Positional specificity of different transcription factor classes within enhancers
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE115438 | GEO | 2018/06/07
REPOSITORIES: GEO
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Project description:Variations in noncoding regulatory sequences play a central role in evolution but interpreting such variations remains difficult even in the context of defined attributes such as transcription factor (TF) binding sites. Here, we systematically link variations in cis-regulatory sequences to TF binding by profiling the allele-specific binding of 27 TFs expressed in a yeast hybrid, which contains two related genomes within the same nucleus. TFs localize preferentially to sites containing their known binding motifs, but occupy only a small fraction of the potential motif-coding genomic sites. Differential binding of TFs to the two orthologues is well explained by sequence variations that alter the consensus core motif, while changes in chromatin accessibility were of little apparent effect. Motif variations that abolish binding when present in one allele show only moderately reduced binding when common to both alleles, suggesting evolutionary compensation. At the promoter level, we report cases of turnover, where binding in the two orthologues localized to different promoter regions, yet most interspecies differences resulted from higher number of binding sites in one of the alleles. Our results suggest that cis variations affecting TF binding occur primarily through the gain and loss of cis-regulatory motifs at accessible regions.
2022-04-29 | GSE196451 | GEO
Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families. Three protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors Cbf1 and Tye7 to double-stranded 44K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 30-bp genomic sequences: 1) ChIP-chip bound probes, 2) ChIP-chip unbound probes, and 3) negative control probes. ChIP-chip bound probes corresponded to genomic regions bound in vivo by Cbf1 or Tye7 (ChIP-chip P < 0.005 in rich medium (YPD) (Harbison et al., Nature 2004, PMID 15343339)) contained at least two consecutive 8-mers with universal PBM E-score > 0.35 (Zhu et al., Genome Research 2009, PMID 19158363). All putative binding sites occurred at the same position within the probes on the array. M-bM-^@M-^\ChIP-chip unboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-chip P > 0.5 and at least two consecutive 8-mers at a more stringent universal PBM E-score threshold of 0.4. Negative control probes corresponded to S. cerevisiae intergenic regions with a maximum 8-mer E-score < 0.3. We also designed probes that contain, within constant flanking regions, all 10-bp sequences that occur within the M-bM-^@M-^\ChIP-chip boundM-bM-^@M-^] probes and contain the E-box CACGTG, but are flanked by synthetic rather than native genomic sequence. Each DNA sequence represented on the array is present in 4 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).
2013-03-29 | E-GEOD-44436 | biostudies-arrayexpress
Project description:Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein’s binding specificity by representing sequence motifs, including the gaps and dependencies between binding-site residues, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For 9 TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases. In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences. Protein binding microarray (PBM) experiments were performed for a set of 86 mouse transcription factors. Briefly, the PBMs involved binding GST-tagged DNA-binding proteins to two double-stranded 44K Agilent microarrays, each containing a different DeBruijn sequence design, in order to determine their sequence preferences. Details of the PBM protocol are described in Berger et al., Nature Biotechnology 2006.
2012-12-13 | E-GEOD-42864 | biostudies-arrayexpress
Project description:Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein’s binding specificity by representing sequence motifs, including the gaps and dependencies between binding-site residues, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For 9 TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases. In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
2012-12-13 | GSE42864 | GEO
Project description:Sequence-specific transcription factors (TFs) regulate gene expression by binding to cognate motifs in promoters and enhancers. However, predicting genomic TF binding events and their quantitative contribution to expression remains a major challenge. In principle, the binding and enhancer activity of specific sites in vivo might depend on: (i) latent properties of the motif instance, (ii) cooperative interactions with other TFs that bind in the immediate vicinity, and (iii) the chromatin state of the sites in the genome. Here, we used massively parallel reporter assays (MPRA) involving 32,115 natural and synthetic enhancers, together with high-throughput in vivo assays, to systematically dissect the contributions of motif affinity, cooperative interactions, and chromatin accessibility to the binding and regulatory activity of genomic sequences that contain motifs for PPARγ, a TF that serves as a key regulator of adipogenesis. We show that PPARγ binding and enhancer activity are governed by distinct features. Genomic PPARγ binding to motif sites is largely governed by on larger-scale features, such as chromatin accessibility, whereas the degree to which a PPARγ motif site enhances transcriptional activity depends on the sequence immediately surround the motif. We detect and functionally validate a network of TFs comprised of multiple functional classes that collaborate with PPARγ to drive transcription. We extensively perturb this network, revealing functional cooperativity among classes of TFs that does not depend on precise positioning. Together, these results present a clear picture of how chromatin and TFs from distinct functional classes interact with PPARγ to determine binding and enhancer activity, and provide a paradigm for studying any TF.
2017-02-03 | GSE84888 | GEO
Project description:Broadly expressed transcriptions factors (TFs) control tissue-specific programs of gene expression through interactions with local TF networks. Prime examples are the circadian clock TFs CLOCK (CLK) and CYCLE (CYC or BMAL1): while they control a core transcriptional circuit throughout animal bodies, downstream clock target genes and circadian physiology are tissue-specific. Here, we use ChIP-seq to determine the regulatory targets of Drosophila CLK and CYC, which we epitope-tagged by homologous recombination. Both TFs have distinct binding sites in heads versus bodies, suggesting that they directly control tissue-specific downstream target genes. Analysis of these context-specific binding sites revealed distinct sequence motifs for putative clock partner factors, including a motif for the GATA factor SERPENT (SRP). SRP indeed synergistically enhances CLK/CYC-mediated activity of a cis-regulatory region bound by CLK/CYC specifically in bodies. These results reveal how universal clock circuits can generate tissue-specific outputs and demonstrate an approach to dissect regulatory interactions more generally. We sequenced ChIP and input samples, as well as M-bM-^@M-^\mockM-bM-^@M-^] samples for which we performed ChIP with the V5 antibody from wildtype w- flies (not carrying the V5 tag) for two independent biological replicates each, summing to 24 libraries in total.
2013-06-01 | E-GEOD-40467 | biostudies-arrayexpress
Project description:Purpose: 224 GSM Samples form GSE32970 and GSE29692 was reanalyzed to find the binding sites of 542 TFs. Methods: 1. iFORM (incorporating Find Occurrence of Regulatory Motifs) is a tool we designed to scan through sequences in search of binding sites that match given motifs. 2. We mapped motif-binding protein information found in TRANSFAC, JASPAR, and UniPROBE databases to 542 coding genes, using GeneCards (Rebhan et al., 1997) and UniProt Knowledgebase (Magrane and Consortium, 2011). The position-specific weight matrices of the 542 TFs, corresponding to 796 motif models, were collected from these databases. 3.We used a uniform set of open chromatin elements (DNaseI hypersensitive sites, DHSs) in 133 ENCODE cell types, based on DNase-seq data produced by the "Open Chromatin" (Duke/UNC/UT-A)(GSE32970) and University of Washington (UW) ENCODE groups (GSE29692)from the project inception in 2007 through the ENCODE January 2011 data freeze. Processing pipeline: Combined all replicates for a given cell type; Subsampled the result at a level of 30 million tags; Ran results through the Stam Lab hotspot pipeline. Hotspots were identified using a relaxed threshold. NarrowPeaks were generated by first thresholding hotspots at FDR 1%, and then locating local maxima of the tag density within the hotspots. The 133 narrowPeaks are base sequences for the given motifs scan. Result: We used our iFORM with a custom library of all 796 motifs to scan for motif instances in 133 ENCODE DHSs cell types separately. Result in 133 files correspond to 133 cell types, each file contains binding sites of 542 TFs in a cell type.
2014-10-12 | GSE53962 | GEO
Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families. Two protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors Cbf1 and Tye7 to double-stranded 44K Agilent microarrays in order to determine the accuracy of our regression models for TF-DNA binding specificity. Briefly, this array contains 30-bp genomic sequences from our initial custom array (Gordan et al 2013, submitted), with 0 through 4 mutations designed at various positions in the genomic sequences. Each sequence in represented in 6 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).
2013-03-29 | E-GEOD-44437 | biostudies-arrayexpress
Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.
2013-03-29 | GSE44436 | GEO
Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.
2013-03-29 | GSE44437 | GEO