title: Comparative analysis of proteome and transcriptome variation in mouse.
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.
authors.affiliation: Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America. aghazalp@ucla.edu
authors.forename: Anatole
authors.initials: A
authors.lastname: Ghazalpour
authors.forename: Brian
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authors.lastname: Bennett
authors.forename: Vladislav A
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authors.lastname: Petyuk
authors.forename: Luz
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authors.lastname: Orozco
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authors.lastname: Hagopian
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authors.lastname: Mungrue
authors.forename: Charles R
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authors.lastname: Farber
authors.forename: Janet
authors.initials: J
authors.lastname: Sinsheimer
authors.forename: Hyun M
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authors.lastname: Kang
authors.forename: Nicholas
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authors.lastname: Furlotte
authors.forename: Christopher C
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authors.lastname: Park
authors.forename: Ping-Zi
authors.initials: PZ
authors.lastname: Wen
authors.forename: Heather
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authors.lastname: Brewer
authors.forename: Karl
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authors.lastname: Weitz
authors.forename: David G
authors.initials: DG
authors.lastname: Camp
authors.forename: Calvin
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authors.lastname: Pan
authors.forename: Roumyana
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authors.lastname: Yordanova
authors.forename: Isaac
authors.initials: I
authors.lastname: Neuhaus
authors.forename: Charles
authors.initials: C
authors.lastname: Tilford
authors.forename: Nathan
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authors.lastname: Siemers
authors.forename: Peter
authors.initials: P
authors.lastname: Gargalovic
authors.forename: Eleazar
authors.initials: E
authors.lastname: Eskin
authors.forename: Todd
authors.initials: T
authors.lastname: Kirchgessner
authors.forename: Desmond J
authors.initials: DJ
authors.lastname: Smith
authors.forename: Richard D
authors.initials: RD
authors.lastname: Smith
authors.forename: Aldons J
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authors.lastname: Lusis
mesh_terms: D017398:Alternative Splicing; D000818:Animals; D020869:Gene Expression Profiling; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D051379:Mice; D020543:Proteome / Q000032:analysis* / Q000235:genetics; D040901:Proteomics; D012333:RNA, Messenger / Q000378:metabolism
chemical_list: D020543:Proteome; D012333:RNA, Messenger