Associations among Wine Grape Microbiome, Metabolome, and Fermentation Behavior Suggest Microbial Contribution to Regional Wine Characteristics

Abstract

Regionally distinct wine characteristics (terroir) are an important aspect of wine production and consumer appreciation. Microbial activity is an integral part of wine production, and grape and wine microbiota present regionally defined patterns associated with vineyard and climatic conditions, but the degree to which these microbial patterns associate with the chemical composition of wine is unclear. Through a longitudinal survey of over 200 commercial wine fermentations, we demonstrate that both grape microbiota and wine metabolite profiles distinguish viticultural area designations and individual vineyards within Napa and Sonoma Counties, California. Associations among wine microbiota and fermentation characteristics suggest new links between microbiota, fermentation performance, and wine properties. The bacterial and fungal consortia of wine fermentations, composed from vineyard and winery sources, correlate with the chemical composition of the finished wines and predict metabolite abundances in finished wines using machine learning models. The use of postharvest microbiota as an early predictor of wine chemical composition is unprecedented and potentially poses a new paradigm for quality control of agricultural products. These findings add further evidence that microbial activity is associated with wine terroir

Importance: Wine production is a multi-billion-dollar global industry for which microbial control and wine chemical composition are crucial aspects of quality. Terroir is an important feature of consumer appreciation and wine culture, but the many factors that contribute to terroir are nebulous. We show that grape and wine microbiota exhibit regional patterns that correlate with wine chemical composition, suggesting that the grape microbiome may influence terroir In addition to enriching our understanding of how growing region and wine properties interact, this may provide further economic incentive for agricultural and enological practices that maintain regional microbial biodiversity.

FIG 1

Map of sampling sites across Napa and Sonoma Counties. Each point represents an individual vineyard from which grapes were harvested for the fermentations monitored in this study. Points are colored by AVA designation, as indicated in the key. The inset illustrates the position of this sampling area within California.

FIG 2

Microbiota exhibits regional variation in musts and wines. (A) Chardonnay. (B) Cabernet Sauvignon. Shown are PCoA comparisons of bacterial weighted UniFrac distance (left two columns) and fungal Bray-Curtis dissimilarity (right two columns) in musts and wines (see column labels), categorized by vineyard (color) and AVA source (shape). Each point represents an individual sample, and sample proximity on the plot is a function of similarity in bacterial and fungal community composition.

FIG 3

Stage of fermentation influences microbial richness. Shown are the mean ± standard deviation (SD) bacterial (A) and fungal (B) richness (observed OTU) in Cabernet Sauvignon and Chardonnay by stage of fermentation. Different lowercase letters indicate significantly different means.

FIG 4

Stage of fermentation influences microbial composition of Cabernet Sauvignon. (A and B) Bacterial weighted UniFrac (A) and fungal Bray-Curtis (B) PCoA comparing similarity among all Cabernet samples, colored by stage of fermentation. (C and D) Relative abundance of bacteria (C) and fungi (D) that differed significantly by stage of fermentation. Only taxa detected at >1% relative abundance are shown in panels C and D. False discovery rate (FDR)-corrected P values are listed for each taxon. The taxon “Leuconostocaceae” represents O. oeni, which belongs to this bacterial family, and all OTU assigned to this taxonomy matched O. oeni by BLASTn.

FIG 5

Must microbiome is correlated with the wine metabolome. Shown are the results from multifactorial analysis (MFA) of must microbiome and wine metabolome profiles of Chardonnay (A to D) and Cabernet Sauvignon (E to G). (A and E) An MFA sample ordination plot demonstrates regional and vineyard segregation of Chardonnay (A) and Cabernet Sauvignon (E). (B and F) A partial-axis MFA plot illustrates category correspondence between metabolite (orange), bacterial (blue), and fungal (red) subcategory coordinates. Partial mean individuals (means of sample ordination for bacterial, fungal, or metabolite profiles) are linked to the subcategory common mean (centroid of all samples for a given region of origin). (C and G) MFA group representations illustrate the relationship between bacterial, fungal, and metabolite profiles with wine origin (region and vineyard). (D) An MFA correlation circle depicts correlations in normalized abundance between all Chardonnay must bacterial taxa (blue), fungal taxa (red), and wine metabolites (orange) along the MFA axes. Metabolite nominal masses are used for clarity; accurate masses are provided in Table S5 in the supplemental material. To improve readability of the plots, only the top correlations in each dimension are shown.

FIG 6

Microbial composition accurately predicts metabolite abundance of select metabolites. Shown are the results from random forest regression of predicted versus observed metabolite intensity in validation samples using a sparse set of predictors (inset, model using all taxa as predictors) to predict abundance of a C₆ keto acid in Cabernet Sauvignon wines (A) and a C₆ acid, ester, or lactone in Chardonnay wines (B). Trend lines indicate a true 1:1 ratio. Bar plots were used to display the ranked feature importance of bacterial (blue bars) and fungal (red bars) taxa used to train the optimized models. MW, molecular weight; MDA, mean decrease in model accuracy if that feature is removed from the model.

FIG 7

Microbiota correlations with must/juice and fermentation characteristics. Shown is the Spearman correlation between must/juice chemistry, fermentation characteristics, and microbiota in musts (left columns) and end of fermentations (EOF [right columns]). Only significant correlations (FDR-corrected P value of <0.05) are shown. As chemical composition was only measured in musts and juices, no data appear for must/juice correlations at end of fermentation (gray boxes at top right corner). NH3, ammonia concentration; NOPA, total nitrogen by o-phthaldialdehyde assay; YAN, yeast assimilable nitrogen.