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

Marianyela Sabina Petrizzelli,Thibault Nidelet,Camille Noûs,Dominique De Vienne,Christine Dillmann,Christoph Kaleta

doi:10.1371/journal.pcbi.1009157

Abstract

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.

Highlights

Phenotypic diversity within the living world results from billions of years of evolution
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
The HeterosYeast dataset provided valuable observations on the genetic diversity of yeast strains involved in the wine-making process at different levels of cellular organization, i.e. phenotypic traits related to life-history or fermentation [21], and quantitative proteomic data [20]

Summary

Introduction

Phenotypic diversity within the living world results from billions of years of evolution. Most evolutionary pressures like mutation, random genetic drift, migration and recombination shape phenotypic diversity by directly changing the genetic composition of populations. The effects of selection are more difficult to predict because fitness is determined by phenotype, which results from a complex interaction between genotype and the environment [1]. An additional layer of complexity results from the fact that life-history traits [2] are the results of processes that occur at the cellular level. There has been a growing interest for a better understanding in evolutionary biology of the so-called genotype-phenotype map Linking cellular processes to high-level phenotypic traits is becoming a new discipline in Biology, known as integrative biology

Methods

Results

Discussion

Conclusion