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

E. Teyssonnière, P. Trébulle, J. Muenzner, V. Loegler, D. Ludwig, F. Amari, M. Mülleder, A. Friedrich, J. Hou, M. Ralser, J. Schacherer
doi: https://doi.org/10.1101/2023.09.18.558197
E. Teyssonnière
1Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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P. Trébulle
3The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
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J. Muenzner
2Charité Universitätsmedizin Berlin, Berlin, Germany
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V. Loegler
1Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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D. Ludwig
2Charité Universitätsmedizin Berlin, Berlin, Germany
4Core Facility High Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
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F. Amari
2Charité Universitätsmedizin Berlin, Berlin, Germany
4Core Facility High Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
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M. Mülleder
4Core Facility High Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
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A. Friedrich
1Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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J. Hou
1Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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M. Ralser
2Charité Universitätsmedizin Berlin, Berlin, Germany
3The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
5Max Planck Institute for Molecular Genetics, Berlin, Germany
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J. Schacherer
1Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
6Institut Universitaire de France (IUF), Paris, France
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  • For correspondence: schacherer@unistra.fr
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Summary

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 co-expression 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 (3.6%). 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.

  • At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species (ρ = 0.165).

  • While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscape

  • Wild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populations

  • Loci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 18, 2023.
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Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast
E. Teyssonnière, P. Trébulle, J. Muenzner, V. Loegler, D. Ludwig, F. Amari, M. Mülleder, A. Friedrich, J. Hou, M. Ralser, J. Schacherer
bioRxiv 2023.09.18.558197; doi: https://doi.org/10.1101/2023.09.18.558197
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Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast
E. Teyssonnière, P. Trébulle, J. Muenzner, V. Loegler, D. Ludwig, F. Amari, M. Mülleder, A. Friedrich, J. Hou, M. Ralser, J. Schacherer
bioRxiv 2023.09.18.558197; doi: https://doi.org/10.1101/2023.09.18.558197

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