Species- wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

• 成果类型: Article
• 署名作者: Teyssonniere, Elie Marcel; Trebulle, Pauline; Muenzner, Julia; Loegler, Victor; Ludwig, Daniela; Amari, Fatma; Muelleder, Michael; Friedrich, Anne; Hou, Jing; Ralser, Markus; Schacherer, Joseph
• 署名单位: Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Centre National de la Recherche Scientifique (CNRS); University of Oxford; Wellcome Centre for Human Genetics; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; Max Planck Society; Institut Universitaire de France
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12369
• DOI: 10.1073/pnas.2319211121
• 发表日期: 2024-05-07
• 关键词: messenger-rna; proteogenomic characterization; protein abundance; regulatory variation; human colon; quantification; unification; traits

摘要

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species - wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.