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Defining the effects of genetic variation on a proteome-wide scale

ABSTRACT: Genetic variation governs protein expression through both transcriptional and post-transcriptional processes. To investigate this relationship, we combined a multiplexed, mass spectrometry-based method for protein quantification with an emerging mouse model harboring extensive genetic variation from 8 founder strains. We collected genome-wide mRNA and protein profiling measurements to link genetic variation to protein expression differences in livers from 192 diversity outcross mice. We observed nearly 3,700 protein-level quantitative trait loci (pQTL) with an equal proportion of proteins regulated directly by their cognate mRNA as uncoupled from their transcript. Our analysis reveals an extensive array of at least five models for genetic variant control of protein abundance including direct protein-to-protein associations that act to achieve stoichiometric balance of functionally related enzymes and subunits of multimeric complexes.

OTHER RELATED OMICS DATASETS IN: PRJNA295057MSV000081696

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Hepatocyte, Liver

SUBMITTER: Joel Chick

LAB HEAD: Steven Gygi

PROVIDER: PXD002801 | Pride | 2016-06-15

REPOSITORIES: Pride

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Defining the consequences of genetic variation on a proteome-wide scale.

Chick Joel M JM Munger Steven C SC Simecek Petr P Huttlin Edward L EL Choi Kwangbom K Gatti Daniel M DM Raghupathy Narayanan N Svenson Karen L KL Churchill Gary A GA Gygi Steven P SP

Nature 20160615 7608

Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we ide ...[more]

PMID: 27309819

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Project description:We aimed at systematically inferring the regulatory functions of host lncRNAs in response to influenza A virus (IAV) and severe acute respiratory syndrome coronavirus (SARS-CoV) in the mouse model, using a ‘guilt-by-association’ approach which relies on finding which lncRNAs have similar expression profiles to protein-coding genes of known function. To build a large panel of diverse host responses to viral infection, we took advantage of the genetic diversity present in the 8 founder strains of the Collaborative Cross (CC) mouse resource. Extensive pulmonary host-response profiling was performed on mock and viral-infected lungs at 2 and 4 days post-infection using total RNA-Seq. Overall lncRNAs accounted for about 40% of total genes differentially expressed upon infections (5,329 DE lncRNAs). To predict the functions of these lncRNAs, we constructed a co-expression network using the weighted correlation network analysis (WGCNA) and identified modules of co-expressed genes. Several lncRNAs were identified as belonging to gene modules associated with viral replication or weight loss, and enriched in various infection-related biological processes such as immune response. In addition, each lncRNA was individually annotated using ranked list of DE genes based on signed correlation with each LncRNA. We predicted that a few lncRNAs may be implicated in more than 50 biological processes or pathways. Finally, we identified the lncRNAs that were positively or negatively correlated with all their neighbors and may have cis enhancer or inhibitor effects. We validated our prediction by examining additional RNASeq dataset of mice treated with IFN-alpha. Altogether, these results provide a broad categorization of lncRNA functions and identify subsets of lncRNAs with potential key roles for respiratory virus pathogenesis. These data are fully accessible through The MOuse NOn-Code Lung database (MONOCLdb) http://monocldb.viromics.washington.edu/ At 2 or 4 days post infection, Collaborative Cross founders mice (n=2-3 for infected conditions, n=2 for mocks) were euthanized and lungs were used for total RNA-Seq.

Dataset Information

Defining the effects of genetic variation on a proteome-wide scale

Publications

Defining the consequences of genetic variation on a proteome-wide scale.

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