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Detalle de: Susceptibility and resistance to ethanol in Saccharomyces strains isolated from wild and fermentative environments

Title Susceptibility and resistance to ethanol in Saccharomyces strains isolated from wild and fermentative environments
Abstract

In this work, we apply statistical modelling techniques to study the influence of increasing concentrations of ethanol on the overall growth of 29 yeast strains belonging to different Saccharomyces and non‐ Saccharomyces species. A modified Gompertz equation for decay was used to objectively estimate the noninhibitory concentration (NIC) and minimum inhibitory concentration (MIC) for the assayed strains to ethanol, which are related to the susceptibility and resistance of yeasts to this compound, respectively. A first ANOVA analysis, grouping strains as a function of their respective Saccharomyces species, revealed that S. cerevisiae was the yeast with the highest, and statistically significant, ethanol resistance value. Then, a second factorial ANOVA analysis, using the origin of strains (wild or fermentative) and their taxonomic classification ( S. cerevisiae , S. paradoxus or S. bayanus var. uvarum ) as categorical predictor variables, showed that no significant differences for the NIC and MIC parameters were found between both ecological niches within the same species, indicative that these physiological characteristics were presumably not modified throughout the adaptation to human‐manipulated fermentative environments. Finally, differences among selected strains with respect to ethanol tolerance were correlated to the initial contents of unsaturated fatty acids, mainly oleic acid. Copyright © 2010 John Wiley & Sons, Ltd.

Apa Reference Arroyo‐López, F.N., Salvadó, Z., Tronchoni, J., Guillamón, J.M., Barrio, E. & Querol, A. (2010). Susceptibility and resistance to ethanol in Saccharomyces strains isolated from wild and fermentative environments. Yeast, 27(12), 1005-1015. 10.1002/yea.1809
Doi 10.1002/yea.1809
Publication Date 8 Sep 2010
Publication Type Article
Researcher Amparo Querol
Ontology Statistical Methods for Biology
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