A quantitative 1H NMR approach for evaluating the metabolic response of Saccharomyces cerevisiae to mild heat stress

Abstract

Effect of growth temperature on the yeast (Saccharomyces cerevisiae) metabolome has been analysed by one-dimensional proton NMR spectroscopy (1H NMR). Potential biomarkers have been first identified by a non-targeted chemometric evaluation of the spectra, followed by a comprehensive analysis of bayesian estimated concentrations of target metabolites in extracts of cells growth either at 30 or 37 °C. Tentative identification of metabolites whose concentrations were affected by this mild heat-shock stress was attempted by partial least squares-discriminant analysis (PLS-DA) on 1H NMR data, combined with Statistical TOtal Correlation SpectroscopY, and further confirmed with empirical data. An extensive assignment for most of the detected NMR signals was performed, with a total number of 38 identified metabolites. Concentrations estimated using automatic BATMAN modelling revealed that bayesian integration is a sufficient approach for obtaining relevant concentration changes of metabolites and biological information of interest. In contrast to when it is applied directly on spectral data, the application of PLS-DA on BATMAN recovered metabolite concentration estimates allowed for a better overview of the investigated samples, since more metabolites were highlighted in the discriminatory model. Observed changes in metabolite concentrations were consistent with the expected process of temperature acclimation, showing alterations in amino acid cellular pools, nucleotide metabolism and lipid composition. The strategy described in this work can thus be proposed as a powerful and easy tool to investigate complex biological processes, from biomarker screening and discovery to the study of metabolite network changes in biological processes.