6533b7d1fe1ef96bd125c86f

RESEARCH PRODUCT

A metabolomic study of yeast/bacteria interactions

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As a complex microbial ecosystem, wine is a particularly interesting model for studying interactions between microorganisms. Contact-independent interactions (indirect interactions) between the yeast Saccharomyces cerevisae and the lactic acid bacterium Oenococcus oeni have a direct effect on malolactic fermentation (MLF), induction and completion, which is an important factor in wine quality. Yeast strains could be classified as MLF+ phenotype if it usually stimulates the bacterial growth or MLF- in the opposite case. The known metabolites that stimulate or inhibit the MLF cannot always explain the phenotypic distinction. In this work, a multidisciplinary workflow combining non-targeted metabolomics, targeted analysis, statistics and network was developed. The main objective was to unravel diverse yeast metabolites involved in yeast-bacteria interaction via a direct comparison of exo-metabolomes of MLF+ and MLF- phenotypes.To that purpose, and for the first time in the research of interspecies microbial interactions, two metabolomics platforms, Fourier Transform Ion Cyclotron Resonance -Mass Spectrometry (FT-ICR-MS) and Liquid Chromatography coupled with Mass Spectrometry (UPLC-Q-TOF-MS) were used in combination. To better visualize the high-throughput data generated from the two platforms, a novel unsupervised statistical method, the MetICA was developed and validated. Compared to classical principal component analysis (PCA), the new method reduced the data dimension in a more robust and reliable way. To extract metabolic features involved in the phenotypic distinction, we have compared different statistical classifiers and selected the best one for each dataset. Putative structures of these biomarkers were validated via MS/MS fragmentation analysis and their physiological roles to bacteria were confirmed in vitro. The discovery of biomarkers was complemented by targeted HPLC (high performance liquid chromatography) analysis. The complementarities between different analytical techniques led to new biomarkers of distinct chemical families, such as phenolic compounds, carbohydrates, nucleotides, amino acids and peptides. Furthermore, metabolic network analysis has revealed connections between yeast biomarkers and suggested bacterial pathways influenced by yeast exo-metabolome.Our multidisciplinary workflow has shown its ability to find new and unexpected molecular evidence of wine yeast-bacteria interaction.