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:Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, often contain similar assortments of sequence-specific transcription factor (TF) binding sites. Knowledge of how these sequence motifs encode multiple, often overlapping gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease. Exploiting natural genetic variation, perturbation of endogenous TF protein levels, and analysis of synthetic regulatory elements using a novel transcription start site (TSS) capturing massively parallel reporter assay approach, here we show that the outcome of TF binding on transcription initiation is position dependent. Analyzing TF motif occurrences relative to the TSS, we identified several motifs with highly preferential and superhelical positioning. We show that these patterns are a combination of a TF's distinct functional profiles: many TFs, including canonical activators like NRF1, NFY, Sp1 or YY1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs can collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TFs, but with different spatial relationships, can result in distinct gene regulatory outcomes and underscore a critical role for TSS data in decoding the regulatory information of our genome.
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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.
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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:Most of the human genome is thought to be non-functional, and includes large segments often referred to as “dark matter” DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells. Nearly half of them associated strongly with known regulatory regions such as promoters and enhancers, often at conserved motif matches and co-localizing with each other. Surprisingly, the other half often associated with genomic dark matter, at largely unique sites, via intrinsic sequence recognition. Dozens of these, which we term “Dark TFs” mainly bind within regions of closed chromatin. Dark TF binding sites are rarely under purifying selection, and are enriched for transposable elements. Many Dark TFs are KZNFs, which contain the repressive KRAB domain, but many are not, and may represent potential pioneer TFs: based on compiled literature information, the Dark TFs exert diverse functions ranging from early development to tumor suppression. Thus, a large fraction of previously uncharacterized human TFs may have unappreciated activities within the dark matter genome.
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Project description:Most of the human genome is thought to be non-functional, and includes large segments often referred to as “dark matter” DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells. Nearly half of them associated strongly with known regulatory regions such as promoters and enhancers, often at conserved motif matches and co-localizing with each other. Surprisingly, the other half often associated with genomic dark matter, at largely unique sites, via intrinsic sequence recognition. Dozens of these, which we term “Dark TFs” mainly bind within regions of closed chromatin. Dark TF binding sites are rarely under purifying selection, and are enriched for transposable elements. Many Dark TFs are KZNFs, which contain the repressive KRAB domain, but many are not, and may represent potential pioneer TFs: based on compiled literature information, the Dark TFs exert diverse functions ranging from early development to tumor suppression. Thus, a large fraction of previously uncharacterized human TFs may have unappreciated activities within the dark matter genome.
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Project description:Most of the human genome is thought to be non-functional, and includes large segments often referred to as “dark matter” DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells. Nearly half of them associated strongly with known regulatory regions such as promoters and enhancers, often at conserved motif matches and co-localizing with each other. Surprisingly, the other half often associated with genomic dark matter, at largely unique sites, via intrinsic sequence recognition. Dozens of these, which we term “Dark TFs” mainly bind within regions of closed chromatin. Dark TF binding sites are rarely under purifying selection, and are enriched for transposable elements. Many Dark TFs are KZNFs, which contain the repressive KRAB domain, but many are not, and may represent potential pioneer TFs: based on compiled literature information, the Dark TFs exert diverse functions ranging from early development to tumor suppression. Thus, a large fraction of previously uncharacterized human TFs may have unappreciated activities within the dark matter genome.
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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.
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Project description:We provide the data collected using the method MPBA, allowing parallel measurement of TF binding to thousands of DNA sequence-variants within cells. The method was applied to quantify binding of dozens of transcription factors (TFs) to libraries of mutated regulatory regions, systematically screening all motif combinations.
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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.
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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.
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