Dataset Information

AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent Enhancer Selection.

ABSTRACT: Enhancer elements are genomic regulatory sequences that direct the selective expression of genes so that genetically identical cells can differentiate and acquire the highly specialized forms and functions required to build a functioning animal. To differentiate, cells must select from among the ?106 enhancers encoded in the genome the thousands of enhancers that drive the gene programs that impart their distinct features. We used a genetic approach to identify transcription factors (TFs) required for enhancer selection in fibroblasts. This revealed that the broadly expressed, growth-factor-inducible TFs FOS/JUN (AP-1) play a central role in enhancer selection. FOS/JUN selects enhancers together with cell-type-specific TFs by collaboratively binding to nucleosomal enhancers and recruiting the SWI/SNF (BAF) chromatin remodeling complex to establish accessible chromatin. These experiments demonstrate how environmental signals acting via FOS/JUN and BAF coordinate with cell-type-specific TFs to select enhancer repertoires that enable differentiation during development.

SUBMITTER: Vierbuchen T

PROVIDER: S-EPMC5744881 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent Enhancer Selection.

Vierbuchen Thomas T Ling Emi E Cowley Christopher J CJ Couch Cameron H CH Wang Xiaofeng X Harmin David A DA Roberts Charles W M CWM Greenberg Michael E ME

Molecular cell 20171201 6

Enhancer elements are genomic regulatory sequences that direct the selective expression of genes so that genetically identical cells can differentiate and acquire the highly specialized forms and functions required to build a functioning animal. To differentiate, cells must select from among the ∼10<sup>6</sup> enhancers encoded in the genome the thousands of enhancers that drive the gene programs that impart their distinct features. We used a genetic approach to identify transcription factors ( ...[more]

PMID: 29272704

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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

AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent Enhancer Selection.

Publications

AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent Enhancer Selection.

Similar Datasets

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