Comparative Analysis of Proteome and Transcriptome Variation in Mouse

Autor: Aldons J. Lusis, Imran N. Mungrue, Richard D. Smith, Nicholas A. Furlotte, Brian J. Bennett, Heather M. Brewer, Karl K. Weitz, Charles Tilford, Hyun Min Kang, Vladislav A. Petyuk, Desmond J. Smith, Janet S. Sinsheimer, Nathan O. Siemers, Christopher C. Park, Isaac M. Neuhaus, Calvin Pan, Raffi Hagopian, David G. Camp, Roumyana Yordanova, Charles R. Farber, Todd G. Kirchgessner, Peter S. Gargalovic, Eleazar Eskin, Ping Zi Wen, Luz D. Orozco, Anatole Ghazalpour
Jazyk: angličtina
Rok vydání: 2011
Předmět:
Proteomics
Cancer Research
Proteome
lcsh:QH426-470
Genetics and Genomics/Animal Genetics
Biology
Genetics and Genomics/Complex Traits
Transcriptome
Mice
03 medical and health sciences
0302 clinical medicine
Inbred strain
Genetics
Animals
RNA
Messenger
Genetics and Genomics/Genomics
Molecular Biology
Gene
Genetics (clinical)
Ecology
Evolution
Behavior and Systematics
Genetics and Genomics/Genetics of Disease
030304 developmental biology
0303 health sciences
Gene Expression Profiling
Alternative splicing
Genetic Variation
Genetics and Genomics
Genetics and Genomics/Gene Expression
Genetics and Genomics/Physiogenomics
Gene expression profiling
Alternative Splicing
lcsh:Genetics
DNA microarray
Genetics and Genomics/Comparative Genomics
030217 neurology & neurosurgery
Genome-Wide Association Study
Research Article
Zdroj: PLoS Genetics
Ghazalpour, A; Bennett, B; Petyuk, VA; Orozco, L; Hagopian, R; Mungrue, IN; et al.(2011). Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genetics, 7(6). doi: 10.1371/journal.pgen.1001393. UCLA: Retrieved from: http://www.escholarship.org/uc/item/3qp1p825
PLoS Genetics, Vol 7, Iss 6, p e1001393 (2011)
ISSN: 1553-7404
1553-7390
DOI: 10.1371/journal.pgen.1001393.
Popis: 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.
Author Summary An old dogma in biology states that, in every cell, the flow of biological information is from DNA to RNA to proteins and that the latter act as a working force to determine the organism's phenotype. This model predicts that changes in DNA that affect the clinical phenotype should also similarly change the cellular levels of RNA and protein levels. In this report, we test this prediction by looking at the concordance between DNA variation in population of mouse inbred strains, the RNA and protein variation in the liver tissue of these mice, and variation in metabolic phenotypes. We show that the relationship between various biological traits is not simple and that there is relatively little concordance of RNA levels and the corresponding protein levels in response to DNA perturbations. In addition, we also find that, surprisingly, metabolic traits correlate better to RNA levels than to protein levels. In light of current efforts in searching for the molecular bases of disease susceptibility in humans, our findings highlight the complexity of information flow that underlies clinical outcomes.
Databáze: OpenAIRE
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