Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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A novel proteomic approach reveals GREB1 as an Estrogen Receptor co-factor

ABSTRACT: Methods for identifying protein-protein interactions have mostly been limited to tagged exogenous expression approaches. We now establish a rapid, robust and comprehensive method for finding interacting proteins using endogenous proteins from limited cell numbers. We apply this approach called ‘Rapid IP-Mass Spectrometry of Endogenous proteins (RIME)’ to identify ER, FoxA1 and E2F4 interacting proteins in breast cancer cells. From small numbers of starting cells, we find a comprehensive collection of known ER, FoxA1 and E2F4 targets, plus a number of novel unexpected interactors. One of the most ER (and FoxA1) associated interactors is GREB1, an estrogen induced gene with almost no known function. We apply RIME, in parallel with ER ChIP-seq, to identify ER protein interactors and ER binding events from solid tumor xenografts, resulting in the validation of the ER-GREB1 interactions. Furthermore, we establish a method for identifying endogenous interacting proteins from solid primary breast cancer samples, whih we apply to validate ER interactions with GREB1 and additional co-factors. Mechanistically, we show that GREB1 is recruited with ER to the chromatin where it functions as an essential estrogen-mediated regulatory factor required for effective ER transcriptional activity. Our novel approach enables, for the first time, the ability for discovery and validation of protein-protein interactions in whole tissue and solid tumors, revealing significant insight into ER regulatory factors. Examination of ERGREB1 and E2F4 genomic binding patterns in cell line and xenograft tumour models

ORGANISM(S): Homo sapiens

SUBMITTER: Silvia Halim

PROVIDER: E-GEOD-41561 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Estrogen receptor-alpha (ER) is the driving transcription factor in most breast cancers, and its associated proteins can influence drug response, but direct methods for identifying interacting proteins have been limited. We purified endogenous ER using an approach termed RIME (rapid immunoprecipitation mass spectrometry of endogenous proteins) and discovered the interactome under agonist- and antagonist-liganded conditions in breast cancer cells, revealing transcriptional networks in breast cancer. The most estrogen-enriched ER interactor is GREB1, a potential clinical biomarker with no known function. GREB1 is shown to be a chromatin-bound ER coactivator and is essential for ER-mediated transcription, because it stabilizes interactions between ER and additional cofactors. We show a GREB1-ER interaction in three xenograft tumors, and using a directed protein-protein approach, we find GREB1-ER interactions in half of ER+ primary breast cancers. This finding is supported by histological expression of GREB1, which shows that GREB1 is expressed in half of ER+ cancers, and predicts good clinical outcome. These findings reveal an unexpected role for GREB1 as an estrogen-specific ER cofactor that is expressed in drug-sensitive contexts. Additional information: All microarray data have been deposited in the GEO database with the accession number GSE37386. All ChIP-seq data have been deposited in the GEO database with the accession number GSE41561. RawMSdata files were processed using Proteome Discoverer v.1.3 (Thermo Scientific). Processed files were searched against the SwissProt human database using the Mascot search engine version 2.3.0. Searches were done with tryptic specificity allowing up to one miscleavage and a tolerance on mass measurement of 10 ppm in MS mode and 0.6 Da for MS/MS ions. Structure modifications allowed were oxidized methionine, and deamidation of asparagine and glutamine residues, which were searched as variable modifications. Using a reversed decoy database, false discovery rate (FDR) was less than 1%. Detailed results including peptide sequences, peptide scores, ion scores, expect values, and Mascot scores have been included in Table S1. SILAC data were analyzed using Proteome Discoverer v.1.3. This analysis only uses unique peptides for ratio analysis. The resulting fold-change ratios for proteins found in both replicates were then converted into log fold-change ratios as represented in the figures. In a few instances where protein ratios were absent due to signal from only one of the two channels, these were ranked highest in the log fold-change table following manual inspection of the raw data (MS and MS/MS) to confirm specificity to a single label.

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A novel proteomic approach reveals GREB1 as an Estrogen Receptor co-factor

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