Computational approaches for the genetic and phenotypic characterization of a S...

• Author(s): Duarte, Ricardo Franco ; Umek, Lan ; Zupan, Blaz ; Schuller, Dorit
• Date: 2009
• Persistent ID: http://hdl.handle.net/1822/10410
• Origin: RepositóriUM - Universidade do Minho
• Subject(s): Saccharomyces cerevisiae; Indigenous yeast; Microsatellite; Genotype; Phenotype; Bayesian classifier; Strain collection; Ethanol resistance; Winemaking
• Document Type: Article
• Language: English

Description

Within this study, we have used a set of computational techniques to relate the genotypes and phenotypes of natural populations of Saccharomyces cerevisiae, using allelic information from 11 microsatellite loci and results from 24 phenotypic tests. A group of 103 strains was obtained from a larger S. cerevisiae winemaking strain collection by clustering with self-organizing maps. These strains were further characterized regarding their allelic combinations for 11 microsatellites and analysed in phenotypic screens that included taxonomic criteria (carbon and nitrogen assimilation tests, growth at different temperatures) and tests with biotechnological relevance (ethanol resistance, H2S or aromatic precursors formation). Phenotypic variability was rather high and each strain showed a unique phenotypic profile. The results, expressed as optical density (A640) after 22 h of growth, were in agreement with taxonomic data, although with some exceptions, since few strains were capable of consuming arabinose and ribose to a small extent. Based on microsatellite allelic information, na¨ıve Bayesian classifier correctly assigned (AUC = 0.81, p < 10−8) most of the strains to the vineyard from where they were isolated, despite their close location (50–100 km). We also identified subgroups of strains with similar values of a phenotypic feature and microsatellite allelic pattern (AUC >0.75). Subgroups were found for strains with low ethanol resistance, growth at 30 ◦C and growth in media containing galactose, raffinose or urea. The results demonstrate that computational approaches can be used to establish genotype–phenotype relations and to make predictions about a strain’s biotechnological potential.