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Liu, Y. ; Forcisi, S. ; Harir, M. ; Deleris-Bou, M.* ; Krieger-Weber, S.* ; Lucio, M. ; Longin, C.* ; Degueurce, C.* ; Gougeon, R.D.* ; Schmitt-Kopplin, P. ; Alexandre, H.*

New molecular evidence of wine yeast-bacteria interaction unraveled by non-targeted exometabolomic profiling.

Metabolomics 12:69 (2016)
Open Access Green as soon as Postprint is submitted to ZB.
Introduction: Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can systematically stimulate (MLF+ phenotype) or inhibit (MLF−) bacteria and the MLF process as a function of numerous winemaking practices, but the underlying molecular evidence still remains a mystery. Objectives: The goal of the study was to elucidate such evidence by the direct comparison of extracellular metabolic profiles of MLF+ and MLF− phenotypes. Methods: We have applied a non-targeted metabolomic approach combining ultrahigh-resolution FT-ICR-MS analysis, powerful statistical tools and a comprehensive wine metabolite database. Results: We discovered around 2500 unknown masses and 800 putative biomarkers involved in phenotypic distinction. For the putative biomarkers, we also developed a biomarker identification workflow and elucidated the exact structure (by UPLC-Q-ToF–MS2) and/or exact physiological impact (by in vitro tests) of several novel biomarkers, such as D-gluconic acid, citric acid, trehalose and tripeptide Pro-Phe-Val. In addition to valid biomarkers, molecular evidence was reflected by unprecedented chemical diversity (around 3000 discriminant masses) that characterized both the yeast phenotypes. While distinct chemical families such as phenolic compounds, carbohydrates, amino acids and peptides characterize the extracellular metabolic profiles of the MLF+ phenotype, the MLF− phenotype is associated with sulphur-containing peptides. Conclusion: The non-targeted approach used in this study played an important role in finding new and unexpected molecular evidence.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Biomarkers ; Ft-icr-ms ; Machine Learning ; Non-targeted Metabolomics ; Uplc-q-tof-ms ; Wine; Lactic-acid Bacteria; Oenococcus-oeni; Saccharomyces-cerevisiae; Malolactic Fermentation; Alcoholic Fermentation; Metabolism; Metabolomics; Strains; Growth; Quantification
ISSN (print) / ISBN 1573-3882
e-ISSN 1573-3890
Journal Metabolomics
Quellenangaben Volume: 12, Issue: 4, Pages: , Article Number: 69 Supplement: ,
Publisher Springer
Publishing Place New York, NY
Reviewing status Peer reviewed