρ ( t ) = r ( t ) t γ , with γ significantly larger than the value of 1/2 expected for a persistent random walk . In their initial work characterizing Lévy walks , Harris and colleagues considered a wide range of Lévy walks as well as several other random walk processes , and finding the best match for T-cell trajectories was to a generalized Lévy walk . As seen in Fig 3 , we find that the mouse-fibroblast data does collapse , with the best fit exponent γ = 0 . 69 ± 0 . 02 as shown in Fig 4 . The best-fit standard Levy walk model collapses at γ = 0 . 58 ± 0 . 03 , which is above the value expected for a persistent random walk but still lower than γ for mouse fibroblast cells . Importantly , the best-fit generalized SPP model also collapses at a similar value of γ = 0 . 67 ± 0 . 03 , suggesting that such a collapse is not sufficient to uniquely identify Lévy walks as a mechanism for superdiffusivity . Moreover , the functional form of the displacement probability distribution ( PDF ) P ( r ( t ) ) provides additional information . It is well-fit by a Gaussian curve , shown as an offset dashed black line in Fig 3 ( A ) and 3 , and a Fig 3 ( B ) shows that non-Lévy version of the generalized SPP model also matches the shape of mouse fibroblast P ( r ( t ) ) very well . In contrast , Fig 3 ( C ) shows that P ( r ( t ) ) for the best-fit standard Lévy walk model has a very different functional form , due to ballistic runs over relatively large distances . However , this mis-fit in the functional form does not rule out Levy walks as a possible mechanism , as it could be corrected by considering a generalized Lévy Walk with more parameters [13] . To truly distinguish between the two mechanisms , we need access to more granular details about the individual cell trajectories . We next study single-cell trajectories . A generalized SPP model with arbitrary distributions for P ( v0 ) and P ( Dr ) has an infinite number of parameters that we could never hope to constrain . As a first step to simplifying our model we constrain functional form of these distributions using experimental data through microscopic statistics , such as velocity and run-time distributions , calculated from single-cell trajectories . This is in contrast to ensemble and time-averaged macroscopic statistics such as MSD and VACF . As shown in Fig 5 ( A ) , we first construct a distribution of cell speeds , determined from the magnitudes of the displacement of nuclei centers-of-mass between image capture events . Our experimental data is consistent with a Gaussian distribution of cell velocities , or equivalently , a distribution of cell speeds of the form P ( v 0 ) = | v 0 | σ v 2 e - ( v 0 - μ v ) 2 σ v 2 , where μv and σv are the mean and standard deviation , respectively , with estimates shown in Table 1 . Therefore , we use this functional form in our generalized model . Next we estimate a distribution P ( Dr ) of rotational diffusion constants ( Dr ) from the distribution of turning angles , shown in Fig 5 ( B ) . Simple active Brownian systems with a single value of Dr will generate a Gaussian distribution of turning angles [21] . A Gaussian distribution of rotational noise broadens this distribution significantly . One can show the expected turning angle distribution in this case is a modified Bessel function of the second kind with an exponential tail , consistent with the numerical simulation data given by the red line in Fig 5 ( B ) . We were unable to match the mouse fibroblast turning angle distribution , which is given by the blue line in Fig 5 ( B ) and has significant weight as the largest values of Δθ , with any Gaussian function for the rotational noise . This suggests that mouse fibroblast cells may have a strongly bimodal distribution of rotational noises , further supported by intermittent run-and-tumble behavior seen in cell trajectories . We choose to capture this bimodal behavior with a noisy run-and-tumble model , where cells have a distribution P ( D r ) = 1 π σ D 2 e - ( D r - μ D ) 2 σ D 2 during runs , which are punctuated by tumbling events . Distribution parameters μD and σD , shown in Table 1 , can be estimated from the distribution P ( Dr ) used to generate the run and tumble distribution of turning angles seen in Fig 5 ( B ) . In our implementation of this model we include possible arbitrary correlations between these distributions through the parameter p , ranging from fully correlated ( p = 1 ) to anti-correlated ( p = −1 ) . We use the Canny algorithm described in the methods section to explicitly identify tumbling events , and the data points in Fig 5 ( C ) show the distribution of times between such events . The red line in Fig 5 ( C ) shows this is well-fit by an exponential distribution with τ0 ≈ 1 hour , and so in our model the distribution of run times τ is given by P ( τ ) = 1 τ 0 e - τ / τ 0 . We note that this is a strong indication that the mouse fibroblasts are not well-described by a Lévy walk model with power-law distributed run times . Specifically , although we focus here on a standard Lévy walk model with fewer parameters than the generalized model used by Harris et al . [13] , it is clear that adding additional parameters to our a Lévy walk will still not generate the P ( τ ) we observe in mouse fibroblasts . The magenta line in Fig 5 ( B ) shows the distribution of turning angles for a noisy run-and-tumble model with the parameters identified above . To confirm that the model parameters we have identified are robust , and to quantify their sensitivity , we vary model parameters around the microscopically determined values and quantify how much this changes their displacement probability distributions . Specifically , we use the linear regression goodness-of-fit parameter ( R2 ) between P ( r ( t ) ) for mouse fibroblast and generalized model trajectories to characterize each parameter configuration and identify a best-fit between our model and mouse fibroblast statistics [29] . Using this method we are able to capture the functional form of P ( r ( t ) ) very well , as shown in Fig 3 . We explored several additional methods to parameterize goodness-of-fit , but because the shape of P ( r ( t ) ) exhibited significant fluctuations as we swept parameter space , nonlinear fitting approaches were inconsistent . Therefore we focus on the more stable linear regression results here . Happily , the configuration of parameters that best matches the macroscopic P ( r ( t ) ) , located at μD = 0 . 09 , σD = 0 . 002 , μv = 1 . 2 , σv = 0 . 8 , p = 0 , τ0 = 10 , is very similar to those identified from microscopic statistics , indicating that the model is consistent with experimental results . A construction of the dynamical matrix as a Hessian in parameter space around this minima and subsequent analysis of local eigenvectors indicates that our system is most sensitive to perturbations in the mean velocity and mean rotational noise as shown in Fig 6 ( A ) , and relatively insensitive to correlations between Dr and v0 parameterized by p ( Fig 6B ) as well as mean run time τ0 ( Fig 6C ) .
Both Lévy walks and heterogeneous SPP models are capable of generating superdiffusive trajectories . Previous studies have focused on one model or the other in order to identify possible mechanisms for superdiffusive cell trajectories . We show that while both types of model are equally capable of matching the large-scale ensemble averaged statistics of mouse fibroblast cells , an analysis of single cell trajectories demonstrates that Lévy walks are not consistent with this data set , despite a very good scaling collapse of the probability displacement distribution with scaling exponent γ > 1/2 . Instead , a careful analysis of turning angle distributions suggests these mouse fibroblasts exhibit heterogeneous speeds , with noisy run-and-tumble behavior . Because superdiffusive cells are able to cover distance faster than diffusive counterparts , it would be useful to adapt the tools developed here to study many more cell types . For example , directed cell motion is known to be a signature of invasiveness in cancer cell lines [30] , and it would be interesting to know if these cell types are altering the mechanisms or timescales for superdiffusion as they become more malignant . To that end , we have created a MATLAB software package for deploying these analyses on generic data sets [31] , which can be used to quantify superdiffusive dynamics and distinguish between different mechanism behavior in cells and active matter . Another important question is whether the tumbling events seen here are cell-autonomous or generated by cell-cell interactions . On the one hand , It is possible that the run and tumble behavior is at least partially cell-autonomous , although no biochemical mechanisms for such behavior have been identified in fibroblasts . To begin to investigate this question , it would be useful to correlate tumbling events with the dynamics of sub-cellular features such as spatio-temporal distributions of focal adhesions [32] , Golgi bodies [33] , or actin waves [30] . This would help us to understand which signaling networks and components of motility machinery are involved in generating tumbling behavior or broad distributions of rotational diffusion . Furthermore , it might be useful to study such behavior on structured or controllable substrates [34] , to tease apart the influence of environment vs . internal circuitry on controlling these timescales . On the other hand , many cell types exhibit contact inhibition of locomotion ( CIL ) [35] , where contact with another cell will either halt their motion or cause them to immediately recoil and begin moving in the opposite direction . It is possible that the tumbling events we see in mouse fibroblast cells are CIL events . In this work mouse fibroblast trajectories were identified from nuclei centers-of-mass , and we do not have direct observations of the cell membrane . Due to this imaging limitation , we were not able to confirm which tumbling events are associated with cell-cell contacts . This would be an interesting avenue of future research , as our cells are seeded at intermediate densities and it is possible that a significant fraction of of tumbling events are caused by cell-cell contacts . If so , this would be an interesting mechanism for generating super-diffusive behavior of a group of cells at intermediate densities , which could contribute to enhanced diffusion of moving cell fronts . In addition to CIL , there could also be additional interactions between cells , such as alignment of motility polarization between neighbors or between cells and the underlying substrate to generate flocking-like behavior [8] . It would be interesting to explore the effect of alignment in a generalized SPP model , to see if heterogeneity causes any significant differences in the flocking transition . From this discussion , it is obvious that a natural extension of our current work is interacting SPP models . If tumbling events are caused by cell-cell contacts , such a model would also allow us to predict how superdiffusivity changes with cell density . In even higher density cell populations and confluent tissues , cells will be in nearly constant contact and steric cell-cell interactions will play an even larger role in constraining cell positions . The effect of super-diffusion , whether generated by a Lévy walk or heterogeneity based model , could potentially alter the high-density behavior of standard SPP models . For example , recent work suggests that groups of cells [36] and packings of SPPs undergo jamming transitions [11 , 37 , 38] . Could the addition of superdiffusive dynamics have an effect on these types of transitions ? Persistent motility can alter the jamming transition—higher speeds and more persistent trajectories allows particles to explore areas of the energy landscape that were previously inaccessible [38] . Similar effects are seen in shape-based models for confluent tissues [36] . The inclusion of both run-and-tumble dynamics as well as varying persistence length through broadly distributed rotational diffusion coefficients in a generalized SPP model could offer an interesting mechanism for tuning jamming . Another emergent feature of self-propelled particle models is motility induced phase separation ( MIPS ) . Persistently moving particles create an inward oriented boundary layer that cage interior particles into a solid phase , while other cells are in a lower density gas phase outside of this boundary [37 , 39] and this effect has recently been implicated in generating colony formation in bacteria [40] . MIPS relies on persistence length to generate this behavior . Our generalized SPP model could reinforce this effect due to relatively persistent run phases , destroy the effect due to tumbling , or perhaps alter the nature of the transition due to enhanced fluctuations , and this is an interesting direction for future study .
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Cells must move through their environment in many different biological processes , from wound healing to cancer invasion to the development of an embryo . There are different ways for cells to explore the physical space around them—ranging from moving along a straight path at constant speed to executing a random walk where the cell changes direction at every time point . Understanding what mechanisms are driving motility patterns in different cell types is important for identifying possible treatments for disease . We found that mouse fibroblast cells moving on a two-dimensional substrate were super-diffusive , meaning that they were able to cover distance faster than a random walk but not as fast as a straight walk . Traditional analysis of cell trajectories was not well-suited to distinguish between different possible mechanisms for super-diffusivity , so we developed a new tool to examine cell trajectories and distinguish between mechanisms . We found that mouse fibroblasts were super-diffusive due to a combination of large fluctuations in speed and “run-and-tumble” behavior , where cells move in a straight line for a while before changing direction rapidly . We expect this tool to be useful for analyzing motion in many other cell types .
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2019
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Identifying the mechanism for superdiffusivity in mouse fibroblast motility
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Stress response networks frequently have a single upstream regulator that controls many downstream genes . However , the downstream targets are often diverse , therefore it remains unclear how their expression is specialized when under the command of a common regulator . To address this , we focused on a stress response network where the multiple antibiotic resistance activator MarA from Escherichia coli regulates diverse targets ranging from small RNAs to efflux pumps . Using single-cell experiments and computational modeling , we showed that each downstream gene studied has distinct activation , noise , and information transmission properties . Critically , our results demonstrate that understanding biological context is essential; we found examples where strong activation only occurs outside physiologically relevant ranges of MarA and others where noise is high at wild type MarA levels and decreases as MarA reaches its physiological limit . These results demonstrate how a single regulatory protein can maintain specificity while orchestrating the response of many downstream genes .
Genetic networks often feature master regulators that control suites of downstream genes . This type of architecture is particularly common in stress response; examples include Msn2 and Crz1 in Saccharomyces cerevisiae , σB in Bacillus subtillis , and the multiple antibiotic resistance activator MarA in Escherichia coli [1–4] . Each of these regulators controls tens to hundreds of diverse downstream targets with widely varying functional roles . For example , MarA regulates small RNAs , metabolic enzymes , efflux pumps , and regulatory proteins [1 , 5] . This diversity of gene products raises two questions: First , how is one signal from an upstream regulator decoded differently by multiple downstream targets ? Second , what are the potential benefits and tradeoffs that define how these genes respond ? To address this , we examined three key properties: activation , transmitted noise , and transmitted information . Although the properties are linked , they can be adjusted in a variety of ways to tailor the response of individual genes . Activation is set by molecular details of the transcription factor and the DNA to which it binds . The resulting transfer function is often described by the sigmoidal Hill function [6] . Here we focus on MarA , which regulates over 60 downstream targets by binding to a well-characterized degenerate binding sequence in their promoters known as the ‘marbox’ [5 , 7] . Previous studies have mapped the activation properties of genes controlled by MarA , finding that the amount of MarA needed to turn on expression varies greatly among its targets , with a 19-fold difference in the dissociation constant ( Kd ) between genes [1] . The majority of downstream genes are only weakly activated unless MarA is overexpressed far beyond physiologically relevant levels [1] . This is due to the high dissociation constants of the downstream promoters [8] , and provokes the question of why these genes are regulated by MarA at all . Noise provides insight into why this may be the case . Recent studies at the single-cell level suggest that although most cells only express low levels of MarA , levels are high in a small subset of the population due to cell-to-cell differences in gene expression [9 , 10] . These single-cell differences allow a subpopulation of cells to survive antibiotic exposure , and survivors can recolonize after the stress has passed [9] . Therefore , noise in MarA may turn on expression of costly downstream genes only in a small subset of the population to hedge against future uncertainty . This role for noise in stress response is observed frequently , such as in sporulation and competence in B . subtilis , which allow subpopulations of cells to survive periods of extreme stress [11–13] . When a noisy input controls downstream targets that have high dissociation constants , low-level fluctuations can be filtered , while larger signals are transmitted [14 , 15] . The relationship between the level of the input and the ability to activate downstream genes defines whether information is transmitted between input and output . This can be quantified using the metric channel capacity , which is the theoretical limit of how well information can be passed through a network ( similar to the diameter of a pipe , or channel ) [16 , 17] . Channel capacity depends both on the bounds of possible input values for the natural system and on the shape of the activation curve [17–19] . For instance , if the realistic range of MarA values are constrained such that a downstream gene is only weakly activated , then the channel capacity is low . As the range of MarA values widens , the channel capacity may either increase , if there is a corresponding increase in downstream gene expression , or remain low , if expression does not change , as in the case where the response is saturated . Therefore the ability to transmit information depends on both the input distribution and where these values fall on the activation curve . Expression of individual downstream genes can be tailored to balance activation , noise , and information transmission in any number of ways . For instance , certain genetic regulatory elements transmit information near the channel capacity [20 , 21] , while others lose considerable information due to noise or active filtering [22 , 23] . Therefore , gene regulation may be tailored to balance these properties . Using MarA and its downstream genes as a case study in multi-gene regulation , we investigated how expression is tailored at the single-cell level . We identified two qualitative classes of promoters: amplifying and filtering . The amplifying promoters generate diversity and have a high channel capacity at low levels of MarA . In contrast , the filtering promoters have low noise and only transmit information when MarA is high . These qualitative differences in promoter classes correlate with the functional differences in the gene products they control . We created chimeric promoters by swapping the MarA binding sequences between downstream promoters from these groups and easily altered their quantitative characteristics . Therefore , this binding sequence can serve as an evolutionary target for tuning output response .
We first asked how individual genes respond to MarA at the single-cell level . To do this , we transformed E . coli MG1655 ΔmarRAB with a plasmid bearing an IPTG-inducible version of marA transcriptionally-fused to red fluorescent protein ( rfp ) . We cotransformed cells containing the inducible marA-rfp plasmid with a second plasmid containing green fluorescent protein ( gfp ) linked to a promoter for a MarA-controlled downstream gene . We then simultaneously measured RFP and GFP levels in individual cells using microscopy . By adding IPTG , we increased MarA ( measured by RFP ) , which activated expression of the downstream promoter ( measured by GFP ) . IPTG induction allowed us to capture a broad range of MarA levels ( S1 Fig ) . These data provide single-cell resolution measurements of downstream gene expression as a function of MarA . Initially , we quantified expression from the promoter PmicF in response to MarA . micF encodes a small RNA that represses the outer membrane porin OmpF , decreasing vulnerability to a number of stressors including antibiotics and osmotic shock [24] . Previous population-level analysis of PmicF has indicated that the promoter has a relatively low Kd , suggesting that it should turn on with low levels of MarA [1] . Consistent with this , PmicF expression increased in response to MarA induction and eventually saturated ( Fig 1A ) . We eliminated the possibility that this result was due to artificial crosstalk between the RFP and GFP channels by constructing a control strain where the PmicF reporter was cotransformed with a plasmid with inducible rfp and no marA and observed no spurious crosstalk effects ( S2 Fig ) . Consistent with previous work on noise propagation [25] , we noted that noise in GFP expression changed along the PmicF activation curve . Diversity in single-cell expression is highest at low levels of MarA . To quantify this effect , we calculated the normalized coefficient of variation as a measure of transmitted noise ( Fig 1B ) . Analytically , this value is proportional to the local slope of the activation curve [19] . In other words , transmitted noise is highest where the Hill function is the steepest . To investigate where on this curve physiological levels of MarA fall , we conducted experiments using the PmicF reporter in two genetic backgrounds . First , we measured the lower bound of the PmicF response using wild type E . coli MG1655 . These represent the natural levels of PmicF under unstressed conditions with basal MarA expression . Second , we measured the upper bound of the PmicF response by using a strain we denote MarA+ , where the chromosomal copies of both MarR binding sites are inactivated . The marRAB operon is induced when repression by MarR is inhibited , therefore by inactivating these repressor binding sites this strain expresses the maximum physiologically realistic level of MarA . Measurements from these two strains have distinctly different levels of PmicF reporter expression ( Fig 1C ) . To confirm that these bounds on the physiological levels of MarA were appropriate we also subjected wild type cells to several chemical stresses . We first used salicylate , the canonical inducer of the marRAB operon , which causes a conformational change in MarR that prevents it from repressing marRAB expression [26] . Using 1 and 3 mM salicylate , we found expression to fall within the upper and lower bounds established by the wild type and MarA+ strains . Additionally , the quinolone ciprofloxacin can indirectly inhibit MarR by increasing intracellular copper levels [26] . Exposure to sublethal levels of ciprofloxacin ( 2 and 4 μg/L ) also resulted in intermediate levels of PmicF expression . Together , these results outline the biologically relevant range of PmicF expression . Although PmicF expression changes dramatically as MarA sweeps across physiologically relevant values , bulk measurements of other MarA-activated genes have suggested that many MarA-regulated genes are not strongly activated [1] . Thus , we next tested expression of PinaA , which has a Kd ten times higher than PmicF [1] . While the exact role of inaA is unknown , it is a pH-inducible gene involved in stress response [27] . In sharp contrast to PmicF , PinaA shows a gradual response to MarA , which never saturates over the tested range ( Fig 1D ) . We quantified transmitted noise and our results show good agreement with the theoretical prediction based on the slope of the fitted Hill function ( Fig 1E ) . In contrast to PmicF , PinaA noise levels remain low across all MarA values . Paralleling the experiments with PmicF , we used the wild type and MarA+ genetic backgrounds to establish the physiologically relevant upper and lower bounds for PinaA expression ( Fig 1F ) . The two distributions are noticeably closer together than in the case of PmicF , which is expected given the gradual slope of the Hill function . Our initial results with PmicF and PinaA reveal that even with a common upstream regulator , there can be categorical differences in downstream gene expression . These differences include population-level response characteristics such as the shape of the activation curve and also single-cell level effects such as variability in gene expression . These effects are related since transmitted noise depends on the slope of the activation curve . To explore how different characteristics of downstream gene activation influence transmitted noise , we used a computational model to simulate systems with different values for the dissociation constant ( Kd ) and Hill coefficient ( n ) . We simulated a noisy activator regulating five downstream genes with different Kd values ( Fig 2A ) . In addition , we included a control not regulated by the activator . We then calculated the transmitted noise for each , and the mean values of our stochastic simulations show excellent agreement with our analytical solutions ( Fig 2B ) . Given the sigmoidal shape of the Hill function , Kd alone can determine whether a promoter filters or amplifies a given input signal . Moreover , Kd is related to the strength of binding between the transcription factor and its associated binding site . This parameter is easily altered by mutations in the binding site and is therefore a potential evolutionary tuning knob . Indeed , the marbox has substantial variation among the myriad of genes regulated by MarA [8] . In contrast to Kd , altering the Hill coefficient n primarily affects the magnitude and shape of the noise response ( Fig 2C and 2D ) . Genes with high n values have high transmitted noise over narrow input ranges , while lower n values correspond to lower , broader responses . We note that other parameters , such as activation and degradation rates , can also influence transmitted noise ( Supplementary Information and S3 Fig ) . In general terms , Kd controls the activator levels where the transmitted noise is highest , while n primarily affects the magnitude of the transmitted noise . We next asked whether the activation and transmitted noise profiles of a diverse set of MarA-regulated genes varied as a function of n and Kd as in our computational simulation . We expanded our single-cell studies to include a total of six MarA-regulated promoters: PmicF and PinaA discussed previously , and the promoters for efflux pump genes PacrAB and PtolC , superoxide dismutase PsodA , and PmarRAB . We selected these genes based on their diverse responses to MarA at the population level [1] . For each , we used IPTG-inducible MarA and measured activation and transmitted noise in the downstream promoters ( Fig 2E and 2F ) . Of the six genes we measured , we observed a range of expression profiles that fall broadly into two groups . First , the ‘amplifying’ group , which includes PmicF and PmarRAB , saturates over the examined range of MarA inputs . These promoters have lower Kd values and larger n values . For these genes , low-level fluctuations in MarA will become large fluctuations in the downstream gene . In contrast , the ‘filtering’ group includes PacrAB , PinaA , PsodA , and PtolC . These genes do not saturate over the MarA range we tested and have lower n values . In this group , low-level fluctuations in MarA are filtered in the downstream gene , attenuating both the signal and the noise . These two classes of genes have categorically different transmitted noise profiles ( Fig 2F ) . The amplifying genes have high transmitted noise peaks at low levels of MarA and drop sharply as MarA increases , while the filtering genes have low , broad transmitted noise curves . While the previous results illustrate the differences in activation and transmitted noise , these findings need to be placed in the context of the biologically relevant levels of MarA . To investigate this we quantified the channel capacity of each promoter . Channel capacity is defined as the maximum mutual information—the potential of the input to inform the output [28] . The channel capacity reflects the ideal distribution of inputs through a target channel for maximizing mutual information [17] . To keep our calculations biologically grounded , we constrained the possible inputs by using estimated endogenous MarA levels . This is critical to our analysis because the physiologically relevant ranges of MarA are limited , and in some cases only span a narrow section of the downstream gene’s activation curve ( Fig 1D and 1F ) . In order to quantify channel capacity for a given promoter , we divided our analysis into two cases that correspond to wild type and MarA+ levels . By mapping the distribution of GFP values that corresponds to wild type and MarA+ through our inducible system , we were able to estimate the MarA distribution that produced each downstream response ( S4 Fig ) . Together , these two distributions represent physiologically relevant estimates of unstressed and stressed MarA levels ( Fig 3A and 3B ) . To provide intuition into the channel capacity of a promoter , we considered how MarA levels that correspond to wild type and MarA+ cells affect genes with amplifying versus filtering properties . At low levels of MarA , amplifying genes like PmicF transmit information well , proportionally mapping input to output . In contrast , the filtering genes like PinaA map the same input to a narrow band of output ( Fig 3C ) . The difference in width between the input and output distributions in the filtering gene corresponds to information loss . In contrast , under MarA+ conditions the channel capacity of the filtering gene PinaA increases , while the amplifying gene PmicF has a lower channel capacity since the promoter saturates and high MarA values all map to the same output ( Fig 3D ) . We asked how the channel capacity varied for the six MarA-regulated genes under wild type and MarA+ conditions . We calculated the channel capacity as a function of Kd and n for the two genetic backgrounds ( Fig 3E and 3F ) . Using parameters derived from experimental data , we plotted the location of each of the downstream genes on the channel capacity heat map . We found that for wild type MarA levels , downstream genes within the filtering class display lower channel capacity than those in the amplifying class ( Fig 3E ) . As the input increases to MarA+ levels , we observed a shift and the filtering genes increased channel capacity , while amplifying genes decreased ( Fig 3F ) . Our calculations for channel capacity quantify what the maximum mutual information is for a bounded range of MarA inputs . Calculating the actual mutual information requires precise knowledge of the MarA input distributions . To estimate this , we calculated mutual information between the MarA distributions from the wild type and MarA+ strains and the downstream gene expression distributions produced by these inputs ( S5 Fig ) . Despite the low channel capacity under wild type conditions for many downstream genes , the MarA input distribution is optimized to transmit information at near channel capacity for the filtering genes . However , these data also suggest that the wild type MarA distribution may not be taking full advantage of the amplifying promoters PmicF and PmarRAB , though we note that the results are very sensitive to the input distributions ( S6 Fig ) , which are produced here as estimates . The amplifying or filtering characteristics of a promoter are determined by how MarA binds . Therefore , it is likely that altering the MarA binding sequence would have a profound effect on the activation profile of the promoter it regulates , and in turn , its transmitted noise and channel capacity . Moreover , although we observed two general classes of promoters , it may be possible to generate promoters with intermediate properties . To investigate this , we created chimeric promoters by swapping the marbox sequences between PmarRAB and PacrAB . We constructed two chimeric promoters , which we denote Pam and Pma . In the Pam chimera we started with PacrAB and replaced its marbox with that from PmarRAB; in the Pma chimera PmarRAB has the PacrAB marbox . We quantified the activation and transmitted noise of these chimeric promoters as before ( Fig 4A and 4B ) . We found that both chimeric promoters have Kd and n parameter values that fall between the two natural promoters , and the corresponding channel capacity is also intermediate as a result ( Fig 4C and 4D ) . This shows that activation , and the transmitted noise and information properties that depend on it , are readily tunable through marbox mutations . This sequence could serve as an ideal target for evolutionary adaptation .
Genetic networks where one master regulator controls multiple downstream genes can efficiently respond to stress by customizing the response of individual genes based on their diverse functions . Using the multiple antibiotic resistance activator MarA as a case study , we show that its downstream targets are individually tailored in the way they respond to MarA and how they transmit noise and information . Understanding the physiological context of MarA proved to be critical; for instance , we found downstream genes that amplified signals under only wild type levels of MarA ( PmicF and PmarRAB ) and also examples that only show a response under high , non-physiological MarA conditions ( PacrAB , PinaA , PsodA , and PtolC ) . These results argue that studies of stress response genes should be coupled with a concrete understanding of the appropriate cellular context . In our experiments with MarA we determined that there were two qualitative classes of downstream genes , which serve to increase variability or transmit critical signals . Ultimately , a cell’s ability to survive stress depends upon expression of multiple downstream genes in a coordinate fashion . The flexibility of MarA responses could balance multiple demands on a particular gene’s expression including cost , desired expression level , and noise . Further , the dissociation constant Kd and Hill coefficient n , are critical in setting a gene’s response . Our work with the chimeric promoters illustrates that these parameters are readily changed by altering the sequence of the marbox . We note that there are two MarA homologs , SoxS and Rob , that may play additional regulatory roles , further underscoring the need for context-dependent measurements [29] . While our experiments focused on activation , the underlying analysis can be extended to other classes of regulation . For instance , transmitted noise is proportional to the local slope of repressors just as it is in activators . As such , this study serves as a framework for contrasting how one gene controls many in the context of noise and information , unconstrained by the method of regulation . In the future it will also be interesting to examine the role of feedback , as previous research has shown that positive feedback has the potential to increase signal transmission without transmitting the associated noise [30] . Throughout our experiments , we show that each of the downstream genes behave differently in wild type and MarA+ strains . These two conditions correspond to approximations for stressed and unstressed states . Both PmarRAB and PmicF control regulatory molecules and therefore these amplifying genes may serve to increase diversity in unstressed conditions . As cells shift to stressed conditions , expression of these genes saturates . This could signify the transition from a bet-hedging state , where diversity is favored , to a state where all cells consistently express the target gene products . In contrast , the filtering genes only engage under high levels of MarA . Efflux pumps and other gene products controlled by these promoters are often costly to the cell and should only be expressed when needed [38] . We have demonstrated the flexibility of multi-gene regulation in stress response networks through a collection of single-cell experiments , computational simulations , and analytical analysis . Our results show how multiple downstream genes can display customized expression given the same input . Straightforward changes in promoter sequence can be used to change activation , noise , and information transmission properties , allowing for a diverse set of possible outcomes that can be tailored to optimize expression of specific gene products . Our findings on the plasticity and specificity of the MarA network provide insight into the role that master regulators can play in diverse stress response networks .
All plasmids were derived from the BioBrick library described in [31] . In order to construct the downstream gene reporter plasmids ( denoted PacrAB , PinaA , PmarRAB , PmicF , PsodA , and PtolC ) we placed the promoter from each upstream of super-folder green fluorescent protein ( gfp ) ( AddGene #63176 ) . These transcriptional reporters were constructed on a plasmid containing the kanamycin resistance marker and low-copy SC101 origin of replication ( from [31] ) . For the activator/reporter experiments , we cotransformed the GFP reporter plasmid with a second plasmid containing the ampicillin resistance marker and medium-copy p15A origin of replication ( pBbA5k from [31] ) . This plasmid places either marA-rfp or rfp under the control of the IPTG-inducible lacUV5 promoter . marA-rfp is a transcriptional fusion of marA and rfp . We used three strains for experiments: wild type E . coli MG1655 , and genetic variants ΔmarRAB and MarA+ . The ΔmarRAB strain is described in [9] . In MarA+ , we used transversion mutations that annihilate the MarR binding sites in the chromosomal copy of the marRAB promoter , preventing repression of the operon . We constructed the chimeric reporter plasmids using PacrAB and PmarRAB with marbox sequences from [7] . Further details on plasmids and strains is provided in Supplementary Information . Cultures were inoculated from single colonies and grown overnight at 37°C with 200 rpm shaking in LB medium with 30 μg/ml kanamycin ( reporter-only ) or 30 μg/ml kanamycin and 100 μg/ml carbenicillin ( activator/reporter ) . Overnight cultures were diluted 1:100 in selective LB . For the activator/reporter experiments , we added 0 , 10 , 20 , 30 , 40 , 50 , 60 , 70 , 80 , 90 , 100 , or 500 μM IPTG and grew cultures for four hours ( Supplementary Information and S1 Fig ) . For the reporter-only experiments , we grew cultures for two hours before adding either salicylate ( 1 or 3 mM ) or ciprofloxacin ( 2 or 4 μg/L ) , then grew them for an additional two hours . Wild type and MarA+ reporter-only strains were grown for four hours without the addition of inducers . For microscopy images , we placed cells on 1 . 5% MGC low melting temperature agarose pads [32] . We used a Nikon Instruments Ti-E microscope to image the cells at 100× magnification . Three images were taken of each pad to ensure that at least 100 cells were imaged under each growth condition . Custom MATLAB scripts were used to extract fluorescence data from individual cells . For the activator/reporter experiments , fluorescence values for cells from all IPTG levels were combined and then binned according to their RFP levels ( S1 Fig ) . To quantify activation of each downstream gene , we used Hill functions: B=αβ ( ( AKd ) n1+ ( AKd ) n ) +cβ ( 1 ) where A is MarA and B is the downstream gene product . α is the promoter strength , Kd is the dissociation constant , n is the Hill coefficient , c is the basal expression level , and β is the degradation and dilution rate . The functions for calculating the normalized level of downstream gene product ( used in Fig 2 ) subtract the basal expression and normalize ( Supplementary Information ) . We calculated transmitted noise using two independent methods in this study . First , transmitted noise is calculated from experimental data as the ratio of the noise in the downstream gene output ( B ) over the noise in the MarA input ( A ) [30] . We note that this quantity is sometimes referred to as ‘noise amplification’ . Noise in an individual gene is calculated as the coefficient of variation , which is the standard deviation ( σ ) divided by the mean ( μ ) for a given gene product level [30 , 33] . However , we needed to account for noise sources not coming directly from fluctuations in MarA , such as those from intrinsic and extrinsic noise [34] . The term S is equal to the noise of the downstream gene without regulation by MarA , and is necessary to fit the analytical solution to the experimentally measured transmitted noise . S does not vary as a function of MarA and is the sum of intrinsic and extrinsic noise sources that are independent of upstream gene regulation . Statistics were determined by bootstrap resampling of one third of the population 100 times . Analytically , transmitted noise is equal to the local slope of the activation curve , normalized by the values of the function about that point [19] . Mathematically , this is equal to the slope of the logarithmic transform of the Hill function [30 , 35] . We used this function to calculate the analytic solutions in all noise plots . Because the transmitted noise is equal to the local slope of the activation curve , Hill functions and transmitted noise curves share the same parameters [19] . We simultaneously fit both curves to experimental data using a differential evolution algorithm with a custom fitness function [36] . For fitness function and exact values of fitted parameters , see Supplementary Information . We modeled the input A and the downstream products B by: A˙=αA−βA+IA ( 4 ) B˙=α ( AKd ) n1+ ( AKd ) n−βB ( 5 ) where α is the promoter strength , Kd is the dissociation constant , and n is the Hill coefficient . In addition , αA is the production rate of A and β describes protein degradation and dilution for both A and B . We simulated intrinsic noise of the input protein IA using an Ornstein-Uhlenbeck process [37] . The intrinsic noise of the input has a standard deviation of α . The correlation time of this noise calculated as Tint/ln ( 2 ) , with Tint of 5 minutes [32] . As with the experimental data , the level of input protein ( αA ) was varied through a range of possible inputs ( 30 log spaced values ) . The parameters used and details of the stochastic simulation are given in Supplementary Information . The channel capacity ( I* ) is dependent on the relationship between input and output and is calculated using the functions from [17 , 19 , 20 , 28]: I* ( A;B ) =log2 ( Z ) +X ( 6 ) Z=∫AminAmax[ ( dB/dA ) 2B+A0*A* ( dB/dA ) 2]12dA ( 7 ) X is a constant that is independent of the parameters of the downstream promoters . It is introduced by the small noise approximation implicit in this calculation of channel capacity . Amin and Amax describe the minimum and maximum input values , which we determine from experimental data by mapping the output from wild type and MarA+ to the data from the activator/report experiments ( S4 Fig ) using the 5th to 95th percentiles from these distributions . A0 is a scaling term for the concentration of the activator to match experimental results . For further details see Supplementary Information .
|
Bacteria can sense and respond to stress in their environment . This process is often coordinated by a master regulator that turns on or off many downstream genes , allowing the cell to survive the stress . However , individual genes encode products that are diverse and optimal expression for each gene may differ . Here , we focus on how expression of diverse downstream genes is optimized by targets of the multiple antibiotic resistance activator MarA . Using single-cell experiments and computational modeling we show that downstream genes process MarA signals differently , with unique activation , noise , and information transmission properties . We find that each downstream gene’s response depends critically on the level of the input MarA . Furthermore , by swapping parts of the regulatory elements of genes we were able to create novel responses . This suggests that these properties can be readily tuned by evolution . Our findings show how a network with diverse downstream genes can be used to process the same command to achieve many distinct outputs , which work together to coordinate the response to stress .
|
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2017
|
Customized Regulation of Diverse Stress Response Genes by the Multiple Antibiotic Resistance Activator MarA
|
Tardigrada , a phylum of meiofaunal organisms , have been at the center of discussions of the evolution of Metazoa , the biology of survival in extreme environments , and the role of horizontal gene transfer in animal evolution . Tardigrada are placed as sisters to Arthropoda and Onychophora ( velvet worms ) in the superphylum Panarthropoda by morphological analyses , but many molecular phylogenies fail to recover this relationship . This tension between molecular and morphological understanding may be very revealing of the mode and patterns of evolution of major groups . Limnoterrestrial tardigrades display extreme cryptobiotic abilities , including anhydrobiosis and cryobiosis , as do bdelloid rotifers , nematodes , and other animals of the water film . These extremophile behaviors challenge understanding of normal , aqueous physiology: how does a multicellular organism avoid lethal cellular collapse in the absence of liquid water ? Meiofaunal species have been reported to have elevated levels of horizontal gene transfer ( HGT ) events , but how important this is in evolution , and particularly in the evolution of extremophile physiology , is unclear . To address these questions , we resequenced and reassembled the genome of H . dujardini , a limnoterrestrial tardigrade that can undergo anhydrobiosis only after extensive pre-exposure to drying conditions , and compared it to the genome of R . varieornatus , a related species with tolerance to rapid desiccation . The 2 species had contrasting gene expression responses to anhydrobiosis , with major transcriptional change in H . dujardini but limited regulation in R . varieornatus . We identified few horizontally transferred genes , but some of these were shown to be involved in entry into anhydrobiosis . Whole-genome molecular phylogenies supported a Tardigrada+Nematoda relationship over Tardigrada+Arthropoda , but rare genomic changes tended to support Tardigrada+Arthropoda .
The superphylum Ecdysozoa emerged in the Precambrian , and ecdysozoans not only dominated the early Cambrian explosion but also are dominant ( in terms of species , individuals , and biomass ) today . The relationships of the 8 phyla within Ecdysozoa remain contentious , with morphological assessments , developmental analyses , and molecular phylogenetics yielding conflicting signals [1–3] . It has generally been accepted that Arthropoda , Onychophora ( velvet worms ) , and Tardigrada ( water bears or moss piglets ) form a monophylum , Panarthropoda [2] , and that Nematoda ( roundworms ) are closely allied to Nematomorpha ( horsehair worms ) and distinct from Panarthropoda . However , molecular phylogenies have frequently placed representatives of Tardigrada as sisters to Nematoda [1 , 3] , invalidating Panarthropoda and challenging models of the evolution of complex morphological traits such as segmentation , serially repeated lateral appendages , the triradiate pharynx , and a tripartite central nervous system [4 , 5] . The key taxon in these disagreements is phylum Tardigrada . Nearly 1 , 200 species of tardigrades have been described [6] . All are members of the meiofauna—small animals that live in the water film and in interstices between sediment grains [6] . There are marine , freshwater , and terrestrial species . Many species of terrestrial tardigrades are cryptobiotic: they have the ability to survive extreme environmental challenges by entering a dormant state [7] . Common to these resistances is an ability to lose or exclude the bulk of body water , and anhydrobiotic tardigrades have been shown to have tolerance to high and low temperatures ( including freezing ) , organic solvents , X- and gamma-rays , high pressure , and the vacuum of space [8–15] . The physiology of anhydrobiosis in tardigrades has been explored extensively , but little is currently known about its molecular bases [16 , 17] . Many other animals have cryptobiotic abilities , including some nematodes and arthropods [18] , and comparison of the mechanisms in different independent acquisitions of this trait will reveal underlying common mechanisms . Central to the development of tractable experimental models for cryptobiosis is the generation of high-quality genomic resources . Genome assemblies of 2 tardigrades , H . dujardini [19–21] and R . varieornatus [22] , both in the family Hypsibiidae , have been published . H . dujardini is a limnoterrestrial tardigrade that is easy to culture [23] , while R . varieornatus is a terrestrial tardigrade and highly tolerant of environmental extremes [24] . An experimental toolkit for H . dujardini , including RNA interference ( RNAi ) and in situ hybridization , is being developed [25] . H . dujardini is poorly cryptobiotic compared to R . varieornatus . H . dujardini requires 48 h of preconditioning at 85% relative humidity ( RH ) and a further 24 h in 30% RH [23] to enter cryptobiosis with high survival , while R . varieornatus can form a tun ( the cryptobiotic form ) within 30 min at 30% RH [26] . Several anhydrobiosis-related genes have been identified in Tardigrada . Catalases , superoxide dismutases ( SODs ) , and glutathione reductases may protect against oxidative stress [27] , and chaperones , such as heat shock protein 70 ( HSP70 ) [28–30] , may act to protect proteins from the denaturing effects of water loss [16 , 31 , 32] . Additionally , several tardigrade-specific gene families have been implicated in anhydrobiosis , based on their expression patterns . Cytosolic abundant heat soluble ( CAHS ) , secretory abundant heat soluble ( SAHS ) , late embryogenesis abundant protein mitochondrial ( RvLEAM ) , mitochondrial abundant heat soluble protein ( MAHS ) , and damage suppressor ( Dsup ) gene families have been implicated in R . varieornatus extremotolerance [22 , 33 , 34] . These gene families were named by their subcellular location or function , and expression of MAHS and Dsup in human tissue culture cell lines resulted in elevated levels of tolerance against osmotic stress and X-ray irradiation ( approximately 4 Gy ) . Surprisingly , analyses of the R . varieornatus genome showed extensive gene loss in the peroxisome pathway and in stress signaling pathways , suggesting that this species is compromised in terms of reactive oxygen resistance and repair of cellular damage [22] . While loss of these pathways would be lethal for a normal organism , loss of these resistance pathways may be associated with anhydrobiosis . Desiccation in some taxa induces the production of anhydroprotectants , small molecules that likely replace cellular water to stabilize cellular machinery . Trehalose , a disaccharide shown to contribute to anhydrobiosis in midges [35 , 36] , nematodes [37] , and artemia [38] , is not present in the tardigrade Milnesium tardigradum [31] . Coupled with the ability of R . varieornatus to enter anhydrobiosis rapidly ( i . e . , without the need for extensive preparatory biosynthesis ) , this suggests that tardigrade anhydrobiosis does not rely on induced synthesis of protectants . Entry into anhydrobiosis in H . dujardini does require active transcription during preconditioning , suggesting the activation of a genetic program to regulate physiology . Inhibition of PP1/2A , an positive regulator of the FOXO transcription factor that induces antioxidative stress pathways , led to high lethality in H . dujardini during anhydrobiosis induction [23] . As R . varieornatus does not require preconditioning , systems critical to anhydrobiosis in R . varieornatus are likely to be constitutively expressed . H . dujardini and R . varieornatus are relatively closely related ( both are members of Hypsibiidae ) , and both have available genome sequences . The R . varieornatus genome has high contiguity and scores highly in all metrics of gene completeness [22] . For H . dujardini , 3 assemblies have been published . One has low contiguity ( N50 length of 17 kb ) and contains a high proportion of contaminating nontardigrade sequence , including approximately 40 Mb of bacterial sequence , and spans 212 Mb [19] . The other 2 assemblies , both at approximately 130 Mb [20 , 21] , eliminated most contamination , but contained uncollapsed haploid segments because of unrecognized heterozygosity . The initial low-quality H . dujardini genome was published alongside a claim of extensive horizontal gene transfer ( HGT ) from bacteria and other taxa into the tardigrade genome and a suggestion that HGT might have contributed to the evolution of cryptobiosis [19] . The extensive HGT claim has been robustly challenged [20 , 21 , 39–41] , but the debate as to the contribution of HGT to cryptobiosis remains open . The genomes of these species could be exploited for understanding the mechanisms of rapid-desiccation versus slow-desiccation strategies in tardigrades , the importance of HGT , and the resolution of the deep structure of the Ecdysozoa . However , the available genomes are not of equivalent quality . We have generated a high-quality genome assembly for H . dujardini , from an array of data including single-tardigrade sequencing [42] and long , single-molecule reads , and using a heterozygosity-aware assembly method [43 , 44] . Gene finding and annotation with extensive RNA sequencing ( RNA-Seq ) data allowed us to predict a robust gene set . While most ( 60% ) of the genes of H . dujardini had direct orthologues in an improved gene prediction for R . varieornatus , levels of synteny were very low . We identified an unremarkable proportion of potential HGTs . H . dujardini showed losses of peroxisome and stress signaling pathways , as described in R . varieornatus , as well as additional unique losses . Transcriptomic analysis of anhydrobiosis entry detected higher levels of regulation in H . dujardini compared to R . varieornatus , as predicted , including regulation of genes with antistress and apoptosis functions . Using single-copy orthologues , we reanalyzed the position of Tardigrada within Ecdysozoa and found strong support for a Tardigrade+Nematode clade , even when data from transcriptomes of a nematomorph , onychophorans , and other ecdysozoan phyla were included . However , rare genomic changes tended to support the traditional Panarthropoda . We discuss our findings in the context of how best to improve genomics of neglected species , the biology of anhydrobiosis , and conflicting models of ecdysozoan relationships .
The genome size of H . dujardini has been independently estimated by densitometry to be approximately 100 Mb [20 , 45] , but the spans of existing assemblies exceed this , because of contamination with bacterial reads and uncollapsed heterozygosity of approximately 30%–60% of the span estimated from k-mer distributions . We generated new sequencing data ( S1 Table ) for H . dujardini . Tardigrades , originally purchased in mixed cultures from Sciento , were cultured with a single algal food source . Illumina short reads were generated from a single , cleaned tardigrade [42] , and Pacific Biosciences ( PacBio ) long single-molecule reads from DNA from a bulk , cleaned tardigrade population ( approximately 900 , 000 animals ) . We employed an assembly strategy that eliminated evident bacterial contamination [46] and eliminated residual heterozygosity . Our initial Platanus [44] genome assembly had a span of 99 . 3 Mb in 1 , 533 contigs , with an N50 length of 250 kb . Further scaffolding and gap filling [47] with PacBio reads and a Falcon [43] assembly of the PacBio reads produced a 104 Mb assembly in only 1 , 421 scaffolds and an N50 length of 342 kb and N90 count of 343 ( Table 1 ) . In comparison with previous assemblies , this assembly has improved contiguity and improved coverage of complete core eukaryotic genes [48 , 49] . Read coverage was relatively uniform throughout the genome ( S1 Fig , S2 Table ) , with only a few short regions , likely repeats , with high coverage . We identified 29 . 6 Mb ( 28 . 5% ) of the H . dujardini genome as being repetitive ( S3 Table ) . Simple repeats covered 5 . 2% of the genome , with a longest repeat unit of 8 , 943 bp . Seven of the 8 longest repeats were of the same repeat unit ( GATGGGTTTT ) n , were found exclusively at 9 scaffold ends , and may correspond to telomeric sequence ( S4 Table ) . The other long repeat was a simple repeat of ( CAGA ) n and its complementary sequence ( GTCT ) n , and spanned 3 . 2 Mb ( 3% of the genome , longest unit 5 , 208 bp ) . We identified eighty-one 5 . 8S rRNA , two 18S rRNA , and three 28S rRNA loci with RNAmmer [50] . Scaffold0021 contains both 18S and 28S loci , and it is likely that multiple copies of the ribosomal RNA repeat locus have been collapsed in this scaffold , as it has very high read coverage ( approximately 5 , 400-fold , compared to approximately 113-fold overall , suggesting approximately 48 copies ) . tRNAs for each amino acid were found ( S2 Fig ) [51] . Analysis of microRNA sequencing ( miRNA-Seq ) data with miRDeep [52] predicted 507 mature miRNA loci ( S1 Data ) , of which 185 showed similarity with sequences in miRbase [53] . We generated RNA-Seq data from active and anhydrobiotic ( “tun” stage ) tardigrades and developmental stages of H . dujardini ( S1 Table ) . Gene finding using BRAKER [54] predicted 19 , 901 genes , with 914 isoforms ( version nHd3 . 0 ) . This set of gene models had higher completeness and lower duplication scores compared to those predicted with MAKER [55] , which uses RNA-Seq and protein evidence ( Predicted proteome based BRAKER: 90 . 7% MAKER: 77 . 9% , genome based BRAKER: 86 . 3% , Metazoan lineage used ) . Minor manual editing of this gene set to break approximately 40 fused genes generated version nHd3 . 1 . These coding sequence predictions lacked 5′ and 3′ untranslated regions . Mapping of RNA-Seq data to the predicted coding transcriptome showed an average mapping proportion of approximately 50%–70% , but the mapping proportion was over 95% against the genome ( S5 Table ) . A similar mapping pattern for RNA-Seq data to predicted transcriptome was also observed for R . varieornatus . Over 70% of the H . dujardini transcripts assembled with Trinity [56] mapped to the predicted transcriptome , and a larger proportion to the genome ( S6 Table ) . RNA-Seq reads that are not represented in the predicted coding transcriptome likely derived from UTR regions , unspliced introns , or promiscuous transcription . We inferred functional and similarity annotations for approximately 50% of the predicted proteome ( Table 2 ) . The H . dujardini nHd . 3 . 0 genome assembly is available on a dedicated ENSEMBL [57] server , http://ensembl . tardigrades . org , where it can be compared with previous assemblies of H . dujardini and with the R . varieornatus assembly . The ENSEMBL database interface includes an application-programming interface ( API ) for scripted querying [58] . All data files ( including supplementary data files and other analyses ) are available from http://download . tardigrades . org , and a dedicated Basic Local Alignment Search Tool ( BLAST ) server is available at http://blast . tardigrades . org . All raw data files have been deposited in International Nucleotide Sequence Database Collaboration ( INSDC ) databases ( National Center for Biotechnology Information [NCBI] and Sequence Read Archive [SRA] , S1 Table ) , and the assembly ( nHd3 . 1 ) has been submitted to NCBI under the accession ID MTYJ00000000 . We compared this high-quality assembly of H . dujardini to that of R . varieornatus [22] . In initial comparisons , we noted that R . varieornatus had many single-exon loci that had no H . dujardini ( or other ) homologues . Reasoning that this might be a technical artifact , we updated gene models for R . varieornatus using BRAKER [54] with additional comprehensive RNA-Seq of developmental stages ( S1 Table ) . The new prediction included 13 , 917 protein-coding genes ( 612 isoforms ) . This lower gene count compared to the original ( 19 , 521 genes ) was largely due to a reduction in single-exon genes with no transcript support ( from 5 , 626 in version 1 to 1 , 777 in the current annotation ) . Most ( 12 , 752 , 90% ) of the BRAKER-predicted genes were also found in the original set . In both species , some predicted genes may derive from transposons , as 2 , 474 H . dujardini and 626 R . varieornatus proteins matched Dfam domains [59] . While several of these putatively transposon-derived predictions have a Swiss-Prot [60] homologue ( H . dujardini: 915 , 37%; R . varieornatus: 274 , 44% ) , most had very low expression levels . One striking difference between the 2 species was in genome size , as represented by assembly span: the R . varieornatus assembly had a span of 55 Mb , half that of H . dujardini ( Table 2 ) . This difference could have arisen through whole genome duplication , segmental duplication , or more piecemeal processes of genome expansion or contraction . H . dujardini had 5 , 984 more predicted genes than R . varieornatus . These spanned approximately 23 Mb and accounted for about half of the additional span . There was no difference in number of exons per gene between orthologues or in the whole predicted gene set . However , comparing orthologues , the intron span per gene in H . dujardini was on average twice that in R . varieornatus ( Fig 1B ) , and gene length ( measured as start codon to stop codon in coding exons ) was approximately 1 . 3-fold greater in H . dujardini ( Table 2 , S3 Fig ) . There was more intergenic noncoding DNA in H . dujardini , largely explained by an increase in the repeat content ( 28 . 6 Mb in H . dujardini versus 11 . 1 Mb in R . varieornatus ) . Whole genome alignments of R . varieornatus and H . dujardini using Murasaki [61] revealed a low level of synteny but evidence for conserved linkage at the genome scale , with little conservation of gene order beyond a few loci . For example , comparison of R . varieornatus Scaffold002 of with H . dujardini scaffold0001 showed linkage , with many orthologous ( genome-wide bidirectional best BLAST hit ) loci across approximately 1 . 7 Mb of the H . dujardini genome ( Fig 1A ) . A high proportion of orthologues of genes located on the same scaffold in H . dujardini were also in one scaffold in R . varieornatus , implying that intrachromosomal rearrangement may be the reason for the low level of synteny ( Fig 1C ) . We defined protein families in the H . dujardini and new R . varieornatus predicted proteomes , along with a selection of other ecdysozoan and other animal predicted proteomes ( S7 Table ) , using OrthoFinder [62] , including predicted proteomes from fully sequenced genomes or predicted proteomes from the fully sequenced genomes and ( likely partial ) transcriptomes in two independent analyses . Using these protein families , we identified orthologues for phylogenetic analysis and explored patterns of gene family expansion and contraction , using KinFin [63] . We identified 144 , 610 protein families in the analysis of 29 fully sequenced genome species . Of these families , 87 . 9% were species specific ( with singletons accounting for 11 . 6% of amino acid span , and multi-protein clusters accounting for 1 . 2% of span ) . While only 12 . 1% of clusters contained members from ≥2 predicted proteomes , they accounted for the majority of the amino acid span ( 87 . 2% ) . H . dujardini had more species-specific genes than R . varieornatus and had more duplicate genes in gene families shared with R . varieornatus ( Table 2 ) . H . dujardini also had more genes shared with nontardigrade outgroups , suggesting loss in R . varieornatus . Many families had more members in tardigrades compared to other taxa , and 3 had fewer members ( 115 had uncorrected Mann-Whitney U-test probabilities <0 . 01 , but none had differential presence after Bonferroni correction ) . In 9 of the families with tardigrade overrepresentation , tardigrades had more than four times as many members as the average of the other species ( S2 Data ) . There were 1 , 486 clusters composed solely of proteins predicted from the 2 tardigrade genomes . Of those , 365 ( 24 . 56% ) had a congruent domain architecture in both species , including 53 peptidase clusters , 27 kinase clusters , and 29 clusters associated with signaling function , including 18 G-protein coupled receptors ( see S3 Data ) . While these annotations are commonly found in clade-specific families , suggesting that innovation in these classes of function is a general feature in metazoan evolution , of particular interest was innovation in the Wnt signaling pathway . Tardigrade-unique clusters included Wnt , Frizzled , and chibby proteins . Of relevance to cryptobiosis , 21 clusters with domain annotation relevant to genome repair and maintenance were synapomorphic for Tardigrada , including molecular chaperones ( 2 ) , histone/chromatin maintenance proteins ( 11 ) , genome repair systems ( 4 ) , nucleases ( 2 ) , and chromosome cohesion components ( 2 ) ( see below ) . HGT is an interesting but contested phenomenon in animals . Many newly sequenced genomes have been reported to have relatively high levels of HGT , and genomes subject to intense curation efforts tend to have lower HGT estimates . We performed ab initio gene finding on the genomes of the model species Caenorhabditis elegans and Drosophila melanogaster with Augustus [64] and used the HGT index approach [65] , which simply classifies loci based on the ratio of their best BLAST scores to ingroup and potential donor taxon databases , to identify candidates . Compared with their mature annotations , we found elevated proportions of putative HGTs in both species ( C . elegans: 2 . 09% of all genes; D . melanogaster: 4 . 67% ) . We observed similarly elevated rates of putative HGT loci , as assessed by the HGT index , in gene sets generated by ab initio gene finding in additional arthropod and nematode genomes compared to their mature annotation ( Fig 2A , S8 Table ) . Thus , the numbers of HGT events found in the genomes of H . dujardini and R . varieornatus are likely to have been overestimated in these initial , uncurated gene predictions , even after sequence contamination has been removed , as seen in the H . dujardini assembly of Boothby et al . [41] . Using the HGT index approach , we identified 463 genes as potential HGT candidates in H . dujardini ( S4 Data ) . Using Diamond BLASTX [66] instead of standard BLASTX [67 , 68] made only a minor difference in the number of potential HGT events predicted ( 446 genes ) . We sifted the initial 463 H . dujardini candidates through a series of biological filters . A true HGT locus will show affinity with its source taxon when analyzed phylogenetically ( a more sensitive test than simple BLAST score ratio ) . Four-fifths of these loci ( 357 ) were confirmed as HGT events by Randomized Axelerated Maximum Likelihood ( RAxML ) [69] analysis of aligned sequences ( Fig 2B ) . For 13 candidates , there were not enough homologues found in public databases to estimate phylogenies . HGT genes are expected to be incorporated into the host genome and to persist through evolutionary time . Only 164 of the RAxML-confirmed H . dujardini candidates had homologues in R . varieornatus , indicating phyletic perdurance ( S2 Data ) . HGT loci will acquire gene structure and expression characteristics of their host metazoan genome . We identified expression at greater than 1 transcript per million ( TPM ) in any library for 338 HGT candidates . While metazoan genes usually contain spliceosomal introns , and 367 of the candidate HGT gene models included introns , we regard this a lower-quality validation criterion , as gene-finding algorithms are programmed to identify introns . Therefore , our highest-credibility current estimate for HGT into the genome of H . dujardini is 133 genes ( 0 . 7% of all genes ) , with a less-credible set , showing conservation , expression , and/or phylogenetic validation of 357 ( 1 . 8% ) and an upper bound of 463 ( 2 . 3% ) . This is congruent with estimates of 1 . 6% HGT candidates ( out of 13 , 917 genes ) for R . varieornatus [22] . The putative HGT loci tended to be clustered in the tardigrade genomes , with many gene neighbors of HGT loci also predicted to be HGTs ( S4 Fig ) . We found 58 clusters of HGT loci in H . dujardini and 14 in R . varieornatus ( S6 Data ) . The largest clusters included up to 6 genes from the same gene family and may have arisen through tandem duplication . These tandem duplication clusters included intradiol ring-cleavage dioxygenases , uridine diphosphate ( UDP ) glycosyltransferases , and alpha/beta fold hydrolases . Several clusters of UDP glycosyltransferases with signatures of HGT from plants were identified in the H . dujardini genome , 1 of which included 6 UDP glycosyltransferases within 12 genes ( loci between the genes bHd03905 and bHd03916 ) . H . dujardini had 40 UDP glycosyltransferase genes , 29 of which were classified as glucuronosyltransferase ( UGT , K00699 ) by Kyoto Encyclopedia of Genes and Genomes ( KEGG ) ORTHOLOG mapping with the KEGG Automatic Annotation Server ( KAAS ) [70] , and of these 27 were HGT candidates . While UGT can function in a number of pathways , we found that the whole ascorbate synthesis pathway , in which UGT metabolizes UDP-D-glucuronate to D-Glucuronate , has been acquired by HGT in H . dujardini . R . varieornatus has only acquired L-gulonolactone oxidase ( S5 Fig ) . Gluconolactonase and L-gluonolactone oxidase were consistently expressed at low levels ( approximately 10–30 TPM ) , but L-ascorbate degradation enzyme L-ascorbate oxidase was not expressed ( TPM < 1 ) . We explored the H . dujardini proteome and the reannotated R . varieornatus proteome for loci implicated in anhydrobiosis . In the new R . varieornatus proteome , we found 16 CAHS loci and 13 SAHS loci and 1 copy each of MAHS , RvLEAM , and Dsup . In H . dujardini , we identified 12 CAHS loci , 10 SAHS loci , and single members of the RvLEAM and MAHS families ( S9 Table ) . Direct interrogation of the H . dujardini genome with R . varieornatus loci identified an additional possible CAHS-like locus and an additional SAHS-like locus . We found no evidence for a H . dujardini homologue of Dsup . Phylogenetic analyses revealed a unique duplication of CAHS3 in R . varieornatus . No SAHS2 orthologue was found in H . dujardini ( S6 Fig ) , and most of the H . dujardini SAHS loci belonged to a species-specific expansion that was orthologous to a single R . varieornatus SAHS locus , RvSAHS13 . SAHS1-like genes in H . dujardini and SAHS1- and SAHS2-like genes in R . varieornatus were locally duplicated , forming SAHS clusters on single scaffolds . R . varieornatus was reported to have undergone extensive gene loss in the stress-responsive transducer of mechanistic target of rapamycin ( mTOR ) pathway and in the peroxisome pathway , which generates H2O2 during the beta-oxidation of fatty lipids . H . dujardini was similarly compromised ( Fig 3A ) . We identified additional gene losses in the peroxisome pathway in H . dujardini , as peroxisome proteins PEK5 , PEK10 , and PEK12 , while present in R . varieornatus , were not found in H . dujardini ( TBLASTN search against genome with an E-value threshold of 1E-3 ) . To identify gene functions associated with anhydrobiosis , we explored differential gene expression in fully hydrated and postdesiccation samples from both species . We compared single individual RNA-Seq of H . dujardini undergoing anhydrobiosis [42] with new data for R . varieornatus induced to enter anhydrobiosis in 2 ways: slow desiccation ( approximately 24 h ) and fast desiccation ( approximately 30 min ) . Successful anhydrobiosis was assumed when >90% of the samples prepared in the same chamber recovered after rehydration . Many more genes were differentially up-regulated by entry into anhydrobiosis in H . dujardini ( 1 , 422 genes , 7 . 1% ) than in R . varieornatus ( fast desiccation: 64 genes , 0 . 5%; slow desiccation: 307 genes , 2 . 2% ) ( S6 Data ) . The fold change distribution of the whole transcriptome of H . dujardini ( mean 8 . 33 , median 0 . 91 ± 69 . 90 SD ) was significantly broader than those of both fast ( 0 . 67 , 0 . 48 ± 2 . 25 ) and slow ( 0 . 77 , 0 . 65 ± 0 . 79 ) desiccation R . varieornatus ( U-test , p-value < 0 . 001 ) ( Fig 3B ) . For the loci differentially expressed in anhydrobiosis ( S7 Data ) , we investigated their membership of gene families with elevated numbers in tardigrades and functional annotations associated with anhydrobiosis . Proteins with functions related to protection from oxidants , such as SOD and peroxiredoxin , were found to have been extensively duplicated in tardigrades . In addition , the mitochondrial chaperone ( BSC1 ) , osmotic stress-related transcription factor NFAT5 , and apoptosis related-gene poly ( ADP-ribose ) polymerase ( PARP ) families were expanded in tardigrades . Chaperones were extensively expanded in H . dujardini ( HSP70 , DnaK , and DnaJ subfamily C-5 , C-13 , and B-12 ) , and the DnaJ subfamily B3 , B-8 was expanded in R . varieornatus . In H . dujardini , we found 5 copies of DNA repair endonuclease XPF , which functions in the nucleotide-excision repair pathway , and , in R . varieornatus , 4 copies of the double-stranded break repair protein MRE11 ( as reported previously [22] ) and additional copies of DNA ligase 4 , from the nonhomologous end-joining pathway . In both R . varieornatus [22] and H . dujardini , some of the genes with anhydrobiosis-related function appear to have been acquired through HGT . All copies of catalase were high-confidence HGTs ( S5 Data ) , and 1 copy was differentially expressed during H . dujardini anhydrobiosis ( expression rises from 0 TPM to 27 . 5 TPM during slow dehydration in H . dujardini ) . R . varieornatus had 11 trehalase loci ( 9 trehalases and 2 acid trehalase-like proteins ) . While H . dujardini did not have an orthologue of trehalose-6-phosphatase synthase ( TPS ) , a gene required for trehalose synthesis , R . varieornatus had a HGT-derived TPS ( S5 Fig ) . Previous studies in M . tardigradum have shown that trehalose does not accumulate during anhydrobiosis , and this is supported by the low expression of the R . varieornatus TPS gene ( 10–20 TPM in active and tun states ) . We note that the R . varieornatus TPS had the highest similarity to TPS from bacterial species in Bacteriodetes , including Chitinophaga , which was one of the contaminating organisms in the Boothby et al . assembly [40] . The R . varieornatus locus contains spliceosomal introns that do not compromise the TPS protein sequence and is surrounded by metazoan-affinity loci . The ascorbate synthesis pathway appears to have been acquired through HGT in H . dujardini , and a horizontally acquired L-gulonolactone oxidase was identified in R . varieornatus ( S5 Fig ) . Several protection-related genes were differentially expressed in anhydrobiotic H . dujardini , including CAHS ( 8 loci of 15 ) , SAHS ( 2 of 10 ) , RvLEAM ( 1 of 1 ) , and MAHS ( 1 of 1 ) . Loci involved in reactive oxygen protection ( 5 SOD genes , 6 glutathione-S transferase genes , a catalase gene , and a LEA gene ) were up-regulated under desiccation . Interestingly , 2 trehalase loci were up-regulated , even though we were unable to identify any TPS loci in H . dujardini . We also identified differentially expressed transcription factors that may regulate anhydrobiotic responses . Two calcium-signaling factors , calmodulin ( CaM ) and a cyclic nucleotide-gated channel ( CNG-3 ) , were both up-regulated , which may drive cAMP synthesis through adenylate cyclase . Although R . varieornatus is capable of rapid anhydrobiosis induction , complete desiccation is unlikely to be as rapid in natural environments , and regulation of gene expression under slow desiccation might reflect a more likely scenario . Fitting this expectation , 5 CAHS loci and a single SAHS locus were up-regulated after slow desiccation , but none were differentially expressed following rapid desiccation . Most R . varieornatus CAHS and SAHS orthologues had high expression in the active state , several over 1 , 000 TPM . In contrast , H . dujardini CAHS and SAHS orthologues had low resting expression ( median 0 . 7 TPM ) and were up-regulated ( median 1 , 916 . 8 TPM ) on anhydrobiosis induction . Aquaporins contribute to transportation of water molecules into cells and could be involved in anhydrobiosis [71] . Aquaporin-10 was highly expressed in R . varieornatus and differentially expressed in anhydrobiotic H . dujardini . M . tardigradum has at least 10 aquaporin loci [72] , H . dujardini has 11 , and R . varieornatus has 10 . The contributions to anhydrobiosis of additional genes identified as up-regulated ( including cytochrome P450 , several solute carrier families , and apolipoproteins ) are unknown . Some genes differentially expressed in both H . dujardini and R . varieornatus slow-desiccation anhydrobiosis were homologous ( S9 Data ) . Of the 1 , 422 differentially expressed genes from H . dujardini , 121 genes were members of 70 protein families that also contained 115 R . varieornatus differentially expressed genes . These included CAHS , SAHS , glutathione-S transferase , and SOD gene families , but in each case H . dujardini had more differentially expressed copies than R . varieornatus . Other differentially expressed gene families were annotated as metalloproteinases , calcium-binding receptors , and G-protein coupled receptors , suggesting that these functions may participate in cellular signaling during induction of anhydrobiosis . Many more ( 887 ) gene families included members that were up-regulated by anhydrobiosis in H . dujardini but unaffected by desiccation in R . varieornatus . These gene families included 1 , 879 R . varieornatus genes; some ( 154 ) were highly expressed in the active state ( TPM > 100 ) . In addition to gene loss , we predicted that the tardigrades might have undergone expansion in gene families active in anhydrobiotic physiology . We identified 3 gene families–each containing members with significant differential expression during anhydrobiosis–that had elevated numbers of members in the tardigrades compared to the other taxa analyzed . H . dujardini and R . varieornatus had more members of OG000684 ( 33 and 8 , respectively ) than any other ( mode of 1 and mean of 1 . 46 copies in the other 28 species , with a maximum of 4 in the moth Plutella xylostella ) . Proteins in OG000684 were annotated with domains associated with ciliar function . OG0002660 contained 3 proteins from H . dujardini and 3 proteins from R . varieornatus but a mean of 1 . 2 from other species . OG0002660 was annotated as fumarylacetoacetase , which acts in phenylalanine metabolism . Fumarylacetoacetase has been identified as a target of the SKN-1-induced stress responses in C . elegans [73] . OG0002103 was also overrepresented in the tardigrades ( 3 in each species ) , while 23 of the other species had 1 copy . Interestingly , the extremophile nematode Plectus murrayi had 4 copies . OG0002103 was annotated as guanosine-5'-triphosphate ( GTP ) cyclohydrolase , involved in formic acid metabolism , including tetrahydrobioterin synthesis . Tetrahydrobioterin is a cofactor of aromatic amino acid hydroxylases , which metabolize phenylalanine . The association of these functions with anhydrobiosis merits investigation . From the two analyses of protein families shared between H . dujardini , R . varieornatus , taxa from other ecdysozoan phyla , and 2 lophotrochozoan outgroup taxa ( one that included only taxa with whole genome data , and a second that also included taxa with transcriptome data ) , we selected putative orthologous protein families . These were screened to eliminate evident paralogous sequences , and alignments were concatenated into a supermatrix . The genomes-only supermatrix included 322 loci from 28 taxa spanning 67 , 256 aligned residues and had 12 . 5% missing data . The alignment was trimmed to remove low-quality regions . The genomes and transcriptomes supermatrix included 71 loci from 37 taxa spanning 68 , 211 aligned residues , had 27% missing data , and was not trimmed . Phylogenomic analyses were carried out in RAxML ( using the General Time Reversible model with Gamma distribution of rates model , GTR+G ) and PhyloBayes ( using a GTR plus rate categories model , GTR-CAT+G ) . We also explored bipartition support from individual gene trees and RAxML and PhyloBayes analyses of 6-state Dayhoff recoded amino acid alignments using the GTR model ( as GTR-CAT cannot be used on these recoded data; S8 Data ) . The genomes-only phylogeny ( Fig 4A ) strongly supported Tardigrada as a sister to monophyletic Nematoda . Within Nematoda and Arthropoda , the relationships of species were congruent with previous analyses , and the earliest branching taxon in Ecdysozoa was Priapulida . RAxML bootstrap and PhyloBayes Bayes proportion support was high across the phylogeny , with only 2 internal nodes in Nematoda and Arthropoda receiving less-than-maximal support . Analysis of individual RAxML phylogenies derived from the 322 loci revealed a degradation of support deeper in the tree , with 53% of trees supporting a monophyletic Arthropoda , 56% supporting Tardigrada plus Nematoda , and 54% supporting the monophyly of Arthropoda plus Tardigrada plus Nematoda . The phylogeny derived from the genomes and transcriptomes dataset ( Fig 4B ) also recovered credibly resolved Nematoda and Arthropoda and , as expected , placed Nematomorpha as sister to Nematoda . Tardigrada was again recovered as sister to Nematoda plus Nematomorpha , with maximal support . Priapulida plus Kinorhyncha was found to arise basally in Ecdysozoa . Unexpectedly , Onychophora , represented by 3 transcriptome datasets , was sister to an Arthropoda plus ( Tardigrada , Nematomorpha , and Nematoda ) clade , again with high support . We tested support for a Nematoda+Tardigrada clade in rare genomic changes [74] in core developmental gene sets and protein family evolution . Rare genomic changes can be used as strong parsimony markers of phylogenetic relationships that are hard to resolve using model-based sequence analyses . An event shared by 2 taxa can be considered to support their relationship where the likelihood of the event is a priori expected to be vanishingly small . HOX genes are involved in anterior-posterior patterning across the Metazoa , with a characteristic set of paralogous genes present in most animal genomes , organized as a tightly regulated cluster . The ancestral cluster is hypothesized to have included HOX1 , HOX2 , HOX3 , HOX4 , HOX5 , and a HOX6-8 like locus and HOX9 . The HOX6-8 and HOX9 types have undergone frequent , independent expansion and contraction during evolution , and HOX clustering has broken down in some species . HOX complements are generally conserved between related taxa , and gain and loss of HOX loci can be considered a rare genomic change . We surveyed HOX loci in tardigrades and relatives ( Fig 5A ) . In the priapulid Priapulus caudatus , 9 HOX loci have been described [75] , but no HOX6-8/AbdA-like gene was identified . All arthropods surveyed ( including representatives of the 4 classes ) had a complement of HOX loci very similar to that of D . melanogaster , with at least 10 loci including HOX6-8 and HOX9 . Some HOX loci in some species have undergone duplication , particularly HOX3/zen . In the mite Tetranychus urticae and the salmon louse Lepeoptheirius salmonis , we identified “missing” HOX genes in the genome . For Onychophora , the sister group to Arthropoda , HOX loci have only been identified through PCR screens [76 , 77] , but they appear to have the same complement as Arthropoda . In H . dujardini , a reduced HOX gene complement ( 6 genes in 5 orthology groups ) has been reported [78] , and we confirmed this reduction using our improved genome ( Fig 5A ) . The same reduced complement was also found in the genome of R . varieornatus [22] , and the greater contiguity of the R . varieornatus genome shows that 5 of the 6 HOX loci are on 1 large scaffold , distributed over 2 . 7 Mb , with 885 non-HOX genes separating them . The H . dujardini loci were unlinked in our assembly , except for the 2 HOX9/AbdB-like loci , and lack of gene level synteny precludes ordering of these scaffolds based on the R . varieornatus genome . The order of the HOX genes on the R . varieornatus scaffolds is not colinear with other , unfragmented clusters , as HOX6-8/ftz and the pair of HOX9/AbdB genes are inverted , and HOX4/dfd is present on a second scaffold ( and not found between HOX3 and HOX6-8/ftz as would be expected ) . The absences of HOX2/pb , HOX5/scr , and HOX6-8/Ubx/AbdA in both tardigrade species is reminiscent of the situation in Nematoda , in which these loci are also absent [79–81] . HOX gene evolution in Nematoda has been dynamic . No Nematode HOX2 or HOX5 orthology group genes were identified , and only a few species had a single HOX6-8 orthologue . Duplication of the HOX9/AbdB locus was common , generating , for instance , the egl-5 , php-3 , and nob-1 loci in Caenorhabditis species . The maximum number of HOX loci in a nematode was 7 , deriving from 6 orthology groups . Loss of HOX3 happened twice ( in Syphacia muris and in the common ancestor of Tylenchomorpha and Rhabditomorpha ) . The independent loss in S . muris was confirmed in 2 related pinworms , Enterobius vermicularis and Syphacia oblevata . The pattern of presence and absence of the Antp-like HOX6-8 locus is more complex , requiring 6 losses ( in the basally arising enoplean Enoplis brevis , the chromadorean Plectus sambesii , the pinworm S . muris , the ancestor of Tylenchomorpha , the diplogasteromorph Pristionchus pacificus , and the ancestor of Caenorhabditis ) . We affirmed loss in the pinworms by screening the genomes of E . vermicularis and S . oblevata as above , and no HOX6-8/Antp-like locus was present in any of the over 20 genomes available for Caenorhabditis . A PCR survey for HOX loci and screening of a de novo assembled transcriptome from the nematomorph Paragordius varius identified 6 putative loci from 5 HOX groups . The presence of a putative HOX2/pb-like gene suggests that loss of HOX2 may be independent in Tardigrada and Nematoda . Gene family birth can be used as another rare genomic marker . We analyzed the whole proteomes of ecdysozoan taxa for gene family births that supported either the Tardigrada+Nematoda model or the Tardigrada+Arthropoda ( i . e . , Panarthropoda ) model . We mapped gene family presence and absence across the 2 contrasting phylogenies using KinFin [63] using different inflation parameters in the Markov Cluster Algorithm ( MCL ) step in OrthoFinder ( S10 Data ) . Using the default inflation value of 1 . 5 , the 2 tardigrades shared more gene families with Arthropoda than they did with Nematoda ( Fig 5B ) . The numbers of gene family births synapomorphic for Arthropoda and Nematoda were identical under both phylogenies , as was expected ( Table 3; Fig 5C; S11 Data ) . Many synapomorphic families had variable presence in the daughter taxa of the common ancestors of Arthropoda and Nematoda , likely because of stochastic gene loss or lack of prediction . However , especially in Nematoda , most synapomorphic families were present in a majority of species ( Fig 5C ) . At inflation value 1 . 5 , we found 6 gene families present that had members in both tardigrades and all 14 arthropods under Panarthropoda , but no gene families were found in both tardigrades and all 9 nematodes under the Tardigrada+Nematoda hypothesis ( S10 Table ) . Allowing for stochastic absence , we inferred 154 families to be synapomorphic for Tardigrada+Arthropoda under the Panarthropoda hypothesis , and 99 for Tardigrada+Nematoda under the Tardigrada+Nematoda hypothesis ( Fig 5D ) . More of the Tardigrada+Arthropoda synapomorphies had higher species representation than did the Tardigrada+Nematoda synapomorphies . This pattern was also observed in analyses using different inflation values and in analyses including the transcriptome from the nematomorph P . varius . We explored the biological implications of these putative synapomorphies by examining the functional annotations of each protein family that contained members from ≥70% of the ingroup species ( Table 3 ) . Under Tardigrada+Arthropoda , 20 families had ≥70% of the ingroup taxa represented , and 6 were universally present . These included important components of developmental and immune pathways , neuromodulators , and others . Two families were annotated as serine endopeptidases , 1 missing in some arthropods that included D . melanogaster Nudel and 1 found in all species . Another synapomorphic family , found in all species , included spätzle orthologues . Spätzle is a cysteine-knot , cytokine-like ligand involved in dorsoventral patterning and is the target of a serine protease activation cascade initiated by Nudel protease . The identification of more than 1 member of a single regulatory cascade as potential gene family births suggests that the pathway may have been established in a Tardigrada+Arthropoda most recent common ancestor . Other Tardigrada+Arthropoda-synapomorphic families were annotated with ommatidial apical extracellular matrix ( eyes shut ) , adipokinetic hormone , neuromodulatory allatostatin-A , drosulfakinin , leucine-rich repeat , thioredoxin , major facilitator superfamily associated , and domain of unknown function DUF4728 domains . However , 9 of the 20 Panarthropoda synapomorphic families had no informative domain annotations . Under Tardigrada+Nematoda , only 5 putatively synapomorphic families had members from ≥70% of the ingroup species . Four of these had domain matches ( proteolipid membrane potential modulator , zona pellucida , RUN , and amidinotransferase domains ) , and 1 contained no proteins with identifiable domains .
We have sequenced and assembled a high-quality genome for the tardigrade H . dujardini , utilizing new data , including single-molecule , long-read sequencing , and heterozygosity-aware assembly methods . Comparison of genomic metrics with previous assemblies for this species showed that our assembly is more complete and more contiguous than has been achieved previously and retains minimal uncollapsed heterozygous regions . The span of this new assembly is much closer to independent estimates of the size of the H . dujardini genome ( 75–100 Mb ) using densitometry and staining . The H . dujardini genome is thus nearly twice the size of that of the related tardigrade R . varieornatus . We compared the 2 genomes to identify differences that would account for the larger genome in H . dujardini . While H . dujardini had approximately 6 , 000 more protein coding genes than R . varieornatus , these accounted for only approximately 23 Mb of the additional span and are not obviously simple duplicates of genes in R . varieornatus . Analyses of the gene contents of the 2 species showed that while H . dujardini had more species-specific genes , it also had greater numbers of loci in species-specific gene family expansions than R . varieornatus and had lost fewer genes whose origins could be traced to a deeper ancestor . H . dujardini genes had , on average , the same structure ( approximately 6 exons per gene ) as did R . varieornatus; however , introns in H . dujardini genes were on average twice the length of their orthologues in R . varieornatus ( 255 bases versus 158 bases ) . Finally , the H . dujardini genome was more repeat rich ( 28 . 5% compared to only 21% in R . varieornatus ) . These data argue against simple whole genome duplication in H . dujardini . The genome of H . dujardini is larger because of expansion of noncoding DNA , including repeats and introns , and acquisition and retention of more new genes and gene duplications than R . varieornatus . The disparity in retention of genes with orthologues outside the Tardigrada , where R . varieornatus has lost more genes than has H . dujardini , suggests that R . varieornatus may have undergone genome size reduction and that the ancestral tardigrade ( or hypsibiid ) genome is more likely to have been approximately 100 Mb than 54 Mb . We await additional tardigrade genomes with interest . While we identified linkage between genes in the 2 tardigrades , local synteny was relatively rare . In this , these genomes resemble those of the genus Caenorhabditis , in which extensive , rapid , within-chromosome rearrangement has served to break close synteny relationships while , in general , maintaining linkage [82] . The absence of chromosomal level assemblies for either tardigrade ( and lack of any genetic map information ) precludes definitive testing of this hypothesis . Boothby et al . made the surprising assertion that 17 . 5% of H . dujardini genes originated through HGT from a wide range of bacterial , fungal , and protozoan donors [19] . Subsequently , several groups including our teams proved that this finding was the result of contamination of their tardigrade samples with cobionts and less-than-rigorous screening of HGT candidates [20 , 21 , 39 , 40] . We found that the use of uncurated gene-finding approaches yielded elevated HGT proportion estimates in many other nematode and arthropod genomes , even where contamination is unlikely to have been an issue . It is thus essential to follow up initial candidate sets of HGT loci with detailed validation . We screened our new H . dujardini assembly for evidence of HGT , identifying a maximum of 2 . 3% of the protein coding genes as potential candidates . After careful assessment using phylogenetic analysis and expression evidence , we identified a likely high-confidence set of only 0 . 7% of H . dujardini genes that originated through HGT . HGT was also low ( 1 . 6% ) in the high-quality R . varieornatus genome . These proportions are congruent with similar analyses of C . elegans and D . melanogaster . Curation of the genome assemblies and gene models may decrease the proportion further . Tardigrades do not have elevated levels of HGT . While tardigrades do not have elevated levels of HGT in their genomes , some HGT events are of importance in anhydrobiosis . All H . dujardini catalase loci were of bacterial origin , as described for R . varieornatus [22] . While trehalose phosphatase synthase was absent from H . dujardini , R . varieornatus has a TPS that likely was acquired by HGT ( S5 Data ) . As M . tardigradum does not have a TPS homologue , while other ecdysozoan taxa do , this suggests that TPS may have been lost in the common ancestor of eutardigrada and regained in R . varieornatus by HGT after divergence from H . dujardini . Genes with likely roles in protection against extreme stress previously identified in R . varieornatus were largely conserved in H . dujardini . Both CAHS and SAHS families had high copy numbers in both species , with independent expansions . However , we did not find a Dsup orthologue in H . dujardini . H . dujardini has similar gene losses to R . varieornatus in pathways that produce reactive oxygen species ( ROS ) and in cellular stress signaling pathways , which suggest that the gene losses occurred before the divergence of the 2 species . This loss of important signaling pathway genes may disconnect signals of stress induction from activating downstream response systems that must be suppressed if anhydrobiosis is to be achieved successfully—for example , cell cycle regulation , transcription and replication inhibition , and apoptosis . As cellular protection and repair pathways were highly conserved , damaged cell systems will still be protected and repaired . Indeed , some stress-related gene families had undergone gene family expansion in 1 or both tardigrades . SOD was duplicated in both species , as was a calcium-activated potassium channel , which has been implicated in cellular signaling during anhydrobiosis [23] . The elevated gene family expansion in H . dujardini compared to R . varieornatus may be related to retention and expansion of induced stress response systems . The transcriptome response to anhydrobiosis differs between the 2 tardigrades . H . dujardini has an induced transcriptomic response where R . varieornatus does not . We found that H . dujardini had more genes differentially expressed on anhydrobiosis than R . varieornatus . As anticipated , more R . varieornatus loci were differentially expressed when desiccated at a slow pace . Genes induced by slow desiccation included CAHS and SAHS genes and antioxidant-related genes . Although most of these genes were highly expressed ( >100 TPM ) in the active state , the induction of these genes may enable higher recovery . CAHS and SAHS loci were also overexpressed on anhydrobiosis in H . dujardini . We found a variety of calcium-related transporters and receptors were differentially expressed on anhydrobiosis . Kondo et al . suggested that cellular signaling using calmodulin and calcium may be required for anhydrobiosis [23] , but it is still unclear how this is related to anhydrobiosis . Calcium and other metal ion concentrations could be increased during dehydration and thus could act as a desiccation signal . Trehalose is known for its role in protecting cellular systems from dehydration [35 , 36 , 83 , 84] . It has been hypothesized that it may not be required for tardigrade anhydrobiosis , as trehalose was not detected in M . tardigradum [31] . Trehalose synthesis via TPS has been lost in H . dujardini , although we found an HGT-origin TPS in R . varieornatus . Unexpectedly , 3 R . varieornatus trehalase loci were differentially expressed on slow desiccation , including 2 with over 200 TPM in the anhydrobiotic state . As trehalose degradation should not be required in the absence of trehalose , there may be an alternative pathway for trehalose synthesis . Our phylogenomic analyses found Tardigrada , represented by H . dujardini and R . varieornatus genomes as well as transcriptomic data from M . tardigradum and Echiniscus testudo , to be sisters to Nematoda , not Arthropoda . This finding was robust to inclusion of additional phyla , such as Onychophora and Nematomorpha , and to filtering the alignment data to exclude poorly represented or rapidly evolving loci . This finding is both surprising and not new . Many previous molecular analyses have found Tardigrada to group with Nematoda , whether using single genes or ever larger gene sets derived from transcriptome and genome studies [1–3] . This phenomenon has been attributed to long branch attraction in suboptimal datasets , with elevated substitutional rates or biased compositions in Nematoda and Tardigrada mutually and robustly driving Bayesian and Maximum Likelihood algorithms to support the wrong tree . Strikingly , in our analyses including taxa for which transcriptome data are available , we found Onychophora to lie outside a ( [Nematoda , Nematomorpha , and Tardigrada] , Arthropoda ) clade . This finding , while present in some other analyses ( e . g . , component phylogenies summarized in [2] ) , conflicts with accepted systematic and many molecular analyses . We note that Onychophora was only represented by transcriptome datasets and that there is accordingly an elevated proportion of missing data in the alignment for this phylum . Developmental and anatomical data do not , in general , support a tree linking Tardigrada with Nematoda . Tardigrades are segmented , have appendages , and have a central and peripheral nervous system anatomy that can be homologized with those of Onychophora and Arthropoda [85 , 86] . In contrast , nematodes are unsegmented , have no lateral appendages , and have a simple nervous system . The myoepithelial triradiate pharynx , found in Nematoda , Nematomorpha , and Tardigrada , is 1 possible morphological link , but Nielsen has argued persuasively that the structures of this organ in nematodes and tardigrades ( and other taxa ) are distinct and thus nonhomologous [5] . H . dujardini has a reduced complement of HOX loci , as does R . varieornatus . Some of the HOX loci missing in the Tardigrada are the same as those absent from Nematoda . Whether these absences are a synapomorphy for a Nematode-Tardigrade clade or simply a product of homoplasious evolution remains unclear . It may be that miniaturization of Nematoda and Tardigrada during adaptation to life in interstitial habitats facilitated the loss of specific HOX loci involved in postcephalic patterning and that both nematodes and tardigrades can be thought to have evolved by reductive evolution from a more fully featured ancestor . It may be intrinsically easier to lose some HOX loci than others . While tardigrades retain obvious segmentation , nematodes do not , with the possible exception of repetitive cell lineages along the anterior-posterior axis during development [87] . We note that until additional species were analyzed , the pattern observed in C . elegans was assumed to be the ground pattern for all Nematoda . More distantly related Tardigrada may have different HOX gene complements to these hypsibiids , and a pattern of staged loss similar to that in Nematoda [79–81] may be found . Assessment of gene family births as rare genomic changes lent support to a Tardigrada+Arthropoda clade , but the support was not striking . There were more synapomorphic gene family births when a Tardigrada+Arthropoda ( Panarthropoda ) clade was assumed than when a Tardigrada+Nematoda clade was assumed . However , analyses under the assumption of Tardigrada+Nematoda identified synapomorphic gene family births at 50% of the level found when Panarthropoda was assumed . We note that recognition of gene families may be compromised by the same “long branch attraction” issues that plague phylogenetic analyses and also that any taxon in which gene loss is common ( such as has been proposed for Nematoda as a result of its simplified body plan ) may score poorly in gene family membership metrics . The short branch lengths that separate basal nodes in the analysis of the panarthropodan-nematode part of the phylogeny of Ecdysozoa may make robust resolution very difficult . We explored the biological implications of the synapomorphies that supported Panarthropoda by examining the functional annotations of each protein family ( S10 Table ) and were surprised that many of these deeply conserved loci have escaped experimental , genetic , or biochemical annotation . One family included spätzle , a cysteine-knot , cytokine-like family involved in dorsoventral patterning as well as immune response , and 2 others were serine endopeptidases , including nudel , which is part of the same pathway as spätzle . This pathway may be a Panarthropod innovation . Thus , our analyses of rare genomic changes lent some support to the Panarthropoda hypothesis , as did analysis of miRNA gene birth [2] , but analysis of HOX loci may conflict with this . The question of tardigrade relationships remains open [4] . While we found support for a clade of Tardigrada , Onychophora , Arthropoda , Nematoda , and Nematomorpha , the branching order within this group remains contentious , and in particular , the positions of Tardigrada and Onychophora are poorly supported and/or variable in our and others’ analyses . Full genome sequences of representatives of Onychophora , Heterotardigrada ( the sister group to the Eutardigrada including Hypsibiidae ) , Nematomorpha , and enoplian , basally arising Nematoda are required . Resolution of the conflicts between morphological and molecular data will be informative , either of the ground state of a nematode-tardigrade ancestor or of the processes that drive homoplasy in rare genomic changes and robust discovery of nonbiological trees in sequence-based phylogenomic studies .
The tardigrade H . dujardini Z151 was purchased from Sciento ( Manchester , United Kingdom ) . H . dujardini Z151 and R . varieornatus strain YOKOZUNA-1 were cultured as previously described [24 , 42] . Briefly , tardigrades were fed Chlorella vulgaris ( Chlorella Industry ) on 2% Bacto Agar ( Difco ) plates prepared with Volvic water , incubated at 18°C for H . dujardini and 22°C for R . varieornatus under constant dark conditions . Culture plates were renewed approximately every 7–8 d . Anhydrobiotic adult samples were isolated on 30 μM filters ( Millipore ) and placed in a chamber maintained at 85% RH for 48 h for H . dujardini; 30% RH for 24 h and additional 24 h at 0% RH for slow-dried R . varieornatus; and 0% RH for 30 min on a 4 cm x 4 cm Kim-towel with 300 μL of distilled water and an additional 2 h without the towel for fast-dried R . varieornatus . Successful anhydrobiosis was assumed when >90% of the samples prepared in the same chamber recovered after rehydration . Genomic DNA for long read sequencing was extracted using MagAttract HMW DNA Kit ( Qiagen ) from approximately 900 , 000 H . dujardini . DNA was purified twice with AMPure XP beads ( Beckman Coulter ) . A 20 kb PacBio library was prepared following the manual “20 kb Template Preparation Using BluePippin Size-Selection System ( 15 kb Size Cutoff ) ” ( PacBio SampleNet—Shared Protocol ) using SMARTBell Template Prep Kit 1 . 0 ( Pacific Biosciences ) and was sequenced using 8 SMRT Cells Pac V3 with DNA Sequencing Reagent 4 . 0 on a PacBio RSII System ( Pacific Biosciences ) at Takara Bio . Briefly , purified DNA was sheared , targeting 20 kb fragments , using a g-TUBE ( Covaris ) . Following end-repair and ligation of SMRTbell adapters , the library was size selected using BluePippin ( Sage Science ) with a size cutoff of 10 kb . The size distribution of the library was assayed on TapeStation 2200 ( Agilent ) and quantified using the Quant-iT dsDNA BR Assay Kit ( Invitrogen ) . MiSeq reads from a single H . dujardini individual ( DRR055040 ) and HiSeq reads are from our previous reports [20 , 21] . For mRNA-Seq to be used in genome annotation , 30 individuals were collected from each of the following conditions in 3 replicates: active and dried adults ( slow dried for R . varieornatus ) , eggs ( 1 , 2 , 3 , 4 , 5 , 6 , and 7 d after laying ) , and juveniles ( 1 , 2 , 3 , 4 , 5 , 6 , and 7 d after hatching ) . Because of sample preparations , R . varieornatus juveniles were sampled until juvenile first day . For gene expression analysis , we sampled approximately 2–3 individuals of fast-dried R . varieornatus . All mRNA-Seq analyses were conducted with 3 replicates . Specimens were thoroughly washed with Milli-Q water on a sterile nylon mesh ( Millipore ) and immediately lysed in TRIzol reagent ( Life Technologies ) using 3 cycles of immersion in liquid nitrogen followed by 37°C incubation . Total RNA was extracted using the Direct-zol RNA kit ( Zymo Research ) following the manufacturer’s instructions , and RNA quality was checked using the High Sensitivity RNA ScreenTape on a TapeStation ( Agilent Technologies ) . For library preparation , mRNA was amplified using the SMARTer Ultra Low Input RNA Kit for Sequencing v . 4 ( Clontech ) , and Illumina libraries were prepared using the KAPA HyperPlus Kit ( KAPA Biosystems ) . Purified libraries were quantified using a Qubit Fluorometer ( Life Technologies ) , and the size distribution was checked using the TapeStation D1000 ScreenTape ( Agilent Technologies ) . Libraries size selected above 200 bp by manually excision from agarose were purified with a NucleoSpin Gel and PCR Clean-up kit ( Clontech ) . The samples were then sequenced on the Illumina NextSeq 500 in High Output Mode with a 75-cycle kit ( Illumina ) as single end reads , with 48 multiplexed samples per run . Adapter sequences were removed , and the sequences were demultiplexed using the bcl2fastq v . 2 software ( Illumina ) . For active and dried adults , RNA-Seq was also conducted starting from approximately 10 , 000 individuals , similarly washed , but RNA extraction was conducted with TRIzol reagent ( Life Technologies ) followed by RNeasy Plus Mini Kit ( Qiagen ) purification . Library preparation and sequencing were conducted at Beijing Genomics Institute . For miRNA-Seq , 5 , 000 individuals were homogenized using Biomasher II ( Funakoshi ) , and TRIzol ( Invitrogen ) was used for RNA extraction; the individuals were then purified by isopropanol precipitation . Size selection of fragments of 18–30 nt using electrophoresis , preparation of the sequencing library for Illumina HiSeq 2000 , and subsequent ( single end ) sequencing were carried out by Beijing Genomics Institute . All sequence data were validated for quality using FastQC [88] . The MiSeq reads from whole genome amplified DNA were merged with Usearch [89] , and both merged and unmerged pairs were assembled with SPAdes [90] as single end . The SPAdes assembly was checked for contamination with BLAST+ BLASTN [68] against the nr [91] database , and no observable contamination was found with blobtools [46] . Illumina data from Boothby et al . [19] were mapped to the SPAdes assembly with Bowtie2 [92] , and read pairs were retained if at least 1 of them mapped to the assembly . These reads were then assembled , scaffolded , and gap closed with Platanus [44] . The Platanus assembly was further scaffolded and gap closed using the PacBio data with PBJelly [93] . Falcon [43] assembly of PacBio data was performed on the DNAnexus platform . Using this Falcon assembly , Platanus assembly was extended using SSPACE-LongReads [47] and gap-filled with PBJelly [93] with default parameters . Single-individual MiSeq reads were mapped to the assembly with BWA , and all contigs with coverage < 1 or length < 1 , 000 bp or those corresponding to the mitochondrial genome were removed . At this stage , 1 CEGMA gene became unrecognized by CEGMA [48] , probably because of multiple PBJelly runs , and therefore , the contig harboring that missing CEGMA gene was corrected by Pilon [94] using the single individual MiSeq reads . We also validated genomic completeness with BUSCO using the eukaryote lineage gene set . mRNA-Seq data ( Development , Active-tun 10k animals ) were mapped to the genome assembly with TopHat [92 , 95] without any options . Using the mapped data from TopHat , BRAKER [54] was used with default settings to construct a GeneMark-ES [96] model and an Augustus [64] gene model , which are used for ab initio prediction of genes . The getAnnotFasta . pl script from Augustus was used to extract coding sequences from the GFF3 file . Similarly , to construct a modified version of the R . varieornatus genomes annotation , we used the development and anhydrobiosis ( S1 Table ) RNA-Seq data for BRAKER annotation . We found that a few genes were misannotated ( MAHS in both species , a CAHS orthologue in R . varieornatus ) , due to fusion with a neighboring gene , and these were manually curated . tRNA and rRNA genes were predicted with tRNAscan-SE [51] and RNAmmer [50] , respectively . BUSCO was used again to validate the completeness of the predicted gene set for both tardigrades . The mRNA-Seq data used to predict the gene models were mapped with BWA MEM [97] against the predicted coding sequences , the genome , and a Trinity [56] assembled transcriptome . We also mapped the mRNA-Seq data used for gene expression analysis ( single individual H . dujardini and fast/slow dry of R . varieornatus ) of the active state and tun state . After SAM to BAM conversion and sorting with SAMtools view and sort [98] , we used QualiMap [99] and bedtools [100] for mapping quality check . To annotate the predicted gene models , we performed similarity searches using BLAST BLASTP [67] against Swiss-Prot , TrEMBL [60] , and HMMER hmmsearch [101] against Pfam-A [102] and Dfam [59] , KAAS analysis for KEGG orthologue mapping [70] , and InterProScan [103] for domain annotation . We used RepeatScout [104] and RepeatMasker [105] for de novo repeat identification . To compare H . dujardini gene models to those of R . varieornatus , we also ran BLAST BLASTP searches against the updated R . varieornatus proteome and TBLASTN search against the R . varieornatus genome and extracted bidirectional best hits with in-house Perl scripts . For miRNA prediction , we used miRDeep [52] to predict mature miRNA within the genome , using the mature miRNA sequences in miRBase [53] . The predicted mature miRNA sequences were then searched against miRBase with ssearch36 [106] for annotation by retaining hits with identity >70% and a complete match of bases 1–7 , 2–8 , or 3–9 . HGT genes were identified using the HGT index approach [65] . Swiss-Prot and TrEMBL were downloaded [60] , and sequences with “Complete Proteome” in the Keyword were extracted . Following the method of Boschetti et al . , an Arthropoda-less and Nematoda-less database was constructed . These databases were searched with DIAMOND [66] using as query all CDS sequences , using the longest transcript for each gene ( DIAMOND BLASTX ) . Hits with an E-value below 1e-5 were kept . The HGT index ( Hu ) was calculated as Bo − Bm , the bit score difference between the best nonmetazoan hit ( Bo ) and the best metazoan hit ( Bm ) , and genes with Hu ≥ 30 were identified as HGT candidates . To assess if ab initio annotation of genomes biases the calculation of the HGT index , we calculated HGT indices for genomes in ENSEMBL-Metazoa [57] that had corresponding Augustus [64] gene models and ran ab initio gene prediction . We analyzed Aedes aegypti , Apis mellifera , Bombus impatiens , Caenorhabditis brenneri , Caenorhabditis briggsae , C . elegans , Caenorhabditis japonica , Caenorhabditis remanei , Culex quinquefasciatus , Drosophila ananassae , Drosophila erecta , Drosophila grimshawi , D . melanogaster , Drosophila mojavensis , Drosophila persimilis , Drosophila pseudoobscura , Drosophila sechellia , Drosophila simulans , Drosophila virilis , Drosophila willistoni , Drosophila yakuba , Heliconius melpomene , Nasonia vitripennis , Rhodnius prolixus , Tribolium castaneum , and Trichinella spiralis . Gene predictions for each organism were conducted using autoAugPred . pl from the Augustus package with the corresponding model ( S8 Table ) . The longest isoform sequences for all genes were extracted for both ENSEMBL and ab initio annotations , and the HGT index was calculated for each gene in all organisms . To assess if using DIAMOND BLASTX biases HGT index calculation , we ran BLAST BLASTX [67] searches with H . dujardini and calculated the HGT index using the same pipeline . The blast-score-based HGT index provided a first-pass estimate of whether a gene had been horizontally transferred from a nonmetazoan species . Phylogenetic trees were constructed for each of the 463 candidates ( based on the HGT index ) along with their best blast hits as described above ( S5 Data ) . Protein sequences for the blast hits were aligned with the HGT candidate using MAFFT [107] . RAxML [69] was used to build 450 individual trees , as 13 of the protein sets had less than 4 sequences and trees could not be built for them . HGT candidates were categorized as prokaryotes , viruses , metazoan , and nonmetazoan ( i . e . , eukaryotes that were nonmetazoan , such as fungi ) based on the monophyletic clades that they were placed in . Any that could not be classified monophyletically were classified as “complex”: these were split into complex non-HGT ( where the complexity was within a metazoan radiation ) or complex HGT ( where HGT was affirmed but affinities remained unclear ) ( S4 Data ) . OrthoFinder [62] with default BLAST+ BLASTP search settings and an inflation parameter of 1 . 5 was used to identify orthogroups containing H . dujardini and R . varieornatus protein-coding genes . These orthogroups were used to identify the R . varieornatus HGT homologues of H . dujardini HGT candidates . HGT candidates were classified as having high gene expression levels if they had an average gene expression greater than the overall average gene expression level of 1 TPM . To identify genes responsive to anhydrobiosis , we explored transcriptome ( Illumina mRNA-Seq ) data for both H . dujardini and R . varieornatus . Individual mRNA-Seq data for H . dujardini [42] before and during anhydrobiosis were contrasted with new sequence data for R . varieornatus similarly treated . We mapped the mRNA-Seq reads to the coding sequences of the relevant species with BWA MEM [97] , and after summarizing the read count of each gene , we used DESeq2 [108] for differential expression calculation , using false discovery rate ( FDR ) correction . Genes with a FDR below 0 . 05 , an average expression level ( in transcripts per kilobase of model per million mapped fragments; TPM ) of over 1 , and a fold change over 2 were defined as differentially expressed genes . Gene expression ( TPM ) was calculated with Kallisto [109] and was parsed with custom Perl scripts . To assess if there were any differences in fold change distributions , we used R to calculate the fold change for each gene ( anhydrobiotic / [active + 0 . 1] ) and conducted a U test using the wilcox . text ( ) function . We mapped the differentially expressed genes to KEGG pathway maps [110] to identify pathways that were likely to be differentially active during anhydrobiosis . For comparison with R . varieornatus , we first aligned the genomes of H . dujardini and R . varieornatus with Murasaki and visualized with gmv [61] . The lower tf-idf anchor filter was set to 500 . A syntenic block was observed between scaffold0001 of H . dujardini and scaffold002 of R . varieornatus . We extracted the corresponding regions ( H . dujardini: scaffold0001 363 , 334–2 , 100 , 664 , R . varieornatus: scaffold002 2 , 186 , 607–3 , 858 , 816 ) , and conducted alignment with Mauve [111] . We determined the number of bidirectional best hit ( BBH ) orthologues on the same scaffold in both H . dujardini and R . varieornatus . We extracted gene pairs that had an identity of more than 90% by ClustalW2 [103] and calculated the identity of the first and last exon between pairs . Tardigrade-specific , protection-related genes ( CAHS , SAHS , MAHS , RvLEAM , and Dsup ) were identified by BLASTP , subjected to phylogenetic analysis using Clustalw2 [103] and FastTree [112] , and visualized with FigTree [113] . HOX loci were identified using BLAST , and their positions on scaffolds and contigs assessed . To identify HOX loci in other genomes , genome assembly files were downloaded from ENSEMBL Genomes [57] or Wormbase ParaSite [114 , 115] and formatted for local search with BLAST+ [68] . Homeodomain alignments were generated using Clustal Omega [116] , and phylogenies estimated with RAxML [69] to classify individual homeodomains . Protein predictions from genomes of Annelida ( 1 species ) , Nematoda ( 9 ) , Arthropoda ( 15 ) , Mollusca ( 1 ) , and Priapulida ( 1 ) were retrieved from public databases ( S7 Table ) . Proteomes were screened for isoforms ( S12 Data ) , and the longest isoforms were clustered with the proteins of H . dujardini and R . varieornatus using OrthoFinder 1 . 1 . 2 [62] at different inflation values ( S10 Data ) . Proteins from all proteomes were functionally annotated using InterProScan [103] . OrthoFinder output was analyzed using KinFin [63] under 2 competing phylogenetic hypotheses: either ( 1 ) “Panarthropoda , ” where Tardigrada and Arthropoda share a more recent common ancestor distinct from Nematoda , or ( 2 ) Tardigrada and Nematoda sharing a more recent common ancestor distinct from Arthropoda . ( see S11 Data for input files used in KinFin analysis ) . Enrichment and depletion in clusters containing proteins from Tardigrada and other taxa was tested using a 2-sided Mann-Whitney-U test of the count ( if at least 2 taxa had non-zero counts ) , and results were deemed significant at a p-value threshold of p = 0 . 01 . A graph representation of the OrthoFinder clustering ( at inflation value = 1 . 5 ) was generated using the generate_network . py script distributed with KinFin . The nodes in the graph were positioned using the ForceAtlas2 layout algorithm implemented in Gephi . Single-copy orthologues between H . dujardini and R . varieornatus were identified using the orthologous groups defined by OrthoFinder . Using the Ensembl Perl API , gene structure information ( gene lengths , exon counts , and intron spans per gene ) were extracted for these gene pairs . To avoid erroneous gene predictions biasing observed trends , H . dujardini genes that were 20% longer or 20% shorter were considered outliers . The whole-genome OrthoFinder clustering at inflation value 1 . 5 was mined for potential single-copy orthologues for phylogenetic analysis . Transcriptome data were retrieved for additional tardigrades ( 2 species ) , a priapulid ( 1 ) , kinorhynchs ( 2 ) , and onychophorans ( 3 ) ( S11 Table ) . Assembled transcripts for E . testudo , M . tardigradum , Pycnophyes kielensis , and Halicryptus spinulosus were downloaded from the NCBI Transcriptome Shotgun Assembly ( TSA ) Database . EST sequences of Euperipatoides kanangrensis , Peripatopsis sedgwicki , and Echinoderes horni were download from NCBI Trace Archive and assembled using CAP3 [117] . Raw mRNA-Seq reads for Peripatopsis capensis were downloaded from NCBI SRA , trimmed using skewer [118] , and assembled with Trinity [56] . Protein sequences were predicted from all transcriptome data using TransDecoder [119] , retaining a single open reading frame per transcript . Predicted proteins from these transcriptomes were used along with the genome-derived proteomes in a second OrthoFinder clustering analysis . We identified putatively orthologous genes in the OrthoFinder clusters for the genome and the genome-plus-transcriptome datasets . For both datasets , the same pipeline was followed . Clusters with 1-to-1 orthology were retained . For clusters with median per-species membership equal to 1 and mean less than 2 . 5 , a phylogenetic tree was inferred with RAxML ( using the LG+G model ) . Each tree was visually inspected to identify the largest possible monophyletic clan , and in-paralogues and spuriously included sequences were removed . Individual alignments of each locus were filtered using trimal [120] and then concatenated into a supermatrix using fastconcat [121] . The supermatrices were analyzed with RAxML [69] with 100 ML bootstraps and PhyloBayes [122] ( see S11 Table for specific commands ) . Trees were summarized in FigTree . A dedicated Ensembl genome browser ( version 85 ) [57] using the EasyImport pipeline [123] was constructed on http://www . tardigrades . org , and the H . dujardini genome and annotations described in this paper and the new gene predictions for R . varieornatus were imported . We used open source software tools where available , as detailed in S11 Table . Custom scripts developed for the project are uploaded to https://github . com/abs-yy/Hypsibius_dujardini_manuscript . We used G-language Genome Analysis Environment [124 , 125] for sequence manipulation .
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Tardigrades are justly famous for their abilities to withstand environmental extremes . Many freshwater and terrestrial species can undergo anhydrobiosis—life without water—and thereby withstand desiccation , freezing , and other insults . We explored the comparative biology of anhydrobiosis in 2 species of tardigrade that differ in the mechanisms they use to enter anhydrobiosis . Using newly assembled and improved genomes , we find that Ramazzottius varieornatus , a species that can withstand rapid desiccation , differs from Hypsibius dujardini , a species that requires extended preconditioning , in not showing a major transcriptional response to anhydrobiosis induction . We identified a number of genetic systems in the tardigrades that likely play conserved , central roles in anhydrobiosis as well as species-unique components . Compared to previous estimates , our improved genomes show much reduced levels of horizontal gene transfer into tardigrade genomes , but some of the identified horizontal gene transfer ( HGT ) genes appear to be involved in anhydrobiosis . Using the improved genomes , we explored the evolutionary relationships of tardigrades and other molting animals , particularly nematodes and arthropods . We identified conflicting signals between sequence-based analyses , which found a relationship between tardigrades and nematodes , and analyses based on rare genomic changes , which tended to support the traditional tardigrade-arthropod link .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"taxonomy",
"horizontal",
"gene",
"transfer",
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] |
2017
|
Comparative genomics of the tardigrades Hypsibius dujardini and Ramazzottius varieornatus
|
Mathematical models predict an exponential distribution of infection prevalence across communities where a disease is disappearing . Trachoma control programs offer an opportunity to test this hypothesis , as the World Health Organization has targeted trachoma for elimination as a public health concern by the year 2020 . Local programs may benefit if a single survey could reveal whether infection was headed towards elimination . Using data from a previously-published 2009 survey , we test the hypothesis that Chlamydia trachomatis prevalence across 75 Tanzanian communities where trachoma had been documented to be disappearing is exponentially distributed . We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation . Model selection by Akaike Information Criteria ( AICc ) suggested the exponential distribution had the most parsimonious fit to the data . Those distributions which do not include the exponential as a special or limiting case had much lower likelihoods of fitting the observed data . 95% confidence intervals for shape parameter estimates of those distributions which do include the exponential as a special or limiting case were consistent with the exponential . Lastly , goodness-of-fit testing was unable to reject the hypothesis that the prevalence data came from an exponential distribution . Models correctly predict that infection prevalence across communities where a disease is disappearing is best described by an exponential distribution . In Tanzanian communities where local control efforts had reduced the clinical signs of trachoma by 80% over 10 years , an exponential distribution gave the best fit to prevalence data . An exponential distribution has a relatively heavy tail , thus occasional high-prevalence communities are to be expected even when infection is disappearing . A single cross-sectional survey may be able to reveal whether elimination efforts are on-track .
Epidemic models hypothesize that the prevalence of infection across communities where an infectious disease is disappearing should approach an exponential distribution . Simulations of mass treatments and decreasing transmission support this . [1–3] However , these epidemic models typically assume similar transmission parameters across communities , while observational studies suggest transmission heterogeneity even amongst neighboring communities . [4] If this hypothesis is consistent with field data , public health stakeholders would benefit by having the ability to forecast prevalence and learn whether a disease was on its way to elimination . Trachoma programs offer an opportunity to test these models . Repeated ocular infection with Chlamydia trachomatis can result in irreversible blindness . Trachoma has been targeted by The World Health Organization ( WHO ) for elimination as a public health concern by the year 2020 . Efforts rely on a multifaceted approach of mass antibiotic distributions to clear infection and hygiene improvements such as promoting facial cleanliness and latrine construction to reduce transmission . Whether due to intervention or secular trend , trachoma is clearly disappearing from many areas . [5–8] A recent study suggested that the prevalence of infection across 24 communities in two separate regions of Ethiopia approached a geometric distribution , the discrete analog of the exponential . Longitudinal evidence confirmed trachoma was indeed disappearing in each of these two areas . [9] Here , we examine a far larger data set from a recent cross-sectional survey in Tanzania to determine the distribution of infection across communities that have received multiple rounds of mass antibiotics and where the prevalence of clinical signs of trachoma was known to be decreasing . We test the hypothesis that the distribution of Tanzanian prevalence data is exponential .
The study was carried out in accordance with the Declaration of Helsinki . Verbal consent was obtained from the local chiefs of each community before randomization . Verbal informed consent from each child participant’s guardian was obtained prior to the examination . This consent process was appropriate given the high rates of illiteracy in the study area and was approved by all institutional review boards .
The exponential distribution had the lowest ( best ) AICc . Note those distributions which include the exponential as a special or limiting case will always achieve a likelihood of having observed the data at least as high as the exponential . However , while the beta , Gumbel , normal , gamma , Weibull , generalized gamma distributions all had slightly better log likelihoods ( slightly better fits ) , these distributions all contained additional parameters and therefore had higher ( worse ) AICc results . The sensitivity analysis yielded the same results as the main analysis , i . e . removing the 0 prevalence villages in the Iramba district had no effect and the exponential distribution gave the most parsimonious fit by AICc . Results from the main analysis are summarized in Table 1 . The fit of the exponential distribution to the data is shown in Fig . 1 along with the fit of those distributions which include the exponential as a special or limiting case . The Cauchy , log-normal , chi , and chi-squared distributions do not include the exponential as a special or limiting case . These distributions gave far worse log likelihoods and AICc than the exponential . The fit of these distributions to the data is shown alongside the exponential in Fig . 2 . 95% confidence intervals of the shape parameter estimates for the beta , gamma , Weibull , and generalized gamma distributions included 1 , consistent with the special case of an exponential distribution ( Table 2 ) . The confidence interval for the location parameter of the truncated normal and Gumbel distributions included negative values , which again is consistent with the exponential . The mixture exponential distribution trivially reduced to a single exponential distribution as the proportion parameter estimate was 0 . 99 and the confidence interval included 1 . With goodness-of-fit testing , we were unable to reject the hypothesis that the observed data came from an exponential distribution ( p = 0 . 30 ) . We found no evidence of spatial autocorrelation . Moran’s I using an inverse weight matrix was- . 09 ( p = 0 . 34 ) and Moran’s I using binary weight matrix was -0 . 02 ( p = 0 . 85 ) .
Here we show that chlamydial prevalence data from Tanzania are consistent with an exponential distribution . A dedicated control program had reduced the prevalence of clinical signs of trachoma 5-fold over 10 years in these Tanzanian communities . Of all distributions tested , the exponential had the most parsimonious fit to the data . Furthermore , the 95% confidence interval for the shape parameter estimate of each of the multi-parameter distributions included the special or limiting case of the exponential . Lastly , goodness-of-fit testing was unable to reject the hypothesis that the observed prevalence data came from an exponential distribution . The Suceptible-Infected-Suceptible ( SIS ) epidemic model is used to study the transmission dynamics of pathogens , such as C . trachomatis , which can repeatedly infect individuals . In its simplest form , this model divides the population into two compartments: those who are susceptible to a disease and those who are infected . Members of the population flow between compartments at rates that reflect how transmissible the disease is and how quickly one recovers from infection . The model assumes similar transmission conditions across communities and it is not obvious the prevalence distribution predicted by the SIS model would be observed with heterogeneous communities . [16 , 17] While a smaller study found a prevalence distribution in Ethiopian communities consistent with the SIS model , there is no reason to believe the findings would apply to this far larger Tanzanian survey . [9] One explanation may be that if systems tend towards states of maximum entropy over time , an exponential distribution would not be unexpected; it has the maximum entropy amongst all continuous distributions with finite mean and non-negative values . [18–20] Furthermore , infection in this cross-sectional survey was a rare event . Individual factors which normally lead to heterogeneity in transmission parameters contribute less and less as outcomes become more rare . [21] Our study has several limitations . Models imply an exponential distribution of infection prevalence when infection is disappearing , however we only had evidence that the clinical signs of trachoma were disappearing . Because the clinical signs ( trachomatous inflammation of the tarsal conjunctiva ) are considered lagging indicators of infection disappears , we assumed infection must have been disappearing as well . [22] It must be noted though that while the prevalence of clinical signs of trachoma is decreasing in these areas of Tanzania from the baseline survey to this 2007–2008 survey , this 2007–2008 survey was not powered to provide district-level estimates . Furthermore , we chose to fit the prevalence data to continuous as opposed to discrete distributions because communities varied in population size . Alternatively , we could have scaled discrete distributions by the mean prevalence , as done previously . [9] Instead , we assumed that reported prevalences were a sample from a binomial distribution , given a true unobserved continuous prevalence . It is possible the prevalence data came from two different exponential distributions . To explore this , we tested a mixture exponential distribution and found that it reduced to a single exponential . Our goodness-of-fit testing assumed independence between samples . To explore this , we performed a Moran’s-I calculation . Though our Moran’s I calculation suggested there was not statistically significant geographical clustering of infection prevalences , this statistic is not perfect and there may still be some clustering . Note that if the observed data were strongly autocorrelated and we had not taken this correlation into account , then our parameter estimates would have had less precision and the exponential would have been more difficult to reject . Thus our analysis was conservative . Our findings have several implications for trachoma control programs . An exponential distribution has a relatively heavy tail compared to a Gaussian distribution and outliers are not uncommon . Therefore we expect occasional high-prevalence communities and such communities do not necessarily suggest transmission hot spots or a failure of control efforts . In fact , models predict infection will disappear from the tail of the distribution as outliers regress to the mean , even if transmission conditions remain the same . [3 , 23] Reports from Nepal , Tanzania , and the Gambia have noted that infection tends to disappear in high-prevalence villages in otherwise hypo-endemic areas . [24–27] Assessing whether trachoma control programs are on-track to eliminate infection can be difficult for public health stakeholders . Large-scale longitudinal surveys of community-wide infection prevalence are costly and resource-intensive to perform . A single cross-sectional survey , on the other hand , is much more feasible . If such a survey reveals the distribution of infection prevalence is approximated by the exponential , control programs could benefit knowing disease is on its way to elimination if transmission conditions remain the same . Further studies are needed to determine whether these findings also apply to clinical activity , the current surrogate for infection used by trachoma programs .
|
Trachoma is the leading infectious cause of blindness and the World Health Organization plans to eliminate it as a public health concern worldwide by the year 2020 . It can be difficult for local trachoma programs to assess whether disease is headed towards elimination in their area . Mathematical infectious disease models describe that when a disease disappears , its prevalence across communities in that area form an exponential distribution . However , this theorem has never been tested with field data . In this study , we take trachoma prevalence data from Tanzania , in an area where trachoma was known to be disappearing , and find that the prevalence forms an exponential distribution . The implications of this study could be applied to other infectious diseases to provide evidence that prevalence is headed towards elimination .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
The Distribution of Ocular Chlamydia Prevalence across Tanzanian Communities Where Trachoma Is Declining
|
Microscopy-based identification of eggs in stool offers simple , reliable and economical options for assessing the prevalence and intensity of hookworm infections , and for monitoring the success of helminth control programs . This study was conducted to evaluate and compare the diagnostic parameters of the Kato-Katz ( KK ) and simple sodium nitrate flotation technique ( SNF ) in terms of detection and quantification of hookworm eggs , with PCR as an additional reference test in stool , collected as part of a baseline cross-sectional study in Cambodia . Fecal samples collected from 205 people in Dong village , Rovieng district , Preah Vihear province , Cambodia were subjected to KK , SNF and PCR for the detection ( and in case of microscopy-based methods , quantification ) of hookworm eggs in stool . The prevalence of hookworm detected using a combination of three techniques ( gold standard ) was 61 . 0% . PCR displayed a highest sensitivity for hookworm detection ( 92 . 0% ) followed by SNF ( 44 . 0% ) and quadruple KK smears ( 36 . 0% ) compared to the gold standard . The overall eggs per gram feces from SNF tended to be higher than for quadruple KK and the SNF proved superior for detecting low egg burdens . As a reference , PCR demonstrated the higher sensitivity compared to SNF and the quadruple KK method for detection of hookworm in human stool . For microscopic-based quantification , a single SNF proved superior to the quadruple KK for the detection of hookworm eggs in stool , in particular for low egg burdens . In addition , the SNF is cost-effective and easily accessible in resource poor countries .
Human hookworms are estimated to infect between 576–740 million people globally and are responsible for a global burden of 3 . 2 million disability-adjusted life years [1] , [2] . Hookworms are a leading cause of iron deficiency anemia and protein malnutrition , especially among pre- and school-aged children and untreated infections are known to result in adverse maternal-fetal outcomes in pregnant women [3] . The principal intervention strategy for hookworm infection is periodic mass drug administration of humans with the benzimidazole drugs , albendazole or mebendazole . Diagnosis of soil-transmitted helminth ( STH ) infections , including hookworm has largely relied on copromicroscopy techniques based on the detection and quantification of eggs in feces . These tests aim to offer simple , reliable and economical options for assessing the prevalence and intensity of STH infections and monitoring the success of drug efficacy trials and helminth control programs . Of these , the Kato-Katz ( KK ) technique is currently the most widely used and accepted diagnostic technique recommended by the World Health Organization ( WHO ) [4] . The KK technique is relatively simple , reproducible , requires minimal equipment and the kit is mostly reusable . Hence the technique is inexpensive and commonly used as a field-based or point-of-care diagnostic test . The major disadvantage of the KK technique , however , is its lack of sensitivity for the detection of low levels and low intensities of STH infections [5] . In addition , hookworm eggs rapidly disappear in cleared slides , resulting in false negative test results if the interval between preparation and examination of the slides is too long ( >30 min ) [6] . For these reasons , it is necessary to increase the sensitivity of the KK technique by examining single fecal samples using multiple KK smears and/or by examining multiple fecal samples over multiple consecutive days [7] , [8] . The sodium nitrate flotation ( SNF ) technique has been used in the veterinary field for diagnosing helminth infections for the last four decades . This method is currently the diagnostic test of choice for enteric parasites in small animals ( e . g . dogs , cats ) and commonly utilized with a commercial reusable stand-up fecal flotation device known as the Fecalyzer ( EVSCO 014008-50 , USA ) . Recent studies suggest that fecal flotation techniques hold promise for the diagnosis of STH infections in humans . A single fecal flotation using the FLOTAC and more recently the mini-FLOTAC device has consistently been shown superior in terms of sensitivity compared to triplicate KK and ether concentration methods for the detection of hookworm eggs in stool [5] , [9] , albeit at the expense of lower egg counts [10] , [11] , [12] , [13] . A number of studies have reported the superior diagnostic parameters of molecular-based diagnostic techniques compared to those of microscopy for the detection of parasite stages in feces , including hookworms [14] , [15] , [16] , [17] . We therefore utilized a previously validated polymerase chain reaction ( PCR ) targeting the internal transcribed spacer ( ITS ) -1 , 5 . 8S and ITS-2 region of hookworms as an additional diagnostic test for assessing the sensitivity of the coproscopy-based methods . This study was conducted to evaluate and compare the diagnostic parameters of the KK and the SNF methods in terms of detection and quantification of hookworm eggs in stool collected as part of a baseline cross-sectional study in Cambodia , with PCR as an additional reference test , .
The research was approved by the Ethics Committee of the Cantons of Basel-Stadt and Baselland ( EKBB , #18/12 , dated 23 February 2012 ) , Switzerland , and by the National Ethics Committee for Health Research , Ministry of Health , Cambodia ( NECHR , #192 , dated 19 November 2011 ) . Written informed consent was obtained from each participant prior to the start of the study . For participants between the ages of 2 and 18 years , written informed consent was obtained from the parents , legal guardian or appropriate literate substitute . All participants were informed of the study's purpose and procedures prior to enrolment . All parasitic infections diagnosed were treated according to the guidelines of the National Helminth Control Program of Cambodia [18] . The study was conducted in Dong village , Rovieng district , Preah Vihear province , Cambodia [9] . In brief , a total of 205 persons were randomly chosen for inclusion in this cross-sectional study . Two fecal samples were collected from each enrolled participant over two consecutive days . On the day of the first visit , informed consent was obtained from all household members and questionnaire interviews were conducted [9] . To all enrolled participants , pre-labeled stool containers were distributed . Participants were asked to defecate during the morning on the following day where stool samples were collected and a second stool container distributed . One half of the collected stool samples were transported at ambient temperature to the laboratory in the Rovieng Health Center within three hours after defecation . One part ( approximately 2 g ) was placed into a 15 ml centrifuge tube containing 8 ml of 10% formalin for examination using SNF and the other part ( approximately 2 g ) was placed into a 15 ml centrifuge tube containing 8 ml of 2 . 5% potassium dichromate for PCR analysis and transported to the School of Veterinary Science , University of Queensland , Gatton campus , Australia . The same collection procedure of fecal samples was carried out in the morning of the second day with samples immediately subjected to a second round of examination by the KK method . For each stool sample two KK smears ( duplicate slides ) were prepared . For each person four KK smears were examined ( two smears on each of two stool samples ) . The preparation of each slide was done following the protocol previously described [19] . Number 120-sized nylon mesh screen was used for filtering the stool and a standard plastic KK template was used to deliver 41 . 7 mg of stool from each sample onto each slide . The smear was examined under light microscope after allowing for clearance for 30 min . Total number of hookworm eggs observed on the slide was counted and noted . Egg counts were multiplied by 24 to obtain the number of eggs per gram feces ( epg ) . SNF was carried out according to a previously described protocol [20] on a single stool sample per study participant . Briefly , the formalin fixative was poured off and a fecal suspension prepared by thoroughly mixing approximately two gram of each stool sample with four times its volume of distilled water . The suspension was strained though a small funnel lined with two layers of surgical gauze directly into a 10 ml centrifuge tube and centrifuged for two min at 3 , 000× g . The supernatant was poured off leaving behind the fecal pellet ( 250 mg ) . Two mL of sodium nitrate solution ( specific gravity 1 . 20 ) was added and the pellet mixed into a slurry using a wooden spatula . Sodium nitrate solution ( specific gravity 1 . 20 , or 315 gm/L of water ) was then filled to the rim of the centrifuge tube , forming a positive meniscus and a 22 mm×22 mm cover slip was carefully placed on top . After 10 min , the cover slip was removed and placed onto a microscope slide . The entire slide was examined under light microscope at 100× magnification in a zig-zag fashion and the total number of hookworm eggs on the coverslip was counted . The observed number was multiplied by four to obtain the epg . Genomic DNA was extracted directly from human fecal samples using the PowerSoil DNA Kit ( Mo Bio , CA , USA ) according to manufacturer's instructions with minor modifications and PCR carried out as previously described [17] . A positive control of each hookworm species and a negative control of distilled water were included in each run . The PCR products were visualized on 1% agarose gels in Sodium Borate ( SB ) buffer and stained by SYBR safe® Nucleic Acid Gel Stain ( Life Technologies , Invitrogen , Eugene , USA ) . The results of the fecal examinations were entered in EXCEL ( Microsoft , USA ) and analyzed by using STATA version 12 . 1 ( StataCorp LP; College Station , TX ) . To estimate sensitivity , specificity and negative predictive value ( NPV ) , results for the three techniques were categorized into positive and negative variables , presented in cross-tabulations , and compared for equal possibilities of being positive by using McNemar's test with 95% confidence interval ( CI ) . The combination of the three techniques was used as diagnostic “gold standard” to estimate the sensitivity and specificity of each technique . Agreement among infection intensities of the two techniques ( only KK and SNF ) was estimated from their mean epg values , using paired student t-test . The “true prevalence” was calculated with the model developed by Marti and Koella , described elsewhere [21] .
For a diagrammatic guide to the study design and summary of the diagnostic results refer to Fig . 1 . The overall prevalence of hookworm infection in humans was 61 . 0% by the combined techniques , 56 . 1% by PCR , 26 . 8% by SNF and 22 . 0% by quadruple KK ( 16 . 6% by day 1 KK , 12 . 2% by day 2 KK , Table 1 ) . The calculated sensitivities , specificities and NPVs with 95% CI are shown in Table 1 . Briefly , the sensitivity of PCR was the highest ( 92 . 0% ) followed by SNF ( 44 . 0% ) , the quadruple KK ( 36 . 0% ) , day 1 KK ( 27 . 2% ) and day 2 KK ( 20 . 0% ) respectively . The specificity of KK , SNF and PCR was assumed 100% . Comparison of the median intensity of hookworm infection by age group is shown in Fig . 2 . The overall epg count from the quadruple KK was higher than those measured using SNF . However , the epg measured using SNF were higher than quadruple KK in two out of five age groups ( 21–30 years , 31–50 years ) . Therefore , there was no significant difference in epg between the two methods . We compared the median epg of the SNF between the samples found only positive by SNF ( median epg: 160 ) to the samples that were analyzed by SNF and KK ( median epg: 448 ) . Using the Mann-Withney U test , there was no significant difference in epg values ( P = 0 . 121 ) . The estimated “true” prevalence for hookworm infection based on the quadruple KK and gold standard were 30 . 3% and 70 . 2% , respectively which is an increase of 8 . 3% from our observed prevalence of 22 . 0% for quadruple KK and increase of 9 . 2% from our observed prevalence of 61 . 0% for gold standard ( Fig . 3 ) .
In the present study , three diagnostic techniques ( KK , SNF and PCR ) were assessed for the qualitative and two techniques ( KK and SNF ) for the quantitative detection of hookworm eggs in fecal samples from humans in Cambodia . Direct comparison of the three diagnostic techniques showed that the PCR assay had a superior sensitivity compared to the SNF , the single , duplicate and quadruplicate KK techniques . The KK when performed in duplicate with stool samples collected over two consecutive days provided a higher sensitivity ( 36 . 0% ) for diagnosing hookworm infection when compared to one day KK ( day 1 or day 2 ) alone ( 27 . 2% and 20 . 0% ) . The ten individuals that were positive by KK and negative by SNF were also found negative on PCR . Therefore it is likely that these 10 positives were false positives on the quadruplicate KK , which is yet another disadvantage of this diagnostic approach . The field of view is poor compared to the SNF and fecal artifacts can be mistaken as helminth eggs . The PCR results are likely explained by two factors: ( i ) false negatives - the inability to amplify these samples could be associated with failure to remove PCR inhibitors in human stool following DNA extraction [22] , ( ii ) false-positive coproscopy results , i . e . that Trichostrongylus eggs detected in stool were misidentified as hookworm eggs . Trichostrongylus columbricformis which is present in humans in neighboring countries such as Lao People's Democratic Republic [23] and Thailand [24] produce eggs very similar to hookworms . Although there are no published reports of human infection with this species in Cambodia , T . columbricformis infection in humans cannot be disregarded because molecular identification of other strongylid nematodes was not attempted in this study . The poorer sensitivity of the KK may be directly related to the significantly smaller amount of filtered feces examined ( 41 . 7 mg ) compared to that of the SNF ( ∼250 mg ) . The addition of a washing step in the SNF procedure coupled with flotation provides a relatively ‘clearer’ and debris-free view of the hookworm eggs , thus making microscopic screening and quantification more accurate and time efficient . For a skilled parasitologist , a single SNF would take a maximum of 30 minutes to perform , including quantification of hookworm eggs . Time is thus a significant advantage for the SNF , both in terms of sampling logistics ( single versus two stool samples ) and preparation and examination of a single instead of duplicate slides . The sensitivity of the KK is further compromised by day-to-day and intra-specimen variation of helminth egg output [13] , [25] , problems related to delay from time of defecation to collection in the field and processing in the laboratories . Unlike the KK , the SNF has the advantage of indefinite formalin-based fixation at room temperature prior to examination . Rapid over-clearing of hookworm eggs by the KK may also lead to false negatives and/or an under-estimation of hookworm egg intensities [25] , [26] . The SNF proved superior to the quadruple KK for the detection of low egg burdens and therefore a better method to monitor the efficacy of anthelmintic treatment programs when worm burdens are expected to be lighter . The calculation of the “true prevalence” was done for the KK and for the gold standard . It takes into consideration the results of each examination day and estimates the prevalence if unlimited number of samples from the same individual would be examined [21] . This calculation is normally performed only for a specific diagnostic method . Yet , we also performed an estimation calculation for the gold standard , assuming the results of KK day 1 , KK day 2 , SNF and PCR as four results of the same diagnostic method , performed on four consecutive days . SNF offers a number of advantages for the detection of hookworm eggs over KK methods . In addition to the superior sensitivity , the SNF did not detect a significant difference in hookworm eggs counts to the KK , a limitation of the FLOTAC technique in which egg counts are consistently reported low by comparison [27] , [28] . The SNF technique is simple , quantitative and can be performed using a simple bench-top centrifuge using 10–15 ml disposable centrifuge tubes , surgical gauze , microscope slides and cover slips . Sodium nitrate can be purchased readily from chemical suppliers and if unavailable , the solution can be replaced with saturated salt of equal specific gravity ( specific gravity 1 . 20 ) . In contrast with SNF , the limitations of the FLOTAC apparatus include its atypical size and the requirements of a large capacity stand-up bucket centrifuge . This requires the procedure be conducted in well-equipped laboratories only [25] , usually not present in areas endemic for hookworm infections , including Cambodia . The reusable Fecalyzer device ( EVSCO 014008-50 , USA ) is widely used and available through veterinary suppliers for less than US 1 . 00 each and may prove an alternative option for conducting SNF in the field or local laboratory . This stand-up fecal flotation device comes with an inbuilt filter and stirrer that in a similar fashion to the mini-FLOTAC , obviates the requirement of a bench-top centrifuge . In conclusion , our comparison of different techniques suggests that PCR is a highly sensitive technique for the detection of hookworm infection in human parasitological surveys . It offers resource-poor communities a logistically feasible , freely available and cost-effective option to monitor the success of hookworm control programs . The SNF holds promise for the detection of human hookworm and potentially other STH infections and may become an essential tool for patient management , monitoring of helminth control programs and anthelmintic drug efficacy studies in areas with no access to the commercially produced parasitological flotation devices .
|
Hookworm infection is widespread in resource-poor countries worldwide . Detection of hookworm eggs in human feces can be done by the Kato Katz technique ( KK ) , sodium nitrate flotation technique ( SNF ) or PCR . This study was conducted to evaluate and compare the diagnostic parameters of the KK and simple SNF in terms of detection and quantification of hookworm eggs , with PCR as an additional reference test in stool , collected as part of a baseline cross-sectional study in Cambodia . PCR demonstrated the highest sensitivity for hookworm detection . By microscopy , SNF of a single stool sample proved superior for the detection of hookworm eggs in feces than quadruple Kato Katz smears . Hookworm egg counts were higher by SNF than those obtained using Kato Katz . Thus , the SNF proved superior to the quadruple Kato-Katz smears for the detection of low egg burdens and for the quantification of egg intensities . We propose the simple SNF is a superior alternative to the Kato-Katz for detection and quantification of hookworm infection in resource poor counties . The test is cost-effective and easily accessible .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"veterinary",
"parasitology",
"parasitology",
"biology",
"and",
"life",
"sciences",
"intestinal",
"parasites",
"veterinary",
"science"
] |
2014
|
Simple Fecal Flotation Is a Superior Alternative to Guadruple Kato Katz Smear Examination for the Detection of Hookworm Eggs in Human Stool
|
Clostridium difficile is the etiological agent of antibiotic-associated diarrhoea ( AAD ) and pseudomembranous colitis in humans . The role of the surface layer proteins ( SLPs ) in this disease has not yet been fully explored . The aim of this study was to investigate a role for SLPs in the recognition of C . difficile and the subsequent activation of the immune system . Bone marrow derived dendritic cells ( DCs ) exposed to SLPs were assessed for production of inflammatory cytokines , expression of cell surface markers and their ability to generate T helper ( Th ) cell responses . DCs isolated from C3H/HeN and C3H/HeJ mice were used in order to examine whether SLPs are recognised by TLR4 . The role of TLR4 in infection was examined in TLR4-deficient mice . SLPs induced maturation of DCs characterised by production of IL-12 , TNFα and IL-10 and expression of MHC class II , CD40 , CD80 and CD86 . Furthermore , SLP-activated DCs generated Th cells producing IFNγ and IL-17 . SLPs were unable to activate DCs isolated from TLR4-mutant C3H/HeJ mice and failed to induce a subsequent Th cell response . TLR4−/− and Myd88−/− , but not TRIF−/− mice were more susceptible than wild-type mice to C . difficile infection . Furthermore , SLPs activated NFκB , but not IRF3 , downstream of TLR4 . Our results indicate that SLPs isolated from C . difficile can activate innate and adaptive immunity and that these effects are mediated by TLR4 , with TLR4 having a functional role in experimental C . difficile infection . This suggests an important role for SLPs in the recognition of C . difficile by the immune system .
Clostridium difficile is a Gram-positive spore-forming intestinal pathogen . It is the leading cause of nosocomial antibiotic-associated diarrhoea among hospital patients and in severe cases can cause pseudomembranous colitis and even death [1] , [2] . The pathogenesis of C . difficile has been attributed to the two major toxins that the bacterium produces [3] , [4]; however , there is currently limited information regarding the recognition of this pathogen by the immune system and the immune response elicited following exposure to this organism . This may be due to the fact that this organism does not produce lipopolysaccharide and therefore has been less well studied than other gastrointestinal pathogens . C . difficile , along with a number of other bacteria , expresses a paracrystalline surface protein array , termed an S-layer , composed of surface layer proteins ( SLPs ) [5] . Two surface layer proteins termed high molecular weight ( HMW ) and low molecular weight ( LMW ) SLPs , form a crystalline regular array that covers the surface of the bacterium . SLPs are known to have a role in binding of C . difficile in the gastrointestinal tract however they may also have other roles [6] . There is now clear evidence that these proteins are important components of C . difficile [7] , and S-layers have previously been described as virulence factors for other bacteria such as Campylobacter fetus and Aeromonas salmonicida [8] , [9] . Their location on the outer surface of the bacteria suggests that they may be involved in immune recognition of the pathogen . Pathogen recognition involves a group of pattern recognition receptors expressed on immune cells called toll-like receptors ( TLRs ) which allow cells of the innate immune system , such as dendritic cells ( DCs ) , to detect conserved patterns of molecules on pathogens [10] . Several studies have highlighted the importance of TLR4 in a number of bacterial infections . For example , the recognition of Mycobacterium tuberculosis , a Gram-positive bacterium , by TLR4 is critical for elimination of the pathogen and containment of the infection to the lungs [11] . Activation of TLR4 initiates downstream signalling which in turn activates nuclear factor kappa beta ( NF-κB ) and interferon regulatory factor 3 ( IRF3 ) via myeloid differentiation factor 88 ( MyD88 ) -dependant and -independent pathways , respectively [12] , [13] . Activation of the MyD88 dependant pathway is mainly an event initiated at the plasma membrane while induction of IRF via the MyD88-independent pathway is dependant on the endocyotosis of TLR4 and requires the presence of CD14 and subsequently TIR-domain-containing adapter-inducing interferon-β ( TRIF ) [14] , [15] . When triggered , TLRs induce strong immune and inflammatory responses , characterised by production of inflammatory cytokines and subsequent activation of T helper ( Th ) cells [16] . The maturation of DCs following activation is characterized by the production of cytokines and changes in the expression of cell surface markers . It is now well established that production of IL-12 promotes Th1 differentiation , IL-4 induces Th2 cells , while IL-23 , IL-6 and IL-1β production by DCs is important in generating Th17 cells [17] , [18] . The importance of Th1 and Th17 cells are well recognised in bacterial clearance [19] . In the present study we tested the hypothesis that SLPs isolated from C . difficile are important for recognition of the pathogen and examined whether recognition of SLPs was mediated by TLR4 . We report that SLPs induce DC maturation and have the ability to subsequently generate Th1 and Th17 responses via TLR4 . Furthermore , we provide evidence that SLPs activate NFκB , but not IRF3 , downstream of TLR4 . Finally , we show that TLR4 has a functional role in experimental C . difficile infection . This is the first study to report a mechanism of recognition of C . difficile by the innate immune system , and suggests that they are important for activating the immune system and subsequent clearance of the pathogen .
BALB/c mice , C3H/HeN and C3H/HeJ mice were purchased from Harlan ( U . K . ) and were used at 10–14 wk of age . TLR2-deficient ( −/− ) [20] , TLR4−/− [21] , MyD88−/− [22] and TRIF−/− [23] , all on a C57BL/6J background , were used in C . difficile infection studies . Animals were housed in a licensed bioresource facility ( Dublin City University or Trinity College Dublin ) and had ad libitum access to animal chow and water . All animal procedures were carried out in accordance with Department of Health and Children Ireland regulations and performed under animal license number B100/3250 . All animal protocols received ethical approval from the Trinity College Dublin Bioresources Ethics Committee . C . difficile infected animals were weighed daily and any mice that became moribund , <15% loss in body weight , were humanely killed . C . difficile ( PCR Ribotype 001; toxin A and B positive; clindamycin resistant; HPA UK reference R13537 , Anaerobe Reference Unit , Public Health Laboratory , University Hospital of Wales ) isolated from a patient with C . difficile-associated disease was used for preparation of SLPs as previously described [6] . Briefly , SLPs were purified from cultures grown anaerobically at 37°C in BHI/0 . 05% thioglycolate broth . Cultures were harvested and crude SLP extracts dialysed and applied to an anion exchange column attached to an AKTA FPLC system ( MonoQ HR 10/10 column , GE Healthcare ) . The pure SLPs were eluted with a linear gradient of 0–0 . 3 mol/L NaCl at a flow rate of 4 mL/min . Peak fractions corresponding to pure SLPs were analysed on 12% SDS–PAGE gels stained with Coomassie blue and assessed for LPS contamination using a Limulus amoebocyte lysate ( LAL ) assay . The individual SLPs ( high and low molecular weight ) were separated by chromatography under the same conditions , but with 8 M urea included in all buffers . The urea was then dialysed out . Additional fractions containing irrelevant proteins were also kept for comparison . Bone marrow-derived immature DCs ( BMDCs ) were prepared by culturing bone marrow cells obtained from the femurs and tibia of mice in RPMI 1640 medium with 10% fetal calf serum ( cRPMI ) supplemented with 10% supernatant from a GM-CSF-expressing cell line ( J558-GM-CSF ) . The cells were cultured at 37°C for 3 days , and the supernatant was carefully removed and replaced with fresh medium with 10% GM-CSF cell supernatant . On day 7 of culture , cells were collected , counted , and plated at 1×106/mL for experiments . BMDCs from C3H/HeN and C3H/HeJ mice were cultured and activated with ovalbumin ( OVA ) peptide ( 323–339; 5 µg/mL ) in the presence of either LPS ( 100 ng/mL ) or SLPs ( 20 µg/mL ) for 24 h . After 24 h , DCs were collected and washed twice in sterile PBS/2% FCS and irradiated with 40 Gy ( 4000 rads ) using a gamma irradiator with a Caesium-137 source . A final concentration of 2×105cells/mL were added to CD4+ T cells , isolated from the spleens of OVA transgenic D011 . 10 mice ( 2×106 cells/mL ) and incubated . On day 5 of co-culture , the supernatant was removed and frozen for cytokine analysis . Fresh medium was added , and the cells were incubated until day 7 and supernatants removed . Newly harvested OVA/SLP or OVA/LPS-activated DCs were added ( 2×105 cells/mL ) with recombinant murine IL-2 ( 10 U/mL; Becton Dickinson ) for the second round of T cell stimulation . At the end-point of the experiment ( day 10 ) , supernatants were removed and frozen for cytokine analysis . DCs were incubated with either SLP ( 20 µg/mL ) or LPS ( 100 ng/mL ) for 24 h . Culture supernatants from this experiment as well as the DC:T cell co-culture experiments were removed and stored at −80°C until analysis . TNF-α , IL-1β , IL-10 and IL-12p70 , IL-12p40 , IL-23 , IFNγ , IL-17 and IL-4 concentrations in cell culture supernatants were analysed by DuoSet ELISA kits ( R&D Systems ) , according to the manufacturer's instructions . DCs were cultured as previously described and incubated with either SLPs ( 20 µg/mL ) or LPS ( 100 ng/mL ) for 24 h . In some experiments a p38 inhibitor ( S8308; 20 µg/mL ) was used . Cells were then washed and used for immunofluorescence analysis . The expression of CD40 , CD80 , CD86 and MHCII was assessed using an anti-mouse CD11c ( Caltag ) , and CD40 , CD80 , CD86 and MHCII ( rat IgG2a , BD Biosciences ) and appropriately labelled isotype-matched antibodies . After incubation for 30 min at 4°C , cells were washed and immunofluorescence analysis was performed on a FACsCalibur ( BD Biosciences ) using Cell Quest software . Human HEK293-TLR4 , HEK293-MD2-CD14-TLR4 and HEK293T were transiently transfected using GeneJuice transfection reagent ( Novagen , Madison , WI ) according to the manufacturer's instructions with a total amount of 220 ng DNA per well comprising of 75 ng ISRE- ( Clontech , Palo Alto , CA ) or κB-luciferase plasmid , 30 ng Renilla-luciferase and empty pcDNA3 . 1 vector as filler DNA . 24 h after transfection , cells were stimulated with LPS ( 100 ng/mL ) or SLPs ( 0–100 µg/mL ) for 6 h before lysis . Firefly luciferase activity was assayed by the addition of 40 µl of luciferase assay mix to 20 µl of the lysed sample . Renilla-luciferase was read by the addition of 40 µl of a 1∶1000 dilution of Coelentrazine ( Argus Fine Chemicals ) in PBS . Luminescence was read using the Reporter microplate luminometer ( Turner Designs ) . The Renilla- luciferase plasmid was used to normalise for transfection efficiency in all experiments . C . difficile ( R13537 ) , described above , was grown on blood agar plates under anaerobic conditions at 37°C for 5 days to generate spores . Spore inoculum was prepared as described by Sambol et al . [24] , the spore concentration was determined by dilution plating onto blood agar plates and stock solutions of 5×106 spores ml−1 were stored at −80°C . TLR2−/− , TLR4−/− , MyD88−/− , TRIF−/− and wild-type mice , all on a C57BL/6J strain background , were infected with C . difficile using an antibiotic-induced model of mouse infection [25] . Mice were treated for 3 days with an antibiotic mixture of kanamycin ( 400 µg/ml ) , gentamicin ( 35 µg/ml ) , colistin ( 850 U/ml ) , metronidazole ( 215 µg/ml ) and vancomycin ( 45 µg/ml ) in the drinking water . Mice were subsequently given autoclaved water . On day 5 , mice were injected i . p . with clindamycin ( 10 mg/kg ) . Mice were infected with 103 C . difficile spores on day 6 by oral gavage . Initial studies determined infection with 103 spores of C . difficile R13537 caused mild transient weight loss and diarrhoea in wild-type C57BL/6J strain mice . Mice that were not treated with antibiotics were also challenged with C . difficile . Animals were weighed daily and monitored for overt disease , including diarrhoea . Moribund animals with >15% loss in body weight were humanely killed . The cecum was harvested from uninfected ( day 0 ) and infected mice at days 3 and 7 and the contents were removed for CFU counts . The cecum was fixed in 10% formaldehyde saline and paraffin sections were hematoxylin and eosin-stained . Evaluation of histopathology was performed as previously described [26] . Briefly slides were scored by two independent investigators , blinded to the study groups , on a 0–3 scale as follows; absence of inflammation and damage was scored 0 , while mild , moderate and severe inflammatory changes were scored 1 , 2 and 3 respectively . The severity of mucosal damage and inflammation was based on the levels of mucosal epithelial damage and erosion , cell inflammation of the lamina propria , crypt abscess formation as well as the incidence and severity of oedema . The contents of cecum were recovered from infected and uninfected mice , weighed and stored frozen . Each sample of cecum material was thawed and homogenised in 1 ml PBS ( pH 7 . 4 ) by vortex mixing in a 1 . 5 ml microcentrifuge tube . The suspension was serially diluted ( 10−1 to 10−4 ) and 50 µl of each dilution was spread in duplicate onto quadrants of Brazier's CCEY plates ( Lab M ) . Plates were incubated under anaerobic conditions at 37°C for 30 h . Colonies were counted and CFU/g determined for each sample . The anti-SLP IgG was measured as previously described [5] . Briefly plates were coated overnight with 2 µg/mL and blocked for 1 h with blocking buffer ( PBS containing 2% nonfat dry milk ) . Serum samples were diluted 1∶50 and further serial 10-fold dilutions of samples were made in antibody buffer ( blocking buffer containing 0 . 05% Tween 20 ) . Bound antibody was detected with HRP-conjugated anti-mouse IgG followed by TMB . Reactions were stopped with 1 M H2SO4 , and ODs were read at 450 nm . One-way analysis of variance ( ANOVA ) was used to determine significant differences between conditions . When this indicated significance ( p<0 . 05 ) , post-hoc Student-Newmann-Keul test analysis was used to determine which conditions were significantly different from each other .
Samples from all stages of the purification process were run on SDS-PAGE gels to demonstrate the purity of the SLPs . Figure 1 clearly shows the presence of multiple bands in the crude extract and only two bands with molecular masses of 42–48 kDa and 32–38 kDa following anion exchange chromatography . Furthermore , we also purified individual high molecular weight ( HMW ) and low molecular weight ( LMW ) proteins which were also seen as single bands at the correct molecular weight on SDS gels . In order to confirm that any activity by the SLPs was attributed to the protein and not a contaminant , we also examined irrelevant proteins which were purified in the same manner but were eluted in different fractions to those of the SLPs . In order to assess whether SLPs could activate DCs we examined their ability to induce TNFα production by these cells . The graph in Figure 1 shows that SLPs induce TNFα production by DCs . This is not seen with the individual LMW and HMW proteins or the irrelevant protein . LPS was used as a positive control . The ability of SLPs to induce cytokine secretion in DCs was found to be dose dependent ( Figure S2 ) . As SLPs and LPS induced DCs to produce a similar profile of cytokines , we examined whether SLPs also activated DCs via TLR4 . Given that the differentiation of naïve CD4+ T cells into Th subsets is determined in part by the cytokines produced by DCs upon activation [17] , we specifically examined the effects of SLPs on these cytokines . Incubation of BMDCs isolated from C3H/HeN with SLPs induced significant production of IL-12p70 ( Figure 2; p<0 . 001 ) , IL-23 ( Figure 2; p<0 . 001 ) and IL-10 , important for Th1 , Th17 and Tr1 responses respectively , and also significant levels of TNFα ( Figure 2; p<0 . 001 ) . Interestingly , there was no significant induction of IL-1β by SLPs . The effects of both LPS and SLPs on cytokine production were completely absent in BMDCs from C3H/HeJ mice , indicating that the activation of DCs by SLPs occurs via TLR4 . DC maturation is also characterized by increased expression of MHC class II , CD40 , CD80 and CD86 [27] , [28] . As SLPs activated DCs via TLR4 , we examined the effects of SLPs on these markers in the presence and absence of TLR4 . Figure 3 demonstrates that SLPs induce DC maturation in a similar manner to LPS , in cells isolated from C3H/HeN mice with increased expression of MHC II , CD40 , CD80 and CD86 . This was completely abrogated in DCs isolated from C3H/HeJ TLR4 mutant mice . Activation of TLR4 results in the subsequent phosphorylation and activation of p38 . In order to further confirm that SLP activated DCs via TLR4 we examined the ability of SLP to induce DC maturation in the presence of a p38 inhibitor . Figure 4 demonstrates that SLP is unable to induce upregulation of MHC II , CD40 , CD80 or CD86 in the presence of a p38 inhibitor . Furthermore , the effects of SLPs on DC maturation markers was also dose dependent ( Figure S3 ) . An important event for the initiation of adaptive immunity is the activation of Th cells by DCs [29] . The DC cytokine production and co-stimulatory marker expression are key to this process . We first wanted to determine whether SLPs could induce a Th1 or Th17 response , given the importance of these responses in bacterial clearance [30] , [31] . Furthermore , since our earlier data demonstrated that activation of DCs by SLPs involves TLR4 , we wanted to determine whether this was critical for generation of subsequent adaptive immune responses . DCs isolated from both C3H/HeN and C3H/HeJ mice were exposed to OVA peptide in the presence of either SLPs or LPS . These DCs were then co-cultured with CD4+ T cells purified from OVA transgenic mice . T cells were exposed to two rounds of activation with DCs and the Th response was characterised . DCs activated with LPS/OVA or SLP/OVA induced a mixed T helper cell response , with significant production of IL-17 , IL-4 and IFNγ on both Day 4 and Day 10 ( Figure 5; p<0 . 001 ) . The dominant response was the production of IL-17 . No response was generated by either LPS/OVA- or SLP/OVA-activated DCs isolated from C3H/HeJ mice . In order to confirm that SLPs activate TLR4 , we performed experiments in which human HEK293 cells were transiently transfected with TLR4 along with the TLR4 accessory proteins , MD2 and CD14 . Non-transfected HEK293 cells were used as a control . Two separate experiments were carried out using luciferase as a reporter gene for activation of the transcription factors NFκB or ISRE ( indicative of interferon regulatory factor 3 ( IRF3 ) activation ) . As expected , neither LPS nor SLP were able to activate ISRE or NFκB in HEK293 cells in the absence of the TLR4 receptor ( Figure 6A&B ) . Exposure of HEK293-TLR4-MD2-CD14 cells to LPS resulted in significant activation of ISRE and NFκB ( Figure 6C&D; p<0 . 001 ) . When increasing concentrations of SLPs were incubated with the HEK293-TLR4-MD2-CD14 cells , there was a dose-dependent activation of NFκB ( Figure 6D; p<0 . 05 , p<0 . 01 , p<0 . 001 ) , but no activation of ISRE ( Figure 6C ) . The lack of effect of SLPs on IRF3 was further confirmed by our observation that SLP did not induce IFNβ production by DCs ( Figure S4 ) . Given that SLP did not activate IRF3 , and that CD14 is important for the endocytosis of the TLR4 complex for subsequent activation of IRF3 [32] , we examined whether SLP required CD14 for activation of TLR4 . We show that LPS activated NF-κB in HEK293-TLR4-MD2-CD14 ( Figure 7C ) cells but not HEK293-TLR4 cells ( Figure 7E ) . In contrast , SLP significantly induced NF-κB in both HEK293-TLR4 ( Figure 7E ) and HEK293-TLR4-MD2-CD14 cells ( Figure 7C; p<0 . 001 ) , suggesting that SLP does not require CD14 for activation of NF-κB downstream of TLR4 . To formally validate the biological relevance of the in vitro cell culture data indicating that C . difficile SLPs interact with TLR4 , wild-type , TLR2−/− , TLR4−/− , MyD88−/− and TRIF−/− mice were infected with the C . difficile strain that SLPs were isolated from . A recently described model of C . difficile infection of antibiotic treated mice was used [25] . Following infection wild-type , TLR2−/− and TRIF−/− mice developed comparable diarrhoea and transient weight loss , which peaked at day 3 post-infection ( Figure 8A ) . In contrast , both TLR4−/− and MyD88−/− mice developed marked weight loss by day 1 , with significantly greater weight loss ( p<0 . 05−0 . 001 ) relative to other groups on days 1–7 ( Figure 8A ) , which was associated with severe diarrhoea . Due to severe morbidity and associated >15% weight loss , 1/7 and 2/7 of TLR4−/− and MyD88−/− groups were humanely killed on day 3 , respectively , with no deaths in wild-type , TLR2−/− or TRIF−/− mice . Consistent with the weigh loss data , both TLR4−/− and MyD88−/− mice had significantly ( p<0 . 05 ) higher numbers of C . difficile spores in the cecum on day 3 compared to wild-type , TLR2−/− and TRIF−/− ( Figure 8B ) . The cecum from TLR4−/− and MyD88−/− had marked inflammatory cell infiltrates with oedema and epithelial disruption on days 3 and 7 post infection , that was significantly ( p<0 . 05−0 . 01 ) greater than the mild inflammation in the cecum of infected wild-type , TLR2−/− or TRIF−/− mice ( Figure 8C , 8D ) . It was notable that uninfected TLR4−/− and MyD88−/− mice had evidence of mild cecal inflammation ( Figure 8C , 8D ) , which is relevant to the known role of TLR4 and MyD88 in basal intestinal homeostasis [33] , [34] . As conventional housed mice are not susceptible to C . difficile infection , we evaluated if the intestinal alterations in TLR4−/− and MyD88−/− mice rendered these mice innately more susceptible to infection . However , TLR4−/− and MyD88−/− mice , and also wild-type , TLR2−/− and TRIF−/− mice , were refractory to infection when exposed to C . difficile without any prior antibiotic treatment ( data not shown ) . These data confirm an in vivo functional role for TLR4 , and not TLR2 , in a MyD88 but not TRIF dependent pathway , in C . difficile infection of antibiotic-treated mice . In order to confirm that SLPs were recognised in the context of the whole bacterium and that TLR4 is necessary for their recognition , wildtype and TLR4−/− mice were infected as before with C . difficile and serum was collected 3 days post infection . Wildtype mice showed an increase in anti-SLP IgG 3 days after infection with C . difficile ( data not shown ) . Figure 9 demonstrates that TLR4−/− mice have no detectable anti-SLP IgG compared to wildtype controls on day 3 post infection .
The significant findings of this study are that SLPs isolated from C . difficile induce maturation of DCs and subsequent generation of T helper cell responses required for bacterial clearance via TLR4 . We also demonstrate the significance of TLR4 in murine infection with C . difficile , with TLR4−/− and MyD88−/− mice displaying a more severe infection than wild type . Interestingly , we found SLPs to activate NFκB but not IRF3 , downstream of TLR4 which correlated with the observation that TRIF−/− mice did not have increased susceptibility or severity of infection . This is the first study to demonstrate a role for TLR4 in infection associated with C . difficile and suggests an important role for SLPs in the generation of the immune response necessary for clearance of this bacterium . SLPs have previously been described as virulence factors for other bacterial infections such as Campylobacter fetus and Aeromonas salmonicida [8] , [9] . There is now significant evidence that SLPs isolated from C . difficile are important components of the pathogen . Specifically , passive immunisation of hamsters with antibodies to these proteins affects the course of C . difficile infection , resulting in prolonged survival of hamsters [6] . While this evidence indicates the importance of these proteins , the way in which they are recognised by and activate the immune system is not clear . Activation of DCs is characterized by the production of cytokines and increased expression of MHCII , as well as co-stimulatory molecules [27] , [29] . We demonstrate that SLPs induce DC maturation , characterised by production of IL-12p70 , TNFα , IL-23 , IL-6 , and increased expression of MHCII , CD40 , CD80 and CD86 . This agrees with some previously reported effects of SLPs [35] . Interestingly , while there are some similarities between the response elicited with SLP and LPS , SLP did not induce IL-1β production , demonstrating a distinct effect of SLPs and further confirming that potential contamination with LPS is not responsible for the effects observed with SLPs . Other evidence was provided by our observation that the effects of SLPs on DCs were not reversed in the presence of polymyxin B , known to bind LPS ( Figure S1 ) . We next conducted experiments in C3H/HeN and C3H/HeJ mice , and showed that the effects of SLPs on DC maturation were mediated through TLR4 . Furthermore , our experiments demonstrated that intact SLPs , containing both the HMW and LMW proteins , were required for DC activation . The significance of this data is two-fold; firstly they demonstrate that the HMW and LMW proteins may need to be associated in their complex for recognition by TLR4; while an additional experiment examining the HMW and LMW proteins after recomplexing would be advantageous , their tight association has been recently demonstrated by Fagan et al . [36] . Secondly , the lack of response to the separated proteins confirms that the effects we observed with SLPs could not possibly be attributed to any contaminating ligand . This is further supported by the fact that an irrelevant protein purified in the same way was unable to elicit these effects on DCs . A number of pathogen derived molecules have now been shown to activate DCs through TLR4 . For example , LPS from Bordetella pertussis and Salmonella enteritidis have been reported to induce TLR4-dependent DC maturation [27] , [37] . Given that the interaction of DC with T cells is required for activation of adaptive immunity , and since SLPs induced potent production of cytokines important in promoting Th1 and Th17 responses [17] , [18] , the data suggest that SLPs may be important in the generation of these responses . We clearly demonstrate that DCs activated with SLPs have the capacity to drive strong Th1 and Th17 responses characterised by production of IFNγ and IL-17 . Indeed the dominant cytokine produced was IL-17 . Not surprisingly , SLPs also induced a weak Th2 response , which concurs with studies demonstrating SLPs to induce an antibody response [6] , [7] . The importance of T helper cells and their cytokines IFNγ and IL-17 are well recognised in bacterial clearance . Both Scid mice ( deficient in T and B cells ) and nude mice ( deficient in T cells ) show high susceptibility to infection with Coxiella burnetii [19] . Another study employing IFNγ−/− mice demonstrated that infection with Bordetella pertussis was exacerbated in the absence of IFNγ [38] . Furthermore , inhibition of IL-17 with a neutralising antibody results in increased infection with Pneumocystis carinii [39] . Since SLPs induce a potent Th1 and Th17 response , our data suggest that they may be important in clearance of C . difficile and that TLR4 is required for this . Several studies have highlighted a role for TLR4 in bacterial clearance; for example activation of TLR4 by Klebsiella pneumoniae has been shown to be critical for induction of IL-17 , known to be important in host defence against bacterial infection [40] . Therefore , we examined whether clearance of C . difficile was impaired in mice without functional TLR4 . We clearly demonstrate that C . difficile infection is more severe in TLR4−/− and MyD88−/− mice with increased weight loss , mortalities , and number of C . difficile spores in the cecum . This suggests that TLR4 and MyD88-mediated signalling are important in the clearance of the bacterium . Furthermore , the lack of an IgG response to SLP in TLR4−/− mice suggests that the recognition of SLP plays a key role in this process . Our findings in MyD88−/− mice concurs with a recent study which showed a more severe intestinal disease following infection with C . difficile in these mice [41] . It is noteworthy that while TLR4−/− and MyD88−/− mice were relatively more susceptible to infection following an antibiotic treatment regime , however , without antibiotic treatment and thus having an intact intestinal microbiota they were resistant to C . difficile infection infected similar to immunocompetent C57BL/6J mice . Recently , Jordan et al demonstrated that mice deficient in functional TLR4 showed increased susceptibility to infection with Rickettsia conorii which was associated with decreased Th1 and Th17 responses [42] . Importantly , rickettsiae do not possess classical endotoxic LPS . Given that C . difficile is a Gram-positive bacterium lacking LPS , our findings that SLPs , the immunodominant antigen on the surface of this bacterium , can activate the innate immune response via TLR4 are particularly significant . Recognition and subsequent binding of LPS to TLR4 results in an intracellular cascade of events involving the adaptor molecules MyD88 , MyD88-like adaptor molecule ( Mal ) , TRIF and TRIF-related adaptor molecule ( TRAM ) , culminating in downstream activation of the transcription factors NFκB and IRF3 for production of pro-inflammatory cytokines and type I interferons , respectively [43] , [44] . In order to confirm that SLPs can indeed activate TLR4 , we examined whether they induced activation of NFκB and IRF3 in human HEK cells transfected with TLR4-MD2-CD14 . We demonstrate that SLPs activate NFκB in a dose dependent manner downstream of TLR4 , however they did not activate ISRE which is indicative of IRF3 activation . The significance of this data is two-fold; firstly , it raised the possibility that SLPs activated TLR4 independently of CD14; given that activation of IRF3 downstream of TLR4 requires endocytosis of the TLR4 complex and its subsequent association with TRIF and TRAM [15]; this finding explains why our experiments in HEK/TLR4 cells clearly show that SLP , but not LPS , activated NKκB in the absence of CD14 . This is further supported by our data showing that mice deficient in TRIF did not get a more severe infection and our data showing that SLP does not induce type 1 IFN in DCs ( Figure S4 ) . Secondly , these data further confirm that our purified SLPs were free from LPS contamination as LPS clearly activates ISRE . A recent report has highlighted the ability of some TLR4 ligands to selectively activate signalling pathways downstream of TLR4 . Specifically , the vaccine adjuvant monophosphoryl lipid A induces strong TRIF-associated responses but only very weak MyD88-associated responses , showing a clear preference for activation of downstream IRF3 [45] . Interestingly , as production of IFNβ ( downstream of IRF3 activation ) is essential for induction of endotoxic shock , the inability of SLPs to activate the ISRE/IRF3 pathway and subsequent IFNβ may explain why numerous groups that administer SLPs to mice do not report any toxicity [46] , [47] . The data presented in this study demonstrate that SLPs activate innate and adaptive immunity via a TLR4-dependent mechanism . Given that the responses activated are critical to bacterial clearance , we propose that recognition of SLPs by TLR4 is important for recognition of the pathogen and the subsequent generation of the appropriate immune response required for bacterial clearance . This is further evidenced by our finding that a more severe disease is present in TLR4−/− mice along with the absence of an antibody response to SLP , suggesting that recognition of SLPs by TLR4 may play a role in determining the outcome of infection . Furthermore , it is now well recognised that TLRs play a key role in host defence against intestinal pathogens and maintenance of tissue homeostasis in the gastrointestinal tract [34] , [48] . It is of great interest that the amino acid sequence of SLP is highly variable between serogroups of C . difficile [49] . It is possible that these sequence differences could affect the recognition of SLPs by the innate immune system and therefore may explain why some strains of C . difficile cause severe infection and a high frequency of recurrence and yet others are associated with minimal clinical symptoms and pathology . While there is currently no known correlation between SLP sequence and virulence other reports suggest that variability of these surface layer proteins may be an important mechanism to escape host defence [50] , [36] and warrants further investigation .
|
Clostridium difficile is the leading cause of antibiotic-associated diarrhoea among hospital patients and in severe cases can cause pseudomembranous colitis and even death . There is currently limited information regarding how this pathogen is recognised by the immune system and the key mechanisms necessary for clearance of the pathogen . C . difficile expresses a paracrystalline surface protein array , termed an S-layer , composed of surface layer proteins ( SLPs ) . Their location on the outer surface of the bacteria suggests that they may be involved in immune recognition of the pathogen . In this study we demonstrate that these SLPs are recognised by toll-like receptor 4 ( TLR4 ) . Activation of TLR4 by SLPs resulted in maturation of dendritic cells and subsequent activation of T helper cell responses which are known to be important in clearance of pathogens . Furthermore , using a murine model of C . difficile infection we show that mice display increased severity of infection in the absence of TLR4 . This is the first study to demonstrate a role for TLR4 in infection associated with C . difficile and suggests an important role for SLPs in the generation of the immune response necessary for clearance of this bacterium .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"immune",
"cells",
"immune",
"activation",
"antigen-presenting",
"cells",
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"adaptive",
"immunity",
"bacterial",
"pathogens",
"t",
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"biology",
"pathogenesis",
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"immunity",
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] |
2011
|
A Role for TLR4 in Clostridium difficile Infection and the Recognition of Surface Layer Proteins
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African animal trypanosomosis is a major obstacle to the development of more efficient and sustainable livestock production systems in West Africa . Riverine tsetse species such as Glossina palpalis gambiensis Vanderplank and Glossina tachinoides Westwood are the major vectors . A wide variety of control tactics is available to manage these vectors , but their removal will in most cases only be sustainable if the control effort is targeting an entire tsetse population within a circumscribed area . In the present study , genetic variation at microsatellite DNA loci was used to examine the population structure of G . p . gambiensis and G . tachinoides inhabiting four adjacent river basins in Burkina Faso , i . e . the Mouhoun , the Comoé , the Niger and the Sissili River Basins . Isolation by distance was significant for both species across river basins , and dispersal of G . tachinoides was ∼3 times higher than that of G . p . gambiensis . Thus , the data presented indicate that no strong barriers to gene flow exists between riverine tsetse populations in adjacent river basins , especially so for G . tachinoides . Therefore , potential re-invasion of flies from adjacent river basins will have to be prevented by establishing buffer zones between the Mouhoun and the other river basin ( s ) , in the framework of the PATTEC ( Pan African Tsetse and Trypanosomosis Eradication Campaign ) eradication project that is presently targeting the northern part of the Mouhoun River Basin . We argue that these genetic analyses should always be part of the baseline data collection before any tsetse control project is initiated .
Tsetse flies ( Diptera: Glossinidae ) are the sole cyclical vectors of human and animal trypanosomoses , two major plagues that are seriously impeding African development . African animal trypanosomosis ( AAT ) is a major obstacle to the development of more efficient and sustainable livestock production systems in West Africa . Since 2008 , the Government of Burkina Faso has embarked on an ambitious tsetse eradication campaign that targets the northern Mouhoun River Basin for its first phase ( http://www . pattec . bf/ ) . The Mouhoun River Basin eradication campaign is implemented under the auspices of the Pan African Tsetse and Trypanosomosis Eradication Campaign ( PATTEC ) , an African Union initiative that was launched in 2001 following an historic decision by the African Heads of State and Government in Lome , Togo , July 2000 ( http://www . africa-union . org/Structure_of_the_Commission/depPattec . htm ) . In the Mouhoun River Basin , Glossina palpalis gambiensis Vanderplank and Glossina tachinoides Westwood are the two remaining tsetse species , after the regression of Glossina morsitans submorsitans Newstead [1]–[3] . The two tsetse species remain very effective vectors of AAT [4] , but local transmission of sleeping sickness ( Human African Trypanosomosis ( HAT ) ) seems to have disappeared from the Mouhoun River Basin [3] . These species inhabit the riparian forests that form habitat galleries along the rivers and the flies' relative abundance is determined by forest ecotype and its level of fragmentation and destruction [2] , [5] . Their particular resilience to habitat fragmentation has been attributed to ( 1 ) their ability to easily adapt to peridomestic situations , ( 2 ) their opportunistic host feeding behaviour [6] , and ( 3 ) their linear habitat that allows them to easily disperse between favourable patches , i . e . riverine forests acting as “genetic corridors” [7] , [8] . Control of tsetse can be achieved through a variety of techniques [9] , including traps , insecticide-impregnated targets [10] , live-baits [11]–[13] , sequential aerosol technique [14] , and the sterile insect technique ( SIT ) [15] . In the past , most control efforts were not sustainable due to either flies surviving the initial interventions , or flies immigrating from untreated regions , or both [16] . The strategic choice between eradication and suppression of a tsetse population is of prime importance as it will have significant economic implications ( see [17] for a review ) . In that respect , knowledge of the genetic structure of the target population can facilitate this critical decision making [18]–[20] . For isolated tsetse populations , eradication is undoubtedly the most cost-effective strategy , as was demonstrated with the sustainable removal of Glossina austeni Newstead from the Island of Unguja , Zanzibar in 1994–1997 [15] . On mainland Africa , the geographical distribution limits of the target tsetse populations are less clearly defined , although complete isolation was recently demonstrated for a G . p . gambiensis population in the Niayes area of Senegal that prompted the Government of Senegal to select an eradication strategy [20] , [21] . In Burkina Faso , G . p . gambiensis populations inhabiting fragmented habitats are genetically structured along the rivers [22] , also in the area that is the target of the national eradication campaign mentioned above . However , a certain level of gene exchange is still sustained among the various populations that inhabit the habitat fragments along the Mouhoun River . Furthermore , G . tachinoides occurs as a panmictic population along its riverine habitat in the same area , due to its more xerophylous nature allowing it to disperse more easily between suitable habitat patches [23]–[25] . As riverine tsetse populations are mainly confined to the riverbeds of the various river systems which are organised in river basins it was proposed to use the “river basin” as a unit of operation in area-wide integrated pest management ( AW-IPM ) programmes [28] against tsetse in West Africa . This assumed that each primary river basin ( and possibly also secondary and tertiary ) contained riverine tsetse populations that were geographically isolated from those belonging to adjacent river basins . If this hypothesis proves to be correct , it would be very beneficial for the present eradication campaign since it would allow limiting the control effort to the Mouhoun River Basin . However , earlier studies have indicated that riverine tsetse flies were able to disperse up to 2km into the savannah areas bordering the riparian forests [7] and a recent genetic study in Burkina Faso suggested that G . p . gambiensis was able to cross the watershed divide between the Mouhoun and the Comoe river basins that contained natural woody savannah [26] . In view of the importance of the Mouhoun eradication project , and the limited number of samples ( three ) used in previous study [26] , it was deemed necessary to expand these studies and to obtain more data on the dispersal potential of the two tsetse species present , as evidenced through genetic structures of the various populations . A more complete picture of the exchange of genes between the various tsetse populations in the area would enable the programme managers to make informed decisions on the establishment of buffer zones between the Mouhoun River Basin and its neighbouring basins , or , alternatively , to expand the eradication campaign to these basins . The present study includes G . tachinoides and two other river basins not considered earlier and also includes areas where the interfluve is very much fragmented , which might impact dispersal of riverine species . Genetic variation at microsatellite DNA loci was thus used to examine the structure of G . p . gambiensis and G . tachinoides populations of the Mouhoun River Basin in relation to those of all its adjacent river basins , i . e . the Niger ( Bani ) , Comoé and Sissili River Basins ( Figure 1 ) . The objective was to assess tsetse population structuring in and between the different river basins , its relation to tsetse fly dispersal amongst adjacent river basins , and its consequences for potential AW-IPM eradication campaigns [27] , [28] .
The study area is located in South-Western Burkina Faso ( latitude 10 . 2 to 12 . 2 N; longitude −5 . 5 to −2 . 0°W ) and encompassed the Mouhoun River Basin ( 8 sampling sites ) and three neighbouring river basins , i . e . the Comoe ( 3 sampling sites ) , the Sissili and the Niger ( 1 sampling site each ) River Basins ( fig . 1 ) . From November 2007 to March 2008 each site was sampled using 5–10 unbaited biconical traps [29] . In each location , the maximal river length sampled was 980 m ( in Darsalamy ) for G . p . gambiensis and 5660 m for G . tachinoides ( Fandiora ) , but was usually lower than 500 m ( Tables 1&2 ) . A total of 296 G . tachinoides and 242 G . p . gambiensis flies were genotyped ( see number of flies genotyped by trapping site in Tables 1&2 ) . G . p . gambiensis was genotyped at 8 microsatellite loci: Gpg 55 . 3 [30] , A10 , B104 , B110 , C102 ( kindly supplied by A . Robinson , Insect Pest Control Laboratory ( formerly Entomology Unit ) , Food and Agricultural Organization of the United Nations/International Atomic Energy Agency [FAO/IAEA] , Agriculture and Biotechnology Laboratories , Seibersdorf , Austria ) , pGp13 , pGp24 [31] , and GpCAG [32] . G . tachinoides was genotyped at 9 microsatellite loci: pGp13 , pGp17 , pGp20 , pGp24 , pGp28 , pGp29 [31] , B104 , C102 and GpCAG . Of these , B104 , B110 , pGp13 , pGp20 , and 55 . 3 are known to be located on the X chromosome . GpCAG and C102 have trinucleotide repeats whereas the others are dinucleotides . Three legs of each individual tsetse fly were removed , transferred to a tube to which 200 µl of 5% Chelex chelating resin was added [33] , [34] . After incubation at 56°C for one hour , DNA was denatured at 95°C for 30 min . The tubes were then centrifuged at 12 , 000 g for two min and frozen for later analysis . The PCR reactions were carried out in a thermocycler ( MJ Research , Cambridge , UK ) 20 µl final volume , using 10 µl of the diluted supernatant from the extraction step as template . After PCR amplification , allele bands were routinely resolved on a 4300 DNA Analysis System from LI-COR ( Lincoln , NE ) after migration in 96-lane reloadable ( 3x ) 6 . 5% denaturing polyacrylamide gels . This method allows multiplexing by the use of two infrared dyes ( IRDye ) , separated by 100 nm ( 700 and 800 nm ) , and read by a two channel detection system that uses two separate lasers and detectors to eliminate errors due to fluorescence overlap . To determine the different allele sizes , a large panel of about 70 size markers was used . These size markers had been previously generated for G . p . gambiensis by cloning alleles from individual tsetse flies into pGEM-T Easy Vector ( Promega Corporation , Madison , WI , USA ) , but were generated for G . tachinoides for this study . Three clones of each allele were sequenced using the T7 primer and the Big Dye Terminator Cycle Sequencing Ready Reaction Kit ( PE Applied Biosystems , Foster City , CA , USA ) . Sequences were analyzed on a PE Applied Biosystems 310 automatic DNA sequencer ( PE Applied Biosystems ) and the exact size of each cloned allele was determined . PCR products from these cloned alleles were run in the same acrylamide gel as the samples , allowing the allele size of the samples to be determined accurately [35] . The gels were read twice by two independent readers using the LIC-OR Saga genotyping software . All datasets were processed with Create V 1 . 1 [36] and converted into the appropriate format as needed . Wright's F-statistics [37] were estimated with Weir and Cockerham's unbiased estimators [38] under Fstat V 2 . 9 . 4 ( Goudet 2003 , updated from [39] ) . FIS is a measure of local inbreeding of individuals relative to inbreeding of subsamples . It is therefore also a measure of reproductive strategy and varies from -1 ( all individuals are heterozygous for the same two alleles within each subsample ) to +1 ( all individuals are homozygous with at least two alleles in subsamples ) and equals 0 when all subsamples conform to genotypic proportions expected under panmixia . It is thus also a measure of deviation from the random mating model within populations . FST measures inbreeding of subsamples relative to the total inbreeding resulting from subdivision . It is therefore also a measure of differentiation among subsamples . It varies between 0 ( no differentiation ) and 1 ( all subsamples fixed for one or the other allele ) . The significant departure from 0 of these parameter estimates was tested by randomisation procedures under Fstat . For this , alleles are randomly exchanged between individuals in each subsample and the proportion of times when a FIS estimate was equal to or higher than the observed one provided the exact P-value of the test . For differentiation between populations , individual were randomised across subsamples and the statistic used here was the log-likelihood ratio G as recommended [40] . Linkage disequilibrium ( LD ) between loci was also tested through randomising association between each locus pair . For each pair of loci the tests were combined across subsamples with the G-based procedure as recommended [41] . All these randomisations ( 10000 in each case ) were undertaken with Fstat 2 . 9 . 4 . For LD , there were as many tests as there were loci pairs ( here possibly 36 ) , we therefore tested the probability of obtaining a proportion higher than the expected one ( 5% ) with a binomial test with k tests , mean 0 . 05 and ks success ( the number of significant pair in linkage disequilibrium at level α = 0 . 05 ) with MultiTest V 1 . 2 [41] . More than three levels ( i . e . individuals , sub-populations and total ) exist within the samples of each tsetse species . Individuals were caught in different traps , in different sites ( i . e . locations ) within three different river basins ( Comoé , Mouhoun and Sissili for G . tachinoides and Comoé , Mouhoun and Niger for G . p . gambiensis ) . Hierfstat version 0 . 03–2 [42] is a package for the statistical software R . This package computes hierarchical F-statistics from any number of hierarchical levels [42] . FTrap/Site represents the homozygosity due to the subdivision into different traps in each site and was tested by randomising individuals between traps within each site . FSite/Basin represents the homozygosity due to subdivision into different sites within each river basin and was tested by randomizing traps ( with all individuals contained ) between sites within the same river basin . FBasin/Total measures the relative homozygosity due to the geographical separation between river basins and was tested by randomizing sites ( with all traps included ) between the three river basins . In all cases we undertook 1000 permutations and the log likelihood ratio as for the FST analysis was the statistic used . These tests were performed with Hierfstat . A user friendly step by step tutorial of how to use HierFstat is available [43] . Some microsatellite loci , noted with an X as last letter , are X linked . These loci were coded as missing data for FIS and null allele analyses and coded as homozygous for the allele present on the X for differentiation and LD tests . Significant FIS can be due to null alleles , stuttering or short allele dominance . We used MicroChecker V 2 . 2 . 3 [44] for stuttering and null alleles . We tested how null alleles can explain the observed FIS using estimates of null allele frequency following either Brookfield's second method [45] or to the method of van Oosterhout et al . [44] as given by MicroCheker . We used these estimates to compute expected blank ( non amplified null homozygotes ) frequency assuming panmixia . For each locus , the sum of all expected blanks across subsamples was compared to the sum of all observed ones with an exact unilateral binomial test with the alternative hypothesis: there were not enough observed blank genotypes as compared to what would be expected under the hypothesis of null alleles in a panmictic population . For X linked loci we also used null allele frequencies ( estimated from females ) directly as the expected proportion of blank ( unamplified ) males expected at these loci and this quantity was also compared with observed blanks with the same method as described above for females at other loci . Confidence intervals ( CI ) were obtained using the standard error of estimates obtained by jackknife over subsamples or by bootstrap over loci , using Fstat , as described in [46] . Sex-biased dispersal was assessed using three tests implemented in Fstat . First , Weir and Cockerham's estimate of FST , was calculated separately in each sex . Next , tests based on the mean ( mAIc ) and the variance ( vAIc ) of Favre et al . 's corrected assignment index AIc [47] were performed ( see Prugnolle and De Meeûs [48] for more details on these tests ) . All three tests are based on a permutation procedure; the sex of each individual is randomly re-assigned in each population ( 10 , 000 permutations ) . The observed difference between male and female FST , the ratio of the largest to the smallest vAIc and the AIc-based t-statistics defined by Goudet [49] were then compared to the resulting chance distributions . For the sex that has a higher dispersal rate , FST and mAIc are expected to be smaller and vAIc is expected to be higher than for the sex that has a lower dispersal rate . This choice of statistics is motivated by the work of Goudet et al . [49] where vAIc was shown to be the most powerful statistic when migration is low ( less than 10% ) , while FST performs better in other circumstances . We also chose to keep mAIc because it may be more powerful in case of complex patterns of sex specific genetic structures [50] , [51] . Tests were all bilateral . Isolation by distance was inferred with Rousset's procedure [52] through the regression FST/ ( 1-FST ) ∼a+bLn ( DG ) . FST/ ( 1-FST ) is a modified measure of differentiation between two subpopulations , a is a constant , Ln ( DG ) is the natural logarithm of the geographical distance between subpopulation pairs for two dimensional data and b the slope of the regression that is related to the product Deσ2 of reproducing ( effective ) adults local density ( De ) by the dispersal surface σ2 ( σ is the mean distance between reproducing adults and their parents ) by the equation Deσ2 = 1/4πb because the neighbourhood size Nb = 1/b = 4πDeσ2 [52] . In that case , the effective number of immigrants per neighbourhood can be computed as Nem = 1/2πb [52] . For one dimensional data , the model becomes FST/ ( 1-FST ) ∼a+bDG and Deσ2 = 1/4b [52] . The significance of the signal was tested with a Mantel test [53] and bootstrap over loci gave 95% confidence intervals for the slope . All isolation by distance procedures were implemented using Genepop 4 [54] with 1 , 000 , 000 iterations . For the sake of power , traps were used as sub-population units for isolation by distance procedures . Effective population sizes were estimated following Waples and Do's method based on linkage disequilibrium and implemented in LDNe [55] , linkage disequilibrium and heterozygosity as implemented by Estim 1 . 2 [56] and following Balloux's method based on heterozygote excess in dioecious populations [57] assuming even sex ratio . For G . tachinoides , since no sub-structuring was observed at the site level , areas of sites were assimilated to the rectangle defined by the approximate gallery forest width ( ∼100 m ) and the mean maximal distance between the two most distant traps in a site ( ∼1000 m ) , being aware that it is a conservative value . This surface S = 100 , 000 m2 was thus used to divide effective population sizes to compute densities . For G . p . gambiensis densities were computed by dividing the population size by the mean minimum distance between two traps ( ∼100 m ) in one dimension along rivers , or by the surface of the rectangle defined by this distance and the approximate gallery forest width ( ∼100 m ) , hence S = 10 , 000 m2 , for two dimensions . This distance of 100 m also corresponds to the range of attraction of a biconical trap , and thus the smallest river section that can be sampled irrespective of the sampling protocol used [58] .
HierFstat analysis only found one significant hierarchical level of population structure in the G . tachinoides samples , i . e . subdivision by sites FSite/Basin = 0 . 026 ( P-value = 0 . 001 ) . Traps ( P-value = 0 . 179 ) and river basin ( P-value = 0 . 707 ) did not significantly contribute to the genetic structure of G . tachinoides . To check for possible disturbing effect of substructuring within sites that may not be detected by HierFstat , we also tested isolation by distance between traps in each of the four sites with the model FST/ ( 1-FST ) ∼a+bDG , appropriate for one dimensional data ( along the river ) . This analysis was feasible in view of the large amount of data available for the Mouhoun River . Absence of population sub-structuring was confirmed by the total absence of any isolation by distance between traps within the Mouhoun River ( all slopes ≤0 , all P-values>0 . 49 ) . In further analyses we only considered sites as subpopulation units for G . tachinoides , except for isolation by distance as explained above . For G . p . gambiensis , two hierarchical levels appeared to contribute significantly to genetic structure , the trap in each site ( FTrap/Site = 0 . 0117 , P-value = 0 . 033 ) and the site in each river basin ( FSite/Basin = 0 . 0379 , P-value = 0 . 001 ) . The analysis therefore revealed that river basins were not important for the genetic structuring of the G . p . gambiensis populations ( P-value>0 . 6 ) . For all further analyses with G . p . gambiensis , the trap was considered as the subpopulation unit and , for population structure analyses ( sex biased dispersal , isolation by distance ) , each site was considered separately , except when specified otherwise . For G . tachinoides , LD tests were carried out with all the 9 loci ( 36 pairs tested ) and with the six most polymorphic loci , i . e . loci with no allele at frequency above or equal to 0 . 9 ( pGp28 and pGp29 excluded , hence 21 pairs remaining ) . In the first case three pairs appeared in significant linkage and two pairs in the second case , which is not significantly above the 5% level in each case ( binomial P-values are respectively 0 . 27 and 0 . 28 ) . For G . p . gambiensis only one test was significant at the 5% level , which is not significantly above the proportion expected under the null hypothesis ( P-value = 0 . 7628 ) . There was a strong and highly significant heterozygote deficit ( FIS = 0 . 227 , 95% CI = [0 . 067 , 0 . 429] in G . tachinoides due to loci pGp17 , pGp20X , pGp24 , pGp28 and B104X ( Figure 2 ) . The four remaining loci , pGp13X , pGp29 , C102 and GPCAG , together provided a pattern conforming with genotypic proportions expected under random mating: FIS = −0 . 005 , P-value = 0 . 5661 . For the other loci , stuttering was observed for pGp17 in all the eight subsamples , and in one subsample for pGp20X . Moreover , null alleles can reasonably explain all FIS as can be seen from Table 3 . Consequently , it was assumed with confidence that stuttering and null alleles totally explained the heterozygote deficits observed at these five loci and we can confidently conclude that the G . tachinoides subsamples conformed to the random mating hypothesis . For G . p . gambiensis the FIS is slightly lower ( FIS = 0 . 137 , 95% CI = [0 . 071 , 0 . 219] ) but still highly significant ( P-value = 0 . 0001 ) ( Figure 3 ) . According to MicroChecker analyses , null alleles provided a reasonable explanation ( Table 4 ) . Nevertheless , individually non significant loci alone still provided a significant positive FIS = 0 . 042 ( P-value = 0 . 0356 ) . Thus neither null alleles nor Wahlund effects alone can explain the pattern observed in this species , as it is often the case for G . p . gambiensis [18] , [22] , [26] . As can be seen from Table 5 , there is a significant genetic signature of sex biased dispersal in G . tachinoides , with the female flies having a lower dispersal rate ( male biased dispersal ) . For G . p . gambiensis several sex biased dispersal tests were carried out:between sites over all river basins and between sites within the Mouhoun river basins , between traps within the Mouhoun river basin and between traps within sites . For the first and second tests , only one male and one female per trap were used , or only a single individual if only one sex was available , per trap and individuals of the same site considered as belonging to the same subpopulation . This data reduction was done to limit as much as possible the confounding effect of the significant differentiation that exists between traps in this species ( see [51] for comments on that matter ) . A single test resulted in a significant P-value ( Table 6 ) , with the mAIc indicating a female biased dispersal . However , it can be seen from Table 6 that biased dispersal genetic signatures are inconsistent across parameters in the same analysis or across analyses for the same parameter . As previously observed [26] , the most obvious conclusion , is that no genetic signature of sex biased dispersal could be detected in G . p . gambiensis at any level . There was a highly significant isolation by distance across traps over the total G . tachinoides sampling zone ( P-value = 0 . 0001 ) with a slope b = 0 . 015 . This results in a neighbourhood size Nb≈67 individuals . Estim did not provide a usable effective population size . Effective population sizes were relatively convergent across Waples and Do's and Balloux's methods . With Waples and Do's method , three sites ( two in Comoe and one in the Mouhoun Basin ) provided outputs different from infinity , with mean Ne = 99 . 4 . Balloux's method gave Ne = 100 . We then assumed an effective subpopulation size of ∼100 . A mean sampling surface as defined above as S∼0 . 1 km2 , resulted in an effective population density of De = Ne/S≈1000 flies per km2 . Rousset's model [52] indicated a mean dispersal per generation of around 73 m for this species , or a migration rate between neighbouring sites of m = 1/2πb = 0 . 11 . For G . p . gambiensis , there was no evidence for isolation by distance in any site along rivers . But this may be due to the very short length of river portions covered in each site . As some sites were however very distant , we further used isolation by distance in a two dimensional framework . Over the entire sampling zone , a significant isolation by distance was detected ( P-value = 0 . 022 ) with slope b = 0 . 015 and a resulting neighbourhood size Deσ2≈67 individuals identical to G . tachinoides . Estim provided an estimate of Ne = 81 and m = 0 . 286 in one trap of the Mouhoun Basin . LDNe provided only usable values for Ne in four traps of the Mouhoun Basin , with mean Ne = 149 . The surface defined above S∼0 . 01 km2 leads to an effective density of G . p . gambiensis De = Ne/S≈8000 ( for Ne = 80 ) or De = 15000 ( for Ne = 150 ) G . p . gambiensis per km2 in the study area . Mean dispersal per generation is thus σ = 26 m or σ = 19 m for Ne = 80 and Ne = 150 respectively , corresponding to migration rates of 0 . 13 and 0 . 07 respectively ( with Rousset's 1997 model in two dimensions ) between neighbouring subpopulations ( traps ) . Using the island model of migration with even sex ratio , published by Vitalis [59] , and in particular using equation 10 from his paper , we checked which parameters could lead to the sex biased dispersal observed in G . tachinoides and the observed difference in FST between female and male flies . As can be seen in Table S1 , the best fit of the model parameters would indicate a very low female migration rate ( less than 0 . 01 and most probably around 0 . 0001 ) , a moderate male migration rate around 0 . 12 ( between 0 . 1 and 0 . 15 ) and subpopulation sizes around 100 individuals ( between 80 and 120 individuals ) . The number of subpopulations and the mutation rate had a small influence on the results . Thus , even if some care must be taken with these values coming from an island model of migration , parameters seem quite convergent with what was inferred from G . tachinoides isolation by distance population structure .
The population genetics data presented here suggest that the savannah area of the watershed divide between two adjacent river basins does not seem to represent a significant barrier to gene flow for the two riverine tsetse species studied . The results corroborate data from an earlier preliminary study that assessed gene flow ( but without clear quantification ) between three populations of G . p . gambiensis inhabiting two tributaries of the Mouhoun and Comoé river basins in Burkina Faso [26] . For both species , isolation by distance between sites of different river basins ( or even at a micro-scale for G . p . gambiensis ) was evidenced , without a particular role of river basins . Nevertheless , for G . palpalis gambiensis , dispersal along rivers ( in one dimension ) is still more efficient than across them ( i . e . in two dimensions ) . During the rainy season , riverine tsetse fly species disperse in the savannah areas neighbouring the river [7] , probably in search of suitable hosts , like cattle , that during that time of the year do not have to enter the riparian forests to find drinking water . It is conceivable that after some days without rain , remaining flies in the savannah areas are quickly forced to find resting sites before facing desiccation and are therefore stimulated to disperse at a higher rate . Following environmental cues such as humidity or temperature gradients , these flies will need to venture back to the closest gallery forest , that might well belong to another river basin system . Tsetse dispersal processes are complex and simple random diffusion models have often been used to capture this complexity [60] . This approach seems to be inadequate as was recently confirmed by an analysis of dispersal data of sterile male Glossina austeni Newstead that were released homogeneously from the air . The recapture data indicated that the sterile flies congregated in the same sites that were also preferred by their wild counterparts [61] . In addition , when riverine tsetse find themselves in unsuitable sites , they are capable of dispersing up to 2km per day to reach suitable habitats ( Bouyer J . , unpublished data ) . The analysis presented here showed that dispersal of G . tachinoides across river basins was ∼3 times higher than G . p . gambiensis , which suggests that G . tachinoides flies have the ability to disperse with ease despite the severe fragmentation of the riparian gallery forests in the study area [2] . G . p . gambiensis dispersed less along fragmented riparian forest habitat and seemed to encounter more difficulties to disperse between the remaining fragments of this suitable habitat . The fact that genetic structuring is not correlated to geographic distance at a local scale in G . tachinoides [25] , and the higher level of genetic structuring observed for G . p . gambiensis populations at the micro-scale [22] corroborate these observations . G . tachinoides is more xerotolerant ( i . e . tolerant for dry conditions ) than G . p . gambiensis , which could lead to a different perception of habitat borders in this species [24] . Mark-release-recapture studies carried out more than 20 years ago [7] showed that , in homogeneous , unfragmented gallery forests , the two species had a similar rate of dispersal . However , capture-mark-release-recapture data do not necessarily correlate with genetic data , as was observed in morsitans group flies [62] , since the former is a direct measure of all kinds of dispersal including hunting dispersal , whereas the latter is an indirect measure of only reproductive dispersal . Our data imply that habitat fragmentation seems to reduce the dispersal capacity of G . p . gambiensis much more as compared to that of G . tachinoides . Similar conclusions were drawn from recent mark-release-recapture experiments in Burkina Faso , where mean dispersal coefficients of 0 . 3 km2 . d−1 and 0 . 05 km2 . d−1 were observed corresponding to mean square displacements of 775 m/day and 316 m/day for male G . tachinoides ( Bouyer , J . , unpublished data ) and G . p . gambiensis [22] respectively . The much lower effective density observed for G . tachinoides as compared to G . p . gambiensis is partially related to the location of the sampling sites , which were mostly along small tributaries of the Mouhoun . These are known to be preferred sites for G . p . gambiensis – hence the name “spring” tsetse fly [5] – but are not favoured by G . tachinoides . During the entire sampling process , the mean number of flies caught per trap per day were 1 . 04 ( s . d . 1 . 06 ) and 0 . 13 ( s . d . 1 . 31 ) for G . p . gambiensis and G . tachinoides , respectively . Tsetse flies are polygynous where the reproductive investment of female flies far outreaches that of the male flies . As such and according to the three main asymmetries of dispersal/philopatry costs between genders favouring biased dispersal ( i . e . the resource-competition hypothesis , the local mate competition hypothesis and the inbreeding hypothesis ) a sex biased dispersal in tsetse flies ( should it exist ) would be biased towards greater mobility of the male sex ( see [47] and references therein ) . Our analysis of the sex biased dispersal in G . tachinoides suggests that female flies indeed disperse very little in fragmented riparian vegetation . This seems to suggest that female G . tachinoides are very conservative in their dispersal behaviour and not only remain close to “known” suitable larviposition sites in these fragmented landscapes , but are also highly philopatric i . e . they deposit their larvae close to their own place of birth . This behaviour would reduce the risk of reinvasion , as only founding females would produce offspring for a new population . This result is at variance with classical mark-release recapture experiments where females were dispersing more than males [7] . One possibility to explain our result would be a sex specific local adaptation rendering immigrant females very unlikely to survive locally . Sex based differences in dispersal were not observed for G . p . gambiensis in the 1980's in Burkina Faso and more recently in Guinea and Burkina Faso [18] , [26] . In this case , both sexes dispersed very little , which was also reflected in a high level of structuring at a more local scale [22] . In conclusion , the data presented here , combined with those from earlier studies [26] , suggest that in Burkina Faso , riverine tsetse populations from adjacent river basins are exchanging genetic material , and can therefore not be considered as biologically isolated . Therefore , potential re-invasion of flies from adjacent river basins will have to be prevented by establishing buffer zones between the Mouhoun and the other river basin ( s ) , in the framework of the PATTEC ( Pan African Tsetse and Trypanosomosis Eradication Campaign ) eradication project that is presently targeting the northern part of the Mouhoun River Basin . Alternatively , the campaign should be extended to adjacent infested basins to sustain the eradication .
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Tsetse flies are insects that transmit trypanosomes to humans ( sleeping sickness ) and animals ( nagana ) . Controlling these vectors is a very efficient way to control these diseases . In Burkina Faso , a tsetse eradication campaign is presently targeting the northern part of the Mouhoun River Basin . To attain this objective , the approach has to be area-wide , i . e . the control effort targets an entire pest population within a circumscribed area . To assess the level of this isolation , we studied the genetic structure of Glossina palpalis gambiensis and Glossina tachinoides populations in the target area and in the adjacent river basins of the Comoé , the Niger and the Sissili River Basins . Our results suggest an absence of strong genetic isolation of the target populations . We therefore recommend establishing permanent buffer zones between the Mouhoun and the other river basin ( s ) to prevent reinvasion . This kind of study may be extended to other areas on other tsetse species .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"veterinary",
"diseases",
"pest",
"control",
"african",
"trypanosomiasis",
"zoonotic",
"diseases",
"neglected",
"tropical",
"diseases",
"veterinary",
"science",
"integrated",
"control",
"agriculture",
"trypanosomiasis"
] |
2011
|
Contrasting Population Structures of Two Vectors of African Trypanosomoses in Burkina Faso: Consequences for Control
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The basic unit of genome packaging is the nucleosome , and nucleosomes have long been proposed to restrict DNA accessibility both to damage and to transcription . Nucleosome number in cells was considered fixed , but recently aging yeast and mammalian cells were shown to contain fewer nucleosomes . We show here that mammalian cells lacking High Mobility Group Box 1 protein ( HMGB1 ) contain a reduced amount of core , linker , and variant histones , and a correspondingly reduced number of nucleosomes , possibly because HMGB1 facilitates nucleosome assembly . Yeast nhp6 mutants lacking Nhp6a and -b proteins , which are related to HMGB1 , also have a reduced amount of histones and fewer nucleosomes . Nucleosome limitation in both mammalian and yeast cells increases the sensitivity of DNA to damage , increases transcription globally , and affects the relative expression of about 10% of genes . In yeast nhp6 cells the loss of more than one nucleosome in four does not affect the location of nucleosomes and their spacing , but nucleosomal occupancy . The decrease in nucleosomal occupancy is non-uniform and can be modelled assuming that different nucleosomal sites compete for available histones . Sites with a high propensity to occupation are almost always packaged into nucleosomes both in wild type and nucleosome-depleted cells; nucleosomes on sites with low propensity to occupation are disproportionately lost in nucleosome-depleted cells . We suggest that variation in nucleosome number , by affecting nucleosomal occupancy both genomewide and gene-specifically , constitutes a novel layer of epigenetic regulation .
In eukaryotic cells , genetic information is organized in chromatin , a highly conserved structural polymer of DNA and histones whose basic unit is the nucleosome [1] . Dynamic changes in the local or global organization of chromatin are required in order to perform most nuclear activities , including replication , transcription , and DNA repair [2] , [3] . Maintenance of such a dynamic structure , in terms of spatial distribution of nucleosomes and proper reorganization during nuclear activities , is considered crucial to preserve cellular identity and to protect cells from genomic instabilities that are among the major causative factors in aging and cancer . Until recently , no gross modifications of nucleosome number in cells were described or even looked for , even if differences in nucleosome linker length were observed between different cell types [4] . However , recent work has showed that aging yeast [5] and mammalian [6] cells contain fewer nucleosomes . We show here that mammalian cells lacking High Mobility Group Box 1 ( HMGB1 ) protein contain a substantially reduced amount of histones and nucleosomes . Yeast cells lacking Nhp6a/Nhp6b proteins , which are functionally similar to HMGB1 [7] , have a very similar phenotype , suggesting that the involvement of HMG-box proteins in controlling nucleosome number is conserved in evolution . HMGB1 is an abundant non-histone chromatin protein that binds to the minor groove of DNA without sequence specificity and , to a large number of nuclear proteins , contributing to the maintenance , retrieval , and expression of genetic information [8] . HMGB1 is composed by two DNA binding domains , called HMG-boxes , followed by a long unstructured tail that appears to modulate the interaction of the HMG-boxes with DNA [9] . HMGB1 binds to nucleosomes at the dyad axis and appears to compete with histone H1 , exerting opposite effects: HMGB1 facilitates nucleosome sliding and makes chromatin more accessible , H1 restrains nucleosome sliding and makes chromatin less accessible [10] , [11] . Hmgb1−/− mice die soon after birth with a complex , pleiotropic phenotype [12] . Yeast cells contain two abundant HMG-box proteins , called Nhp6a and Nhp6b , which are composed of a single , non-sequence-specific HMG-box and are functionally redundant , since the loss of only one of the two Nhp6 proteins causes a very mild phenotype . Both mammalian cells lacking HMGB1 and yeast cells lacking both Nhp6a and -b are viable , although they display a number of defects [12] , [13] . Specifically , the nhp6a/b double mutant ( henceforth nhp6 ) yeast cells and Hmgb1−/− MEFs display genomic instability and hypersensitivity to DNA-damaging agents; nhp6 cells have a shorter life span and increased levels of extrachromosomal rDNA circles ( a hallmark of senescence ) [14] . Curiously , in both mammalian and yeast mutants , a given dose of UV irradiation appeared to produce almost twice as many thymidine dimers as in wild type cells [14] . We show here that these cells are also more sensitive to ionizing radiation , which is due to a genomewide reduction in DNA-binding proteins , notably histones . Thus , both mammalian Hmgb1−/− cells and nhp6 yeast cells have fewer nucleosomes . This raised the critical question of where available nucleosomes are located when they are fewer . We found that , at least in yeast ( but most likely also in mammalian cells ) , the reduction in nucleosome number does not alter nucleosome spacing and location , but reduces nucleosomal occupancy in a non-uniform way in different sites and is associated with an overall increase in transcript abundance and a specific alteration in the expression of a subset of genes .
Previous results indicated that a given dose of UV irradiation produced almost twice as many thymidine dimers in mammalian cells lacking HMGB1 and yeast cells lacking Nhp6 proteins compared to wild type cells [14] . We then asked whether ionizing radiation also produced more DNA damage in Hmgb1−/− cells . We irradiated primary wild type and Hmgb1−/− MEFs with 10 Gy of gamma rays; we measured the formation of single-stranded and double-stranded DNA breaks in individual cells by means of the comet assay [15] , whereby in the presence of an electrophoretic field short DNA fragments migrate out of the lysed cell and into the agarose , whereas intact DNA remains confined ( Figure 1A , left ) . The tail moment , which is a measure of DNA fragmentation , indicated that Hmgb1−/− MEFs contained more DNA breaks before irradiation ( Figure 1A , right ) . The number of DNA breaks induced by irradiation was higher in Hmgb1−/− cells; this could not be ascribed to defective DNA repair since the cells were subjected to the assay immediately after irradiation , before DNA repair could deal with the breaks . We also quantitated γH2AX levels after irradiation with gamma rays ( Figure 1B ) : substantially more H2AX is phosphorylated in Hmgb1−/− cells relative to wild type cells after 1 h , but the difference subsides after 6 h . This suggests that Hmgb1−/− cells can repair effectively double strand breaks . Ionizing radiation generates hydroxyl radicals , which in turn react with DNA producing a large number of chemical modifications , including DNA breaks . Our results suggest that the DNA of Hmgb1−/− MEFs is more accessible to hydroxyl radicals . DNA-bound proteins protect DNA from the attack of hydroxyl radicals; this property is exploited in protocols of hydroxyl radical footprinting . Nucleosomes shield DNA from hydroxyl radicals , and chromatin structure is a major factor determining DNA radiosensitivity [16] . We then hypothesized that the DNA of Hmgb1−/− cells is less protected by associated proteins , and in particular by histones . We thus measured histone content in Hmgb1−/− and wild type cells . We accumulated by Coulter counting an equal number of Hmgb1−/− and wild type MEFs , blocked in G0/G1 by serum starvation , and measured their DNA content by PicoGreen fluorescence and their histone content by quantitative immunoblotting ( Figure 2A , B ) . While the amount of DNA was not statistically different between wild type and mutant cells , the amounts of core histones H2A , H2B , H3 , and H4 , linker histone H1 , and the variant histone H2AX were all reduced by about 20% in Hmgb1−/− MEFs . On the contrary , beta-actin content ( a common control for protein loading ) was about 50% higher in Hmgb1−/− MEFs , whose cytoplasm appears larger than that of wild type MEFs even at an early passage ( unpublished data ) . The abundance of other proteins , like peroxiredoxin-2 , did not change in Hmgb1−/− MEFs . We confirmed these results in HeLa cells stably transfected with a plasmid expressing HMGB1 shRNA ( HeLa knockdown , henceforth KD ) or a control plasmid . HMGB1 expression was almost abolished by the HMGB1 shRNA ( Figure S1A ) , and cycling KD cells ( Figure S1B ) contained less core and linker histones ( about 80% compared to the control HeLa cells ) and more beta-actin ( about 120% ) ( Figure S1C , upper panel ) . We then compared the entire proteomes of control and KD cells by stable isotope labeling with amino acids in cell culture ( SILAC ) [17] . Control cells were grown for 8 passages in either light medium ( Arg0 Lys0 ) or medium containing C13 , N15-labelled arginine and lysine ( Arg10 Lys8 ) ; KD cells were grown in light medium only . Light and heavy cells were mixed 1∶1 before lysis , subjected to SDS-PAGE and in-gel trypsin digestion; peptides were quantitated by liquid chromatography coupled to tandem mass spectrometry ( LC-MS/MS ) ( Figure S2A ) . HMGB1-derived tryptic peptides were absent in KD cells , as expected ( Figure S2B ) . In the control experiment ( which compared heavy and light HMGB1-containing control cells ) the ratios of light to heavy proteins had a narrow log-normal distribution ( standard deviation close to 0 . 13 ) . In contrast , when comparing light KD and heavy control cells these ratios showed a much wider distribution ( standard deviation close to 0 . 37 ) ( Figure S2C ) . The abundance of most proteins changed slightly but significantly , albeit few proteins showed a change larger than 2-fold ( MS tracings for a few representative peptides are shown , Figure S2B ) . We then investigated the relative abundance of all histone-derived peptides that do not bear post-translational modifications; we excluded peptides known to bear modifications because a difference in their abundance could be due to variations in modification , rather than to a difference in the quantity of the histone protein . Notably , peptides from core and linker histones were reduced by about 25% in HMGB1-depleted cells ( p<10−5 by two-sample Wilcoxon test ) ( Figures 2C , S2D ) . Variant histones H2AX and H2AZ were also significantly reduced ( Figure S3 ) . These experiments were repeated on wild type and Hmgb1−/− MEFs , with comparable results ( unpublished data ) . Taken together , SILAC and quantitative immunoblotting indicated that cells lacking HMGB1 contain a coordinately reduced amount of all histones . A lower histone content might be due to compensatory mutations selected in response to the lack of HMGB1 in Hmgb1−/− cells . In this case , rare mutant cells might be selected during in vitro culture and expand into viable clones . Alternatively , all cells might be able to modulate histone content in response to their physiological state , including the available level of HMGB1 protein . To distinguish between the possibilities , we transfected HeLa cells with 21-mer double-stranded HMGB1 siRNA , verified the disappearance of HMGB1 , and grew the cells for 10 d until the amount of HMGB1 returned to normal ( Figure 2D ) . Notably , the amount of histone H3 decreased concomitantly with the decrease in HMGB1 , down to less than 80% of the starting level , and then recovered concomitantly with the recovery in HMGB1 content . HeLa cells transfected with control firefly luciferase siRNA showed no change in either HMGB1 or histone H3 content . Since there was no gross cell death after siRNA transfection , cells with a reduced histone content are not rare clones selected from a large cell population; rather , most cells in the population down-regulate histone content in response to a lack of HMGB1 , and this regulation is reversible . To further establish the physiological interdependence between HMGB1 and histone content we examined MEFs derived from Hmgb1+/− heterozygous embryos; these have one half the amount of HMGB1 protein and contain about 90% of the normal amount of histones , which is intermediate between the amount in wild type and in Hmgb1−/− MEFs ( unpublished data ) . Finally , we verified that Hmgb1−/− embryo livers contain a 20% reduction in histone content ( Figure S4 ) , further excluding that the observed histone reduction in cultured cells can be due to culture conditions . Histones are predominantly associated with DNA to form nucleosomes . Thus , a severe reduction in histone content should translate in a corresponding reduction in nucleosomally organized DNA . To verify the hypothesis that the DNA of Hmgb1−/− cells might be wrapped into fewer nucleosomes , cells were partially lysed and chromatin was digested with increasing amounts of micrococcal nuclease ( MNase ) . At higher MNase concentrations , the amount of remaining ( nucleosome-protected ) DNA was reduced by about 30% in Hmgb1−/− MEFs ( Figure 3A , quantification with PicoGreen ) . Agarose electrophoresis indicated that the total amount of MNase-resistant DNA is reduced in Hmgb1−/− MEFs , at all concentrations of MNase ( Figure 3B ) . However , at low MNase concentration ( 0 . 5 U/ml , Figure 3B ) , Hmgb1−/− samples contained more of higher molecular weight DNA ( Figure 3B ) . This result was repeated several times and most likely indicates that a minor fraction of the chromatin of Hmgb1−/− cells becomes more resistant to digestion , in contrast to the major fraction which becomes more accessible . The average spacing between nucleosomes was very similar in Hmgb1−/− and wild type MEFs ( Figure 3B ) , contrary to what is expected if available nucleosomes were uniformly redistributed over the genome . Similar results were obtained with KD cells ( Figure S1C , D ) . The conclusion from these experiments is that mammalian cells can survive and proliferate with substantially fewer nucleosomes . The availability of cells with fewer nucleosomes allowed us to test the widely held opinion that nucleosomes limit transcription in vivo , as they do in vitro by impeding the progress of RNA polymerases [18] , [19] . We quantified total nucleic acids in KD and control HeLa cells by FACS after acridine orange staining ( Figure 3C ) [20] . Whereas the DNA content was similar in KD and control cells , the RNA content is about 1 . 3 times higher in KD cells . Both polyA+ mRNA and the 47S rRNA precursor are similarly increased ( Figure 3D ) . Although global transcript abundance increases in cells lacking HMGB1 , so that most transcripts will be more abundant , the expression of individual genes can also change relative to each other . We thus measured the relative representation of each transcript within an identical amount of RNA extracted from cells . Relative representation in a fixed amount of RNA automatically normalizes away the global increase of about 30% in total RNA amount in HeLa KD cells . The comparison in relative amount is instructive to identify which genes deviate the most from the average effect . Our analysis indicates that about 13% of transcripts ( 1 , 080 over 8 , 027 on the Illumina platform; p<0 . 01; Figure S5A ) are over-represented ( 577 genes ) or under-represented ( 503 genes ) from the average 30% increase in KD HeLa cells . The Gene Ontology categories significantly affected at the mRNA level ( p<0 . 05 , Wilcoxon test ) are indicated in Figure S6A . These broadly correspond to the Gene Ontology categories significantly affected at the protein level ( Figure S6B ) . Since the absence of HMGB1 leads to a decrease in nucleosome number , we investigated whether HMGB1 was directly involved in chromatin assembly , as suggested by early experiments [21] . We tested the effect of HMGB1 on histone deposition onto naked DNA using a simple , commercially available assay ( Chromatin Assembly Kit by Active Motif ) . Linearized plasmid DNA was mixed with soluble histones , the histone chaperone NAP , and the remodeling factor ACF . After incubation for 15 min at 27°C , the assembled chromatin was digested with micrococcal nuclease , and an aliquot was run on an agarose gel ( Figure 4A , upper panel ) . No DNA remained after nuclease digestion if histones were absent from the assembly reaction ( lane 3–4 ) , whereas a clear band of mononucleosomal size was present in the presence of histones ( lane 5 ) . We then added to the reaction mix increasing concentrations of HMGB1 , and we noted a highly significant dose-dependent increase in the mononucleosome band ( lanes 6–9 ) , reaching a maximal yield at 1 µg/ml . At higher HMGB1 concentrations , the efficiency of nucleosome deposition decreased ( lanes 10–12 ) . At the optimal HMGB1 concentration , nucleosome formation was 3 . 5 times faster in the presence than in the absence of HMGB1 ( Figure 4B , upper panel ) . Direct quantification of nuclease-resistant DNA by PicoGreen confirmed the data obtained by gel electrophoresis ( Figure 4A and B , lower panels ) . Yeast Nhp6 proteins are functionally equivalent to HMGB1 in mammalian cells , and nhp6 yeast mutants are more sensitive to UV irradiation [14] . We therefore verified whether yeast nhp6 cells also have reduced histone and nucleosome content . We synchronized yeast cells in G1 by treatment with alpha factor pheromone , collected an equal number of wild type and nhp6 cells , and measured their DNA content with PicoGreen and their histone content by quantitative immunoblotting . nhp6 cells contained about 65% of the amount of histones compared to the wild type , and their chromatin was more accessible to digestion by MNase ( Figure 5A , B ) . Moreover , 2D gel analysis indicated that the supercoiling of the 7 . 0 kb yRp17 plasmid was reduced by about three turns in nhp6 cells , equivalent to about three nucleosomes fewer than in the wild type ( Figure 5C ) . Finally , nhp6 cells contain about 1 . 2 times more RNA than wild type cells ( Figure 5D ) . We conclude that the phenotypes of nhp6 and Hmgb1 mutants are largely similar . A transient model of nucleosome depletion in yeast was examined previously [22] . In the UKY403 yeast strain , the sole copy of histone H4 is under the control of the GAL1 promoter . In glucose medium , UKY403 cells lost around 50% of nucleosomes by 6 h , relative to a control strain with a wild type H4 gene , and the expression of 15% of genes increased and the expression of 10% of genes decreased more than 3-fold . We then looked at the relative expression of genes in wild type and nhp6 cells and compared them to those in the UKY403 strain . By Affymetrix analysis we found that out of 5 , 447 genes , 219 are up and 251 are down in nhp6 relative to wild type cells ( 1 . 5-fold threshold and p<0 . 05 ) ( Figure S5B ) . The Gene Ontology categories that are significantly affected are shown in Figure S7 . The correlation between gene expression profiles in UKY403 and nhp6 cells ( Figure 5E ) rises from r2<0 , when nucleosomes are not depleted in UKY403 , to almost 0 . 16 after 2 h ( p<10−33 ) , and remains almost constant thereafter . Since about half of the genes transcriptionally affected by nucleosome depletion in the UKY403 strain are also affected by slow growth , we asked whether our results were influenced by the slow growth of the nhp6 mutant relative to its wild type counterpart [13] . Indeed , the correlation between the two strains is much stronger for the growth-related gene subset; however , the correlation for the genes unresponsive to changes in growth rate is only slightly smaller ( r2 = 0 . 15 , p<10−5 ) than the one for all genes . Taken together , these data suggest that nucleosomal depletion affects transcription profiles in broadly similar ways in strains where histone H4 is depleted or Nhp6a/b proteins are lacking . Research in the last few years has highlighted the importance of nucleosome positioning in the control of transcription; we therefore asked how nucleosome depletion affects genomewide nucleosome positioning . The hypothesis of statistical positioning states that nucleosomes space themselves between barriers [23] . In this case , the position of nucleosomes should vary when their number is lower ( Figure 6A , hypothesis 1 ) . According to the alternative hypothesis that DNA sequence is the major determinant of nucleosome positioning , nucleosome limitation could lead to the selective loss of a minority of nucleosomes ( hypothesis 2 ) . Alternatively , nucleosomes might occupy the same positions , but spend less time on each of them ( hypothesis 3; nucleosome “occupancy” of individual sequences is reduced ) . When we applied high throughput sequencing to MNase-resistant DNA from nhp6 and wild type cells , we found that the distribution of sequence reads was very similar . Representative snapshots of the nucleosome maps are shown in Figure 6B; a complete browsable form is available at the website indicated in Materials and Methods . The number of times a specific base pair appears in sequence reads , divided by the total number of sequence reads , is the relative occupancy of that base pair . Relative occupancies of all base pairs can then be compared between strains; a density dot plot allows a visual representation of such a comparison . The comparison between biological replicates gives a density plot where most bases cluster around the diagonal ( Figure 6C , right ) . The comparison between nhp6 and wild type cells ( Figure 6C , left ) gives a density plot which is more dispersed about the diagonal and has a characteristic skew with more points below the diagonal at low occupancy and more points above the diagonal at high occupancy . This result is inconsistent with a global redistribution of nucleosomes over the genome ( hypothesis 1 in Figure 6A ) , which would give a smeared density plot with a lot of points close to the axes ( base pairs occupied in one strain but not in the other ) . Our result is also inconsistent with the disappearance of nucleosomes from a minority of sites ( hypothesis 2 ) , which would give a density plot with two separate sub-populations , as simulated in Figure S8A . In fact , the similarity of the nhp6/wt density plot to that of biological replicates indicates that most base pairs that are occupied by nucleosomes in wild type cells are also occupied in nhp6 cells . A complete identity between nucleosome positions in the two strains ( although with reduced occupancy in one strain ) would give the same density plot of biological replicates . Thus all three hypotheses depicted schematically in Figure 6A do not correspond to observation , but hypothesis 3 comes closest . We next moved from coverage at individual base pairs to examination of nucleosome positions . We used template filtering [24] to call nucleosome positions and confirmed that they are highly conserved ( Figure 6B , “nuc calls” ) . Almost half of the nucleosomes are centered around the same position in both strains , many are offset by about 10 base pairs and some by 20 pairs ( Figure S8B ) ; we note that 10-bp shifts correspond to those expected from the rotational periodicity of DNA wrapped around the nucleosome . Only about 30% of nucleosomes had shifted by more than 20 base pairs . This confirms that most nucleosomes occupy approximately the same location in the two strains . However , in nhp6 cells fewer nucleosomes were unambiguously called ( 45 , 441 versus 53 , 643 ) , and the read peaks that identify nucleosome edges were broadened in the nhp6 sequencing data ( Figure 6B ) . This suggests that some nucleosomes may shift from a single favored position into a superposition of multiple overlapping positions ( “fuzzy nucleosomes”; [23] ) ; beyond a certain degree of fuzziness , nucleosomes would not be called by the algorithm . The length of DNA predicted to be covered by nucleosomes was reduced on average and had increased variability in nhp6 cells ( Figure S8C ) , consistent with increased fuzziness . As observed at the single base pair level ( Figure 6C ) , many nucleosomal sites are either less or more relatively occupied in nhp6 cells . This is clearly visible in the snapshots in Figure 6B , showing three different loci with decreased , unchanged , and increased relative occupancy , respectively . Absolute occupancy is proportional to the nucleosome number , and thus is reduced by about 30% in nhp6 cells . As a result , on some sites absolute occupancy in nhp6 cells may be comparable to that in the wild type ( but not higher ) , whereas on the vast majority of sites it will be reduced or very reduced . High-resolution primer extension analysis confirmed a similar position of nucleosomes in the ars1 locus , but with higher accessibility of nucleosome-covered sequences ( and thus lower absolute occupancy ) in nhp6 cells ( Figure S9A ) . Nucleosome ChIP ( using an antibody against histone H3 ) also was in agreement with reduced nucleosome occupancy of the ars1 locus in nhp6 cells ( Figure S9B ) . Overall , these results are in accordance with increased chromatin accessibility in the nhp6 mutant and suggest that nucleosomes have increased mobility on the sites they occupy ( either intrinsic or catalyzed by nucleosome remodelling complexes ) . Nucleosomal organization of the control regions of genes is considered most important for gene expression . In yeast , nucleosomes are regularly arranged on protein-coding genes , starting from the transcriptional start site ( TSS ) . A nucleosome-depleted region ( NDR , also called nucleosome-free region , NFR ) of about 140 bp is generally found just upstream of the TSS and is surrounded by two well-positioned nucleosomes , called −1 and +1 nucleosome , respectively . We aligned genes by their TSS and ranked them by the severity of nucleosome loss in nhp6 cells relative to wild type ( Figure 7A , heatmap in the center ) . All genes had reduced occupancy of the −1 nucleosomes ( green streak in the heatmap ) , and genes with more severe nucleosome loss at the 5′ end also had reduced nucleosome occupancy over the gene body . Once again , we observed that the genes with more severe nucleosome loss in nhp6 cells ( Figure 7A , center ) were the ones already low in nucleosome occupancy in the wt ( Figure 7A , right , red line ) . A few genes appeared to have relatively increased occupancy in nhp6 cells ( red genes in the bottom of Figure 7A ) ; these genes belong primarily to the Gene Ontology categories “metabolism” and “cell wall” . None of these genes , however , appeared to have increased absolute nucleosome occupancy ( i . e . , after considering that nucleosome number is reduced by about 30% in nhp6 cells ) . Nucleosome by nucleosome , median relative occupancy over the promoter and the TSS of all genes ( from the −1 to the +1 nucleosome ) was lower in nhp6 cells , and median relative occupancy for the +2 , +3 , and +4 nucleosomes was slightly higher ( thick lines in Figure 7B ) . Relative occupancy of all nucleosomal sites is more variable in nhp6 cells ( the thin lines in Figure 7B indicate the lower and upper quartiles of occupancy ) . Lower nucleosome occupancy in the control regions correlates with increased gene expression ( Figure 7A , left , blue line , and Figure S8D ) . In all our analyses , from the correlation of base pair occupancy to the distribution of nucleosomes over genes , a theme stands out: the reduction in nucleosome number is associated with an increase in the variability of relative occupancy . From the point of absolute occupancy , we have already pointed out that some sites might be similarly occupied in wild type and nhp6 strains , while sites that are intermediately occupied in the wild type are less occupied in nhp6 cells , and weakly occupied sites in the wild type are much less occupied in nhp6 cells . The skew in the density plot of Figure 6C visually represents this pattern of more pronounced loss of occupancy in nhp6 cells from sites that are already less occupied in the wild type . Unequal occupancy of nucleosomal sites in vivo is expected , since ( 1 ) in vitro the probability of nucleosome occupancy on different sites can vary by a factor of up to 5 , 000 [25] and ( 2 ) histone octamers are insufficient to package all the genome [26] . Thus , in physiological conditions some sites will be occupied close to 100% of the time ( “saturated” ) and some much less . Based on these considerations , we designed a model to account for the characteristic pattern of nucleosomal occupancy in nhp6 cells . We assume that all sites compete for a finite pool of histones that is insufficient for all of them , and that each site has a certain probability of being occupied , that depends from histone availability . We also posit that the probability of occupation versus availability of histones is a hyperbolic function and is different for each site ( Figure 8A ) . This model recalls formally the formation of a complex between two macromolecules , and we can thus assign a dissociation constant ki to each nucleosome . The occupancy O of site i is then Oi = x/ ( x+ki ) , where ki is the dissociation constant and x is the concentration of available histones . A decrease in the availability of histones will result in a skewed desaturation , with heavy nucleosome loss at sites of high dissociation constant and mild loss at sites of low dissociation constant ( Figure 8A , B ) . This will increase the variability of relative occupancy . Based on the relative occupancy of wild type sites , the model should be able to predict the genomewide occupancy for a certain decrease in available histones ( details in Figure S8E ) . We then used our model to simulate the relative occupancies in a population of cells which have a 30% reduction in histone content ( Figure 5A ) . The density dot plot comparing simulated and observed occupancy in nhp6 cells ( Figure 8C , right ) is almost symmetrical about the diagonal and corrects the observed systematic skew in the density dot plot of nph6/wt relative occupancies ( Figure 8C , left ) , although the dispersion of values is not decreased substantially . We also plotted the distribution of the number of nucleosomes at each occupancy value ( Figure 8D ) ; our model correctly predicts the approximate shape of the distribution for nhp6 cells and the position of the mode . The fitting between the observed and predicted nhp6 occupancies is optimal at the nucleosome content actually observed for nhp6 cells . An alternative model based on statistical positioning does not justify our observations , since it predicts that both position and spacing of nucleosomes would be changed when histone content is reduced ( Figure S8F , red line ) , contrary to what we observed . Overall , our model justifies the disproportionate loss of nucleosomes from weakly occupied sites , and the increase in relative occupancy at the more occupied sites ( Figure 8D , far tail of the distribution ) . We then asked whether the sites with reduced occupancy in nhp6 cells are the ones with lower intrinsic ability to form nucleosomes . To this aim , we compared our dataset with the dataset obtained by reconstituting yeast chromatin in vitro [27] . The comparison of changes in nucleosomal occupancy between our nhp6/wt dataset and Kaplan's in vitro/in vivo dataset is shown in Figure 8E . The Pearson correlation coefficient between datasets is r2 = 0 . 46 ( p<10−6 ) , indicating that the sites that most lose occupancy in nhp6 cells correspond to the ones with lower occupancy in reconstituted chromatin; conversely , the sites that lose less occupancy in nhp6 cells correspond to the ones that most easily reform chromatin in vitro ( Figure 8E ) .
We show here that the absence of HMGB1 or Nhp6a/b proteins causes substantial histone and nucleosome depletion in mammalian and yeast cells; surprisingly , nucleosome depletion is compatible with cell survival . While our work was in progress , a substantial reduction in the histone content in chromatin was described in aging yeast cells [5]; forced expression of plasmid-borne histone genes was shown to increase the lifespan of yeast cells . Our results independently confirm that yeast cells can contain a variable amount of chromatinized histones and that reduced histone content leads to aging: nhp6 yeast mutants , which we show have a reduced histone and nucleosome content , have a reduced lifespan and an increased level of extrachromosomal ribosomal DNA circles , which are a hallmark of aging [14] . Histone depletion has also been demonstrated in aging mammalian cells [6] . We also confirm a correlation between nucleosome depletion and an increase in DNA damage [6] . We suggest that a decrease in nucleosome number increases DNA damage because DNA is more exposed to DNA damaging agents , as indicated by in vitro experiments showing that nucleosomes protect DNA from hydroxyl radicals [16] . Reactive oxygen species , including hydroxyl radicals , are produced also by basal metabolism [28] , [29] , which may explain an increase in DNA breaks in non-irradiated Hmgb1−/− cells . We also report that nucleosome depletion correlates with a global increase in transcript abundance . Our observation in living cells supports the current notion ( based on experiments in vitro ) that nucleosomes limit the accessibility of DNA to the transcription machinery . We have asked what happens to the basic organization of eukaryotic genomes when only a limited number of nucleosomes can be formed . The prevailing view was that histones are deposited until all DNA is packaged . Indeed , Kornberg and Stryer [30] proposed that the fairly uniform spacing of nucleosomes along DNA arises from the “statistical positioning” of nucleosomes between fixed barriers , so that when the DNA is saturated with histones , each nucleosome finds itself within a narrow distribution of distances from the preceding nucleosome . As a consequence , when the number of nucleosomes is reduced , the distance between nucleosomes should increase . This is not what we find , neither in yeast nor in mammalian cells . At least in yeast , nucleosomes largely occupy the same positions also when nucleosome number is lower than usual; nucleosome occupancy drops , but not uniformly . In general , sites that are highly occupied in wild type cells remain highly occupied also in nuclesosome-poor nhp6 cells , whereas nucleosomes are mostly lost from sites that already had low occupancy in wild type . This creates a ranking of sites for nucleosome occupancy , which can be at least partially explained by a model that assumes formally that each site has its own affinity for histones available for deposition . This is certainly compatible with models that affirm that nucleosome position is driven largely by DNA sequence . Indeed , the comparison between Kaplan's and our datasets indicates that the sites that are more nucleosome-depleted in nhp6 cells are the ones with lower intrinsic propensity to form nucleosomes . Anyway , our model does not exclude strong contributions to nucleosomal location by active processes such as nucleosome deposition , sliding , eviction , or remodeling , as will be discussed in the following sections . Nucleosomal position is measured directly in yeast , by identifying the borders of MNase-resistant DNA sequences , as is relative nucleosomal occupancy , by dividing the number of reads of a specific MNase-resistant sequence by the total number of MNase-resistant sequence reads . However , absolute nucleosomal occupancy is calculated by multiplying relative occupancy by the amount of nucleosomally organized DNA . We consider that DNA remaining after MNase digestion represents nucleosomally organized DNA , but an alternative explanation is often favored: chromatin may become more accessible to MNase not because of a variation in nucleosome number but because of unspecified changes in higher-order chromatin organization . This alternative explanation is not compatible with the topological analysis of plasmid supercoiling ( Figure 5C ) , which does not depend on MNase digestion nor in fact on any other type of chromatin accessibility , but only on the number of nucleosomes residing on the plasmid at the time of extraction . Most of all , the alternative explanation does not fit with the reduced abundance of histones: since only about 0 . 1% of histones are not engaged within nucleosomes [31] , a reduced amount of histones must be reflected in a decreased number of nucleosomes . Measuring histone content is therefore critical in interpreting alterations in chromatin organization . Nucleosomes were also recently mapped in nhp6 cells by Dowell et al . [32]; our results , despite the different approach ( high throughput sequencing versus hybridization to tiled chips ) , broadly agree with theirs , but the interpretations are different . Dowell et al . infer that Nhp6 proteins stabilize nucleosomes directly , possibly by interacting with them; we infer that nucleosome occupancy is substantially reduced in most nucleosomal locations , due to decreased histone content . Although they are different , the two interpretations are not mutually exclusive , and indeed specific interaction with Nhp6 proteins might explain the preferential loss of nucleosome +1 at the 5′ end of genes ( Figure 7A , B ) [32] . Other yeast mutants ( for example , spt10; [33] ) show an “altered” organization of chromatin , with increased accessibility to MNase digestion and altered plasmid topology . We speculate that if histone abundances were measured in these mutants , they might turn out to have reduced histone content and decreased global nucleosomal occupancy . This might be a fairly common phenotype that was overlooked so far . The same argument—that decreased histone content must correspond to fewer nucleosomes—also applies to mammalian cells . Genomewide nucleosomal location and occupancy are more difficult to determine , however . The decrease in nucleosome number does not cause an increase in internucleosomal distance ( Figures 3B and S1D ) , but we cannot show rigorously that nucleosome position is conserved , both because of the significant effort and cost of resequencing entire mammalian genomes and because the majority of sequences in mammals are repeated and cannot be assigned to a specific genome position . Thus , even if all mappable nucleosome borders were conserved , non-mappable nucleosome borders ( which are the majority in the mammalian genome ) might be substantially altered . Despite remaining uncertainties on nucleosomal positions and occupancy in mammalian cells lacking HMGB1 , the similarity in phenotype between mammalian and yeast mutants is striking . This suggests that HMG-box proteins might have been involved in determining the number of nucleosomes since the emergence of eukaryotes . HMGB1 and Nhp6 proteins are functionally similar , and both bend DNA . Since DNA must be bent to wrap around histone octamers and this entails a high energy of activation , DNA-bending proteins might lower the activation energy and provide a chaperone activity on DNA for nucleosome assembly . Expression of a Nhp6 mutant protein unable to bend DNA does not revert the phenotype of Δnhp6 cells [32] , suggesting that DNA bending is required for the correct chromatinization of the yeast genome . We observed that there is a significant correlation in the yeast genome between the intrinsic propensity to assemble nucleosomes in vitro from high-salt solutions of histones [27] and the nucleosomal occupancy in nhp6 cells ( Figure 8E ) ; this suggests that sites that have an intrinsic difficulty in assembling nucleosomes most need the presence of a DNA-bending protein . Likewise , we argue that HMGB1 can provide a DNA chaperone activity for nucleosome assembly in mammalian cells: we show that in vitro HMGB1 accelerates nucleosome assembly onto naked DNA . A chaperone activity only changes the rate of the biological reaction ( by lowering the activation energy ) , and not the equilibrium; thus , we suggest that nucleosome assembly is never at equilibrium in living cells and that the absence of a DNA chaperone will move nucleosome assembly further away from the equilibrium level . This hypothesis has not been formally tested , but we note that the high rate of nucleosome dynamics in living cells makes it very unlikely that equilibrium is ever reached . We also note that a delay or reduction in nucleosome assembly would lead to a decrease in histone biosynthesis or an increase in histone degradation , or both , via the activity of several feedback control loops [31] , [34] . As a consequence , the steady state level of histones in the cell would fall , which is what we observe . HMG-box proteins can also affect nucleosome dynamics in different ways . For example , we had previously shown that HMGB1 enhances nucleosome remodeling in vitro [10] , and the group of Karen Vasquez had shown that histones are not acetylated after DNA damage in the absence of HMGB1 [35] . In yeast , Nhp6 proteins are non-essential components of the FACT complex ( in contrast , the absence of the other components Pob3 and Spt16 causes lethality ) [7]; the absence of Nhp6 proteins can thus affect nucleosome remodeling associated to transcription . All these activities can affect nucleosomal occupancy directly or indirectly . Nucleosomal occupancy over promoters is a powerful determinant of gene expression . Since we show that genomewide nucleosomal occupancy varies non-uniformly in response to nucleosome depletion , specific transcriptional profiles are expected to ensue . This is exactly what we observe . In both mammalian Hmgb1−/− cells and yeast nhp6 cells , specific genes have increased or decreased expression relative to the rest . These gene-specific effects are distinct from the overall increase in transcription that also takes place and might depend in part on the specific interactions of HMGB1 and Nhp6 proteins with nucleosomes and transcription factors [32] . In turn , changes in gene expression can determine changes in the cell cycle and in cell metabolism , which can lead to further changes in nucleosomal occupancy and gene expression; this makes it difficult to disentangle cause from effect in the phenotype of nhp6 yeast cells and Hmgb1−/− mammalian cells . However , the correlation of transcriptomic profiles in nhp6 cells ( Figure 5E ) and in cells where transcription of histone H4 has been shut off [22] suggests that histone depletion is by itself partially responsible for the altered expression of a subset of genes . We therefore propose that overall histone content and the associated modulation of the genomewide and gene-specific nucleosomal landscapes represents a novel layer of epigenetic control of transcription .
Hmgb1−/− MEFs and their control wild type MEFs were isolated from same-mother embryos deriving from Hmgb1+/− crosses [12] . Since Hmgb1−/− MEFs accumulate chromosome rearrangements with continuous culturing [14] , only cells up to 8 population doublings from embryo isolation were used . MEFs from different mothers gave consistent results in our experiments , and batch-to-batch variation was minimal . Wild type and Hmgb1−/− MEFs were synchronized in G0–G1 by serum starvation for 36 h and gamma-irradiated with 10 Gy by using a 137Cs source ( Biobeam 2000 ) . The Alkaline Comet Assay was performed as described [15] immediately after irradiation . The extent of DNA damage was measured by calculating the tail moment ( Comet Assay II software , Perceptive Instruments ) . Wild type and nhp6 cells [14] were grown in YPD medium at 30°C . For protein quantification cells were arrested in G1 by synthetic alpha-factor pheromone ( 5 µg/ml ) and checked for synchronization by FACS analysis after Sytox staining [36] . Stable HMGB1 knockdown HeLa cells were prepared by transfection with plasmid HMGB1shRNA-pSuperior . puro or , as a mock control , with the empty vector pSuperior . puro ( Invitrogen ) [37] . Transfected cells were selected with puromycin and single resistant clones were picked , amplified , and analyzed for HMGB1 expression by western blot . Only clones with <10% HMGB1 were used . Transient gene silencing was carried out by performing at 1-d intervals four consecutive transfections of small interfering RNA duplexes ( siRNA ) against HMGB1 transcript using Lipofectamine 2000 ( Invitrogen ) . siRNA for HMGB1 and for firefly luciferase , as a negative control , were purchased from Dharmacon . The Quant-iT PicoGreen dsDNA Kit was used as described by the manufacturer ( Invitrogen ) . Seventy-five µl of diluted PicoGreen reagent were added to each well of a 96-well plate containing either phage λ DNA at known concentrations or the sample ( 100 µl final volume ) . After incubation for 5 min at room temperature , fluorescence was measured using a Victor3 plate reader ( Ex/Em filters: 485 nm/535 nm , exposure 1 . 0 s ) . DNA concentration of the samples was determined by interpolation of the fluorescence intensity values against the standard curve . For western blot analysis , whole-cell extracts were prepared by direct lysis of a defined number of cells in SDS-PAGE sample buffer . DNA from 106 cells was extracted with the DNeasy tissue Kit ( Qiagen ) and quantified by PicoGreen to confirm cell count . Following electrophoresis , blots were probed with primary antibodies mouse anti-γH2AX ( Upstate ) , rabbit anti-total H2AX ( Upstate ) , rabbit anti-H3 ( Abcam ) , rabbit anti-H2A ( Abcam ) , rabbit anti-H2B ( Abcam ) , rabbit anti-H4 ( Abcam ) , sheep anti-H1 ( Abcam ) , mouse anti-peroxiredoxin-2 ( AbFrontier ) , and mouse or rabbit anti-β-actin ( Sigma ) for mammalian and yeast , respectively , and visualised using the ECL detection kit ( GE Healthcare ) or the ECL Plex fluorescent western blotting system ( GE Healthcare ) . Sixteen-bit images were acquired with FLA-9000 ( Fuji Film ) ; signals were within the linear part of the dynamic range . Quantification of western blot signals was performed with ImageQuant software ( GE Healthcare ) . SILAC-labelled cells were harvested and mixed 1∶1 . Proteins were extracted in SDS-PAGE sample buffer and separated by one-dimensional electrophoresis . Protocols for protein processing and peptide desalting and concentration are described [38] , [39] . Peptides were analyzed by nanoflow liquid chromatography on an Agilent 1100 LC system ( Agilent Technologies Inc . ) coupled to LTQ-FT ultra ( Thermo Fisher Scientific ) . Mass spectrometric data were analyzed for protein identification and peptide quantification with MaxQuant algorithm . 2×105 cells resuspended in DMEM with 10% FCS were permeabilized by gently adding 0 . 4 ml ice-cold permeabilizing solution ( 0 . 1% Triton X-100 , 80 mM HCl , 150 mM NaCl ) and stained with 1 . 2 ml ice-cold acridine orange staining solution ( 37 mM citric acid , 126 mM Na2HPO4 , 150 mM NaCl , 1 mM EDTA , 6 µg/ml Acridine Orange ) . As control , cells were treated with 100 µg/ml RNase A for 10 min after permeabilization . Using the 488 nm laser for excitation , DNA and RNA fluorescence at 530/30 and 610/20 nm , respectively , was recorded with a LSRII flow cytometer ( BD Biosciences ) . Total RNA from 5×106 control and KD HeLa cells was extracted using RNeasy tissue kit ( Qiagen ) . DNA from 106 cells was extracted and quantified by PicoGreen to confirm cell count . Slot blots of total RNA were prepared by denaturing total RNA at 68°C in 1× SSC , 50% formamide , and 5% formaldehyde for 15 min and cooled on ice . Serial 1∶2 dilutions of RNA starting from 400 , 000 cells were applied to a Nitrocellulose membrane ( Protran , Schleicher & Schuell ) using a minifold apparatus ( Schleicher & Schuell ) . Total RNA was hybridized to 32P end-labeled oligo-dT ( 25 mer ) and to 47S leader sequence oligonucleotide ( GGAGACGAGAACGCCTGACACGCACGGCACGGAGCCAGC ) , to detect polyA+ mRNA and 47S rRNA precursor , respectively . The membranes were exposed to an imaging plate ( BAS-IP SR 2025 , Fuji ) and the radiograms were analyzed by BAS-5000 imager ( Fuji ) . Different exposure times were used to obtain densitometric scans ( 16-bit ) in the linear response range . Quantification of RNA slot blot signals was performed with ImageQuant software ( GE Healthcare ) . Nuclei were isolated as described [40] . Briefly , 107 cells were resuspended in 5 ml ice-cold resuspension buffer ( 10 mM Tris-Cl pH 7 . 4 , 15 mM NaCl , 60 mM KCl , 1 mM EDTA , 0 . 1 mM EGTA , 0 . 15 mM spermine , 0 . 5 mM spermidine , 1 mM DTT , 0 . 5 mM PMSF ) containing 5% sucrose and 0 . 1% NP-40 . Nuclei were pelletted by centrifugation in a swinging bucket rotor at 100–120 g for 20 min at 4°C through a sucrose “pad” solution ( resuspension buffer containing 10% sucrose ) , washed in ice-cold buffer , and digested with micrococcal nuclease ( Sigma-Aldrich cat . N5386 ) for 5 min at 25°C . DNA was purified with the DNeasy Tissue Kit ( Qiagen ) and electrophoresed on 1 . 2% agarose gels . Chromatin was assembled on linear DNA with Chromatin Assembly Kit according to the manufacturer's instructions ( Active Motif ) , with two modifications: we used 0 . 8 µg histones per 1 µg DNA , and we added either BSA or HMGB1 to a total amount of 10 or 1 µg protein/ml in the titration and time-course experiments , respectively . pGEM-T vector DNA ( Promega ) was digested with SacII , and the linearized plasmid was purified from gel with Gel Extraction kit ( Qiagen ) . Calf thymus HMGB1 was purified as described [41] . Assembled chromatin was digested for 4 min with the Enzymatic Shearing Cocktail provided in the kit . The residual DNA after digestion was quantified by PicoGreen and electrophoresed on a 1 . 5% agarose gel . The gel was stained with Syber Safe ( Molecular Probes ) and scanned with FLA-9000 ( Fuji Film ) . Cells were harvested from a 300 ml culture grown to OD = 0 . 5 and resupended in 10 ml of a buffer containing 1 M sorbitol , 50 mM Tris-HCl pH 7 . 5 , and 10 mM ß-mercaptoethanol . Thirty million cells were incubated for 10 min at 30°C in the presence of 0 . 05 mg Zymolyase 100T . The spheroplasts were harvested , resuspended in 3 . 6 ml nystatin buffer ( 50 mM NaCl , 1 . 5 mM CaCl2 , 20 mM Tris-HCl pH 8 . 0 , 1 M sorbitol , 100 µg/ml nystatin ) , and divided into 0 . 4 ml aliquots . The samples were incubated with MNase ( from 6 . 4 units/ml in 2× dilutions ) at 25°C for 15 min . The reaction was stopped with 1% SDS , 5 mM EDTA ( final concentrations ) . Samples were incubated with Proteinase K ( 40 µg/sample ) at 56°C for 2 h . DNA was then purified by three phenol/chloroform extractions and ethanol precipitation . RNase treatment ( 35 µg/sample ) was also performed . The DNA was then electrophoresed in 1 . 2% agarose gels at 1 . 75 V/cm and visualized by EtBr staining [42] . Circular DNA from yeast cells was prepared by alkaline lysis of spheroplasts [43] . Briefly , cells were treated with Zymolyase as reported for chromatin preparation . Spheroplasts were kept on ice for 10 min with two volumes of 0 . 2 M NaOH , 1% SDS; 1 . 5 volumes of 3 M potassium acetate ( pH 4 . 8 ) were then added and the mixture kept on ice for 45 min . After centrifugation , two volumes of ethanol were added to the supernatant in order to precipitate the circular DNA forms . 2D topoisomer analysis was performed essentially as described [44] . Plasmid DNA , prepared as above , was run on 1% agarose gels in the first direction for 21 h at 60 V , in chloroquine buffer ( 30 mM NaH2PO4 , 36 mM Tris , 0 . 5 mM Na-EDTA , 10 µg/ml chloroquine ) . The gel was then equilibrated in the same buffer containing 30 µg/ml chloroquine for 2 h and run in the perpendicular direction at 40 V in the same buffer with recirculation for 16 h at room temperature . Finally , the gel was blotted and hybridized with the specific probe ( plasmid yRp17 digested with EcoRI ) . The signal of each band in the topoisomer arch was measured with a PhosphorImager . Chromatin from wild type and nhp6 yeast cells was prepared as described and digested with MNase . The sample containing mostly mononucleosome-sized DNA ( 12 units MNase/108 cells , 25°C , 15 min ) was run on a 1 . 2% agarose gel . The mononucleosome band was excised from the gel and used to prepare libraries suitable for the Illumina GA IIx sequencer: the samples were end-repaired , A-tailed , and adapter-ligated ( following each step the reaction was cleaned up using the Qiaquick PCR purification kit ) . Samples were subjected to 12 cycles of PCR amplification before selection of the 250–300 bp fraction from a 2% agarose gel . The library was extracted using the Qiaquick gel extraction kit , quantified using the Quant-iT dsDNA HS Assay , and quality checked using the Agilent bioanalyzer 2100 system . Two biological replicates for both wild type and mutant were sequenced in independent experiments producing 9 . 65–10 . 08 M reads at 51 bp . The reads were aligned to the yeast genome using Novoalign producing around 76% unique alignments . High throughput sequence reads were mapped to the yeast genome , and nucleosome positions were extracted using “Template Filtering” [24] . A total of 53 , 643 positioned nucleosomes were found in the wild type versus 45 , 441 nucleosomes in nhp6 . To compare and model the occupancy we considered only nucleosomes that were detected both in wild type and nhp6 cells . To model occupancy , we assumed that the occupancy of each nucleosome is a hyperbolic function of available histones . The occupancy O of nucleosome i is defined by Oi = x/ ( x+ki ) , where x is an unknown parameter of the concentration of available histones and ki is the dissociation constant . In a simple interpretation , x can be taken as the concentration of free histones before deposition on DNA , for example during S phase . x was set to 1 for the wild type sample and ki was extracted using the measured wild type occupancy . Average occupancy in nhp6 cells is reduced to 70% of the wild type ( based on the measured amount of MNase-resistant DNA ) ; using this parameter , the model returns a concentration of available histones of 0 . 5 and the occupancy of each single nucleosome . We removed the top 1% highly occupied nucleosomes to avoid overdependence of the model on the extreme values and overvaluation of correlations . Total RNAs from control and KD HeLa cells were extracted using RNeasy tissue kit ( Qiagen ) . Yeast RNA extraction was performed using standard procedures [43] . RIN ( RNA Integrity Number ) of each sample was determined with the 2100 Bioanalyzer ( Agilent ) to assess RNA quality; samples with RIN<8 were discarded . We analyzed four technical replicates for HeLa cells and three biological replicates for yeast cells . For HeLa cells , total RNA was reverse transcribed with the Illumina TotalPrep RNA Amplification kit ( Ambion ) and cRNA was generated with a 14 h in vitro transcription reaction . cRNA was then eluted and purified . Washing , staining , and hybridization were performed according to the standard Illumina protocol . RNAs were hybridized to Illumina HumanHT-12 V3 . 0 expression beadchip; datasets were first quantile normalized ( without background subtraction ) in BeadStudio v . 3 . 0 , then expression data were rescaled by mean centering and standardization; the differentially expressed genes were identified by t test , p<0 . 01 . For yeast , Affymetrix Yeast 2 . 0 chips were hybridized and scanned according to the manufacturer's recommendation . 7G scanner data extracted as . cel files were then analyzed in GeneSpring GX 11 . 0 . 1 . We used quantile normalization using RMA summarization algorithm with baseline transformation to the median of all samples . We then identified genes with transcript levels differing more than 1 . 5-fold ( p<0 . 05 with Benjamini-Hochberg correction for multiple testing ) . Data from mammalian and yeast cells were visualized by hierarchical clustering in TmeV ( v . 4 . 5 . 1 ) , choosing Euclidean metric and average linkage . The complete nucleosome map of wild type and nhp6 cells is available at http://genome . ucsc . edu/cgi-bin/hgTracks ? hgS_doOtherUser=submit&hgS_otherUserName=Assafwe&hgS_otherUserSessionName=NH6PA . HeLa transcriptome is available on the GEO website with the accession number GSE18721 . Yeast transcriptome is available on the GEO website with the accession number GSE23711 .
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The accurate preservation and correct retrieval of genetic information is crucial for all living organisms . In eukaryotes , whether single-celled yeast or complex mammals , the DNA containing the genetic information is wrapped around beads of histone proteins to form structures called nucleosomes along the length of the DNA; this packaging arrangement helps protect the genome from damage and may restrict access to the genetic information . Until recently , the amount of histones and , consequently , the number of nucleosomes in the cell were considered fixed . Here , we show that in both mammalian and yeast cells that lack a single protein—HMGB1 in mammals or Nhp6a/b in yeast—the abundance of histones and nucleosomes decreases by 20%–30% . Contrary to expectations , we found that in yeast the nucleosomes do not redistribute along DNA when they are fewer: they largely maintain their positions , but the amount of time each specific DNA site spends wrapped in a nucleosome ( i . e . , its occupancy ) decreases . Sequences that are already less frequently occupied than others in normal yeast cells lose disproportionally more nucleosomes in the mutant yeast that lack Nhp6a/b . This gives rise to a global increase in transcription and specific alterations in the expression of certain genes . This study thus contributes to a deeper understanding of how the DNA is packaged and organized . It also suggests that the cell's histone content might contribute in an important way to gene regulation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome",
"expression",
"analysis",
"protein",
"abundance",
"dna",
"transcription",
"genome",
"sequencing",
"chromatin",
"chromosome",
"biology",
"gene",
"expression",
"biology",
"proteomics",
"molecular",
"biology",
"biochemistry",
"cell",
"biology",
"genomics",
"molecular",
"cell",
"biology",
"computational",
"biology"
] |
2011
|
Substantial Histone Reduction Modulates Genomewide Nucleosomal Occupancy and Global Transcriptional Output
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In the yeast Saccharomyces cerevisiae and most other eukaryotes , mitotic recombination is important for the repair of double-stranded DNA breaks ( DSBs ) . Mitotic recombination between homologous chromosomes can result in loss of heterozygosity ( LOH ) . In this study , LOH events induced by ultraviolet ( UV ) light are mapped throughout the genome to a resolution of about 1 kb using single-nucleotide polymorphism ( SNP ) microarrays . UV doses that have little effect on the viability of diploid cells stimulate crossovers more than 1000-fold in wild-type cells . In addition , UV stimulates recombination in G1-synchronized cells about 10-fold more efficiently than in G2-synchronized cells . Importantly , at high doses of UV , most conversion events reflect the repair of two sister chromatids that are broken at approximately the same position whereas at low doses , most conversion events reflect the repair of a single broken chromatid . Genome-wide mapping of about 380 unselected crossovers , break-induced replication ( BIR ) events , and gene conversions shows that UV-induced recombination events occur throughout the genome without pronounced hotspots , although the ribosomal RNA gene cluster has a significantly lower frequency of crossovers .
Recombination occurs in both meiotic and mitotic cells . In budding yeast , there are about 100 meiotic crossovers per cell [1] . Although mitotic recombination events in S . cerevisiae are about 105-fold less frequent than meiotic exchanges [2] , homologous recombination ( HR ) is important for the repair of double-stranded DNA breaks ( DSBs ) that occur spontaneously or that are induced by DNA damage . Yeast strains that lack HR grow more slowly than wild-type strains , and are sensitive to DNA damaging agents [3] . In HR events in diploid cells , the broken chromosome is repaired utilizing an intact sister chromatid or homolog as a template . Most organisms also have a pathway termed “non-homologous end-joining” ( NHEJ ) in which the broken ends are re-joined by a mechanism that does not require sequence homology . In diploid cells of S . cerevisiae , HR is much more important than NHEJ for repair of DNA damage [4] . We will first discuss pathways of HR , followed by a description of UV-induced DNA damage , and the recombinogenic effects of this damage . DSBs can be repaired by a number of different HR pathways [5] . For all of these pathways , the broken DNA ends are processed by 5′ to 3′ degradation , followed by invasion of the processed chromosome end into either a sister chromatid or a homolog ( Figure 1 ) . In the synthesis-dependent strand annealing ( SDSA ) pathway , after strand invasion and DNA synthesis , the invading broken end is displaced and reanneals to the other broken end . The resulting product has a region of heteroduplex DNA and mismatches within the heteroduplex can be repaired to yield a gene conversion event unassociated with a crossover ( Figure 1A ) . Alternatively , the broken ends can both engage in pairing with the intact chromosome resulting in a double Holliday junction ( Figure 1B ) . This structure can be resolved to yield a crossover or non-crossover . As in the SDSA pathway , mismatches within the heteroduplex region can be repaired to generate a conversion event . Lastly , invasion of one broken end can result in the generation of a replication structure that duplicates sequences from the other chromosome from the point of invasion to the end of the chromosome ( break-induced replication , BIR; Figure 1C ) . One consequence of mitotic recombination is to cause loss of heterozygosity ( LOH ) for markers near the initiating lesion ( gene conversions ) or extending distal from the initiating lesion to the end of the chromosome ( crossovers and BIR events ) . In Figure 2 , we show the repair of DSBs in diploid mitotic cells by HR involving the homolog . In Figure 2A , we show the repair of a single broken chromatid ( G2 event ) using the homolog as a template . The red and black colors indicate that the two homologs have single-nucleotide polymorphisms ( SNPs ) that allow the detection of recombination events . Figure 2A shows a crossover between chromatids 2 and 3 . If chromatids 1 and 3 segregate into one daughter cell ( D1 ) , and 2 and 4 segregate into the other ( D2 ) , a reciprocal pattern of LOH would be observed . Segregation of unrecombined chromatids 1 and 4 into one cell and the recombined chromatids 2 and 3 into the other would not lead to LOH . These two patterns of segregation are equally frequent in yeast [6] . Our previous studies [2] , [7] showed that most ( 80% ) crossovers are associated with gene conversion events ( indicated by boxes in Figure 2 ) . In Figure 2B , we show a conversion event unassociated with a crossover which produces an interstitial LOH event in one of the daughter cells . The conversion events shown in Figure 2A and 2B are termed “3∶1” events since three of the chromatids have one type of SNP and one has the other within the boxed region . A BIR event produces a region of LOH that extends to the telomere in one but not both daughter cells ( Figure 2C ) . The 3∶1 conversion events shown in Figures 2A and 2B are expected from the repair of a single DSB generated in S or G2 of the cell cycle . In addition , since the chromosome with the DSB acts as a recipient of information derived from the intact chromosome , these conversion events have the pattern expected if the recombinogenic DSB was on the black chromosome [4] . We observed previously , however , that over half of the mitotic conversion events had a different form from that shown in Figures 2A and 2B . In Figure 2D , we show a conversion event unassociated with a crossover in which both daughter cells have an interstitial region of LOH that is homozygous for the same SNPs; these events are called “4∶0” conversions . We interpret 4∶0 events as resulting from the repair of two broken sister chromatids in which the DSBs are located at the same positions . One simple mechanism to obtain this pattern of breakage is that the recombinogenic DSB is generated in G1 , the broken chromosome is replicated , and the two resulting broken chromatids are repaired in G2 ( Figure 2D ) . The alternative model in which the DSB is generated and repaired in G1 is ruled out because such events would not be associated with LOH for markers located distal to the conversion event [8] . If the two broken chromatids are repaired to generate conversion tracts of the same lengths , a 4∶0 event is generated . If one conversion tract is longer than the other , repair of two broken sister chromatids can also generate hybrid 3∶1/4∶0 conversion tracts [2] , [7] . Our previous studies indicated that most spontaneous crossovers had conversion events consistent with a G1-initiated DSB rather than a G2-initiated DSB [9] , [10] , and spontaneous events resembled those induced by gamma rays in G1-synchronized yeast cells [11] . UV results in DNA lesions that are both mutagenic and recombinogenic . The primary types of lesions caused by low dosages of UV-C ( ∼254 nm ) are pyrimidine dimers including cyclobutane dimers ( CPDs ) and ( 6-4 ) photoproducts ( 6-4 PPs ) [3] . Although CPDs can be reversed in yeast by the action of photolyase , the repair of most lesions in wild-type cells likely reflects nucleotide excision repair ( NER ) . In NER , multiple proteins act to excise a short oligonucleotide containing the damaged bases . The resulting 30-nucleotide gap is filled in by DNA polymerase delta and/or epsilon [12] , and the remaining nick is sealed by Lig1p . In yeast , as in many other organisms , UV-induced lesions are more quickly repaired in transcribed genes than in non-transcribed regions [3] . Although most UV-induced lesions are removed quickly by this error-free process , a small fraction of the 30-nucleotide gaps are expanded by the action of Exo1p , resulting in large RPA-coated gaps [13] , [14] . These RPA-coated regions recruit Mec1p/Ddc2p and the 9-1-1 complex , followed by subsequent recruitment of other components of the DNA damage checkpoint [15] . In addition to checkpoints triggered by the action of Exo1p , if unrepaired lesions persist into the S-phase , single-stranded regions may also be generated during the re-start of blocked replication forks . Strong activation of Mec1p by UV is observed in S-phase cells , presumably by this mechanism [16] . Although it is clear from many previous studies that UV greatly elevates the frequency of mitotic recombination in yeast [17]–[23] , the recombinogenic mechanism is not well understood . There are two types of models . First , it is possible that the recombinogenic lesion is generated by NER . Consistent with this model , Galli and Schiestl ( 1999 ) [20] observed that UV of G1-synchronized cells was not recombinogenic unless the cells were allowed to replicate . They concluded that the recombinogenic lesion was likely to represent an NER-associated gap that was replicated to produce the recombination-stimulating DSB . This model predicts that the gene conversion events associated with UV-treatment of G1-synchronized cells would be exclusively 3∶1 conversion events ( Figure 2A ) . In a preliminary study [7] , however , we found that about half of the observed UV-induced conversions were 3∶1 events and about half were 4∶0 events ( Figure 2D ) . This observation is inconsistent with the simplest form of the model proposed by Galli and Schiestl . An alternative model is that the unexcised dimers and other DNA lesions are the recombinogenic lesion . For example , replication forks stalled at an unexcised dimer may engage in replication re-start or be broken . Although both re-start and the repair of an S-phase DSB would be expected to involve an interaction with the sister chromatid [24] , some fraction of these events could involve the homolog , resulting in LOH . Kadyk and Hartwell ( 1993 ) [21] showed that UV stimulates recombination between both sister-chromatids and homologs in NER-proficient cells . In rad1/rad1 ( NER-deficient ) diploids , conversions , but not crossovers , were stimulated by UV in a replication-dependent manner [21] . One complication in interpreting this result is that Rad1p is involved with multiple recombination-related reactions [25]–[27] in addition to its role in NER . Regardless of this ambiguity , it is likely that unexcised dimers are recombinogenic . The summary of studies performed thus far is that some fraction of UV-induced recombination events reflects lesions resulting from NER and another fraction reflects unexcised dimers . In the experiments described below , we examine mitotic crossovers and gene conversion events induced by UV in diploid cells . In G1-synchronized cells treated with high doses of UV , most of the events reflect the repair of two broken sister chromatids whereas at low doses , most events reflect repair of a single broken chromatid . We also show that UV induces crossovers more efficiently than BIR events . We mapped the distribution of about 100 UV-induced LOH events selected on chromosome V and about 400 unselected LOH events throughout the genome . We found that the unselected events were widely distributed throughout the genome with no very strong hotspots . The ribosomal RNA gene cluster , however , was significantly “cold” for crossovers compared to the rest of the genome .
In order to determine different types of mitotic recombination and to determine whether the conversion events are of the 3∶1 or 4∶0 configuration , we used a method of identifying recombination events that allows the recovery of both daughter cells with the recombinant chromosomes . The system used in the present study ( Figure 3 ) is similar to that employed previously [2] , [28] . Near the telomere of chromosome V , one homolog ( shown in black in Figure 3A ) has an insertion of SUP4-o , an ochre-suppressing tRNA gene . The diploid is also homozygous for the ade2-1 ochre mutation . Diploids homozygous for the ade2-1 mutation and zero , one or two copies of SUP4-o form colonies that are red , pink , and white , respectively [28] . In most of the experiments described below , G1-synchronized diploid cells were plated and immediately irradiated with UV . If the resulting DNA damage induces a crossover between the heterozygous SUP4-o gene and the centromere of chromosome V before the first cell division , a red/white sectored colony will be formed ( Figure 3A ) . Since formation of a sectored colony requires a crossover , followed by the segregation pattern in which each daughter cell receives one recombined chromosome and one unrecombined chromosome ( Figures 2A and 3A ) , only half of the crossovers induced in the first division following irradiation result in LOH . If the UV-induced DNA damage is not repaired in the first cell cycle but persists into subsequent cell cycles , a pink/white/red sectored colony could be produced ( Figure 3B ) . As described below , most of the events induced by UV treatment in G1-synchronized cells generate a red/white sectored colony rather than a tri-colored colony . Neither gene conversion events unassociated with a crossover nor BIR events on chromosome V result in a red/white sectored colony . As will be shown below , such events can be detected as unselected events in cells that have a selected crossover on chromosome V . The transition between heterozygous markers and homozygous markers in the sectored colony locates the position of the crossover . To detect the position of the selected crossover on chromosome V and to detect unselected LOH events throughout the genome , we used a diploid strain ( PG311 ) derived from mating two sequence-diverged haploid strains: W303a and YJM789 [2] , [7] , [29] . These two strains differ by about 52 , 000 SNPs . We detect LOH using microarrays that examine 13 , 000 of these SNPs [7] , allowing mapping of most events to a resolution of about 1 kb . Each SNP is represented by four 25-bp probes , two with W303a sequences ( Watson and Crick ) and two with YJM789 sequences . At the hybridization temperature optimized for the whole probe set , W303a genomic DNA hybridizes strongly to W303a oligonucleotides with very weak cross-hybridization to the corresponding YJM789 oligonucleotides , and vice versa for YJM789 genomic sequences . Genomic DNA is isolated from each sector of red/white sectored colonies , labeled with Cy5-tagged nucleotides , and competitively hybridized to the SNP microarray with genomic DNA from the untreated strain labeled with Cy3-tagged nucleotides . By assaying the ratio of hybridization of the differentially-tagged samples to each oligonucleotide [7] , we can readily map LOH events . The transition between heterozygous and homozygous markers should be located near the site of the recombinogenic DNA lesion . Figure 4 shows the analysis of one red/white sectored colony ( 59RW ) . In this figure , we show the normalized ratio of hybridization of genomic sequences to W303a- and YJM789-specific oligonucleotides on chromosome V with red lines and black lines , respectively; CEN5 is located near coordinate 152 kb . In the top part of Figure 4A , we depict the pattern of hybridization of genomic DNA isolated from the red sector . The ratio of hybridization is about 1 for all SNPs from coordinate 105 kb to the right telomere , indicating that SNPs in this region are heterozygous . In the red sector , SNPs centromere-distal to coordinate 105 kb on the left arm are homozygous for the W303a-derived SNPs whereas the genomic DNA from the white sector becomes homozygous at approximately the same position for YJM789-derived SNPs . In Figure 4B , the same recombination event is depicted at higher resolution; each square and diamond shows the level of hybridization to an individual YJM789-specific or a W303a-specific SNP , respectively . As shown in this figure , the red sector has a single transition between heterozygous and homozygous SNPs whereas the white sector has three transitions . The pattern of these transitions indicates that the crossover is associated with a 3∶1/4∶0 hybrid conversion tract . Most of our experiments involve UV treatment of G1-synchronized cells with 15 J/m2; the experimental parameters used for each experiment are in Table S1 . PG311 is hemizygous at the MAT locus ( MATa/MATα::NAT ) , allowing its synchronization in G1 using the alpha pheromone [11] . The synchronized cells were plated onto solid medium and immediately irradiated at doses varying between 1 and 15 J/m2 . Even at the maximum dose of UV , cell viability was 70% . No sectored colonies were observed in cells that were not treated with UV . Based on our earlier study of spontaneous crossovers in the same strain [2] , the rate of crossovers in untreated cells is 1 . 1×10−6/division in the 120 kb interval between CEN5 and the SUP4-o marker . Relative to this rate , UV treatment stimulated sector formation by factors of 1500 ( 1 J/m2 ) , 1600 ( 5 J/m2 ) , 5000 ( 10 J/m2 ) , and 8500 ( 15 J/m2 ) . The strong stimulation of mitotic crossovers by UV is consistent with previous studies [23] . In some studies [2] , [4] , [28] , the frequency of mitotic recombination events is higher in diploids that express both mating types than in diploids that express only one mating type . Consequently , we compared the frequency of red/white sectored colonies in G1-synchronized cultures of PG311 and PSL101 ( the MATa/MATα progenitor of PG311 ) . Because PSL101 cannot be synchronized in G1 using alpha pheromone , both strains were synchronized in G1 by growing the cells into stationary phase ( Text S1 ) . After treatment of the G1-synchronized cells with 15 J/m2 of UV , 0 . 4% ( 0 . 2–0 . 9% , 95% confidence limits ) of the PG311 colonies formed red/white sectors compared to 0 . 6% ( 0 . 4–1% ) of the PSL101 colonies . Although the confidence limits are wide , these results indicate that mating type heterozygosity does not have a large effect on the frequency of UV-induced mitotic crossovers in our system . In addition to red/white sectored colonies , in the irradiated samples , we also observed pink/red and pink/white/red colonies . Such colonies could represent non-reciprocal recombination events ( for example , BIR events ) , persistence of recombinogenic DNA damage beyond the first cell cycle , or an artifact ( two closely-located independent cells ) . To exclude sectors formed artifactually , we micromanipulated individual G1-irradiated ( 15 J/m2 dose ) single cells to specific positions on plates with solid medium , and monitored their subsequent development to form sectored or unsectored colonies . From a total of 970 isolated irradiated single cells , we observed eleven sectored colonies of the following types: seven red/white colonies , two pink/red colonies , and two pink/white/red colonies . From our SNP microarray analysis of the LOH patterns on chromosome V in these colonies ( described in Text S1 and Figure S1 ) , we found that all seven of the red/white colonies represented crossovers induced during the first cell cycle . The two pink/red sectored colonies reflected chromosome loss , resulting in a monosomic red sector and a pink sector . Only one of the pink/white/red colonies was a consequence of a UV-induced recombination event in the second division ( Figure 3B , Figures S1 and S2 ) . In summary , of the nine sectored colonies in which sectoring reflected a UV-induced crossover , eight occurred prior to the first cell division and only one occurred after the first cell division , indicating that most UV-induced DNA lesions are rapidly repaired . We used SNP microarrays to analyze 47 sectored colonies of G1-synchronized cells treated with 5 , 10 or 15 J/m2 of UV ( Tables S2 and S3 ) . 80% of the colonies were from cells treated with 15 J/m2 . Nine of these colonies were derived from the single-cell experiments described above . 45 of the 47 sectored colonies examined had patterns of LOH on chromosome V consistent with a reciprocal crossover on the left arm of chromosome V . In one of the two exceptional colonies , there was a loss of one copy of chromosome V . In the other colony , there were two independent conversions that resulted in LOH events that were unassociated with a crossover . These two sectored colonies were not used in our subsequent analysis of selected events on chromosome V , although data from these colonies were used to analyze unselected recombination events . In addition to the selected LOH events on chromosome V , we observed an average of eight unselected LOH events per sectored colony . As described below , our analysis of the 45 selected and 381 unselected events ( 300 gene conversion events unassociated with crossovers , 60 crossovers , and 21 BIR events ) allowed us to determine several important features of the UV-induced recombination events: 1 ) the patterns of gene conversion in selected and unselected recombination events , 2 ) the lengths of gene conversion tracts associated or unassociated with crossovers , and 3 ) the locations of selected and unselected recombination events induced by UV . Since the frequency of selected sectored colonies in cells irradiated with 15 J/m2 was about 1% , and the selected interval on chromosome V is about 1% of the genome , we expect about one unselected crossover per irradiated cell , roughly the observed frequency ( 60 unselected crossovers/47 sectored colonies ) . One interpretation of our observation of frequent DSCBs in G1-irradiated cells is that the repair of two very closely-spaced single-stranded DNA lesions induced by 15 J/m2 results in DSCBs in the G1-synchronized cells , whereas SCBs reflect DNA lesions on one strand . Thus , the productions of DSCBs by this mechanism would be proportional to the square of UV dosage , whereas the frequency of SCBs would be linearly proportional to the UV dosage . By this model ( details to be discussed below ) , one might expect that a low dose of UV should have a relatively higher frequency of SCBs . Consequently , we examined the frequency and types of recombination events induced in G1-synchronized cells by 1 J/m2 . As expected , the frequency of red/white sectored colonies was reduced in cells irradiated with 1 J/m2 relative to cells irradiated with 15 J/m2 ( 1 . 6×10−3/division versus 9 . 4×10−3/division ) . Ten sectored colonies were examined by whole-genome microarrays . Only four unselected events were observed . This frequency ( 0 . 4 events/sectored colony ) was about twenty-fold less than that observed in samples irradiated with 15 J/m2 ( 8 events/sectored colony ) . Consequently , in the additional thirty-six sectored colonies examined , we used microarrays specific for detecting LOH on chromosome V . The depictions of the LOH events in the 1 J/m2 irradiated samples that had the same patterns as observed for the 15 J/m2 samples are shown in Table S2; the numbers of samples with specific classes of events are shown in parentheses in this table . Patterns of LOH that were unique to the 1 J/m2 samples are shown in Table S6 . The coordinates for these LOH events are shown in Table S7 . The distribution of the LOH events on chromosome V for the 1 J/m2 samples was not significantly different from that observed for the 15 J/m2 samples or the spontaneous events using the same “binning” procedure and statistical test described above . The median length of conversion events associated with crossovers on chromosome V in cells irradiated with 1 J/m2 was 4 . 3 kb ( 2 . 3 kb–8 . 2 kb; 95% confidence limits ) kb . In cells irradiated with 15 J/m2 , the median length of conversion tracts associated with crossovers on chromosome V was 6 . 7 ( 4 . 2–13 kb ) . The distributions of tract lengths analyzed by the Mann-Whitney test showed that these distributions were not significantly different ( p = 0 . 12 ) . A striking difference was observed in the distributions of events diagnostic of SCBs and DSCBs in cells irradiated with 1 and 15 J/m2 of UV . Of selected events on chromosome V in cells irradiated with 1 J/m2 , we observed 5 crossovers unassociated with conversion , 31 SCB events , and 10 DSCB events . In contrast , in cells irradiated with 15 J/m2 , most of the selected events on chromosome V were DSCB events ( Figure 8 ) . By the Fisher exact test , the difference in the numbers of SCB and DSCB events induced by the two different UV treatments is very significant ( p<0 . 0001 ) . The conclusion that G1-synchronized cells have different recombinogenic DNA lesions induced by different UV doses will be discussed further below .
As noted in previous studies , UV very effectively induces mitotic recombination in yeast [7] , [10] , [20] , [21] , [23] , [38] . In experiments involving heteroallelic recombination in synchronized cells , UV is somewhat more recombinogenic in G1-synchronized cells than in G2-synchronized cells [18] , [21]; our results support these observations . Kadyk and Hartwell ( 1992 ) [24] concluded that DSBs induced by X-rays in G2-synchronized cells were repaired primarily by sister-chromatid recombination , whereas X-ray treatment of G1-synchronized cells effectively stimulated recombination between homologs . Our previous interpretation of both spontaneous and DNA damage-induced crossovers is also consistent with this conclusion [2] , [7] , [29] . We argue that most spontaneous crossovers between homologs are initiated by a DSB in G1 in one chromosome , and replication of the broken chromosome produces two sister chromatids broken at the same position . Since these lesions cannot be repaired by sister-chromatid recombination , they are repaired by recombination with the homolog . Although it is likely that DSBs formed in S or G2 are primarily repaired by sister-chromatid recombination , some DSBs generated in G2 are repaired by interaction with the homolog [11] . As observed in our previous studies [2] , [7] , [29] , the mitotic conversion tracts are long compared to those observed in meiosis , and the tracts associated with crossovers are longer than the tracts unassociated with crossovers . Most of the conversion events are explicable as a consequence of repair of one broken chromatid or two sister chromatids broken at the same position by the standard HR pathways shown in Figure 1 , with only conversion-type MMR and not restoration-type MMR . About 15% of the conversion events , however , are more complex , requiring “patchy” repair of mismatches within a heteroduplex ( mismatches corrected by both conversion-type repair and restoration-type repair within one heteroduplex ) , and/or branch migration of the Holliday junction . The fraction of complex conversion tracts in the current study is similar to those observed in our previous studies [7] , [29] . Although these events ( described in detail in Text S1 and Figures S5 , S6 , S7 , S8 , S9 , S10 , S11 , S12 , S13 , S14 , S15 ) are explicable by modifications of the standard models shown in Figure 1 , it is possible that some of these conversion events involve a substantially different mechanism such as multiple template switching events during BIR . In this context , template switching during BIR has been observed in experiments in which linear DNA fragments are transformed into yeast [39] . In addition to the complex tracts , it is possible that the very long conversion tracts reflect BIR rather than mismatch repair in a heteroduplex; 16% of the conversion events unassociated with crossovers are greater than 10 kb in length , and the longest exceeds 50 kb . Finally , it should be pointed that , although single BIR events would not be expected to generate crossovers , a model for production of a crossover by a double BIR event is shown in Figure S4 of Lee et al . ( 2009 ) [2] . A central issue is the nature of the recombinogenic DNA damage generated by UV . Based on the mechanism of NER and on the observation that unrepaired pyrimidine dimers block replication , there are two obvious potential sources of DSBs [40] . First , if a DNA molecule with an unrepaired gap resulting from NER is replicated before filling-in of the gap and ligation , the net result would be a pair of sister chromatids with a single DSB ( Figure 9A ) . Alternatively , if a replication fork encounters an unrepaired UV-induced lesion , breakage of the fork could also result in a single broken chromatid ( Figure 9B ) . Based on the observation that UV treatment of G1- or G2-synchronized cells was not recombinogenic unless cells were allowed to divide , Galli and Schiestl ( 1999 ) [20] suggested that cell division was required to convert DNA lesions to recombinogenic lesions , consistent with both of the possibilities described above; their assay detected only intrachromatid deletions . Kadyk and Hartwell ( 1993 ) [21] found that unrepaired UV lesions stimulate gene conversion events between homologs , but have little effect on mitotic crossovers . This conclusion may be affected by the use of the rad1 mutation to prevent dimer excision , since rad1 strains have reduced frequencies of crossovers in some assays [41] . Both of the models discussed above predict that UV-induced DNA damage in G1-synchronized cells would produce primarily gene conversion events involving a single broken chromatid ( SCBs ) . In our study , about two-thirds of the conversion events in which cells were irradiated with 15 J/m2 reflect two broken sister chromatids , but only one-quarter of the conversions reflect two broken sister chromatids in cells irradiated with 1 J/m2 ( Figure 8 ) . Thus , there is a qualitative change in the nature of the DNA lesion with increasing UV dose . In addition , since our single-cell experiments demonstrate that UV-induced lesions are recombinogenic during the first division following treatment , DSCBs cannot be explained as reflecting the segregation of a chromosome with an unrepaired G2-associated DSB from the previous division . We suggest that most DSCBs are a consequence of a DSB in G1 . Although UV damage is generally regarded as an agent that produces DNA nicks rather than DSBs , a gel-based detection of the conversion of a circular chromosome to a linear chromosome indicated that a dose of 40 J/m2 produces 5 to 10 DSBs in G2-synchronized cells [42] . There are several related mechanisms by which NER could produce a DSB in G1 cells . First , the excision tracts resulting from removal of two closely-opposed dimers could result in very short ( <6 bp ) unstable duplex regions between the repair tracts , resulting in a DSB ( Figure 9C ) . A second model is that , following the removal of two closely-opposed dimers by NER , one or both of the resulting short gaps is expanded by Exo1p ( Figure 9D ) . A third related model is that the excision tract generated by NER is expanded into a large single-stranded gap that is cleaved by an endonuclease to yield the DSB ( Figure 9E ) . Based on our results and those of others , it is likely that UV produces a variety of recombinogenic lesions . In our experiments , at a low dose of UV ( 1 J/m2 ) , we observed primarily SCBs , consistent with the two models shown in Figures 9A and 9B . At 15 J/m2 , we observed DSCBs more frequently than SCBs . This observation supports models shown in Figure 9C and 9D that require closely-opposed lesions , and argues against the model shown in Figure 9E in which the relative fraction of DSCB and SCB events would be expected to be independent of the density of the NER tracts . It can be calculated that diploid cell irradiated with 15 J/m2 have about 7500 dimers/genome [43]; if these dimers are distributed randomly , we expect about 35 closely-opposed ( separated by ≤75 bases ) dimers , enough to explain the detected DSCB events . It is likely that the number of closely-spaced dimers is greater than that determined by this calculation . Lam and Reynolds ( 1987 ) [43] found that the fraction of dimers located within 15 base pairs of each other is greater than expected from a random distribution , and this fraction is somewhat independent of UV dose . These dimers may be responsible for the DSCB events detected in strains treated with the 1 J/m2 UV dose . In summary , we suggest that low doses of UV primarily result in SCBs as a consequence of replication of a chromosome with a NER-generated DNA gap in one strand , or an unrepaired dimer resulting in breakage of one arm of the replication fork . In contrast , we suggest that high doses of UV often result in DSCB events as a consequence of a G1-generated DSB , reflecting cellular enzymes acting on closely-opposed dimers . Although this explanation seems straightforward , we cannot exclude more complex explanations of our data . For example , it is possible that the very large number of UV-induced lesions at high doses may overwhelm the DNA repair systems , resulting in changes in the use of repair pathways . In addition , we stress that our analysis based on interhomolog recombination does not yield an estimate of the relative frequencies of UV-induced recombinogenic lesions produced in G1 , S , and G2 , since most recombinogenic lesions produced in S and G2 are likely repaired by sister-chromatid recombination [24] , a mechanism that does not lead to LOH [7] . The UV-induced recombination events were broadly distributed throughout the genome; no strong recombination hotspots were detected . The distribution of UV-induced genomic LOH events is expected to be a function of multiple factors such as: 1 ) the distribution of DNA damage , particularly the distribution of closely-opposed dimers , 2 ) the relative frequency of dimer repair by recombinogenic and non-recombinogenic pathways , and 3 ) the relative frequency of repair of recombinogenic DNA damage by sister-chromatid recombination , non-homologous end-joining , and recombination between homologs . The regions on each chromosome that were examined for LOH events are in Table S8 . It has been shown recently that the distribution of UV-induced pyrimidine dimers observed in vivo in yeast is primarily a function of the density of TT , TC , CT , and CC sequences in the genome ( [44] , Sheera Adar and Jason Lieb , personal communication ) . As previously discussed , in cells irradiated with 15 J/m2 , the calculated frequencies of AA/TT dinucleotides among our selected ( 0 . 221 ) and unselected ( 0 . 218 ) conversion events are very close to the frequencies on the left arm of chromosome V ( 0 . 219 ) and the whole genome ( 0 . 217 ) . In contrast , the frequency of these dinucleotides in the ribosomal DNA ( 0 . 19 ) is very significantly less ( p<0 . 0001 ) than the frequency in the whole genome . Thus , the observed reduction in UV-induced recombination in the ribosomal DNA , at least in part , may be a consequence of a reduced frequency of dimer formation . Interestingly , the two motifs that are underrepresented in the unselected conversion events ( tRNA and solo LTRs in Table S9 ) also have frequencies of AA/TT dinucleotides that are considerably less than the genomic frequency ( 0 . 124 for tRNA genes and 0 . 205 for the solo LTRs ) . Since the tRNA genes and the solo LTRs are smaller than the average conversion tract size , however , it is unlikely that the relative lack of AA/TT dinucleotides is the only factor influencing the frequency of UV-induced conversion events that include these elements . Finally , we note that the frequency of AA/TT dinucleotides in non-coding RNA genes , which are significantly over-represented in the DSCB conversion tracts ( Table S9 ) , is also higher ( 0 . 23 ) than the frequency for the whole genome . A more detailed discussion of the relationship between various chromosome elements and conversion tracts ( Tables S9 and S10 ) is given in Text S1 . Although dimer formation has a simple relationship to DNA sequence , the rate of NER-mediated repair of the dimers is enhanced by transcription and reduced by chromatin silencing and other aspects of chromatin structure [45]–[47] . Our discussion of dimer repair will be limited to NER , since our experiments were done under conditions in which photoreactivation was prevented . In general , dimer repair is rapid in yeast with the majority of dimers being removed within two hours [48] . Our observation that UV treatment of G1-synchronized cells primarily results in recombination in the first cell cycle following radiation is consistent with efficient dimer repair . Nonetheless , Teng et al . ( 2011 ) [44] found genomic regions in which dimer repair was delayed . To test whether these long-lasting lesions could be more recombinogenic than lesions that were quickly repaired , we determined whether the chromosome regions containing the long-lasting lesions were over-represented in our unselected gene conversion tracts ( details of the analysis in Text S1 ) . There was not a significant enrichment of the regions with long-lasting lesions in our unselected gene conversion tracts . Most previous studies of BIR involve transforming linear fragments of DNA or using strains in which the interacting homologous sequences are flanked by non-homologous regions [4] , [49] . In contrast , our ability to distinguish BIR from crossovers is based on the recovery of both cells containing recombinant products . Among unselected LOH events examined in G1-synchronized cells irradiated with 15 J/m2 , we observed 60 crossovers and 21 BIR events . By the microarray analysis , as described previously , we detect only half of crossovers . All BIR events , however , can be detected . We conclude , therefore , that crossovers are induced about six-fold more than BIR events . This conclusion is in agreement with previous observations of spontaneous recombination events [50] , and events induced by the I-SceI endonuclease [51] performed by others . About 60% of the UV-induced BIR events appear to be randomly distributed , whereas the remainder have a breakpoint located within 50 kb of the telomere . We suggest that there are two types of BIR events , the “classic” type in which one of the chromosome fragments is lost prior to second end capture , and a second type that is initiated by degradation of one of the two homologs beginning at the telomere . In two previous studies [52] , [53] , LOH events near the telomere were observed in strains with mutations in genes affecting telomere structure or replication ( CDC13 and EST1 ) . It was not determined in these studies whether these LOH events were crossovers or BIR events . Since the BIR events in our study were induced by UV , one interpretation of our results is that high doses of UV are associated with telomere uncapping or some other telomere defect . An alternative explanation of our observation that BIR events are enriched near the telomere is that such events are more efficiently initiated and completed than events located more internally on the chromosome , as demonstrated by Donnianni and Symington [49] . In meiosis in S . cerevisiae , roughly half of the conversion events are associated with crossovers [1] , [54] . The fraction of conversions associated with crossovers varies in different studies from <5% to 50% [4] . In the unselected events induced by 15 J/m2 , we observed 300 conversions without an observable crossover , and 60 crossovers . Because of the pattern of chromosome segregation , we expect only half of the crossovers will lead to LOH distal to the exchange . We calculate that there are likely 240 conversions unassociated with crossovers and 120 associated with crossovers . Thus , we conclude that about one-third of the unselected conversion events are associated with a crossover , similar to our previous conclusion based on a smaller number of events [7] . In our previous analysis of gamma ray-treated G2-synchronized diploids , we observed about two unselected chromosome aberrations per irradiated cell [31] . We found that most of these events reflected homologous recombination between Ty elements located at non-allelic positions . In our current study , although UV treatment induced about eight unselected LOH events per cell irradiated with 15 J/m2 , we did not detect any large deletions , duplications , or translocations . The difference in these two studies is likely to reflect the total number of DSBs and other recombinogenic lesions generated by the two treatments . In the Argueso et al . study , the gamma ray dose ( 800 Gray ) produces about 250 DSBs/cell . Based on the estimate that 40 J/m2 of UV results in 5–10 DSBs in G2-synchronized cells [42] , we expect only about two DSBs/cell as the result of irradiating G1-synchronized cells with 15 J/m2 . Since Ty elements , the main target for chromosome rearrangements , represents only a small fraction of the genome ( 2% ) , the likelihood of a UV-induced DSB within Ty elements is small , although DSBs located near Ty elements can also contribute to Ty-Ty exchanges [55] . Of the 360 unselected conversion and crossover events induced by UV , 14 included a Ty element; it is unclear whether these events initiated within or nearby the Ty . Although the lack of recombinogenic lesions within or near Ty elements may be sufficient to explain the dearth of chromosome rearrangements , other factors may also be important , since UV does not effectively stimulate recombination between non-allelic Ty elements [56] , [57] .
Most of our experiments were done with the diploid strain PG311 , a hybrid that is heterozygous for about 52 , 000 SNPs [2] . The PG311 genotype is: MATa/MATα::NATade2-1/ade2-1 can1-100/can1Δ::SUP4-o ura3-1/URA3 trp1-1/TRP1 his3-11 , 15/HIS3 leu2-3 , 112/LEU2 V9229::HYG/V9229 V261553::LEU2/V261553 GAL2/gal2 RAD5/RAD5 . Additional features of this strain are described in Text S1 ( Supplemental Materials and Methods ) . We also describe the strains JSC24 , JSC25 and PSL101 , all of which are isogenic to PG311 except for the specified alterations . The experiments to measure recombination within the ribosomal RNA genes were done with the diploid AMC45 that is heterozygous for markers flanking the array and within the array [34] . The diploid YYy13 is a MATα::NAT derivative of AMC45 . Unlike the other strains used in our analysis , AMY45 and YYy13 are not isogenic with PG311 . Standard rich growth medium ( YPD ) and omission media were used for these experiments [58] . We also used standard conditions for tetrad analysis , transformation , and DNA isolation . In most of our experiments , PG311 cells were synchronized in G1 using α-factor or in G2 using nocodazole as described by Lee and Petes [11] . After two hours of treatment with these agents , the cells were plated on medium lacking arginine and irradiated with UV using a TL-2000 UV Translinker; doses varied between 1 and 15 J/m2 . Following the UV treatment , the plates were covered with foil to prevent light-associated removal of dimers , and incubated for two days to allow the formation of sectored colonies . In some experiments , modifications of this protocol were employed as described in Supplemental Materials and Methods . LOH events in PG311 and related strains were detected using SNP microarrays . For each SNP , these Agilent-constructed microarrays contain four oligonucleotides , one pair that hybridizes to the YJM789-derived SNP allele and another that hybridizes to the W303a-derived SNP allele [7] . About 13 , 000 SNPs distributed throughout the genome were examined . A short description of the use of SNP microarrays is in the Results section and additional details are given in St . Charles et al . ( 2012 ) [7] . In brief , genomic DNA from the experimental strain was labeled with Cy5-dUTP and control DNA from the fully heterozygous strain JSC24-2 was labeled with Cy3-dUTP . The two DNA samples were then hybridized in competition to the SNP microarrays . The microarray was examined using a GenePix scanner . By measuring the ratio of hybridization of the two differentially-labeled samples , we could determine which SNPs were heterozygous and which were homozygous . Most of our statistical analysis involved chi-square analysis , the Fisher exact test , or the Mann-Whitney test . These tests were done using the VassarStat Website ( http://vassarstats . net/ ) or the functions associated with Excel . To calculate 95% confidence limits on the median , we used Table B11 of Altman ( 1990 ) [59] .
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Nearly every living organism has to cope with DNA damage caused by ultraviolet ( UV ) exposure from the sun . UV causes various types of DNA damage . Defects in the repair of these DNA lesions are associated with the human disease xeroderma pigmentosum , one symptom of which is predisposition to skin cancer . The DNA damage introduced by UV stimulates recombination and , in this study , we characterize the resulting recombination events at high resolution throughout the yeast genome . At high UV doses , we show that most recombination events reflect the repair of two sister chromatids broken at the same position , indicating that UV can cause double-stranded DNA breaks . At lower doses of UV , most events involve the repair of a single broken chromatid . Our mapping of events also demonstrates that certain regions of the yeast genome are relatively resistant to UV-induced recombination . Finally , we show that most UV-induced DNA lesions are repaired during the first cell cycle , and do not lead to recombination in subsequent cycles .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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Genome-Wide High-Resolution Mapping of UV-Induced Mitotic Recombination Events in Saccharomyces cerevisiae
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Control of rabies requires a consistent supply of dependable resources , constructive cooperation between veterinary and public health authorities , and systematic surveillance . These are challenging in any circumstances , but particularly during conflict . Here we describe available human rabies surveillance data from Iraq , results of renewed sampling for rabies in animals , and the first genetic characterisation of circulating rabies strains from Iraq . Human rabies is notifiable , with reported cases increasing since 2003 , and a marked increase in Baghdad between 2009 and 2010 . These changes coincide with increasing numbers of reported dog bites . There is no laboratory confirmation of disease or virus characterisation and no systematic surveillance for rabies in animals . To address these issues , brain samples were collected from domestic animals in the greater Baghdad region and tested for rabies . Three of 40 brain samples were positive using the fluorescent antibody test and hemi-nested RT-PCR for rabies virus ( RABV ) . Bayesian phylogenetic analysis using partial nucleoprotein gene sequences derived from the samples demonstrated the viruses belong to a single virus variant and share a common ancestor with viruses from neighbouring countries , 22 ( 95% HPD 14–32 ) years ago . These include countries lying to the west , north and east of Iraq , some of which also have other virus variants circulating concurrently . These results suggest possible multiple introductions of rabies into the Middle East , and regular trans-boundary movement of disease . Although 4000 years have passed since the original description of disease consistent with rabies , animals and humans are still dying of this preventable and neglected zoonosis .
The first written record of disease consistent with rabies is in the Laws of Eshunna , a Sumerian city in ancient Mesopotamia . Largely corresponding to the region of what is now the Republic of Iraq , Mesopotamia encompassed the Euphrates and Tigris river systems and is considered by many to be the birthplace of civilisation . Some of the earliest archaeological records of the domestication of dogs also originate from the area , and dogs are thought to have had religious significance during that time [1] . Those Eshunnian laws , written almost 4000 years ago , warn of fines for owners of uncontrolled ‘mad’ dogs that bite humans [2] . The association of disease with infected dogs is consistent with the knowledge that rabies is spread through the saliva of infected animals , and that dogs are the major reservoir for infections of humans in many endemic areas [3] . It wasn't until the last century , that rabies virus , a single stranded RNA virus in the family Rhabdoviridae , genus Lyssavirus , was identified as the causative agent [4] . Baghdad was established as the centre of the Arab world during the middle ages , and endured repeated changes in rule until the region came under the control of the Ottoman Empire in the 14th century . Although information on rabies incidence during Ottoman rule is scarce , the speed at which the medical authorities adopted Pasteur's vaccine after the development was first published in 1885 , illustrates the importance of the disease at that time [5] . The current borders of Iraq were demarcated by the Treaty of Sèvres in 1920 , when the Ottoman Empire was fragmented after World War I . After a brief period under control of Great Britain , Iraq first became an independent Kingdom in 1932 , and then a Republic when the monarchy was overthrown in 1958 . An effective hospital based health system was developed , but was reported to have deteriorated toward the end of the 20th century due to conflict , embargoes and sanctions [6] , [7] . Increase in conflict from 2003 has had further effects on disease control , with migration of health professionals and restrictions on travel and resources [7] . Rabies is considered endemic in most countries in the Middle East , but establishing the true burden is prevented by a relative lack of systematic surveillance and reporting [8] . Previous studies have provided valuable insight into the molecular epidemiology of canine rabies in the Middle East , but isolates from Iraq have not previously been available for analysis [9] , [10] . The Middle East and Eastern Europe Rabies Expert Bureau ( MEEREB ) network was established in 2010 to improve regional collaboration in rabies control and has improved exchange of information [11] . Although Iraq is not currently represented in the network , rabies is reported in both dogs and wildlife in neighbouring countries , with dogs the main reservoir of rabies to humans . Iraq's two largest neighbours , Turkey and Iran , both reported similar estimates of annual human rabies incidence of 0 . 02/million and 0 . 025/million respectively in 2009 , and similar levels of PEP administration , at 1 , 700 and 2 , 290 per million population during the same year . Control measures in Turkey have effectively reduced dog rabies to restricted foci in urban areas . However , despite this reduction in dog rabies and implementation of control measures , rabies has re-emerged in the Aegean region , highlighting the complexities of controlling the disease [5] . There is minimal systematic surveillance for animal rabies in Iraq , and no laboratory confirmation of diagnosis . Vaccination is compulsory for dogs , but the majority of the urban dog population is considered ownerless , free-roaming and therefore presumed unvaccinated . There are no coordinated dog vaccination or sterilisation campaigns and dog population control has traditionally been attempted through culling , known to be an insufficient measure [12] . Rabies is also sporadically reported in wildlife , particularly in western regions , but the prevalence in wildlife , and the role of wildlife in maintenance and transmission of RABV to domestic animals and humans is poorly understood . To address the lack of available data on rabies and to inform control strategies , sampling was initiated in the Baghdad region in April 2010 . Here we report results of laboratory diagnosis and virus characterisation from these initial sampling efforts alongside official surveillance data for human rabies across Iraq .
Human rabies is notifiable in Iraq through regional public health offices in each of the 18 Governorates ( provinces ) . Private and public health centres and hospitals report rabies cases based on a clinical definition of encephalitis , combined with hydrophobia and history of animal bite . There is no routine laboratory diagnosis undertaken . The regional public health offices report to the Zoonoses Section of the Centre for Disease Control ( CDC ) in Baghdad , who also collate post-exposure prophylaxis and reported animal bite numbers . These anonymized data on human rabies cases , animal bites and post-exposure prophylaxis were reviewed for the period 2001–2010 . Analysis of the data was approved by the AHVLA Ethics Committee . Differences in rabies incidence between groups ( age , sex and rural/urban habitation ) were assessed using Chi-squared tests . Expected frequencies for age and area of habitation were taken from a separate recent household survey undertaken by others [13] ( Table 1 ) .
Data on reported rabies cases were supplied by all 18 regional public health offices . In the 10 years between 2001 and 2010 , there was an average of 17 ( SD 6 . 9 ) human rabies cases reported annually in Iraq ( Figure 1a ) . There was a three-fold increase in reported cases between 2003 and 2005 and , although the number of cases has varied from year to year , there has not been less than 15 cases reported per year since 2005 . Human rabies incidence for Iraq during 2009 is estimated from these data at 0 . 89 deaths per million population , using a population estimate of 30 million [13] ( Table 1 ) . Children are over represented among rabies cases in Iraq . An estimated 40% of the population is under 15 years of age [13] , yet 63% of cases occur in this age group ( X2 = 48 . 4 , p = 0 . 0001 ) . Rabies is also more frequently reported in rural areas than urban areas , with 83% of cases reported in rural areas despite only 29% of the population living in rural areas [13] ( X2 = 283 , p = 0 . 0001 ) . However , there has also been an apparent three-fold increase in the number of cases reported in Baghdad over the past ten years ( albeit not statistically significant ) , with an average of 2 cases per year reported in between 2001 and 2002 , and 6 cases reported per year between 2009 and 2010 ( Figure 1b ) . There is an extreme bias towards males , with eight cases in males reported for every one case in a female despite a population sex ratio of 1∶1 [13] ( X2 = 122 , p = 0 . 0001 ) . There is regional variation in the number of reported cases , with Governorates in the centre of the country reporting the highest incidence per 100 , 000 population during 2001–2010 ( Figure 2 and Table S2 ) . Rabies prophylaxis is available in Iraq , although is not always initiated , and is rarely completed . The five-dose ( Essen ) regime is most frequently followed , and although a large proportion of dog bite victims receive the first vaccination ( 75% ) a much lower number complete the full course ( 7% ) . From 2002 to 2004 there were less than 1000 dog bites reported annually in Baghdad , corresponding to an incidence of 20 ( 95% CI 18 . 76–21 . 24 ) bites per 100 , 000 people , based on a population estimate of 5 million [18] . In the years between 2007 and 2010 the average had increased to 3300 bites reported per year ( Figure 1c ) , corresponding to an annual incidence of 46 ( 95% CI 44 . 27–47 . 40 ) bites per 100 , 000 people , using a population estimate of 7 . 2 million [19] ( paired incident rate test , p<0 . 0001 ) ( Figure 1c ) . Three out of 40 brain samples were positive for rabies virus by both FAT and RT-PCR . The three positive samples yielded unique partial nucleoprotein gene ( N-gene ) sequences ( Genbank accession numbers JX524176-8 ) . Phylogenetic analysis using a 400 base pair region of the N-gene showed that the viruses are closely related , forming a well supported clade separated from other published sequences ( Figure 3 ) . A relaxed molecular clock model applied to these data ( assuming constant virus population size ) suggests they share a common ancestor approximately 22 years ago ( 95% HPD 14–32 years ) with viruses in the cosmopolitan lineage of RABV , from neighbouring countries including Turkey , Iran , and Syria .
Rabies is a preventable disease , and yet the data presented here demonstrate that it remains a significant public and animal health challenge in Iraq . All except two of the 18 Governorates reported human rabies cases during the period of study , indicating that rabies is endemic and widespread across the country . The reported incidence of human rabies far exceeds that reported by some neighbouring countries . Incidence during 2009 is estimated from these data at 0 . 89 deaths per million population , compared with 0 . 025 for Turkey and 0 . 02 for Iran [11] . There was an increase in reported cases for the whole country after 2003 and a three-fold increase in reported cases in Baghdad in the ten years between 2001 and 2010 . This increase coincides with a period of intense conflict in Iraq , with the potential to have widespread direct and indirect consequences on disease control . These include well documented affects of the migration of health professionals and restrictions on travel , resources and sanitary conditions previously reported to cause increases in infectious diseases such as typhoid , measles and mumps in Iraq [6] , [20] . As rabies is a zoonotic disease , with domestic dogs as primary reservoir host in many regions , changes to the free-roaming dog population will also have a large effect on human rabies incidence [3] . The effects of conflict on municipal services and disruption of human habitation are likely to have an impact on the urban dog population . The doubling of reported dog bites in Baghdad reported here coincide with the increase in human rabies cases and anecdotal reports of a mass expansion of the free-roaming dog population in Baghdad . It is likely therefore , that the increase in human rabies is due to an increase in the free roaming dog population and associated increase in dog bites . In addition to the increase in risk of zoonotic disease , the dog population increase has animal welfare implications . Historical approaches to manage dog populations in Baghdad have included pro-active culling in areas where large accumulations of free roaming dogs are reported . Although this temporarily results in fewer free roaming dogs , it is increasingly recognised that indiscriminate culling is not a long-term solution to dog population control , or to reducing rabies prevalence , has welfare implications and can make the situation worse [3] , [12] . Animal birth control ( ABC ) programs , where sterilisation is combined with rabies vaccination , have proven effective in reducing or stabilising free-roaming dog populations , and also reducing rabies incidence [21] , [22] but they may not be necessary in many socioeconomic settings [3] , [23] , [24] , [25] . Therefore , where dog population management is used , it should be according to international guidelines and only after assessment of the local dog population [26] . Promotion of responsible dog ownership and appropriate legislative measures are also recommended as longer term solutions for controlling rabies in Iraq [12] . As with all studies using human rabies surveillance data , the data presented here have limitations . The low reported numbers of human rabies cases in Baghdad preclude robust statistical comparison , meaning that the apparent increase in cases in Baghdad could be the result of annual variation rather than a genuine increase in cases . In addition , human rabies cases are currently only diagnosed based on clinical data without laboratory confirmation . The incidence of other diseases with overlapping clinical presentations such as bacterial or viral encephalitis , could affect reporting if they are misdiagnosed as rabies , or if rabies cases are incorrectly diagnosed as other diseases [27] , [28] . There are few published data on the incidence of encephalitidies in Iraq , but the region is at risk from pathogens , including tick borne encephalitis and West Nile virus [29] , which could potentially be misdiagnosed as rabies . Finally , reporting effort may change overtime with changes in staff and resources , or between public health offices . Therefore we cannot control for possible change in reporting effort over time , or between Governorates . The low numbers of bite victims completing a full course of post exposure prophylaxis is a significant risk to public health , but is a problem that is not unique to Iraq [30] . The reasons for failure to start or complete PEP given in similar contexts include failure to present for primary health care , cost of treatment , and misunderstanding of the risks [30] . Therefore increased public awareness and education may increase PEP uptake and reduce rabies deaths . To reduce the cost of PEP , a strategy adopted by some rabies endemic countries , is a reduced dose administered intradermally , which could be considered in Iraq [31] . The extreme male: female bias in cases is likely to be due to different occupational and domestic roles , causing different levels of exposure to potentially rabid animals but could also be affected by different tendency to present for treatment between the sexes [30] , [32] , [33] . Assuming negligible regional bias in reporting , the high numbers of cases in rural areas relative to the population means rural areas should be a priority for public awareness and rabies control . There is no reported laboratory confirmation of rabies in Iraq , and circulating strains have not been previously characterised . In this study , brain samples for rabies diagnosis were analysed on an opportunistic basis from animals euthanased during the sampling period with one or more signs consistent with rabies . The prevalence of rabies in the sampled population ( 3/40 ) is lower than might be expected in a rabies endemic country . The clinical criteria for the sampled population included all animals with one or more sign consistent with rabies . Considering many signs of rabies are common to other diseases , this definition will include many non-rabid animals . All three positive samples were from Northern Baghdad , and two of the three positive cases were cattle . Cattle are considered dead-end hosts for rabies , and therefore these cases in cattle are likely to be spill over of the virus from undetected cases in dogs , or wildlife , in the area . This is supported by the phylogenetic evidence , suggesting these cases are from the same lineage circulating in dogs . The species bias towards large and economically important livestock animals provides supporting evidence to the hypothesis that the incidence is higher than these results suggest , and that many dog or wildlife cases go undetected or unreported . In addition , due to the necessary opportunistic nature of sampling , absence of cases in other areas does not imply freedom from rabies , and the same public and animal health interventions should be applied in all areas . Security concerns were a key restriction on sampling location and strategy in this study , and more comprehensive and systematic surveillance would be required to provide estimates of the incidence of animal rabies . There are few isolates available from the region for phylogenetic analysis and hence sequence data from these cases therefore provide valuable information . A previously sequenced RABV reportedly from a dog in Iraq [34] was subsequently determined to have been incorrectly recorded , and it in fact originated in Afghanistan [Freuling , C . , FLI Germany , pers . comm . ] . Consequently , the present report is the first to characterise rabies strains in Iraq . The three sequences are unique over the region studied , but form a separate clade that diverged from related viruses approximately 22 ( HPD 14–32 ) years ago . The most closely related viruses ( with published sequence available ) to this new Iraqi clade are from multiple different regions: Turkey , Iran , Syria and Georgia , which lie to the North East and West of Iraq . Turkey and Iran both have strains from other clades in the tree circulating contemporaneously with those in the Iraq clade . This information , combined with previously published evidence for a recent origin of RABV diversity in parts of the Middle East [10] suggests multiple introductions of rabies into the region , and likely transboundary movements of disease . Knowing the direction of any transboundary movements would help guide regional control policies , but would require a larger number of rabies sequences from the region to reduce sampling bias and increase resolution of phylogeographic analysis . These viruses circulating in the Middle East fall within the cosmopolitan lineage of RABV , suggested to have been spread worldwide by human movements in the 18th and 19th centuries [10] , [35]–[37] . Estimates using these data for the most recent common ancestor for this European/Middle Eastern clade of the cosmopolitan lineage ( 101 years ago , HPD 59–114 ) concur with previous estimates [10] . These close relationships between viruses from Iraq and neighbouring countries reiterate that rabies does not respect cultural or political barriers and elimination of rabies must be approached at a regional level , with global cooperation from international veterinary ( OIE/FAO ) and health ( WHO ) providers . This is already being addressed through NGOs , and the MEEREB network but will require consistent commitment and resources [11] , [38] . Despite the on-going conflict , notable efforts , with international support , are being made to improve primary health care in addition to hospital based healthcare in Iraq [39] , [40] . In addition , collaboration between environmental , human and veterinary health departments is essential at a local level to eliminate rabies in dogs , and thereby reduce the risk to humans [41] .
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Control of rabies requires cooperation between government departments , consistent funding , and an understanding of the epidemiology of the disease obtained through surveillance . Here we describe human rabies surveillance data from Iraq and the results of renewed sampling for rabies in animals . In Iraq , it is obligatory by law to report cases of human rabies . These reports were collated and analysed . Reported cases have increased since 2003 , with a marked increase in Baghdad 2009–2010 . There is no system for detecting rabies in animals and the strains circulating in Iraq have not previously been characterized . To address this , samples were collected from domestic animals in Baghdad and tested for rabies . Three out of 40 were positive for rabies virus . Comparison of part of the viral genetic sequence with other viruses from the region demonstrated that the viruses from Iraq are more closely related to each other than those from surrounding countries , but diverged from viruses isolated in neighbouring countries approximately 22 ( 95% HPD 14–32 ) years ago . Although 4000 years have passed since the original description of disease consistent with rabies , animals and humans are still dying of this preventable and neglected zoonosis .
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2013
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Rabies in Iraq: Trends in Human Cases 2001–2010 and Characterisation of Animal Rabies Strains from Baghdad
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Many natural prion diseases of humans and animals are considered to be acquired through oral consumption of contaminated food or pasture . Determining the route by which prions establish host infection will identify the important factors that influence oral prion disease susceptibility and to which intervention strategies can be developed . After exposure , the early accumulation and replication of prions within small intestinal Peyer’s patches is essential for the efficient spread of disease to the brain . To replicate within Peyer’s patches , the prions must first cross the gut epithelium . M cells are specialised epithelial cells within the epithelia covering Peyer’s patches that transcytose particulate antigens and microorganisms . M cell-development is dependent upon RANKL-RANK-signalling , and mice in which RANK is deleted only in the gut epithelium completely lack M cells . In the specific absence of M cells in these mice , the accumulation of prions within Peyer’s patches and the spread of disease to the brain was blocked , demonstrating a critical role for M cells in the initial transfer of prions across the gut epithelium in order to establish host infection . Since pathogens , inflammatory stimuli and aging can modify M cell-density in the gut , these factors may also influence oral prion disease susceptibility . Mice were therefore treated with RANKL to enhance M cell density in the gut . We show that prion uptake from the gut lumen was enhanced in RANKL-treated mice , resulting in shortened survival times and increased disease susceptibility , equivalent to a 10-fold higher infectious titre of prions . Together these data demonstrate that M cells are the critical gatekeepers of oral prion infection , whose density in the gut epithelium directly limits or enhances disease susceptibility . Our data suggest that factors which alter M cell-density in the gut epithelium may be important risk factors which influence host susceptibility to orally acquired prion diseases .
Prion diseases ( transmissible spongiform encephalopathies ) are a unique group of subacute neurodegenerative diseases that affect humans and animals . During prion disease , aggregations of PrPSc , an abnormally folded isoform of cellular PrP ( PrPC ) , accumulate in affected tissues . Prion infectivity co-purifies with PrPSc and constitutes the major , if not sole , component of the infectious agent [1–3] . Many natural prion diseases , including natural sheep scrapie , bovine spongiform encephalopathy ( BSE ) , chronic wasting disease in cervids , and variant Creutzfeldt-Jakob disease in humans ( vCJD ) , are acquired peripherally , such as by oral consumption of prion-contaminated food or pasture . The precise mechanism by which orally-acquired prions are propagated from the gut lumen across the epithelium to establish host infection is uncertain . In the U . K . relatively few vCJD cases have fortunately occurred despite widespread dietary exposure to BSE [4] , suggesting that the acquisition of prions from the gut lumen may differ between individuals . Further studies are clearly necessary to precisely characterise the cellular route that prions exploit to establish infection after oral exposure , and how alterations to this cellular route , both intrinsic and extrinsic , can affect disease susceptibility . Treatments which prevent the accumulation and replication of prions in host lymphoid tissues can significantly reduce disease susceptibility [5–9] . Therefore , identification of the cellular route by which prions are first transported across the gut epithelium to achieve host infection will identify an important factor which influences oral prion disease susceptibility and to which intervention strategies can be developed . Following oral exposure the early accumulation and replication of prions upon follicular dendritic cells ( FDC ) within the gut associated lymphoid tissues ( GALT ) , such as Peyer’s patches of the small intestine , is essential for efficient neuroinvasion [7 , 10–13] . FDC are a unique subset of stromal cells resident within the primary B cell follicles and germinal centres of lymphoid tissues [14] . After amplification upon the surface of FDC [15] , the prions then infect neighbouring enteric nerves and spread along these to the CNS ( a process termed neuroinvasion ) where they ultimately cause neurodegeneration and death of the host [16–19] . The follicle-associated epithelia ( FAE ) which covers the lumenal surfaces of the Peyer’s patches contains a unique population of epithelial cells , termed M cells . These highly phagocytic epithelial cells are specialized for the trans-epithelial transfer of particulate antigens and microorganisms from the gut lumen ( termed transcytosis ) [20] , an important initial step in the induction of efficient mucosal immune responses against certain pathogenic bacteria [21 , 22] and the commensal bacterial flora [23] . A variety of bacterial and viral pathogens including Brucella abortus [24] , Salmonella Typhimurium [25] , Yersinia enterocolitica [26] , norovirus [27 , 28] and reovirus [28] appear to exploit the transcytotic activity of M cells to cross the gut epithelium and infect the host . The food-borne botulinum neurotoxin [29] has also been suggested to exert its toxicity after transcytosis by M cells [29] . Independent studies suggest orally administered prions may similarly be transported by M cells into host tissues [9 , 30–32] and that this transport may be important to establish host infection [9] . Other studies have also suggested that prions can be transported across the gut epithelium via enterocytes , independently of M cells [16 , 33 , 34] , however to what extent enterocyte-transported prions contribute to the establishment of host infection has not been assessed . The differentiation of M cells from uncommitted precursors in the intestinal crypts is critically dependent on stimulation from the cytokine known as RANKL ( receptor activator of nuclear factor-κB ligand ) . This cytokine is expressed by subepithelial stromal cells beneath the FAE in Peyer’s patches , and signals via its receptor RANK ( receptor activator of nuclear factor-κB ) which is expressed by epithelial cells throughout the intestine [35] . Accordingly , M cell-differentiation is blocked in RANKL-deficient mice or following in vivo RANKL-neutralization with anti-RANKL antibody [35] . RANKL stimulation induces a program of gene expression in intestinal epithelial cells which includes the transcription factor SPIB . Expression of SPIB by intestinal epithelial cells is essential for their differentiation and functional maturation into M cells [22 , 36 , 37] . We have previously reported that the early accumulation of prions upon FDC in Peyer’s patches and subsequent neuroinvasion were blocked in mice in which M cells were transiently depleted by RANKL-neutralization using anti-RANKL antibody [9] . However , since RANKL-RANK signalling has multiple roles in the immune system , a more refined model is required to specifically determine the role of M cells in oral prion disease pathogenesis . In the current study a unique conditional knockout mouse model was used in which RANK expression was specifically deleted only in the intestinal epithelium ( RANKΔIEC mice ) [23 , 38] . In these mice the complete loss of M cells prevents M cell-mediated antigen uptake from the gut lumen , without altering other RANKL-RANK signalling events required for normal immune development and function [23 , 38] . Using these mice our data clearly show that M cells are critically required for the initial trans-epithelial transfer of prions across the gut epithelium into Peyer’s patches in order to establish host infection . Certain pathogenic bacteria [25 , 39] or exposure to inflammatory stimuli such as cholera toxin [40] can significantly increase the density of M cells in the intestine . Inflammation or pathogen infection can also influence prion disease pathogenesis by enhancing the uptake , or expanding the distribution , of prions within the host [11 , 41–43] . This raised the hypothesis that exposure to inflammatory stimuli that enhance M cell-density might increase oral prion disease susceptibility by enhancing the uptake of prions from the gut lumen . We show that increased M cell-density at the time of oral exposure dramatically enhanced the uptake of prions from the gut lumen , decreased survival times and increased disease susceptibility by approximately 10-fold . Our data provide a significant advance in our understanding of how prions exploit M cells to initially infect Peyer’s patches and how factors that increase the density of M cells in the gut epithelium , such as concurrent pathogen infection , may have the potential to increase susceptibility to orally-acquired prion infection .
Our previous study showed that oral prion infection was blocked after transient M cell-depletion by treatment with anti-RANKL antibody , implying a functional role for M cells in the trafficking of prions from the lumen into GALT in vivo [9] . Although the major phenotype observed in the intestine was a transient loss of mature M cells , RANKL-RANK signalling is also important in immune system and lymphoid tissue development . Therefore , systemic RANKL neutralization by treatment with anti-RANKL antibody could have affected other important cellular processes involved in prion pathogenesis . To exclude these , we used a more refined model of M cell-deficiency , RANKΔIEC mice [23 , 38] , to further elucidate the role of M cells in the transport of prions from the intestinal lumen into GALT . These mice are specifically deficient in Tnfrsf11a ( which encodes RANK ) only in Vil1-expressing intestinal epithelial cells . As previously published [23] , whole-mount immunostaining for the mature M cell marker glycoprotein 2 ( GP2; [21 , 22] ) revealed an absence of GP2+ M cells in the FAE of the Peyer’s patches of RANKΔIEC mice compared to control ( RANKF/F ) mice ( Fig 1A & 1B ) . Coincident with the loss of RANK expression in the gut epithelium was a significant reduction in area of the FAE ( Fig 1C ) . Assessment of the uptake of fluorescent latex microbeads from the gut lumen into Peyer’s patches is a reliable in vivo method to compare the functional ability of M cells to acquire and transcytose particulate antigens . Here , RANKΔIEC mice and RANKF/F control mice ( n = 3/group ) were orally gavaged with 2x1011 200 nm fluorescent microbeads , and 24 h later the number of microbeads in their Peyer’s patches quantified by fluorescence microscopy . This duration was selected to ensure sufficient time for the beads to transit through the intestine and be transcytosed by M cells in the FAE overlying the Peyer’s patches [13] . Coincident with the absence of mature GP2+ M cells , RANKΔIEC mice had substantially less fluorescent microbeads within the subepithelial dome ( SED ) regions of their Peyer’s patches when compared to controls ( Fig 1D ) , indicating a dramatic reduction in the ability to sample particulate antigen from the gut lumen . RANK-dependent GP2+ M cells have been described in the epithelium of the nasal associated lymphoid tissue ( NALT ) [44 , 45] . The abundance of GP2+ M cells in the NALT was unaffected in RANKΔIEC mice ( Fig 1E ) , highlighting the intestinal specificity of the model . In addition to being transported through M cells , prions have also been observed trafficking into Peyer’s patches through the large LAMP1+ endosomes of FAE enterocytes [16] . Immunohistochemical ( IHC ) analysis of LAMP1 expression showed that these endosomes were still present in the FAE of RANKΔIEC mice ( Fig 1F ) . If the presence of these endosomes in the FAE was dependent on RANKL-RANK signalling , we reasoned that the abundance of LAMP1+ immunostaining would be decreased in the FAE of RANKΔIEC mice . However , morphometric analysis indicated equivalent areas of LAMP1+ immunostaining in the FAE of RANKΔIEC and RANKF/F mice ( Fig 1G ) . These data suggest that the presence of LAMP1+ endosomes in the FAE was not RANKL-RANK signalling dependent . Antigens that are transcytosed by M cells are released into their basolateral pockets where they are sampled by lymphocytes and mononuclear phagocytes ( MNP; a heterogeneous population of macrophages and classical dendritic cells; DC ) [46–48] . The acquisition of prions by MNP such as CD11c+ classical DC may mediate their initial transport to FDC [8 , 16 , 49] , and the subsequent transfer of prions from FDC to the peripheral nervous system [50–52] . IHC and morphometric analysis revealed a significant reduction in the % area of CD11c-specific immunostaining in the SED of the Peyer’s patches from RANKΔIEC mice ( Fig 2A & 2B ) , whereas the % area of CD68-specific immunostaining ( indicative of tissue macrophages ) was equivalent in RANKΔIEC and RANKF/F mice ( Fig 2A & 2C ) . Analysis of the intestinal lamina propria ( LP ) showed a similar trend ( Fig 2D–2F ) . Following replication upon FDC , the prions subsequently infect enteric nerves ( both sympathetic and parasympathetic ) to reach the CNS where they ultimately cause neurodegenerative disease [16 , 18] . Our IHC analysis of the expression of the neuronal synaptic vesicle marker synaptophysin 1 suggested that the magnitude of the enteric innervation in the LP was similar in the intestines of RANKΔIEC and RANKF/F mice ( Fig 2G & 2H ) . Together these data demonstrate that RANKΔIEC mice represent a refined model in which to study the specific role of M cells in oral prion disease pathogenesis . The early replication of many prion strains upon FDC within the B cell-follicles of the draining lymphoid tissues is essential for their efficient transmission to the CNS after peripheral exposure [5–7 , 15] . FDC in mice characteristically express high levels of CD21/35 ( complement receptors 2 & 1 , respectively ) . Our IHC analysis showed that the area of CD21/35-specific immunostaining in Peyer’s patches of 10 wk old RANKΔIEC and RANKF/F mice was similar ( Fig 3A & 3B ) , suggesting that the size of the FDC networks ( CD21/35+ cells ) in the Peyer’s patches of each mouse strain was equivalent . The replication of prions upon FDC is critically dependent on their expression of PrPC [15 , 53 , 54] . Morphometric analysis also indicated that the magnitude of the PrPC-expression co-localized upon CD21/35+ FDC in the Peyer’s patches ( Fig 3A & 3C ) and mesenteric lymph nodes ( MLN ) ( Fig 3D & 3E ) of RANKΔIEC mice and RANKF/F mice was similar . We next determined whether the FDC in the lymphoid tissues of RANKΔIEC mice were capable of accumulating prions to a similar extent as those of control mice . After injection by the intra-peritoneal ( i . p . ) route high levels of prion accumulation and replication are first detected in the spleen within 35 d post infection ( dpi ) [53] . The prions are then subsequently disseminated around the host via the blood and lymph to most other secondary lymphoid tissues [55] . Furthermore , by 140 dpi the prions are also detectable within Peyer’s patches . Since the prions do not need to cross the gut epithelium to eventually infect the Peyer’s patches after injection by the i . p . route , RANKΔIEC and RANKF/F were injected with a 1% dose of ME7 scrapie prions via this route and tissues collected at 140 dpi , to determine whether the FDC in the lymphoid tissues of RANKΔIEC mice were capable of accumulating prions . Prion disease-specific accumulations of PrP ( referred to as PrPd ) were detected by immunostaining for the abnormal aggregates of PrP characteristically present only in affected tissues [6 , 9 , 11 , 13 , 53 , 56] , complimented with paraffin-embedded tissue ( PET ) blot analysis of adjacent membrane-bound sections to confirm that these aggregates contained relatively proteinase-K ( PK ) -resistant prion disease-specific PrPSc [57] . Abundant accumulations of PrPSc were evident in association with FDC ( CD21/35+ cells ) in the Peyer’s patches , MLN and spleens of RANKΔIEC and RANKF/F mice ( Fig 4A–4C ) . These data clearly show that the FDC in the Peyer’s patches , MLN and spleen of RANKΔIEC mice were functionally capable of acquiring and accumulating prions , and that the dissemination of prions between lymphoid tissues was not impaired . Importantly , these data also suggest that the cause of any difference in prion pathogenesis between RANKΔIEC and RANKF/F mice observed after oral exposure would be restricted to effects on M cells in the gut epithelium . Within weeks after oral exposure , high levels of ME7 scrapie prions first accumulate upon FDC in the Peyer’s patches and subsequently spread to the MLN and spleen [7–9 , 11 , 13] . The initial replication of prions upon FDC in the Peyer’s patches is essential for the efficient transmission of disease to the CNS [7 , 11 , 13] . In order to determine the effect of specific M cell-deficiency on oral prion disease pathogenesis , RANKΔIEC mice and RANKF/F ( control ) mice were orally exposed to a moderate dose of ME7 scrapie prions ( 50 μl of a 1% brain homogenate from a mouse clinically-affected with ME7 scrapie prions; [7 , 9 , 11 , 13 , 58] ) . At intervals after exposure the accumulation of PrPd and PrPSc in tissues from 4 mice/group were compared by IHC and PET blot analysis , respectively , as above . As anticipated , at 105 dpi , abundant accumulations of PrPd ( middle row , brown ) and PrPSc ( lower row , black ) were detected in association with FDC ( CD21/35+ cells , upper row , brown ) in the Peyer’s patches , MLN and spleen of RANKF/F control mice ( Fig 5A ) . However , no PrPSc accumulations were detected in the same tissues from RANKΔIEC mice ( Fig 5A ) . Mice on a C57BL/6 background typically succumb to a moderate dose of ME7 scrapie prions by ~340 d after oral exposure [9 , 13] . However , RANKΔIEC mice ( n = 8 ) remained free of the clinical signs of prion disease up to at least 440 dpi , at which point no PrPd or PrPSc was detected in their Peyer’s patches , MLN , spleen ( Fig 5B ) , spinal cords or brains ( Fig 5C ) by IHC and PET blot analysis . Together these data clearly show that M cells are essential for the initial uptake of prions from the gut lumen into Peyer’s patches in order to establish host infection , since oral prion disease pathogenesis was blocked in the specific absence of M cells in RANKΔIEC mice . Certain pathogen infections or inflammatory conditions can enhance M cell-differentiation within the intestine [25 , 39 , 40] . We therefore reasoned that alterations to M cell-density in the gut epithelium may significantly alter oral prion disease pathogenesis and susceptibility . The density of functionally mature M cells in the intestine can be promoted in mice through exogenous administration of RANKL [22 , 35] . Recombinant RANKL was prepared and its ability to stimulate M cell-differentiation was confirmed in in vitro intestinal enteroids derived from RANKΔIEC and RANKF/F mice [23 , 36] . As anticipated , RANKL-treatment of enteroids from RANKF/F ( control ) mice induced robust expression of several M cell-associated genes ( Marcksl1 , Anxa5 , Spib , Ccl9 , and Gp2; [22] ) without significantly altering expression of genes associated with other intestinal lineages , including Paneth cells ( Lyz1 , Lyz2 ) and intestinal stem cells ( Lgr5 ) ( S1 Fig ) . No induction of expression of M cell-specific genes was observed in RANKL-treated enteroids derived from RANKΔIEC mice . Next , C57BL/6 mice ( n = 4/group ) were treated daily with RANKL to induce M cell-differentiation and tissues harvested on d 3 , coincident with the peak period of induction of M cell gene expression in the gut epithelium [22 , 35] . A parallel group of mice were treated with PBS as a control . IHC and morphometric analysis revealed that RANKL-treatment induced a significant increase in the number of GP2-expressing ( mature ) and SPIB-expressing ( differentiating and mature ) M cells within the FAE of Peyer’s patches ( Fig 6A–6C ) and also in the villous epithelium ( Fig 6D–6F ) . This increase in M cells was associated with increased functional ability to acquire particulate antigen from the gut lumen , demonstrated by a significant increase in the number of 200 nm microbeads transcytosed into the SED of Peyer’s patches and villous cores 24 h after their administration by oral gavage ( Fig 6G–6I ) . Although a small increase in the area of LAMP1+ immunostaining was observed in the FAE after RANKL treatment , the abundance of LAMP1+ immunostaining was unchanged in the villous epithelium ( Fig 6J–6L ) . We also determined whether RANKL-treatment affected other important parameters considered to be required for prion infection . IHC and morphometric analysis suggested there was no significant difference in the area of CD21/35+ ( indicative of FDC size ) or PrPC+ immunostaining in the Peyer’s patches ( S2A–S2C Fig ) or MLN ( S2D & S2E Fig ) of RANKL-treated mice when compared to PBS-treated controls . This implied that RANKL-treatment had no significant effect on FDC status in the Peyer’s patches or MLN . IHC and morphometric analysis also indicated that the % area of CD11c+ immunostaining in the SED of the Peyer’s patches ( Fig 7A & 7B ) and the LP ( Fig 7D & 7E ) did not differ between tissues from PBS- and RANKL-treated mice . Although the % area of CD68+ immunostaining was equivalent in the SED of the Peyer’s patches ( Fig 7A & 7C ) , a significant increase was observed in the LP of RANKL-treated mice ( Fig 7D & 7F ) . No difference in the % area of synaptophsyin 1+ immunostaining was observed in the LP ( Fig 7G & 7H ) , suggesting that RANKL-treatment did not significantly affect the magnitude of the enteric innervation in the intestine . Together , these data demonstrate that RANKL-treatment promotes M cell-differentiation in the FAE of Peyer’s patches and villous epithelium without significant effects on other key cells ( FDC , CD11c+ cells and enteric nerves ) considered to play an important role in oral prion disease pathogenesis . To determine whether increased M cell-density in the intestine altered oral prion disease susceptibility , groups of C57BL/6 mice were treated daily with RANKL ( or PBS as a control ) for 4 d as above , and between the 3rd and 4th treatments ( coincident with the peak period of induction of M-cell gene expression in the gut epithelium [22 , 35] ) the mice were orally exposed to either a moderate ( 1% ) or limiting ( 0 . 1% ) dose of ME7 scrapie prions . Exposure of C57BL/6 mice to a 1% dose of prions typically yields a clinical disease incidence of 100% in the recipients , whereas a 0 . 1% dose has a much lower incidence allowing the effects of RANKL-treatment on both survival time and prion disease susceptibility to be assessed . As anticipated , following oral exposure to a moderate ( 1% ) dose of ME7 scrapie prions , all PBS and RANKL-treated mice developed clinical disease . However , the RANKL-treated mice succumbed to clinical disease approximately 17 d earlier with a shorter mean survival time when compared to PBS-treated control mice ( PBS-treated mice , mean 346±25 d , median 343 d , n = 7/7; RANKL-treated mice , mean 329±18 d , median 322 d , n = 8/8; Fig 8A ) . When mice were orally exposed to a limiting ( 0 . 1% ) dose of prions only three of eight PBS-treated mice succumbed to clinical disease with individual survival times of 371 , 378 and 420 d ( Fig 8A ) . The five remaining PBS-treated mice did not develop clinical prion disease up to 525 dpi . In contrast , RANKL-treatment significantly enhanced prion disease pathogenesis as seven of eight RANKL-treated mice exposed to a limiting dose of prions succumbed to clinical disease with significantly shorter survival times ( Fig 8A; RANKL-treated mice , mean 352±22 d , median 350 d , n = 7/8; P<0 . 0078 , Log-rank [Mantel-Cox] test ) . Only one of the eight RANKL-treated mice exposed to a limiting dose of prions was free of the clinical signs of prion disease up to at least 525 dpi . The brains of all mice that developed clinical signs of prion disease in each treatment group had the characteristic spongiform pathology ( vacuolation ) , astrogliosis , microgliosis and PrPSc accumulation typically associated with terminal infection with ME7 scrapie prions ( Fig 8B ) . The distribution and severity of the spongiform pathology was also similar in the brains of all the clinically-affected mice ( Fig 8C & 8D ) , indicating that RANKL treatment did not alter the course of CNS prions disease after neuroinvasion had occurred . In contrast , no histopathological signs of prion disease were detected in the brains of any of the clinically-negative mice . As expected , at the terminal stage of disease high levels of PrPSc were maintained upon FDC in the Peyer’s patches , MLN and spleen of all clinically-affected mice . However , no evidence of PrPSc accumulation within these lymphoid tissues was observed in any of the orally-exposed clinically-negative mice ( S3 Fig ) . These data show that all the clinically-negative mice were free of prions in their lymphoid tissues and brains , and therefore highly unlikely to succumb clinical prion disease after substantially extended survival times , had the observation period been extended beyond 525 dpi . Our data suggested that RANKL-treatment significantly increased susceptibility to orally-administered prions . Indeed , no significant difference in disease incidence or mean survival time was observed in the RANKL-treated mice exposed to a 0 . 1% dose of prions when compared to PBS-treated control mice given a 10X higher ( 1% ) dose ( PBS/1% vs . RANKL/0 . 1% , P = 0 . 205; Log-rank [Mantel-Cox] test; Fig 8A ) . Together , these data demonstrate that increased M cell-deficiency in the gut epithelium following RANKL-treatment significantly enhances oral prion disease susceptibility by approximately 10-fold . Although certain concurrent pathogen infections or inflammatory stimuli may have multiple effects on the gut epithelium , our data suggest that factors such as these that modify M cell-density in the intestine [25 , 39 , 40] may represent important risk factors which can significantly influence susceptibility to orally-acquired prion infections . Prion replication within Peyer’s patches is essential for efficient neuroinvasion after oral exposure [10–13] . We therefore determined whether the decreased survival times and increased prion disease susceptibility in orally-exposed RANKL-treated mice were associated with the earlier accumulation of prions in their lymphoid tissues . Mice were treated with RANKL ( or PBS as a control ) and orally exposed to a 1% dose of ME7 scrapie prions as above , and culled at intervals afterwards ( n = 4/group ) . Abundant accumulations of PrPSc were clearly evident in association with FDC in the Peyer’s patches , MLN and spleen of RANKL-treated mice by 70 dpi , and were undetectable in the majority of the tissues from the PBS-treated animals at this time ( Fig 9A & 9B ) . To compare prion infectivity levels between the treatment groups , spleen homogenates were prepared and injected intracerebrally ( i . c . ) into groups of tga20 indicator mice ( n = 4/spleen homogenate ) . As the expression level of PrPC controls the prion disease incubation period , tga20 mice which overexpress PrPC are extremely useful as indicator mice in prion infectivity bioassays as they succumb to disease with much shorter survival times than conventional mice [59] . Significantly high levels of prion infectivity were detected in three of four of the spleens collected from the RANKL-treated mice at 70 dpi , whereas only one of four spleens from the PBS treated spleen contained detectable levels of prion infectivity ( P<0 . 0002 , Log-rank [Mantel-Cox] test; Fig 9C ) . By 105 dpi abundant accumulations of PrPSc were detected at equivalent frequencies in the lymphoid tissues of the PBS- and RANKL-treated animals ( Fig 9D ) . These data show that an increased density of M cells in the intestinal epithelium at the time of oral exposure enhanced the uptake of prions from the gut lumen , as the RANKL-treated mice accumulated prions within their lymphoid tissues significantly earlier than control mice . Although a rare occurrence in the steady-state , certain pathogenic microorganisms can stimulate the direct sampling of the gut lumenal contents by classical DC [60–63] . Whether this direct sampling activity by classical DC also contributes to the efficient uptake of orally-administered prions in the steady-state is uncertain [8 , 16 , 49] . Since RANKL was administered systemically in the current study , it is plausible that this treatment may have stimulated the direct sampling of the lumenal contents by cells other than M cells such as classical DC . An additional experiment was performed to test this hypothesis . As shown above , RANKΔIEC mice are unable to accumulate prions in their Peyer’s patches due to the specific lack of M cells ( Fig 5 ) . Since RANK-deficiency in RANKΔIEC mice is restricted only to intestinal epithelial cells [23] , we reasoned that if the effects of RANKL-treatment on disease pathogenesis were independent of their effects on M cells , then RANKL-treatment would also facilitate the uptake of prions into the Peyer’s patches of RANKΔIEC mice . To address this issue , RANKΔIEC mice were treated with RANKL and orally exposed to a 1% dose of ME7 scrapie prions as in the previous experiment . At 105 dpi Peyer’s patches and MLN were collected and analysed for the presence of PrPSc as before . As anticipated , abundant accumulations of PrPSc were detected in association with FDC in the Peyer’s patches and MLN of orally-exposed C57BL/6 wild-type ( WT ) control mice by 105 dpi . However , no PrPSc was detected in tissues from RANKL-treated RANKΔIEC mice ( S4 Fig ) . These data clearly show that RANKL-treatment was unable to restore prion accumulation in the Peyer’s patches and MLN of RANKΔIEC mice , indicating that the major effects of RANKL-treatment on oral prion disease pathogenesis were due to effects on M cell-deficiency in the intestinal epithelium . RANKL-treatment stimulates M cell-differentiation within the FAE of the Peyer’s patches and also in the villous epithelium ( Fig 6; [22 , 35 , 64] ) . We therefore considered it plausible that the enhanced prion pathogenesis we observed in RANKL-treated mice was due to the increased uptake of prions by the M cells induced in the villous epithelium . If RANKL-treatment had stimulated the uptake of prions predominantly via villous M cells , we reasoned that this would have facilitated the earlier transport of prions directly to the MLN [65] . An additional experiment was designed to test this hypothesis . Lymphotoxin-β-deficient ( LTβ-/- ) mice lack Peyer’s patches and most peripheral lymph nodes , but retain MLN and the spleen [66] . These mice also lack FDC in their remaining lymphoid tissues , as constitutive LT-stimulation is essential for their maintenance [67] , and are refractory to oral prion infection [10 , 11] . Peyer’s patches-deficient LTβ-/- mice were γ-irradiated and reconstituted with LTβ-expressing ( WT ) bone marrow ( termed WT→LTβ-/- mice , hereinafter ) and tissues collected at 2 . 5 weekly intervals ( n = 4 mice/group ) . Although the formation of FDC networks within the MLN and spleens of WT→LTβ-/- mice is restored by 5 wk after reconstitution ( Fig 10A ) , WT→LTβ-/- mice remain refractory to oral prion disease [11] as Peyer’s patches , not the MLN , are the essential early sites of prion accumulation and neuroinvasion after oral exposure [11 , 13] . The reconstitution of LTβ-/- mice with WT bone marrow also induces the differentiation and maturation of isolated lymphoid follicles ( ILF ) throughout the small intestine [11 , 68 , 69] . Mature ILF characteristically contain a single organized B cell-follicle , a network of FDC , and an M cell-containing FAE at the lumenal surface [11 , 13 , 68] . Since we have shown that mature small intestinal ILF are important sites of prion accumulation and neuroinvasion [11 , 13] , it was necessary to ensure there were no ILF with M cell-containing FAE in the intestines of WT→LTβ-/- mice at the time of RANKL-treatment and prion exposure . Whole-mount immunostaining of three individual 2 cm sections of small intestine from each WT→ LTβ-/- mouse showed that ILF with developed FAE containing GP2+ M cells were not present until 12 . 5 post-reconstitution ( Fig 10B & 10C ) . These data revealed a window of opportunity between 5–10 wk post-reconstitution during which the small intestines of WT→LTβ-/- mice lacked FAE and M cell-containing GALT , but possessed FDC within their MLN . This FAE-deficient model was therefore used to determine whether RANKL-treatment facilitated the direct delivery of prions from the gut lumen to the MLN . At 7 . 5 wk post-reconstitution WT→LTβ-/- mice ( n = 3-4/group ) were treated with RANKL ( or PBS as a control ) for 4 d and orally-exposed to prions as before , and prion infectivity levels determined in their MLN 28 d later . Tissues were assayed for prion infectivity at this time after oral exposure to determine whether RANKL-treatment of WT→LTβ-/- mice facilitated the earlier replication of prions within the MLN . Consistent with our previous data showing that Peyer’s patches in the small intestine , not the MLN , are the important early sites of prion accumulation after oral exposure [11 , 13] , prion infectivity was undetectable in the MLN of the PBS control-treated WT→LTβ-/- mice . Similarly , prion infectivity was also undetectable in the MLN of the RANKL-treated WT→LTβ-/- mice . In each instance all the recipient tga20 indicator mice ( n = 4/MLN homogenate tested ) were free of clinical disease up to 200 dpi ( Fig 10D ) and had no histopathological signs of prion disease in their brains ( spongiform pathology and PrPd deposition; Fig 10E ) . These data clearly show that RANKL-treatment did not stimulate the early transport of prions directly to the MLN . This suggests that the enhanced prion disease pathogenesis observed in RANKL-treated mice was due to the increased uptake of prions from the gut lumen by M cells in the FAE of the Peyer’s patches , rather than by villous M cells .
Here we show that the density of M cells in the gut epithelium directly influences oral prion disease pathogenesis and susceptibility . In the specific absence of M cells , the accumulation of prions in Peyer’s patches and subsequent neuroinvasion was blocked , demonstrating that prion translocation across the gut epithelium in association with M cells is essential to establish host infection . Our data also imply that an absence or reduction in M cell-abundance may significantly reduce susceptibility to many naturally acquired prion diseases such as vCJD in humans , CWD in cervids and natural sheep scrapie . For example , in the UK most clinical vCJD cases have predominantly occurred in young adults ( median age at death , ~28 years ) [4] , but epidemiological data indicate that this age-related susceptibility is not simply due to the exposure of young individuals to greater levels of the BSE agent through dietary preference [70] . We have previously shown that the density of functionally mature M cells in the Peyer’s patches of aged mice is substantially reduced [71] , suggesting that the reduced susceptibility of aged mice to oral prion infection [72] is at least in part due to the inefficient uptake of prions from the gut lumen by M cells . We also show that increased M-cell density at the time of oral exposure exacerbated prion disease pathogenesis: the uptake of prions from the gut lumen was enhanced , and as a consequence , survival times were decreased and disease susceptibility was increased approximately 10-fold . The density of M cells in the gut epithelium can be modified by the presence of certain pathogenic bacteria or inflammatory stimuli [25 , 39 , 40] . Although these stimuli may have multiple effects on the gut epithelium which can influence the integrity of this barrier , data in the current study provide a significant advance in our understanding of how factors that increase the density of M cells in the gut epithelium may increase susceptibility to orally-acquired prion infection . For example , the enteroinvasive bacterium Salmonella Typhimurium can specifically and rapidly transform enterocytes in the FAE of Peyer’s patches into M cells in order to facilitate host infection [25] . Furthermore , an independent study has shown that concurrent infection with S . Typhimurium significantly increased oral prion disease susceptibility [43] . Although this observation was originally attributed to the colitis induced by the pathogen in the large intestine , data in the current study suggest a role for effects on M cells in the small intestine cannot be excluded . During the BSE epidemic in the UK it is estimated that approximately 500 , 000 infected cattle were slaughtered for human consumption [73] . Despite the widespread dietary exposure of the UK human population to BSE prions , clinical cases of vCJD have fortunately been rare ( Ref . [4]; 178 definite or probable cases , as of 5th December 2016; www . cjd . ed . ac . uk/documents/figs . pdf ) . This implies that the ability to acquire prions from the gut lumen may differ between individuals . Studies using transgenic mice expressing human PrPC suggest that the transmission of BSE to humans is restricted by a significant species barrier [74] . After interspecies prion exposure , the processing and amplification of prions upon FDC in lymphoid tissues is important for their adaptation to the new host and to achieve neuroinvasion [75 , 76] . Thus , it is plausible that factors which increase the density of M cells in the small intestine may enable a greater burden of prions to enter Peyer’s patches , increasing the probability that more will be able to avoid clearance by cells such as macrophages , [11 , 77] . This may provide a greater opportunity for prion quasi-species present within the original inoculum with zoonotic potential to be selected and undergo adaptation and amplification upon FDC [78] . These effects may help to reduce the transmission barrier to some orally acquired prion strains . Enterocytes within the FAE overlying the Peyer’s patches specifically contain large LAMP1+ endosomes [16] . A detailed high resolution IHC-based study has shown that within the first day following oral exposure of mice to prions , PrPSc was detected within these large LAMP1+ endosomes of FAE enterocytes , and to a lesser extent in M cells [16] . These FAE enterocyte-associated endosomes have been proposed as a potential M cell-independent route through which lumenal proteins and prions may also be taken up into Peyer’s patches [16] . In the current study the presence and abundance of the large LAMP1+ endosomes within FAE enterocytes was unaffected in M cell-deficient RANKΔIEC mice . These data clearly show that the specific lack of M cells in the FAE , rather than an absence of the large LAMP1+ endosomes within FAE enterocytes , was responsible for the blocked prion accumulation in Peyer’s patches . Furthermore , the accumulation of prions in the Peyer’s patches , MLN and spleens of orally-exposed M cell-deficient RANKΔIEC mice was undetectable up to at least 440 d after exposure . As abundant prion accumulation is typically evident in these tissues in conventional ( WT ) mice by 105 d after exposure , this implies that in the absence of M cells , any prions that do enter the Peyer’s patches via alternative routes may be of insufficient magnitude to establish infection . Indeed PrPSc was also undetectable in the lymphoid tissues and CNS of these mice up to at least 440 dpi . Instead the prions that are acquired from the gut lumen by these M cell-independent routes are most likely sequestered and destroyed by cells such as macrophages , which are considered to degrade prions [77] , rather than being efficiently transported to FDC where they undergo amplification before neuroinvasion [7 , 10 , 13 , 15] . RANKΔIEC mice show reduced IgA production and delayed germinal centre responses in their Peyer’s patches [23] . This suggests that antigens that are transcytosed by M cells are preferentially targeted to the FDC-containing B-cell follicles to initiate antibody responses . Therefore , M cells , in contrast to FAE enterocytes with large LAMP1+ endosomes , may be considered to facilitate the efficient transfer of prions from the gut lumen to FDC in the B-cell follicles of Peyer’s patches . A separate IHC-based study also has proposed that the uptake of scrapie-affected brain homogenate across the jejunal epithelium of lambs occurs independently of M cells [34] . However , if prions do efficiently establish infection within Peyer’s patches after their translocation across the gut epithelium by enterocytes , one would not expect the specific absence of M cells in RANKΔIEC mice to block oral prion disease susceptibility . In the above in vivo study [34] , large quantities of scrapie-affected brain homogenate were injected directly into the lumen of ligated loops of jejunum . The presence of a large bolus of prions concentrated within the lumen of these ligated loops may have facilitated prion uptake into alternative cellular compartments to those utilized following exposure to physiologically relevant doses via the oral cavity . Although evidence of prions ( PrPd ) was detected in the underlying LP of these lambs , it was interesting to note that no intraepithelial PrPd accumulations were detected by IHC [34] . Whether the prions were transiently present in enterocytes and/or M cells soon after exposure , but at levels below the reliable detection limit or in a conformation which could not be detected by the IHC protocols used , remains to be determined . By comparison , in the study by Kujala and colleagues discussed above [16] , PrPSc was detected within the FAE during the first day after oral exposure using highly sensitive cryo-immunogold electron microscopy . M cells unlike the neighbouring enterocytes have a very narrow cytoplasm due to the presence of the MNP-containing basolateral pocket [20] . Thus it is also plausible that the prion transit time through M cells may be extremely rapid , restricting the ability of IHC to reliably detect low levels of prions or other particles which are being transcytosed through them . Surgical manipulation and manual compression of the intestine can temporarily inhibit intestinal motility and induce intestinal inflammation with activation of resident macrophages , as occurs during postoperative ileus [79 , 80] . These factors may have a significant influence on the uptake of prions from the lumen of surgically-ligated intestinal loops . Using extremely sensitive PrPSc-based detection assays , two independent studies reported the presence of low/trace levels of prions in the blood-stream within minutes of oral exposure [81 , 82] . The cellular route through which the prions initially gained access to the blood-stream was not determined in these studies . Urayama and colleagues [82] suggested that the levels of PrPSc that initially contaminated the blood-stream after oral exposure were sufficient to initiate infection in the brain . However , data from several studies show that prion replication upon FDC in Peyer’s patches in the small intestine is essential to establish host infection after oral exposure [7 , 8 , 10–13] . Furthermore , in the temporary absence of FDC at the time of oral exposure , prion disease susceptibility is blocked [6] . Thus although PrPSc may be detected in the blood-stream soon after oral exposure using highly sensitive assays [81 , 82] , data elsewhere indicate that the levels of prions that are initially within it are unable to directly establish host infection and achieve neuroinvasion . After uptake by M cells , CD11c+ classical DC are considered to deliver prions towards FDC , as their transient depletion reduces susceptibly to oral prion disease [8] . A partial reduction in CD11c+ immunostaining was observed in the SED of Peyer’s patches from RANKΔIEC mice , implying a partial reduction in the abundance of these cells . M cells specifically express the chemokine CCL9 [22] which mediates the attraction of certain classical DC populations towards the FAE [83] . Thus , the reduced CD11c+ immunostaining in the SED of RANKΔIEC mice may be a consequence of the absence of attraction of CD11c+ cells towards the basolateral pockets of M cells . This partial reduction in CD11c+ immunostaining in SED region alone could not account for the complete block of prion accumulation observed in RANKΔIEC mice , as our previous data show that the depletion of CD11c+ cells ( >85% ) prior to oral exposure does not block neuroinvasion [8] . Although the germinal centre response is delayed in RANKΔIEC mice [23] , our data suggested that FDC status was unaffected in these mice . Furthermore , the FDC in the Peyer’s patches , MLN and spleen of these mice were capable of accumulating high levels of PrPSc after injection of prions by the i . p . route . We have also previously shown that an absence of germinal centres themselves does not influence peripheral prion disease pathogenesis [84] . The GALT in the small intestine such as the Peyer’s patches , not those in the large intestine , are the major early sites of prion uptake , replication and neuroinvasion after oral exposure [11 , 13 , 16] . RANKL-RANK signalling is also necessary for the induction of M cell-differentiation within the large intestine , but in contrast to its role in the small intestine , it does not induce their maturation . As a consequence , GP2-expressing functionally mature M cells are scarce in the FAE overlying the large intestinal GALT [64] . Consistent with this , systemic RANKL-treatment also does not increase the abundance of functionally mature M cells in the FAE overlying the caecal patches or in the conventional epithelium of large intestine [64] . These data suggest that the effects of systemic RANKL-treatment on oral prion disease pathogenesis observed in the current study were due to an increased abundance of mature M cells specifically in the small intestine . In the steady state , functionally mature M cells are confined to the FAE overlying the Peyer’s patches and are extremely rare within the villous epithelium . However , systemic RANKL-treatment , as used here , significantly increases the abundance of mature M cells in the FAE overlying Peyer’s patches and throughout the villous epithelium [22 , 35 , 64] . Therefore , it is plausible that the effects of systemic RANKL-treatment on oral prion disease pathogenesis were in part due to the enhanced uptake of prions by villous M cells , facilitating their more efficient delivery to the MLN . Using LTβ-/- mice reconstituted with WT bone marrow ( WT→LTβ-/- mice ) , we generated mice that lacked Peyer’s patches or other M cell-containing GALT structures ( ILF ) in their small intestines , but retained MLN which contained mature FDC . If the major effect of RANKL-treatment on oral prion pathogenesis was due to uptake by villous M cells and enhanced delivery from the LP to the MLN , the accumulation of prions in the MLN would likewise be enhanced in these mice after RANKL-treatment . However , our data clearly show that RANKL-treatment did not enhance the accumulation of prions within the MLN of WT→LTβ-/- mice . This demonstrates that the major effect of RANKL-treatment on oral prion disease pathogenesis and susceptibility was due to the increased uptake of prions across the FAE overlying the Peyer’s patches in the small intestine . The absence of detectable levels of prion infectivity in the MLN at the time of analysis suggests that any low levels of prions that do reach this tissue immediately after oral exposure are either not delivered to FDC in the MLN as efficiently as they are in the Peyer’s patches , or are of insufficient magnitude to establish infection on FDC and are thus most likely degraded by macrophages [11 , 77] . Our IHC analysis implied that the abundance of CD68+ macrophages was increased in the LP after RANKL-treatment , suggesting that it is also plausible that any prions that had been acquired by villous M cells were subsequently sequestered and destroyed in the LP by macrophages . Classical DC in the LP of the intestine are considered to deliver lumenal antigens directly to MLN [65] . Here , RANKL-treatment of RANKΔIEC mice did not restore prion accumulation in their Peyer’s patches and MLN following oral exposure , demonstrating that RANKL-treatment did not alter the uptake of prions from the gut lumen by non-epithelial cells , such as classical DC . Our data suggest that direct sampling of the lumenal contents by classical DC in the LP [60–63] is also unlikely to contribute significantly to prion uptake from the gut lumen , as this too would result in the direct delivery of prions to the MLN [65] . In conclusion , we show that the initial uptake and transfer of prions across the gut epithelium in association with M cells is essential to establish host infection . Importantly , we also demonstrate that the density of M cells in the FAE overlying the Peyer’s patches in the small intestine directly controls the efficiency of oral prion infection . In the specific absence of M cells , the uptake and accumulation of prions in Peyer’s patches and their subsequent spread to the MLN and spleen is blocked . In contrast , oral prion disease susceptibility was enhanced approximately 10-fold in mice in which M cell-deficiency in the gut epithelium was increased . Thus , M cells could be considered as the gatekeepers of oral prion infection whose density directly limits or enhances disease susceptibility . Further studies are necessary to determine whether most orally acquired prion strains similarly exploit intestinal M cells to establish host infection after oral exposure , but data from independent in vivo and in vitro studies using mouse-passaged RML scrapie prions [30] , Fukuoka-1 prions [31] , BSE prions [32] and 263K hamster prions [17] imply a similar requirement . Antigen sampling M cells are also present in the FAE overlying the NALT in the nasal cavity [44 , 45] , but data from the analysis of prion disease pathogenesis in hamsters implies that the requirement for M cell-mediated uptake may vary depending on the route of exposure [85] . After intra-nasal exposure some transient uptake of 263K prions was observed in M cells within the FAE overlying the NALT , but a greater magnitude of paracellular transport across the epithelia within the nasal cavity was also noted [85] . Although certain concurrent pathogen infections , inflammatory stimuli and aging may have multiple effects on the gut epithelium , our data suggest that factors such as these that can modify M cell-density in the small intestine [25 , 39 , 40 , 71] may represent important risk factors which can significantly influence susceptibility to orally-acquired prion infections . Our data also raise the possibility that the density of M cells in the gut epithelium may similarly influence susceptibility to other important orally-acquired bacterial and viral pathogens which are considered to exploit M cells to infect the host [24–28] .
All studies using experimental mice and regulatory licences were approved by both The Roslin Institute’s and University of Edinburgh’s ethics committees . All animal experiments were carried out under the authority of a UK Home Office Project Licence ( PPL60/4325 ) within the terms and conditions of the strict regulations of the UK Home Office ‘Animals ( scientific procedures ) Act 1986’ . Where necessary , anaesthesia appropriate for the procedure was administered , and all efforts were made to minimize harm and suffering . Mice were humanely culled by a UK Home Office Schedule One method . The following mouse strains were used in this study where indicated: C57BL/6J; Villin-cre ( Tg ( Vil-cre ) 997Gum/J strain; The Jackson Laboratory , Bar Harbor , ME ) ; RANKfl/fl , which have loxP sites flanking exons 2 and 3 of Tnfrsf11a ( which encodes RANK ) [23]; LTβ-/- [86]; tga20 , which overexpress PrPC [59] . All mice were bred and maintained on a C57BL/6J background and housed under SPF conditions . Bone-marrow from the femurs and tibias of donor mice was prepared as single-cell suspensions ( 3x107–4x107 viable cells/ml ) in HBSS ( Life Technologies , Paisley , UK ) . Recipient adult LTβ-/- mice ( 6–8 weeks old ) were γ-irradiated ( 10 Gy ) and 24 h later reconstituted with 100 μl bone-marrow by injection into the tail vein . Glutathione S-transferase—RANKL fusion protein was prepared as described [35] . To enhance M-cell-density in the gut epithelium mice were treated with RANKL in vivo as previously described [22 , 35]: d 0 injected with RANKL by a combination of i . p . and subcutaneous injection ( 50 μg/ea . ) ; d 1 , 50 μg RANKL by subcutaneous injection; d 2 , 50 μg RANKL by subcutaneous injection; d 3 , 50 μg RANKL by subcutaneous injection . Mice were orally exposed to prions or gavaged with fluorescent microbeads on d 2 after the onset of RANKL treatment . For oral exposure , mice were fed individual food pellets doused with 50 μl of a 1% ( containing approximately 2 . 5 X 104 i . c . ID50 units ) or 0 . 1% ( w/v ) dilution of scrapie brain homogenate prepared from mice terminally-affected with ME7 scrapie prions according to our standard protocol [7–9 , 11 , 13 , 72] . During the dosing period mice were individually housed in bedding- and food-free cages . Water was provided ad libitum . A single prion-dosed food pellet was then placed in the cage . The mice were returned to their original cages ( with bedding and food ad libitum ) as soon as the food pellet was observed to have been completely ingested . The use of bedding- and additional food-free cages ensured easy monitoring of consumption of the prion-contaminated food pellet . For i . p . exposure , mice were injected with 20 μl of a 1% dilution of scrapie brain homogenate . Following prion exposure , mice were coded and assessed weekly for signs of clinical disease and culled at a standard clinical endpoint . The clinical endpoint of disease was determined by rating the severity of clinical signs of prion disease exhibited by the mice . Following clinical assessment , mice were scored as “unaffected” , “possibly affected” and “definitely affected” using standard criteria which typically present in mice clinically-affected with ME7 scrapie prions . Clinical signs following infection with the ME7 scrapie agent may include: weight-loss , starry coat , hunched , jumpy behaviour ( at early onset ) progressing to limited movement , upright tail , wet genitals , decreased awareness , discharge from eyes/blinking eyes , ataxia of hind legs . The clinical endpoint of disease was defined in one of the following ways: i ) the day on which a mouse received a second consecutive “definite” rating; ii ) the day on which a mouse received a third “definite” rating within four consecutive weeks; iii ) the day on which a mouse was culled in extremis . Survival times were recorded for mice that did not develop clinical signs of disease or were culled when they showed signs of intercurrent disease . Prion diagnosis was confirmed by histopathological assessment of vacuolation in the brain . For the construction of lesion profiles , vacuolar changes were scored in nine distinct grey-matter regions of the brain as described [87] . For bioassay of prion infectivity individual MLN or spleen were prepared as 1% ( wt/vol ) homogenates in physiological saline . For each tissue homogenate groups of tga20 indicator mice ( n = 4/homogenate ) were injected i . c . with 20 μl of each homogenate . The prion infectivity titre in each sample was determined from the mean incubation period in the indicator mice , by reference to a dose/incubation period response curve for ME7 scrapie-infected spleen tissue serially titrated in tga20 mice using the relationship: y = 9 . 4533–0 . 0595x ( where y is log ID50 U/20 μl of homogenate , and x is the incubation period; R2 = 0 . 9562 ) . Whole-mount immunostaining was performed as previously described [9] . Peyer’s patches , NALT and pieces of small intestines were fixed with BD Cytofix/Cytoperm ( BD Biosciences , Oxford , UK ) , and subsequently immunostained with rat anti-mouse GP2 mAb ( MBL International , Woburn , MA; 5 μg/ml ) . Following addition of primary Ab , tissues were stained with Alexa Fluor 488-conjugated anti-rat IgG Ab ( Life Technologies ) , rhodamine-conjugated Ulex europaeus agglutinin I ( UEA-1; Vector Laboratories Inc . , Burlingame , CA; 20 μg/ml ) and Alexa Fluor 647-conjugated phalloidin to detect f-actin ( Life Technologies; 4 U/ml ) . Intestines , MLNs and spleens were also removed and snap-frozen at the temperature of liquid nitrogen . Serial frozen sections ( 6 μm in thickness ) were cut on a cryostat and immunostained with the following antibodies: FDC were visualized by staining with mAb 7G6 to detect CR2/CR1 ( CD21/35; BD Biosciences; 1 μg/ml ) or mAb 8C12 to detect CR1 ( CD35; BD Biosciences; 1 . 25 μg/ml ) ; cellular PrPC was detected using PrP-specific polyclonal antibody ( pAb ) 1B3 [88] ( 1/1000 dilution ) ; B cells were detected using rat anti-mouse B220 mAb ( clone RA3-6B2 , Life Technologies; 5 μg/ml ) ; MNP were detected using hamster anti-mouse CD11c mAb ( clone N418 , Bio-Rad , Kidlington , UK; 5 μg/ml ) or rat anti-mouse CD68 mAb ( clone FA-11 , Biolegend , Cambridge , UK; 5 μg/ml ) ; rat anti-mouse CD107a ( clone 1D4B; Biolegend; 2 . 5 μg/ml ) to detect LAMP1; nerve synapses were detected using rabbit anti-synaptophysin 1 ( Synaptic Systems , Göttingen , Germany; 1/150 dilution ) . For the detection of SPIB in paraformaldehyde-fixed sections , antigen retrieval was performed with citrate buffer ( pH 7 . 0 , 121°C , 5 min . ) prior to immunostaining with sheep anti-mouse SPIB polyclonal antibody ( R&D Systems , Abingdon , UK; 0 . 4 μg/ml ) . Appropriate species and immunoglobulin isotype control Ab were used as controls ( S5 Fig ) . Where appropriate , sections were counter-stained with DAPI ( 2 . 86 μM ) to detect cell nuclei ( Life Technologies ) . For the detection of disease-specific PrP ( PrPd ) in intestines , MLN , spleens and brains , tissues were fixed in periodate-lysine-paraformaldehyde fixative and embedded in paraffin wax . Sections ( thickness , 6 μm ) were deparaffinised , and pre-treated to enhance the detection of PrPd by hydrated autoclaving ( 15 min , 121°C , hydration ) and subsequent immersion formic acid ( 98% ) for 10 min . Sections were then immunostained with 1B3 PrP-specific pAb ( 1/1000 dilution ) . For the detection of astrocytes , brain sections were immunostained with anti-glial fibrillary acidic protein ( GFAP; DAKO , Ely , UK; 1/400 dilution ) . For the detection of microglia , deparaffinised brain sections were first pre-treated with citrate buffer and subsequently immunostained with anti-ionized calcium-binding adaptor molecule 1 ( Iba1; Wako Chemicals GmbH , Neuss , Germany; 0 . 5 μg/ml ) . For the detection of FDC in intestines , MLN and spleens , deparaffinised sections were first pre-treated with Target Retrieval Solution ( DAKO ) and subsequently immunostained with anti-CD21/35 mAb . PET immunoblot analysis was used to confirm the PrPd detected by immunohistochemistry was proteinase K-resistant PrPSc [57] . Membranes were subsequently immunostained with 1B3 PrP-specific pAb ( 1/4000 dilution ) . For light microscopy , following the addition of primary antibodies , biotin-conjugated species-specific secondary antibodies ( Stratech , Soham , UK ) were applied and immunolabelling was revealed using HRP-conjugated to the avidin-biotin complex ( ABC kit , Vector Laboratories ) and visualized with DAB ( Sigma , Dorset , UK ) . Sections were counterstained with haematoxylin to distinguish cell nuclei . For fluorescent microscopy , following the addition of primary antibody , streptavidin-conjugated or species-specific secondary antibodies coupled to Alexa Fluor 488 ( green ) , Alexa Fluor 594 ( red ) or Alexa Fluor 647 ( blue ) dyes ( Life Technologies ) were used . Sections were counterstained with either DAPI or Alexa Fluor 647-conjugated phalloidin and subsequently mounted in fluorescent mounting medium ( DAKO ) . Images of whole-mount immunostained tissues and cryosections were obtained using a Zeiss LSM710 confocal microscope ( Zeiss , Welwyn Garden City , UK ) . For morphometric analysis , images were analysed using ImageJ software ( http://rsb . info . nih . gov/ij/ ) as described on coded sections [89] . Background intensity thresholds were first applied using an ImageJ macro which measures pixel intensity across all immunostained and non-stained areas of the images . The obtained pixel intensity threshold value was then applied in all subsequent analyses . Next , the number of pixels of each colour ( black , red , green , yellow etc . ) were automatically counted . For these analyses , data are presented as the proportion of positively-stained pixels for a given IHC marker per total number of pixels ( all colours ) in the specific area of interest ( eg: SED , FAE , LP etc . ) . In each instance , typically 3–6 images were analysed per mouse , from tissues from multiple mice per group ( n = 4–8 mice/group ) . Full details of all the sample sizes for each parameter analysed are provided in every figure legend . Mice were given a single oral gavage of 2x1011 of Fluoresbrite Yellow Green labelled 200 nm microbeads ( Polysciences , Eppelheim , Germany ) in 200 μl PBS . Mice were culled 24 h later and Peyer’s patches and small intestine segments were snap-frozen at the temperature of liquid nitrogen . Serial frozen sections ( 6 μm in thickness ) were cut on a cryostat and counterstained with DAPI . Images of SED from three Peyer’s Patches ( duodenal , jejunal and ileal ) and 8 LP areas per mouse ( n = 3–4 mice/group ) from 3 non-sequential sections ( total 21–31 SED , or 24 LP areas per mouse studied ) were typically acquired using a Nikon Eclipse E400 fluorescent microscope using Micro Manager ( http://www . micro-manager . org ) . For example , each Peyer’s patch was trimmed until at least one SED region was visible and 20 sections collected . The 1st , 10th and 20th sections were then analysed . Tissue auto-fluorescence was subtracted from displayed images using ImageJ , the size of the area of interest in each section was then measured and the number of beads determined using the cell counter function in ImageJ and the bead density calculated . Intestinal crypts were dissociated from mouse small intestine using Gentle Cell Dissociation Reagent ( Stemcell Tech , Cambridge , UK ) and used establish enteroids by cultivation in Matrigel ( BD Bioscience ) and Intesticult medium ( Stemcell Tech ) as described [23 , 90] . Where indicated , some wells were treated with RANKL ( 100 ng/ml ) . Enteroids were cultivated in triplicate and either passaged after 5 d of cultivation [90] or harvested for mRNA expression analysis as described [23] . Total RNA was isolated from the enteroid cultures using RNA-Bee ( AMS Biotechnology , Oxfordshire , UK ) followed by treatment with DNase I ( Ambion , Warrington , UK ) . First strand cDNA synthesis was performed using 1 μg of total RNA and the First Strand cDNA Synthesis kit ( GE Healthcare , Bucks , UK ) as described by the manufacturer . PCR was performed using the Platinum-SYBR Green qPCR SuperMix-UDG kit ( Life Technologies ) and the Stratagene Mx3000P real-time qPCR system ( Stratagene , CA , USA ) . The qPCR primers ( S1 Table ) were designed using Primer3 software [91] . The cycle threshold values were determined using MxPro software ( Stratagene ) and normalized relative to Gapdh . All data are presented as mean ± SD . Unless indicated otherwise , differences between groups were analysed by a Student's t-test . In instances where there was evidence of non-normality ( identified by the Kolmogorov-Smirnov , D’Agostino & Pearson omnibus , or Shapiro-Wilk normality tests ) , data were analysed by a Mann-Whitney U test . Survival rates were analysed using the Log-rank ( Mantel-Cox ) test . Values of P<0 . 05 were accepted as significant .
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Prion diseases are infectious neurodegenerative disorders that affect humans and animals . Many natural prion diseases are orally acquired through consumption of contaminated food or pasture . An understanding of how prions infect the intestine will help identify factors that influence disease susceptibility and allow the development of new treatments . After oral infection prions first accumulate within the lymphoid tissues that line the intestine ( known as Peyer’s patches ) before they spread to the brain where they cause neurodegeneration . To do this , the prions must first cross the intestinal epithelium , a single layer of cells that separates the body from the gut contents . M cells are found within the epithelium that covers the Peyer’s patches and are specialised to transport large particles and whole bacteria across the gut epithelium . We show that M cells act as the gatekeepers of oral prion infection . In the absence of M cells , oral prion infection is blocked , whereas an increase in M cells increases the risk of prion infection and shortens the disease duration . Therefore , our data demonstrate that factors such as pathogen infection , inflammation and aging , which alter the abundance of M cells in the intestine , may be important risk factors which influence susceptibility to orally-acquired prion infections .
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2016
|
Increased Abundance of M Cells in the Gut Epithelium Dramatically Enhances Oral Prion Disease Susceptibility
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Iron is an essential cofactor , but it is also toxic at high levels . In Schizosaccharomyces pombe , the sensor glutaredoxin Grx4 guides the activity of the repressors Php4 and Fep1 to mediate a complex transcriptional response to iron deprivation: activation of Php4 and inactivation of Fep1 leads to inhibition of iron usage/storage , and to promotion of iron import , respectively . However , the molecular events ruling the activity of this double-branched pathway remained elusive . We show here that Grx4 incorporates a glutathione-containing iron-sulfur cluster , alone or forming a heterodimer with the BolA-like protein Fra2 . Our genetic study demonstrates that Grx4-Fra2 , but not Fep1 nor Php4 , participates not only in iron starvation signaling but also in iron-related aerobic metabolism . Iron-containing Grx4 binds and inactivates the Php4 repressor; upon iron deprivation , the cluster in Grx4 is probably disassembled , the proteins dissociate , and Php4 accumulates at the nucleus and represses iron consumption genes . Fep1 is also an iron-containing protein , and the tightly bound iron is required for transcriptional repression . Our data suggest that the cluster-containing Grx4-Fra2 heterodimer constitutively binds to Fep1 , and upon iron deprivation the disassembly of the iron cluster between Grx4 and Fra2 promotes reverse metal transfer from Fep1 to Grx4-Fra2 , and de-repression of iron-import genes . Our genetic and biochemical study demonstrates that the glutaredoxin Grx4 independently governs the Php4 and Fep1 repressors through metal transfer . Whereas iron loss from Grx4 seems to be sufficient to release Php4 and allow its nuclear accumulation , total or partial disassembly of the Grx4-Fra2 cluster actively participates in iron-containing Fep1 activation by sequestering its iron and decreasing its interaction with promoters .
Since iron ( Fe ) is essential but also toxic , its uptake from the extracellular environment and its intracellular availability from a “disposable Fe pool” are tightly regulated . All cell types display wide transcriptome changes upon Fe starvation . These responses are triggered in very distinct ways in each organism , but the final gene expression programs are quite similar in essence: they are meant to temporally increase Fe import and decrease Fe storage and usage . In Schizosaccharomyces pombe , the repressors Fep1 and Php4 mediate the transcriptional response to Fe depletion [1] ( Fig . 1A ) . When Fe is not limiting , Fep1 represses the expression of several genes which mediate Fe uptake and/or increase the intracellular available Fe pool , such as those coding for the reductive high-affinity transporter Fio1 [2] , the non-reductive importer Str3[3] or the ferrichrome sinthetase Sib2 [4] . Fep1 de-represses transcription of these genes under Fe deprivation [5] , but is localization remains nuclear [6] . Php4 , on the contrary , has Crm1-dependent cytosolic localization under basal conditions . When Fe is scarce , Php4 accumulates in the nucleus and represses transcription of genes activated by the Pho2/3/5 complex , acting as a transcriptional repressor [7] . These more than 80 repressed genes , according to microarray analysis [8] , include those coding for the vacuole Fe importer Pcl1 , the Fe-sulfur ( Fe-S ) cluster-containing protein Sdh4 and the Fe-S cluster assembly protein Isa1 [1] . Fep1 also represses the php4 gene under basal conditions [1] , whereas Php4 blocks fep1 expression under Fe depleted conditions [8] . In Saccharomyces cerevisiae , the Fe starvation response is based on the activation of the positive transcription factors Aft1/2 , and in post-transcriptional regulation of mRNA stability by the RNA-binding proteins Cth1/2 ( for a recent review , see [9] ) . The only common element in the cascades of budding and fission yeast seems to be the Fe sensor glutaredoxin 4 ( Fig . 1A ) . The S . cerevisiae redundant glutaredoxins Grx3/4 have been described to participate not only in Fe sensing , but also in metal delivery to Fe-containing proteins [10] . Grx3/4 are monothiol glutaredoxins containing an Fe-S cluster between two subunits of the glutaredoxins or between a heterodimer with the bacterial BolA-like protein Fra2 ( for a review , see [11] ) . Regarding Fe signaling , Fe deprivation promotes nuclear accumulation of a positive transcription factor , Aft1/2 [12 , 13] , after disassembly from the apo-Grx4-Fra2 heterodimer [14 , 15] . In fission yeast , there is only one cytosolic monothiol glutaredoxin , Grx4 , which was first described to be essential for growth [16] , although cells devoid of Grx4 can grow under semi-anaerobic conditions[7] . Grx4 is a repressor of Php4 activity under basal conditions , since deletion of grx4 triggers constitutive nuclear localization of Php4 and repression of genes such as pcl1 under Fe-rich conditions [7] . Fep1 is also regulated by Grx4: in the absence of this glutaredoxin , the expression of Fep1-dependent genes such as fio1 cannot be induced [17 , 18] . Grx4 contains an N-terminal thioredoxin domain and a C-terminal glutaredoxin domain , each one of which containing a unique cysteine ( Cys ) residue . The role of their thiol groups in the protein’s function as an Fe sensor has been studied with conditional mutants or tagged versions of Grx4 with conflicting results: the interaction with Fep1 is disturbed in one of the Cys mutants according to one of the reports [18] but not the other [17] . The molecular events connecting Fe levels and activity of this signaling cascade are unknown . We have performed a biochemical and genetic study of the different components of the pathway . Not only Grx4 but also Fep1 are Fe-binding proteins . We have purified recombinant proteins and show here that there is a Fe-S bridging cluster between two Grx4 subunits and between Grx4 and Fra2 , a new component of this S . pombe cascade . Genetic data suggest that Fe-containing Grx4 homodimer is sufficient for cytosolic retention of Php4 , although Fra2 seems to partially contribute to this retention; Fe starvation should promote accumulation of apo-Grx4 , and release of Php4 . On the contrary , the Fe-S cluster bridging Grx4 and Fra2 is required for regulation of Fep1 activity; upon Fe starvation , total or partial disassembly of this cluster may sequester Fe from Fep1 and decrease its affinity for DNA .
In S . cerevisiae , the redundant proteins Grx3/4 regulate two important processes of cell survival and adaptation: delivery of Fe to proteins and regulation of the transcription factor Aft1 , activator of a transcriptional response to Fe starvation [12 , 13] ( for a review , see [11] ) . To confirm whether the same dual function applies to the S . pombe homolog Grx4 ( Fig . 1A ) , we generated a Δgrx4 strain by selection under anaerobic conditions , and tested first the effect of such gene deletion in growth . As shown in Fig . 1B , the lack of Grx4 jeopardizes the cell’s capacity to grow on plates in the presence of oxygen even in rich media ( YE ) , and especially in the respiratory-prone minimal media ( MM ) . Php4 and Fep1 are dispensable for this aerobic function ( Fig . 1B ) . Similarly , liquid aerobic growth is impaired only in cells lacking Grx4 . Regarding Fe signaling , we first compared the effect of different Fe chelators in the growth of fission yeast . We tested chelators such as the membrane-permeable dipyridyl ( DIP ) , desferroxamine ( Dx ) , a siderophore which chelates Fe extracellularly , or the extracellular Fe chelator bathophenanthroline disulfonate ( BPS ) . As shown in Supplemental information ( S1A Fig ) , addition of different concentrations of these chelators impairs or fully halts the growth of wild-type S . pombe cultures . DIP is the only compound able to immediately cease the growth on fission yeast in liquid cultures , while the extracellular chelators allowed several cell cycles since they only blocked iron acquisition by eliminating the extracellular iron sources . Using as a reference the sub-toxic concentrations on liquid cultures for the different chelators ( that is , those that reduced the growth rates but did not fully inhibit growth ) , we then tested on solid plates that cells lacking Php4 or Grx4 , but not Δfep1 cells , were more sensitive than wild-type strain to grow in the presence of DIP ( Fig . 1C ) , Dx or BPS ( S1B Fig ) . On the contrary , an excess of Fe only affects cells lacking Fep1 or Grx4 ( Fig . 1C ) . To explain these phenotypes , we first analyzed the response of wild-type cells to two different chelators , BPS and DIP . While activation of Fep1-dependent genes such as fio1 occurred with similar kinetics upon treatment with BPS or DIP at low and high concentrations , repression of Php4-genes such as pcl1 was much weaker in the presence of the extracellular chelator BPS than with the permeable DIP ( Fig . 1D ) : BPS treatment induced repression of pcl1 at lower rates and its mRNA never reached the levels accomplished by DIP treatment . In order to test whether Grx4 is also essential for the induction of the Fep1- and Php4-dependent changes of gene expression upon Fe deprivation , we decided to use high doses of DIP ( 250 μM ) to highlight the effects on Php4-dependent genes , which are more dramatic with this chelator . Furthermore , most work in the characterization of the iron starvation response in S . pombe has been performed using DIP at this concentration ( see [1 , 8] , and references there-in ) . As shown in Fig . 1E , in the presence of DIP wild-type cells up-regulate transcription of Fep1-dependent genes , whereas they down-regulate the expression of Php4-dependent ones . The inactivation of the Fep1 and Php4 repressors upon DIP and basal conditions , respectively , is dependent on Grx4 , since Δgrx4 cells can not signal-activate genes such as fio1 , str3 , sib2 or php4 and constitutively repress genes such as pcl1 , isa1 or fep1 ( Fig . 1E ) . We then confirmed with immuno-fluorescence ( S2A Fig ) or fluorescent microscopy ( S2B Fig ) that Grx4 is localized at both the cytosol and the nucleus , Fep1 is constitutively nuclear , and Php4 shifts from the cytosol to the nucleus upon Fe deprivation . Using double tagged strains , we then tested whether Grx4-GFP would interact with the repressors before and/or after stress . We immuno-precipitated Grx4-GFP , and used commercial antibodies against the Myc-tag to check the in vivo binding to Php4-Myc ( Fig . 1G ) or Fep1-Myc ( Fig . 1F ) . As shown in Fig . 1G , the association between Grx4-GFP and Php4-Myc is significantly disturbed under Fe deprived conditions , while binding of Grx4-GFP and Fep1-Myc is constitutive ( Fig . 1F ) . These results suggest that the interaction of Grx4 with Php4 , but not with Fep1 , is partially disturbed upon Fe starvation . Mammalian Grx2 , a nuclear-mitochondrial glutaredoxin , was the first thioredoxin fold-containing protein reported to have a Fe-S cluster [19] . More recently , the redundant monothiol Grx3/4 glutaredoxins of S . cerevisiae were also reported to be Fe-S cluster-containing proteins , and to use two glutathione ( GSH ) moieties to hold the cluster [10 , 12 , 13] . We over-expressed a TEV-cleavable GSH-S-transferase ( GST ) -TEV-HA-Grx4 fusion protein in Escherichia coli , and noticed than the cell pellets ( Fig . 2A ) and the early supernatants had brownish color when compared with bacteria over-expressing GST alone . The color disappeared during protein purification . By comparing tagged and un-tagged proteins , we verified that the HA tag did not affect the properties of recombinant Grx4 . We hypothesized that Grx4 can assemble an oxygen-sensitive Fe-S cluster , and attempted to reconstitute it under anaerobic conditions . We incubated recombinant TEV-cleaved apo-HA-Grx4 with Fe , inorganic sulfide and GSH in the presence of the E . coli Fe-S cluster catalyzer IscS [20] . As observed by UV-visible spectroscopy , two shoulders in the 390–650 nm regions could be detected after Fe-S cluster reconstitution , with an apparent extinction coefficient at 398 nm of 3 . 2 mM-1 cm-1 ( Fig . 2B , C ) ; the samples lost , although not completely , these visible spectra peaks after oxygen exposure ( Fig . 2D ) and became colorless . GSH was required for Fe-S cluster reconstitution ( Fig . 2E ) , which suggests that the tripeptide coordinates the Fe-S cluster , as previously shown for other monothiol glutaredoxins . To corroborate the relevance of GSH in cluster formation and in the role of Grx4 in Fe sensing , we analyzed the transcriptome of strain Δgcs1 in response to Fe deprivation . Cells lacking gcs1 , coding for glutamate-Cys ligase [21] , are able to grow in GSH-containing rich media , but cultures halt their growth few hours after cells are shifted to minimal media , as expected . Under these circumstances , the transcriptome of Δgcs1 cells displays constitutive repression of Php4-genes and constitutive de-repression of Fep1-dependent genes ( Fig . 2F ) ; this last feature is completely dependent on the presence of Grx4 ( Fig . 2G ) . These results suggests that GSH is required to allow the assembly of an Fe-S cluster in Grx4 , and that this cluster is essential for the function of Grx4 as a Fe deprivation sensor and signal transducer . Grx4 has one Cys residue in the thioredoxin domain and another one in the glutaredoxin domain ( Fig . 3A ) . We substituted the endogenous grx4 locus with mutant versions with a Cys-to-Ser codon substitution in either the thioredoxin or the glutaredoxin domains . Fission yeast cells expressing Grx4 . C35S behaved very similar to wild-type cells regarding both aerobic growth ( Fig . 3B , C ) and activation of the transcriptional Fe starvation response ( Fig . 3D ) . On the contrary , Grx4 . C172S was unable to fulfill any function of Grx4 , since cells expressing this mutant protein display phenotypes very similar to Δgrx4 cells ( Fig . 3B , C , D ) . It is important to point out that the grx4 . C172S strain was isolated under semi-anaerobic conditions . We next over-expressed TEV-cleavable GST- ( TEV ) -HA-Grx4 . C35S and C172S fusion proteins in E . coli , and noticed again than the cell pellets for the wild-type and Grx4 . C35S tagged proteins were clearly brownish , while those of cells over-expressing the Grx4 . C172S fusion protein were colorless and similar to those of bacteria over-expressing GST alone ( Fig . 3E ) . Again , we attempted to reconstitute the oxygen-sensitive metallocenters under anaerobic conditions . As shown in Fig . 3F ( left panel ) , reconstitution of recombinant Grx4 . C35S yielded a protein with similar visible spectrum to that of wild-type Fe-Grx4 . However , the presence of Cys172 was required for cluster assembly in vitro , since reconstitution could not be observed for mutant Grx4 . C172S ( Fig . 3F , right panel ) . These results suggest that the Fe-S cluster of Grx4 is essential for both functions: aerobic growth and Fe signaling via Php4 and Fep1 . The Fe-S cluster of the redundant Grx3/4 proteins in S . cerevisiae was also reported to be oxygen-sensitive [14] . The cluster was more stable if the protein was co-expressed in bacteria with Fra2 , a protein originally shown at the genetic level to be required to transduce an Fe starvation signal to the yeast transcriptional activator Aft1 [14 , 15 , 22] . The S . pombe genome has an S . cerevisiae’s fra2 homolog , SPAC8C9 . 11 ( Fig . 4A ) . We performed in vitro reconstitution of the Grx4 Fe-S cluster in the presence of equimolar recombinant Fra2 , yielding a coloured sample with different UV-visible spectrum ( apparent ε398: 5 . 0 mM-1 cm-1 ) to that formed within a Grx4 homodimer , suggesting the formation of an Fe-S cluster bridging Grx4 and Fra2 ( Fig . 4B , solid line ) . This cluster was not sensitive to the presence of oxygen ( Fig . 4C ) . The presence or absence of GSH in the Grx4-Fe-Fra2 reconstitution process only moderately affected the resulting visible spectra ( Fig . 4D ) . Indeed , in vitro studies with the S . cerevisiae GRX4-FRA2 heterodimer suggest that only one GSH molecule , and not two , is present in the holo-heterodimer [14] . The Cys172 in the glutaredoxin domain , but not Cys35 , of Grx4 is important for the assembly of the Fe-S cluster between Grx4 and Fra2 ( S3A Fig ) . We constructed a Δfra2 strain , and observed that it also displays aerobic growth defects , although of less severity that cells lacking Grx4: in the absence of Fra2 , cells grow as wild-type on YE plates , but the growth is strongly impaired on respiratory-prone MM plates , where many Fe-containing proteins mediate redox reactions ( Fig . 4E ) . With regards to Fe deprivation , Δfra2 cells are only mildly sensitive to iron chelators such as DIP or BPS on solid plates ( S3B Fig ) , and the growth in liquid cultures is only moderately affected by these chelators , when compared to cells lacking Grx4 ( S3C Fig ) . Regarding the transcriptional response to Fe deprivation , cells lacking Fra2 are able to repress Php4-dependent genes upon DIP treatment as wild-type cells , but cannot efficiently induce the Fep1-dependent Fe uptake genes ( Fig . 4F ) . Therefore , Php4 can be retained in the cytosol by Grx4 in the absence of Fra2 under Fe repleted conditions , but inactivation of Fep1 upon addition of DIP requires both Grx4 and Fra2 . Interestingly , the use of BPS as a chelator revealed a marginal role of Fra2 not only in Fep1 regulation but also in Php4 cytoplasmic retention during normal growth conditions: in cells lacking Fra2 , activation of Php4 as determined by repression of pcl1 is much faster and dramatic that in wild type cells , which suggest that Grx4-Fe-Fra2 is able to retain Php4 in the cytosol with more efficiency than Fe-Grx4 alone ( Fig . 4G ) . The role of Fra2 on Fep1 function is not to promote the association between Grx4 and Fep1 , since this interaction is maintained in cells lacking Fra2 ( S3D Fig ) . Similar to Grx4 , Fra2 displays dual cytoplasmic/nuclear localization that is not affected by treatment with DIP , according to fluorescence microscopy ( Fig . 4H ) . Indeed , both proteins constitutively interact as shown by co-immuno-precipitation ( Fig . 4I ) . It is worth mentioning that during the course of this study , the group of Labbé proposed that S . pombe Fra2 participates with Grx4 in the inactivation of the Fep1 repressor [23] . Activation of Php4 upon Fe starvation seems to be straightforward: addition of chelators probably promotes Fe-S cluster disassembly from Grx4-Fra2 , and the apo-protein may lose affinity for Php4 , which is then accumulated at the nucleus . Grx4 is also important for Fep1 function , but contrary to what happens for Php4-dependent genes , the levels of Fep1-dependent ones in Δgrx4 cells do not mimic a Fe-starvation situation; instead , they are constitutively repressed ( Fig . 1E ) . Similarly , Grx4 . C172S should mimic a Fe starvation situation , but the transcriptome of cells expressing this mutant protein reveals that only Php4 genes are fully repressed , and on the contrary Fep1 genes cannot be activated ( Fig . 3D ) . How is then the Fe starvation signal transferred to Fep1 ? Fep1 belongs to the GATA family of transcriptional repressors ( for a review , see [24] ) . It contains two zinc finger motifs for DNA binding at the N-terminal domain , flanking a Cys-rich region with four important Cys residues , which when mutated completely disturbed in vitro and in vivo DNA binding and gene repression , respectively [6] ( Fig . 5A ) . Furthermore , the ability of recombinant Fep1 to bind to DNA in vitro was greatly diminished when the protein was purified from Fe-starved cultures [2] . We over-expressed a TEV-cleavable GST-Fep1 fusion protein in E . coli , and again the cell pellets had brownish color when compared with bacteria over-expressing GST alone ( Fig . 5B ) . The GST-Fep1 protein , before ( Fig . 5C , solid line ) or after TEV cleavage , retained the brown color during purification , and displayed a characteristic visible spectrum . Multiple Cys-to-Ser substitutions at the Cys-rich domain fully abrogated the brownish color of cells over-expressing the transcription factor ( Fig . 5B ) , and flattened the visible spectrum of the purified protein ( Fig . 5C ) . When HA-tagged Fep1 was expressed in S . pombe Δfep1 cells from an episomal plasmid , the tagged protein was able to repress Fe import genes under basal conditions , whereas mutated HA-Fep1 . C4S could not ( Fig . 5D ) . Therefore , binding of Fe by Fep1 seems to be required for its role as a transcriptional repressor . The GST-tagged full length Fep1 protein was however very susceptible to degradation , and we therefore purified a more stable , shorter GST fusion protein containing only the first 245 N-terminal amino acids ( S4A Fig ) : this truncated protein still retained Fe , as determined both in vivo ( Fig . 5E ) and as a purified protein ( Fig . 5F ) . This N-terminal Fep11–245 protein still relies on the four Cys residues for metal binding ( Fig . 5E , F ) , and is more stable than the full length protein . In order to determine whether Fep1 is an Fe-S protein or it just directly coordinates Fe , we determined the stoichiometry of Fe-to-protein and inorganic sulfide-to-protein , using a bona fide Fe-S cluster containing protein , the E . coli SoxR transcription factor , as a control [25] . Thus , we purified SoxR ( S4A Fig ) , which displayed the characteristic UV-visible spectrum of its [2Fe-2S] cluster ( S4B Fig ) [25] , and determined a 0 . 9:1 ratio for both Fe and acid labile sulfide ( Table 1 ) to SoxR monomer . On the contrary , we measured a 1:1 Fe-to-protein ratio in purified GST-Fep11–245 , which was dependent on the presence of the four Cys residues at the N-terminal domain , but could not detect inorganic sulfide ( Table 1 ) . This suggests that the Fe is not bound to Fep1 in the form of a Fe-S cluster . To confirm this result , we obtained an apo form of Fep1 upon purification of GST-Fep11–245 in thiol-containing buffers ( 1 mM β-mercaptoethanol ) , and managed to recover in vitro the peaks of absorption of the Fe-containing protein upon anaerobic incubation with Fe and reducing agents , in the absence of a sulfide donor ( Fig . 5G , red solid line ) . This result suggests that Fe is bound to Fep1 directly to the Cys residues located between the zinc fingers , and not as a Fe-S cluster . Our results indicate that Fep1 is a Fe-containing protein . Since the Fep1 . C4S mutant , unable to bind Fe in vitro , displays de-repressed gene transcription ( Fig . 5D ) , a simple hypothesis is that intracellular Fe depletion could then render apo-Fep1 and activation of Fe import genes . Why are then Grx4 and Fra2 required for this de-repression ? We explored the possibility that these proteins are just facilitators of the loss of Fe from Fe-Fep1 , where the metal should be tightly bound to the protein backbone . Indeed , under longer DIP chelator treatments we observed de-repression of Fep1-dependent genes even in the absence of Grx4 ( 8 hours; Fig . 6A ) or Fra2 ( 2–4 hours; Fig . 6A ) . In S . cerevisiae , Fe-GRX3/GRX4 participates in the Fe-S cluster assembly pathway , where it probably transfers its iron-sulfur cluster towards the protein NAR1 , a protein involved in the assembly of cytosolic and nuclear iron-sulfur proteins ( for a review , see [26] . Our in vivo data suggest that Grx4-Fra2 may be ‘facilitators’ in the loss of Fe by Fep1 , by promoting a ‘reverse’ Fe transfer reaction , as opposed to the proposed role of this complex in iron-sulfur cluster assembly [26] . We then design an in vitro approach to investigate reverse Fe transfer from Fep1 to Grx4-Fra2 . We performed reconstitution of the Fe-S cluster of Grx4-apo-Fra2 under anaerobic conditions in the absence of inorganic Fe and with the only metal supply of Fe-containing GST-Fep11–245 ( Fig . 6B ) . After 2 hours of incubation the UV-visible spectrum shoulders of Fe-containing Fep1 were lost ( Fig . 6C ) , concomitant to the assembly of the Fe-S cluster to yield Grx4-Fe-Fra2 ( Fig . 6D ) . Importantly , this reverse metal transfer process is abolished if apo-GST-Fep1 . C4S ( S5A Fig ) or Grx4 . C172S ( S5B Fig ) are components of the reaction . Furthermore , we could not detect Fe transfer from Grx4-Fe-Fra2 to apo-GST-Fep11–245 using the same experimental conditions ( S5C Fig ) . We propose that upon Fe loss from the Fe-S cluster bridging Grx4 and Fra2 , the heterodimer can then sequester in a reverse transfer process the Fe from Fep1 , which would then become inactive as a transcriptional repressor ( Fig . 6E ) .
Regulation of the intracellular Fe pools largely depends on the activation of Fe uptake and inhibition of Fe storage and consumption during Fe starvation . In S . pombe , this process fully depends on Grx4 , a monothiol glutaredoxin reported in other eukaryotic organisms to regulate both Fe traffic and Fe signaling events . We have generated grx4 and fra2 knock out strains , and mutants integrated at the chromosomal grx4 locus , to unambiguously determine their participation in both processes . We demonstrate that Grx4-Fra2 is a Fe-S cluster-containing heterodimer , which is essential for both Fe delivery and Fe sensing/signaling . Grx4-Fra2 is localized at the cytosol and the nucleus before and after Fe starvation . Addition of chelators to the cell cultures triggers a Fe starvation response , probably by eliminating the Fe-S clusters in the Grx4-Fra2 dimers . However , the molecular events regulating Php4 and Fep1 are quite distinct . Php4 is the only component of this complex Fe signaling cascade that is regulated at the level of subcellular localization: it is retained at the cytosol under rich Fe conditions in a Grx4-dependent manner . Under Fe deprivation , the association of Grx4-Fra2 to Php4 is significantly disturbed , as demonstrated here by co-immuno-precipitation analysis ( Fig . 1G ) , and Php4 then accumulates at the nucleus , where it represses Fe usage genes . Fra2 was recently described to be fully dispensable for the Php4-dependent regulation of transcription in response to the permeable Fe chelator DIP [23] , but we show here that in Δfra2 cells the kinetics of Php4 activation as a repressor in response to the mild chelator BPS are much faster than in wild-type cells ( Fig . 4G ) . We propose that Fe-containing Grx4 homodimers can retain Php4 in the cytosol but with less efficiency than Grx4-Fe-Fra2 , and therefore BPS can more easily achieve Php4 activation in a Δfra2 background than in wild-type cells . The loss of Fe from the Grx4-Fra2 dimers seems to be the triggering event: our in vitro reconstitution experiments demonstrate that Cys172 in Grx4 is required for cluster assembly in the homo ( Fig . 3F ) or heterodimer ( S3A Fig ) , and cells expressing only Grx4 . C172S display a transcriptional program to Fe deprivation identical to that of cells lacking Grx4 , with constitutive repression of Php4-dependent genes ( Fig . 3D ) . We propose that Fe-containing Grx4-Grx4 or , to a better extent , Grx4-Fra2 complexes are capable of binding to and sequestering Php4 in the cytosol , and that Fe-S cluster loss either by Fe starvation of by expression of the constitutive apo-form Grx4 . C172S render cells with constitutively nuclear Php4 . The molecular events ruling the Grx4-dependent Fep1 activity are not so straightforward . Instead of displaying constitutive transcription of Fe import genes , cells expressing Grx4 . C172S cannot activate them in response to Fe deprivation ( Fig . 3D ) . Thus , loss of metal binding in Grx4 upon Fe starvation cannot be sufficient for Fep1 inactivation as a repressor . We show here that Fep1 is also a Fe-binding protein , as demonstrated by characterization of recombinant GST-Fep1 ( Fig . 5C and Table 1 ) . The presence of Fe in other recombinant GATA repressors , such as SRE of Neurospora crassa [27] or Sre1 of the pathogenic fungus Histoplasma capsulatum [28] , has already been described . Fep1 has some Cys residues of the N-terminal domain essential for metal binding ( Fig . 5B , C and Table 1 ) and transcriptional repression activity ( Fig . 5D ) . This Fe is probably tightly bound to the protein: while moderate Fe decreases ( i . e . 1 . 5 h long DIP treatments ) are not sufficient to withdraw the metal from Fep1 in the absence of Grx4 or Fra2 and de-repress transcription , longer chelator exposures ( 2 or 8 h ) are able to accomplish it in Δfra2 or Δgrx4 strains , respectively ( Fig . 6A ) . Which is then the role of the Grx4-Fra2 heterodimer in Fep1 inactivation as a repressor ? We propose that the Fe-S cluster in the Grx4-Fra2 complex may be partially or totally dismantled upon metal starvation , and the Fe-lacking Grx4-Fra2 can then induce reverse metal transfer from Fep1 ( Fig . 6C , D ) , as recently proposed in vitro for the monothiol glutaredoxin Grx3-Fra2 heterodimer and the downstream Fe-S cluster carrier proteins Isc1 [29] . We also suggest that the Fe-S cluster between Grx4 and Fra2 is specifically important to bridge these two proteins , but not for binding to and inactivating Fep1 . This is supported by the following facts: ( i ) the cluster between Grx4 . C172S and Fra2 cannot be reconstituted in vitro ( S3A Fig ) ; ( ii ) the in vivo binding between Grx4 and Fep1 is only mildly affected in cells expressing Grx4 . C172S-GFP ( S6A Fig ) ; and ( iii ) the kinetics of activation of Fe import genes upon DIP in cells lacking Fra2 are almost identical to those of cells expressing the Grx4 . C172S mutant ( compare Fig . 6A with S6B ) . The fact that Grx4 regulates by different mechanisms two transcriptional repressors is intriguing . With the use of the chelator BPS , which naturally depletes extracellular Fe and which therefore acts indirectly on the intracellular Fe levels , it has been surprising to detect important differences in the kinetics of regulation of Fep1- and Php4-dependent genes . Thus , activation of genes coding for Fe importers occurs immediately , whereas repression of Fe usage genes is not as fast and it is less pronounced as in response to DIP ( Fig . 1D ) . Therefore , and at least after BPS exposure which seems to induce a more physiological Fe starvation than DIP , fission yeast attempts to induce Fe import as a first wave of action , with only a mild down-regulation of Fe storage and consumption . This hierarchical activation of Fe import prior to repression of Fe usage may be a rather general strategy of cells upon Fe starvation: at least in S . cerevisiae , the expression of Cth2 , a protein which promotes degradation of mRNAs encoding Fe-containing proteins , depends at the level of transcription on Aft1 , a transcription factor which responds to Fe deprivation and triggers transcription of Fe import genes , and therefore occurs later on time than the activation of Fe import [30] . Cells lacking Grx4 or , to a lesser extent , Fra2 display severe growth defects in the presence of oxygen that are not shared by cells lacking Php4 or Fep1 ( Fig . 1B and4E ) . This fact prompts us to speculate that S . pombe Grx4 has an essential role in Fe delivery towards Fe-containing proteins , as it has been proposed for the S . cerevisiae homolog GRX4 [10] . Some of the aerobic phenotypes of cells lacking Grx4 could arise from the constitutive repression of Php4-dependent genes ( Fig . 1E , right panel ) , most of which are essential for respiratory growth [8] . We have , however , dismissed this possibility with the characterization of cells lacking both Grx4 and Php4: the expression of respiratory-related genes is wild-type in this cell background under normal Fe conditions , and nevertheless cells are still defective to grow under aerobic conditions ( S7A–S7B Fig ) . Another fact that supports the idea of Grx4 and Fra2 participating in a process other than sensing Fe depletion to activate signaling cascades is the intracellular concentrations of these components . Thus , while Php4 and Fep1 are present in 1–3 thousand copies per cell , the concentration of Grx4 and Fra2 has been described to be in the order of 19 and 16 thousand copies per cell , respectively , according to a recent proteomic report [31] . We have confirmed these 5–15-fold higher concentrations of Grx4 and Fra2 with respect to the transcriptional repressors Fep1 and Php4 with C-terminal myc tagging of the four gene loci and immuno-blotting ( S7C Fig ) . Contrary to what has been proposed for S . cerevisiae , not only Grx4 but also Fra2 may contribute to Fe delivery to downstream effectors in the Fe-S cluster assembly pathway in S . pombe . Whether general Fe-containing proteins are defective in Fe content and activity in Δgrx4 Δphp4 cells , where the concentrations of Fe-containing proteins are as in wild-type cells , will have to be analyzed .
Origins and genotypes of strains used in this study are outlined in S1 Table . Details on their construction and growth conditions , as well as on plasmids construction , are provided in S1 Text . For survival on solid plates , S . pombe strains were grown in YE , diluted and spotted in YE or MM medium agar plates as described previously [32] . Growth curves were also measured as previously described [32] . Details are provided in S1 Text . Total RNA from exponentially growing S . pombe cells in YE , with or without treatment with the Fe chelators DIP ( 0 . 1 or 0 . 25 mM ) or BPS ( 25 μM ) , was extracted , processed and analyzed as previously reported [33] . Cells were fixed with formaldehyde , treated with zymolyase in the presence of sorbitol , and resulting spheroplasts were incubated with polyclonal anti-Grx4 , or monoclonal anti-HA ( 12CA5 ) antibodies . After incubation with corresponding secondary antibodies , cells were analyzed directly by fluorescence microscopy as described previously [34] . Details are provided in S1 Text . Fluorescence microscopy and image capture was performed as previously described [34] . Analysis was performed as previously described [35] . Bacteria strain FB810 [36] transformed with the pGEX-2T-TEV derivatives were grown at 18°C for efficient protein expression as described in S1 Text . Analysis of the color of cell pellets , purification of GST-tagged proteins with glutathione-sepharose beads and cleavage with Tev protease is also described in S1 Text , as well as purification of the untagged control bacterial protein SoxR . Cluster reassembly and Fe transfer reactions were performed under anaerobic conditions in a Forma Anaerobic System ( Thermo Electron Corporation ) . Details on the iron reconstitution reactions of recombinant wild-type or mutant Grx4 ( 50–60 μM ) with or without equimolar amounts of Fra2 , or GST-Fep11–245 , are detailed in S1 Text , as well as the Fe transfer reactions between Fe-GST-Fep11–245 and apo-Grx4-Fra2 . Fe [37] and acid labile sulfide [38] quantification was performed as previously described with the modifications indicated in S1 Text . TCA extracts and Western blot was performed as previously described [39] .
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Iron is an essential biometal but it is also toxic , and therefore its intracellular availability from disposable iron pools is tightly regulated . From bacteria to higher eukaryotes , iron starvation triggers complex genetic responses to exacerbate the otherwise limited iron uptake and decrease intracellular iron storage and usage . These responses are triggered in very distinct ways in each organism . In fission yeast , two transcriptional repressors , Php4 and Fep1 , mediate the iron usage/iron import cellular response to iron starvation , respectively , and a glutaredoxin Grx4-Fra2 heterodimer governs both repressors . We show here that iron is an essential component of the Grx4-Fra2 heterodimers and of the transcriptional repressor Fep1 . Under normal iron conditions , iron-containing Grx4 maintains Php4 retained in the cytosol , and iron depletion forces their dissociation and Php4 nuclear accumulation . On the other hand , iron-bridged Grx4-Fra2 is bound to Fep1 at repressed promoters , and iron depletion forces reverse metal transfer from Fep1 to Grx4-Fra2 , and transcriptional de-repression . These complex molecular events occur upon iron scarcity to induce iron import and decrease iron usage , and explains how a single protein complex , Grx4-Fra2 , can both activate and inactivate transcription to mount a survival response .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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A Cascade of Iron-Containing Proteins Governs the Genetic Iron Starvation Response to Promote Iron Uptake and Inhibit Iron Storage in Fission Yeast
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The innate immune system of plants consists of two layers . The first layer , called basal resistance , governs recognition of conserved microbial molecules and fends off most attempted invasions . The second layer is based on Resistance ( R ) genes that mediate recognition of effectors , proteins secreted by pathogens to suppress or evade basal resistance . Here , we show that a plant-pathogenic fungus secretes an effector that can both trigger and suppress R gene-based immunity . This effector , Avr1 , is secreted by the xylem-invading fungus Fusarium oxysporum f . sp . lycopersici ( Fol ) and triggers disease resistance when the host plant , tomato , carries a matching R gene ( I or I-1 ) . At the same time , Avr1 suppresses the protective effect of two other R genes , I-2 and I-3 . Based on these observations , we tentatively reconstruct the evolutionary arms race that has taken place between tomato R genes and effectors of Fol . This molecular analysis has revealed a hitherto unpredicted strategy for durable disease control based on resistance gene combinations .
Long periods of co-evolution of plants and microorganisms have led to complex mechanisms of attack and defence , involving the innate immune system of plants and virulence factors of pathogens [1] . The first layer of plant defence , called basal immunity , is based on recognition of conserved microbial molecules but can be suppressed by microbial virulence factors known as “effectors” . Plants respond to this suppression by employing a second layer of defence , Resistance ( R ) gene-based immunity , which relies on recognition of effectors [2] . In turn , at least bacterial pathogens have found ways to manipulate or evade this second layer of defence [3] . It is unclear to what extent this capacity exists in eukaryotic plant pathogens like oomycetes and fungi . Like bacteria , many plant-pathogenic fungi secrete proteins that are recognized by R-genes [4] , [5] . One of these fungi is Fusarium oxysporum , a common soil inhabitant . It propagates asexually and is mostly harmless . However , pathogenic and host-specific clonal lines have evolved that cause severe diseases in crops , such as banana , cotton , cucumber , melon and tomato [6] , [7] . Many of these diseases are caused by colonisation of the water-conducting xylem system of the roots followed by upward growth through xylem vessels , with wilting and death as a dramatic result . Strains of F . oxysporum that cause wilt of tomato plants are grouped in forma specialis ( f . sp . ) lycopersici . Several polymorphic resistance ( R ) genes have been identified in the tomato gene pool that each confer resistance against a subset of F . oxysporum f . sp . lycopersici ( Fol ) strains . These are I ( for Immunity ) , I-1 , I-2 and I-3 [8] . Races of Fol are named historically according to the R gene that is effective against them: the I gene and the ( unlinked ) I-1 gene are effective against race 1 , race 2 overcomes I and I-1 , but is stopped by I-2 , while race 3 overcomes I , I-1 and I-2 but is blocked by I-3 [9] . Race 1 strains have been further divided into subgroups based on whether or not they are able to ( partially ) overcome I-2 or I-3 [9] , [10] . Based on the gene-for-gene hypothesis [11] , it is assumed that disease resistance conferred by R genes in tomato requires ‘matching’ avirulence ( AVR ) genes in Fol . The I gene originates from Solanum [Lycopersicon] pimpinellifolium and resides on chromosome 11 [12] , [13] , while the I-1 gene is located on chromosome 7 in another wild relative of tomato , Solanum [Lycopersicon] pennellii [14] . The I-2 gene has been cloned and encodes an R protein of the common NB-LRR class [15] . The I-3 gene has not yet been cloned [16] , but the matching AVR gene has: it encodes a small protein , Six1 ( “Secreted in xylem 1” ) , which is secreted by Fol during colonization of the xylem system [17] and contributes to fungal virulence [9] . Six1 is now called Avr3 to indicate its gene-for-gene relationship with the I-3 resistance gene . We describe here the identification and analysis of a second avirulence factor of Fol , Avr1 . Surprisingly , this protein does not only act as an avirulence factor in conjunction with the I gene , but also suppresses disease resistance mediated by I-2 and I-3 .
In an initial analysis of the xylem sap proteome of tomato plants infected with Fol race 1 using 2-D gel electrophoresis and mass spectrometry , three small secreted proteins of Fol were identified in addition to Avr3 ( Six1 ) , named Six2 , Six3 and Six4 , and their genes cloned [18] . We now find that one of these , Six4 , is not secreted by Fol race 2 ( Fig . 1 ) . For reasons detailed below , we now call this protein Avr1 . Like the AVR3 ( SIX1 ) gene , AVR1 is surrounded by repetitive elements ( Fig . 2A ) . In all of the race 1 strains we examined , PCR experiments detected the presence of AVR1 and no sequence polymorphism was detected in the coding regions of seven isolates from different clonal lines ( see [9] for the list of strains; 17 of these are race 1 , 23 are race 2 or 3 ) . AVR1 was not detected in race 2 or 3 strains by PCR nor is AVR1 present in the genome sequence of the race 2 strain 4287 ( Fusarium oxysporum Sequencing Project; Broad Institute of Harvard and MIT ( http://www . broad . mit . edu ) ) . Absence of AVR1 or closely related genes in the race 2 and race 3 strains used in this study was confirmed by DNA gel blot analysis ( Fig . 2B , lanes 4 and 7 , respectively ) . To test whether AVR1 is indeed responsible for avirulence of Fol on plants carrying the I gene , we created an AVR1 gene knock-out in a race 1 strain ( Fol004 ) through Agrobacterium-mediated transformation ( Fig . 2 ) . For the AVR1 gene , the frequency of homologous recombination leading to gene knock-out turned out to be extremely low , with only a single knock-out mutant obtained out of ∼200 transformants ( Fig . 2B , lane 2 ) . A disease assay with this mutant ( avr1Δ ) confirmed that indeed deletion of AVR1 leads to breaking of I-mediated disease resistance ( Fig . 3A , panel A , quantified in Fig . 3B ) . Re-introduction of AVR1 in the avr1Δ strain ( Fig . 2B , lane 3 ) restored the original avirulence phenotype ( results not shown ) . In addition , we found that disease resistance conferred by the unlinked I-1 gene in tomato also depends on recognition of Avr1 , since the avr1Δ strain ( but not its parental strain ) is virulent on a plant line carrying I-1 ( line 90E402F , results not shown ) . This suggests that I and I-1 express the same resistance specificity . To confirm that the AVR1 gene is sufficient to trigger recognition by the I gene , we transformed AVR1 to a race 2 strain ( Fol007 ) and a race 3 strain ( Fol029 ) that do not contain AVR1 ( Fig . 2B , lanes 4–9 ) and are virulent on I-containing tomato lines . Ten independent transformants ( six of race 2 and four of race 3 ) containing AVR1 were unable to cause disease on I-containing plants ( Fig . 3A , panels B and C , quantified in Fig . 3B ) , confirming the avirulence character of AVR1 . In contrast to Avr3 [9] , Avr1 is dispensable for full virulence towards plants that do not contain R genes against Fol ( results not shown ) . Although all Fol strains possess an intact AVR3 gene , most race 1 strains nevertheless cause disease on plants carrying only the I-3 gene [9] . One explanation for this is that Avr1 itself is involved in suppression of I-3 mediated disease resistance . To test this , we inoculated a plant line containing only the I-3 gene with the set of Fol strains described above . The results clearly show that Avr1 indeed has this suppressive activity: deletion of AVR1 in race 1 leads to loss of virulence towards I-3 plants ( Fig . 3A , panel D , quantified in Fig . 3B ) , while introduction of AVR1 in race 2 or race 3 leads to gain of virulence towards I-3 plants ( Fig . 3A , panels E and F , quantified in Fig . 3B ) . Furthermore , we discovered that Avr1 also suppresses I-2-mediated disease resistance ( Fig . 3A , panels D and E , quantified in Fig . 3B ) . This means that the ability of some race 1 strains to cause disease on I-2 plants , as observed earlier [10] , is likely to be caused by suppression of I-2 rather than loss of AVR2 . In accordance with earlier observations using I-3 plants [9] , we found that virulence due to suppression of I-2 and I-3 is partial compared to strains lacking the corresponding AVR gene ( Fig . S1 ) . It should be noted that not all race 1 strains are virulent on I-2 and/or I-3 plants [9] , [10] , even though all contain AVR1 with identical sequences ( results not shown ) . Apparently , suppression of R gene-based immunity by Avr1 is dependent on unknown factors in the genetic background of the fungus . Since suppression works in Fol007 ( race 2 ) and Fol029 ( race 3 ) , the genetic background in which AVR1 is effective is not restricted to race 1 strains . Our observation that Avr1 is not required for virulence to plants without I genes may be due to the existence of other effectors that are redundant for such an activity . Alternatively , the role of Avr1 is restricted to the suppression of I-2 and I-3-mediated disease resistance . A mechanistic explanation for the latter role could be that Avr1 interferes directly with Avr2 and Avr3 . However , at least Avr3 accumulates in xylem sap and remains unaltered in the presence of Avr1 [9] , [18] . A direct interaction between the two proteins could also not be demonstrated in vitro by pull down experiments ( results not shown ) . Unlike bacteria , pathogenic fungi are not known to inject proteins directly into plant cells , but many are known to secrete small , frequently cysteine-rich , but otherwise unrelated proteins during colonization of plants [5] . Avr1 , like Avr3 , falls within this group , the predicted mature protein having 184 residues including 6 cysteines and lacking homology to other proteins [18] . The mode of action of most of these small secreted proteins has remained unclear . Molecular targets have been described for Avr2 and Avr4 from the leaf mold Cladosporium fulvum: Avr2 is a protease inhibitor [19] while Avr4 binds chitin in the fungal cell wall and protects it against attack by plant chitinases [20] . These two proteins act in the apoplast to enhance fungal virulence , but others act inside plant cells [4] . Uptake from the apoplast by plant cells has been shown directly for ToxA , a small secreted protein that acts as a host-selective toxin [21] . This may also occur with Avr2 , since I-2 is a cytoplasmic protein [15] . Avr1 , then , may interfere with the uptake of Avr2 and Avr3 . Alternatively , it may be taken up itself and interfere with I-2 and I-3 or with signal transduction processes downstream of these R proteins ( Fig . 4 ) . Suppression of effector-triggered ( R gene-mediated ) immunity has been observed in bacteria [3] , [22] , [23] . In plant pathogenic fungi , suppression of avirulence by unlinked loci has been demonstrated by genetics in rust fungi [24] . In the flax rust fungus , two dominant alleles or tightly linked genes at the I ( “inhibitor” ) locus suppress – sometimes partially – either one ( M1 ) or several ( M1 , L1 , 7 , 8 , 10 ) R genes out of 30 against flax rust [24] , [25] . The flax rust inhibitor locus is not itself linked to avirulence . Here , we report the identification of a fungal avirulence factor that suppresses disease resistance conferred by two R genes . Interpreting this phenomenon in terms of molecular arms races between plants and their pathogens [1] , we envisage the following scenario . During evolution of the tomato-Fol pathosystem , I-2 and I-3 have evolved to recognize , respectively , Avr2 and Avr3 . Since Avr3 is required for full virulence of Fol , evasion of I-3 recognition through loss of the AVR3 gene would entail a serious fitness penalty . This explains why all Fol strains analysed so far retained AVR3 [9] , [26] . Point mutations in AVR3 preventing recognition have not been found either [9] . A possible explanation for this is that the I-3 protein operates in accordance with the guard model , in which not the Avr3 protein itself but the effect it has on its virulence target is recognized [27] . In any case , Fol has ( partially ) regained virulence towards I-3-containing plants by acquisition of AVR1 , which , as shown here , suppresses the function of I-3 . Subsequently , tomato responded to this ‘invention’ with the employment of the I gene , or the unlinked I-1 gene , to specifically recognize and respond to Avr1 . Apparently , I and I-1 are themselves insensitive to the suppressive effect of Avr1 ( Fig . 4 ) . The agricultural ‘arms race’ between Fol and tomato is different from the natural one because it is dictated by successive R gene deployment in commercial cultivars [8] . The I gene from the wild tomato relative Solanum [Lycopersicon] pimpinellifolium was the first R gene to be introgressed into tomato cultivars to resist Fusarium wilt in the 1940s [12] . At that time , Fol strains without Avr1 may already have been present in some locations , since I-breaking race 2 strains were quickly discovered [28] even though major outbreaks did not occur before 1960 [29] . The I-2 gene , also from S . pimpinellifolium and directed against Avr2 , was introduced in commercial cultivars in the 1960s to protect tomato against Fol race 2 [29] , [30] . The combination of I and I-2 was effective for about two decades until the appearance of race 3 in both Australia and North America [31] , which probably emerged from a race 2 background through selection for loss or mutation of AVR2 . To combat race 3 , the I-3 gene was introgressed from S . pennellii [31] . From the results presented here , we deduce that the combination of I ( or I-1 ) and I-3 may yield durable resistance of tomato to Fusarium wilt disease of tomato , since I-3 is directed against a virulence factor ( Avr3 ) and I ( and I-1 ) against the suppressor of I-3 ( Avr1 ) . The molecular toolbox that is now gradually filling up ( Avr1 , Avr3 , I-2 ) will help us to define host targets and evolutionary bottlenecks that govern the arms race in the Fol-tomato pathosystem . It also may allow development of new strategies for breeding plants with durable resistance against fungal pathogens .
The following tomato lines were used ( Fol resistance genes between brackets ) : GCR161 ( I ) [32] , 90E402F ( I-1 ) [31] , [33]; 90E341F ( I-2 ) [29] and E779 ( I-3 ) [31] , C32 ( no I gene ) [32] . The following Fol strains were used: Fol004 ( race 1 ) , Fol002 ( race 2 ) , Fol007 ( race 2 ) , Fol029 ( race 3 ) , Fol004avr1Δ ( Fol004 with AVR1 deleted by gene replacement ) , Fol004avr1Δ+AVR1 ( Fol004avr1Δ transformed with AVR1 ) , Fol007+AVR1 ( Fol007 transformed with AVR1 ) , Fol029+AVR1 ( Fol029 transformed with AVR1 ) . See Rep et al . ( 2005 ) [9] for a more detailed description of the wild type Fol strains . Proteins present in xylem sap of tomato plants infected with Fol were isolated and separated with 2-dimensional gel electrophoresis as described earlier [18] , using for the first dimension an Immobiline DryStrip of 13 cm , pH 6–11 NL ( Amersham Biosciences ) . Ten day old seedlings of tomato were inoculated with a fungal spore suspension and disease was scored after three weeks as described earlier [17] . The outcome of the disease assays was quantified in two ways: 1 ) average plant weight above the cotyledons and 2 ) phenotype scoring according to a disease index ranging from zero ( no disease ) to four ( heavily diseased or dead ) [17] . The AVR1 disruption construct was made by PCR amplification of AVR1 upstream and downstream sequences for homologous recombination , and their insertion in front of and behind the hygromycin resistance gene in the vector pRW2h ( see below ) : an upstream fragment , from 714 bp to 1 bp upstream of the start codon , was cloned into pRW2h between the PacI and KpnI sites , and a downstream fragment , from 375 bp after the start codon to 537 bp downstream of the stop codon , was cloned into pRW2h between the XbaI and BssHII sites ( see Fig . 2A for location of the primers ) . The construct for complementation was made by amplification of a AVR1 expression cassette from 714 bp upstream of the start codon to 537 bp downstream of the stop codon ( Fig . 2A ) , which was inserted between the XbaI and StuI sites of pRW1p ( see below ) . Transformation of these constructs to Fol was done with Agrobacterium as described earlier [34] . pRW2h is a binary vector for Agrobacterium-mediated transformation of fungi . It was made through insertion of a NheI-XbaI fragment from pAN7 . 1 , carrying the hygromycin resistance gene hph under control of the Aspergillus ( Emericella ) nidulans gpd promoter and trpC terminator [35] , into the unique XbaI site of pPZP-201BK [36] . Similarly , pRW1p was derived from pPZP-201BK through insertion of a NheI-XbaI fragment from pAN8 . 1 [35] carrying the phleomycin resistance gene ble under control of the same gpd promoter and trpC terminator . Genomic DNA of F . oxysporum was isolated according to Raeder and Broda [37] , digested with HindIII and BamHI , separated in a 1% agarose gel and blotted to Hybond N+ according to Sambrook et al . [38] . The probe containing the AVR1 ORF and 3′ sequences ( 1402 bp , Fig . 2A ) was generated by PCR and contains sequences from 72 bp upstream to 537 bp downstream of the ORF . The probe was radioactively labelled with α32P dATP using the DecaLabel™ DNA labeling kit from MBI Fermentas ( Vilnius , Lithuania ) . Hybridization was done overnight at 65°C in 0 . 5M phosphate buffer pH 7 . 2 containing 7% SDS and 1 mM EDTA . Blots were washed at 65°C with 0 . 2 X SSC , 0 . 1% SDS . The position of sequences hybridizing to the probe were visualized by phosphoimaging ( Molecular Dynamics ) . The AVR1 ( SIX4 ) locus: AM234064 The Avr1 ( Six4 ) protein: CAJ84000
|
In agriculture , the most environmentally friendly way to combat plant diseases is to make use of the innate immune system of plants , for instance by crossing into crop varieties polymorphic resistance genes that occur in natural populations of the crop plant or its close relatives . Plant pathogens , however , have co-evolved with their host plants and have developed ways to overcome the immune system . To effectively make use of components of the plant immune system , it is therefore important to understand the co-evolution of plants and their pathogens at the molecular level . For the interaction between a fungal pathogen and tomato , this paper presents a breakthrough in this respect . A small protein secreted by some strains of the fungus Fusarium oxysporum was found to suppress the activity of two disease resistance genes of tomato . However , a third resistance gene specifically targets this suppressor protein and renders the plant fully resistant against fungal strains that produce it . With this insight , together with knowledge of the genetic variation in the pathogen population , a combination of resistance genes is suggested that is expected to confer durable resistance in tomato against Fusarium wilt disease .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology/plant-biotic",
"interactions"
] |
2008
|
Suppression of Plant Resistance Gene-Based Immunity by a Fungal Effector
|
We use high-density single nucleotide polymorphism ( SNP ) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture . We first develop a theoretical framework for detecting an individual's presence within a mixture , then show , through simulations , the limits associated with our method , and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures , including mixtures where an individual contributes less than 0 . 1% of the total genomic DNA . These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture , and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination . These findings also suggest that composite statistics across cohorts , such as allele frequency or genotype counts , do not mask identity within genome-wide association studies . The implications of these findings are discussed .
Resolving whether an individual's genomic DNA is present at trace amounts within a complex mixture containing DNA from numerous individuals is of interest to multiple fields . Within forensics , determining whether a person contributed their DNA to a mixture is typically a manual process that requires extensive experience and careful training . Furthermore , different laboratories can often come to different conclusions due to differences in methodology or lab intervariability . In large part , forensically identifying whether a person is contributing less than 10% of the total genomic DNA to a mixture is not easily done , is difficult to automate , and is highly confounded with the inclusion of more individuals . Within the field of forensics , as well as the field of human genetics , there is a base assumption that it is not possible to identify individuals using pooled data ( e . g . allele frequency ) from SNP data . In this paper we investigate the accuracy of such assumptions . Numerous methods examining DNA mixtures currently exist , most of these addressing mixtures with smaller numbers of individuals within forensics studies [1]–[3] . Using short tandem repeats ( STR ) is a common method to generate DNA genotyping profiles and allows for identification of the various alleles and their relative quantity within the mixture [4]–[7] . Frequently , STRs on the Y chromosome are useful when resolving the male components of the mixture [8] . Nevertheless , these methods based on STRs expectedly suffer from limited power when using severely degraded DNA [8] , [9] . Mitochondrial DNA ( mtDNA ) based on hypervariable region sequencing is useful when analyzing degraded DNA due to its high copy number and improved stability . Profiles for mtDNA can also be combined with STR analysis for better identification [10] . Nonetheless , mtDNA has weaknesses , including the uniparental mode of inheritance and lower discrimination power that can be moderately mediated by using the whole mitochondrial genome or known surrounding single nucleotide polymorphisms ( SNPs ) [11] , [12] . Informative SNPs have been used to help resolve problems with using mtDNA [11] , [13] , [14] but have not been used wholly or separately as the discriminatory factor , or on the same scale as we propose . In this study , we assess the feasibility of using hundreds of thousands of SNPs assayed on a high-density microarray as a means to resolve trace contributions of DNA to a complex mixture . High-density SNP genotyping arrays have predominately been developed as tools for geneticists to identify common genetic variants that predispose an individual to disease . In the context of forensic mixtures , SNPs are traditionally analyzed by genotype ( e . g . AA , AT , or TT ) and thought to be non-ideal for resolving mixtures . In fact , it is argued that their poor performance in the analysis of mixed DNA samples is one of the primary reasons SNP genotyping arrays have not become adopted by the forensics community [8] , [15] . However , most SNP assays are inherently quantitative at one or both alleles , requiring a genotype calling algorithm to digitize the inherently analog information of a SNP assay [16] . Within this paper , we specifically exploit raw allele intensity measures for analysis of DNA with mixed samples . We demonstrate an approach for rapidly and sensitively determining whether a trace amount ( <1% ) of genomic DNA from an individual is present within a complex DNA mixture . We focus on solving the problem forensically , whereby the problem is much more difficult due to the multiple sources of experimental noise that would further mask identification . Our method can be interpreted as a cumulative analysis of shifts in allele probe intensities in the direction of the individual's genotype . Similarly , we can also interpret our method as measuring the difference of two distances: the distance of the individual from a reference population and the distance of an individual from the mixture . Our method does not require knowledge of the number of individuals in the mixture and we demonstrate robustness for discriminating mixtures composed of over a thousand individuals . We first give a theoretical justification for our method with modifications for known factors including homogeneity of the mixture and accuracy of our reference populations . We then proceed to simulate the effects of three combinations of variables when using SNP microarrays , including probe measurement noise , fraction of the person of interest's DNA in the mixture , and the number of SNPs probed . Finally in a series of proof-of-principle experiments using both Affymetrix and Illumina microarrays , we demonstrate resolving whether an individual is within a series of complex mixtures ( 2 to 200 individuals ) when the individual contributes trace levels ( at and below 1% ) of the total genomic DNA . We finally discuss the implications of these results in the context of forensics and population genetics .
A total of 8 complex mixtures were constructed ( See Table 1 ) . All DNA samples were checked for concentration in triplicates using the Quant-iT PicoGreen dsDNA Assay Kit by Invitrogen ( Carlsbad , CA ) . For accuracy , an eight point standard curve was prepared using Human Genomic DNA from Roche Diagnostics ( Cat#: 11691112001 , Indianapolis , IN ) . The median concentrations were calculated for each individual DNA sample . Shown in Table 1 , two main mixtures ( mixtures A and B ) were composed in duplicates resulting in a total of 4 mixtures . Mixture A was composed of 41 HapMap CEU individuals ( 14 trios minus one individual ) and mixture B was composed of 47 HapMap CEU individuals ( 16 trios minus one individual ) . Two CEU males were combined in a single mixture so that one individual ( NA12752 ) contributed 90% ( 675 ng ) of the DNA in the mixture , while the other individual ( NA07048 ) contributed only 10% ( 75 ng ) DNA into the mixture by concentration . Two CEU individuals , a female and a male , were combined in a single mixture so that one individual ( NA10839 ) contributed 90% ( 675 ng ) of the DNA in the mixture , while the other individual ( NA07048 ) contributed only 10% ( 75 ng ) DNA into the mixture by concentration . Two CEU males were combined in a single mixture so that one individual ( NA12752 ) contributed 99% ( 742 . 5 ng ) of the DNA in the mixture , while the other individual ( NA07048 ) contributed only 1% ( 7 . 5 ng ) DNA into the mixture by concentration . Two CEU individuals , a female and a male , were combined in a single mixture so that one individual ( NA10839 ) contributed 99% ( 742 . 5 ng ) of the DNA in the mixture , while the other individual ( NA07048 ) contributed only 1% ( 7 . 5 ng ) DNA into the mixture by concentration . Two mixtures were combined into a single mixture so that each of the original mixtures contributed the same amount of genomic DNA by volume into the final mixture . CAU2 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository . Mixture A1 was constructed as above and contained 41 CEU individuals . Two mixtures were combined into a single mixture so that each mixture contributed the same amount of genomic DNA by volume into the final mixture . CAU3 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository . Mixture B2 was constructed as above . Two mixtures were combined into a single mixture with Mixture A2 comprising of 5% of the mixture and the CAU3 comprising of 95% of the mixture . CAU3 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository . Mixture A2 was constructed as above . Two mixtures were combined into a single mixture with Mixture B1 comprising of 5% of the mixture and the CAU2 comprising of 95% of the mixture . CAU2 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository . Mixture B1 was constructed as above . Four cohorts were assayed on the Illumina ( San Diego , CA ) HumanHap550 Genotyping BeadChip v3 , one cohort was assayed on the Illumina ( San Diego ) HumanHap450S Duo , and three cohorts were assayed on the Affymetrix ( Emeryville , CA ) Genome-Wide Human SNP 5 . 0 array , with each cohort being assayed on a single chip . Probe intensity values were extracted for analysis from the file folders generated by the BeadScan software for the Illumina platform , and from Affymetrix GTYPE 4 . 008 software for the Affymetrix data , as described in previous studies [6] . We recognize there are multiple approaches to derive a test-statistic to evaluate the hypotheses that a person is within a mixture , and these are discussed further in later sections . In this primary approach we take a frequentist rather than a Bayesian approach , recognizing that both are possible and each has unique advantages . An overview of our approach is described in Figure 1 , and this method can be summarized as the cumulative sum of allele shifts over all available SNPs , where the shift's sign is defined by whether the individual of interest is closer to a reference sample or closer to the given mixture . We first introduce our method in terms of genotyping a given SNP for a single person , which addresses the original design of SNP genotyping microarrays for the field of human genetics . We then proceed to adapt our method for mixtures and pooled data . Current genotyping microarray technology can assay millions of SNPs . Genotypes are expected to result from an assay and data is categorical in nature , e . g . AA , AB , BB , or NoCall where A and B symbolically represent the two alleles for a biallelic SNP . However , as evident from copy number , calling algorithm , and pooling-based GWA studies [6] , [17] , raw preprocessed data from SNP genotyping arrays is typically in the form of allele intensity measurements that are proportional to the quantity of the “A” and “B” alleles hybridized to a specific probe ( or termed features ) on a microarray [16] . Individual probe intensity measurements are derived from the fluorescence measurement of a single bead ( e . g . Illumina ) or 5 micron square on a flat surface ( e . g . Affymetrix ) . On a genotyping array , multiple probes are present per SNP at either a fixed number of copies ( Affymetrix ) or a variable number of copies ( Illumina ) . For example , recent generation Affymetrix arrays typically have 3 to 4 probes for the A allele and B allele respectively , whereas Illumina arrays have a random number of probes averaging approximately 18 probes per allele . With 500 , 000+ SNPs , there are millions of probes ( or features ) on a SNP genotyping array . One should note that there are considerably different sample preparation chemistries prior to hybridization between SNP genotyping platforms and thus probes behave differently on the respective platforms . Before we discuss resolving mixtures , we summarize ‘genotype calling’ in the context of data from a single individual at a single SNP . SNP genotyping algorithms typically begin by transforming normalized data into a ratio or polar coordinates . For simplicity , we will utilize a ratio transformation Yi = Ai/ ( Ai+kiBi ) , where Ai is the probe intensity for the A allele and B is the probe intensity for the B allele for the jth SNP . Multiple papers have shown that Yj transformation approximates allele frequency , where kj is the SNP specific correction factor accounting for experimental bias and is easily calculated from individual genotyping data [6] , [17] . Thus with this transformation , Yi is an estimate of allele frequency ( termed pA ) for each SNP . Since most individuals contain two copies of the genome for autosomal SNPs , values for the A allele frequency ( pA ) in a single individual may be 0% , 50% , or 100% for the A allele at AA , AB , or BB , respectively . Equivocally Yi will be approximately 0 , 0 . 5 , or 1 , varying from these values due to measurement noise . By example and assuming kj = 1 , probe intensity measurements of Aj = 450 and Bj = 550 yield Yj = 0 . 45 and this SNP would be likely called AB . For a single individual , we thus expect to see a trimodal distribution for Y across all SNPs since only AA , AB , or BB genotype calls are expected . However , in a mixture of multiple individuals , the assumptions of the genotype-calling algorithm are invalid , since only AA , AB , BB , or NoCall are given regardless of the number of pooled chromosomes . However , this does not prevent us from extracting information and meaning from the relative probe intensity data . In our approach , we compare allele frequency estimates from our mixture ( termed M , where Mi = Ai/ ( Ai+kiBi ) ) to estimates of the mean allele frequencies of a reference population . The selection of the reference population is important and will be discussed later . For now , we assume that the reference population has a similar ancestral make-up as that of the mixture . We refer to having similar population substructure , ethnicity , or ancestral components interchangeably , and define similar ancestral components for an individual or mixture as having similar allele frequencies across all SNPs . We let Yi , j be the allele frequency estimate for the individual i and SNP j , where Yi , j∈{0 , 0 . 5 , 1} , from a SNP genotyping array . We then compare absolute values for two differences . The first difference |Yi , j−Mj| measures how the allele frequency of the mixture Mj at SNP j differs from the allele frequency of the individual Yi , j for SNP j . The second difference |Yi , j−Popj| measures how the reference population's allele frequency Popj differs from the allele frequency of the individual Yi , j for each SNP j . The values for Popj can be determined from an array of equimolar pooled samples or from databases containing genotype data of various populations . Taking the difference between these two differences , we obtain the distance measure used for individual Yi: ( 1 ) Under the null hypothesis that the individual is not in the mixture , D ( Yi , j ) approaches zero since the mixture and reference population are assumed to have similar allele frequencies due to having similar ancestral components . Under the alternative hypothesis , D ( Yi , j ) >0 since we expect that the Mj is shifted away from the reference population by Yi's contribution to the mixture . In the case of D ( Yi , j ) <0 , Yi is more ancestrally similar to the reference population than to the mixture , and thus less likely to be in the mixture . Consistent with the explanation of Figure 1 , D ( Yi , j ) is positive when Yi , j is closer to Mj and D ( Yi , j ) is negative when Yi , j is closer to Popj . By sampling 500 K+ SNPs , one would generally expect D ( Yi , j ) to follow a normal distribution due to the central limit theorem . In our analysis , we take a one-sample t-test for this individual , sampled across all SNPs , and thus obtain the test statistic: ( 2 ) In equation ( 2 ) we assume μ0 is the mean of D ( Yk ) over individuals Yk not in the mixture , SD ( D ( Yi ) ) is the standard deviation of D ( Yi , j ) for all SNPs j and individual Yi , and s is the number of SNPs . We assume μ0 is zero since a random individual Yk should be equally distant from the mixture and the mixture's reference population and so . Under the null hypothesis T ( Yi ) is zero and under the alternative hypothesis T ( Yi ) >0 . In order to account for subtle differences in ancestry between the individual , mixture , and reference populations among other biases we normalize our allele frequency estimates to a reference population . Different populations will have different mean SNP allele frequencies based on ancestry , admixture , and population bottlenecks . An obvious assumption of this type of analysis is that the reference population must be either ( a . ) accurately matched in terms of ancestral composition to the mixture and person of interest or ( b . ) limited to analysis of SNPs with minimal ( or known ) bias towards ancestry . It is first important to recognize that any single SNP will have only a small effect on the overall test-statistic . Moreover , it is realistic that ancestry of the reference population could be determined by analysis of a small subset of SNPs , followed by analysis of a person's contribution to the mixture with a separate set of SNPs ( recognizing that nearly 500 , 000 SNPs are assayed ) . In the absence of SNP-specific ancestral information towards allele frequency as was assumed in our study , we can also use normalization methods that leverage the fact that we have assayed hundreds of thousands of SNPs and consequentially have largely sampled the distribution of the test-statistic . In essence , we fit the test-statistic to a second reference population matched to the individual of interest to account for ancestry differences that do not effect the overall distribution of allele frequencies . Thus under the assumption of similar test-statistic distributions , normalizing SNP data from the mixture to a reference population reduces the effect of systematic biases on allele frequency from the microarray or , to an extent , towards ancestry at a cost of power . While not necessary in this study , the effect of ancestry on allele frequency could be more directly managed by SNP selection combined with extensive allele frequency data across multiple ancestrally diverse populations . Ideally , one would use a subset of SNPs to identify ancestry of the individual and then match them to a reference population . Moreover , SNPs that are stable for allele frequency across populations ( low Fst ) or at have a common distribution of allele frequencies would be preferable . Identifying such a set of SNPs and more appropriately considering ancestral biases are reserved for future database studies whereby genotype data of an ancestrally diverse set of individuals is available . Pre-compiled UNIX binaries are available for a software implementation of our method and can be found at http://public . tgen . org/dcraig/deciphia . Our software is able to run analysis using raw data from either Affymetrix or Illumina or by using genotype calls . The software is also able to normalize our test statistic using the reference population and/or adjust the mean test statistic using a specified individual . Additionally , the user can restrict the SNPs considered to a subset of the total available SNPs . For raw input data we are able to match the distribution of signal intensities for each raw data file to that of the mixture input file ( see platform specific analysis ) . Finally , multiple test statistics and distance calculations are implemented including our original test statistic , Pearson correlation , Spearman rank correlation and Wilcoxon sign test . With the Affymetrix platform we were able to use genotypes for each individual and found similar results with the Illumina platform . Additionally , we were able to use the raw CEL files from the HapMap dataset [18] found at http://www . HapMap . org . To overcome the differences in distribution of signal intensity between CEL files , we matched the distribution of the signal intensities to the distribution of the mixture's CEL file . This was achieved by ordering allele frequencies on a given chip ( and allele frequencies in the mixture ) . We then substituted the ith allele frequencies from the mixture of interest for the ith allele frequencies for the given chip . Without this adjustment , there was difficulty resolving any individual in any mixture due to the fact that we did not account for off-target cross-hybridization . This type of adjustment is the preferred type of normalization method when raw data is available for the mixture , person of interest , and reference population . For the Illumina platform we used the genotypes from the HapMap dataset [18] for both the person of interest and the reference populations instead of raw intensity values as we had for the Affymetrix platform . For the mixture we used raw intensity values . This set of data mimics the case where raw data may not be available but genotype calls are available . We use a simple method to reduce errors between different microarrays , where we normalize each microarray by dividing by the mean channel intensity for each respective channel . This was performed on the raw data for the mixture only . We note that this platform specific adjustment is not needed when the raw data for a person's genotype is present on the same platform . In the Illumina specific example , we utilized only the calls from the HapMap without having platform specific genotype data . Theoretically , it should be possible to use a library of Yi means for AA , AB , and BB to map genotype calls to expected Yi values to each SNP for individually genotyped samples , but this was not necessary for our analysis . Simulation was used to test the efficacy of using high-density SNP genotyping data for resolving mixtures . The key variables of the simulation are: the number of SNPs s , the fraction f of the total DNA mixture contributed by our person of interest Yi , and the variance or noise inherent to assay probes vp . In the simulations , theoretical mixtures were composed by randomly sampling individuals from the 58C Wellcome Trust Case-Control Consortium ( WTCCC ) dataset [19] . After removing duplicates , relatives and other data anomalies , a total of 1423 individuals remained for sampling . The genotype calls for these individuals were provided from the WTCCC and were previously genotyped on the Affymetrix 500 K platform . Within each simulation , we randomly chose N individuals to be equally represented in our mixture and then computed the mean allele frequency ( Yi ) of our mixture for each SNP . SNPs j with an observed Yij below 0 . 05 or above 0 . 95 in the reference population were removed due to their potential for having false positives and low inherent information content . We then simulated a microarray that would contain a mean of 16 probes for simplicity , approximating the mean number of probes found on the Illumina 550 K , Illumina 450S Duo and Affymetrix 5 . 0 platforms ( 18 . 5 , 14 . 5 and 4 respectively ) . For each SNP j we added to the Yij of each probe a Gaussian noise based off the previously measured probe variance . When fixed , we set probe variance to 0 . 006 when simulating Affymetrix 5 . 0 arrays , and to 0 . 001 for both Illumina 550 K and Illumina 450S Duo arrays . The allele frequency of the mixture was then calculated to be the mean of these probe values . A mixture size of N is equivalent to saying that an individual's DNA represents f = 1/Nth of the total DNA in the mixture . We tested equimolar mixtures ranging from 10 individuals to 1 , 000 individuals . Using this design , we tested each individual for their presence where they contributed between 10% and 0 . 1% genomic DNA to the total mixture . To obtain significance levels ( p-values ) for testing the null hypothesis , we sampled from the normal distribution . We note that we do not have enough samples to test the tail of our distribution and therefore our p-values are not completely accurate ( e . g . below 10−6 ) . Nonetheless , p-values are expected to be sufficiently accurate to qualitatively assess the limits of our method . To examine empirically the efficacy of our method we formed various known mixtures of DNA from HapMap individuals and genotyped the mixtures on three different platforms . Listed in Table 1 and detailed in the methods are the compositions of the different mixtures formed and the platforms they were assayed across . The use of mixtures of HapMap individuals has several advantages . First , we can be confident of the genotype calls because in most cases more than one platform has been used to identify the consensus genotype . Second , trios are available , which allow for evaluation of identifying an individual using a relative's genotype data . Third , by using mixtures of multiple HapMap individuals we can evaluate our ability to resolve each individual within the mixture . Therefore we have constructed simple two-person mixtures as well as complex mixtures containing contributions from 40+ individuals . For each mixture , we used the HapMap CEU individuals not present in the mixture as our reference population for the mixture .
We performed simulations to examine the trade-off between the number of SNPs considered , the fraction of the DNA mixture belonging to our person of interest , and the probe variance or noise of the microarray . To examine empirically the efficacy of our method we formed various known mixtures of DNA from HapMap individuals and genotyped the mixtures on three different platforms .
Within this study , we develop a theoretical framework for resolving mixtures using high-density SNP array data , use simulation to test the limitations of these approaches , and experimentally demonstrate rapid and robust determination of whether individuals are within an assayed mixture . Our results show a remarkable ability to identify trace amounts of an individual's DNA within highly complex mixtures . These results further suggest novel forensic applications where the existence of DNA from numerous other individuals currently hampers the ability to identify the presence of any single individual . Whereas few conclusions can be drawn by a SNP measurement that is slightly biased ( less than 1% ) towards an individual's genotype , considerable confidence is gained by statistical analysis of the cumulative aggregate of all measurements across millions of SNPs . While in hindsight this conclusion seems obvious , it represents a fundamental paradigm shift in thinking about the utility of SNPs at resolving mixtures . The approach described here uses the ratio of intensity measures from common biallelic SNPs . As a result , one expects more robust scaling in DNA quantity or quality at any given SNP . We assume neither a known number of individuals present in the mixture nor the presence of equal amounts of DNA from each individual within the mixture . Described in simplistic terms , we determine whether a person is in a mixture by comparing a statistically describable distance measure between the individual and the mixture versus the individual and the reference population . The analytical framework presented within this study builds upon pioneering approaches for assessing and quantifiably calculating whether a person is within a mixture . These methods have frequently employed match probability estimation after inferring genotypes using STRs , where the probability of two unrelated individuals sharing a combination of markers is calculated [8] . Exclusion probabilities give a calculation based on the probability of excluding a random individual [20] . Nevertheless , many of these methods rely on assuming the number of individuals in the mixture [1] ( which is not necessary in our analysis ) and have been applied only to STR markers . One can also consider using other statistical approaches . For example , likelihood ratios are also commonly used when testing which hypothesis is favored by DNA evidence [21] . Adapting to the overall framework presented in this study , one might compute the likelihood ratio of two hypotheses: the individual contributes to the mixture and the individual does not contribute to the mixture . The proper prior odds ratio can then be given based on the current situation or context , and then would be combined with the likelihood ratio to give a posterior odds ratio . In this approach , using SNP microarrays for allele frequencies or allele counts could be used to calculate the probability of the observed mixture's allele frequency or individual of interest's genotype . This Bayesian approach could build from the methods presented here and , depending on the scenario , has attractive strengths including creation of explicit hypotheses ( e . g . that a person and/or related individuals are within the mixture ) and inclusion of specific priors ( e . g . informativeness towards ancestry SNPs ) . Overall , it is clear there are multiple analytical methods for resolving complex mixtures and depending on the objective , other methods may be more suitable . Regardless of method , it is clear that the perception that SNPs cannot be easily used to resolve mixtures is no longer valid . Given the results of this study , it is possible to speculate on future research assessing the viability of using commonly handled surfaces as a forensics source . In the context of degraded samples , further research will be needed to choose which SNPs ( of millions assayed SNPs ) provide sufficient amplifiable DNA or show less allelic bias at low concentrations . Further , the theoretical principles described here will apply to mitochondrial variants . Regardless of the artifacts encountered , the large number of assayed SNPs may allow for partitioning sets of SNPs for different analyses , such that a small subset of SNPs becomes reserved for detecting specific artifacts , such as biases in allele amplification or ancestry . Additional areas of future research include conversion tables using haplotype or imputation frameworks to convert between SNPs and microsatellite markers . Finally , it is important to consider these findings in light of GWA studies . Indeed , the push to develop high-density SNP genotyping arrays is largely driven by the desire to identify common variants predisposing to a disease . For many GWA studies , the overall cost of genotyping thousands of individuals is substantial . However since genotype data is transferable and can be combined with data from other studies , there is a considerable effort to make experimental data publicly available . As part of this effort , many studies provide pooled allele frequency data in the form of summary statistics ( e . g . allele frequencies or genotype counts ) , in part to mask individual-level genotype data . Though counter-intuitive , our findings show a clear path for identifying whether specific individuals are within a study based on summary-level statistics . Such approaches may have specific utility for identifying redundant individuals when new individual-level genotype data is combined with previous studies sharing only summary statistics . Considering privacy issues with genetic data , it is now clear that further research is needed to determine how to best share data while fully masking identity of individual participants . However , since sharing only summary data does not completely mask identity , greater emphasis is needed for providing mechanisms to confidentially share and combine individual genotype data across studies , allowing for more robust meta-analysis such as for gene-environment and gene-gene interactions .
|
In this report we describe a framework for accurately and robustly resolving whether individuals are in a complex genomic DNA mixture using high-density single nucleotide polymorphism ( SNP ) genotyping microarrays . We develop a theoretical framework for detecting an individual's presence within a mixture , show its limits through simulation , and finally demonstrate experimentally the identification of the presence of genomic DNA of individuals within a series of highly complex genomic mixtures . Our approaches demonstrate straightforward identification of trace amounts ( <1% ) of DNA from an individual contributor within a complex mixture . We show how probe-intensity analysis of high-density SNP data can be used , even given the experimental noise of a microarray . We discuss the implications of these findings in two fields: forensics and genome-wide association ( GWA ) genetic studies . Within forensics , resolving whether an individual is contributing trace amounts of genomic DNA to a complex mixture is a tremendous challenge . Within GWA studies , there is a considerable push to make experimental data publicly available so that the data can be combined with other studies . Our findings show that such an approach does not completely conceal identity , since it is straightforward to assess the probability that a person or relative participated in a GWA study .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"genetics",
"and",
"genomics/genomics",
"genetics",
"and",
"genomics",
"genetics",
"and",
"genomics/bioinformatics",
"genetics",
"and",
"genomics/medical",
"genetics",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2008
|
Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
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Leishmaniasis is one of the most diverse and complex of all vector-borne diseases worldwide . It is caused by parasites of the genus Leishmania , obligate intramacrophage protists characterised by diversity and complexity . Its most severe form is visceral leishmaniasis ( VL ) , a systemic disease that is fatal if left untreated . In Latin America VL is caused by Leishmania infantum chagasi and transmitted by Lutzomyia longipalpis . This phlebotomine sandfly is only found in the New World , from Mexico to Argentina . In South America , migration and urbanisation have largely contributed to the increase of VL as a public health problem . Moreover , the first VL outbreak was recently reported in Argentina , which has already caused 7 deaths and 83 reported cases . An inventory of the microbiota associated with insect vectors , especially of wild specimens , would aid in the development of novel strategies for controlling insect vectors . Given the recent VL outbreak in Argentina and the compelling need to develop appropriate control strategies , this study focused on wild male and female Lu . longipalpis from an Argentine endemic ( Posadas , Misiones ) and a Brazilian non-endemic ( Lapinha Cave , Minas Gerais ) VL location . Previous studies on wild and laboratory reared female Lu . longipalpis have described gut bacteria using standard bacteriological methods . In this study , total RNA was extracted from the insects and submitted to high-throughput pyrosequencing . The analysis revealed the presence of sequences from bacteria , fungi , protist parasites , plants and metazoans . This is the first time an unbiased and comprehensive metagenomic approach has been used to survey taxa associated with an infectious disease vector . The identification of gregarines suggested they are a possible efficient control method under natural conditions . Ongoing studies are determining the significance of the associated taxa found in this study in a greater number of adult male and female Lu . longipalpis samples from endemic and non-endemic locations . A particular emphasis is being given to those species involved in the biological control of this vector and to the etiologic agents of animal and plant diseases .
Leishmaniasis is a vector-borne neglected infectious disease of worldwide incidence and its most severe clinical form is visceral leishmaniasis ( VL ) . Each year VL causes an estimated 500 , 000 new cases and more than 59 , 000 deaths [1] , a death toll that is only surpassed by malaria among the parasitic diseases [2] . Furthermore , both figures are approximations since VL is frequently not recognized or not reported [3]–[4] . Leishmaniasis is transmitted through the bite of two phlebotomine sandfly genera , Phlebotomus in the Old World and Lutzomyia in the New World . In Latin America VL is caused by Leishmania infantum chagasi and transmitted by Lutzomyia longipalpis [5] . This phlebotomine sandfly is only found in the New World , with a wide distribution from Mexico to Argentina [6] . The geographical distribution of leishmaniasis has undoubtedly expanded and is now being reported in areas that were previously non-endemic . The worldwide phenomenon of urbanisation , closely related to the sharp increase in migration , is one of the major risk factors that is making leishmaniasis a growing public health concern for many countries around the world [7] and Argentina is not an exception . Between 1925 and 1989 only 14 leishmaniasis human cases were reported in Argentina and none was attributed to Le . chagasi . Moreover , there were only two isolated reports of Lu . longipalpis ( in 1953 and 2000 ) which were not associated with VL [8] . Nevertheless , this situation has changed dramatically , mostly due to an indiscriminate advance of urbanisation , and the first Argentine VL outbreak was recently reported [9] . From 2006 to date the morbidity and mortality toll of this disease have amounted to 83 human cases ( 35% corresponding to children under ten years of age ) , 7 deaths and more than 7 , 000 infected dogs ( National Health Surveillance System , Epidemiology Bureau , National Ministry of Health , Argentina ) . In the natural environment , phlebotomine larvae feed on organic matter from soil [10] , while adults from both sexes feed on sugars from plant sources [11]–[12] . Only female adults need blood to obtain necessary proteins for the development of their eggs [5] . It is widely accepted that many insects derive their microbiota from the surrounding environment , such as the phylloplane of food plants or the skin of the animal host , and although the degree of persistence of strains of the ingested species is unknown , these microorganisms can influence the insect life cycle [13] . To comprehensively understand the biology of insects , microorganisms must be considered as a very important component of the ecological system [14] . Moreover , an inventory of the associated microbiota of phlebotomine sandflies , especially of wild specimens , would aid in understanding the annual and regional variations recorded for this disease [15] and in the development of novel strategies for controlling these vectors , among others [13] . One serious obstacle for the biological control of VL sandfly vectors is that their precise breeding sites are poorly known . Furthermore , its practical application seems to be limited to the adult VL vector stage [16] because , as Lu . longipalpis larvae appear to be thinly dispersed [17] , this complicates the employment of biolarvicides in the field . There is scanty information on the microbial colonisation of Lu . longipalpis and it is not yet clear if they possess an indigenous community . Previously , midgut bacteria were examined from wild and laboratory reared Lu . longipalpis populations [18]–[20] which showed a predominance of Gram negative bacteria . Various genera found ubiquitously in the environment ( water , soil and debris ) were identified in these studies , including Acinetobacter , Serratia , Pseudomonas , Stenotrophomonas , Flavimonas and Enterobacter . These bacteria have also been found associated with the gut of several other insects [21]–[24] , suggesting they are a part of the natural or transient microbiota . Prior studies on guts and malpighian tubes from wild P . papatasi and P . tobbi showed a high incidence of mycoses which were similar to Aspergillus sclerotiorum and Saccharomyces cerevisiae [25] . Various types of virus have also been found infecting phlebotomine sandflies [26] , including Vesiculovirus [27]–[28] and Cytoplasmic Polyhedrosis Virus [29] . Furthermore , in addition to Leishmania , Trypanosoma , Endotrypanum and possibly other trypanosomatids [30] , neotropical sandflies may harbour other parasites including microsporidians [31]–[32] , gregarines [33]–[36] , some Plasmodium spp . that parasitise lizards [37] and nematodes [38]–[41] . Nevertheless , there is little information on the pathological effects these parasites may produce in their sandfly hosts . Metagenomics facilitates the culture-independent analysis of microbial communities [42] , an approach which does not require prior assumptions about the composition of the target community . Metagenomic sequencing of communities containing eukaryotes , in particular protists , is mostly cost-prohibitive because of their enormous genome sizes and low gene coding densities [43] . Nevertheless , from an ecological perspective , excluding eukaryotes from a metagenomic analysis compromises the ability to assess the microbial community in its entirety . A possible approach to bypass the problem of large amounts of non-coding eukaryotic sequence data consists in obtaining molecular data at the RNA level . Given the recent VL outbreak in Argentina and with the ultimate goal of identifying possible biological control agents , this study used unbiased high-throughput pyrosequencing technology [44] to compare the diversity of the taxonomic groups associated with wild male and female adult Lu . longipalpis from endemic ( Posadas , Misiones ) and non-endemic ( Lapinha Cave , Minas Gerais ) VL locations in Argentina and Brazil , respectively . As in this study phlebotomine sandflies were considered environmental samples , the term “associated with” was used here in its broadest sense , referring to a wide variety of possible interactions ranging from casual associations due to random environmental contact ( e . g . , plant pathogenic fungi spores adhering to the hairy surface of the sandflies when sugar-feeding on plants ) to closer pathogenic or symbiotic interactions ( e . g . , protists that parasitise phlebotomines or permanent gut microbiota , respectively ) . This analysis revealed the presence of sequences from bacteria , fungi , protists , plants and metazoans .
This study was carried out in strict accordance with the recommendations in the Manual for the Use of Animals/FIOCRUZ ( Manual de Utilização de Animais/FIOCRUZ ) of Fundação Oswaldo Cruz , FIOCRUZ , Ministry of Health of Brazil ( National decree Nr 3 , 179 ) . The protocol was approved by the Ethics Committee for the Use of Animals of the Fundação Oswaldo Cruz - FIOCRUZ , Ministry of Health of Brazil ( Nr 242/99 ) . Lu . longipalpis specimens from the non-endemic VL location , Lapinha Cave ( Minas Gerais , Brazil ) , situated in the Sumidouro National Public Park , were kindly provided by Dr . Paulo Pimenta ( Laboratory of Medical Entomology , Centro de Pesquisas René Rachou , Fundação Oswaldo Cruz , FIOCRUZ ) . Sandflies from this location were chosen as reference because they have been extensively studied . Lu . longipalpis specimens from the endemic VL location , Posadas ( Misiones , Argentina ) , where they occur in high density , were kindly provided by Dr . María Soledad Santini and Mr . Enrique Adolfo Sandoval ( Research Network for Leishmaniasis in Argentina , REDILA , and Posadas Municipality Quality of Life Department ) . Captures were made using CDC light traps [45] on the 15th and 26th of May 2009 in the Lapinha Cave and in Posadas , respectively . The Lapinha Cave ( S19 33 42 . 42 W43 57 34 . 96 ) is a network of interconnected caves located in a vast tropical savanna ecoregion called cerrado , characterised by great plant and animal biodiversity . The trap was left 50–80 cm above ground level in an external small annex cave ( 2 mt long ) where a chicken was kept to attract the sandflies and as a source of food ( see Table 1 for a detailed description of the site ) . Posadas , the densely populated capital city of the province of Misiones , is located in the subtropical fields and grasslands ecoregion . In the Posadas area this ecoregion contacts the Paranaense forest and has a savanna-type landscape . The trap was installed in the peridomicile of a worst-case scenario homestead ( domestic animals , dense vegetation , nearby spring ) ( S27 23 . 266 W55 53 . 403 ) ( see Table 1 for a detailed description of the site ) . Sandflies were transported alive in a nylon cage to the corresponding laboratories in Belo Horizonte ( Minas Gerais ) and Posadas ( Misiones ) and no mortality was registered on arrival . Other insect species were captured together with the sandflies including hymenopterans , lepidopterans and mosquitoes . Sandflies were killed at low temperature , identified and separated according to sex , and stored alternatively in Tri-Reagent ( Molecular Research Center Inc . , Cincinnati , OH ) or RNAlater® ( Qiagen ) . A total of four groups of 100 sandflies each , two per location , were separated and named according to: SS1 , females from the Endemic VL location ( EVL females ) ; SS2 , EVL males; PP1 , females from the Non-Endemic VL location ( NEVL females ) ; and PP2 , NEVL males . Individual samples were ground in Tri-Reagent ( Molecular Research Center Inc . , Cincinnati , OH ) with a Teflon pestle and total RNA was immediately extracted , according to the manufacturer's instructions . Total RNA was amplified using a modified sequence-independent amplification protocol [46] . Briefly , M-MuLV Reverse Transcriptase ( Fermentas , Vilnius , Lithuania ) was used for a first-strand reverse transcription which was initiated with a random octamer linked to a specific primer sequence ( 5′-GTT TCC CAG TAG GTC TCN NNN NNN N-3′ ) [47] . cDNA was then amplified with the Expand Long Template PCR System ( Roche ) using a 1∶9 mixture of the above primer and a primer targeting the specific primer sequence ( 5′-CGC CGT TTC CCA GTA GGT CTC-3′ ) [48] . The following profile was used: initial denaturation cycle at 94°C for 2 minutes; five low stringency cycles with denaturation at 94°C for 30 seconds , 25°C for 30 seconds and 68°C for 6 minutes , were followed by 30 cycles at 94°C for 30 seconds , 55°C for 30 seconds and 68°C for 6 minutes and a final extension cycle at 68°C for 5 minutes . Pooled samples were submitted for high-throughput pyrosequencing ( Macrogen Inc . , Korea ) . Reads were submitted to the NCBI Sequence Read Archive ( SRA ) ( submission SRA026595 ) under accessions SRR089611 ( adult EVL female Lu . longipalpis; Posadas , Misiones , Argentina; SS1 ) , SRR089612 ( adult EVL male Lu . longipalpis; Posadas , Misiones , Argentina; SS2 ) , SRR089613 ( adult NEVL female Lu . longipalpis; Lapinha Cave , Minas Gerais , Brazil; PP1 ) and SRR089614 ( adult NEVL male Lu . longipalpis; Lapinha Cave , Minas Gerais , Brazil; PP2 ) . Reads ranged in size from approximately 100 to 1200 base pairs ( bp ) ( 350 bp average ) . Raw sequence reads were trimmed to remove sequences derived from the amplification primer . With the purpose of reducing database search efforts and improving the homology detection sensitivity [49] , Cd-hit [50] was used to generate non-redundant nucleotide datasets but these represented less than 1% in every case ( data not shown ) . For this reason , singlet sequences were used for the nucleotide database search . Non-redundant ( nt ) and non-human , non-mouse ESTs ( est-others ) NCBI databases last modified on 23/04/10 and 25/04/10 , respectively , were downloaded locally ( ftp://ftp . ncbi . nlm . nih . gov/blast/db/ ) . After trimming , singlet sequences were compared to these databases using BLASTN ( nucleotide homology ) [51] , with a 1e-50 cutoff E-value . The resulting BLAST alignments were analysed and classified according to their taxonomical hierarchies using custom applications written in Mathematica ( Wolfram Mathematica 7; available upon request ) . 16S sequences were confirmed by alignment to type-species 16S rRNA sequences from the Ribosomal Database Project ( http://rdp . cme . msu . edu/ ) [52]–[53] . Hits for every taxon were individually revised and confirmed and only those which showed unequivocal results were included in the final analysis . Fisher's Exact Test [54] ( p<0 . 05 ) was used to establish the significance of sequences in the different samples using a custom application written in Mathematica ( Wolfram Mathematica 7; available upon request ) .
BLASTN analysis of the high-throughput sequencing data identified bacteria in females from both locations ( SS1 and PP1 ) and in NEVL males ( PP2 ) ( Figures 1 and 2 ) . Bacteria were identified mostly by homology to completely sequenced bacterial genomes ( 7 reads ) , followed by rRNA genes ( 4 reads ) and lastly to plasmid sequences ( 2 reads ) ( Table S1 ) . Ten different bacterial types were identified , six of which showed homology at the species level ( five to genomic sequences and one to plasmid sequences ) and four to diverse uncultured environmental samples ( three to 16S rRNA genes and one to genomic sequences ) . The bacterial composition was different and unique in every case and included sequences from Ralstonia pickettii , Anoxybacillus flavithermus , Geobacillus kaustophilus , Streptomyces coelicolor , Propionibacterium acnes , Acinetobacter baumannii , uncultured Veillonella sp . and uncultured bacterium clones isolated from environmental samples ( cow faeces , wetland soil and water ) ( Figure 2; Table S1 ) . The totality of identified bacteria showed a predominance of Gram negative rods ( 53 . 8% , 7 reads ) and a significant proportion of Gram positive bacteria ( 38 . 5% , 5 reads ) , in accordance with previous studies [18]–[20] , [55] . Of all the bacterial sequences that were identified in this study , only A . baumannii , which was found in NEVL males ( PP2 ) , had been previously identified in adult female Lu . longipalpis . This species had been isolated from female laboratory reared specimens from the same non-endemic VL location ( Lapinha Cave ) [20] and from wild female specimens from endemic VL locations in Brazil ( Jacobina , Bahia , and São Luís , Maranhão ) [18] . Fungi were only found in NEVL males and females ( PP1 and PP2 ) ( Figure 1 ) . A total of four different species was identified by homology to rRNA genes ( Figure 2; Table S1 ) . These differed between males and females and have not been found to date associated with phlebotomines . The identified species included Peronospora conglomerata , Cunninghamella bertholletiae , Mortierella verticillata and Toxicocladosporium irritans ( Figure 2; Table S1 ) . Protist sequences were only identified in EVL female and male specimens ( SS1 and SS2 ) ( Figure 1 ) , of which the vast majority ( 99 . 8% ) were found in males ( Figure 2; Table S1 ) . Protists were identified by homology to sequenced rRNA genes ( 360 reads , 61 . 4% ) , cDNA ( 208 reads , 35 . 5% ) and chromosomal sequences ( 18 reads , 3 . 1% ) ( Table S1 ) . Ten species and one genus of apicomplexan parasites were identified that parasitise Diptera ( Ascogregarina taiwanensis , Psychodiella chagasi ) , birds ( Eimeria tenella , Sarcocystis falcatula , Sarcocystis cornixi ) , mammals ( Cryptosporidium muris , Sarcocystis arieticanis , Besnoitia besnoiti , Plasmodium falciparum , Plasmodium berghei ) and reptiles , birds and mammals ( Sarcocystis sp . ) ( Figure 2; Table S1 ) . Most of the apicomplexan sequences ( 64 . 8% , 379 reads ) were homologous to the mammalian parasites C . muris , S . arieticanis , B . besnoiti , P . falciparum and P . berghei . Of these , more than half ( 53 . 6% , 203 reads ) were homologous to published cDNA libraries and the rest to rRNA genes ( 41 . 7% , 158 reads ) and chromosomal DNA ( 4 . 7% , 18 reads ) . The second most numerous group of apicomplexan sequences was homologous to the avian parasites E . tenella , S . cornixi and S . falcatula rRNA genes ( 28 . 2% , 165 reads ) and the rest were homologous to the dipteran parasites A . taiwanensis and P . chagasi rRNA genes ( 7% , 41 reads ) . Metazoan sequences ( mammals , birds and reptiles ) were also found in all the samples and included Homo sapiens , Gallus gallus and Anolis carolinensis ( Figures 1 and 2 ) . Homo sapiens was identified by homology to genomic chromosomal sequences ( 16 reads ) . Gallus gallus was identified by homology to genomic chromosomal sequences ( 46 reads ) , cDNA ( 18 reads ) and rRNA genes ( 1 read ) . Anolis carolinensis was identified by homology to cDNA ( 1 read ) ( Table S1 ) . Human sequences were found in males and females from both locations , whereas chicken sequences were found in NEVL females ( PP1 ) and EVL males ( SS2 ) and lizard sequences were only found in NEVL females ( PP1 ) ( Figures 1 and 2 ) . A total of ten different plant species were identified in males and females from both locations , namely Elaeis guineensis , Capsicum annuum , Juglans hindsii , Artemisia annua , Brassica napus , Vitis vinifera , Solanum tuberosum , Nicotiana tabacum , Oryza sativa and Rhapidophyllum hystrix ( Figures 1 and 2 ) . All plant sequences were identified by homology to cDNA libraries ( 56 reads ) , except for R . hystrix which was identified by homology to rRNA genes ( 1 read ) ( Table S1 ) . EVL males and females showed a greater number of species ( 5 and 6 species , respectively ) , followed by NEVL males ( 4 species ) and lastly NEVL females ( 3 species ) ( Figure 2 ) . However , these differences were significant ( p<0 . 05 ) only between females from both locations ( Table 2 ) . EVL females showed the highest number of plant sequences ( 23 reads ) , followed by EVL and NEVL males ( 13 reads ) and lastly NEVL females ( 8 reads ) ( Figure 2 ) . The only case in which these differences were not significant ( p<0 . 05 ) was between EVL females and NEVL males ( Table 3 ) . C . annuum ( bell pepper ) was found in EVL males and females and in NEVL males . E . guineensis ( African oil palm ) was found in EVL females and NEVL males and females . S . tuberosum ( potato ) was found in EVL males and females and in NEVL females . J . hindsii ( Northern California walnut ) was found in EVL males and females and A . annua ( sweet wormwood ) was found in males from both locations . R . hystrix ( needle palm ) and B . napus ( rapeseed ) were only found in EVL females and V . vinifera ( grapevine ) was only found in EVL males . O . sativa ( rice ) was only found in NEVL females and N . tabacum ( tobacco ) was only found in NEVL males ( Figure 2 ) .
This is the first study to survey taxa associated with an infectious disease vector applying an unbiased and comprehensive metagenomic approach . To ensure an unbiased description of the microbial community , the rationale chosen for this study included the extraction of total RNA and sequence-independent amplification . Total RNA was extracted from wild adult male and female Lu . longipalpis from an endemic ( Posadas , Misiones ) and a non-endemic ( Lapinha Cave , Minas Gerais ) VL location in Argentina and Brazil , respectively , and submitted to high-throughput pyrosequencing [44] . Given the high background level of vector sequences ( ∼85% ) , this approach proved to be very sensitive since it enabled the identification of taxa present in percentages up to 0 . 00036% . Moreover , as the different taxa were identified by homology to both rRNA and mRNA , the chosen approach was adequate for the objectives of this study . The bacterial community identified in females from both locations and in NEVL males was distinct in every case . The only results in common with previous studies of gut microbiota from wild and laboratory reared female Lu . longipalpis and laboratory reared female P . duboscqi [18]–[20] , [55] , included the prevalence of Gram negative bacteria and the identification of A . baumanni , which in this study was found in NEVL males . Although previous reports established an essential basis for phlebotomine gut microbiota current knowledge , in these studies bacteria were identified using standard bacteriological methods . Consequently , those descriptions did not consider the remaining 99% of unculturable environmental microbes [56] . Hence the differences with this study , which applied a culture independent unbiased high-throughput approach that bypassed cloning of environmental DNA . Interestingly and in accordance with results from this study , in previous reports the proportion of bacteria isolated from wild dipterans has been low . Studies on the midgut microbiota of wild mosquitoes , isolated bacteria from less than 50% of the specimens and the numbers of bacteria varied between individuals [57]–[58] . In a more recent study which used culture dependent and independent screening of field-collected Anopheles , bacteria were found in 15% of the mosquitoes , few of the mosquitoes harboured more than one bacterial species and only one species was found in more than one mosquito [23] . Only one bacterial type was found in EVL females ( SS1 ) , which corresponded to an unculturable bacterium originally isolated from cow faeces ( Figure 2 ) . Furthermore , a sequence match against RDP [52] indicated high similarity with Alistipes sp . , a Gram negative anaerobic bacteria found in human faeces ( Figure 2 ) . Five bacterial species were found in NEVL females ( PP1 ) , four of which were Gram positive ( Figure 2 ) . Of these species , R . pickettii , A . flavithermus , G . kaustophilus and S . coelicolor , were originally isolated from contaminated lake sediment , waste water [59] , deep-sea sediment [60] and soil [61] , respectively . Interestingly , A . flavithermus and G . kaustophilus are thermophilic . Even though R . pickettii 12D was originally isolated from contaminated lake sediment , it is a ubiquitous microorganism found in water and soil [62] and is emerging as an opportunistic pathogen found in a wide variety of clinical samples [63] . P . acnes [64] is a universal inhabitant of human skin and is found at high population densities on the fat-rich areas of the face , scalp and upper trunk [65] . Four bacterial types were found in NEVL males ( PP2 ) , 50% of which were Gram negative ( Figure 2 ) . One of these bacterial types was the multidrug-resistant A . baumannii [66] , which is recovered from natural environments and has emerged as an important opportunistic pathogen worldwide [67] . Another of the bacterial types corresponded to uncultured Veillonella sp . isolated from human skin [68] . The other two bacteria were uncultured bacterial types originally recovered from environmental samples . In one case , BLASTN analysis indicated homology both to an uncultured bacterium from a water sample ( Atlantic Ocean ) and to Leifsonia xyli , a sugar-cane pathogen [69] . The other bacterial sequence corresponded to a proteobacterium clone isolated from wetland soil [70] . In summary , bacteria identified in this study are ubiquitous in the diverse environments these sandflies frequent ( faeces , soil , water , sediment , plants , human skin ) and which were present in both sampling sites ( Figure 1 , Table 1 ) . Hence , possibly they were indicative of the behavioural patterns and feeding habits of these sandflies and are probably part of their transient microbiota . However , more in depth research is required to determine these interactions . Four different species of fungi were found in NEVL Lu . longipalpis ( PP2 and PP1 ) , which differed between males and females ( Figure 2 ) . In previous reports for P . papatasi and P . tobbi , mycoses with a high incidence rate were found in the guts and malpighian tubes of wild specimens . Similar fungi cultured from guts of laboratory reared P . papatasi were identified as A . sclerotiorum and S . cerevisiae [25] . Microsporidians , which are highly pathogenic for some insects [71] , have also been found parasitising neotropical sandflies [32] , [72] . The two species identified in this study in NEVL females were P . conglomerata [73] , a plant pathogen ( mildew ) , and C . bertholletiae [74] , a common soil fungus and a rare cause of zygomycosis in humans . On the other hand , the species found in NEVL males were M . verticillata [75] , a genus commonly found in soil and a zygomycete which also causes zygomycosis in animals , and T . irritans [76] , which belongs to a genus of foliar pathogens [77] . Given the very high vegetation density in the Lapinha Cave area ( Figure 1 , Table 1 ) , a possible scenario is that plant pathogenic spores adhered to the sandflies' hairy surface during sugar-feeding on infected plants . This suggested Lu . longipalpis has a putative capacity of casual dispersal of plant pathogens , among others ( see below ) . In conclusion , fungi identified in this study are found ubiquitously in the environments frequented by sandflies ( plants and soil ) , which were abundant in the sampling site ( Lapinha Cave ) , and so were probably indicative of their sugar-feeding habits and behavioural patterns . Protist sequences were only found in EVL male and female specimens , of which the vast majority were found in males ( Figure 2 ) . Nearly 90% of the identified apicomplexans corresponded to coccidians ( genera Cryptosporidium , Eimeria , Sarcocystis and Besnoitia , 88 . 7% ) and the rest to gregarines ( genera Ascogregarina and Psychodiella , 7% ) and haemosporidians ( Plasmodium spp . , 4 . 3% ) . The absence of leishmanial sequences was not unexpected considering the rate of infection of sandflies with Leishmania is generally very low ( 0 . 01–1% ) [78] , even in endemic areas [5] . Gregarines have been reported in over 20 species of sandflies and Ascogregarina spp . have only been described in mosquitoes [79] . Given A . taiwanensis sequences were found in EVL males in this study , this could indicate that the parasite also infects Lu . longipalpis . Genus Psychodiella comprises 3 species with host specificity to phlebotomine sandflies: P . chagasi , P . saraviae and P . mackiei [35] , [79] . In the New World , only P . chagasi and P . saraviae have been found parasitising Lutzomyia spp . and P . chagasi seems to infect a large range of neotropical species [33]–[36] . In this study , P . chagasi sequences were found in EVL males . The exact pathology caused by gregarines is unknown , but in Lu . longipalpis the parasite can reduce longevity and egg production and the level of parasitaemia can reach over 80% in laboratory colonies [80] . Notwithstanding , the use of P . chagasi as a control method in the field has not been considered efficient because the parasite seems to have a limited range and a minimal effect on sandfly biology under natural conditions [81] . The fact that in this study P . chagasi was found in randomly caught wild specimens , suggested it could be a more efficient control method under natural conditions than what was previously reported . The free-living oocyst stage of coccidians is discharged by infected animals through their faeces . Sandflies are found around human habitations and breed in specific organic wastes , exploiting the accumulation of organic matter produced by domestic animals and poor sanitary conditions such as faeces , manure , rodent burrows and leaf litter [5] . Since the EVL sampling site ( Posadas ) was a worst case-scenario homestead which included dense vegetation , various domestic animals and abundant organic matter ( Figure 1 , Table 1 ) , this could account for the presence of these parasites in EVL males and females . On the other hand , female sandflies suck blood from different animal species including humans , bovines , pigs , equines , dogs , opossums , birds , various rodents and reptiles [5] , [82] and , additionally , some Plasmodium spp . that parasitise lizards have been found in sandflies [37] . In the field it is common to see lek-like aggregations of males and females assembled on or near hosts where blood feeding and mating occur [83]–[84] . This behaviour could account for the presence of haemosporidian sequences ( blood borne parasites ) in EVL males . Nevertheless , as these sequences were found only in males , this suggested males , and not females , would be the primary source of Plasmodium spp . Furthermore , P . falciparum was recently identified by PCR in faecal samples from gorillas [85] and considering the EVL sampling site had a significant amount of organic matter ( Figure 1 , Table 1 ) , it is highly feasible that males acquired these microorganisms from human faeces . Alternatively , EVL males could have acquired these microorganisms by contact with other vectors bearing P . falciparum ( i . e . , Anopheles spp . ) during transportation . In any case , these results suggest that , due to their behavioural patterns , Lu . longipalpis could be implicated in the casual dispersal of parasites of medical and veterinary importance . Human sequences were found in males and females from both locations , whereas chicken sequences were found in NEVL females and EVL males and lizard sequences were only found in NEVL females ( Figures 1 and 2 ) . Lu . longipalpis is ubiquitous in dwellings where sanitary conditions are poor and domestic animals , such as dogs , chickens and pigs , are kept in and around the houses . In this kind of environment , the sandfly tends to congregate at outdoor sites , including animal sheds , where leks easily form on abundant , stationary hosts [7] , [84] . In this context , as previously mentioned , the EVL location ( Posadas ) was a worst case-scenario homestead that kept dogs , chickens and a cat , had dense vegetation and a nearby spring ( Figure 1 , Table 1 ) . In the NEVL location ( Lapinha Cave ) , a chicken was kept to attract sandflies and as a source of food ( Figure 1 , Table 1 ) . Furthermore , 35 species of lizards can be found in the Minas Gerais region [86] . As female sandflies blood feed on different animal species such as birds , reptiles and humans [5] , [82] , the presence of these sequences in females was not unexpected . Contrariwise , it was unexpected to find human and chicken sequences associated with males . Nonetheless , this could be due to their previously mentioned behavioural patterns of aggregation and courtship , where male sandflies are often seen over the host where they form leks , attracting females for a blood meal and increasing their chance for mating [83]–[84] . Alternatively , the trap itself was another area of close contact between male and female sandflies and with other potential vectors of medical importance . Consequently , males could have acquired these sequences by contact during transportation . A total of ten different plant species were identified in males and females from both locations . Capsicum annuum ( bell pepper ) , Elaeis guineensis ( African oil palm ) and Solanum tuberosum ( potato ) were identified in three of the four Lu . longipalpis samples . Juglans hindsii ( Northern California walnut ) was found in both EVL samples and Artemisia annua ( sweet wormwood ) was found in both male samples . Rhapidophyllum hystrix ( needle palm ) and Brassica napus ( rapeseed ) were only found in EVL females and Vitis vinifera ( grapevine ) was only found in EVL males . Oryza sativa ( rice ) and Nicotiana tabacum ( tobacco ) were only found in NEVL females and males , respectively ( Figure 2 ) . As adults from both sexes feed on sugars from different plant sources [12] , this diversity could be indicative of the different feeding preferences and/or food source availability . Moreover , as sugar meals are not composed primarily by cells , plant RNA could also have originated from other sources . Namely , pollen dispersed by wind could have adhered to the sandflies' hairy surface or , alternatively , these vectors could be casual pollinators during sugar-feeding . For a more comprehensive understanding of these results , a few limitations of the chosen approach should be considered . In the first place , homology searches are circumscribed to the number and quality of sequences in the databases at the time of analysis . The relatively high number of sequences which showed no significant hits ( ∼14% ) was a clear indication of this . Moreover , if the query corresponds to a given organism that has not yet been sequenced , the hit will probably coincide with the most related organism found in the database . Notwithstanding and given this situation , the results from the homology search will provide a close approximation to the real case-scenario . Another aspect is that , similarly to a previous study [48] and in order to obtain as much environmental data as possible , specimens were neither surface cleaned nor dissected to extract their guts . As they were not surface cleaned , some ( or all ) of the identified taxa could have been surface contaminants , acquired during transportation by contact with other captured species , i . e . hymenopterans , lepidopterans and mosquitoes , or during manipulation in the lab . In the latter case , even though samples were manipulated with extreme care , this was still a potential source of contamination . Nevertheless , if contamination occurred during manipulation , it was plausible to expect the same contaminating species in males and females from the same location ( when specimens were identified and separated according to sex ) or from both locations ( when total RNA was extracted ) . The only species present in all four samples was Homo sapiens and , consequently , contamination during manipulation was a possibility for these reads . Notwithstanding , as some biological control agents act by surface contact , such as Beauveria bassiana [39] , [87] , and since the ultimate goal of this study was to identify possible biological control agents for this neglected infectious disease vector , had the specimens been surface cleaned , this information could have been lost together with other valuable environmental data . On the other hand , as the gut was not separated from the rest of the specimen and , consequently , they were not analysed independently , it was not possible to classify the observed taxa in putative surface contaminants and gut inhabitants , among others . Therefore , the possible role of putative permanent gut residents could not be inferred , such as influence on the insect development cycle or on the parasite transmission ability . Nevertheless , even a careful extraction process would not preclude the possibility of cross-contamination between the gut and the rest of the specimen and/or loss of information . In this sense , the chosen approach ensured that no data was lost and , notwithstanding the aforementioned limitations , enabled the identification of taxa that could putatively influence sandfly development and which have become the target of ongoing studies to determine their significance and location in the sandfly . Finally , the diversity of bacterial , fungal , protist , plant and metazoan sequences found in this study in wild adult Lu . longipalpis from endemic and non-endemic locations , mostly confirmed their feeding habits and behavioural patterns . Nevertheless , it also suggested that these vectors could possibly be a chance source of dispersal of various animal and plant diseases , such as coccidiosis and malaria . This is particularly significant since the geographical distribution of this vector is undoubtedly expanding [7] . The fact that RNA was obtained from these animal and plant pathogens would indicate that they were biologically active , but this cannot be determined with the present results and further studies must be performed to establish the significance of these findings . The identification of gregarines in wild Lu . longipalpis specimens could indicate that these parasites are a more efficient control method under natural conditions than what was previously suggested [81] . This is specially meaningful as studies on biological control of phlebotomines are still scarce and its practical application seems to be limited to the adult VL vector stage [16] . The employment of biolarvicides in the field is difficult due to the diversity of habitats in which this vector can reproduce and evidence that Lu . longipalpis larvae appear to be thinly dispersed and not concentrated in any particular microhabitat [17] . Nevertheless , as the number of samples analysed in this study was limited , a greater number of specimens must be studied to establish the significance of these results . Current studies are underway to analyse the presence and establish the significance of the taxa found in this study in a greater number of adult male and female Lu . longipalpis samples from endemic and non-endemic locations . A particular emphasis is being given to those taxa implicated in the biological control of this vector and to the etiologic agents of animal and plant diseases .
|
Leishmaniasis is a vector-borne disease with a complex ecology and epidemiology . It has three main clinical forms of which visceral leishmaniasis ( VL ) is the most severe , as it is fatal if untreated . It is caused by a protist parasite , Leishmania spp . , and is transmitted to humans by phlebotomine sandflies . The best method to interrupt any vector-borne disease is to reduce man-vector contact . Vector-targeted strategies are particularly attractive because the vectorial capacity to transmit infectious diseases to humans is proportional to vector density and , in an exponential way , to vector survival . Biological control is an effective means of reducing or mitigating pests through the use of natural enemies and is more environmentally friendly than traditional insecticide treatments . Nevertheless , there is very scanty information on the biological control of sandflies and their potential control agents . In this context , a detailed knowledge of the microorganisms that are associated with these vectors would aid in the development of novel strategies for controlling them . This is the first study to survey the taxa associated with leishmaniasis vectors and , more importantly , with any infectious disease vector , using an unbiased and high-throughput approach .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"biota",
"microbiology",
"metagenomics",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"environmental",
"protection",
"infectious",
"diseases",
"biology",
"vectors",
"and",
"hosts",
"ecology",
"vector",
"biology",
"leishmaniasis",
"genomics",
"genetics",
"and",
"genomics"
] |
2011
|
Metagenomic Analysis of Taxa Associated with Lutzomyia longipalpis, Vector of Visceral Leishmaniasis, Using an Unbiased High-Throughput Approach
|
Trinucleotide repeat expansion is the genetic basis for a sizeable group of inherited neurological and neuromuscular disorders . Friedreich ataxia ( FRDA ) is a relentlessly progressive neurodegenerative disorder caused by GAA·TTC repeat expansion in the first intron of the FXN gene . The expanded repeat reduces FXN mRNA expression and the length of the repeat tract is proportional to disease severity . Somatic expansion of the GAA·TTC repeat sequence in disease-relevant tissues is thought to contribute to the progression of disease severity during patient aging . Previous models of GAA·TTC instability have not been able to produce substantial levels of expansion within an experimentally useful time frame , which has limited our understanding of the molecular basis for this expansion . Here , we present a novel model for studying GAA·TTC expansion in human cells . In our model system , uninterrupted GAA·TTC repeat sequences display high levels of genomic instability , with an overall tendency towards progressive expansion . Using this model , we characterize the relationship between repeat length and expansion . We identify the interval between 88 and 176 repeats as being an important length threshold where expansion rates dramatically increase . We show that expansion levels are affected by both the purity and orientation of the repeat tract within the genomic context . We further demonstrate that GAA·TTC expansion in our model is independent of cell division . Using unique reporter constructs , we identify transcription through the repeat tract as a major contributor to GAA·TTC expansion . Our findings provide novel insight into the mechanisms responsible for GAA·TTC expansion in human cells .
Trinucleotide repeat disorders are caused by the expansion of unstable tandem repeats to a pathogenic size above disease-specific length thresholds [1]–[5] . Disease-associated trinucleotide repeat arrays include CAG·CTG , CGG·CCG , and GAA·TTC sequences . Disease pathology in these disorders is often progressive and usually involves a neurodegenerative phenotype . Friedreich ataxia ( FRDA ) is a relentlessly progressive neurodegenerative disorder caused by GAA·TTC repeat expansion within the first intron of the frataxin ( FXN ) gene [6] . FRDA is autosomal recessive and is the only currently known human disorder associated with GAA·TTC repeat expansion . The normal range of GAA·TTC sequences within this intronic region is between 6 and 36 repeats , while affected individuals have expansions ranging from 120 to 1700 uninterrupted repeats , most commonly 600 to 900 triplets [6]–[9] . Transcription-dependent structure formation by expanded GAA·TTC repeats and/or heterochromatin-mediated gene silencing have been proposed as likely causes of reduced FXN expression in FRDA [10]–[16] . The length of the repeat tract directly correlates with disease severity [17] , [18] , but our current understanding of the mechanisms governing GAA·TTC repeat expansion in FRDA is incomplete and is the focus of this study . While intronic GAA·TTC sequences within the normal size range ( <36 triplets ) are stably maintained , uninterrupted premutation ( 36–120 triplets ) and expanded ( >120 triplets ) alleles display intergenerational and somatic instability , consisting of both contraction and expansion [8] , [9] , [19]–[27] . Interruptions within the repeat tract stabilize premutation alleles during germline transmission [9] and in peripheral leukocytes from GAA·TTC carriers [24] , but the effects of interruptions on the stability of expanded alleles have not been reported . The intergenerational dynamics of GAA·TTC instability are dependent on the mode of inheritance; paternal transmission results in a bias towards repeat contraction , while maternal transmission leads to both repeat expansion and contraction [19] , [20] , [22] . Somatic instability in FRDA appears to be tissue-specific as to whether repeat contraction or expansion predominates . Analysis of GAA·TTC allele size in multiple tissues from FRDA patients found a general contraction bias during aging in most tissues examined [26] . However , a bias towards age-dependent expansion is seen in disease relevant tissues of FRDA patients , notably the dorsal root ganglia ( DRG ) of the central nervous system , which suggests that the somatic expansion bias in these tissues directly contributes to disease progression [27] . The unstable nature of the disease-associated repeat sequences is generally attributed to the ability of these sequences to adopt non-B DNA structures [3] , [4] , but the cellular processes potentiating instability have yet to be fully elucidated . Studies using bacteria , yeast , and patient cell lines demonstrated a strong association between replication and GAA·TTC instability , consisting mostly of contractions [24] , [28]–[30] . The somatic expansion bias within post-mitotic neurons of the spinal cord suggests that mechanisms other than replication , such as transcription and/or post-replicative DNA repair , could be the primary forces driving GAA·TTC repeat expansion in FRDA patients . GAA·TTC repeat sequences have been shown to adopt DNA triplex and triplex-associated structures in vitro and in bacteria [11]–[14] , [31] , [32] , leading to stalled transcription within the promoter distal half of the GAA·TTC repeat region [13] , [14] . The association between transcription and structure formation by GAA·TTC repeat sequences , coupled with the high levels of FXN expression in the spinal cord [6] , suggests that there may be a relationship between transcription and GAA·TTC expansion . However , the contribution of transcription to GAA·TTC expansion within the genomic context has not been thoroughly examined . The current lack of information regarding the mechanisms responsible for GAA·TTC expansion in FRDA is partly due to the absence of a good experimental model for GAA·TTC repeat expansion . Bacterial and yeast models of GAA·TTC instability display a pronounced contraction bias [24] , [29] , [30] . While a recently developed system was able to capture the rare GAA·TTC expansion events in yeast by a selection scheme , this limited analysis to single-event expansions [33] . Lymphoblastoid cell lines derived from FRDA patients have proven to be inconsistent models for the analysis of GAA·TTC instability and require meticulous small-pool PCR techniques to analyze the rare expansion events [23] , [34] . While mouse models have proven valuable in reproducing the tissue-specific expansion seen in FRDA patients [35] , [36] , a more homogeneous and rapid system readily capable of experimental manipulation would provide a valuable tool for mechanistic studies of GAA·TTC repeat expansion . Here we present a novel model for studying trinucleotide repeat expansion in human cells . In our model system , uninterrupted GAA·TTC repeat sequences display high levels of genomic instability , with an overall tendency towards progressive expansion . Using this system , we characterize the relationship between repeat length and expansion . We further differentiate key mechanistic processes regarding GAA·TTC expansion in human cells . We demonstrate that GAA·TTC repeat expansion in our model is independent of cell division rates . Using unique reporter constructs , we identify transcription through the repeat tract as a major contributor to GAA·TTC repeat expansion .
In order to analyze the dynamics of GAA·TTC repeat stability within the context of the human genome , we utilized tandem reporter constructs containing uninterrupted ( GAA·TTC ) n repeat arrays integrated into the genome of an HEK 293 host cell line ( Figure 1A ) . The tandem reporter constructs utilize two self-cleaving ribozymes in order to isolate transcription elongation through the insert region of the construct . These constructs have been previously tested and characterized [37] . Stable cell lines were established using Flp-recombinase mediated recombination , which allowed for single-copy integration at a consistent chromosomal location and orientation in all cell lines used in this study . Antibiotic selection following construct transfection produced colonies derived from individual cells with an integrated reporter construct ( see Materials and Methods ) . Therefore , all cell lines used in this study are single-cell clonal lineages . We confirmed single-copy integration by Southern blot analysis ( as shown in Figure 1B ) . We constructed the ( GAA·TTC ) n repeat arrays using an in vitro ligation strategy [38] in order to circumvent bacterial propagation , which often leads to the contraction of large GAA·TTC sequences and could result in the preferential selection of repeats stabilized by interruptions . The repeat inserts were sequenced prior to transfection to ensure that the repeat tracts in our cell lines were uninterrupted . We analyzed the stability of GAA·TTC repeat sequences within our cell lines ( Figure 1B and 1C ) . A ( GAA·TTC ) 352 insert was initially chosen in order to analyze the stability of a repeat allele within the size range expected to be unstable in FRDA . A cell line made with a ( GAA·TTC ) 352 insert was serially passaged over a 10 week period . Genomic DNA samples were isolated at Day 0 ( W0 ) and after 1 , 2 , 4 , and 10 weeks in culture ( W1–W10 ) . Subsequent sizing of the GAA·TTC insert by Southern blot ( Figure 1B ) and PCR ( Figure 1C ) analyses showed the progressive expansion of the GAA·TTC repeat insert as a function of time in culture . Expansion was detected as early as W1 ( Figure 1B and 1C ) . Southern blot analysis confirmed that the observed instability is restricted to the ( GAA·TTC ) n region of the integrated construct ( Figure 1B ) . EcoRV digestion of the genomic DNA cuts the integrated tandem construct upstream of the GAA·TTC insert region and between the 5′ hRLUC and 3′ hRLUC region of the construct ( Figure 1A ) . Our results show that the expansion is localized to the 5′ hRLUC probe region containing the GAA·TTC insert , while the 3′ hRLUC probe region remains stable ( Figure 1B ) . PCR analysis of the GAA·TTC insert reproduced the results obtained by the Southern blot analysis and showed a gain of roughly 119 triplets at W4 ( Figure 1C ) and a gain of 284 triplets at W10 ( Figure 1C ) . PCR and sequencing analysis of the DNA sequence flanking the GAA·TTC repeat region showed that instability is restricted to the repeat tract , while the flanking sequence remains unaffected ( Figure S1 and Figure S2 ) . PCR analysis allows for a more efficient and accurate analysis for GAA·TTC insert sizing and will be used for sizing analysis throughout this study . We wanted to further characterize the dynamics of GAA·TTC instability within the cell population during culturing by analyzing the size distribution of individual GAA·TTC repeat alleles in that population . End-point dilution of the parental ( GAA·TTC ) 352 cell line at W4 ( Figure 1B and 1C ) produced colonies derived from individual cells within the population . Size analysis of GAA·TTC sequences from these clonal cell isolates revealed a mixed pool of GAA·TTC repeat alleles , ranging in size from 264 to 1000 repeat units ( Figure 1D ) . Of 9 clones , 8 represented expansion events relative to the transfected ( GAA·TTC ) 352 insert and one deletion product was detected ( lane 1 in Figure 1D ) . In several of these colonies , multiple amplification products were detected . The detection of different sized alleles in the individual colonies is likely due to continued instability during the growth of the colony . The mixed distribution of repeat alleles in Figure 1D illustrates the mosaicism of individual GAA·TTC repeat alleles within the cell population , which is a characteristic commonly seen in the somatic tissues of FRDA patients [26] , [27] . The wide-ranging instability of individual repeat alleles illustrated in Figure 1D suggests that the progressive expansion displayed in Figure 1B and 1C does not represent the uniform expansion of every allele in the cell population . Figure 1B and 1C likely represent an expansion bias among the majority of repeat alleles , with larger and smaller outlier alleles within the population . PCR and Southern blot analysis of a large pool of mixed sized repeat alleles is prone to detect the most common alleles in that population , therefore the products shown in Figure 1B and 1C are likely a reflection of this tendency . It is important to note that there is no selective pressure acting on the GAA·TTC repeat inserts within the integrated reporter construct during culturing . Therefore , there is unlikely to be any sampling bias favoring repeat expansion over deletion . We next analyzed the relationship between repeat length and stability within our cell lines ( Figure 2 ) . The duration between construct transfection and initial insert sizing varies among the different clones ( see Materials and Methods ) . The repeat lengths used throughout this report are in reference to the transfected insert sequence and do not account for any gains in repeat size between transfection and the beginning of the time-course experiments . Time-course analysis of repeat stability was performed using cell lines harboring 11 , 44 , 88 , 176 , and 1000 GAA·TTC repeats ( Figure 2A ) . ( GAA·TTC ) 11 inserts are within the normal size range of GAA·TTC repeats found within the first intron of the FXN allele and repeats within this range have been shown to be below the reported initiation threshold for instability in simple replication models and human cells [23] , [24] , [38] . As expected , the ( GAA·TTC ) 11 insert sequence remained stable over the 4 week time-course as indicated by the tight banding pattern of the PCR amplification product at each time-point ( Figure 2A ) . The ( GAA·TTC ) 44 underwent a modest level of expansion , which is in agreement with the initiation threshold for instability reported by others [23] , [24] ( Figure 2A ) . PCR mobility profile analysis ( Figure 2B ) further illustrates the stability of the ( GAA·TTC ) 11 insert and the modest expansion of the ( GAA·TTC ) 44 repeat insert during the time-course experiments . Substantial expansion of the ( GAA·TTC ) 88 and ( GAA·TTC ) 176 inserts was observed during the 4 week time-course with the larger ( GAA·TTC ) 176 sequence demonstrating a more rapid expansion when compared to the ( GAA·TTC ) 88 sequence ( Figure 2A ) . PCR mobility profile analysis of the ( GAA·TTC ) 88 sequence showed a gain of 9 triplets when comparing the peak intensities of the amplified products at W0 and W4 , while the ( GAA·TTC ) 176 sequence showed a gain of 140 triplets over the same time period ( Figure 2B ) . These results demonstrate that GAA·TTC repeat sequences undergo a length-dependent increase in expansion rate in our cellular model . Previous analysis using peripheral blood samples of FRDA patients revealed a contraction bias in GAA·TTC sequences greater than 500 repeats in length [23] , while post-mortem analysis of GAA·TTC sequences ranging from 350–1030 repeats in the dorsal root ganglia of FRDA patients demonstrated an age-dependent expansion bias [27] . To analyze the stability dynamics of larger GAA·TTC sequences within our cell lines , we performed time-course experiments using a cell line isolated from the end-point dilution of the ( GAA·TTC ) 352 cell line ( lane 9 in Figure 1D ) . This cell line contained a ( GAA·TTC ) 1000 insert sequence ( Figure 2A ) . Sizing of the insert sequence showed the continued expansion of the ( GAA·TTC ) 1000 insert from W0 to W4 ( Figure 2A and 2B ) , confirming that larger GAA·TTC inserts continue to expand in our system . To determine if the observed expansion is due to the length of the DNA sequence inserted into poly-linker region of our construct , rather than sequence composition , we analyzed the stability of a semi-repetitive 2 . 1 kilobase ( kb ) tetramer insert sequence ( Figure 2A ) . The tetramer sequence is composed of four identical non-repetitive 529 bp fragments and is equivalent in size to a ( GAA·TTC ) 700 insert sequence [37] . We have previously shown that this tetramer insert has a neutral affect on transcription through the insert region of the reporter constructs [37] . The tetramer sequence remained stable over the duration of the time-course ( Figure 2A and 2B ) , indicating that the observed expansion of GAA·TTC repeat sequences in these cell lines is not solely a function of insert length . This suggests that GAA·TTC expansion in our cell lines must be due to certain properties intrinsic to these triplet repeats . Previous studies have shown that interruptions in the purity of the GAA·TTC repeat tract have a stabilizing effect on these sequences [9] , [24] , possibly by interfering with the formation of secondary structures by the repeat . Time-course analysis of repeat stability was performed on four individual cell lines with ( GAA·TTC ) 176 insert sequences . The mean increase in repeat size among three of these cell lines was 61 . 1±11 . 6 triplets , while the fourth cell line gained only 12 triplets after 3 weeks in culture ( Figure 3A ) . Sequencing of the first three inserts did not detect any interruptions . Sequencing of the GAA·TTC insert region within the fourth cell line identified two separate interrupting point mutations ( Figure S3 ) . An A→T mutation was detected approximately 118 triplets into the repeat region from the 5′ end and a T→G mutation was detected 31 triplets into the repeat region from the 3′ end ( Figure S3 ) . Expansion of the repeat sequence either upstream or downstream of the interruption distributes the base signal when sequencing from a polymorphic population , as represented in Figure S3 . These shifts can mask the detection of potential interruptions during automated sequencing , with mutations towards the edges of a repetitive run being more readily detected . Careful visual inspection of the primary sequencing data is needed when seeking to identify potential interruptions within unstable tandem repeat arrays . The identification of multiple interruptions within the GAA·TTC sequence in this cell line suggests that even a couple of point mutations that interrupt the purity of the repeat sequence can greatly reduce the rate of repeat expansion . To further analyze the effects of interruptions on GAA·TTC repeat expansion , we created a cell line containing a repeat insert with an interrupting hexamer ( TCAATT ) that creates an Mfe I restriction endonuclease recognition site situated between two repetitive tracts , ( GAA ) 88MfeI ( GAA ) 90 . Sequencing did not detect any interruptions in the either of the two repeat tracts flanking the interruption within this construct , but visual inspection of the sequencing chromatogram confirmed the presence of the interrupting hexamer ( Figure S3 ) . Stability analysis revealed that this interrupting hexamer sequence reduces the rate of expansion when compared to uninterrupted ( GAA·TTC ) 176 insert sequences ( Figure 3B ) . The ( GAA ) 88MfeI ( GAA ) 90 insert gained only 22 triplets after 3 weeks in culture , compared to the 61 . 1±11 . 6 mean triplet gain by the uninterrupted ( GAA·TTC ) 176 insert sequences . The primer pair used for PCR amplification adds 338 bp to the 5′ end of the GAA·TTC insert and 100 bp to the 3′ end . Digestion of the amplified product containing the Mfe I site interruption yields two distinct bands representing the two repeat tracts , which allowed us to investigate whether there is preferential expansion at one end of the repeat or expansion from both sides of the interrupting mutation ( Figure 3C ) . Analysis of the digested products at W0 , W2 , and W3 showed more expansion within the promoter distal ( GAA·TTC ) 90 repeat tract than within the promoter proximal ( GAA·TTC ) 88 repeat tract ( Figure 3C ) . Incomplete digestion was used to highlight the visible expansion of the full-length fragment , which indicates that the observed preferential expansion within the promoter distal tract is not simply due to differential mobility between the two digested fragments . Complete digestion at each time-point has been achieved at lower DNA concentrations ( not shown ) , thereby excluding the possibility that a portion of the interrupting mutations were lost during culturing . These results confirm that GAA·TTC expansion rates are affected by the purity of the repeat sequence and suggest that expansion is biased towards the promoter distal end of the repeat tract . The stability of GAA·TTC repeat sequences is affected by the orientation of the repeat array during plasmid vector propagation in bacteria , yeast and transiently transfected mammalian cells [24] , [39]–[42] . To analyze the effects of repeat orientation on GAA·TTC expansion in our system , we created cell lines with reverse oriented ( CTT·AAG ) 176 insert sequences in our expression constructs . The rates of expansion within these reverse oriented inserts were compared to the expansion rates of the forward oriented insert sequences ( Figure 3D ) . Two separate reverse oriented ( CTT·AAG ) 176 inserts gained 33 and 26 triplets , compared to the 61 . 1±11 . 6 mean triplet gain by the forward oriented ( GAA·TTC ) 176 inserts after 3 weeks in culture . Sequencing did not detect any interruptions in the purity of either reverse oriented insert . These results demonstrate that GAA·TTC expansion levels are affected by the orientation of the repeat sequence within the genome . Replication has been shown to influence GAA·TTC repeat stability in previous model systems [24] , [28]–[30] , [39] . To analyze the influence of replication on GAA·TTC expansion in our cell lines , we sought to alter cell division rates during culturing and analyze the influence on GAA·TTC stability ( Figure 4 ) . A cell line containing a ( GAA·TTC ) 352 insert was cultured in low-serum growth media ( LS , 0 . 5% FBS ) , which resulted in a 5–10 fold decrease in cell division rate . During the 4 week experimental period , cell lines grown in LS media were passaged only twice due to reduced cell division , while cell lines carried in normal growth media ( NS , 5% FBS ) underwent 10 passages . Expansion rate of the ( GAA·TTC ) 352 insert was unaffected after 4 weeks in LS growth media when compared to the control insert grown in NS growth media for 4 weeks ( Figure 4B ) . An additional time-course was performed in which the cell lines were grown in normal growth media ( 5% FBS ) at or near confluency in order to reduce cell divisions due to crowding ( HD in Figure 4 ) . The cell lines were split and reseeded at ∼90% confluence every third day . Sizing analysis demonstrated that the expansion rate of the ( GAA·TTC ) 352 insert was unaffected by cellular confluency when compared to the control NS insert ( Figure 4B ) . Luciferase reporter expression levels showed that basal transcription levels within the integrated reporter constructs remain unchanged during culturing in the various growth conditions ( Figure 4C ) . These results demonstrate that the observed GAA·TTC expansion is independent of replication rate or cell confluency in our cellular model and further indicates that GAA·TTC repeat expansion in our model correlates better with time in culture than with the number of cell divisions . Induced transcription levels have previously been shown to promote the contraction of CAG·CTG and GAA·TTC repeat sequences in human cell lines [43]–[45] . We found a similar response in our model system ( Figure S4 ) . While these previous studies aimed to produce instability through induced transcription from stable alleles , this current study differs in that the GAA·TTC repeat sequences undergo robust expansion at basal transcription levels . The human cytomegalovirus immediate-early enhancer/promoter ( CMVIE ) is well known for directing very high levels of transgene expression in human cells . In our construct , this promoter is regulated by a pair of tetracycline operator sites near the transcription start site . While this affords a degree of inducibility , luciferase expression values in our cell lines indicated high basal levels of transcription through our constructs in the absence of promoter induction ( Figure 4C ) . Background transcription is likely due to promoter leakage , but could be due to high levels of local transcription within the genomic region near our constructs . Since expansion occurs in the absence of promoter induction , we wanted to further reduce as much as possible any transcription through the repeat region of our construct by utilizing the well characterized transcription termination signals of the human β-globin gene ( HBB; NM_000518 ) . We have previously created tandem constructs designed to test the efficiency of transcription termination by defined sections of the polyadenylation sequence from HBB [37] . Here , we introduced these transcription termination sequences upstream of the GAA·TTC inserts within the polylinker region of the tandem construct in order to reduce transcription through the repeat tract ( Figure 5A ) . The HBB2 sequence is a 1300 bp segment containing the poly ( A ) addition site and the putative co-transcriptional cleavage ( CoTC ) element within HBB , which is thought to enhance transcription termination [46]–[48] . The HBB3 sequence is a 2000 bp segment also containing the poly ( A ) site and CoTC element plus additional downstream sequence . By analyzing the expression ratio of the hRLUC and FLUC reporters in our constructs , we were able to quantitate transcription rates through the GAA·TTC insert region ( Figure 5B ) . The HBB2 and HBB3 sequences reduce transcription through the repeat region to less than 1% ( . 001 and . 002 respectively ) of the control TAN construct ( Figure 5B ) , which is in agreement with our previous findings [37] . To analyze the effects of decreased transcription on GAA·TTC expansion over time , we performed time-course analyses of repeat stability in the TAN , HBB2 , and HBB3 constructs with ( GAA·TTC ) 176 insert sequences ( Figure 5C ) . A significant ( p< . 05 for HBB2 and HBB3 ) decrease in expansion levels was observed when the GAA·TTC repeat sequences were positioned downstream of the transcription termination elements in HBB2 and HBB3 as compared to the control TAN construct ( Figure 5C ) . After 3 weeks in culture , the ( GAA·TTC ) 176 inserts gained 13 . 5±5 . 2 triplets when downstream of the HBB2 termination sequence and 21 . 3±13 . 4 triplets when downstream of the HBB3 termination sequence . The ( GAA·TTC ) 176 inserts gained 61 . 1±11 . 6 triplets in the TAN construct ( Figure 5C ) . Sequencing did not detect any interruptions within the GAA·TTC region of the HBB or TAN constructs . The decreased expansion levels in the HBB2 and HBB3 constructs are unlikely to be due to the additional sequence inserted immediately upstream of the GAA·TTC insert region . The HBB3 construct has 700 bp of added downstream sequence that is not present in the HBB2 construct ( Figure 5A ) , therefore the sequence immediately upstream of the GAA·TTC repeat region differs between the two constructs . Analysis of GAA·TTC expansion in a separate construct containing a shorter HBB termination element also reduced expansion levels ( Figure S5 ) , but we were only able to obtain a single clone due to the difficulty of creating these cell lines using in vitro ligation . These findings indicate that the observed decreased expansion levels within the HBB constructs are not due to the sequence composition inserted upstream of the repeat insert . Although the effects of spacing , or other small differences conferred by the HBB insertions , cannot be ruled out , our results , taken together , indicate that transcription through the repeat tract is a major contributor to expansion . Ideally , we would like analyze the influence of induced transcription on the reduced GAA·TTC expansion levels within the HBB constructs , but transcription termination within these constructs is highly efficient and we do not observe appreciable levels of transcription induction through the repeat region , as analyzed by hRLUC expression levels ( data not shown ) . Our finding that decreased transcription levels reduce GAA·TTC repeat expansion rates supports our hypothesis that transcription contributes to the progressive GAA·TTC repeat expansion seen in human cells . The residual GAA·TTC expansion in the HBB constructs could be due to antisense transcription or to other factors , in addition to transcription , that contribute to GAA·TTC repeat expansion .
In this study , we present a human cellular model of progressive GAA·TTC repeat expansion . Our model recapitulates key features of GAA·TTC instability in FRDA . The continued expansion of large GAA·TTC repeats and the observed mosaicism of repeat alleles in our system are characteristic of instability within the dorsal root ganglia ( DRG ) of FRDA patients [27] , one of the primary affected tissues in FRDA . The level of instability and prominent expansion bias in our system has not been achieved in previous cellular models of trinucleotide repeat instability . Examination of GAA·TTC instability in patient samples requires the analysis of thousands of individual repeat alleles using small-pool PCR in order to detect significant levels of variability in the cell population [26] , [27] . In our cellular model , we are able to detect high levels of expansion in a matter of weeks using standard PCR techniques . The robust expansion observed in our system could be aided by the lack of selective pressure against repeat expansion in our luciferase reporters . In FRDA patient cells , the continued expansion of GAA·TTC repeats within the first intron of the FXN gene results in the length-dependent reduction of frataxin expression , which eventually leads to cell death . Larger repeat alleles would be selected against as the dying cells are removed from the population , resulting in larger alleles going undetected during PCR sizing analysis . Tissues samples from FRDA patients and mouse models used for GAA·TTC repeat analyses are likely to be heterogeneous mixtures of different cell types . Any expansion biases specific to certain cell lineages , notably the DRG , would be underrepresented due to the presence of alleles from other cell types in the population . The obvious expansion bias observed in our model system could be due to the homogenous nature of the cells in culture . The continuous expansion of repeat alleles in our model provides us with an advantageous system for mechanistic studies of repeat expansion in human cells . Errors during mitotic replication , due to structure formation by the GAA·TTC repeat sequence and/or strand-slippage events , were previously proposed as the primary mechanisms by which GAA·TTC repeat expansion occurs [30] , [33] , [41] . However , replication models of GAA·TTC expansion , derived from data using simple replication systems displaying pronounced contraction biases , generally conflict with instability data obtained from patient studies and mouse models . In FRDA patients and transgenic mouse models , GAA·TTC instability in proliferating tissues consisted predominantly of contraction events [26] , [35] , while a GAA·TTC expansion bias was predominantly localized to post-mitotic neurons within the spinal cord [27] , [36] . While replication may promote GAA·TTC contraction , we hypothesize that GAA·TTC expansion occurs via a separate non-replicative mechanism . Given that it is repeat expansion that results in disease , it is important to distinguish repeat contraction from repeat expansion during mechanistic studies of trinucleotide repeat instability . We have shown that GAA·TTC expansion in our cell lines is independent of cell division rates , which supports our hypothesis that GAA·TTC expansion , unlike contraction , is not mechanistically linked to cellular replication . Previous reports using plasmid replication models in E . coli , S . cerevisiae and transiently transfected mammalian cells demonstrated a relationship between repeat stability , repeat orientation , and repeat distance from replication origins [28] , [30] , [39] , [41] . A common finding among these studies was that GAA·TTC repeats demonstrate higher levels of instability , consisting mostly of contractions , when the purine ( GAA ) strand serves as the template for lagging strand synthesis . Recently , Shishkin et al . [33] found that GAA·TTC expansion in yeast was unaffected by the repeat orientation relative to replication origins and that , unlike repeat contraction , replication fork stalling was not involved in GAA·TTC expansion . In our model , reversing the repeat orientation decreases the rate of expansion , yet we have shown that expansion in the less stable forward orientation is independent of cell division . Therefore the differential stability between the two orientations in our system is unlikely to involve replication fork dynamics . While we are unable to rule out possible chromosomal positioning effects on GAA·TTC stability in our system , the strong expansion bias and the relatively low level of repeat contraction could be due to the lack of replication-mediated instability in our cellular model . Using novel tandem reporter constructs , we have shown that transcription levels through the repeat tracts contribute to GAA·TTC expansion in our model system . By introducing the polyadenylation signal and terminator of the HBB gene upstream of the repeat , and thereby reducing transcription through the repeat tract , we were able to decrease expansion levels . This is the first report to establish a relationship between transcription levels and GAA·TTC expansion rates , and to correlate transcription levels with the expansion of any disease-associated trinucleotide repeat sequence in human cells . Our findings support a transcription-dependent mechanism for GAA·TTC expansion . Transcription-dependent expansion is consistent with GAA·TTC instability data obtained from patient samples and mouse models , which found an expansion bias within the neurons of the dorsal root ganglia [27] , [36] , where FXN expression levels are the among the highest of all tissues [6] . Structure formation by the GAA·TTC repeat sequence is likely to be a key event in the mechanism leading to GAA·TTC expansion . Our group has previously shown that transcription through expanded GAA·TTC repeat sequences is associated with the formation of a transient DNA triplex structure and an RNA·DNA hybrid in vitro and in live bacteria , which leads to transcriptional arrest at the promoter distal duplex-triplex junction [13] , [14] . Our finding that GAA·TTC repeat expansion is biased towards the distal end of the repeat tract supports a model in which structure formation and stalled transcription complexes at the promoter distal end of the repeat tract facilitates expansion within this region . We have shown that expansion in our cellular model initiates at a length of approximately 44 triplets and this repeat length correlates with the shortest repeat found to be associated with RNA·DNA hybrid formation in our earlier study [14] , which suggests that structures , such as a transient RNA·DNA hybrid , are involved in the expansion process . Out of register re-annealing of the non-template strand after removal of the RNA hybrid could result in the formation of slipped-stranded structures , which are thought to be key intermediates in the process leading to CAG·CTG expansion [49] . Interruptions in the purity of the repeats would reduce the likelihood of slipped-strand formation by acting as a reference point for annealing within the repeat tract and would thereby reduce expansion levels , as demonstrated in this study . We have also shown that reversing the orientation of the repeats relative to the promoter reduces expansion levels . This effect could be due to an altered potential for transcription-associated structure formation in the reverse orientation relative to the promoter . While Shishkin et al . [33] demonstrated that GAA·TTC expansion was unaffected by the repeat orientation relative to replication origins , the orientation of the repeat relative to the promoter in their constructs remained constant and any orientation-dependent transcriptional influence on expansion could have been overlooked . While we observed reduced expansion levels in the HBB constructs and in the TAN constructs with reverse oriented repeats , expansion was not completely abolished . It is possible that antisense transcription passes through the GAA·TTC insert , which we cannot detect with our reporters . Antisense transcription could be responsible for the residual expansion within these constructs . Components of the DNA repair machinery have been implicated in the expansion of CAG·CTG repeat sequences [50]–[56] , but this association has not yet been made regarding GAA·TTC repeat sequences . Any transcription-mediated model of GAA·TTC expansion is likely to involve post-replicative DNA repair due to the requirement of newly synthesized DNA to facilitate this expansion . In studies utilizing triplex-forming oligonucleotides ( TFOs ) , components of the nucleotide excision repair pathway ( NER ) were shown to bind DNA triplex structures in vitro [57] , [58] and triplex-associated mutagenesis was found to be dependent on the transcription-coupled NER ( TC-NER ) repair pathway in mammalian cells [59] . The mismatch repair ( MMR ) complex MutSβ ( MSH2–MSH3 ) , which is required for CAG·CTG expansion in mice , was also shown to interact with the NER machinery in the recognition of TFO-directed psoralen DNA inter-strand cross-links [60] , suggesting that MutSβ may be involved in the recognition and repair of DNA triplex structures . Slipped-strand structures formed by GAA·TTC repeats could also be recognized and processed by MutSβ , as has been shown for CAG·CTG repeats [49] , [61] . Therefore , transcription-associated structure formation by the GAA·TTC repeat sequence and the subsequent arrest of transcription could lead to the induction of NER , TC-NER , MMR , or an interaction between these various pathways that would lead to expansion during repair . Strand-slippage and/or reiterative synthesis of repeat units during the gap-filling stage of the excision repair pathways would lead to the recursive accumulation of small expansion events , which would account for the progressive GAA·TTC expansion observed over weeks in our cellular model and in the dorsal root ganglia of FRDA patients during aging . Interestingly , when transcription was induced within the TAN constructs , we observed a decreased rate of expansion and an increase in deletion products during prolonged periods of culturing . This increase in repeat contraction during periods of induced transcription agrees with earlier studies examining CAG·CTG repeat contraction in human cells [43] , [44] . Both in our system and the selection assay for CAG·CTG contraction reported by Lin et al . [43] induced transcription is driven by modified CMVIE promoters , which are well known for generating high levels of transgene expression . Chromosome fragile sites are often linked to regions of repetitive DNA , including trinucleotide repeats . GAA·TTC repeats were previously shown to be frequent sites of double-strand breaks in yeast [41] . Very high levels of transcription generated during promoter induction could promote strand breaks within the repeat region , the repair of which may favor trinucleotide repeat contraction over expansion . We believe that the GAA·TTC repeat contraction observed during high transcription levels and the incremental GAA·TTC expansion seen in our system at basal and reduced transcription levels occur via separate mechanisms . Both in bacteria and yeast , double-strand breaks within the GAA·TTC repeat region were shown to promote rapid repeat contraction [41] , [62] , leading us to propose that the repair of double-strand breaks generated during extended periods of induced transcription is responsible for the observed repeat contraction over time . Basal transcription levels within the TAN construct or the reduced transcription levels within the HBB constructs are likely to be more representative of transcription levels produced from the native FXN gene . This study has identified transcription levels as a key regulator of GAA·TTC expansion in human cells . Progressive GAA·TTC expansion in the neurons of FRDA patients has been postulated to contribute to disease progression during aging . Transcription-driven expansion could partially explain the expansion bias of GAA·TTC repeats in the post-mitotic neurons of FRDA patients . The neurons of the DRG are the primary sites of degeneration in FRDA and are among the tissues in which FXN gene expression is the highest [6] . The findings of this study provide support for a model in which high gene expression and low cell turnover would promote the progressive expansion of intronic GAA·TTC repeat sequences , thereby reducing FXN mRNA levels , causing cell death and neuronal degeneration . Potential therapies aimed at alleviating the transcriptional deficit at the FXN gene in FRDA patients should take into consideration the possibility that elevating transcription levels from the FXN promoter could exacerbate expansion and inhibit therapeutic effectiveness .
Construction of the tandem reporter constructs , tetramer insert sequence and polyadenylation regions has been described previously [37] . The capped in vitro ligation strategy used to create the ( GAA·TTC ) n repeat inserts has previously been described [38] . The repeat inserts were cut with SpeI & BamHI and ligated into the tandem vector polylinker region cut with NheI and BamHI . The reverse orientation ( CTT·AAG ) 176 inserts were cut with XbaI & BglII and ligated into the tandem vector polylinker region cut with NheI and BamHI . The TCAATT hexamer used to create the Mfe I recognition sequence was added to the 3′ end of a ( GAA·TTC ) 88 insert using an oligonucleotide fragment and was ligated to a second ( GAA·TTC ) 88 to create the ( GAA ) 88T CAATT ( G AA ) 90 insert fragment . The non-human sequences ( GFP/CAT ) flanking the polylinker site serve as unique priming sites for insert sizing . The insert region was sequenced for impurities within the repeat arrays prior to transfection . Our reporter constructs were integrated into the genome of Flp-In T-REx-293 cell lines ( Invitrogen ) using the Invitrogen Flp-In T-REx system following the supplier's protocol . Transfection was performed using the Lipofectamine 2000 transfection reagent ( Invitrogen ) . Selection for successful construct integration was performed by culturing transfected cells in media containing hygromycin B ( 75 µg/ml ) and blasticidin-HCL ( 15 µg/ml ) . Individual colonies were isolated after approximately 2 weeks under antibiotic selection . The colonies were then expanded for approximately 2 weeks . Insert sizing using PCR analysis was done approximately 4 weeks post-transfection . Cell lines were maintained in Dulbecco's modified Eagle's medium high glucose ( Invitrogen ) and 5% fetal bovine serum ( Sigma ) at 5% CO2 . Time-course experiments were conducted by serially passaging the cell lines . Cells were split 1∶10 approximately every third day using trypsinization . Genomic DNA was isolated at the indicated time-points when the cells were ∼80% confluent . The end-point dilution was performed by seeding the parental cell line at 10–20 cells per 100 mm plate . These cells formed individual colonies that were then isolated and expanded . The genomic DNA was extracted and the GAA·TTC insert was sized . Genomic DNA was isolated from the cell lines using DNAzol Reagent ( Invitrogen ) following the supplier's directions . PCR amplification was performed in 50 µl reactions ( 100 ng template; 0 . 2 µM primers; 1 mM dNTP mix ( Stratagene ) ; 2 . 5 units polymerase; 1× enzyme buffer ) for 30 cycles . Either Paq5000 DNA polymerase ( Stratagene ) or Herculase II Fusion polymerase ( Stratagene ) enzymes were used for PCR amplification using the manufacturer supplied reaction buffer specific to each enzyme . 1 . 3 M betaine was included in reactions performed using the Herculase II Fusion Enzyme . Primer pairs specific to the TAN construct include: MGF3102 5′-ggtcttgtagttgccgtcgt-3′ forward , MGR3533 5′-caactgactgaaatgcctcaa-3′ reverse; annealing: 58°C; product size: ( ( GAA ) n×3 ) +438 bp . Repeats amplified from the HBB2 constructs were amplified using H2-2574 forward primer: 5′-aggtctgctggctcccttat-3′ with MGR3533 reverse; annealing 55°C; product size: ( ( GAA ) n×3 ) +445 bp . Repeats amplified from the HBB3 constructs were amplified using H3-791 forward primer: 5′- cacagatgattcaataacaaacaaaa-3′ with MGR3533 reverse; annealing 55°C; product size: ( ( GAA ) n×3 ) +501 bp . Amplified products containing 88 GAA·TTC repeat inserts or less were analyzed by electrophoresis using 1 . 4% agarose gels , while larger fragments were resolved using 1% agarose gels . 1 Kb Plus DNA Ladder ( Invitrogen ) was used as the size marker . Gels were stained using 1 . 3 µg/ml ethidium bromide . Gel images were obtained using the Kodak Gel Logic 440 imaging system . Software analysis of the gel images and profile analysis of the insert PCR mobility distribution were obtained using the Kodak molecular imaging software ( version 4 . 0 ) . Graphical representation of the software analysis was created using Prism 4 graphing software and Canvas 8 graphics software . Statistical analysis of GAA·TTC repeat expansion was performed using the Student's t-test for unpaired data with unequal variance . 10 µg of genomic DNA was digested to completion using EcoRV and BglII restriction endonucleases , ethanol precipitated , and resuspended in 1× TE buffer ( 10 mM Tris , 1 mM EDTA , pH 8 ) . 5× loading buffer ( glycerol , 2 . 5 mM EDTA , bromophenol blue , xylene cyanol ) was added to sample ( s ) before electrophoresis using a 1% agarose gel . The agarose gels containing the digested samples were soaked in 0 . 1 N HCl for 20 min to depurinate the DNA prior to transfer . The gels were rinsed in distilled water and soaked in 0 . 4 N NaOH for 15 min to denature the DNA for probe binding . The gels were rinsed in distilled water and set up for transfer . Capillary blot transfer was performed for 12 h using 5× SSC transfer buffer ( 750 mM sodium chloride , 75 mM sodium citrate ) and Hybond-N+ transfer membrane ( Amersham Biosciences ) . Post-transfer , the DNA samples were crosslinked to the membrane using a 1 min UV exposure . [32P] Riboprobe synthesis was performed using the pSP72/5′hRLUC ( 5′ hRLUC probe ) and the pSP72/3′hRLUC ( 3′ hRLUC probe ) HindIII linearized templates . Hybridization was performed overnight at 65°C in ULTRAhyb hybridization buffer ( Ambion ) . The hybridized blot was washed in a series of SSC/SDS mixtures increasing in stringency every 5 min . The washed blot was exposed to film for 24 h and 1 week at −80°C . The Dual-Luciferase reporter assay system ( Promega ) was used according to the manufacturers directions . Cells were seeded in a 48-well tissue culture treated plate and induced with doxycycline ( 1 µg/ml ) for 24 h . Cells were washed in PBS and lysed in passive lysis buffer ( Promega ) . Cell lysates were aliquoted into a Greiner 96-well plate ( Sigma ) and analyzed using a Turner Biosystems Veritas plate-reader luminometer ( Turner Biosystems , Sunnyvale , CA ) with an integration time of 10 sec according to the Promega dual luciferase reagent protocol . For the culturing experiment , the cells were isolated after 2 weeks in the various culturing conditions and analyzed for luciferase expression . Statistical analysis of relative luciferase expression values was performed using the Student's t-test for unpaired data with unequal variance .
|
The human genome is comprised of the DNA base sequences used by the cell as a blueprint to direct proper cellular function . Changes in this sequence , known as genomic instability , often interfere with vital cellular functions , resulting in genetic disorders . Repetitive DNA sequences are particularly susceptible to genomic instability . Trinucleotide repeat disorders are caused by three base repeat sequences that increase in size when passed from parent to child and during aging . Trinucleotide repeat expansion results in disease when the size of the repeat sequence increases into the pathogenic size range . Our understanding of the mechanisms responsible for these repeat length changes is incomplete and modeling repeat expansion in human cells has proven difficult . Here , we have developed a unique human cellular model of GAA·TTC trinucleotide repeat expansion , the causative mutation in Friedreich ataxia . Using this model , we characterize GAA·TTC expansion in human cells and identify gene transcription as a key regulator of GAA·TTC repeat expansion . The findings of this study provide novel insight into the mechanisms contributing to trinucleotide repeat expansion in human cells and present new implications for certain therapeutic approaches in Friedreich ataxia .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/disease",
"models",
"molecular",
"biology/transcription",
"elongation",
"neurological",
"disorders/neuromuscular",
"diseases",
"genetics",
"and",
"genomics/gene",
"expression"
] |
2009
|
Progressive GAA·TTC Repeat Expansion in Human Cell Lines
|
JC polyomavirus ( JCV ) carriers with a compromised immune system , such as in HIV , or subjects on immune-modulating therapies , such as anti VLA-4 therapy may develop progressive multifocal leukoencephalopathy ( PML ) which is a lytic infection of oligodendrocytes in the brain . Serum antibodies to JCV mark infection occur only in 50–60% of infected individuals , and high JCV-antibody titers seem to increase the risk of developing PML . We here investigated the role of human leukocyte antigen ( HLA ) , instrumental in immune defense in JCV antibody response . Anti-JCV antibody status , as a surrogate for JCV infection , were compared to HLA class I and II alleles in 1621 Scandinavian persons with MS and 1064 population-based Swedish controls and associations were replicated in 718 German persons with MS . HLA-alleles were determined by SNP imputation , sequence specific ( SSP ) kits and a reverse PCR sequence-specific oligonucleotide ( PCR-SSO ) method . An initial GWAS screen displayed a strong HLA class II region signal . The HLA-DRB1*15 haplotype was strongly negatively associated to JCV sero-status in Scandinavian MS cases ( OR = 0 . 42 , p = 7×10−15 ) and controls ( OR = 0 . 53 , p = 2×10−5 ) . In contrast , the DQB1*06:03 haplotype was positively associated with JCV sero-status , in Scandinavian MS cases ( OR = 1 . 63 , p = 0 . 006 ) , and controls ( OR = 2 . 69 , p = 1×10−5 ) . The German dataset confirmed these findings ( OR = 0 . 54 , p = 1×10−4 and OR = 1 . 58 , p = 0 . 03 respectively for these haplotypes ) . HLA class II restricted immune responses , and hence CD4+ T cell immunity is pivotal for JCV infection control . Alleles within the HLA-DR1*15 haplotype are associated with a protective effect on JCV infection . Alleles within the DQB1*06:03 haplotype show an opposite association . These associations between JC virus antibody response and human leucocyte antigens supports the notion that CD4+ T cells are crucial in the immune defence to JCV and lays the ground for risk stratification for PML and development of therapy and prevention .
Progressive multifocal leukoencephalopathy ( PML ) was first described neuropathologically during the fifties by Karl Erik Åström [1] . It took until 1971 when JC virus ( JCV ) was isolated from brain tissue of a patient with PML , since then JCV was accepted as the causative agent of PML [2] . PML used to be a rare demyelinating disease of the central nervous system , mainly seen in patients with lymphoproliferative disease or AIDS . Now several different drugs that interfere with immune functions , such as natalizumab , efalizumab , mycophenolate mofetil , fumaric acid , rituximab , tacrolimus , and possibly azathioprine , cyclosporine and cyclophosphamide have been associated with an increased risk of developing PML . For natalizumab and efalizumab the strongest associations were seen in patients without an underlying disease that predispose for PML itself [3]–[7] . Thus , it is of major importance to develop measures to prevent or treat the condition , including understanding of factors allowing persons to acquire the virus , as carriers , a requisite for later risk for PML . In patients with multiple sclerosis ( MS ) treated with natalizumab previous immunosuppressive therapy , an increased duration of therapy , and the positive detection of anti-JCV IgG antibodies as surrogate for the infection with JCV have been established as risk factors for PML [8]–[12] . The anti-JCV antibody status in MS patients is determined by a commercial two step-ELISA . Around 40–50% of the adults are anti-JCV antibody negative [11] , [13]–[15] . The cut-off of the commercial assay have been validated in large multicentre cohorts of MS patients with data on JC viruria available , and the false negative rate ( sero-negative , but DNA excretion in urine ) was estimated with around 2 . 5% [9]–[11] . In contrast , a recent study that also measured JCV excretion in urine in a comparably small study population ( n = 67 ) indicated a much higher false negative rate of 37% , however , these cases displayed considerably lower JCV DNA copy numbers in the urine . Hypothetically , a vast majority of persons might be exposed to an ubiquitous virus such as JCV , proposed as contamination marker for human excretions , [16] but differ in replicative activity of a persistent asymptomatic infection , and potentially connected to this , the individual level of immune response to the virus . This view would fit with recent serological observations of a continuous anti-JCV reactivity in larger populations , [17] and might imply that actually not the true absence of the JCV infection , but rather the level of the replicative activity of the persistent JCV infection determines the individual risk of developing PML [18] . This risk might then critically depend on host genetic factors that determine the immune response to the virus , and protect from e . g . the spread of the virus from places of peripheral persistency or latency to the brain . Genes of particular interest in this respect are the HLA class I and class II genes where different variants with different peptide presenting abilities may affect the effectiveness of CD4+ and CD8+ T cell immune defence . Our aim was therefore to test the host genetic regulation of HLA genes in the immune response to JCV . We used anti-JCV antibody status and anti-JCV antibody levels as surrogate for the identification of persons carrying a JCV infection in significant and clinically relevant levels and tested association to HLA class I and class II genes .
Clinical characteristics and demographic data of the included patients and controls are displayed in table 1 . Anti-JCV antibody status and levels were determined in the same laboratory for all individuals with an ELISA based method [9] . In this study we selected the HLA complex for scrutiny in view of its potent immune regulatory functions . We performed a meta-analysis of association of markers on chromosome 6 for both anti-JCV antibody status and normalized anti-JCV antibody levels ( anti-JCV nOD ) of results obtained in the three separate cohorts of individuals shown in table 1 . This indicated a strong association signal in the HLA class II region for both anti-JCV antibody status and anti-JCV nOD values ( figure 1 ) . The most significant markers for the two analyses ( rs34454257 for the anti-JCV antibody status and rs3129860 for anti-JCV nOD values ) map 42 . 6 and 145 . 7 kb upstream of the HLA-DRB1 gene in the direction of the HLA class I genes . With this association signal on chromosome 6p21 , it was of interest to determine the particular class II gene variants which were associated . Several HLA-alleles showed association to anti-JCV antibody status in both Scandinavian MS cases and controls ( Table 2 ) . The table is organised based on common established extended haplotypes found in the Caucasian population [19]–[21] . It is noteworthy that the DRB1*15-DQA1*01:02-DQB1*06:02-haplotype , the most strongly associated MS genetic risk factor , was found to be negatively associated with the positive detection of anti-JCV antibodies . The OR for DRB1*15 was 0 . 42 in Scandinavian MS cases and 0 . 53 in controls . This association was replicated in German MS cases ( OR for DRB1*15 0 . 54 Table 3 ) . Other alleles in this haplotype , DQB1*06:02 and DQA1*01:02 , also showed strong protective associations , as expected , since they are in LD with DRB1*15:01 . In contrast , the DRB1*13-DQA1*01:03-DQB1*06:03-haplotype was positively associated with anti-JCV antibody status , with an OR = 1 . 62 in Scandinavian MS cases , OR = 1 . 55 in Swedish controls ( Table 2 ) and OR = 1 . 58 in German MS cases ( Table 3 ) . In addition the DRB1*03-DQA1*05-DQB1*02 and DQA1*05-DQB1*03:01 haplotypes showed a positive association to anti-JCV antibody status , while the DQA1*05-DQB1*01:01-haplotype was negatively associated with anti-JCV antibody status among controls . The DRB1*15-DQA1*01:02-DQB1*06:02-haplotype also showed an association to lower anti-JCV nOD levels in a linear regression analysis among JCV seropositive individuals , with a significance level of p≤0 . 001 in the Scandinavian cohorts ( Table 4 ) . For DQB1*06:02 beta was between −0 . 218 and −0 . 366 in the different cohorts ( Table 4 and 5 ) . DRB1*13 showed an association to higher anti JCV nOD levels among Scandinavian MS cases , p = 0 . 02 , beta = 0 . 197 , but not in German MS cases or Swedish controls ( Table 4 and 5 ) . In addition DQB1*03:01 and DQA1*05 showed an association to higher transformed anti-JCV nOD levels among Swedish controls ( Table 4 ) . Most of the HLA associations to both anti-JCV antibody status and anti-JCV nOD levels remained similar when other nominally associated HLA alleles for the same gene are included in the regression analysis ( Table 2 to 5 ) . The OR for the association of the presence of DRB1*15 and for DRB1*15 homozygotes for JCV antibody status did not differ , suggestive of a dominant DRB1*15 effect ( Figure 2A ) . Conversely , the DRB1*13 homozygotes showed a slightly stronger association compared to presence of DRB1*13 , although the 95%CI do overlap . DRB1*13/15 heterozygotes were not significantly associated with JCV seropositivity indicating that the effect of the two haplotypes counteract each other . Similar results were seen for the DQA1 locus ( Figure 2B ) , but here the DQA1*01:03/05 heterozygotes are associated with an OR as high as 5 . 23 . Consistent with the effect on the qualitative anti-JCV status , the DRB1*15 haplotype appeared to act dominantly also on anti-JCV nOD levels as presence of DRB1*15 showed a similar association to DRB1*15 homozygotes , while the DRB1*11 haplotype had an additive effect ( Table 6 ) . The effect of these two haplotypes cancel each other out as DRB1*11/15 heterozygotes showed no association to anti-JCV nOD levels . We reanalysed the association of SNPs on chromosome 6 to anti-JCV antibody status and anti-JCV nOD levels when including all HLA alleles that remained associated in the multivariate analysis as covariates . This lead to an almost complete abolishment of the association peak on chromosome 6 , with the most significant remaining associations being p = 0 . 0001 for a handful of markers ( data not shown ) . This indicates that the association we observed in the HLA region was almost completely explained by the HLA alleles listed in tables 2–4 .
We here demonstrate a host genetic HLA complex mediated influence on anti-JCV antibody status and anti-JCV antibody levels as surrogate for the susceptibility of the infection with JCV or the activity of the infection with JCV , respectively . We report a strong negative association with anti-JCV antibody positivity , and to a lesser extent , to anti-JCV nOD levels , for the HLA-DRB1*15-DQA1*01:02-DQB1*06:02-haplotype in all three datasets . In contrast , the DRB1*13-DQA1*01:03-DQB1*06:03-haplotype is associated to increased signs of JCV carriage as assessed serologically . We further find that the DRB1*15-DQA1*01:02-DQB1*06:02 haplotype acts dominantly , one copy being sufficient to reduce the ability to form anti-JCV antibodies while the DRB1*13-DQA1*01:03-DQB1*06:03 haplotype acts in an additive fashion . Neither of the haplotypes dominates over the other . A recent study demonstrated considerable variation in which JCV peptides were recognized by T cells [22] . A most straight forward interpretation of the present findings is that the DRB1*15:01 haplotype displays class II molecules that are especially able to present JCV antigens/peptides that are instrumental in activating CD4+ T cells that support the elimination or control of the virus upon exposure to the host . Hence , the opposite would be valid for the haplotype associated with increased carriage of the JCV . Thus hypothetically a large proportion of those persons being sero-negative might have encountered the virus , but had an efficient immune response following primary infection , with low viral turnover or the lack of viral persistency , and low anti-JCV IgG as consequence . Recent serological studies support such a concept: antibody reactivity as measured by ELISA resembles a continuum from non-reactive to highly reactive in particular in persons not excreting the virus in urine [9] . This led to the introduction of a second-step confirmation test when determining the anti-JCV sero-status . However , this pattern of continuous reactivity is also seen with alternative assay formats , which suggest that a vast majority of persons have been exposed to JCV , but have a level of the antibody response to JCV below the assay cut-off , possibly due to an efficient control of the virus with low viral turn-over [13] , [17] . This would also explain the higher false negative rate of serological studies observed in recent publications [23] , [24] . Although CD8+ cells , restricted by class I molecules are critical in eliminating virus infected cells , antigen specific CD4+ HLA class II restricted cells are crucial for providing T cell help through a variety of cytokines and activation of antigen presenting dendritic cells [25] . The findings may pave the way for finding epitopes in JCV critical for immune defence which could impact on vaccination strategies . Any direct clinical implications of the data , or use , for example in risk stratifications , remain to be determined . The DRB1*13-DQA1*01:03-DQB1*06:03-haplotype shows a positive association to anti-JCV antibody serostatus , and was also to higher anti-JCV nOD levels . Hypothetically , a less effective viral immune control with higher viral turnover may be consistent with a chronically higher stimulation of the B cell arm of the immunity resulting in higher antibody levels . This might help us understand why patients that develop PLM during therapy with natalizumab had increased anti-JCV antibody levels already prior to development of PML , and why it might be rational to include the level of the anti-JCV response into PML risk stratification strategies [26] . Recent data suggests that PML-specific viral mutations are acquired intra-individually e . g . in the VP1-region and the regulatory region of the viral genome [27] , [28] . It is tempting to speculate that viral PML-specific mutations , although being a random event , are more likely to occur in persons with an inefficient control of the infection with JCV . The host genetic data presented here might therefore be a first step helping to understand how the interplay of host- and viral genetic factors might lead to the development of PML in some , but not all persons exposed to certain immunosuppressive therapies . Our study is however lacking a sufficient number of cases of PML and is therefore not designed and empowered to test this directly . There is one previous paper studying the HLA association to PLM [29] . In this paper 123 Caucasian PML cases , the majority whom were HIV positive , were compared with a large group of HIV positive individuals . The study was limited to the association of HLA class I antigens . While A3 was found to be nominally negatively associated with PML , B18 was found to be positively associated . We do not find any of these alleles associated with anti-JCV antibody formation in our study . However , the A3 association to PML possibly is explained by the same effect as the DRB1*15-DQA1*01:02-DQB1*06:02 association we see in our study , considering that A3 can be present on the same extended haplotype . Studies in larger cohorts of PML patients with appropriate controls testing the association of class II antigens are warranted . A recent investigation has studied the stimulation of CD4+ T cells by pools of JCV peptides among healthy donors with different HLA-DRB1 alleles [22] . For haplotypes where we see an increased OR for sero-status and positive correlation to JCV-Ab levels ( DRB1*13 and DRB1*03 ) they observe reduced stimulation of CD4+ cells , while the opposite was true for DRB1*15 . Hence , both antibody response and T-cell response to JCV infection are affected by HLA-class II antigens , which is consistent with our observations of potent HLA class II gene variant effects in large cohorts of persons . HLA associations to some viral infections have been seen previously . The HLA class II genes were recently reported as host genetic factors influencing the IgG response to EBNA1 , an Epstein Barr virus-related protein [30] . In addition , there are well documented class II allelic influences on Hepatitis C [31] and recently a highly associated SNP in the HLA region was demonstrated in relation to human papilloma virus infection [32] . HLA class II associations have also been seen in chronic hepatitis B infections as well as response to hepatitis B vaccination [33]–[35] . Association to the HLA class I related MICB gene have also been reported to hypovolemic shock caused by dengue viral infection and HIV viral load [36] , [37] . Another MIC gene , MICA has been associated to hepatitis C virus induced hepato cellular carcinoma [38] . In our data , after adjusting the association for HLA class II associated alleles the most strongly associated marker in the class I region is rs3094014 ( p<0 . 02 ) which is in LD with both the MICB ( r2 0 . 76 for rs3132468 associated with dengue fever ) and MICA ( r2 0 . 90 for rs2596542 reported to be associated with hepatitis C virus induced hepato cellular carcinoma ) using European 1000 genomes and HapMap data and may therefore represent the same signal . Interestingly , the DRB1*15 haplotype is also the most strongly associated genetic risk factor for MS [39] . Consistent with this , the demographic data in our study suggests that anti-JCV antibody positivity is somewhat lower among MS cases ( 59% ) , compared to controls ( 66% , p = 0 . 02 ) . A protection against the establishment of a persistent JCV infection with positive detection of anti-JCV antibodies provided by the DRB1*15 haplotype would then explain the lower sero-prevalence among cases . The presence of an association with the DRB1*15-haplotype in controls also indicates that the association is not likely due to an aberrant immune response to JCV infection in MS cases . In conclusion , we here demonstrate strong associations of class II gene variants on JCV infection . Hence , CD4+ T cells , restricted by class II molecules are crucial in the host control of JCV infection . Our data is of importance for a better understanding of JCV infection and virus-host interactions , and might pave the way for new developments for an improved PML risk stratification , and preventive or curative future anti-JCV therapies .
The study was approved by the regional ethical committees in each country involved; Stockholm regional Ethical Review Board ( Sweden ) , the Ethical review boards at the Heinrich-Heine Universität Düsseldorf and the Technische Universität München ( Germany ) and the Danish Ethical Committee Review Board for Copenhagen and Frederiksberg ( Denmark ) . All participants provided written informed consent . A Scandinavian dataset consisting of 2015 Swedish persons with MS from two separate studies EIMS [40] and IMSE [41] with 1259 population based controls , and 157 Danish MS patients treated with natalizumab in Copenhagen . HLA-genotypes and anti-JCV antibody status were available for 1621 MS cases and 1064 controls ( table 1 ) . A German dataset of 745 MS patients , 718 with GWAS data , was used for replication . The cohort was recruited from multiple sites in Germany and included persons treated with interferon-beta for at least 6 months . GWAS genotyping for these MS cases had been performed in the same laboratory with the same chip as the Scandinavian datasets . HLA-genotypes came from three different sources . Low resolution Sequence Specific amplification ( Olerup , Saltsjöbaden , Sweden ) [42] were for genotyping of 2115 Swedish cases and controls for HLA-A , 2140 for HLA-DRB1 , and 161 for HLA-C . For HLA-B a Luminex based reverse PCR-SSO ( One Lambda , Inc . , Canoga Park , CA , USA ) was used for 173 persons [39] . And finally imputation either using HLA*IMP [43] with genotypes from the IMSGC WTCCC2 MS GWAS , [44] or with HLA*IMP:2 [45] using genotypes from the Immunochip [46] was used . The former was used for both the Danish and German cohort while both were used in the Swedish cohort . Imputed HLA data was available for 1105 Swedish persons from the IMSGC WTCCC2 and for 2220 Swedish persons using Immunochip genotypes . In cases where the genotypes for any individual were discordant between platforms , the following order of precedence was used: classical , Immunochip imputed , GWAS imputed . A quality value for allele probability of 0 . 7 was used as a threshold for imputed HLA genotypes . In this study we used SNP genotypes from MS GWAS study to analyse association to the HLA region [44] . Genome-wide SNP markers were genotyped as part of the IMSGC WTCCC2 MS GWAS on the Human660-Quad chip , and genotype calling and markers were quality controlled as previously described [44] . Cases with previous intravenous IgG treatment were excluded . In the Scandinavian cohort all persons were of Scandinavian ancestry , and all MS cases fulfilled the McDonald or Poser criteria for MS . For the German MS cases , a total number of 749 cases were GWAs genotyped; 25 persons were removed as they were outliers in the principal component analysis ( PCA ) , 4 were removed due to unsuccessful genotyping , and 2 because of natalizumab treatment at blood draw . JCV serology response was determined from plasma or serum using a two-step assay , [9] performed at Focus Diagnostics ( Cypress , CA , USA ) and sponsored by Biogen Idec ( Cambridge , MA , USA ) . In the first step of the ELISA assay optical density ( OD ) were measured . Samples with OD>0 . 25 were considered positive while samples with OD<0 . 10 were considered negative . For samples in the intermediary interval ( 0 . 10–0 . 25 ) a second assay step was used to determine the percentage of inhibition during a pre-incubation with soluble JCV-like particles . Samples in this intermediary interval with an inhibition >40% were considered positive while those with an inhibition <40% were classified as negative . The assay has been estimated to have a false negative rate for JCV carriage of 2–3% . For the quantitative analysis , normalised OD values ( nOD ) from the first step ELISA were transformed using rank based transformation in the GenABEL-package in R [47] .
|
JC virus infection can lead to progressive multifocal leukoencephalopathy in individuals with a compromised immune system , such as during HIV infections or when treated with immunosuppressive or immunomodulating therapies . Progressive multifocal leukoencephalopathy is a rare but potentially fatal disease characterized by progressive damage of the brain white matter at multiple locations . It is therefore of importance to understand the host genetic control of response to JC virus in order to identify patients that can be treated with immunomodulating therapies , common treatments for autoimmune diseases , without increased risk for progressive multifocal leukoencephalopathy . This may also lead to development of preventative or curative anti-JC virus therapies . We here identify genetic variants being associated with JC virus antibody development; a negative association with the human leucocyte antigen DRB1*15-DQA1*01:02-DQB1*06:02 haplotype and a positive association with the DRB1*13-DQA1*01:03-DQB1*06:03 haplotype among controls and patients with multiple sclerosis from Scandinavia . We confirmed the associations in patients with multiple sclerosis from Germany . These associations between JC virus antibody response and human leucocyte antigens imply that CD4+ T cells are crucial in the immune defence and lay the ground for development of therapy and prevention .
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2014
|
JC Polyomavirus Infection Is Strongly Controlled by Human Leucocyte Antigen Class II Variants
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A SNP upstream of the INSIG2 gene , rs7566605 , was recently found to be associated with obesity as measured by body mass index ( BMI ) by Herbert and colleagues . The association between increased BMI and homozygosity for the minor allele was first observed in data from a genome-wide association scan of 86 , 604 SNPs in 923 related individuals from the Framingham Heart Study offspring cohort . The association was reproduced in four additional cohorts , but was not seen in a fifth cohort . To further assess the general reproducibility of this association , we genotyped rs7566605 in nine large cohorts from eight populations across multiple ethnicities ( total n = 16 , 969 ) . We tested this variant for association with BMI in each sample under a recessive model using family-based , population-based , and case-control designs . We observed a significant ( p < 0 . 05 ) association in five cohorts but saw no association in three other cohorts . There was variability in the strength of association evidence across examination cycles in longitudinal data from unrelated individuals in the Framingham Heart Study Offspring cohort . A combined analysis revealed significant independent validation of this association in both unrelated ( p = 0 . 046 ) and family-based ( p = 0 . 004 ) samples . The estimated risk conferred by this allele is small , and could easily be masked by small sample size , population stratification , or other confounders . These validation studies suggest that the original association is less likely to be spurious , but the failure to observe an association in every data set suggests that the effect of SNP rs7566605 on BMI may be heterogeneous across population samples .
Body mass index ( BMI ) is a heritable measure of obesity that is routinely obtained in large cohorts , is correlated with other measures of obesity , and predicts morbidity and mortality from obesity-related diseases [1–4] . Thus , BMI is a readily accessible trait that can be used to screen for genetic variants that increase an individual's risk for obesity and its complications . There have been more than one hundred publications reporting association between common genetic variants and BMI , but few of the associations have been reproducible in multiple populations [5] . Genotyping of variants has increased exponentially in scale over the past few years , and much more comprehensive screens of common genetic variation for association with obesity are now possible . The poor rate of reproducible findings in association studies in general and obesity in particular are likely due to a combination of false-positive results , underpowered attempts to reproduce associations with modest effects , systematic bias due to technical artifacts or population stratification , and perhaps true heterogeneity in effect across populations due to differences in genetic or environmental modifiers [6 , 7] . Thus , new reports of association require rapid , well-powered studies to validate true associations or identify false positives that could otherwise trigger unwarranted investigation of spurious findings . Recently , Herbert and colleagues , including several of the authors of this study , reported a novel association between homozygosity for the minor allele of a single nucleotide polymorphism ( SNP ) , rs7566605 , and increased BMI [8] . The SNP has no known function , and the closest gene codes for the insulin signaling protein type 2 ( INSIG2 ) , a hijacking protein in the endoplasmic reticulum that , in response to changes in lipid levels , impedes the movement of sterol regulatory element binding proteins to the Golgi apparatus for processing and ultimately its release to act as a nuclear transcription factor and regulator of lipid biosynthesis [9–11] . Animal data suggests a role for INSIG2 in increasing triglyceride level in rats [12] , as well as linkage to obesity phenotypes in mice [13] . The association of SNP rs7566605 with obesity was initially found in a set of related individuals from the Framingham Heart Study ( FHS ) offspring cohort [8] . The SNP was genotyped in five additional cohorts , and the association was observed again in four of these , including population-based studies , case-control samples , and family-based cohorts . However , no significant association was found in a fifth cohort ( the Nurses Health Study [NHS] ) , where a slight trend in the opposite direction was seen . Approximately 10% of individuals were homozygous for the minor allele ( C/C ) , and in a meta-analysis of the case-control samples ( including the NHS cohort and excluding the FHS discovery cohort ) , these individuals had a 22% increased risk of obesity ( defined as BMI ≥ 30 kg/m2 ) . In the NHS cohort alone , the 95% confidence interval ( CI ) for the odds ratio ( OR ) for obesity was 0 . 58–1 . 13 . Subsequently , two further groups reported no evidence of association in large cohorts , and a third found association only for people on the overweight end of their population [14–17] . We considered several possible explanations for observing an association in four cohorts but not in the fifth . The failure to observe association in the NHS sample could be due to more modest effects in this cohort and therefore inadequate sample size , population stratification , ascertainment bias , other unmeasured confounders , or any combination of these . It is also possible that evidence in the four cohorts was falsely positive , for any of a combination of reasons that could include hidden population substructure , technical artifacts , or statistical fluctuations causing false positives . However , because of the consistency across multiple cohorts , including studies with family-based design , we felt that these explanations were less likely . Finally , it is also possible that the association is heterogeneous across populations , either due to differences in ascertainment , or differences in genetic or environmental modifiers . Of these possibilities , it is most critical to assess first whether the original associations were spurious , so as to avoid further efforts expended on a false finding . Our primary objective was therefore to test additional large populations to evaluate further the validity and generalizability of this association . By studying these additional populations , including a sample with longitudinal data , we hoped to better assess the strength and consistency of the association between increased BMI and the risk genotype at rs7566605 , and perhaps generate hypotheses about any inconsistencies in this association .
Descriptions of the cohorts used in this study are presented in Table 1 , Table S1 , and in the Methods . These nine cohorts are drawn from eight different populations and include a total of almost 17 , 000 individuals . The cohorts were not ascertained for BMI , except for the Essen study cohort , which was selected from the upper ( BMI ≥ 30 kg/m2 ) and lower ( BMI < 20 kg/m2 ) ends of the BMI distribution of their population and a portion of the African-American sample that was enriched for obese individuals . We tested for association with obese ( BMI ≥ 30 kg/m2 ) versus non-obese ( BMI <30 kg/m2 ) and also with BMI as a continuous trait , to mimic the association tests performed in the initial publication . All analyses were performed under a recessive model , with the prior hypothesis that C/C homozygotes would have a higher BMI than individuals in the other two genotype classes . The frequency of C/C homozygotes was increased in obese individuals compared to non-obese control individuals in several cohorts ( Table 2 ) . Nominally significant ( two-tailed p < 0 . 05 ) associations between obesity ( BMI ≥ 30 kg/m2 ) and the C/C were present in three samples: the Iceland cohort ( OR = 1 . 29 , 95% CI = 1 . 06–1 . 57 , p = 0 . 0064 ) , the Essen cohort ( OR = 1 . 75 , 95% CI = 1 . 15–2 . 68 , p = 0 . 008 ) , and in one of six exam cycles within the longitudinal data from the FHS cohorts ( Table 2 ) . In the Iceland cohort , the homozygote C/C genotype was associated with a 0 . 69 kg/m2 increment in BMI , which is in good agreement with the effect observed by Herbert et al . [1] . The KORA S3 , Maywood , and Scandinavian cohorts , and five of six exam cycles in the FHS cohort , did not show nominally significant associations under a recessive model . The Scandinavian , FHS , and Maywood samples may have been too small to achieve statistical significance with an association of the magnitude estimated by Herbert et al . ( OR = 1 . 22 ) . The Scandinavian cohort had an estimated OR ( 1 . 25 ) similar to the original report , but a p value of 0 . 46 and a wide 95% CI around the estimated OR ( 0 . 69–2 . 24 ) . In particular , this cohort had only 120 people with BMI > 30 kg/m2 , and the power to achieve nominal significance for an OR of 1 . 22 ( as estimated in the original report ) is only 15% . The estimated OR in the Maywood cohort was 0 . 88 but the CIs were also wide ( p = 0 . 68 , 95% CI = 0 . 49–1 . 59 ) , which suggests that the sample was also underpowered to find this modest association and/or that the effect in this sample is smaller than in the original report . The KORA S3 sample was much larger ( 851 obese and 3 , 233 non-obese ) , but had an OR of 0 . 90 , with a 95% CI of 0 . 71–1 . 16 , suggesting that the association is either more modest or absent in this cohort , limited to a particular subgroup of this population ( see Discussion ) , and/or that when several samples are tested , some statistical fluctuation either away from or toward the null is expected . Association tests in the FHS cohort between the C/C genotype and obesity showed some apparent variability , achieving significance in some but not all of the six exams , with p values ranging from 0 . 003–0 . 51 ( Table 2 ) ; correcting the best p value for having tested six exams suggests that the totality of these findings are consistent with a replication ( corrected p value = 0 . 018 ) . There was no formal evidence of heterogeneity across the six exams ( p = 0 . 47 ) , and the 95% CIs for all exams include an OR of 1 . 22 ( Table 2 ) . We also analyzed the five population-based samples—Maywood , Iceland , KORA S3 , Scandinavia , and FHS ( see Methods for details ) —for association with BMI as a continuous trait , again under a recessive model controlling for age and gender . We saw similar results to those observed for the dichotomous analysis , with nominally significant associations between C/C homozygotes and increased BMI observed in the Iceland and FHS cohorts but not in KORA S3 , Maywood , or Scandinavia ( Table 2 ) . When we analyzed association with BMI at each exam cycle from FHS separately , there was no significant evidence of association in a recessive model . The effect estimates trended in the same direction ( exam 3 , two-tailed p value = 0 . 096 ) ( Table 2 ) as did estimates in the analysis using z-scores for BMI ( see Methods ) and mean z-score over six exams ( unpublished data ) . Finally , we tested SNP rs7566605 for association with increased BMI in three family-based samples , using PBAT [18] . Two of the three cohorts showed an association between SNP rs7566605 and BMI as a continuous trait under a recessive model ( Table 3 ) . ( A dichotomous analysis was not done in these cohorts , because the definition of obesity we used for the remainder of the samples [BMI > 30 kg/m2] was not applicable to the children that made up a substantial part of each cohort . ) The family-based portion of the Scandinavian cohort was composed of adults , but the incidence of obesity was only 13% ( n = 66 ) , limiting the power of a dichotomous analysis . Because BMI changes rapidly during childhood , we compared the results for the pediatric cohorts using three different measured outcomes: BMI , BMI adjusted for age and gender , and BMI-for-age percentile ( Centers for Disease Control and Prevention 2000 National Center of Health Statistics ) ; the p values for the corresponding FBAT statistics were essentially identical in each cohort ( unpublished data ) . To estimate the overall significance and effect size in the samples we studied , we performed a pooled analysis for both the unrelated and family-based cohorts . These combined analyses , which included both cohorts that showed association and those that did not , yielded independent , statistically significant associations for both the unrelated samples ( Table 4 ) and the family-based samples ( Table 3 ) . Combining the p values of the family-based studies using Fisher's method provided evidence of replication ( Fisher's combined p = 0 . 004; Table 3 ) . For the unrelated samples ( Table 4 ) , we compared obese and non-obese people , and performed a combined analysis using each exam cycle of the FHS cohort in turn . Since the Essen cohort was ascertained as a severe obesity cohort with non-age matched controls , we tested for heterogeneity between studies using a modified Breslow-Day test [19 , 20] . There was evidence for heterogeneity when including the Essen cohort ( p values for homogeneity = 0 . 007–0 . 08 ) so this cohort was excluded from the combined analyses . Mantel-Haenszel two-tailed p values ranged from 0 . 011 using FHS exam 3 to 0 . 054 using FHS exam 6 ( Table 4 ) . In these combined analyses , the estimated OR for obesity ( BMI > 30 kg/m2 ) associated with the C/C homozygous genotype ranged from 1 . 13 to 1 . 18 , somewhat lower than the effect size estimated by the original report [8] . There was also modest evidence of heterogeneity; p values for homogeneity ranged from 0 . 03 to 0 . 20 , depending on which exam from FHS was included in the combined analysis ( Table 4 ) , suggesting that there might be some real variability in effect size across the samples in this study .
Association testing in these nine cohorts shows further evidence that individuals homozygous for the C/C genotype at SNP rs7566605 have a higher BMI and a higher risk of obesity . The association is detectable in diverse cohorts , in children as well as in adults , and in both family-based and population-based samples . The association is not likely to be due to stratification because it was seen in family-based samples such as Costa Rica and CAMP , which are immune to stratification , and because the original publication also described associations in family-based testing [8] . The effect of ascertainment on these analyses could potentially provide confounding of the association in four of these studies . Because index children in family-based studies in CAMP and Costa Rica were ascertained on the basis of asthma , a spurious association between SNP rs7566605 and BMI could be found if the SNP of interest was directly associated with asthma . However , none of the other cohorts were ascertained in this manner , lessening concerns about this source of bias as a potential cause of false-positive associations . In addition , the Scandinavian sample was ascertained as control subjects for a diabetes case control study ( similar to the NHS in the original report ) . A further bias could potentially have been introduced by the selection of non-obese people in the Essen cohort who have a younger mean age than the obese people from this cohort ( Table 1 ) . The lean controls ( mean BMI = 18 . 2 kg/m2 ) are less likely to be obese later in life , but a small portion of them could be misclassified as non-obese , which would tend to bias the estimate toward the null . Of note , the combined analysis remains significant even if we include this study ( unpublished data ) . The longitudinal nature of the FHS data may provide a clue to a possible cause for inconsistency in the association between SNP rs7566605 and obesity . In this cohort , a stronger effect on BMI was seen in the data from the first three exams than in the last three exams . The individuals at each exam are largely overlapping , making confounders less likely to explain a positive association in the early exam data and a lack of association in later data . Assuming that the association in this cohort is not a false positive due to statistical fluctuation , then the passage of time is the most likely explanation for the diminution of the association in this cohort . The decreasing evidence of association in theory could be due to an interaction with age , namely decreasing effect size with increasing age . Alternatively , a change in the environment could have diminished the strength of the association over time; this would be , in theory , consistent with a well documented “secular trend” of increased obesity over the relevant time period [21 , 22] . A preliminary and post hoc examination of the FHS data suggests that age may play an important role in modifying the strength of the association ( unpublished data ) . This hypothesis would also be consistent with stronger effects in controls matched for early-onset disease ( such as asthma ) than in controls matched for later-onset diseases ( such as diabetes ) . Finally , an additional post hoc analysis of the KORA S3 data suggests a stronger association in the most severely obese individuals ( OR for BMI ≥ 38 kg/m2 was 1 . 78 , 95% CI , = 0 . 99–3 . 21 , p = 0 . 054 ) , who perhaps became obese at an earlier age . Although these hypotheses are speculative at this time , they and other possibilities could and should be tested by a formal meta-analysis of our data , recent studies showing no association [14–16] , and additional data that are likely to emerge . We ( I . M . H . and colleagues ) are in the process of organizing a meta-analysis to reexamine the INSIG2 association in light of these hypotheses to better understand the relationship of this gene to obesity in the population . In summary , the association of SNP rs7566605 with higher BMI is found in diverse populations . The number of studies in which a nominal association has been observed ( five out of the nine cohorts reported here ) appears more frequently than expected by chance . However , a more precise assessment of this apparent excess of associations will depend on the availability of a complete set of studies of this polymorphism . Large sample sizes were required to observe the association , but even some large samples have not demonstrated an association with this allele , possibly due to modification by age or other issues related to ascertainment . A combined analysis of both positive and negative studies presented here suggests that the association is valid but also suggests the possibility of heterogeneity across populations . Additional data , both positive and negative , ideally from large samples with good information regarding potential confounders and in a format suitable for meta-analysis , would be required to confirm the existence of heterogeneity and to further refine the estimate of the effect of this SNP on BMI in different populations . However , the evidence to date suggests that this variant has a detectable influence on BMI in a diverse range of populations .
DNA samples were obtained from a large group of 5 , 187 Icelanders . The study group was composed of individuals who participated in studies of the genetic etiology of cardiovascular and metabolic diseases and the majority of these subjects were recruited as unaffected relatives of probands or as controls and did not have any history of metabolic or cardiovascular diseases . All participants in the study signed informed consent . All personal identifiers associated with tissue samples , clinical information , and genealogy were encrypted by the Icelandic Data Protection Authority , using a third-party encryption system in which the Data Protection Authority maintains the code [23] . Association testing was done according to that of the KORA S4 study design described in Herbert et al [1] . OR of genotype G1 ( C/C ) compared to genotype G0 ( G/C + G/G ) was calculated by [n ( G1 ) /m ( G1 ) ]/[n ( G0 ) /m ( G0 ) ] , where n and m denote genotype counts in obese and non-obese individuals , respectively . The genotyping procedure has been previously described [24] . Genotype call rate was 97 . 3% . p value and CI were adjusted for relatedness of the individuals using simulations as previously described [25] . In each simulation , genotypes for the SNP are simulated through the Icelandic genealogy and the association test repeated treating those genotypes as real genotypes . By repeating this procedure 50 , 000 times we get the standard deviation of log ( OR ) under the null hypothesis of no association , which is used to calculate both the p value and the CI . We regressed the log transformed values for BMI on C/C carrier status by adjusting for age and sex in the multiple regressions as shown in Table 2 . In the Southern German region of Augsburg , which includes the city of Augsburg and the two surrounding counties , population-based surveys of the 25–74-y-old population were implemented in 1984 as part of the World Health Organization's Multinational Monitoring of Trends and Determinants in Cardiovascular Disease [MONICA] ) project and continued since 1996 within the German Kooperative Gesundheitsforschung in the Region Augsburg ( KORA ) platform . The third survey , KORA S3 , which was the study used in our analysis , was conducted in 1994–1995 . Subjects ( 4 , 856 ) were recruited via registry according to the same protocol as the fourth survey ( KORA S4 ) performed in 1999–2001 , which was part of the initial replication samples in Herbert et al . The KORA surveys were described previously [22 , 26] . Genotyping was performed using a MALDI-TOF mass spectometry system ( MassEXTEND; Sequenom , http://www . sequenom . com ) and the call rate was 99 . 3% . DNA samples were obtained from 1 , 515 unrelated people from the offspring generation of the FHS [27] . We considered the possibility of overlap between the “unrelated plate” of the offspring cohort used here and with the family-based panel , approximately half of which was used in the analysis in the Herbert et al . report . There were 283 people who overlap between the “unrelated plate” and the full family-based panel , so these 283 people were excluded from the analyses reported here . The samples were genotyped using allele-specific primer extension of amplified products with detection by MALDI-TOF mass spectroscopy using a Sequenom platform as previously described [28–30] . Genotype call rate was 99 . 1% with no discordancies among replicate samples . Association testing was done with linear regression using BMI log transformed and adjusted for age and gender at all six exams . DNA samples were obtained from 874 unrelated people , self-described as African-Americans , from the same cohort as was described in the original association report [8] . Unrelated people were selected from this population for genotyping . In 270 families , the most obese sibling was chosen to enrich the sample for obese people in the case-control comparison . These were not included in the quantitative trait analysis as described below in Statistical Analysis . Samples were genotyped as previously described [8 , 29] . Genotype call rate was 97 . 9% with no discordancies among replicate samples . Association testing was done with linear regression modeling of using log BMI corrected for age and gender with genotype in a recessive and additive model . DNA samples were obtained from 1 , 381 adults from Marburg , of which 990 were obese cases ( BMI ≥ 30 kg/m2; mean BMI 36 . 02 ± 5 . 38 kg/m2 ) and 391 were lean controls ( BMI ≤ 20 kg/m2 , mean BMI 18 . 17 ± 1 . 00 kg/m2 [31] . Genotyping was carried out by PCR-RFLP with Bsp143I ( digests the C-allele ) ( primers: 5′-TGAAGTTGATCTAATGTTCTCTCTCC-3′ and 5′-AAACCAAGGGAATCGAGAGC-3′ ) . Association analysis under the recessive model , by χ2 testing . Nuclear families ( 415 ) of children with asthma in the Central Valley of Costa-Rica , a relative genetic isolate of predominantly Spanish and Amerindian ancestry [32 , 33] . Children and their families were enrolled as described previously [34] and anthropometric measurements of all probands included weight and height . However , this population was not ascertained based on morphometric phenotypes . Genotyping was performed using the Illumina BeadStation 500G system ( http://www . illumina . com ) . Genotyping completion rate was >99 . 8% with no discordances among replicate genotypes . Of the 415 families with genotypic data , 408 had complete phenotypic data and were included in the analysis . The Childhood Asthma Management Program ( CAMP ) is a multicentered North American clinical trial designed to investigate the long-term effects of inhaled antiinflammatory medications in children with mild to moderate asthma [35] . Children ages 5 through 12 were eligible for inclusion in the study if they had a diagnosis of asthma and no other clinically significant conditions . Height and weight measurements were collected on these children during the prerandomization period . Of the 1 , 041 children originally enrolled , 968 children and 1 , 518 parents contributed DNA samples for genetic studies . Complete nuclear families ( 408 ) of self-described non-Hispanic white race with baseline BMI measurements are included here . Genotyping was performed using the Sequenom genotyping platform . The unrelated sample consisted of individuals from the Botnia Study chosen as control subjects from two cohorts to study diabetes . The first group were controls from a Scandinavian sample of 471 case-control pairs individually matched for gender , age , BMI , and geographic region in Sweden and Finland . The second group were from a Swedish sample of 514 case-control pairs who were individually matched for gender , age and BMI . Subjects were characterized as unaffected for diabetes by glucose tolerance testing as previously described [29] . The family cohort was comprised of 512 unaffected siblings from a Scandinavian sample of 1 , 189 siblings with and without diabetes , as previously described [36 , 37] . The samples were genotyped using by an allele-specific primer extension of amplified products with detection by MALDI-TOF mass spectroscopy using a Sequenom platform as previously described [28 , 29] . Genotype call rate was 96 . 5% with one Mendel error in one family and no discordancies among replicate samples . The genotype data in each population was tested for deviation from Hardy-Weinberg and found to be consistent ( p value > 0 . 01 ) . Tests for association of rs7566605 with obesity were performed for the five population-based cohorts under a recessive model , classifying non-obese people as BMI < 30 kg/m2 and obese as BMI ≥ 30 kg/m2 . Significance was assessed using a χ2 test with one degree of freedom and two-tailed p values were reported . The Mantel-Haenszel method was used for the combined analysis , and testing for heterogeneity was performed using the Breslow-Day test , as described previously [7 , 19 , 20] . For the four samples that had population-based components , an association analysis was performed using BMI as a continuous trait , adjusting for age and gender . A second analysis of the FHS cohort was done to make use of longitudinal data collected across six exams , approximately 4 y apart spanning 26 years from 1971–1997 . For each exam , Z scores were calculated by the following process: within each decade of life and gender , log BMI was regressed against age . A Z score was then calculated for these age-adjusted BMIs based on the mean and standard deviation within each decade and gender for each exam . These were then analyzed using standard regression methods ( implemented in SAS ) for each exam individually , and also for the mean of all available Z scores across the six exams . For the KORA S3 , Maywood , and Scandinavian cohort analyses we used standard linear regression with log transformed BMI and adjusted for age and gender . The linear regression analysis in the Maywood cohort excluded 270 people , who had been selected as the most obese person in their family , to avoid possible bias . The Iceland analysis was done with log transformed BMI as a continuous trait under a recessive model , adjusting for age and sex in the multiple regression ( sex + age + sex × age ) . Association testing of rs7566605 in the family-based cohorts was performed using the FBAT-approach as implemented in PBAT [18 , 38] , with BMI as a quantitative ( continuous ) trait adjusted for age and gender by Z score under a recessive model . For the Costa Rica and CAMP populations , tests were also done for the outcome BMI adjusted for age and gender , and BMI-for-age percentile ( Centers for Disease Control and Prevention 2000 National Center of Health Statistics ) . Because these studies were similarly sized , a combined analysis was performed using Fisher's method for combining p values , in which twice the negative sum of the natural log of k one-tailed p values is distributed as a χ2 distribution with 2k degrees of freedom [39] . In this method , a one-tailed p value for an effect in the opposite direction is first corrected by subtracting the p value from one; as all the effects in our studies were in the same direction , this correction was not necessary .
The National Center for Biotechnology Information ( NCBI ) ( http://www . ncbi . nlm . nih . gov ) accession numbers for the gene and gene product discussed in this paper are INSIG2 ( NM_016133 ) and INSIG2 ( NP_057217 ) .
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Obesity is an epidemic in the United States of America and developing world , portending an epidemic of related diseases such as diabetes and heart disease . While diet and lifestyle contribute to obesity , half of the population variation in body mass index , a common measure of obesity , is determined by inherited factors . Many studies have reported that common sequence variants in genes are associated with an increased risk for obesity , yet most of these are not reproducible in other study cohorts , suggesting that some are false . Recently , Herbert et al . reported a slightly increased risk of obesity for people carrying two copies of the minor allele at a common variant near INSIG2 . We present our attempts to further evaluate this potential association with obesity in additional populations . We find evidence of increased risk of obesity for people carrying two copies of the minor allele in five out of nine cohorts tested , using both family- and population-based testing . We indicate possible reasons for the varied results , with the hope of encouraging a combined analysis across study cohorts to more precisely define the effect of this INSIG2 gene variant .
|
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"Abstract",
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"Materials",
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"homo",
"(human)",
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2007
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The Association of a SNP Upstream of INSIG2 with Body Mass Index is Reproduced in Several but Not All Cohorts
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To assess the effectiveness of community-wide deployment of insecticide–impregnated collars for dogs- the reservoir of Leishmania infantum–to reduce infantile clinical visceral leishmaniasis ( VL ) . A pair matched–cluster randomised controlled trial involving 40 collared and 40 uncollared control villages ( 161 [95% C . L . s: 136 , 187] children per cluster ) , was designed to detect a 55% reduction in 48 month confirmed VL case incidence . The intervention study was designed by the authors , but implemented by the Leishmaniasis Control Program in NW Iran , from 2002 to 2006 . The collars provided 50% ( 95% C . I . 17·8%–70·0% ) protection against infantile VL incidence ( 0·95/1000/yr compared to 1·75/1000/yr ) . Reductions in incidence were observed across 76% ( 22/29 ) of collared villages compared to pair–matched control villages , with 31 fewer cases by the end of the trial period . In 11 paired villages , no further cases were recorded post–intervention , whereas in 7 collared villages there were 9 new clinical cases relative to controls . Over the trial period , 6 , 835 collars were fitted at the beginning of the 4 month sand fly season , of which 6 . 9% ( 95% C . I . 6 . 25% , 7 . 56% ) were lost but rapidly replaced . Collar coverage ( percent dogs collared ) per village varied between 66% and 100% , with a mean annual coverage of 87% ( 95% C . I . 84·2 , 89·0% ) . The variation in post-intervention clinical VL incidence was not associated with collar coverage , dog population size , implementation logistics , dog owner compliance , or other demographic variables tested . Larger reductions and greater persistence in incident case numbers ( indicative of transmission ) were observed in villages with higher pre-existing VL case incidence . Community–wide deployment of collars can provide a significant level of protection against infantile clinical VL , achieved in this study by the local VL Control Program , demonstrating attributes desirable of a sustainable public health program . The effectiveness is not dissimilar to the community-level protection provided against human and canine infection with L . infantum .
Visceral leishmaniasis ( VL ) is a protozoan vector–borne parasitic disease of humans following infection with Leishmania donovani or L . infantum , characterized by prolonged fever , wasting , splenomegaly , and hepatomegaly , and >95% case–fatality in the absence of treatment[1] . Leishmania are transmitted by female phlebotomine sand flies . Leishmaniasis is a Neglected Tropical Disease ( NTD ) strongly associated with poverty and malnutrition[2] , resulting in a global incidence of 50 , 000 to 90 , 000 new VL cases per year[1] . Difficulties in reducing VL case burdens arise due to the current lack of a human vaccine , limited safe therapeutic drugs , need for improved vector control , and a better understanding of transmission dynamics[3–5] . VL due to L . donovani is anthroponotically transmitted , whereas VL due to L . infantum is a zoonosis involving infectious domestic dog reservoirs[6] , and uncertain role of humans[7] , in maintaining transmission[8] . Otherwise traditionally known as “infantile VL” , zoonotic VL is a disease mainly of young children[9–14] , although case age-distributions may vary e . g . [15 , 16] . Regional VL control programs focus on human case detection and treatment , adult sand fly vector control , and for dogs , optional strategies including canine vaccination , topical insecticide protection , chemotherapeutic treatment , or euthanasia[17–19] . Anthroponotic VL has been targeted for elimination as a public health problem ( <1 case/10 , 000 people per year at district levels ) in the Indian subcontinent by 2020 , with substantial investment , technical and political support[3 , 5 , 20] . This has contributed to significant reductions in incidence between 2012 and 2017 [1 , 12 , 21] . In contrast , investment to reduce zoonotic VL in endemic Latin America , central Asia and Caucasia , are minimal by comparison; no such reductions in incidence are observed notably in the Americas where >90% of cases occur [22 , 23] . Vector and animal reservoir control are necessary to reduce zoonotic VL incidence as treatment of human clinical cases , though necessary , is unlikely to impact on the transmission cycle . Insecticides topically applied to dogs as slow release insecticide–impregnated collars have been extensively tested in different regions , showing that they are effective against sand fly vectors[24–28] , and reduce infection risk in dogs[29–37] . Evidence that collars also can reduce human infection incidence is limited to a single cluster randomized trial conducted in NW Iran[31] . However , there are no peer-reviewed studies to test the impact of this approach on human clinical VL disease . In collaboration with the regional VL control program authorities in NW Iran , and with access to VL case records provided by the Ministry of Health ( MoH ) , we had the opportunity to evaluate the impact of community–wide distribution of collars against clinical VL incidence , and as conducted under operational conditions . The principal aims of the study were ( i ) to measure the efficacy of the collar intervention against VL case incidence; ( ii ) to evaluate the operational logistics of collar implementation; and ( iii ) to assess likely causes for the variation in intervention effectiveness .
The study was conducted in the rural communities of the Kalaybar and Ahar administrative districts of East Azerbaijan province , NW Iran ( 38688131N; 4321696E ) from 2002 to 2006 . Villages were located at altitudes of 369-1305m ( Table 1 ) . The main economic activity in the area is agriculture , cash crops and particularly sheep farming . Dogs are kept as shepherd dogs or household guard dogs , and small numbers of livestock ( sheep , goats , cattle , chickens ) are kept in shelters variably near or attached to houses . Houses are constructed of plastered or unplastered stone , cement , or brick , and rooves constructed of cement , thatched or corrugated iron . Dried manure is generally stocked in piles away from houses as a source of fertilizer and fuel . Temperatures ranged from 1°C ( in January ) to 23°C ( in July/August ) , and rainfall from 18mm ( in August ) to 76mm ( in May ) . In the region , 100–150 new VL cases were reported annually between 1998 and 2005 , representing 45% of the total VL cases in Iran , the vast majority being children <10yrs of age[9–11 , 38] . At the time of the study , pediatric L . infantum infection incidence measured by Direct Agglutination Test ( DAT ) seroconversion was 2·4% compared to 1 . 9% by Leishmanin skin test ( LST ) reaction [31] . Canine DAT seroconversion incidence was 7%[31] , with reported regional seroprevalences of 11%-22% [31 , 38 , 39] . In the same locations , the canine to human seroprevalence ratio was 1 . 4: 1 ( 11% vs 8% ) [31] compared to 3 . 1:1 ( 22% vs 7% ) six years earlier in 1995[39] . Transmission is predominantly peridomestic , with human exposure being independent of age and sex , and associated with endophilic Phlebotomus sand fly vectors and infected dogs[39–43] . The risk of childhood Leishmania seropositivity is associated with dog ownership , village dog density ( 28/km2 , 95% C . I . : 23 . 6–32 . 1 ) , and the dog to human ratio[39] . Two species of sand fly , Phlebotomus ( Larroussius ) perfiliewi transcaucasicus and Ph . ( L . ) kandelakii , are likely vectors in this region , being seasonally active for 4 months ( late June to October ) [41 , 44] . The VL control program hinges on the District Ministry of Public Health ( DTARH ) which is responsible for the District Health Centers ( DHCs ) in Kalaybar and Ahar . These centers coordinate activities of 12 provincial Rural Health Centers ( RHCs ) , which are run by trained medical staff and health officers . Each RHC supervises about ten village health posts ( khaneh behdasht ) where resident Health Promoters ( behvarz ) are responsible for disease surveillance , control implementation , and facilitate suspect VL cases attendance at the RHCs . The RHCs provide free VL diagnostic testing and treatment services , and refer those needing more specialist hospitalization to the DHCs or district hospital . Clinical VL is a notifiable disease . The intervention effect was computed using random–effects Poisson regression to test differences in the cumulative numbers of incident VL cases per cluster ( = village ) , expressed as an incidence risk ratio ( IRR ) . The model included variables describing the trial design: the attributed year of infection , the log10 transformed childhood baseline village incidence , and the pair–matching structure ( as a cluster term ) . The number of children ≤10yrs per village , being the well documented high risk group , was set as the model offset parameter . The variance in cluster ratios of children to total population size were similar between treatment arms ( Table 1 ) . Additional demographic variables , listed in Table 1 , were then individually tested by log–likelihood ratio test [LRT] of nested models , as potential modifiers of the unadjusted intervention effect estimates . A secondary analysis following[45] tested the normalized residuals of the observed /expected case ratios for pair-matched clusters ( Supplementary S1 ) , where the expected number of cases per cluster were generated first by fitting the Poisson model , simultaneously adjusting for significant covariates as described above , but excluding the intervention term . Then , normalized ( square root transformed ) residuals of the observed /expected case ratios were tested by Student’s paired t-test where the results of each cluster are given equal weighting[45] . Differences in these ratios were also confirmed by applying a non–parametric two–sided weighted signed rank test . The relationship between post-intervention VL incidence differences between pair-matched villages , and pre-intervention incidence , was examined by linear regression ( illustrated in Fig 4 ) . The relationships between village incidence pre–and post–intervention was evaluated by negative binomial regression ( nbreg ) , and by Spearman’s rank correlation ( illustrated in Fig 5 ) . Data were analysed in Stata v . 15 . 1 ( StataCorp LP , College Station , TX ) . The trial protocol was approved by the Regional Committee of Medical Ethics , Tabriz University of Medical Sciences , Iran . The University of Warwick’s Biomedical and Scientific Research Ethics Committee ( BSREC ) confirmed that the trial raised no significant ethical concerns due to its use of secondary anonymised human case data . There was no local animal ethical committee at the time of the study . Informed written consent was obtained from village leaders , and from dog owners to fit collars , and dogs were monitored on a regular basis by village Health Promotors for any adverse reactions to the collars .
The median age of the 70 reported clinical cases during the trial period was 1 . 6yrs ( range: 0 . 5–5yrs ) , with M:F sex ratios of 25:21 and 12:12 in control and collar arms , respectively . There were no significant differences in case age distributions between trial arms ( Poisson regression: z = -0·97 , P = 0·331 ) , nor between intervention years ( non–parametric test for trend: z = -1·54 , p = 0·124 ) . Over the trial period , 6 , 835 dogs were fitted with collars before the beginning of the sand fly season ( 1 , 682 , 1 , 722 , 1 , 689 , and 1 , 742 dogs annually in the 4 years ) . Of these , 6 . 9% ( 95% C . I . 6 . 25% , 7 . 56% ) were lost per year , respectively 7·3% ( 123/1 , 682 ) , 6 . 4% ( 110/1722 ) , 6 . 9% ( 117/1689 ) and 7 . 0% ( 122/1742 ) . The mean annual coverage ( percent of total dogs collared ) was 87% ( 95% C . I . 84·2 , 89·0% ) being consistent across years ( Kruskal–Wallis test: χ2 ( 3 ) = 0·260 , p = 0·967 ) , although it varied significantly between villages ( range: 65·7%–100% ) ( Kruskal–Wallis test: χ2 ( 39 ) = 0·122 , p = 0·0001 ) ( Fig 6 ) . Coverage in any single village and year was not associated with the village dog population size ( z = 1·14 , p = 0·26 ) . The variation in post-intervention VL incidence between pair-matched villages were not associated with the mean or variance in village collar coverage ( t<0 . 67 , p>0 . 51 , r2 = 0 . 008 ) ; similar coverage was observed in villages with no incident VL cases , as those with >0 incident VL cases ( Fig 6 ) .
A significant finding of this study was that the intervention effect was achieved under the operational conditions as implemented by the DTARH VL control program . After ensuring that the design and village randomisation processes met CRT analytical requirements , the authors had minimal contact with authorities . The authors supplied known numbers of collars to the DHCs who distributed them to the villages , and collated details of the collar implementation . This provided the opportunity to measure the logistics and likely sustainability of programmatic scale–up . Collars were fitted within only 15 days of village supply , and collar losses were rapidly replaced , suggesting coverage levels were maintained during the transmission seasons . Neither coverage level nor time to complete collar fitting were related to the total number of dogs eligible for a collar , concluding that lower village coverage levels were not due to time constraints . Feasibility of a regional–level scale–up was indicated by DTARH taking only 16 days to deliver collars to all 40 villages spread over an area of 15 , 000km2 , and total collars being fitted within one month from collar provision to the first village . Collars were socially accepted , visibly distinguished treated dogs , and should not require replacement every 3–4 weeks , unlike alternative topical formulations . Coupled with the short transmission season , effective collar duration , and rapid replacement rate , the feasibility of effective sustainable scale–up is indicated . Collar losses in this region of between 1% per month[31] and 1 . 7% ( 95% CI: 1 . 6% , 1 . 9% ) per month ( this study ) , were not substantially higher than in other endemic regions: 0·8% in Brazil[37] and 0·6–0·7% in Italy[34 , 54] , though exceptional rates , 7·8% and 8·2% , in both continents have been reported[32 , 33] . Sustainability will be governed also by costs and cost–effectiveness . Considering the cost of collars alone , in this study , collars were donated by the manufacturer . However , based on €14 per collar , the annual material cost would have equated to approximately €24 , 000 , or €96 , 000 over 4 years . That is €4 , 364 per VL case averted compared to case numbers in the control arm . This value is for a period with 70% relative decline in reported case numbers from the late 1990s to 2014[9] . Better cost–effectiveness may be expected as the cyclical waning in case numbers inevitably reverses in non–intervention regions . One key knowledge gap is how best to implement dog collar programs: currently unknown is the minimum community coverage level necessary to reduce transmission , equivalent to vaccination coverage thresholds calculated to maintain herd immunity[55] . Mathematical models suggest that increasing collar coverage from 70% to 90% could cause moderate to substantial reductions in both canine and human infection prevalences[32 , 56 , 57] . However , three empirical studies that report coverage of 70%–100% failed to corroborate these predictions[31 , 34 , 37] . Without known target thresholds , collar campaigns may result only in protection of the individual dog or its household . Of relevance to NTD elimination strategies , our data suggest that greater initial reductions in incidence may be achieved in communities with higher compared to lower preexisting transmission pressure . One prediction is that by reducing transmission , infection in the lower-prevalence settings becomes more clustered , thus more stable and more resilient to interventions[58] . Approaching elimination will require different control strategies to those used in the initial attack phase . For example , heterogeneities in household sand fly densities , and the transmission potential of individual vectors and reservoirs[53 , 59 , 60] , imply the need for high coverage levels to encounter such transmission hotspots . In conclusion , the results of this study promote the community-wide application of insecticide–impregnated collars as a public health tool to reduce clinical VL burdens , not just human and canine infection . The observed level of impact was achieved working within the existing MoH infrastructure , demonstrating many desirable attributes of a potentially sustainable control program that could be scaled–up with minimal additional technical capacity training .
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Zoonotic visceral leishmaniasis is a sand fly-borne disease of humans and dogs caused by the intracellular parasite Leishmania infantum . Dogs are the proven reservoir . The disease is of global health significance , and usually fatal unless treated . There are limited options to reduce transmission . Insecticide-treated dog collars have been shown to protect dogs against infectious bites from sand fly vectors , resulting in reductions of new infections in both dogs and humans . However , there have been no studies to demonstrate the public health benefits of this approach i . e . the impact on clinical VL incidence . This study assessed the effectiveness of community-wide deployment of insecticide–impregnated collars on dogs to reduce the incidence of clinical visceral leishmaniasis in children , the high risk age-group . Collars were fitted to dogs in 40 endemic villages over 4 consecutive years by the regional public health authorities in NW Iran . The case incidence of infantile visceral leishmaniasis in these villages was compared to that in 40 untreated villages at the end of the intervention period . The community-wide deployment of collars proved to provide a 50% reduction in the development of the disease in children . This effect was achieved under the operational conditions of the regional routine health authorities . We conclude that the implementation of insecticide-impregnated collars should be considered in strategic scale-up operations against zoonotic visceral leishmaniasis .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
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2019
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Insecticide–impregnated dog collars reduce infantile clinical visceral leishmaniasis under operational conditions in NW Iran: A community–wide cluster randomised trial
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During development of the vertebrate body axis , Hox genes are transcribed sequentially , in both time and space , following their relative positions within their genomic clusters . Analyses of animal genomes support the idea that Hox gene clustering is essential for coordinating the various times of gene activations . However , the eventual collinear ordering of the gene specific transcript domains in space does not always require genomic clustering . We analyzed these complex regulatory relationships by using mutant alleles at the mouse HoxD locus , including one that splits the cluster into two pieces . We show that both positive and negative regulatory influences , located on either side of the cluster , control an early phase of collinear expression in the trunk . Interestingly , this early phase does not systematically impact upon the subsequent expression patterns along the main body axis , indicating that the mechanism underlying temporal collinearity is distinct from those acting during the second phase . We discuss the potential functions and evolutionary origins of these mechanisms , as well as their relationship with similar processes at work during limb development .
Hox genes play essential roles in patterning during the development of metazoans . In many species , they are found clustered in the genome , such as in vertebrates , which contain four Hox gene clusters ( HoxA to HoxD ) , due to the additional two rounds of genome amplification that accompanied their emergence from early chordates . These genes are required to confer regional identities along the rostral to caudal body axis , a task that mostly depends upon particular combinations of HOX proteins found at a given anterior-posterior level , since genes of all four clusters are expressed in largely overlapping domains [1] , [2] . In mouse , combined mutations produce drastic effects on the specification of extended body regions , as exemplified by the inactivation of genes belonging to the paralogy group 10 , which triggered the appearance of ectopic ribs along the lumbar and sacral regions [3] . Therefore , a precise spatial distribution of these transcription factors must be orchestrated so as to ensure proper specification . These regionalized expression domains are in part controlled at a transcriptional level , by using an intrinsic property of the gene clusters , conserved from insects to vertebrates and referred to as spatial collinearity [4]–[7]: the order of genes along the chromosome correlates with their successive anterior limits of expression along the body axis . Vertebrates display yet another type of collinearity whereby the relative timing of Hox gene activation during development follows the gene sequence , such that genes lying at one extremity of a cluster are activated earlier and more rostrally than genes located near the other extremity [8] , [9] . Murine Hoxd genes thus become activated in the most posterior part of the embryo between late embryonic day 7 . 75 ( E7 . 75 ) for Hoxd1 and early E9 for Hoxd13 . This temporal progression was proposed to be a molecular clock ( the ‘Hox clock’ ) controlling the proper timing of axial specification by coordinating the rostral-caudal positions of the various expression boundaries [10] . While this view has found some support in studies of early limb patterning , where a strong correlation exists between the onset of Hox gene expression in the incipient limb bud and the extent of expression along the anterior to posterior axis [11] , the situation in the developing major body axis appeared more complex . First , it was noticed early on [12] , [13] that Hox transgenes could be expressed with rather faithful anterior boundaries , yet not necessarily with the exact expression timing . Secondly , targeted Hox cluster modifications in vivo , which changed the timing of activation , induced patterning problems even without modification of the late expression boundaries [14] . Finally , spatial collinearity is still observed , to some extent , in animals where Hox genes are not clustered such as the larvacean Oikopleura [15] . Altogether , these observations suggest that , while gene clustering may be an absolute requirement for implementing the temporal sequence of activation ( see [10] , [16] , [17] ) , important aspects of spatial regulation do not require tight clustering . So far , the relationships between the time of Hox gene activation and their expression territories have been best documented in developing limbs ( e . g . [11] , [18] ) , i . e . in structures which do not obligatorily implement the same regulatory mechanisms than those at work in the developing trunk , to activate this gene family ( see [19] , [20] ) . In this work , we assess the importance of genomic clustering for the temporal and spatial collinear regulations of Hox genes during the development of the major body axis . We use mutant mice where the HoxD cluster is split into two independent sub-clusters , as well as a collection of deletion and duplication alleles . We show that temporal activation relies upon a balance between a repressive activity , mediated via the centromeric neighborhood of the cluster , and an activating effect mediated by the telomeric region . Remarkably , however , modifications in this early time sequence are not systematically translated into concurrent alterations in the subsequent spatial distribution of transcripts , which mostly depends upon local , interspersed regulatory elements . Consequently , temporal and spatial collinear controls appear to be mechanistically uncoupled .
We evaluated whether the integrity of a Hox gene cluster is essential for temporal collinearity during early trunk development , by using a targeted inversion that splits the HoxD cluster into two smaller , independent gene clusters [21] . One of the inversion breakpoints was located between Hoxd10 and Hoxd11 , and the other at the Itga6 ( integrin alpha 6 ) locus , about 3 megabases ( Mb ) centromeric to HoxD . The inversion separates the most ‘posterior’ part of the cluster ( Hoxd11 , Hoxd12 and Hoxd13 ) along with the adjacent 5′ region , from the rest ( Hoxd10 to Hoxd1 ) , thus allowing to evaluate the importance of regulatory influences associated with either the telomeric ( Figure 1A , yellow ) or the centromeric ( Figure 1A , purple ) neighborhoods of the cluster . We first looked at the early expression of Hoxd11 and Hoxd10 , those genes immediately flanking the breakpoint . At E9 , Hoxd11 is normally transcribed in the most posterior aspect of the embryo , around the remnants of the primitive streak , as well as in adjacent mesoderm [22] . In situ hybridizations on mutant embryos carrying only an inverted cluster showed no detectable Hoxd11 transcripts at this stage ( Figure 1B ) . We examined progressively later developmental stages and , until 12 . 5 days , saw no transcription of Hoxd11 in the trunk of mutant embryos ( Figure 1B , lower panel ) . This effect was certainly more dramatic than the delay observed upon the loss of region VIII alone , a small DNA region that is deleted in one of the parental strains used for the inversion [14] , [21] . Hoxd11 , however , was expectedly expressed in the distal limb domain and the genital bud . These two domains were previously shown to depend upon late acting , global regulatory sequences lying centromeric to the cluster that kept the same relative position with Hoxd11 in the inverted configuration [23] , [24] . We then looked at both Hoxd12 and Hoxd13 and dramatic reductions in mRNA levels were scored ( Figure 1C and D ) , suggesting that a long-range enhancer sequence , located on the telomeric side of the gene cluster , was required for the activation of these posterior Hoxd genes in the major body axis . Consequently , animals homozygous for the inversion lacked the functions of the three most posterior genes and expectedly displayed an anterior transformations of the sacral region ( Figure S1A , B ) , thereby phenocopying the combined loss of function mutations of these three genes in cis [25] , [26] . In contrast , and consistent with the observed gene expression in the developing distal limb , digits remained unchanged in this inversion . Interestingly , however , this down-regulation of posterior Hoxd gene transcription could not be entirely explained by moving genes away from a potential activating sequence , for transgenic analyses of both the Hoxd11 and Hoxd12 loci had identified local cis-acting elements capable to elicit expression in the trunk when integrated randomly in the genome [27] , [28] . These elements are present in the sub-cluster containing Hoxd13 , Hoxd12 and Hoxd11 and their inability to function in the context of a split cluster thus suggested a negative effect exerted by the centromeric neighborhood over these transcription units . The analysis of Hoxd10 and Hoxd9 expression in the same mutant stock , at early stages , showed premature or elevated expression , respectively , consistent with these genes escaping such a repressive effect , due to their presence within the other sub-cluster , i . e . three Mb further apart ( Figure 1E , F ) . Although this up-regulation was only transient , some mutant animals displayed clear skeletal abnormalities located at body levels much more anterior than the late expression boundaries of the corresponding genes ( Figure S1C , D ) . The appearance of similar abnormal phenotypes after a transient gain of function was previously observed for the same gene , yet in a different genetic context [29] . Altogether , these data suggested the existence of a regulatory balance between a positive regulation , located telomeric to the cluster , and a repression , coming from the centromeric side , both acting on several genes and at a distance , to properly activate the HoxD cluster in the developing trunk . We challenged this view by looking at the timing of activation , in vivo , of a Hoxd11/lacZ reporter transgene positioned at various places along the gene cluster via successive loxP-dependent deletions ( Figure 2 ) . Following the above-mentioned hypothesis , the repressive effect per se exerted on the transgene should not be modified in such configurations , since only the relative distance to the activating sequence is progressively reduced . When placed within the Evx2-Hoxd13 intergenic region , the transgene did not produce any signal at an early stage ( Figure 2A , upper panel ) . Likewise , when a small deletion brought the transgene at the position of Hoxd11 , no signal was scored ( Figure 2B , upper panel ) . However , lacZ activity was detected whenever the transgene was placed further towards the telomeric extremity of the cluster , ( Figure 2C and D , upper panels ) , well before the expected transcriptional onset for Hoxd11 under normal conditions . Because the largest deletion had removed the entire cluster , leaving behind the Hoxd11/LacZ reporter transgene only , we concluded that at least part of the activation mechanism was located outside the complex ( Figure 2D ) . Subsequently , however , all transgene relocations allowed for robust expression ( Figure 2 , lower panels ) , showing that the lack of early transcription was not caused by an inability to activate the transgene in a given context . Rather , it reflected a delay in the activation process . We next looked at the impact of various deletions upon the activation timing of endogenous Hoxd genes located 5′ to the breakpoints , i . e . genes brought closer to the telomeric end of the cluster . In E8 to E9 embryos , a developmental window during which the most posterior Hoxd genes are normally silent , we systematically detected their premature transcription in the deleted configurations ( Figure 3A–I ) . For example , any deletion which would bring Hoxd13 closer to the 3′ end of the cluster led to its premature activation , regardless whether it was next to the breakpoint ( Figure 3A , B ) or further apart ( Figure 3C , D ) . Similar effects were observed for Hoxd11 ( Figure 3E–H ) and for Hoxd10 ( Figure 3I ) . We then used two alleles carrying internal duplications and looked at the expression timing of those genes lying centromeric to the duplicated DNA segments . Three genes placed in such relative positions were analyzed and displayed a distinct delay in their transcriptional activation ( Figure 3J–L ) . For example , the cis-duplication of the Hoxd8 to Hoxd10 DNA segment postponed activation of both Hoxd11 ( Figure 3K ) and Hoxd13 ( Figure 3J ) . Here again , as for premature activations , several adjacent genes responded in a coherent manner to this regulatory re-allocation , suggesting the existence of a global , rather than local , mechanism of activation . Altogether , the relative position of a Hox gene within the HoxD cluster seems to largely determine its transcriptional timing in the primary body axis; the closer to the telomeric extremity , the earlier a gene was expressed in the developing trunk . To assess the relationships between the time of gene activation and the subsequent distribution of transcript in space , we re-visited the dynamics of Hoxd expression territories along the major body axis . The first transcripts were scored at the basis of the allantois , at the most posterior aspect of the gastrulating embryo ( e . g . Figure 3A ) . Soon after , transcripts appeared in various mesoderm derivatives and in the neural plate , in a precise sequence that was best determined for Hoxd10 to Hoxd13 . In mesoderm , transcripts were first detected as two distinct lateral lines , matching the lateral plate mesoderm , rather than in PSM or in the neural plate . Positive cells were found from about the level of the joining of the splanchnopleural and somatopleural layers of the lateral plate mesoderm ( Figure 4; arrowheads ) , slightly ventral to the intermediate ( nephric ) mesoderm whenever the section was rostral enough to identify this latter structure ( not shown ) . Subsequently , however , expression of these posterior Hoxd genes was clearly observed within paraxial mesoderm , still in the presomitic areas , as well as in the adjacent spinal cord ( Figure 4 ) . We investigated whether this generic progression in gene activation was conserved when the timing of activation was changed or , alternatively , if the mutant genomic context would modify tissue specificity along with the time variation . The general tendency is exemplified by the case of Del ( 8-10 ) , where premature activation of Hoxd11 was detected in the mesoderm of the body wall , yet not in the most dorsal cells ( Figure 4 ) . As for the wild type situation ( but here in younger embryos ) , mesodermal expression was initially scored ventral to the pre-somitic mesoderm , whereas no transcripts were detected in neuro-epithelial cells . Subsequently , when Hoxd11 appeared in the wild type embryo ( Figure 4E ) , the mutant embryo , at a similar body level , was already positive for these transcripts in lateral plate mesoderm , in pre-somitic mesoderm as well as in the closing neural tube ( Figure 4F ) . We concluded that premature Hoxd gene activation along the major body axis did not induce indiscriminate ectopic gene expression . Instead , premature activations followed the expected sequence in the detection of signals , within the various embryonic layers . In marked contrast , no coherent impact on transcript distribution could be scored in our mutants , when analyzed at later stages . For example , the expression of both Hoxd9 and Hoxd11 was largely anteriorized , whenever the adjacent DNA was deleted up to the Hoxd4 locus ( Figure 5B , F ) . This was usually not the case for those genes located at more centromeric positions: while Hoxd9 was clearly anteriorized in the Del ( i-8 ) when placed near Hoxd4 ( Figure 5B ) , Hoxd10 showed a wild type expression pattern in the same deletion ( Figure 5C and data not shown ) , indicating that whatever the nature of the underlying mechanism is , it may act locally rather than at a global level . Two deletions sharing the same telomeric breakpoint confirmed this observation: firstly , Hoxd11 was expressed too anteriorly in Del ( i-10 ) mutant embryos , the ectopic domains recapitulating Hoxd4 specific domains ( Figure 5F ) . Secondly , a shorter deletion leaving in place a gene-free DNA fragment ( Del ( 8-10 ) ) did not elicit the same response , even though the relative position of Hoxd11 towards the telomeric part the cluster was as in the Del ( i-10 ) allele ( Figure 5E ) . In this case , the intergenic DNA fragment located between Hoxd8 and Hoxd4 , present in Del ( 8-10 ) but removed from Del ( i-10 ) , likely isolated Hoxd11 from enhancers located around Hoxd4 . Interestingly , these two deleted alleles displayed similar timing of premature activations ( see Figure 3E and F ) . Therefore , while the effect of changing a gene's position upon its timing of activation was highly predictable , its subsequent spatial expression domain was impossible to anticipate . This observation was echoed by other alleles where neighboring gene expression was drastically reduced , if not abrogated . For example , the combined deletion of Hoxd9 to Hoxd12 led to the disappearance of Hoxd13 expression in the tail and tailbud ( Figure 5; compare G to I ) . However , when the extent of the deletion was slightly decreased , some expression was recovered ( Figure 5H ) . More importantly , no anterior gain of expression was scored for either configuration , despite premature activation at earlier stages ( compare to Figure 3A , B ) . Altogether , these spatial reallocations of transcript domains could be best explained by local , context-dependent modifications due to the effects of various breakpoints upon nearby-located enhancer sequences , rather than as direct consequences of the modified timing of activation . We also analyzed the expression dynamics of genes lying telomeric of various breakpoints in our deleted stocks . After deletions , these genes occupied relative positions closer to the ‘repressive influence’ emanating from the centromeric neighborhood , whereas their positions with regard to the telomeric side of the cluster remained unchanged . In E8 to E9 . 5 embryos , genes brought closer to the centromeric extremity via a deletion were consistently down-regulated , as exemplified by Hoxd3 , Hoxd4 and Hoxd9 ( Figure 6A–H ) . The same effect was scored for genes lying further away from the breakpoint , such as Hoxd3 in the Del ( i-10 ) and Del ( 8-9 ) ( Figure 6B , C ) . Repression from the centromeric side contributed to this phenomenon , as transgenic approaches could exclude the deletion of distant promoters as the sole causative factor . Such transgenic analyses have defined local regulatory elements , as well as promoters , driving spatially correct expression for Hoxd4 [30] , [31] . Although these remained undisturbed , a clear down-regulation of Hoxd4 was noticed ( Figure 6A ) . Likewise , the observed weakening in Hoxd9 transcription ( Figure 6H , L ) recalled an earlier observation whereby a Hoxd9/LacZ transgene was down-regulated when transposed into the Evx2 to Hoxd13 intergenic region [32] . Although we observed a clear posteriorization for Hoxd4 in the mesoderm at later stages that became more pronounced the closer the gene was brought to the centromeric extremity ( Figure 6I , J; arrowheads ) , other genes retained their anterior-posterior expression boundaries , yet changing their level of expression: in Del ( 9-4 ) mutant embryos , Hoxd3 showed a slight but consistent increase of expression ( Figure 6K ) , whereas a deletion sharing the same 3′ breakpoint ( Del ( 11-4 ) ) induced a decrease for the same gene ( data not shown ) . Also , Hoxd9 was down-regulated in the trunk when moved next to Hoxd13 ( Figure 6L ) . Expression analysis of genes located in 3′ of the breakpoints at these later stages thus did not reveal any coherent tendency . Rather , the diversity of the observed modifications pointed to independent , local regulatory reallocations , similar to what happened to Hoxd genes lying in 5′ of the respective breakpoints . Therefore , gene position with respect to either the centromeric , or telomeric extremities of the Hoxd gene cluster did not substantially affect spatial collinearity , in contrast to our observations regarding temporal collinearity .
Ever since collinearity was reported in vertebrates , pointing to a functional conservation between the way arthropods and vertebrates organize their body plans [4] , [6] , both the underlying molecular mechanisms and the nature of the associated evolutionary constraints have been discussed ( see [16] , [17] , [33] ) . Differences in developmental strategies between diptera and vertebrates made it unlikely that the same genetic cascade would act upstream the Hox gene family . In search for an alternative mechanism , the observation of temporal collinearity , in vertebrates , suggested the timing of Hox gene activation as an important parameter in establishing the positions of the future transcript domains . However , while vertebrate Hox genes need to be clustered to properly achieve temporal control , clustering is not essential in all cases where spatial collinearity is observed ( e . g . [15] ) . Here , we further challenged the causal link between temporal and spatial collinearities during trunk elongation in the mouse and we conclude that the final collinear distribution of Hoxd gene expression domains along the developing body axis is not strictly the function of their timing of activation during early development . Our approach reveals a correspondence between the location of a gene relative to both extremities of the cluster and its timing of transcription , whereby proximity to the telomeric end is translated into precocity of activation . Accordingly , the onset of gene activation is likely controlled by a timing mechanism originating in the telomeric neighborhood of the HoxD cluster . Since this early mechanism seems to be shared by developing limb buds [11] , we confirm the suggestion that it was co-opted from the trunk to tetrapod limb . However , unlike in developing limbs , we failed to see a coherent impact of our engineered heterochronies on the spatial distribution of transcripts along the anterior-posterior axis at later stages . At these stages , transcript distributions mostly depend upon local regulations , interspersed within the gene cluster , in marked contrast with the early events observed by using the same mutant strains , implying that different mechanisms exist for the early temporal and late spatial collinear processes in the trunk ( Figure 7 ) . These mechanistic differences support the existence of at least two distinct phases in the activation of Hox genes during axial development [8]; first a time-sequenced activation along the primitive streak and the node , controlled by globally acting opposite regulatory influences , followed by a second wave of activation controlled by local cues in tissues derived from these cells such as the various mesoderm derivatives and the neurectoderm . A biphasic activation [34] , [35] could also explain why some early defects associated with temporal perturbations were transient and not carried along to later stages of development [32] , as they only affect the early phase . The mechanism involved in the late phase of activation may involve local effects such as enhancer sharing and/or competition [36] , which could be easily disturbed in our genetic configurations leading to unpredictable outcomes . Regarding the early temporal activation , while global regulatory influences may rely upon remote enhancer sequences ( e . g . [24] ) , they could as well involve , or be combined with- , processes such as chromatin modifications or chromosome looping [37] . For instance , the premature activations described in our set of deletions might reflect the successive removal of sequences , which evolved within the cluster to secure proper repression . While we do not rule out such a possibility , we think it can hardly account for some previously published results . In particular , a full inversion of the HoxD cluster lead to the premature activation of the inverted ‘posterior’ genes , even though , internally , the gene cluster remained untouched [38] . The respective functional contribution of each phase of activation to the primary body axis is unclear . In the mouse embryo , while the necessity to establish correct expression boundaries has been largely documented through various genetic approaches , the function of the early temporal sequence of activation is less explicit . Because this temporal process has been thus far associated only with animals where ( an ) integral Hox gene cluster ( s ) is ( are ) present , it may be one of the major constraints that kept Hox genes together . The analyses of additional animal species will be informative in this respect . Both instructive and restrictive contexts can be considered ( non-exclusively ) when looking for the ‘raison d'être’ of temporal collinearity . In the former , a need exists for a precise time-sequence in the transcriptional activation of these genes and important direct functional outputs of this process may occur , perhaps at a time and in a cellular population that have so far escaped our analyses . An example of such an early mechanism is the observed delay in ingression , during gastrulation , of epiblast cells containing abnormal combinations of HOX proteins [39] . Alternatively , temporal collinearity simply illustrates the necessity , for the developing embryo , not to activate the most posterior Hox gene ( s ) too early , a situation detrimental to embryonic development . This is suggested both by the early lethality associated with the inversion of the complete HoxD cluster , Hoxd13 becoming activated at the expected time for Hoxd1 [38] , and by the premature expression of Hoxd10 and Hoxd9 in the split cluster ( this work ) . Whichever mechanism evolved to prevent the most posterior gene ( s ) to be expressed too early may have incidentally generated a graded timescale for those genes located in between and hence this series of genes is transcribed following their genomic order , without any particular functional relevance in itself . The question as to which type of collinearity evolved first , i . e . whether the time-sequence preceded the spatial organization of the expression domains , or vice versa [40] , [41] is concerned with the segmental status of the ancestral animal where this genetic system was implemented . If this animal indeed had a meristic organization , as a result of a time-sequenced addition of segments , it makes sense that temporal collinearity was already at work there and was then used as a ground for evolving spatial collinearity . In this case , particular collinear Hox expression domains found in animals having lost this developmental time sequence , such as in diptera , may have been progressively taken over by different regulatory mechanisms , disconnecting space from time ( such as gap genes ) . The evidence is compelling , however , that even animals containing an atomized Hox gene cluster still show reminiscences of spatial collinearity , suggesting that the timing mechanism was built on the top of an already constrained gene cluster . Altogether , we consider it unlikely that an animal species will ever be found , which contains a broken Hox gene cluster , develops following a simultaneous segmentation process and implements temporal collinearity . Accordingly , any species displaying a clear time sequence in the ontogeny of its metameric aspect should have an intact Hox cluster , associated with a transcriptional time-sequence . Also , it should not be taken for granted that the ancestral Hox gene collinear function will still be found in extant animal species . Different collinear mechanisms can co-exist with one another and the implementation of a collinear regulation may have paved the way for its replacement by a more efficient strategy . For example , a mere distance effect to a remote enhancer could set up a time sequence in the appearance of transcripts encoded by contiguous genes , a situation selected due to a particular adaptive value . Once in place , this genomic topology may facilitate the evolution of yet a different progressive regulation , for example the spreading of chromatin modifications . Over time , the accumulation of such secondary mechanisms could take over the initial constraint for these genes to remain clustered , making it possible for an ancestral mechanism to turn into a fossil regulation and disappear from this particular phylogenetic branch .
The mutant strains used in this study , except for the Del ( 4-9 ) allele , were described previously: The inversion allele Inv ( Itga6-HoxDrVIII ) was obtained by sequential targeted recombination ( STRING; [21] ) . The targeted Hoxd11-lacZ transgene TgH[d11/lac] and the associated Del ( 11-13 ) , Del ( 4-13 ) and Del ( 1-13 ) were produced using loxP/Cre mediated site-specific recombination in ES cells [25] , [32] , [42] , [43] . The remaining set of deletion and duplication alleles were all produced in vivo using targeted meiotic recombination ( TAMERE; [44]: Del ( 1-10 ) [38]; Del ( i-9 ) , Del ( 8-10 ) , Del ( 9-10 ) , Del ( 10 ) [45]; Del ( i-8 ) , Del ( i-10 ) , Del ( 9-12 ) , Del ( 10-12 ) , Dup ( i-9 ) , Dup ( i-10 ) [11] . The Del ( 4-9 ) allele was obtained by TAMERE , using as parental lines the Del ( 4-13 ) and L5 , the latter strain carrying a single loxP sites between Hoxd10 and Hoxd9 [45] . Crosses were generally carried out using animals heterozygous for the respective alleles . For those crosses involving duplication alleles , the mother was heterozygous for a chromosome deficient for the gene to be analyzed such that +/Del embryos were used as control and Dup/Del as experimental embryos . These experiments are in agreement with the Swiss law concerning animal protection . They are subject to an official authorization delivered by representative of the government . Genotyping was performed on isolated yolk sac DNA using either simplex or duplex PCR protocols . Mutant and control embryos were marked before performing WISH for subsequent identification . Embryos younger than E10 were re-typed after WISH , using standard DNA extraction procedures [46] . Noon on the day of the vaginal plug was considered as E0 . 5 . Embryos were dissected in PBS and fixed from 4 h to overnight in 4% PFA . Whole mount in situ hybridization ( WISH ) was performed according to standard protocols , with both mutant and control embryos processed in the same well to maintain identical conditions throughout the procedure . Probes were as before: Hoxd3 [47] , Hoxd4 [48] , Hoxd8 [49] , Hoxd9 [50] , Hoxd10 and Hoxd11 [51] , Hoxd12 [22] , Hoxd13 [52] . Whole mount detection of beta-galactosidase reporter activity was carried out as described [53] . Embryos were dissected in PBS and fixed shortly in 2% PFA for 5′ to 15′ . For histology , embryos after WISH were cryoprotected in 30% sucrose and embedded in OCT compound . Sectioning was performed on a Leica CM1850 cryostat at 12–16 mm .
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Hox genes encode proteins that control embryonic development along the head-to-tail axis . These genes are clustered in one site on the chromosome and their respective positions within the cluster determine their time and place of activation . Here , by using a large set of targeted mutations disturbing the integrity of the gene cluster , we show that the spatial organization of expression domains does not directly depend upon the timing of activation as was previously suggested . This uncoupling between space and time in the regulation of these Hox genes coincides with the existence of two major phases of regulation . The first is time-dependent and involves global regulatory influences , located outside the gene cluster , whereas the second relies upon more local regulatory elements , likely interspersed between the genes , inside the cluster . These results provide the bases for future analyses of collinear mechanisms and indicate that different types of collinearities are not necessarily related , neither in function , nor in their evolutionary histories .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/animal",
"genetics",
"genetics",
"and",
"genomics/gene",
"expression",
"developmental",
"biology/pattern",
"formation",
"genetics",
"and",
"genomics/chromosome",
"biology",
"developmental",
"biology/molecular",
"development",
"evolutionary",
"biology/developmental",
"molecular",
"mechanisms",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
] |
2009
|
Uncoupling Time and Space in the Collinear Regulation of Hox Genes
|
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