Producing a wide range of volatile secondary metabolites Saccharomyces cerevisiae influences wine, beer, and bread sensory quality and hence selection of strains based on their volatilome becomes pivotal. A rapid on-line method for volatilome assessing of strains growing on standard solid media is still missing. OBJECTIVES: Methodologically, the aim of this study was to demonstrate the automatic, real-time, direct, and non-invasive monitoring of yeast volatilome in order to rapidly produce a robust large data set encompassing measurements relative to many strains, replicates and time points. The fundamental scope was to differentiate volatilomes of genetically similar strains of oenological relevance during the whole growing process. METHOD: Six different S. cerevisiae strains (four meiotic segregants of a natural strain and two laboratory strains) inoculated onto a solid medium have been monitored on-line by Proton Transfer Reaction-Time-of-Flight-Mass Spectrometry for 11 days every 4 h (3540 time points). FastGC PTR-ToF-MS was performed during the stationary phase on the 5th day. RESULTS: More than 300 peaks have been extracted from the average spectra associated to each time point, 70 have been tentatively identified. Univariate and multivariate analyses have been performed on the data matrix (3640 measurements × 70 peaks) highlighting the volatilome evolution and strain-specific features. Laboratory strains with opposite mating type, and meiotic segregants of the same natural strain showed significantly different profiles. CONCLUSIONS: The described set-up allows the on-line high-throughput screening of yeast volatilome of S. cerevisiae strains and the identification of strain specific features and new metabolic pathways, discriminating also genetically similar strains, thus revealing a novel method for strain phenotyping, identification, and quality control.

Non-invasive real time monitoring of yeast volatilome by PTR-ToF-MS / Khomenko, I; Stefanini, I; Cappellin, L; Cappelletti, V; Franceschi, P; Cavalieri, D; Märk, Td; Biasioli, F.. - In: METABOLOMICS. - ISSN 1573-3882. - ELETTRONICO. - (2017), pp. 118-131.

Non-invasive real time monitoring of yeast volatilome by PTR-ToF-MS

Stefanini, I;Cavalieri, D;
2017

Abstract

Producing a wide range of volatile secondary metabolites Saccharomyces cerevisiae influences wine, beer, and bread sensory quality and hence selection of strains based on their volatilome becomes pivotal. A rapid on-line method for volatilome assessing of strains growing on standard solid media is still missing. OBJECTIVES: Methodologically, the aim of this study was to demonstrate the automatic, real-time, direct, and non-invasive monitoring of yeast volatilome in order to rapidly produce a robust large data set encompassing measurements relative to many strains, replicates and time points. The fundamental scope was to differentiate volatilomes of genetically similar strains of oenological relevance during the whole growing process. METHOD: Six different S. cerevisiae strains (four meiotic segregants of a natural strain and two laboratory strains) inoculated onto a solid medium have been monitored on-line by Proton Transfer Reaction-Time-of-Flight-Mass Spectrometry for 11 days every 4 h (3540 time points). FastGC PTR-ToF-MS was performed during the stationary phase on the 5th day. RESULTS: More than 300 peaks have been extracted from the average spectra associated to each time point, 70 have been tentatively identified. Univariate and multivariate analyses have been performed on the data matrix (3640 measurements × 70 peaks) highlighting the volatilome evolution and strain-specific features. Laboratory strains with opposite mating type, and meiotic segregants of the same natural strain showed significantly different profiles. CONCLUSIONS: The described set-up allows the on-line high-throughput screening of yeast volatilome of S. cerevisiae strains and the identification of strain specific features and new metabolic pathways, discriminating also genetically similar strains, thus revealing a novel method for strain phenotyping, identification, and quality control.
2017
118
131
Khomenko, I; Stefanini, I; Cappellin, L; Cappelletti, V; Franceschi, P; Cavalieri, D; Märk, Td; Biasioli, F.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1107741
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