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Cell Rep. 2017 Jun 27;19(13):2853-2866. doi: 10.1016/j.celrep.2017.06.016.

An Integrative Framework Reveals Signaling-to-Transcription Events in Toll-like Receptor Signaling.

Author information

1
Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
2
Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
3
Molecular Physiology and Cell Biology Section, Leibniz-Institute for Molecular Pharmacology (FMP), 13125 Berlin, Germany.
4
WPI Immunology Frontier Research Center, Osaka University, 3-1 Yamada-oka, Suita, Osaka 565-0871, Japan.
5
Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Department of Biology, MIT, Cambridge, MA 02142, USA. Electronic address: aregev@broadinstitute.org.
6
Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Center for Immunology and Inflammatory Diseases and Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129, USA. Electronic address: nhacohen@mgh.harvard.edu.
7
Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA. Electronic address: chevrier@fas.harvard.edu.

Abstract

Building an integrated view of cellular responses to environmental cues remains a fundamental challenge due to the complexity of intracellular networks in mammalian cells. Here, we introduce an integrative biochemical and genetic framework to dissect signal transduction events using multiple data types and, in particular, to unify signaling and transcriptional networks. Using the Toll-like receptor (TLR) system as a model cellular response, we generate multifaceted datasets on physical, enzymatic, and functional interactions and integrate these data to reveal biochemical paths that connect TLR4 signaling to transcription. We define the roles of proximal TLR4 kinases, identify and functionally test two dozen candidate regulators, and demonstrate a role for Ap1ar (encoding the Gadkin protein) and its binding partner, Picalm, potentially linking vesicle transport with pro-inflammatory responses. Our study thus demonstrates how deciphering dynamic cellular responses by integrating datasets on various regulatory layers defines key components and higher-order logic underlying signaling-to-transcription pathways.

KEYWORDS:

TLRs; Toll-like receptors; large-scale in vitro kinase assay; pathogen-sensing pathways; phosphoproteomics; protein-protein interactions; signaling; transcriptional network analysis

PMID:
28658630
PMCID:
PMC5551420
DOI:
10.1016/j.celrep.2017.06.016
[Indexed for MEDLINE]
Free PMC Article

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