Macrowine 2021
IVES 9 IVES Conference Series 9 New molecular evidence of wine yeast-bacteria interaction unraveled by untargeted metabolomic profiling

New molecular evidence of wine yeast-bacteria interaction unraveled by untargeted metabolomic profiling

Abstract

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can consistently stimulate (MLF+ phenotype) or inhibit (MLF- phenotype) malolactic bacteria and the MLF process as a function of numerous winemaking practices, but the molecular evidence behind still remains a mystery. In this study, such evidence was elucidated by the direct comparison of extracellular metabolic profiles of MLF+ and MLF- yeast phenotypes. Untargeted metabolomics combining ultrahigh-resolution FT-ICR-MS analysis, powerful machine learning methods and a comprehensive wine metabolite database, discovered around 800 putative biomarkers and 2500 unknown masses 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 vivo tests) of several novel biomarkers, such as gluconic acid, citric acid, caffeic acid-sulfate, palmitic acid and tripeptide Pro-Phe-Val. In addition to new biomarkers, molecular evidence was reflected by unprecedented chemical diversity (more than 3000 discriminant masses) that characterized MLF+ and MLF- phenotypes. Distinct chemical families such as phenolic compounds, carbohydrates, amino acids and peptides characterize the extracellular metabolic profiles of the MLF+ phenotype, whereas the MLF- phenotype is associated with sulphur-containing peptides. Moreover, the location of MLF+ biomarkers in the yeast metabolic network indicated the potential involvement of specific pathways in MLF stimulation. The untargeted approach used in this study played a significant role in discovering new and unexpected molecular evidence of wine yeast-bacteria interaction.

This work will appear in the accepted article in Metabolomics (Volume 12 issue 5). (http://link.springer.com/journal/11306).

Publication date: May 17, 2024

Issue: Macrowine 2016

Type: Article

Authors

Youzhong Liu*, Cedric Longin, Claudine Degueurce, Hervé Alexandre, Magali Deleris-Bou, Marianna Lucio, Mourad Harir, Philippe Schmitt-Kopplin, Régis Gougeon, Sara Forcisi, Sibylle Dr. Krieger-Weber

*Université de Bourgogne

Contact the author

Tags

IVES Conference Series | Macrowine | Macrowine 2016

Citation

Related articles…

Full automation of oenological fermentations and its application to the processing of must containing high sugar or acetic acid concentrations

Climate change and harvest date decisions have led to the evolution of must quality over the last decades. Increases in must sugar concentrations are among the most obvious consequences, quantitatively. Saccharomyces cerevisiae is a robust and acid tolerant organism. These properties, its sugar to ethanol conversion rate and ethanol tolerance make it the ideal production organism for wine fermentations. Unfortunately, high sugar concentrations may affect S. cerevisiae and lead to growth inhibition or yeast lysis, and cause sluggish or stuck fermentations. Even sublethal conditions cause a hyperosmotic stress response in S. cerevisiae which leads to increased formation of fermentation by-products, including acetic acid, which may exceed legal limits in some wines.

DNA and type of grain: which factor does better explain sensory differences of sessile and pedunculate oaks?

Sessile oak and pedunculate oak have shown several differences of interest for enological purposes. Tannic and aromatic composition among sessile oak or pedonculate oak has been well studied. Sessile oak is generally more aromatic than pedunculated, while the later is more tannic. This scientific point of view is rarely applied to classify oak in cooperages. Most coopers use the type of grain to distinguish wide and thin grain.

Extraction of pathogenesis-related proteins and phenolics in Sauvignon Blanc as affected by different

The composition of wine is largely determined by the composition of pre-fermentation juice, which is influenced by extraction of grape components. Different grape harvesting and processing conditions could affect the extraction of grape components into juice. Among these grape components, pathogenesis-related (PR) proteins are of great concern for white wine maker as they are the main cause of haze formation in finished white wine. If not removed before bottling, these PR proteins may progress into haze through the formation of complex with phenolics under certain conditions. Thaumatin-like proteins (TLPs) and chitinases are the main constituents of PR proteins found in protein haze.

An excessive leaf-fruit ratio reduces the yeast assimilable nitrogen in the must

Yeast assimilable nitrogen (YAN) in the grape must is a key variable for wine quality as a source of aroma precursors. In a situation of YAN deficiency, a foliar urea application upon the vine at veraison enhances YAN concentration and facilitates must fermentation. In 2013, Agroscope investigated the impact of leaf-fruit ratio on the nitrogen (N) assimilation and partitioning in grapevine Vitis vinifera cv. Chasselas following foliar-urea application with the aim of improving its efficiency on the YAN concentration.

Mean polymerization degree of proanthocyanidins of grape seeds, skins and wines from Agiorgitiko (cv. Vitis vinifera): Differences among vintages

Grape phenolic compounds are very important constituents of red wine because, in addition to their antioxidant properties, they contribute to color, astringency and bitterness, oxidation reactions, interactions with proteins and ageing behavior of wines. The aim of our study was to assess the structural characteristics of grape and wine proanthocyanidins of Agiorgitiko variety and to evaluate the influence of the vintage year. Twelve vineyard locations were designated in the Nemea wine region. For three consecutive years (2012-2014), the grapes were harvested at technological maturity and the method of phloroglucinolysis was employed to determine the mean degree of polymerization (mDP) and subunit composition of the samples.