Comparative analysis of proteome and transcriptome variation in mouse.

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.

High-resolution mapping of gene expression using association in an outbred mouse stock.

Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL "hotspots"), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock.

Novel QTLs for HDL levels identified in mice by controlling for Apoa2 allelic effects: confirmation of a chromosome 6 locus in a congenic strain.

Atherosclerosis is a complex disease resulting from the interaction of multiple genes, including those causing dyslipidemia. Relatively few of the causative genes have been identified. Previously, we identified Apoa2 as a major determinant of high-density lipoprotein cholesterol (HDL-C) levels in the mouse model. To identify additional HDL-C level quantitative trait loci (QTLs), while controlling for the effect of the Apoa2 locus, we performed linkage analysis in 179 standard diet-fed F(2) mice derived from strains BALB/cJ and B6.C-H25(c) (a congenic strain carrying the BALB/c Apoa2 allele). Three significant QTLs and one suggestive locus were identified. A female-specific locus mapping to chromosome 6 (Chr 6) also exhibited effects on plasma non-HDL-C, apolipoprotein AII (apoAII), apoB, and apoE levels. A Chr 6 QTL was independently isolated in a related congenic strain (C57BL/6J vs. B6.NODc6: P = 0.003 and P = 0.0001 for HDL-C and non-HDL-C levels, respectively). These data are consistent with polygenic inheritance of HDL-C levels in the mouse model and provide candidate loci for HDL-C and non-HDL-C level determination in humans.

Dissection of multigenic obesity traits in congenic mouse strains.

Previous quantitative trait locus mapping (QTL) identified multigenic obesity (MOB) loci on mouse Chromosome (Chr) 2 that influence the interrelated phenotypes of obesity, insulin resistance, and dyslipidemia. To better localize and characterize the MOB locus, three congenic mouse strains were created. Overlapping genomic intervals from the lean CAST/Ei (CAST) strain were introgressed onto an obesity-susceptible C57BL/6 (BL6) background to create proximal (15 Mb-73 Mb), middle (63 Mb-165 Mb), and distal (83 Mb-182 Mb) congenic strains. The congenic strains showed differences in obesity, insulin, and lipid traits consistent with the original QTL analysis for the locus. Importantly, characterization of the MOB congenics localized the effects of genes that underlie obesity-related traits to an introgressed interval (73-83 Mb) unique to the middle MOB congenic. Conversely, significant differences between the lipid and insulin profiles of the middle and distal MOB congenics implicated the presence of at least two genes that underlie these traits. When fed an atherogenic diet, several traits associated with metabolic syndrome were observed in the distal MOB congenic, while alterations in plasma lipoproteins were observed in the middle MOB congenic strain.