Author response: Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways
Benoît BallesterAlejandra Medina-RiveraDominic SchmidtMar Gonzàlez-PortaMatthew CarlucciXiaoting ChenKyle ChessmanAndré J. FaureAlister PW FunnellÂngela GonçalvesClaudia KutterMargus LukkSuraj MenonWilliam McLarenKlara StefflovaStephen WattMatthew T. WeirauchMerlin CrossleyJohn C. MarioniDuncan T. OdomPaul FlicekMichael D. Wilson
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Full text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract As exome sequencing gives way to genome sequencing, the need to interpret the function of regulatory DNA becomes increasingly important. To test whether evolutionary conservation of cis-regulatory modules (CRMs) gives insight into human gene regulation, we determined transcription factor (TF) binding locations of four liver-essential TFs in liver tissue from human, macaque, mouse, rat, and dog. Approximately, two thirds of the TF-bound regions fell into CRMs. Less than half of the human CRMs were found as a CRM in the orthologous region of a second species. Shared CRMs were associated with liver pathways and disease loci identified by genome-wide association studies. Recurrent rare human disease causing mutations at the promoters of several blood coagulation and lipid metabolism genes were also identified within CRMs shared in multiple species. This suggests that multi-species analyses of experimentally determined combinatorial TF binding will help identify genomic regions critical for tissue-specific gene control. https://doi.org/10.7554/eLife.02626.001 eLife digest Stretches of DNA called cis-regulatory modules (or CRMs for short) could help researchers to identify the regions of DNA that are most important for controlling genes. CRMs are regions where multiple transcription factors—proteins that control when and how genes are expressed—bind to DNA. As important biological pathways are often regulated by more than one transcription factor, CRMs are therefore a good target when looking for DNA regions that, if mutated, are likely to cause disease. If a stretch of DNA performs an important role, it is often conserved throughout evolution. This is often observed for genes that make proteins. Indeed, DNA regions that specify critical amino acids that make up proteins are often conserved across distantly related species. However, unlike the changes made to the amino acid encoding parts of genes, it is currently a challenge to predict which changes in the rest of the genome will affect gene expression. One reason for this challenge is that transcription factor binding sites are rapidly evolving. This rapid evolution means that strictly comparing DNA sequences between species may fail to identify where transcription factors like to bind in the genome. Numerous experimental efforts have therefore been made to map these sites. These have revealed that there are a huge number of regions in the human genome that can bind transcription factors: hundreds of thousands of sites, far more than there are genes. For this reason, there is a great interest in revealing which of these regulatory regions are critical for maintaining normal levels and timings of gene expression. Ballester et al. compared the binding sites of four transcription factors responsible for regulating liver function in humans, macaques, mice, rats, and dogs. About two-thirds of these binding sites were found in CRMs. Less than half of the CRMs in humans were also CRMs in another species—but Ballester et al. found that these shared CRMs are predominantly in charge of regulating the essential biological pathways that allow the liver to function correctly. In addition, Ballester et al. identified several examples of disease-causing DNA mutations in shared CRMs that affected the expression of genes that make up pathways such as the blood clotting cascade. Genome-wide association studies also uncovered common variants for liver-related traits that were enriched for the CRMs found in more than one species, further supporting their importance. As transcription factors work in different ways in different tissues, further studies are now required to expand these observations to organs other than the liver. Future work is also needed to investigate the function of thousands of conserved CRMs whose role in liver gene regulation remains unknown. https://doi.org/10.7554/eLife.02626.002 Introduction The combinatorial binding of transcription factors to DNA define the gene regulatory regions that are essential for achieving spatial and temporal gene expression (Zinzen et al., 2009; Gerstein et al., 2012; Hardison and Taylor, 2012). The rapid increase in empirically determined TF bound motifs (Badis et al., 2009; Jolma et al., 2013; Weirauch et al., 2013), sequenced genomes (Goode et al., 2010; Lindblad-Toh et al., 2011; 1000 Genomes Project Consortium et al., 2012), and genome-wide profiling of DNA–protein interactions has given us unprecedented insight into the location of gene regulatory regions in multiple tissue and cell types. In particular, experimental results obtained by chromatin immunoprecipitation (ChIP), FAIRE, and DNase I footprinting assays in combination with high-throughput sequencing have unmasked what was previously a hidden landscape of active DNA regions (Rhee and Pugh, 2011; Furey, 2012; Neph et al., 2012). The compendium of ChIP-seq determined DNA-binding for 119 different proteins in 72 cell experiments produced by the Encyclopedia of DNA Elements (ENCODE) consortium alone has revealed that the number of TF binding events greatly exceeds the number of genes in the genome and that over 8% of the genome can be bound by at least one TF (ENCODE Project Consortium, 2012). The large number of TF bound genomic regions highlights the growing need for rational strategies for distilling these protein–DNA interactions into functional and non-functional categories. It has recently been shown that unlike TF binding events with high TF occupancy levels (measured by ChIP signal), genomic regions with low TF occupancy levels are not responsible for patterned reporter gene expression in Drosophila (Fisher et al., 2012). It remains to be seen how TF occupancy levels relate to functional gene expression in other species. Comparing DNA between species has long been employed to identify transcription factor (TF) binding sites that comprise gene regulatory regions (e.g., Tagle et al., 1988; Lindblad-Toh et al., 2011). Indeed, functional reporter gene expression assays have shown that many highly conserved mammalian non-coding regions serve as developmental limb and nervous system enhancers (Pennacchio et al., 2006). In contrast, other tissues including the heart (Blow et al., 2010; May et al., 2012), liver (Kim et al., 2011), and adult brain (Visel et al., 2013) possess many functional enhancers that do not show such deep phylogenetic preservation at the DNA level. An increasingly used way to identify tissue and species-specific gene regulatory regions is to compare experimentally determined TF–DNA interactions or histone modifications between species (Kunarso et al., 2010; Mikkelsen et al., 2010; Schmidt et al., 2010, 2012; Xiao et al., 2012; Cotney et al., 2013; Paris et al., 2013). For example, we previously established that the target genes of CEBPA and HNF4A, as identified from gene expression studies of conditional liver TF knockout mice, were enriched for TF binding shared in multiple species (Schmidt et al., 2010). Similarly, functional Drosophila enhancers are more likely to be found in regions with conserved TF binding events detected by ChIP (Paris et al., 2013). Associating common genetic variation with complex traits is another powerful way to identify functional regulatory DNA in the human genome. Over 80% of the most significant single nucleotide polymorphisms (SNPs) associated with human phenotypes and disease occur within non-coding regions of the genome (Hindorff et al., 2009). Recent integrative analyses have shown that open chromatin regions obtained for a specific cell type (e.g., DNase I hypersensitivity sites in T-cells) are enriched for reported GWAS SNPs. Importantly, this GWAS enrichment appeared most significant when the DNAse data was ascertained in a cell type relevant to the phenotype studied (Maurano et al., 2012; Reddy et al., 2012; Schaub et al., 2012). Examples of regulatory DNA mutations that explain differences in disease gene function are increasingly being discovered (e.g., Musunuru et al., 2010) and there is tremendous interest in methods that can predict which non-coding variants are of functional consequence (Schaub et al., 2012; Ward and Kellis, 2012a, 2012b). To test whether evolutionary conservation of cis-regulatory modules (CRMs) gives insight into human gene regulatory function, we determined transcription factor (TF) binding locations of four liver-enriched TFs in liver tissue from: two primates (human and macaque) estimated to have diverged 29 million years ago; two rodents (mouse and rat) estimated to have diverged 25 million years ago; and dog which diverged during the mammalian radiation along with primate and rodent lineages (Hedges et al., 2006). The liver is a suitable tissue for studying vertebrate gene regulation. It is a relatively homogenous tissue with approximately 75% of the nuclei in the liver coming from hepatocytes (Marcos et al., 2006). Both the relative homogeneity and the large cell numbers that can be isolated from diverse organisms under physiologically optimal conditions lend itself well to comparative studies. We focus on four TFs required for liver cell specification and gene function (HNF4A, CEBPA, ONECUT1, and FOXA1) (Kyrmizi et al., 2006). Together, several studies have demonstrated that these four TFs work together directly and indirectly to drive liver-specific function (Plumb-Rudewiez et al., 2004). Using liver as a model tissue, we demonstrate how a combinatorial analysis of TF occupancy across multiple species can highlight conserved and species-specific biological processes, as well as potential mechanistic actions of disease variants. Results Determining combinatorial binding in multiple mammalian species The genome-wide occupancy of four transcription factors (HNF4A, CEBPA, ONECUT1, and FOXA1) was determined in primary liver in five species (Homo sapiens [Hsap], Macaca mulatta [Mmul], Canis familiaris [Cfam], Mus musculus [Mmus], and Rattus norvegicus [Rnor]) using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) (Figure 1, Figure 1—figure supplement 1A, Figure 1—source data 1). The antibodies used for the four TFs have been raised against conserved epitopes and have previously been validated in ChIP experiments in mouse and human ChIP studies (Figure 1—source data 1D). As expected from previous multi-species ChIP study of CEBPA and HNF4A (Schmidt et al., 2010), the known binding motifs for the four TFs was virtually identical between species and occurred close to the ChIP-seq binding summit (Figure 1—figure supplement 1B,C). Figure 1 with 3 supplements see all Download asset Open asset Overview of ChIP-seq, CRM construction, and multiple-species comparisons. ChIP-seq peaks were determined for four liver TFs in five mammals. (A) CRMs were constructed by merging ChIP-seq peaks whose summits occurred within 300 bp and consisted of at least two distinct TFs. Remaining peaks were designated as singletons. (B) Whole genome 9-way EPO multiple sequence alignments (MSA) were used to project CRMs/Singletons across the five species. A CRM was considered shared if its position in the EPO MSA overlapped a CRM in a second species by a minimum of 10 bp. Neither the content nor order of TFs within the CRM was required to be classified as a 'Shared' CRM. A singleton in one species was considered 'Shared' if it overlapped the same TF in a second species. (C) Relative to human, the average % of shared CRMs is shown. Human CRMs (comprised of any two TFs) that overlap a CRM from a second species are shown with empty circles. Human CRMs containing at least one of each TF (all 4 TFs) were compared to all identified CRMs in a second species (purple circles). (D) The percentage of human CRMs and singletons in different phylogenetic categories that can be found aligned within the EPO MSAs for each of the five species is shown. https://doi.org/10.7554/eLife.02626.003 Figure 1—source data 1 Quality control for ChIP-seq, CRM construction, and multi-species comparisons. https://doi.org/10.7554/eLife.02626.004 Download elife-02626-fig1-data1-v1.docx Similar to what was observed for previous CEBPA and HNF4A ChIP-seq experiments, only a minority of ONECUT1 and FOXA1 bound regions overlapped orthologous, TF-bound regions in a second species, a relationship we refer to here as "shared" TF binding (see Figure 1A,B, Figure 1—figure supplement 2A). The rapid evolution of TF binding is further supported by comparisons within primate and rodent orders that are separated by less than 25 million years (Springer et al., 2003). For example, on average, between 21 and 37% of TF binding events in human are found in the orthologous location in macaque and 21–31% between mouse and rat for each of the TFs assayed (Figure 1—figure supplement 2A). Tissue-specific TFs are known to bind in close proximity to form cis regulatory modules (CRMs). Similar to what has been done for multi-TF binding analyses in Drosophila (Zinzen et al., 2009) and mouse (Stefflova et al., 2013), we defined CRMs by clustering at least two proximal heterotypic TF binding events (Figure 1A). The number of liver TFs forming clusters falls off sharply when the distance between them is greater than 150 bp, which is less than the average width of the TF bound regions we detected by ChIP-seq (Figure 1—figure supplement 3). We built CRMs by merging TF binding events whose summits were within 300 bp of each other (Figure 1). Using this summit-based clustering, we found that approximately two thirds of the human liver TF binding events were incorporated into CRMs (Figure 1—figure supplement 1A). We found that the shared CRM categories were robust to using a more permissive peak caller or calling peaks on individual biological replicates (Figure 1—source data 1E). As we found for individual TFs, the location of CRMs appears to have evolved rapidly (Figure 1C). For example, we found that only ∼35% of human CRMs had a CRM in the orthologous macaque genomic region. Similarly, ∼32% of mouse CRMs were found as CRMs in the orthologous location in the rat genome (Figure 1—figure supplement 2C). This divergence of CRM occupancy was consistent between different lineages separated by the similar evolutionary distances (Figure 1—source data 1F), robust to the multiple sequence alignments (MSA) used to detect orthologous CRMs, and also robust to different overlap methods chosen to infer CRM conservation between species (Figure 1—source data 1G). Figure 1D shows that most (>93%) of human CRMs and singletons we detect are found in the EPO MSA (Paten et al., 2008) with macaque, which suggests that the rapid turnover observed between human and macaque CRMs is not due to characteristics of the multiple alignment. CRMs containing all four TFs are on average more highly shared with a CRM from a second species (e.g., 53% of human CRMs with all four TFs are shared with a macaque CRM), indicating increased selection pressure on higher order combinatorial TF binding (Figure 1C, Figure 1—figure supplement 2C). TF binding events shared in multiple species are more likely to be found within CRMs (72% of shared human TF binding events are in CRMs vs 27% that are classified as singletons; hypergeometric test, p = 8.48 × 10−238). For example, 32 of the 35 CEBPA binding events previously found to be bound in orthologous regions in five vertebrate species (Schmidt et al., 2010) fell within CRMs identified in this study. Comparative genomic analysis of combinatorial TF binding creates biologically meaningful categories from in vivo ChIP-seq data To test how combinatorial binding and TF binding conservation relate to liver gene function, we classified our set of human CRMs (n = 31,765) and singletons (n = 43,824) into phylogenetic categories (Figure 2, Figure 2—source data 1). CRMs were categorized as one of the following: shared only in human and macaque (Primates only, n = 4672); shared in human plus at least one non-primate (Beyond primates, n = 7631); and shared in at least three species (Deeply shared n = 5046) (Figure 2). The 43,824 singletons not residing in CRMs (44%) were categorized in the same manner (Figure 2). Figure 2 Download asset Open asset Annotation of human regulatory regions using interspecies combinatorial transcription factor binding. (A) Human liver ChIP-seq data from ONECUT1, HNF4A, FOXA1, and CEBPA were assembled into CRMs consisting of at least 2 of the 4 TFs. The CRMs or single TFs were then broken down into categories based on their overlap with ChIP-seq data in macaque, dog, mouse, and rat. Singletons and CRMs were considered shared if they overlapped at least 10 bp with another TF bound region in the EPO multiple sequence alignment (MSA). (B) Experimentally determined combinatorial binding at the blood coagulation F7 locus. Raw sequencing reads from ChIP-seq experiments: CEBPA (red), HNF4a (green), ONECUT1 (yellow), and FOXA1 (green) are overlaid and called peaks are displayed for each species. ChIP-seq determined TF binding events were assembled into CRMs (black bars) underneath the enriched regions (peaks). Grey lines are drawn to illustrate shared CRMs using the EPO-MSA. https://doi.org/10.7554/eLife.02626.008 Figure 2—source data 1 Table of CRMs and singletons along with the phylogenetic categories they were assigned. File coordinates are for the hg18 assembly. https://doi.org/10.7554/eLife.02626.009 Download elife-02626-fig2-data1-v1.xlsx TF binding events contained in CRMs were enriched for their respective TF's DNA binding motif, in addition to other liver TF binding motifs, including those profiled in this study (Supplementary file 1). Supporting this observation, we find that both Deeply shared and human only CRMs overlap significantly with relevant ENCODE genome-wide experimental data sets, including ChIP peaks for HNF4A, FOXA1, and CEBPB in the liver cancer cell line HepG2 (e.g., p < 10−149 and p < 10−212 for HNF4 respectively; Supplementary file 1). The fold enrichment was higher for Deeply shared CRMs than for human only CRMs (e.g., 20.5 vs 11.8 for HNF4A). We also found significant overlap with TFs not tested in our study. For example, 51% of the Deeply shared CRMs and 20% of the human only CRMs overlap binding peaks for SP1 in HepG2 cells (p < 10−149 and p < 10−212 respectively; Supplementary file 1). Again the enrichment for these additional TFs was higher in the Deeply shared CRM category than the human only category (e.g., 17.6 vs 8.4-fold). SP1 and HNF4A have previously been shown to cooperatively regulate gene expression in HepG2 cells (Sugawara et al., 2007). In this manner, Deeply shared CRMs can be used to enrich for additional TFs that might play global combinatorial roles in liver gene regulation when used in conjunction with other data sets from related cell types. A comparison of shared CRMs to human-specific CRMs reveals an increase in the number of liver-related biological pathways, diseases, and known target genes of liver enriched TFs (Figure 3). We used the enrichment tool GREAT (McLean et al., 2010) to perform functional enrichments. GREAT's default setting assigns TF binding events to a basal region around every gene (5 kb upstream, 1 kb downstream). ChIP-seq peaks that fall within the basal regulatory region of each gene, as well as the genomic sequence that spans between the basal region of that gene and the nearest gene's basal region (within a maximum of 1 Mb) are used to generate functional enrichments. The most significant enrichments that were unique to the shared liver CRMs include: liver disease (Binomial FDR q-value = 4.53 × 10−130) from the Disease Ontology database and metabolism of lipids and lipoproteins (q = 2.96 × 10−73) from MSigDB Pathway (Figure 3A,B, Figure 3—source data 1A). Figure 3 Download asset Open asset Phylogenetic filtering of experimentally determined liver TF binding events yield distinct functional enrichments. Results were obtained using the programming interface for the online enrichment tool GREAT version 2.02 (McLean et al., 2010) and plotted with custom R scripts. Up to five of the most significant enrichments obtained for each of the six analyses are listed on the left. The −log10 of binomial Q values for Disease ontology, HGNC gene family, and MSigDB are shown along the x-axis. Bars with a black asterisk indicate significant enrichments using GREAT default parameters (binomial and hypergeometric FDR Q-value significance at P ≤ 0.05 with at least twofold region enrichment). The size of the asterisk is proportional to the fold enrichment obtained for the given database. See Figure 3—source data 1 for complete list of Q-values, fold enrichments, genes giving the enrichments along with results from additional databases. (A) Enrichment analysis of any CRM shared in human plus at least one additional species is shown on the left and human only CRMs are shown on the right (Figure 3—source data 1A). (B) Human CRMs (left panel) shared in human and at least one non-primate (Beyond Primates) is shown vs Human CRMs (right panel) shared in human and macaque but no other species (Primate only) (Figure 3—source data 1B). (C) Enrichment analysis of shared CEBPA CRMs and singletons (Figure 3—source data 1C). (D) Enrichment analysis of shared HNF4A CRMs and singletons (Figure 3—source data 1D). (E) Enrichment analysis of shared FOXA1 CRMs and singletons (Figure 3—source data 1E). (F). Enrichment analysis of shared ONECUT1 CRMs and singletons (Figure 3—source data 1F). (G) Human TFs in CRMs and Singletons were categorized by the number of species in which they are shared with. Profiles of constrained elements (sequence conservation) in a 1-kb window around CRMs or singletons were calculated using GERP scores from the 29-way multiple sequence alignments. (H) Genomic location of CRMs and Singletons. Proportion of single TFs located near transcription start sites (TSS) increases to >50%, but remains stable for CRMs at ∼20%. https://doi.org/10.7554/eLife.02626.010 Figure 3—source data 1 Functional enrichment results obtained for CRMs and singletons using GREAT. https://doi.org/10.7554/eLife.02626.011 Download elife-02626-fig3-data1-v1.xlsx The most significant liver-related enrichments obtained using human only CRMs were for biological oxidations (q = 1.95 × 10−39; in MSigDB Pathway). These enrichments were driven by genes involved in the metabolism of xenobiotics by the cytochrome P450 gene family (q = 2.24 × 10−19; HGNC gene family database) (Figure 3A). Given the liver's major contribution to the detoxification of xenobiotics and the well established species-specificity of the proteins involved in the process (Gonzalez and Nebert, 1990), these results suggest that evolutionary filtering of CRMs has the potential to enrich for both conserved and species-specific biological pathways. Singleton TF binding events were predominantly enriched for their respective motif, but were not enriched for the motifs from the other three TFs profiled in this study (Supplementary file 1). Supporting this, comparisons against all ENCODE TF binding data show that for HNF4A, CEBPA, and FOXA1 singletons, the top ChIP-seq peak association in HepG2 cells corresponded to the TF assayed. HNF4A singletons were enriched for FOX family motifs, albeit not the same FOXA1 motif obtained from CRMs and singleton FOXA1 peaks. Comparing normalized sequence read counts in the HNF4A singletons, and HNF4A-containing CRMs lacking FOXA1 peaks, it is clear that pervasive weak FOXA1 ChIP-seq signal occur at HNF4A binding sites (Figure 4A). Further supporting this hypothesis is the similarity of FOXA1 to a portion of the HNF4A motif (Figure 4B), and a recent study that showed a close association of HNF4A with FOXA1 motifs (Guo et al., 2012). Figure 4 with 1 supplement see all Download asset Open asset Comparison of TF occupied regions classified as CRMs and singletons. (A) Regions of ±5 kb are represented around the center of CRMs or singletons. Reads centered on the summit of each TF are counts subtracted by input reads in 100 bp bins plus and minus 5 kb from the summit. Colored boxes indicate CRMs or singletons where a peak was called for a given factor: CEBPA (red), HNF4A (blue), ONECUT1 (orange), and FOXA1 (green). Looking at read counts for all four factors reveal that many of the HNF4A singleton in fact have weak FOXA1 signal. (B) Alignment of FOXA1 de novo ChIP-seq motif to the HNF4A motif. Motif comparison (alignment) was performed using compare-matrices from RSAT. The program calculates the correlation between two matrices shifting positions; the correlation is normalized based on the width of the alignment to avoid high correlation based on few flanking positions. https://doi.org/10.7554/eLife.02626.012 Figure 4—source data 1 Comparison of motif matches between CRMs and singletons. Chi-square test for differences between the number of peaks associated with CRMs and singletons, for each TF, that contained at least one predicted motif using three different p-value thresholds for motif scanning: stringent (10−4), moderate (10−3) and lenient (10−2). Blue shadows highlight siginficnat p-values. https://doi.org/10.7554/eLife.02626.013 Download elife-02626-fig4-data1-v1.xlsx We asked whether CRMs or singletons differ with regards to the quality of their TF binding motifs. Peaks for each TF were scanned using the RSAT tool matrix-scan with the best position weight matrices (PWM) for each TF. We set three p-value threshold cut offs (stringent:10−4, moderate:10−3 and lenient 10−2) based on the comparison between the theoretical and empirical PWM weight score distribution observed for each peak collection as previously described (Medina-Rivera et al., 2011). As expected, motifs were identified in the vast majority of their corresponding peak set using the lenient motif threshold. Similarly, for both singletons and CRMs, the moderate and stringent motif searches returned the highest fraction motifs in their corresponding peak set. Interestingly, for both the moderate and stringent motif searches, the singleton TF binding sites had a significantly greater fraction of high quality motifs than they did for CRMs (e.g., 56% for singletons vs 33% for CRMs for the stringent cutoff). This trend was observed for all four TFs in this study (Figure 4—figure supplement 1, Figure 4—source data 1). Our results in primary liver tissue are supported by an integrative analysis of ENCODE cell lines, which showed that active chromatin states are depleted of regulatory motif instances relative to all regions bound by a given TF (Ernst and Kellis, 2013). After collapsing the four TFs into CRMs, there were still over 40,000 singleton TF binding events. We asked if shared singletons show any distinct genomic properties. Shared singleton TF binding events show high DNA constraint (Figure 3G), and a larger fraction are found close to the transcription start site of annotated genes compared to the CRMs shared in the equivalent number of species (Figure 3H). Unlike the equivalently shared CRMs, shared singleton TF binding events gave fewer and less significant enrichments that than the equivalently shared CRMs (Figure 3C–F). Relative to CRMs, the enrichments unique to shared singletons, did not appear to be overtly liver specific. For example, no significant liver disease ontology enrichments were found for shared singleton CEBPA binding events; however, several cancer disease enrichments from various tissues, such as in situ carcinoma (q = 1.91 × 10−5), were obtained (Figure 3C). One consideration about comparing singletons to CRMs is that singleton TF binding events are likely to become CRMs as more factors are tested and more peaks are called (see Figure 1—source data 1E for a comparison of the stability singleton and CRM categories). Nonetheless, by focusing on the singletons that remain singletons in orthologous regions in two or more species, we have been able to detect distinct genomic properties that warrant future study. Shared combinatorial TF binding associates with highly expressed liver-specific genes To compare the functional properties of shared and species-specific CRMs/singletons, we then looked at how combinatorial binding and evolutionary constraint correlated with gene expression (Figure 5). Human TF binding events in CRMs and singletons were categorized by the number of species they were shared in and then associated with the nearest gene. Human liver mRNA expression level of the nearest gene was determined by RNA-seq (Kutter et al., 2011). Genes nearest to human-specific singletons and CRMs were not significantly different in their expression levels (p = 0.221). In contrast, gene expression levels near TFs in shared CRMs were significantly higher than those near shared singleton-associated genes (p = 2.4 × 10−16) (Figure 5A). This striking p-value is due to several CRMs being found close to highly expressed liver genes including albumin, fibrinogen (FGA, FGB, FGG), and several acute phase response genes (e.g., CRP, SAA1 etc). We therefore broke down each CRM and singleton by transcription factor, which still revealed a significantKeywords:
Cis-regulatory module
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The transcription factor PU.1 is crucial for the development of many hematopoietic lineages and its binding patterns significantly change during differentiation processes. However, the 'rules' for binding or not-binding of potential binding sites are only partially understood. To unveil basic characteristics of PU.1 binding site selection in different cell types, we studied the binding properties of PU.1 during human macrophage differentiation. Using in vivo and in vitro binding assays, as well as computational prediction, we show that PU.1 selects its binding sites primarily based on sequence affinity, which results in the frequent autonomous binding of high affinity sites in DNase I inaccessible regions (25–45% of all occupied sites). Increasing PU.1 concentrations and the availability of cooperative transcription factor interactions during lineage differentiation both decrease affinity thresholds for in vivo binding and fine-tune cell type-specific PU.1 binding, which seems to be largely independent of DNA methylation. Occupied sites were predominantly detected in active chromatin domains, which are characterized by higher densities of PU.1 recognition sites and neighboring motifs for cooperative transcription factors. Our study supports a model of PU.1 binding control that involves motif-binding affinity, PU.1 concentration, cooperativeness with neighboring transcription factor sites and chromatin domain accessibility, which likely applies to all PU.1 expressing cells.
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A new computational method uses gene expression databases and transcription factor binding specificities to describe regulatory elements in the Drosophila A/P patterning network in unprecedented detail.
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Detailed knowledge of the mechanisms of transcriptional regulation is essential in understanding the gene expression in its entirety. Transcription is regulated, among other things, by transcription factors that bind to DNA and can enhance or repress
Sp3 transcription factor
TAF2
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Transcription
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Identification of transcription factor binding sites is an important step towards the understanding of the transcription regulation.A reliable prediction of transcription factor binding sites can help to identify the target genes of transcription factors and infer the relationship between the positions of binding sites and regulation activity of transcription factors.But the specificity of recognition results achieved by the current algorithms is quite low;therefore,algorithms that can identify binding sites more efficiently are required.By using the data from JASPAR,a position-correlation score function is constructed,which considers the conservation and pseudocount according to the thorough analysis about features of transcription factor binding sites,and use these functions to predict the transcription factor binding sites of Drosophila melanogaster,the false positive rates are lower than 0.02%.
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Transcription
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Trans-acting
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To understand the structure and function of gene regulatory networks, it is important to first catalogue the components. Measurable constituents of networks include cis-regulatory elements, identified by their conservation and ability to drive expression; transcription factor binding motifs, identified by protein binding; transcription factors, identified by their necessity in network function; and target genes, identified by their conditional expression. The heart of a regulatory network is the transcription factor, which is dedicated to its role in the network. Transcription factors must be activated and regulate downstream targets in a discrete and reproducible fashion. Any deviation in network function may result in the collapse of the network and death of the animal. Thus, a network must be robust enough to function under a variety of biological conditions. However, network redundancies are inefficient in terms of fitness and lost during the course of evolution. The network structure and function reflects these evolutionary realities: strong sequence conservation of cis-regulatory elements coupled with widespread stochastic transcription factor binding, and ancient transcription factor conservation coupled with overlapping activation of targets. The evolution of functional transcription factor networks therefore must be a balance between conservation and flexibility.
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Abstract Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.
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Reliable prediction of transcription factor binding sites can help to identify the target genes of transcription factors and infer the relationship between binding sites and regulation activity of transcription factors.However,the performance of some algorithms has been unreliable with respect to poor specificity,false-positive predictions.Therefore,more efficient algorithms are required.Based on the conservation analysis of transcription factor binding site sequences,a novel position-correlation scoring function(PCSF) algorithm for predicting transcription factor binding sites is proposed by using the high quality database of transcription factor binding site sequences downloaded from JASPAR.The results indicate that under the optimal cut-offs of this algorithm,the false positive rates are lower than 0.01% for all transcription factors.
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Abstract Background Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. Results We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor’s targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. Conclusion We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.
DNA binding site
General transcription factor
Transcription
Sp3 transcription factor
TCF4
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