Dataset Information

Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling.

ABSTRACT: Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human proteins of various functional classes. Among them, we recovered many known PDIs for transcription factors (TFs). We identified a large number of unanticipated PDIs for known TFs, as well as for previously uncharacterized TFs. We also found that over three hundred unconventional DNA-binding proteins (uDBPs)--which include RNA-binding proteins, mitochondrial proteins, and protein kinases--showed sequence-specific PDIs. One such uDBP, ERK2, acts as a transcriptional repressor for interferon gamma-induced genes, suggesting important biological roles for such proteins.

SUBMITTER: Hu S

PROVIDER: S-EPMC2774939 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling.

Hu Shaohui S Xie Zhi Z Onishi Akishi A Yu Xueping X Jiang Lizhi L Lin Jimmy J Rho Hee-sool HS Woodard Crystal C Wang Hong H Jeong Jun-Seop JS Long Shunyou S He Xiaofei X Wade Herschel H Blackshaw Seth S Qian Jiang J Zhu Heng H

Cell 20091001 3

Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human ...[more]

PMID: 19879846

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Project description:Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118-310, targeting ?-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.

Dataset Information

Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling.

Publications

Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling.

Similar Datasets

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