Comprehensive Mapping of Key Regulatory Networks that Drive Oncogene Expression
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1-s2.0-S2211124720314157-main.pdf
Description
Published version
Size
5.33 MB
Format
Adobe PDF
Checksum (MD5)
ec8077f335493f5856a8a1e51fbc3161
Author(s)
Lin, Lin
Holmes, Benjamin Ray
Shen, Max Walt
Gifford, David K
Date Issued
November 2020
Journal
Cell reports
Publisher
Elsevier BV
Citation
Lin, Lin et al. “Comprehensive Mapping of Key Regulatory Networks that Drive Oncogene Expression.” Cell reports, 33, 8 (November 2020): 108426 © 2020 The Author(s)
Version
Final published version
Abstract
Gene expression is controlled by the collective binding of transcription factors to cis-regulatory regions. Deciphering gene-centered regulatory networks is vital to understanding and controlling gene misexpression in human disease; however, systematic approaches to uncovering regulatory networks have been lacking. Here we present high-throughput interrogation of gene-centered activation networks (HIGAN), a pipeline that employs a suite of multifaceted genomic approaches to connect upstream signaling inputs, trans-acting TFs, and cis-regulatory elements. We apply HIGAN to understand the aberrant activation of the cytidine deaminase APOBEC3B, an intrinsic source of cancer hypermutation. We reveal that nuclear factor κB (NF-κB) and AP-1 pathways are the most salient trans-acting inputs, with minor roles for other inflammatory pathways. We identify a cis-regulatory architecture dominated by a major intronic enhancer that requires coordinated NF-κB and AP-1 activity with secondary inputs from distal regulatory regions. Our data demonstrate how integration of cis and trans genomic screening platforms provides a paradigm for building gene-centered regulatory networks.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.celrep.2020.108426
