Comprehensive meta-analysis of Signal Transducers and Activators of Transcription (STAT) genomic binding patterns discerns cell-specific cis-regulatory modules
© Kang et al.; licensee BioMed Central Ltd. 2013
Received: 23 August 2012
Accepted: 1 January 2013
Published: 16 January 2013
Cytokine-activated transcription factors from the STAT (Signal Transducers and Activators of Transcription) family control common and context-specific genetic programs. It is not clear to what extent cell-specific features determine the binding capacity of seven STAT members and to what degree they share genetic targets. Molecular insight into the biology of STATs was gained from a meta-analysis of 29 available ChIP-seq data sets covering genome-wide occupancy of STATs 1, 3, 4, 5A, 5B and 6 in several cell types.
We determined that the genomic binding capacity of STATs is primarily defined by the cell type and to a lesser extent by individual family members. For example, the overlap of shared binding sites between STATs 3 and 5 in T cells is greater than that between STAT5 in T cells and non-T cells. Even for the top 1,000 highly enriched STAT binding sites, ~15% of STAT5 binding sites in mouse female liver are shared by other STATs in different cell types while in T cells ~90% of STAT5 binding sites are co-occupied by STAT3, STAT4 and STAT6. In addition, we identified 116 cis-regulatory modules (CRM), which are recognized by all STAT members across cell types defining a common JAK-STAT signature. Lastly, in liver STAT5 binding significantly coincides with binding of the cell-specific transcription factors HNF4A, FOXA1 and FOXA2 and is associated with cell-type specific gene transcription.
Our results suggest that genomic binding of STATs is primarily determined by the cell type and further specificity is achieved in part by juxtaposed binding of cell-specific transcription factors.
KeywordsSTAT GAS motif Meta-analysis ChIP-seq Cis-regulatory module CRM
In Drosophila the single STAT (Stat92E), in conjunction with one cytokine (UPD), controls an array of developmental processes ranging from immune responses and heart development to the specification of border cells in the ovary and primordial germ cell formation in the gonads . In contrast, mammals have seven STATs (1–4, 5A, 5B and 6) . Although these STATs recognize similar, if not identical, DNA sequence motifs in vitro they execute cell- and context-specific functions in addition to overlapping and redundant functions. Yet, cell-specific gene expression patterns are obtained despite different cells being exposed in vivo to similar, and in some cases identical, cytokines. The appropriate execution of these programs is determined by several regulatory layers . These include a large number of membrane receptors that have the ability to differentially activate individual STATs, cellular STAT levels, the affinity of STATs to receptors and their cognate JAKs and possibly the ability of STATs to recognize regulatory sequences only in certain contexts, such as composite promoter elements or chromatin configuration. In fact, evidence is emerging that specific chromatin remodeling is required for STAT binding to a subset of loci [4, 5].
Direct STAT binding to cognate genomic targets will, at least in part, execute cytokine stimuli. With this in mind, new and critical insight into common and cell-specific functions of STATs could come from genome-wide STAT occupancy data sets. However, it is not clear to what extent different members of the STAT family share genetic targets. In particular STAT binding to the canonical GAS (gamma interferon-activated sequence) motif (TTCnnnGAA), the extent of cell specificity and the influence of STAT concentration on their ability to occupy genomic sites are poorly understood. Large-scale chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) studies have explored in vivo binding of five different STATs in a number of different cell types exposed to several cytokines. We have now comparatively reanalyzed this resource of 29 data sets and provide insight into the complexity of common and selective STAT binding patterns that are unique to, as well as shared between, different cell lineages.
Results and discussion
Meta-analysis of ChIP-seq data sets reveals cell context as the major defining factor controlling STAT binding to specific GAS sites
The Signal Transducer and Activator of Transcription (STAT) family consist of seven transcription factors (TFs) called STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B and STAT6, which upon activation by cytokines bind to specific sequences called GAS motifs (TTCnnnGAA) [3, 6, 7]. To determine the extent of genomic binding of each STAT member in various cell contexts, we collected available STAT (1, 3, 4, 5 and 6) ChIP-seq and control data sets from 11 independent studies (gene expression omnibus, http://www.ncbi.nlm.nih.gov/geo/) [8–19] and re-analyzed them using the same analysis pipeline (Additional files 1, 2 and Methods). Since the number of significant peaks is sensitive to algorithms [20–22], we used three different peak-calling programs, MACS (version 1.4.2), HOMER (version 3.10) and Qeseq (version 0.2.2) as Chen et al. suggested [20, 21, 23, 24]. The combined peaks were categorized into three classes (high-, intermediate- and low-confidence) according to the number of algorithms that detected the peaks (Additional file 1). In this regard, the high- and intermediate-confidence peaks should be more reliable than low-confidence peaks due to the fact that any two different algorithms identified them as significant peaks (good signal-to-noise ratio).
STATs regulate gene expression by cell-specific binding to distinct sets of GAS motifs
Gene sets targeted by all STAT members independent of cell type and cytokine stimulus generate a JAK-STAT signature
Distinct transcription factors work in concert with STATs
In this study, we explored the extent to which GAS motifs throughout the mammalian genome are occupied by any given STAT in various cell types subjected to different cytokine stimuli. Due to a paucity of available STAT1, STAT4 and STAT6 ChIP-seq data, results were mainly derived from STAT5 ChIP-seq data sets. Our meta-analysis confirmed the increment of STAT binding on GAS sites upon cytokine induction (Figure 1A). Most of the up to 100,000 sequences occupied by STATs (up to 94%) contained a GAS motif (Additional file 2). Since the mouse genome harbors more than one million GAS sites (TTCnnnGAA, perfect match), up to 10% of these are occupied by STATs at any given moment. However, the probability of being bound by a STAT protein is not equal for all GAS sites. John et al. demonstrated that up to 95% of de novo glucocorticoid receptor binding sites are pre-determined by chromatin accessibility . In accordance with this, our meta-analysis of genome-wide STAT binding sites in 29 different cell contexts showed that the binding of STATs to GAS sites was mostly defined by the cell type compared to other features such as the type of STATs and the cytokine (Figure 1B). Therefore, the capacity of STATs to access specific GAS sites seems to be pre-determined by the cell type via open chromatin and this notion was validated by previous studies using DNase-seq and STAT5 ChIP-seq in mouse liver [16, 49]. In support of this, each cell type displayed a unique STAT binding pattern. The majority of the STAT-bound GAS sites were located near genes with cell-specific expression patterns. This result can explain the cell-specific aspect of STATs that transmits signals for the growth-, survival- and differentiation-related genes corresponding to a given cell type. In contrast to cell-type restricted binding obtained for most sites, we detected 116 highly conserved GAS sites whose recognition by STATs transcended cell types. These CRCCs, which were targeted by any member of the STAT family regardless of cell type and cytokine stimulus, included classical JAK-STAT signature genes, such as Stat1, Socs2, Socs3, Cish, Ifnar2 and Irf9 genes. Thus, our analysis strengthens previous knowledge that STATs recognize GAS motifs nearby target genes upon cytokine induction and further shows that STATs target cell-type specific as well as common JAK-STAT signature genes.
A previous study using an aneuploid mouse strain carrying human chromosome 21 revealed that transcriptional outputs are determined primarily by genetic sequence besides epigenetic and cellular environment . Several studies demonstrated that nucleosome positions are also determined by nucleotide sequences and therefore, successfully predicted ~50% of in vivo nucleosome positions solely based on DNA sequences [51, 52]. These findings highlighted the importance of DNA sequences involved in gene regulation. In this regard, binding motifs for distinct transcription factors were enriched around the center of STAT binding sites in specific cell types, suggesting that cell-specific gene regulation of STATs might be driven by cooperative activity of cell-type restricted or enriched TFs. For instance, STAT5 is known to interact with RUNX1 physically in vitro which is corroborated by our finding in T cells . The binding motif of HNF4A, which is a key TF in liver , coincided significantly with STAT5 binding sites in liver, while none of the other STATs showed any significant association with HNF4A in the different cell types. CEBPA, which is sufficient to promote differentiation of growth-arrested 3T3-L1 cells , was significantly over-represented within flanking regions of STAT5 in 3T3-L1 cells. Indeed, the integration of nine TF ChIP-seq data sets from liver and 3T3-L1 cells revealed that STAT5 coincided with cell-enriched TFs. CEBPA, CEBPB, CEBPD and GR coincided with STAT5 in the 3T3-L1 cells, while CEBPA, FOXA1, FOXA2 and HNF4A significantly associated with STAT5 binding sites in female mouse liver. However, E2F4 and p300 were not significantly associated with the STAT5 binding sites in both cell types suggesting that only defined cell-specific co-TFs are related with STATs. Moreover, this finding is supported by a recent study demonstrating that SMAD3, a master transcription factor generating cell-type specific effects of TGFβ signaling, coincided with OCT4 in ES cells, MYOD1 in myotubes and PU.1 in pro-B cells . Collectively, the cooperative activity of STATs with associated TFs appeared to control cell-type specific genes in concordance with previous studies [9, 55], while the accessibility of their target GAS sites seems to be pre-determined by epigenetic features including chromatin configurations . Future studies will be required to elucidate which TFs are pioneer factors that recruit co-TFs and/or influence chromatin modifications or bystanders.
ChIP-seq data sets
All data were downloaded from the GEO website (http://www.ncbi.nlm.nih.gov/geo/) . A list of all ChIP-seq data sets can be found in Additional file 2. If aligned files were not provided, we downloaded corresponding unaligned files (.fastq) from the SRA website (http://www.ncbi.nlm.nih.gov/sra) and mapped sequenced reads (tags) to the mouse reference genome (mm9) using the Bowtie aligner with the same parameters as described previously [56, 57]. All data sets were converted to BED files (mm9) (http://genome.ucsc.edu/FAQ/FAQformat).
Systematic evaluations of available peak-calling algorithms demonstrated that there are substantial variations in sensitivity and specificity among the programs [20–22]. To identify significant peaks representing STAT binding sites, we analyzed the BED files with three independent peak-calling programs as suggested by Chen et al. : MACS (version 1.4.2), HOMER (version 3.10) and Qeseq (version 0.2.2) with default parameters. Next, all the identified peaks were merged into a single data set. All the merged peaks were categorized into three classes (high-, intermediate- and low-confidence) according to the number of algorithms that detected the peaks (Additional file 1). The low-confidence peaks seem to be false positives since only one of the algorithms detected the regions as binding sites (Figure 1A). Therefore, we only used high- and intermediate-confidence peaks for the rest of the analyses.
To estimate overall similarity of genomic STAT binding sites, the mouse genome was divided into 500-bp bins and the numbers of overlaps were calculated between all possible pairs. Hierarchical clustering was performed using the Cluster 3.0 program  with the average linkage algorithm. The percentage of the overlaps was used to draw the heat map in Figure 1B.
PeakSplitter was used to pinpoint the centers of STAT binding sites with corresponding wig files generated by MACS . To identify significantly over-represented motifs around the centers of STAT binding sites, a web-based de novo motif identification program called MEME-ChIP was used with the default setting (http://meme.sdsc.edu/meme/) . The MEME-ChIP program predicts the top three motifs by E-value, which is an estimate of the expected number of motifs in a similarly sized set of random sequences. The top three significant motifs in each set of the top 600 STAT binding sites (+/− 75 bp around the peak centers, sorted by the height of peaks) are shown in Figure 4A. In order to verify the identified motifs, TOMTOM, which compares identified motifs with the known motifs , was used.
Co-transcription factor identification
To identify co-transcription factors in Figure 4B, we used a custom Perl script (MOODS algorithm) with available 130 TFBS position frequency matrices (p value < 0.001, http://jaspar.cgb.ki.se/) . The script is available as Additional file 6. For each TFBS matrix, the number of STAT binding sites containing at least one TFBS within 150 bp (−75 bp ~ peak center ~ +75 bp) was counted and defined as motif-covered sites. The proportion of the motif-covered sites was calculated as ratio of motif-covered sites to total number of sites. The motif enrichment score was calculated using the MOODS algorithm implemented in the script. For each site, the highest motif enrichment score for each TFBS was used. The motif-covered sites and motif enrichment score were then normalized with the values from the same calculation of a background set containing 100,000 random regions (150 bp). To get significantly associated co-TFs with a given set of STAT binding sites, we set the normalized motif-covered site threshold as 0.2 and the motif enrichment score threshold as 1.5. The TFBSs above the thresholds were regarded as significantly associated co-TFs with a given STAT.
Estimation of empirical P-values
To estimate the significance of STAT5 binding (−10 kb ~ peak center ~ + 10 kb) to gene expression (Figure 5B), we randomly resampled genes among all genes with replacement (the size of the resample was equal to the size of the given STAT5 target genes) and the mean expression values of the resampled set were calculated. This procedure was repeated 10000 times. Then, P-values were empirically computed as the number of times the mean value of a randomly-resampled set was greater than or equal to the observed mean expression value.
Common STAT controlled CRMs
Gamma interferon-activated sequence
Chromatin immunoprecipitation followed by high throughput sequencing
Transcription start site
Transcription factor binding site
Mouse embryonic fibroblast
- ES cell:
Embryonic stem cell.
We thank all the members of the Laboratory of Genetics and Physiology for helpful discussions.
The Intramural Research Programs (IRP) of NIDDK at the National Institutes of Health (NIH), USA. Funding for the open access charge: the World Class University Program, Ministry of Education, Science and Technology, through the National Research Foundation of Korea, South Korea (R31-10069); WCU Research Center, Dankook University.
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