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Synergistic action of master transcription factors controls epithelial-to-mesenchymal transition.


ABSTRACT: Epithelial-to-mesenchymal transition (EMT) is a complex multistep process in which phenotype switches are mediated by a network of transcription factors (TFs). Systematic characterization of all dynamic TFs controlling EMT state transitions, especially for the intermediate partial-EMT state, represents a highly relevant yet largely unexplored task. Here, we performed a computational analysis that integrated time-course EMT transcriptomic data with public cistromic data and identified three synergistic master TFs (ETS2, HNF4A and JUNB) that regulate the transition through the partial-EMT state. Overexpression of these regulators predicted a poor clinical outcome, and their elimination readily abolished TGF-?-induced EMT. Importantly, these factors utilized a clique motif, physically interact and their cumulative binding generally characterized EMT-associated genes. Furthermore, analyses of H3K27ac ChIP-seq data revealed that ETS2, HNF4A and JUNB are associated with super-enhancers and the administration of BRD4 inhibitor readily abolished TGF-?-induced EMT. These findings have implications for systematic discovery of master EMT regulators and super-enhancers as novel targets for controlling metastasis.

SUBMITTER: Chang H 

PROVIDER: S-EPMC4824118 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Synergistic action of master transcription factors controls epithelial-to-mesenchymal transition.

Chang Hongyuan H   Liu Yuwei Y   Xue Mengzhu M   Liu Haiyue H   Du Shaowei S   Zhang Liwen L   Wang Peng P  

Nucleic acids research 20160228 6


Epithelial-to-mesenchymal transition (EMT) is a complex multistep process in which phenotype switches are mediated by a network of transcription factors (TFs). Systematic characterization of all dynamic TFs controlling EMT state transitions, especially for the intermediate partial-EMT state, represents a highly relevant yet largely unexplored task. Here, we performed a computational analysis that integrated time-course EMT transcriptomic data with public cistromic data and identified three syner  ...[more]

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