Lifestyle, Lineage, and Geographical Origin Influence Temperature-Dependent Phenotypic Variation across Yeast Strains during Wine Fermentation

Saccharomyces cerevisiae yeasts are a diverse group of single-celled eukaryotes with tremendous phenotypic variation in fermentation efficiency, particularly at different temperatures. Yeast can be categorized into subsets based on lifestyle (Clinical, Fermentation, Laboratory, and Wild), genetic lineage (Malaysian, Mosaic, North American, Sake, West African, and Wine), and geographical origin (Africa, Americas, Asia, Europe, and Oceania) to start to understand their ecology; however, little is known regarding the extent to which these groupings drive S. cerevisiae fermentative ability in grape juice at different fermentation temperatures. To investigate the response of yeast within the different subsets, we quantified fermentation performance in grape juice by measuring the lag time, maximal fermentation rate (Vmax), and fermentation finishing efficiency of 34 genetically diverse S. cerevisiae strains in grape juice at five environmentally and industrially relevant temperatures (10, 15, 20, 25, and 30 °C). Extensive multivariate analysis was applied to determine the effects of lifestyle, lineage, geographical origin, strain, and temperature on yeast fermentation phenotypes. We show that fermentation capability is inherent to S. cerevisiae and that all factors are important in shaping strain fermentative ability, with temperature having the greatest impact, and geographical origin playing a lesser role than lifestyle or genetic lineage

Saccharomyces cerevisiae FLO1 Gene Demonstrates Genetic Linkage to Increased Fermentation Rate at Low Temperatures

Low fermentation temperatures are of importance to food and beverage industries working with Saccharomyces cerevisiae. Therefore, the identification of genes demonstrating a positive impact on fermentation kinetics is of significant interest. A set of 121 mapped F1 progeny, derived from a cross between haploid strains BY4716 (a derivative of the laboratory yeast S288C) and wine yeast RM11-1a, were fermented in New Zealand Sauvignon Blanc grape juice at 12.5°. Analyses of five key fermentation kinetic parameters among the F1 progeny identified a quantitative trait locus (QTL) on chromosome I with a significant degree of linkage to maximal fermentation rate (Vmax) at low temperature. Independent deletions of two candidate genes within the region, FLO1 and SWH1, were constructed in the parental strains (with S288C representing BY4716). Fermentation of wild-type and deletion strains at 12.5 and 25° confirmed that the genetic linkage to Vmax corresponds to the S288C version of the FLO1 allele, as the absence of this allele reduced Vmax by ∼50% at 12.5°, but not at 25°. Reciprocal hemizygosity analysis (RHA) between S288C and RM11-1a FLO1 alleles did not confirm the prediction that the S288C version of FLO1 was promoting more rapid fermentation in the opposing strain background, suggesting that the positive effect on Vmax derived from S288C FLO1 may only provide an advantage in haploids, or is dependent on strain-specific cis or trans effects. This research adds to the growing body of evidence demonstrating the role of FLO1 in providing stress tolerance to S. cerevisiae during fermentation

The Chemical Reaction of Glutathione and <i>trans</i>-2-Hexenal in Grape Juice Media To Form Wine Aroma Precursors: The Impact of pH, Temperature, and Sulfur Dioxide

The aldehyde 3-<i>S</i>-glutathionylhexanal is an intermediate which is produced during the formation of the wine aroma precursor 3-<i>S</i>-glutathionylhexanol, after the reaction of glutathione with <i>trans</i>-2-hexenal. This study was conducted to assess whether the chemical, as opposed to the enzymatic, production of 3-<i>S</i>-glutathionylhexanal could occur at a significant rate in grape juice. LC–MS/MS was used in low- and high-resolution modes, in combination with functional group derivatization, to identify and quantitate products. In comparison to cysteine, glutathione was found to induce less cyclized products on reaction with <i>trans</i>-2-alkanals and the glutathione-derived products were more reactive to hydrogen sulfite. The zero-order rates for 3-<i>S</i>-glutathionylhexanal formation in model grape juice were 1.08 ± 0.08 and 0.45 ± 0.05 mg/(L·day) glutathione equivalents at 25 and 13 °C, respectively, and the reaction rate increased 3-fold by increasing the pH from 3.2 to 3.8. 3-<i>S</i>-Glutathionylhexanal was detected in all five white grape juices examined. The concentration of the aldehyde could be increased by up to 10-fold after being released from hydrogen sulfite, demonstrating a potentially novel source for the production of varietal thiol aroma compounds in wine

New Precursors to 3-Sulfanylhexan-1-ol? Investigating the Keto–Enol Tautomerism of 3-S-Glutathionylhexanal

The volatile thiol compound 3-sulfanylhexan-1-ol (3SH) is a key impact odorant of white wines such as Sauvignon Blanc. 3SH is produced during fermentation by metabolism of non-volatile precursors such as 3-S-gluthathionylhexanal (glut-3SH-al). The biogenesis of 3SH is not fully understood, and the role of glut-3SH-al in this pathway is yet to be elucidated. The aldehyde functional group of glut-3SH-al is known to make this compound more reactive than other precursors to 3SH, and we are reporting for the first time that glut-3SH-al can exist in both keto and enol forms in aqueous solutions. At wine typical pH (~3.5), glut-3SH-al exists predominantly as the enol form. The dominance of the enol form over the keto form has implications in terms of potential consumption/conversion of glut-3SH-al by previously unidentified pathways. Therefore, this work will aid in the further elucidation of the role of glut-3SH-al towards 3SH formation in wine, with significant implications for the study and analysis of analogous compounds

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction of complex unknown relations between the input data and desired output parameters. Recently, soft sensors have been applicable in predicting the prices and vintages of New Zealand Pinot noir wines based on chemical parameters. However, the previous sample size did not adequately represent the diversity of provenances, vintages, and price points across commercially available New Zealand Pinot noir wines. Consequently, a representative sample of 39 commercially available New Zealand Pinot noir wines from diverse provenances, vintages, and price points were selected. Literature has shown that wine phenolic compounds strongly correlated with wine provenances, vintages and price points, which could be used as input data for developing soft sensors. Due to the significance of these phenolic compounds, chemical parameters, including phenolic compounds and pH, were collected using UV-Vis visible spectrophotometry and a pH meter. The soft sensor utilising Naive Bayes (belongs to ML) was designed to predict Pinot noir wines’ provenances (regions of origin) based on six chemical parameters with the prediction accuracy of over 75%. Soft sensors based on decision trees (within ML) could predict Pinot noir wines’ vintages and price points with prediction accuracies of over 75% based on six chemical parameters. These predictions were based on the same collected six chemical parameters as aforementioned

Unraveling the Mystery of 3-Sulfanylhexan-1-ol: The Evolution of Methodology for the Analysis of Precursors to 3-Sulfanylhexan-1-ol in Wine

Volatile polyfunctional thiol compounds, particularly 3-sulfanylhexan-1-ol (3SH) and 3-sulfanylhexyl acetate (3SHA), are key odorants contributing to the aroma profile of many wine styles, generally imparting tropical grapefruit and passionfruit aromas. 3SH and 3SHA are present in negligible concentrations in the grape berry, juice, and must, suggesting that they are released from non-volatile precursors present in the grape. The exploration of the nature and biogenesis of these precursors to 3SH and 3SHA has proven important for the elucidation of polyfunctional thiol biogenesis during alcoholic fermentation. The development and validation of appropriate analytical techniques for the analysis of 3SH precursors in enological matrices have been extensive, and this review explores the analysis and discovery of these precursor compounds. The development of analytical methods to analyze 3SH precursors, from the selection of the analytical instrument, sample preparation, and methods for standardization, will first be discussed, before highlighting how these techniques have been used in the elucidation of the biogenesis of 3SH and 3SHA in grape wines. Lastly, the future of thiol precursor analysis will be considered, with the development of new methods that greatly reduce the sample preparation time and enable multiple precursors, and the thiols themselves, to be quantitated using a single method

Contribution of Grape Skins and Yeast Choice on the Aroma Profiles of Wines Produced from Pinot Noir and Synthetic Grape Musts

The aroma profile is a key component of Pinot noir wine quality, and this is influenced by the diversity, quantity, and typicity of volatile compounds present. Volatile concentrations are largely determined by the grape itself and by microbial communities that produce volatiles during fermentation, either from grape-derived precursors or as byproducts of secondary metabolism. The relative degree of aroma production from grape skins compared to the juice itself, and the impact on different yeasts on this production, has not been investigated for Pinot noir. The influence of fermentation media (Pinot noir juice or synthetic grape must (SGM), with and without inclusion of grape skins) and yeast choice (commercial Saccharomyces cerevisiae EC1118, a single vineyard mixed community (MSPC), or uninoculated) on aroma chemistry was determined by measuring 39 volatiles in finished wines using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC-MS). Fermentation medium clearly differentiated the volatile profile of wines with and without yeast, while differences between EC1118 and MSPC wines were only distinct for Pinot noir juice without skins. SGM with skins produced a similar aroma profile to Pinot noir with skins, suggesting that grape skins, and not the pulp, largely determine the aroma of Pinot noir wines

The importance of outlier rejection and significant explanatory variable selection for pinot noir wine soft sensor development

Sensory attributes are essential factors in determining the quality of wines. However, it can be challenging for consumers, even experts, to differentiate and quantify wines' sensory attributes for quality control. Soft sensors based on rapid chemical analysis offer a potential solution to overcome this challenge. However, the current limitation in developing soft sensors for wines is the need for a significant number of input parameters, at least 12, necessitating costly and time-consuming analyses. While such a comprehensive approach provides high accuracy in sensory quality mapping, the expensive and time-consuming studies required do not lend themselves to the industry's routine quality control activities. In this work, Box plots, Tucker-1 plots, and Principal Component Analysis (PCA) score plots were used to deal with output data (sensory attributes) to improve the model quality. More importantly, this work has identified that the number of analyses required to fully quantify by regression models and qualify by classification models can be significantly reduced. Based on regression models, only four key chemical parameters (total flavanols, total tannins, A520nmHCl, and pH) were required to accurately predict 35 sensory attributes of a wine with R2 values above 0.6 simultaneously. In addition, for classification models to accurately predict 35 sensory attributes of a wine at once with prediction accuracy above 70%, only four key chemical parameters (A280nmHCl, A520nmHCl, chemical age and pH) were required. These models with reduced chemical parameters complement each other in sensory quality mapping and provide acceptable accuracy. The application of the soft sensor based on these reduced sets of key chemical parameters translated to a potential reduction in analytical cost and labour cost of 56% for the regression model and 83% for the classification model, respectively, making these models suitable for routine quality control use

Addition of volatile sulfur compounds to yeast at the early stages of fermentation reveals distinct biological and chemical pathways for aroma formation

8restrictedInternationalInternational coauthor/editorVolatile sulfur compounds (VSCs) greatly influence the sensory properties and quality of wine and arise via both biological and chemical mechanisms. VSCs formed can also act as precursors for further downstream VSCs, thus elucidating the pathways leading to their formation is paramount. Short-term additions of exogenous hydrogen sulfide (H2S), ethanethiol (EtSH), S-ethylthio acetate (ETA), methanethiol (MeSH) and S-methylthio acetate (MTA) were made to exponentially growing fermentations of synthetic grape medium. The VSC profiles produced from live yeast cells were compared with those from dead cells and no cells. Interestingly, this experiment allowed the identification of specific biochemical and/or chemical pathways; e.g. most of the conversion of H2S to EtSH, and the further step from EtSH to ETA, required the presence of live yeast cells, as did the conversion of MeSH to MTA. In contrast, the reaction from MTA to MeSH and ETA to EtSH was due primarily to chemical degradation. Ultimately, this research unravelled some of the complex interactions and interconversions between VSCs, pinpointing the key biochemical and chemical nodes. These pathways are highly interconnected and showcase the complexity of both the sulfur pathways in yeast and the reactive chemistry of sulfur-containing compoundsrestrictedKinzurik, Matias I; Deed, Rebecca C; Herbst-Johnstone, Mandy; Slaghenaufi, Davide; Guzzon, Raffaele; Gardner, Richard C; Larcher, Roberto; Fedrizzi, BrunoKinzurik, M.I.; Deed, R.C.; Herbst-Johnstone, M.; Slaghenaufi, D.; Guzzon, R.; Gardner, R.C.; Larcher, R.; Fedrizzi, B

Iterative synthetic strategies and gene deletant experiments enable the first identification of polysulfides in: Saccharomyces cerevisiae

New evidence on the role of H2S as a gasotransmitter suggests that the true signalling effectors are polysulfides. Both oxidized polysulfides and hydropolysulfides were synthesized and their presence in S. cerevisiae was observed for the first time. A single gene-deletant approach allowed observation of the modulation of polysulfide species and levels.</p