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Title: Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

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 revealedmore » 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. Molecular & Cellular Proteomics 16: 10.1074/mcp.M116.060301, 121–134, 2017.« less

Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
1372993
Report Number(s):
PNNL-SA-124098
Journal ID: ISSN 1535-9476; 48666; 453040220
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Molecular and Cellular Proteomics
Additional Journal Information:
Journal Volume: 16; Journal Issue: 1; Journal ID: ISSN 1535-9476
Publisher:
American Society for Biochemistry and Molecular Biology
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Environmental Molecular Sciences Laboratory

Citation Formats

Wang, Jing, Ma, Zihao, Carr, Steven A., Mertins, Philipp, Zhang, Hui, Zhang, Zhen, Chan, Daniel W., Ellis, Matthew J. C., Townsend, R. Reid, Smith, Richard D., McDermott, Jason E., Chen, Xian, Paulovich, Amanda G., Boja, Emily S., Mesri, Mehdi, Kinsinger, Christopher R., Rodriguez, Henry, Rodland, Karin D., Liebler, Daniel C., and Zhang, Bing. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction. United States: N. p., 2016. Web. doi:10.1074/mcp.M116.060301.
Wang, Jing, Ma, Zihao, Carr, Steven A., Mertins, Philipp, Zhang, Hui, Zhang, Zhen, Chan, Daniel W., Ellis, Matthew J. C., Townsend, R. Reid, Smith, Richard D., McDermott, Jason E., Chen, Xian, Paulovich, Amanda G., Boja, Emily S., Mesri, Mehdi, Kinsinger, Christopher R., Rodriguez, Henry, Rodland, Karin D., Liebler, Daniel C., & Zhang, Bing. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction. United States. doi:10.1074/mcp.M116.060301.
Wang, Jing, Ma, Zihao, Carr, Steven A., Mertins, Philipp, Zhang, Hui, Zhang, Zhen, Chan, Daniel W., Ellis, Matthew J. C., Townsend, R. Reid, Smith, Richard D., McDermott, Jason E., Chen, Xian, Paulovich, Amanda G., Boja, Emily S., Mesri, Mehdi, Kinsinger, Christopher R., Rodriguez, Henry, Rodland, Karin D., Liebler, Daniel C., and Zhang, Bing. Fri . "Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction". United States. doi:10.1074/mcp.M116.060301.
@article{osti_1372993,
title = {Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction},
author = {Wang, Jing and Ma, Zihao and Carr, Steven A. and Mertins, Philipp and Zhang, Hui and Zhang, Zhen and Chan, Daniel W. and Ellis, Matthew J. C. and Townsend, R. Reid and Smith, Richard D. and McDermott, Jason E. and Chen, Xian and Paulovich, Amanda G. and Boja, Emily S. and Mesri, Mehdi and Kinsinger, Christopher R. and Rodriguez, Henry and Rodland, Karin D. and Liebler, Daniel C. and Zhang, Bing},
abstractNote = {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. Molecular & Cellular Proteomics 16: 10.1074/mcp.M116.060301, 121–134, 2017.},
doi = {10.1074/mcp.M116.060301},
journal = {Molecular and Cellular Proteomics},
issn = {1535-9476},
number = 1,
volume = 16,
place = {United States},
year = {2016},
month = {11}
}

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