Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses

Ido Amit(Broad Institute), Manuel Garber(Broad Institute), Nicolas Chevrier(Harvard University), Ana Paula Leite(Broad Institute), Yoni Donner(Broad Institute), Thomas Eisenhaure(Harvard University), Mitchell Guttman(Broad Institute), Jennifer K. Grenier(Broad Institute), Weibo Li(Harvard University), Or Zuk(Broad Institute), Lisa Schubert(Nanostring Technologies (United States)), Brian Birditt(Nanostring Technologies (United States)), Tal Shay(Broad Institute), Alon Goren(Broad Institute), Xiaolan Zhang(Broad Institute), Zachary D. Smith(Broad Institute), Raquel P. Deering(Harvard University), Rebecca C. McDonald(Harvard University), Moran N. Cabili(Broad Institute), B Bernstein(Broad Institute), John L. Rinn(Broad Institute), Alex Meissner(Broad Institute), David E. Root(Broad Institute), Nir Hacohen(Broad Institute), Aviv Regev(Broad Institute)
Science
September 3, 2009
Cited by 493Open Access
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Abstract

Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data but have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We applied this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins, which enabled the construction of a network model consisting of 24 core regulators and 76 fine-tuners that help to explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly applicable, comprehensive, and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.


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