Exploring the Saccharomyces cerevisiae Volatile Metabolome: Indigenous versus Commercial Strains

Abstract

Winemaking is a highly industrialized process and a number of commercial Saccharomyces cerevisiae strains are used around the world, neglecting the diversity of native yeast strains that are responsible for the production of wines peculiar flavours. The aim of this study was to in-depth establish the S. cerevisiae volatile metabolome and to assess inter-strains variability. To fulfill this objective, two indigenous strains (BT2652 and BT2453 isolated from spontaneous fermentation of grapes collected in Bairrada Appellation, Portugal) and two commercial strains (CSc1 and CSc2) S. cerevisiae were analysed using a methodology based on advanced multidimensional gas chromatography (HS-SPME/GC×GC-ToFMS) tandem with multivariate analysis. A total of 257 volatile metabolites were identified, distributed over the chemical families of acetals, acids, alcohols, aldehydes, ketones, terpenic compounds, esters, ethers, furan-type compounds, hydrocarbons, pyrans, pyrazines and S-compounds. Some of these families are related with metabolic pathways of amino acid, carbohydrate and fatty acid metabolism as well as mono and sesquiterpenic biosynthesis. Principal Component Analysis (PCA) was used with a dataset comprising all variables (257 volatile components), and a distinction was observed between commercial and indigenous strains, which suggests inter-strains variability. In a second step, a subset containing esters and terpenic compounds (C10 and C15), metabolites of particular relevance to wine aroma, was also analysed using PCA. The terpenic and ester profiles express the strains variability and their potential contribution to the wine aromas, specially the BT2453, which produced the higher terpenic content. This research contributes to understand the metabolic diversity of indigenous wine microflora versus commercial strains and achieved knowledge that may be further exploited to produce wines with peculiar aroma properties.

Fig 1. Schematic representation of the main stages for S. cerevisiae metabolome determination.

This includes yeast growth, sample, preparation and metabolites extraction, GC×GC analysis and data processing.

Fig 2. Volatile metabolome of S. cerevisiae strains represented by GC×GC total ion chromatogram contour plots: BT2453 and BT2652 corresponding to strains isolated from wine, and CSc1 and CSc2, corresponding to commercial yeast strains.

Fig 3. Heatmap representation of GC×GC peak areas of metabolites from four S. cerevisiae strains.

The metabolites were organized by chemical families, and with the indication of the number of compounds per family. Areas are normalized by applying a logarithm function. Each line corresponds to one metabolite, and each column corresponds to each strain (BT2453, BT2652, CSc1 and CSc2, each one with three independent cultures). The terpenic profile is highlighted at the right side: β -Myrcene (121), α-Terpinene (122), Limonene (123), β-Ocimene (124), Dihydromyrcenol (125), γ-Terpinene (126), 6,10-Dihydromyrcenol (127), α-Terpinolene (128), Linalool (129), α-Terpineol (130), Nerol (131), Isogeraniol (132), Geraniol (133), Citral (134), Geranylacetate (135), β-Farnesene (136), α-Farnesene (137), Nerolidol (138).

Fig 4. Schematic representation proposed to explain S. cerevisiae metabolic pathways related to terpenic and esters chemical families.

(PP—Diphosphate) [34,35,37].

Fig 5. PC1 × PC2 analysis applied to all chemical families from metabolome of the four yeasts strains: BT2453, BT2652, CSc1 and CSc2.

Scores scatter plot (A) and PC1 (B) and PC2 (C) loadings plots.

Fig 6. PC1 × PC2 analysis applied to terpenic compounds and esters from metabolome of the four yeasts strains: BT2453, BT2652, CSc1 and CSc2.

Scores scatter plot (A) and PC1 (B) and PC2 (C) loadings plots.