Comparative Analysis of Proteome and Transcriptome Variation in Mouse

Anatole Ghazalpour(University of California, Los Angeles), Brian J. Bennett(University of California, Los Angeles), Vladislav Petyuk(Pacific Northwest National Laboratory), Luz D. Orozco(University of California, Los Angeles), Raffi Hagopian(University of California, Los Angeles), Imran N. Mungrue(University of California, Los Angeles), Charles R. Farber(University of Virginia), Janet S. Sinsheimer(University of California, Los Angeles), Hyun Min Kang(Michigan United), Nicholas A. Furlotte(University of California, Los Angeles), Christopher C Park(University of California, Los Angeles), Ping-Zi Wen(University of California, Los Angeles), Heather M. Brewer(Pacific Northwest National Laboratory), Karl Weitz(Environmental Molecular Sciences Laboratory), David Camp(Environmental Molecular Sciences Laboratory), Calvin Pan(University of California, Los Angeles), Roumyana Yordanova(Bristol-Myers Squibb (United States)), Isaac Neuhaus(Bristol-Myers Squibb (United States)), Charles Tilford(Bristol-Myers Squibb (United States)), Nathan O. Siemers(Bristol-Myers Squibb (United States)), Peter S. Gargalovic(Bristol-Myers Squibb (United States)), Eleazar Eskin(University of California, Los Angeles), Todd G. Kirchgessner(Bristol-Myers Squibb (United States)), Desmond Smith(University of California, Los Angeles), Richard Smith(Environmental Molecular Sciences Laboratory), Aldons J. Lusis(University of California, Los Angeles)
PLoS Genetics
June 9, 2011
10.1371/journal.pgen.1001393
Cited by 645Open Access
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Abstract

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.

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