Dataset Information

Combinatorial binding of transcription factors in the pluripotency control regions of the genome.

ABSTRACT: The pluripotency control regions (PluCRs) are defined as genomic regions that are bound by POU5F1, SOX2, and NANOG in vivo. We utilized a high-throughput binding assay to record more than 270,000 different DNA/protein binding measurements along incrementally tiled windows of DNA within these PluCRs. This high-resolution binding map is then used to systematically define the context of POU factor binding, and reveals patterns of cooperativity and competition in the pluripotency network. The most prominent pattern is a pervasive binding competition between POU5F1 and the forkhead transcription factors. Like many transcription factors, POU5F1 is co-expressed with a paralog, POU2F1, that shares an apparently identical binding specificity. By analyzing thousands of binding measurements, we discover context effects that discriminate POU2F1 from POU5F1 binding. Proximal NANOG binding promotes POU5F1 binding, whereas nearby SOX2 binding favors POU2F1. We demonstrate by cross-species comparison and by chromatin immunoprecipitation (ChIP) that the contextual sequence determinants learned in vitro are sufficient to predict POU2F1 binding in vivo.

SUBMITTER: Ferraris L

PROVIDER: S-EPMC3129248 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Combinatorial binding of transcription factors in the pluripotency control regions of the genome.

Ferraris Luciana L Stewart Allan P AP Kang Jinsuk J DeSimone Alec M AM Gemberling Matthew M Tantin Dean D Fairbrother William G WG

Genome research 20110428 7

The pluripotency control regions (PluCRs) are defined as genomic regions that are bound by POU5F1, SOX2, and NANOG in vivo. We utilized a high-throughput binding assay to record more than 270,000 different DNA/protein binding measurements along incrementally tiled windows of DNA within these PluCRs. This high-resolution binding map is then used to systematically define the context of POU factor binding, and reveals patterns of cooperativity and competition in the pluripotency network. The most p ...[more]

PMID: 21527551

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Project description:The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strategy (GRS) associated with each response gene. Here, we examined whether the GRSs of target genes may be inferred from stimulus-response (input-output) datasets, which remains an unresolved model-identifiability challenge. We developed a mechanistic modeling framework and computational workflow to determine the identifiability of all possible combinations of synergistic (AND) or non-synergistic (OR) GRSs involving three transcription factors. Considering different sets of perturbations for stimulus-response studies, we found that two thirds of GRSs are easily distinguishable but that substantially more quantitative data is required to distinguish the remaining third. To enhance the accuracy of the inference with timecourse experimental data, we developed an advanced error model that avoids error overestimates by distinguishing between value and temporal error. Incorporating this error model into a Bayesian framework, we show that GRS models can be identified for individual genes by considering multiple datasets. Our analysis rationalizes the allocation of experimental resources by identifying most informative TF stimulation conditions. Applying this computational workflow to experimental data of immune response genes in macrophages, we found that a much greater fraction of genes are combinatorially controlled than previously reported by considering compensation among transcription factors. Specifically, we revealed that a group of known NFκB target genes may also be regulated by IRF3, which is supported by chromatin immuno-precipitation analysis. Our study provides a computational workflow for designing and interpreting stimulus-response gene expression studies to identify underlying gene regulatory strategies and further a mechanistic understanding.

Dataset Information

Combinatorial binding of transcription factors in the pluripotency control regions of the genome.

Publications

Combinatorial binding of transcription factors in the pluripotency control regions of the genome.

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