Dynamic genome‐scale modeling of Saccharomyces cerevisiae unravels mechanisms for ester formation during alcoholic fermentation

Abstract

Fermentation employing Saccharomyces cerevisiae has produced alcoholic beverages and bread for millennia. More recently, S. cerevisiae has been used to manufacture specific metabolites for the food, pharmaceutical, and cosmetic industries. Among the most important of these metabolites are compounds associated with desirable aromas and flavors, including higher alcohols and esters. Although the physiology of yeast has been well‐studied, its metabolic modulation leading to aroma production in relevant industrial scenarios such as winemaking is still unclear. Here we ask what are the underlying metabolic mechanisms that explain the conserved and varying behavior of different yeasts regarding aroma formation under enological conditions? We employed dynamic flux balance analysis (dFBA) to answer this key question using the latest genome‐scale metabolic model (GEM) of S. cerevisiae. The model revealed several conserved mechanisms among wine yeasts, for example, acetate ester formation is dependent on intracellular metabolic acetyl‐CoA/CoA levels, and the formation of ethyl esters facilitates the removal of toxic fatty acids from cells using CoA. Species‐specific mechanisms were also found, such as a preference for the shikimate pathway leading to more 2‐phenylethanol production in the Opale strain as well as strain behavior varying notably during the carbohydrate accumulation phase and carbohydrate accumulation inducing redox restrictions during a later cell growth phase for strain Uvaferm. In conclusion, our new metabolic model of yeast under enological conditions revealed key metabolic mechanisms in wine yeasts, which will aid future research strategies to optimize their behavior in industrial settings.

Insights into Yeast Response to Chemotherapeutic Agent through Time Series Genome-Scale Metabolic Models

“…In order to reduce the complexity some efforts focus on simplifying genome scale metabolic models (GSMM) to enable targeted kinetic models [1]. GSMMs, firstly used to estimate flux distributions in steady state (FBA: Flux Balance Analysis; pFBA: Parsimonious FBA) [2][3][4] and to predict pseudo dynamic flux behaviors (dFBA: Dynamic FBA) [5][6][7]. Integrative analysis of transcriptomics and GSMMs is also common especially in higher organisms in order to make use of tissue or cell type specific GSMMs [8,9].…”