Comparative Analysis of Proteome and Transcriptome Variation in Mouse Anatole Ghazalpour1*., Brian Bennett1., Vladislav A. Petyuk2., Luz Orozco3., Raffi Hagopian1, Imran N. Mungrue1, Charles R. Farber4, Janet Sinsheimer3, Hyun M. Kang 5 , Nicholas Furlotte6, Christopher C. Park7, Ping-Zi Wen1, Heather Brewer2, Karl Weitz2, David G. Camp I2I , Calvin Pan3 , Roumyana Yordanova8, Isaac Neuhaus8, Charles Tilford8, Nathan Siemers8, Peter Gargalovic9, Eleazar Eskin6, Todd Kirchgessner9, Desmond J. Smith7, Richard D. Smith2, Aldons J. Lusis1,3,10,11 1 Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America, 2 Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States of America, 3 Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America, 4 Department of Medicine, Department of Biochemistry and Molecular Genetics, and Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America, 5 Biostatistics Department, University of Michigan Ann Arbor, Ann Arbor, Michigan, United States of America, 6 Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America, 7 Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America, 8 Department of Applied Genomics, Bristol-Myers Squibb, Princeton, New Jersey, United States of America, 9 Department of Atherosclerosis Drug Discovery, Bristol-Myers Squibb, Princeton, New Jersey, United States of America, 10 Molecular Biology Institute, University of California Los Angeles, Los Angeles, California, United States of America, 11 Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, California, United States of America 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. Citation: Ghazalpour A, Bennett B, Petyuk VA, Orozco L, Hagopian R, et al. (2011) Comparative Analysis of Proteome and Transcriptome Variation in Mouse. PLoS Genet 7(6): e1001393. doi:10.1371/journal.pgen.1001393 Editor: Michael Snyder, Stanford University School of Medicine, United States of America Received July 13, 2010; Accepted May 10, 2011; Published June 9, 2011 Copyright: 2011 Ghazalpour et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This research was supported in part by the American Heart Association AHA0825204F (AG); US National Institute of Health NIH grants HL28481, HL30568, HL094322 (AJL); R01 NS050148 (DJS); RR18552 (RDS); Ruth L. Kirschstein NIH F32 Fellowship 5F32DK074317 (CRF); and NRSA GM07104 and NRSA T32- HG002536 (LO). Proteomic analyses were performed in the Environmental Molecular Sciences Laboratory, a US Department of Energy (DOE) national scientific user facility located at the Pacific Northwest National Laboratory (PNNL) in Richland, Washington. PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the DOE under Contract DE-AC05-76RL01830. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: aghazalp@ucla.edu . These authors contributed equally to this work. Introduction An underlying assumption in many biological studies is the concordance of transcript and protein levels during the flow of information from DNA to phenotype. Clearly, protein levels are greatly influenced by post-translational processing and inherent variations in stability but, in general, it is assumed that perturbations of transcript levels are substantially correlated with PLoS Genetics | www.plosgenetics.org 1 June 2011 | Volume 7 | Issue 6 | e1001393