Data integration uncovers the metabolic bases of phenotypic variation in yeast

Autor: Marianyela Petrizzelli, Camille Noûs, Dominique de Vienne, Thibault Nidelet, Christine Dillmann
Přispěvatelé: Institute Curie, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Sciences Pour l'Oenologie (SPO), Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Cogitamus Laboratory, ANR11-IDEX-0003-02
Rok vydání: 2020
Předmět:
Metabolic Processes
Proteomics
Enzyme Metabolism
Yeast and Fungal Models
Biochemistry
Database and Informatics Methods
0302 clinical medicine
Databases
Genetic
Biology (General)
Enzyme Chemistry
Protein Metabolism
0303 health sciences
education.field_of_study
Proteomic Databases
Eukaryota
Phenotype
Enzymes
Chemistry
Experimental Organism Systems
Physical Sciences
Trait
Glycolysis
Research Article
Applicability domain
QH301-705.5
Population
Saccharomyces cerevisiae
Computational biology
Biology
Research and Analysis Methods
Saccharomyces
03 medical and health sciences
Model Organisms
[SDV.BBM]Life Sciences [q-bio]/Biochemistry
Molecular Biology
education
030304 developmental biology
Genetic diversity
Organisms
Fungi
Chemical Compounds
Computational Biology
Biology and Life Sciences
Proteins
Carbon Dioxide
biology.organism_classification
Yeast
Metabolism
Biological Databases
Biological Variation
Population
Fermentation
Enzymology
Animal Studies
030217 neurology & neurosurgery
Zdroj: PLoS Computational Biology
PLoS Computational Biology, Public Library of Science, 2021, 17 (7), ⟨10.1371/journal.pcbi.1009157⟩
PLoS Computational Biology, Vol 17, Iss 7, p e1009157 (2021)
ISSN: 1553-734X
1553-7358
DOI: 10.1101/2020.06.23.166405
Popis: The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results. This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.
Author summary The integration of data at different levels of cellular organization is an important goal in computational biology for understanding the way the genotypic variation translates into phenotypic variation. Novel profiling technologies and accurate high-throughput phenotyping now allows genomic, transcriptomic, metabolic and proteomic characterization of a large number of individuals under various environmental conditions. However, the metabolic fluxes remain difficult to measure. In this work, we take advantage of recent advances in genome-scale functional annotation and constraint-based metabolic modeling to provide a mathematical framework that allows to estimate internal cellular fluxes from protein abundances and elucidate the biological mechanisms underlying phenotypic variation. Applied to yeast as a model system, this approach highlights that the negative correlation between production traits such as maximum population size and growth and fermentation traits is explained by a differential usage of energy production pathways. The ability to identify molecular and metabolic bases of the variation of integrated traits through population studies has a broad applicability domain.
Databáze: OpenAIRE
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