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

Abstract

International audienceIntroduction 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

Lactobacillus plantarum, a New Biological Tool to Control Malolactic Fermentation: A Review and an Outlook

“…. The interrelationships between LAB and wine yeast [30] or other wine microorganisms and the method of vinification have been reported to be the most influential factors to affect LAB growth. The wine pH is one of the most important factors that limits LAB growth and MLF in wine [2,29,31] and determines the type of LAB which will be present.…”

“…More recently, other bacterial growth inhibiters derived from yeast metabolism have been reported, such as medium-chain fatty acids [44] and yeast peptides (between 5 and 10 kDa) [45,46]. More recently, Liu et al [30] reported certain peptides being stimulating for O. oeni. These effects depend on the nature and the level of fatty acids in the wine or the size of the yeast peptides, and can be exacerbated by low pH.…”

Effect of the inoculation strategy of non-Saccharomyces yeasts on wine malolactic fermentation

“…The performance of MLF by LAB is affected by the intrinsic properties of wine, which are mostly determined by yeasts (Balmaseda et al, 2018). The effects of yeasts on MLF can be either inhibitory, for example the production of ethanol or the nutrient exhaustion (Arnink and Henick-Kling, 2005), or stimulating, such as the production of citric and pyruvic acids (Liu et al, 2016). These effects depend on the concentration of the compounds in wine, which, in turn, depends on species and strains (Balmaseda et al, 2018).…”

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

“…Since the beginning, there was an ongoing need to develop improved analytical platforms that would allow the determination of a large number of metabolites within the grape and wine metabolome. The use of high-resolution MS instrumentations, such as, fourier transform mass spectrometry (FTICR-MS) and ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-ToF-MS) in wine analysis in an untargeted manner is now making it possible to detect metabolites with high precision and mass accuracy [32,[116][117][118]128]. In addition to revealing the true complexity of the wines, this approach is now adding another dimension in terms of obtaining exact mass for formula calculation with retention time information of unknown molecules [117].…”

“…In addition to revealing the true complexity of the wines, this approach is now adding another dimension in terms of obtaining exact mass for formula calculation with retention time information of unknown molecules [117]. Liu, Forcisi, Harir, Deleris-Bou, Krieger-Weber, Lucio, Longin, Degueurce, Gougeon, Schmitt-Kopplin and Alexandre [128] also applied untargeted metabolite profiling using FTICR-MS and UPLC-Q-ToF-MS to determine the outcomes from the interaction of malolactic bacteria and yeasts that either stimulate (MLF+) or inhibit (MLF−) malolactic fermentation. In this study, they were able to detect 3000 discriminant masses that characterized the phenotypes of both MLF+ and MLF− yeast strains in addition to determining MLF− biomarkers.…”