New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

• Published in: Food chemistry Vol. 211; pp. 509 - 520
• Main Authors: Franco-Duarte, Ricardo, Umek, Lan, Mendes, Inês, Castro, Cristiana C., Fonseca, Nuno, Martins, Rosa, Silva-Ferreira, António C., Sampaio, Paula, Pais, Célia, Schuller, Dorit
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15-11-2016; Elsevier B.V
• Subjects: Computational Biology; Data-fusion; Fermentation; Food Handling; Genetic Variation; Matrix factorization; Metabolome; Metabolomics; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Science & Technology; Wine - analysis; Wine - microbiology; Wine yeasts; Wine yeasts; Metabolomics; Matrix factorization; Data-fusion; Saccharomyces cerevisiae
• ISSN: 0308-8146; 1873-7072; 0308-8146
• DOI: 10.1016/j.foodchem.2016.05.080

•A yeast strain collection was phenotypic, genetic and metabolically characterized.•We adapted computational approaches to analyze the yeast pheno-metabolome.•Results can be used to identify features in order to select good winemaking strains.
During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of most-correlated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.