Selection of S. cerevisiae genotypes

Recently Knight and Goddard17 isolated 3,900 S. cerevisiae from native forests, vineyards and the spontaneous ferments of Vitis vinifera var Sauvignon Blanc fruit from six major regions in NZ (Hawke’s Bay, Martinborough, Nelson, Wairau Valley, Awatere Valley and Central Otago). Microsatellite genotype profiling of these isolates revealed the presence of 295 different genotypes. Bayesian population structure methods and statistical analyses of the resulting ancestry profiles29, showed significantly distinct sub-populations residing in each of these regions17. Here we use the genetic ancestry profiles produced from Bayesian analysis, in combination with regional allele frequencies, to select S. cerevisiae genotypes from each of these regional sub-populations that span and represent the genetic diversity within each region. Genotypes that belong to the main inferred population correlating with each region, that also maximize the diversity of alleles present in each region, were selected, including at least one genotype that harbored regionally unique alleles. Supplementary Table S1 shows the Bayesian ancestry profiles for the genotypes originally analyzed by Knight and Goddard17: the inferred sub-populations common in each regions are noted and the genotypes selected for use in this study are highlighted. The allele frequencies within each regional population are shown in Supplementary Table S2 with the alleles harbored by the selected genotypes in this analysis highlighted. Due to the large diversity of alleles observed in each regional population and the constraints on the number of ferments we could perform and analyze here, clearly not every allele could be represented. Rather, we included those genotypes harboring the more common alleles in each region: genotypes selected ensured that the average proportion of each population that harbored the represented alleles was no lower than 60% (Supplementary Table S2).

Ferment Performance

All ferments were conducted using the same commercially derived batch of homogenized and sterilized Sauvignon Blanc juice from Marlborough in NZ. Six individual S. cerevisiae genotypes from each region and co-inoculations of all six genotypes representing regional populations, were fermented in triplicate across three separate batches totaling 126 ferments. The extent to which sugars were fermented was analyzed by weight loss30 and most lost approximately 25 g indicating complete fermentation given the 220 g of sugar in the juice initially. One genotype from the Wairau Valley failed to ferment at all and was removed from all analyses. Eleven single genotype ferments, all in the third batch, displayed significantly less weight loss than the remaining ferments (F 1, 108 = 905.9, P < 0.0001), indicating incomplete fermentation which is known to affect the volatile profiles of wines31. Also consistent with incomplete fermentation, the final concentration of ethanol in these ferments reduced (Dataset S1). It would also be expected that these ferments would have higher residual sugar but curiously the residual sugar reported for all of these ferments is below 2.5 g/L suggesting the majority of the sugar has been consumed (Dataset S1). To confirm this observation the wines from the third batch were also analyzed for residual sugar using an alternate enzymatic assay (Megazyme D-Fructose/D-Glucose assay kit), which confirmed the low residual sugar levels, reporting concentrations between 0–1.1 g/L. This suggests these ferments may not have had as much sugar at the start of fermentation, potentially caused by incomplete mixing of the initial juice before allocation into flasks. We therefore conservatively removed these ferments from all further analyses. Lag phase, the time taken for fermentation to initiate, differed significantly between batches (F 2, 89 = 7.73, P = 0.0008) and since each batch contained one replicate of each sample, this was controlled for in subsequent statistical analyses by introducing a “batch” factor.

Chemical profiles produced by single genotype ferments correlate with region of microbe origin

We quantified the concentrations of 39 volatile compounds and wine quality parameters produced in each of the 112 successful ferments using targeted GC-MS and FTIR analyses. First we analyzed the volatile profiles deriving from ferments conducted by single yeast genotypes only. A Permutational Multivariate Analysis of Variance (PERMANOVA) employing a full factorial model with “region” and “batch” as main effects and where permutations kept replicates of each genotype together, revealed that both factors significantly affected volatile profiles (both P = 0.001), but provided no evidence of an interaction between these main effects (Table 1a). The R2 value for the region effect was greatest reporting the geographic origin of the S. cerevisiae genotypes explained approximately 10 % of the total variation in the chemical profiles (Table 1a). The lack of significance for the interaction term indicates this result is not confounded by the differences between batches. In addition, we analyzed these differential chemical profiles by accounting for human perception thresholds of compounds. Where available, we used empirically determined odor activity values (OAVs) to standardize the various chemical concentrations in these ferments32,33. The results of the subsequent PERMANOVA agreed with the initial analyses and again revealed a highly significant effect of the region of S. cerevisiae isolation on these wine phenotypes (Region: R2 = 0.127, P = 0.002; Table 1b). Thus, we can categorically reject the null hypothesis and move to accept that there is a significant correlation between the region of isolation of S. cerevisiae and aroma profiles in wine.

Table 1 Summary of all PERMANOVA analyses. Full size table

Regional pairwise PERMANOVA analyses revealed different degrees of distinction between the chemical profiles produced by S. cerevisiae genotypes originating from different regions (Supplementary Table S3). P-values can be misleading when multiple comparisons are performed34 and it has been argued that more emphasis should be placed on the magnitude of the effect when dissecting differences35: we therefore examined the magnitude of the F-statistics from these multiple comparisons as a measure of the strength of evidence for a regional effect (i.e. the higher the F-statistic, the stronger the support for a regional effect). The chemical profiles of yeasts originating from Nelson are the most distinct compared to other regions with the mean of the pairwise F-statistics involving this region being the highest at 3.20 (Fig. 1; Supplementary Table S3). Nelson’s similarity to all regions is low with the exception of the Awatere Valley (Fig. 1). The Awatere and Wairau Valleys are the most similar to other regions (Fig. 1) and report the least distinct chemical profiles compared to other regions with mean F-statistics of 1.19 and 1.73 respectively (Supplementary Table S3). Central Otago, Martinborough and Hawke’s Bay are intermediate with a mix of both highly similar and more distinct relationships with other regions (Fig. 1; Supplementary Table S3).

Figure 1 A map of the regions the tested genotypes of S. cerevisiae were isolated from and the strength of regional differentiation in the chemical profiles as indicated by F-statistics from pairwise PERMANOVA analyses (Supplementary Table S3). Wider lines indicate weaker regional distinctions in the chemical profiles produced (i.e. less distinct chemical profiles), while thinner lines indicate stronger regional distinction (i.e. more distinct chemical profiles). The inset indicates the portion of NZ highlighted in the larger map. The outline of the map of NZ was obtained from www.spraypaintstencils.com, where it is freely available and all modifications were performed by the Authors in Microsoft Power Point. Full size image

To effectively visualize the differences in chemical profiles, the data were transformed and plotted using Constrained Correspondence Analysis (CCA)36. Overall a large overlap is observed between chemical profiles derived from genotypes from different regions (Fig. 2); however, the chemical profiles of Central Otago genotypes cluster in the upper half and those from Nelson mostly toward the lower left quadrant, with the exception of the three replicate samples from one genotype that are located in the upper right quadrant (Fig. 2a). The genotypes from Wairau and Awatere Valleys have the largest ellipses indicating a larger variability in the chemical profiles of these samples (Fig. 2b).

Figure 2 CCA of the 105 single genotype ferments analyzed. (a) All sample points colored by region. (b) Regional averages and 50 % ellipses. Full size image

Chemical drivers of regional differentiation in single ferment samples

Next we evaluated which components of the volatile profiles might be driving these differences in wine phenotype. Individual ANOVA analyses were performed for each of the chemical properties measured. As explained above, F-statistics are reported here in place of P-values as they are a more appropriate measure of support for multiple comparisons. We designate F-statistics larger than two as having a sizeable effect (i.e. region explains more the twice the variation in the model compared to the residuals) and thus 29 of the 39 compounds vary with respect to the region of origin of the yeast genotype (Supplementary Table S4). R2 values range from zero to 38% of the variation being explained by the S. cerevisiae genotype region of isolation, but no one class of chemical compound is exclusively responsible for the regional signal for wine phenotypes (Supplementary Table S4).

CCA additionally provides vectors indicating the direction and magnitude of influence that each chemical property has on the positioning of the sample aroma profiles within the plot and potentially provides a mechanism to infer which chemicals differentiate each region. Four compounds (three esters and one fatty acid) have the greatest impact on the distribution of these wine phenotypes generally with vectors of a magnitude larger than 0.25 (Fig. 3a); however these chemical compounds are not necessarily correlated to the differentiation calculated between regions. To focus on and visualize the vectors of the chemical properties most important to the differences in chemical profiles between ferments conducted by yeasts derived from different regions we identified those chemicals that reported R2 values above 0.25 and F-statistics above 5 in the individual ANOVA analyses (Fig. 3b,c; Supplementary Table S4). This reveals that concentrations of ethyl isobutyrate and ethyl-2-methyl butanoate, which have apple and sweet fruit sensory descriptors, are on average both greatest in the ferments conducted by the genotypes deriving from Nelson and least in those from Central Otago and Martinborough. In addition, concentrations of ethyl butanoate (sensory descriptors of peach, apple and sweet) are on average greatest in ferments conducted by genotypes derived from Martinborough and least in ferments conducted by genotypes derived from Nelson (Fig. 3). β-damascenone (sensory descriptors of apple, honey and floral) concentrations are on average greater in the ferments conducted by yeast genotypes derived from the Awatere and Wairau Valleys comprising the larger Marlborough region and least from the ferments conducted by genotypes deriving from the Hawke’s Bay. Together this paints an intuitively sensible picture and reveals that the differential wine phenotype signatures driven by yeasts derived from different regions are not one-dimensional but multi-faceted.

Figure 3 Visualization of the chemicals that individually explain more than 25% of the regional variation as calculated by ANOVA analyses. (a) The direction and magnitude of all chemical loading vectors, with labels for the chemicals that reported a magnitude above 0.25. The blue circles represent the position of 0.1 and 0.25. (b) The chemical loading vectors in the CCA plot for those that reported an R2 value for region larger than 0.25 and an F-statistics larger than 5 in the ANOVA analyses (Supplementary Table S4). The blue circles represent the position of 0.1 and 0.25. (c) The same chemical loading vectors reported in b with respect to the regional centers of the chemical profiles. Full size image

The genetic basis for differences in chemical profiles

While not exclusively genetically determined, the types and concentrations of metabolites produced by S. cerevisiae are significantly influenced by yeast genotype37,38,39. It is thus not surprising that a Mantel test evaluating the correlation between S. cerevisiae genotype genetic distance (using microsatellite profiles)17 and volatile chemical profile distance (calculated using Jaccard dissimilarity) reveal they are significantly correlated (R2 = 0.189; P < 0.0001). This formally allows us to accept the alternate hypothesis at the core of this study: that there is a significant correlation between the genetic relatedness of natural S. cerevisiae sub-populations and their effect on resulting wine phenotypes. Additionally, PERMANOVA analysis using the assignment of genotypes to inferred genetic clusters calculated using InStruct17 as a factor, as opposed to region of origin, increased the R2 value by 0.051 to 0.151 or 15% (P = 0.007). Some of the genotypes do not have a high proportion of ancestry to any one inferred population and thus have mixed ancestry to different regions (Dataset S1). If these hybrid genotypes are removed and only those genotypes with a ‘clean’ geographic signal are analyzed, the PERMANOVA analysis reveals an increase in the R2 for the factor “region” to 0.198 (P = 0.006), double that of the original analysis (Table 1c).

The effect of regionally co-fermented genotypes and blended wines on volatile profiles

There is evidence to show that the presence of other yeasts during fermentation, be they conspecifics or other species, may affect the subsequent volatile profiles of wine compared to the profiles produced when genotypes ferment in isolation25,40,41,42. We moved to evaluate whether interactions between genotypes from each region may affect and potentially alter regional signals for wine phenotypes. We compared the volatile profile of regional co-ferments, produced by inoculating all six genotypes from a region together in equal proportions, to regional blends, created by mixing the final wine produced by single genotypes from each region in equal proportions. PERMANOVA reveals that the type of ferment (co-ferment or blend) has a significant effect on chemical profiles (R2 = 0.061, P = 0.014; Table 1d). Again CCA was used to visualize the differences between the chemical profiles and while overlap between the blends and co-ferments is evident, the blended ferments show less variability than the co-ferments and are typically placed in the lower right of the plot (Supplementary Fig. S1a). Individual chemical ANOVA and the resulting CCA plot show the main differences between the co-ferments and blends are driven by ethyl decanoate, ethyl dodecanoate, ethyl octanoate and ethyl acetate (Supplementary Fig. S1b).

While the co-fermentation of multiple genotypes significantly affects the phenotype of wine compared to blending, it appears to erode signal for wine phenotype regionality, as PERMANOVA analysis reveals no strong regional co-ferment effect on volatile profiles (R2 = 0.346, P = 0.073; Table 1e). However, this may be an issue of statistical power—only three replicates of regional co-ferments and blends were implemented compared to the six volatile profiles from each of six genotypes from each region in the initial analysis. It is worth noting that the P-value for the effect of region reported by the co-ferments is marginal (P = 0.073), but the value for blends is not (P = 0.196) (Table 1 e,f) and might suggest that blending more greatly erodes any signal for regional wine phenotype than co-fermentation does.