ArticlePLoS biology2011
Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.
Article in PLoS biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed, 106 citations in OpenAlex.
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- Physiological and Multi-Omics Approaches for Explaining Drought Stress Tolerance and Supporting Sustainable Production of Rice.Frontiers in plant science · 2021Review
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- A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize.Genome research · 2020Article
- Adaptive laboratory evolution induced novel mutations in Zymomonas mobilis ATCC ZW658: a potential platform for co-utilization of glucose and xylose.Journal of industrial microbiology & biotechnology · 2020Article
- Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels.PLoS genetics · 2020Article
- Prolyl 4-hydroxylase alpha 1 protein expression risk-stratifies early stage colorectal cancer.Oncotarget · 2020Article
- DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories.PLoS genetics · 2019Article
- Exploiting Interdata Relationships in Next-generation Proteomics Analysis.Molecular & cellular proteomics : MCP · 2019Review
- Characterization of Proteome Variation During Modern Maize Breeding.Molecular & cellular proteomics : MCP · 2019Article
- Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines.Frontiers in genetics · 2019Article
- Row versus column correlations: avoiding the ecological fallacy in RNA/protein expression studies.Briefings in bioinformatics · 2018Review
- Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells.PLoS biology · 2018Article
- Nature's fat-burning machine: brown adipose tissue in a hibernating mammal.The Journal of experimental biology · 2018Review
- Methods, Tools and Current Perspectives in Proteogenomics.Molecular & cellular proteomics : MCP · 2017Review
- Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease.PLoS genetics · 2017Observational
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6 authors at 5 institutions in 1 country.
Funding
Abstract
Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.