Large-Scale Discovery of ERK2 Substrates Identifies ERK-Mediated Transcriptional Regulation by ETV3

The mitogen-activated protein kinase (MAPK) extracellular signal–regulated kinase 2 (ERK2) is ubiquitously expressed in mammalian tissues and is involved in a wide range of biological processes. Although MAPKs have been intensely studied, identification of their substrates remains challenging. We have optimized a chemical genetic system using analog-sensitive ERK2, a form of ERK2 engineered to use an analog of adenosine 5′-triphosphate (ATP), to tag and isolate ERK2 substrates in vitro. This approach identified 80 proteins phosphorylated by ERK2, 13 of which are known ERK2 substrates. The 80 substrates are associated with diverse cellular processes, including regulation of transcription and translation, mRNA processing, and regulation of the activity of the Rho family guanosine triphosphatases. We found that one of the newly identified substrates, ETV3 (a member of the E twenty-six family of transcriptional regulators), was extensively phosphorylated on sites within canonical and noncanonical ERK motifs. Phosphorylation of ETV3 regulated transcription by preventing its binding to DNA at promoters for several thousand genes, including some involved in negative feedback regulation of itself and of upstream signals.Massachusetts Institute of Technology (Eugene Bell Career Development Chair)David H. Koch Institute for Integrative Cancer Research at MIT (Graduate Fellowship)Massachusetts Institute of Technology (Whitaker Health Science Fellowship)National Science Foundation (U.S.) (Graduate Research Fellowship)Pfizer Inc.National Institutes of Health (U.S.) (grant ES002109)National Institutes of Health (U.S.) (grant R01DK42816)National Institutes of Health (U.S.) (grant R01CA118705)National Institutes of Health (U.S.) (grant U54CA112967

Direct Recruitment of Polycomb Repressive Complex 1 to Chromatin by Core Binding Transcription Factors

Polycomb repressive complexes (PRCs) play key roles in developmental epigenetic regulation. Yet the mechanisms that target PRCs to specific loci in mammalian cells remain incompletely understood. In this study we show that Bmi1, a core component of Polycomb Repressive Complex 1 (PRC1), binds directly to the Runx1/CBFβ transcription factor complex. Genome-wide studies in megakaryocytic cells demonstrate significant chromatin occupancy overlap between the PRC1 core component Ring1b and Runx1/CBFβ and functional regulation of a considerable fraction of commonly bound genes. Bmi1/Ring1b and Runx1/CBFβ deficiencies generate partial phenocopies of one another in vivo. We also show that Ring1b occupies key Runx1 binding sites in primary murine thymocytes and that this occurs via PRC2-independent mechanisms. Genetic depletion of Runx1 results in reduced Ring1b binding at these sites in vivo. These findings provide evidence for site-specific PRC1 chromatin recruitment by core binding transcription factors in mammalian cells.Stem Cell and Regenerative Biolog

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

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 β-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.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109

ChIP-Seq reveals functional role of p300.

<p>A. EMT marker genes bound by p300 in U87H cells. Shown are genome browser tracks for p300 bound regions near several EMT marker genes, where the horizontal axis represent coordinates along the genome and the height of the solid area represents the number of ChIP-Seq reads mapped to a position in the genome. For each region we show this signal from the ChIP sample that used an antibody specific to p300 (bottom track) and the signal from the sample that used an IgG antibody for non-specific binding (top track). Arrow indicates direction of transcription. B. Regions that are more hypersensitive (HS) in the U87H cells were significantly enriched for overlap with p300 binding regions (p<1E-05) compared to a background of all regions called hypersensitive in U87H cells, for a range of peak calling thresholds of hypersensitivity specified on the x-axis tick marks. Enrichment p-values computed by Fisher exact test are indicated immediately below each set of bars.</p

PCST constructed from the U87 datasets.

<p>This is a composite network representing the union of the optimal solution to the original PCST problem and 10 suboptimal solutions where 15 percent of the nodes must be different from the optimal solution. TF: transcription factor. Node weight: the log2 fold changes in phosphorylation from the phosphoproteomic data comparing U87H to U87DK cells, or values from the expression regression procedure using the mRNA microarray, DNase-Seq and transcription factor motif data. The absolute value of node weights was used as penalty values for the PCST algorithm.</p

High-, mid- lower-ranked nodes by the PCST network and the experiments used to validate their importance.

<p>For cell viability assays, the inhibitors used are listed. Note that some inhibitors have multiple targets. For ChIP-Seq, the antibody used is listed.</p

Enriched Gene Ontology (GO) categories of p300 target genes in U87H cells.

<p>1,969 genes within 10 kb of 7,657 high stringency peaks called by MACS (p<1E-07) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002887#pcbi.1002887-Zhang1" target="_blank">[112]</a> were input into the DAVID functional annotation tool <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002887#pcbi.1002887-Yamoutpour1" target="_blank">[120]</a> to identify enriched Biological Process terms with FDR <5%.</p

Validation of targets predicted by network connectivity by cell viability assays.

<p>A. Cell viability for treatment with compounds targeting high-scoring nodes (high-ranked targets), intermediate-scoring nodes (mid-ranked targets) and low-scoring nodes (lower-ranked targets), at 0.5 µM concentration of 17-AAG, 5 µM for harmine (due to low solubility in DMSO) and 10 µM concentration of others. The color bar at the top of each target corresponds to its relative ranking within the interactome. B. Dose response curves of compounds targeting high-scoring nodes and lower-scoring nodes for those that can be fitted to the four-parameter log-logistic model (lack-of-fit test p-value>0.05). P-values between cell lines were computed by comparing the model where one curve was fitted to the data from each cell line to the null model where one shared curve was fitted to the data from both cell lines.</p

NetSig: network-based discovery from cancer genomes

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes

Direct Recruitment of Polycomb Repressive Complex 1 to Chromatin by Core Binding Transcription Factors

Polycomb repressive complexes (PRCs) play key roles in developmental epigenetic regulation. Yet the mechanisms that target PRCs to specific loci in mammalian cells remain incompletely understood. In this study we show that Bmi1, a core component of Polycomb Repressive Complex 1 (PRC1), binds directly to the Runx1/CBFβ transcription factor complex. Genome-wide studies in megakaryocytic cells demonstrate significant chromatin occupancy overlap between the PRC1 core component Ring1b and Runx1/CBFβ and functional regulation of a considerable fraction of commonly bound genes. Bmi1/Ring1b and Runx1/CBFβ deficiencies generate partial phenocopies of one another in vivo. We also show that Ring1b occupies key Runx1 binding sites in primary murine thymocytes and that this occurs via PRC2-independent mechanisms. Genetic depletion of Runx1 results in reduced Ring1b binding at these sites in vivo. These findings provide evidence for site-specific PRC1 chromatin recruitment by core binding transcription factors in mammalian cells.National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Science Foundation (U.S.) (Award DB1-0821391)National Institutes of Health (U.S.) (P30-ES002109