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

AbstractIntroduction 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.ObjectivesThe goal of the study was to elucidate such evidence by the direct comparison of extracellular metabolic profiles of MLF+ and MLF− phenotypes.MethodsWe have applied a non-targeted metabolomic approach combining ultrahigh-resolution FT-ICR-MS analysis, powerful statistical tools and a comprehensive wine metabolite database.ResultsWe 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.ConclusionThe non-targeted approach used in this study played an important role in finding new and unexpected molecular evidence.

[1]  M. Dols-Lafargue,et al.  Distribution and Functions of Phosphotransferase System Genes in the Genome of the Lactic Acid Bacterium Oenococcus oeni , 2013, Applied and Environmental Microbiology.

[2]  S. Kanaya,et al.  KEGGscape: a Cytoscape app for pathway data integration , 2014, F1000Research.

[3]  Kathleen J. Arnink,et al.  Influence of Saccharomyces cerevisiae and Oenococcus oeni Strains on Successful Malolactic Conversion in Wine , 2005, American Journal of Enology and Viticulture.

[4]  I. J. Evans,et al.  A Novel Family of Small Cysteine-rich Antimicrobial Peptides from Seed of Impatiens balsamina Is Derived from a Single Precursor Protein* , 1997, The Journal of Biological Chemistry.

[5]  J. Martinussen,et al.  Nucleotide metabolism and its control in lactic acid bacteria. , 2005, FEMS microbiology reviews.

[6]  Karsten Suhre,et al.  MassTRIX: mass translator into pathways , 2008, Nucleic Acids Res..

[7]  P. Henschke,et al.  Interaction of pH, ethanol concentration and wine matrix on induction of malolactic fermentation with commercial ‘direct inoculation’ starter cultures , 2003 .

[8]  P. Maeng,et al.  TCA cycle‐independent acetate metabolism via the glyoxylate cycle in Saccharomyces cerevisiae , 2011, Yeast.

[9]  Douglas B. Kell,et al.  Statistical strategies for avoiding false discoveries in metabolomics and related experiments , 2007, Metabolomics.

[10]  F. Mathieu,et al.  Impact of the co-culture of Saccharomyces cerevisiae-Oenococcus oeni on malolactic fermentation and partial characterization of a yeast-derived inhibitory peptidic fraction. , 2010, Food microbiology.

[11]  C. Charpentier,et al.  Yeast protein extracts: an alternative fining agent for red wines , 2015, European Food Research and Technology.

[12]  E. Bartowsky.  Oenococcus oeni and malolactic fermentation – moving into the molecular arena , 2005 .

[13]  Lothar Willmitzer,et al.  Discrimination of wine attributes by metabolome analysis. , 2010, Analytical chemistry.

[14]  P. Bidan,et al.  Croissance des bacteries lactiques a partir des principaux constituants azotes du vin , 1977 .

[15]  K. Suhre,et al.  DI-ICR-FT-MS-based high-throughput deep metabotyping: a case study of the Caenorhabditis elegans–Pseudomonas aeruginosa infection model , 2015, Analytical and Bioanalytical Chemistry.

[16]  Philippe Schmitt-Kopplin,et al.  The chemodiversity of wines can reveal a metabologeography expression of cooperage oak wood , 2009, Proceedings of the National Academy of Sciences.

[17]  F. Radler,et al.  The metabolism of several carboxylic acids by lactic acid bacteria , 1984, Zeitschrift fur Lebensmittel-Untersuchung und -Forschung.

[18]  G. L. Miller.  Use of Dinitrosalicylic Acid Reagent for Determination of Reducing Sugar , 1959 .

[19]  H. Alexandre,et al.  Cytofluorometric detection of wine lactic acid bacteria: application of malolactic fermentation to the monitoring , 2012, Journal of Industrial Microbiology & Biotechnology.

[20]  Xiaohui Lin,et al.  A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. , 2012, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[21]  G. Fleet,et al.  Chemical and cytological changes during the autolysis of yeasts , 1995, Journal of Industrial Microbiology.

[22]  C. Davis,et al.  Growth and Metabolism of Selected Lactic Acid Bacteria in Synthetic Wine , 1995, American Journal of Enology and Viticulture.

[23]  A. Bekatorou,et al.  Malolactic fermentation in wine with Lactobacillus casei cells immobilized on Delignified cellulosic material. , 2005, Journal of agricultural and food chemistry.

[24]  H. Alexandre,et al.  Protease A activity and nitrogen fractions released during alcoholic fermentation and autolysis in enological conditions , 2001, Journal of Industrial Microbiology and Biotechnology.

[25]  G. de Revel,et al.  A HPLC fluorescence-based method for glutathione derivatives quantification in must and wine. , 2010, Analytica chimica acta.

[26]  J. François,et al.  Reserve carbohydrates metabolism in the yeast Saccharomyces cerevisiae. , 2001, FEMS microbiology reviews.

[27]  Michael R. Yeaman,et al.  Mechanisms of Antimicrobial Peptide Action and Resistance , 2003, Pharmacological Reviews.

[28]  P. Henschke,et al.  Standardised methodology for testing malolactic bacteria and wine yeast compatibility , 2003 .

[29]  Coral Barbas,et al.  Method validation strategies involved in non-targeted metabolomics. , 2014, Journal of chromatography. A.

[30]  M. Witting,et al.  Integrating analytical resolutions in non-targeted wine metabolomics , 2015 .

[31]  H. Alexandre,et al.  Oenococcus oeni preference for peptides: qualitative and quantitative analysis of nitrogen assimilation , 2006, Archives of Microbiology.

[32]  F. Remize,et al.  Dual effect of organic acids as a function of external pH in Oenococcus oeni , 2007, Archives of Microbiology.

[33]  M. Toit,et al.  Malolactic Fermentation: The ABC’s of MLF , 2016 .

[34]  Patrícia Branco,et al.  Identification of novel GAPDH-derived antimicrobial peptides secreted by Saccharomyces cerevisiae and involved in wine microbial interactions , 2013, Applied Microbiology and Biotechnology.

[35]  P. Schmitt‐Kopplin,et al.  How Subtle Is the “Terroir” Effect? Chemistry-Related Signatures of Two “Climats de Bourgogne” , 2014, PloS one.

[36]  M. Bertuccioli,et al.  Quantification and characterization of cell wall polysaccharides released by non-Saccharomyces yeast strains during alcoholic fermentation. , 2012, International journal of food microbiology.

[37]  Steve A. Kupina,et al.  Evaluation of a Fourier Transform Infrared Instrument for Rapid Quality-Control Wine Analyses , 2003, American Journal of Enology and Viticulture.

[38]  S. Torriani,et al.  Selection criteria and tools for malolactic starters development: an update , 2011, Annals of Microbiology.

[39]  C. G. Edwards,et al.  Inhibition of malolactic fermentation by a peptide produced by Saccharomyces cerevisiae during alcoholic fermentation. , 2007, International journal of food microbiology.

[40]  N. Hertkorn,et al.  Kendrick-Analogous Network Visualisation of Ion Cyclotron Resonance Fourier Transform Mass Spectra: Improved Options for the Assignment of Elemental Compositions and the Classification of Organic Molecular Complexity , 2011, European journal of mass spectrometry.

[41]  P. Schmitt‐Kopplin,et al.  Liquid chromatography-mass spectrometry in metabolomics research: mass analyzers in ultra high pressure liquid chromatography coupling. , 2013, Journal of chromatography. A.

[42]  V. Bajic,et al.  DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm , 2015, PloS one.

[43]  F. Mattivi,et al.  The influence of storage on the “chemical age” of red wines , 2014, Metabolomics.

[44]  F. Clementi,et al.  Interactions between Saccharomyces cerevisiae and malolactic bacteria: preliminary characterization of a yeast proteinaceous compound(s) active against Oenococcus oeni , 2005, Journal of applied microbiology.

[45]  J. François,et al.  New Insights into Trehalose Metabolism by Saccharomyces cerevisiae: NTH2 Encodes a Functional Cytosolic Trehalase, and Deletion of TPS1 Reveals Ath1p-Dependent Trehalose Mobilization , 2007, Applied and Environmental Microbiology.

[46]  E. Schleicher,et al.  Insulin Sensitivity Is Reflected by Characteristic Metabolic Fingerprints - A Fourier Transform Mass Spectrometric Non-Targeted Metabolomics Approach , 2010, PloS one.

[47]  H. Lemaresquier.  Inter‐relationships between strains of Saccharomyces cerevisiae from the Champagne area and lactic acid bacteria , 1987 .

[48]  M. Angelis,et al.  Distinct signatures of host–microbial meta-metabolome and gut microbiome in two C57BL/6 strains under high-fat diet , 2014, The ISME Journal.

[49]  J. Moreno,et al.  Changes in gluconic acid, polyols and major volatile compounds in sherry wine during aging with submerged flor yeast cultures , 2003, Biotechnology Letters.

[50]  J. Converse,et al.  Ripper procedure for determining sulfur dioxide in wine: collaborative study. , 1980, Journal - Association of Official Analytical Chemists.

[51]  T. H. Lee,et al.  Practical Implications of Malolactic Fermentation: A Review , 1985, American Journal of Enology and Viticulture.

[52]  D. Kell,et al.  Metabolic footprinting and systems biology: the medium is the message , 2005, Nature Reviews Microbiology.

[53]  Juan C Aon,et al.  Exometabolome analysis reveals hypoxia at the up-scaling of a Saccharomyces cerevisiae high-cell density fed-batch biopharmaceutical process , 2014, Microbial Cell Factories.

[54]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[55]  Iwona Gientka,et al.  Shikimate pathway in yeast cells: enzymes, functioning, regulation - a review. , 2009 .

[56]  Young-Shick Hong,et al.  NMR‐based metabolomics in wine science , 2011, Magnetic resonance in chemistry : MRC.

[57]  R. Lovitt,et al.  Studies on growth and metabolism of Oenococcus oeni on sugars and sugar mixtures , 2005, Journal of applied microbiology.

[58]  Douglas M. Hawkins,et al.  Assessing Model Fit by Cross-Validation , 2003, J. Chem. Inf. Comput. Sci..

[59]  F. M. Saguir,et al.  Effect of Dipeptides on the Growth of Oenococcus oeni in Synthetic Medium Deprived of Amino Acids , 2004, Current Microbiology.

[60]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[61]  H. Alexandre,et al.  Effects of yeast proteolytic activity on Oenococcus oeni and malolactic fermentation. , 2006, FEMS microbiology letters.