Helmholtz Gemeinschaft


Proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Item Type:Article
Title:Proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction
Creators Name:Wang, J. and Ma, Z. and Carr, S.A. and Mertins, P. and Zhang, H. and Zhang, Z. and Chan, D.W. and Ellis, M.J.C. and Townsend, R.R. and Smith, R.D. and McDermott, J.E. and Chen, X. and Paulovich, A.G. and Boja, E.S. and Mesri, M. and Kinsinger, C.R. and Rodriguez, H. and Rodland, K.D. and Liebler, D.C. and Zhang, B.
Abstract:Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this "guilt-by-association" (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies.
Keywords:Algorithms, Chromosome Mapping, Epithelial-Mesenchymal Transition, Gene Expression Profiling, Gene Regulatory Networks, Mass Spectrometry, Neoplasms, Neoplastic Gene Expression Regulation, Oligonucleotide Array Sequence Analysis, Protein Interaction Maps, Proteomics, Web Browser
Source:Molecular & Cellular Proteomics
Publisher:American Society for Biochemistry and Molecular Biology
Page Range:121-134
Date:1 January 2017
Official Publication:https://doi.org/10.1074/mcp.M116.060301
PubMed:View item in PubMed

Repository Staff Only: item control page


Downloads per month over past year

Open Access
MDC Library