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Transcriptional responses to fatty acid are coordinated by combinatorial control.


ABSTRACT: In transcriptional regulatory networks, the coincident binding of a combination of factors to regulate a gene implies the existence of complex mechanisms to control both the gene expression profile and specificity of the response. Unraveling this complexity is a major challenge to biologists. Here, a novel network topology-based clustering approach was applied to condition-specific genome-wide chromatin localization and expression data to characterize a dynamic transcriptional regulatory network responsive to the fatty acid oleate. A network of four (predicted) regulators of the response (Oaf1p, Pip2p, Adr1p and Oaf3p) was investigated. By analyzing trends in the network structure, we found that two groups of multi-input motifs form in response to oleate, each controlling distinct functional classes of genes. This functionality is contributed in part by Oaf1p, which is a component of both types of multi-input motifs and has two different regulatory activities depending on its binding context. The dynamic cooperation between Oaf1p and Pip2p appears to temporally synchronize the two different responses. Together, these data suggest a network mechanism involving dynamic combinatorial control for coordinating transcriptional responses.

SUBMITTER: Smith JJ 

PROVIDER: S-EPMC1911199 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Transcriptional responses to fatty acid are coordinated by combinatorial control.

Smith Jennifer J JJ   Ramsey Stephen A SA   Marelli Marcello M   Marzolf Bruz B   Hwang Daehee D   Saleem Ramsey A RA   Rachubinski Richard A RA   Aitchison John D JD  

Molecular systems biology 20070605


In transcriptional regulatory networks, the coincident binding of a combination of factors to regulate a gene implies the existence of complex mechanisms to control both the gene expression profile and specificity of the response. Unraveling this complexity is a major challenge to biologists. Here, a novel network topology-based clustering approach was applied to condition-specific genome-wide chromatin localization and expression data to characterize a dynamic transcriptional regulatory network  ...[more]

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