Data integration uncovers the metabolic bases of phenotypic variation in yeast.

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 underlyi...

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Autores principales:	Marianyela Sabina Petrizzelli, Dominique de Vienne, Thibault Nidelet, Camille Noûs, Christine Dillmann
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ff87de2b9a9d4cd59397d3d3f3dd651e
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spelling	oai:doaj.org-article:ff87de2b9a9d4cd59397d3d3f3dd651e2021-12-02T19:57:31ZData integration uncovers the metabolic bases of phenotypic variation in yeast.1553-734X1553-735810.1371/journal.pcbi.1009157https://doaj.org/article/ff87de2b9a9d4cd59397d3d3f3dd651e2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009157https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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.Marianyela Sabina PetrizzelliDominique de VienneThibault NideletCamille NoûsChristine DillmannPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009157 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Marianyela Sabina Petrizzelli Dominique de Vienne Thibault Nidelet Camille Noûs Christine Dillmann Data integration uncovers the metabolic bases of phenotypic variation in yeast.
description	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.
format	article
author	Marianyela Sabina Petrizzelli Dominique de Vienne Thibault Nidelet Camille Noûs Christine Dillmann
author_facet	Marianyela Sabina Petrizzelli Dominique de Vienne Thibault Nidelet Camille Noûs Christine Dillmann
author_sort	Marianyela Sabina Petrizzelli
title	Data integration uncovers the metabolic bases of phenotypic variation in yeast.
title_short	Data integration uncovers the metabolic bases of phenotypic variation in yeast.
title_full	Data integration uncovers the metabolic bases of phenotypic variation in yeast.
title_fullStr	Data integration uncovers the metabolic bases of phenotypic variation in yeast.
title_full_unstemmed	Data integration uncovers the metabolic bases of phenotypic variation in yeast.
title_sort	data integration uncovers the metabolic bases of phenotypic variation in yeast.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ff87de2b9a9d4cd59397d3d3f3dd651e
work_keys_str_mv	AT marianyelasabinapetrizzelli dataintegrationuncoversthemetabolicbasesofphenotypicvariationinyeast AT dominiquedevienne dataintegrationuncoversthemetabolicbasesofphenotypicvariationinyeast AT thibaultnidelet dataintegrationuncoversthemetabolicbasesofphenotypicvariationinyeast AT camillenous dataintegrationuncoversthemetabolicbasesofphenotypicvariationinyeast AT christinedillmann dataintegrationuncoversthemetabolicbasesofphenotypicvariationinyeast
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Data integration uncovers the metabolic bases of phenotypic variation in yeast.

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