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. 2021 Feb 4;16(2):e0225615. doi: 10.1371/journal.pone.0225615

An indigenous Saccharomyces uvarum population with high genetic diversity dominates uninoculated Chardonnay fermentations at a Canadian winery

Garrett C McCarthy 1,#, Sydney C Morgan 1,¤,*,#, Jonathan T Martiniuk 2, Brianne L Newman 1, Stephanie E McCann 1, Vivien Measday 2, Daniel M Durall 1
Editor: Luca Cocolin3
PMCID: PMC7861373  PMID: 33539404

Abstract

Saccharomyces cerevisiae is the primary yeast species responsible for most fermentations in winemaking. However, other yeasts, including Saccharomyces uvarum, have occasionally been found conducting commercial fermentations around the world. S. uvarum is typically associated with white wine fermentations in cool-climate wine regions, and has been identified as the dominant yeast in fermentations from France, Hungary, northern Italy, and, recently, Canada. However, little is known about how the origin and genetic diversity of the Canadian S. uvarum population relates to strains from other parts of the world. In this study, a highly diverse S. uvarum population was found dominating uninoculated commercial fermentations of Chardonnay grapes sourced from two different vineyards. Most of the strains identified were found to be genetically distinct from S. uvarum strains isolated globally. Of the 106 strains of S. uvarum identified in this study, four played a dominant role in the fermentations, with some strains predominating in the fermentations from one vineyard over the other. Furthermore, two of these dominant strains were previously identified as dominant strains in uninoculated Chardonnay fermentations at the same winery two years earlier, suggesting the presence of a winery-resident population of indigenous S. uvarum. This research provides valuable insight into the diversity and persistence of non-commercial S. uvarum strains in North America, and a stepping stone for future work into the enological potential of an alternative Saccharomyces yeast species.

Introduction

Modern winemaking often involves the inoculation of grape must with one or more commercial yeast strains, usually belonging to the dominant winemaking yeast species Saccharomyces cerevisiae. However, single-strain fermentations have been shown to produce less complex wines than fermentations conducted by multiple yeast species and strains [13]. Furthermore, the commercial S. cerevisiae strains used in these inoculated single-strain fermentations may be more aggressive than indigenous yeasts; indeed, commercial strains used previously at wineries have been identified entering and dominating uninoculated (spontaneous) fermentations in subsequent vintages [48]. The loss of indigenous yeast strains during the winemaking process can result in the reduction of regional character, because the local consortium of microorganisms can contribute to the expression of terroir in wine [3, 9]. In recent years, many winemakers have become interested in conducting uninoculated fermentations in an attempt to encourage a diversity of yeast species and strains to participate in alcoholic fermentation. Although vineyard-derived yeasts were originally thought to be the ones conducting uninoculated fermentations, a growing body of evidence has shown that uninoculated fermentations are actually conducted by winery-resident yeast strains [4, 7, 10, 11]. These yeasts may be of commercial or indigenous origin, but have established themselves as residents of the winery environment, and are capable of entering and conducting fermentations over multiple vintages.

Although most uninoculated fermentations at commercial wineries are conducted by strains of S. cerevisiae [4, 7, 10, 11], some wineries contain established populations of Saccharomyces uvarum that are able to conduct and complete alcoholic fermentation [1215]. S. uvarum belongs to the Saccharomyces sensu stricto clade, and is the furthest relative from S. cerevisiae within this clade [16, 17]. The Holarctic S. uvarum population, which originated in the northern hemisphere [1820], includes both natural S. uvarum strains isolated from the soil and bark of Quercus (oak) trees as well as industrial strains isolated from cider, beer, and wine fermentations. It is believed that the Holarctic population evolved alongside other Saccharomyces species, as 95% of Holarctic S. uvarum strains possess introgressed regions from Saccharomyces eubayanus, Saccharomyces kudriavzevii, and S. cerevisiae throughout their genome [18, 19]. However, the history of S. uvarum research is difficult to trace, because S. uvarum has had many names, and has even shared names with now distinct species. In the past, S. uvarum has been referred to as Saccharomyces bayanus var. uvarum [13, 14, 21, 22] or simply Saccharomyces bayanus [23, 24]. To complicate matters, many commercial S. cerevisiae strains have been marketed incorrectly as strains of S. bayanus [25]. However, S. uvarum is now known to be a pure species, distinct from S. bayanus, which itself is a hybrid of the pure species S. uvarum and S. eubayanus [16, 19, 26, 27].

S. uvarum is a cryotolerant yeast usually found in association with white wine fermentations in cool-climate wine regions [12, 13, 15, 28], but has also been associated with cider production [23, 24] and some traditional fermentations [29, 30]. During fermentation, S. uvarum produces lower levels of ethanol, acetic acid, and acetaldehyde, and higher levels of glycerol, succinic acid, malic acid, isoamyl alcohol, isobutanol, and ethyl acetate, as compared to S. cerevisiae [3134]. Additionally, because of its ability to conduct fermentation at lower temperatures, S. uvarum may produce wines with more balanced aroma profiles [35]. However, few studies have been conducted to investigate the origins, genetic diversity, and enological potential of this yeast, and thus more research is needed on this topic.

The overall objective of this study was to identify the presence and genetic diversity of S. uvarum strains conducting uninoculated fermentations at a commercial winery in the Okanagan Valley wine region of British Columbia, Canada, and place this population within the context of global S. uvarum strains. Two years before this current study was conducted, a highly diverse, indigenous population of S. uvarum was identified at this same commercial winery [15]. We were interested in investigating the persistence of S. uvarum strains in the winery environment over multiple vintages. A secondary objective (addressed by sampling fermentations with grapes sourced from two different vineyards) was to investigate whether the geographical origin or chemistry of the grapes played a role in determining the fungal communities and S. uvarum populations present in the fermentations.

Materials and methods

Experimental design and sampling

This study was conducted in association with Mission Hill Family Estate Winery during the 2017 vintage. The fungal diversity and community composition of grapes from two different vineyards were followed from the vineyard into the winery and throughout alcoholic fermentation. Samples for high-throughput amplicon sequencing (Illumina MiSeq) were taken from grapes in the vineyard just prior to harvest, as well as at four stages of fermentation in the winery. Samples for Saccharomyces uvarum population analysis were taken at three stages of alcoholic fermentation.

Vineyard grape samples

Two Chardonnay vineyards managed by Mission Hill Family Estate Winery in the Okanagan Valley of British Columbia, Canada, were selected for this study (Vineyard 2 and Vineyard 8). Vineyard 2 is located approximately 120 km south of the winery, and Vineyard 8 is located approximately 90 km south of the winery; the two vineyards are approximately 30 km apart. The exact locations of these vineyards, along with the dates of grape sample collection, can be viewed in Table 1. Both vineyards had been herbicide-free since 2016 (2017 was the second herbicide-free vintage), and were transitioning from conventional to organic viticulture practices.

Table 1. Summary of vineyards used in this study.
Vineyard name Closest municipality Latitude Longitude Sample collection Date of commercial harvest
Vineyard 2 Osoyoos, BC 49.000603 -119.418712 2017-09-04 2017-09-05
Vineyard 8 Oliver, BC 49.221125 -119.559834 2017-09-18 2017-09-19

Each vineyard was divided into six sampling sections (achieved by dividing the number of rows in the vineyard by six), and was further sub-divided into potential sampling sites within each section. Each post in the vineyard was planted approximately 15 feet (4.57 m) apart, with five vines planted in between each post (within each panel). A potential sampling site was defined as three consecutive panels (equalling approximately 15 vines), and one sampling site was randomly selected within each sampling section, for a total of six samples per vineyard (S1 Fig). The following restrictions were placed on randomization: sampling occurred at least three posts inward from the edge of each vineyard, and at least two rows away from neighbouring vineyard blocks, to minimize the potential for contamination from nearby roads or other grape varietals. Sampling was performed by aseptically collecting one cluster from each vine in the sampling site, on both sides of the row, for a total of 30 clusters per sample (approximately 2–3 kg). The 30 clusters from each sampling site were placed into a sterile bag (one bag per sampling site, six bags per vineyard) and transported on ice back to the laboratory at The University of British Columbia (Kelowna, BC, Canada) for same-day processing. Each bag containing grape clusters was then gently crushed and homogenized by hand for 10 min, after which time 2 mL samples of the juice were collected and frozen at -80°C to await further processing for high-throughput amplicon sequencing.

Winery samples

The winery portion of this study was conducted at Mission Hill Family Estate Winery, a large commercial winery in British Columbia, Canada that conducts both inoculated and uninoculated (spontaneous) fermentations of many different grape varietals and makes wines in many different styles. The Chardonnay grapes for this study were sourced from two vineyards (Vineyard 2 and Vineyard 8), as described above. The must from each vineyard was crushed and pressed into large stainless steel tanks to undergo a settling period before being transferred into 285 L stainless steel barrels (La Garde, SML Stainless Steel, Québec, Canada). Must from Vineyard 2 spent two days in the settling tank, while must from Vineyard 8 spent eight days in the settling tank. Six stainless steel barrels were used in this experiment: three contained must exclusively from Vineyard 2, and three contained must exclusively from Vineyard 8. Because Vineyard 2 is located further south than Vineyard 8 (Table 1), the grapes from this vineyard ripened earlier, and were harvested before those from Vineyard 8, in order to achieve similar sugar concentrations at crush. Although the fermentations from each vineyard began at different times, they did overlap in the same cellar for 20 days during sampling. The cellar where the fermentations were conducted was maintained at 12°C.

Samples were taken from the stainless steel barrels at four stages of uninoculated (spontaneous) alcoholic fermentation as defined by their sugar concentration (approximated by Brix level): cold settling (22°Brix, prior to the start of AF), early (14–18°Brix), mid (6–10°Brix), and late (2 ± 0.1°Brix). To each barrel, 20 mg/L total sulfur dioxide was added in the form of potassium metabisulfite (K2S2O5) during the cold settling stage, 250 ppm Lallemand® Fermaid K complex yeast nutrient was added between the cold settling and early stages, and 100 ppm Laffort® THIAZOTE mineral nutrient was added between the early and mid stages. Samples were collected in sterile 50 mL centrifuge tubes and were transported on ice to the laboratory at The University of British Columbia (Kelowna, BC, Canada) for processing. Subsamples (2 mL) were washed and frozen at -80°C for high-throughput amplicon sequencing, while other subsamples were processed immediately for culture-dependent S. uvarum strain typing.

Chemical analyses

Chemical parameters for the grape must and wine were taken from the stainless steel barrels at the cold settling and late stages, respectively. Yeast assimilable nitrogen (YAN), titratable acidity, volatile acidity, malic acid, pH, residual sugar, ethanol content, fructose, and glucose were measured using a WineScanTM wine analyzer (Foss, Hillerød, Denmark).

Saccharomyces uvarum strain typing

Colony isolation and DNA extraction

Samples from the early, mid, and late stages of fermentation were serially-diluted and plated onto YEPD media (1% (w/w) yeast extract (BD BactoTM, Sparks, MD, USA), 1% (w/w) bacterial peptone (HiMedia Labs, Mumbai, India), 2% (w/w) dextrose (Fisher Chemical, Fair Lawn, NJ, USA), 2% (w/w) agar (VWR, Solon, OH, USA)), with the addition of 0.01% (v/v) chloramphenicol (Sigma-Aldrich, St. Louis, MO, USA) to prevent bacterial growth, and 0.015% (v/v) biphenyl (Sigma-Aldrich, St. Louis, MO, USA) to prevent filamentous fungal growth [12, 3638]. Plates were incubated at 28°C for 48 h and stored at 4°C. Plates containing 30–300 colonies were selected for colony isolation and DNA extraction. Grape samples from the vineyard and the cold settling stage samples were not analyzed for S. uvarum strains, because Saccharomyces yeasts are rarely found in must before the onset of alcoholic fermentation [39], and are present on healthy grapes in very low abundance compared to other microbes [40].

From the early, mid, and late stage plates, 47 single yeast colonies were isolated onto Wallerstein Nutrient media (WLN) (Sigma-Aldrich, St. Louis, MO, USA), a differential medium used to identify non-S. cerevisiae yeasts. Two controls were used to distinguish between S. cerevisiae and S. uvarum colonies: Lalvin® BA11 (Lallemand, Montreal, QC, Canada) and CBS 7001 (Westerdijk Fungal Biodiversity Institute, Utrecht, Netherlands), respectively. The WLN plates were incubated at 28°C for 48 h, and then stored at 4°C. Presumed S. cerevisiae isolates appeared cream-coloured, while presumed S. uvarum isolates appeared green (S2A Fig). In order to determine the colour of potential S. cerevisiae x S. uvarum hybrids on WLN plates, the following isolates were streaked onto a WLN plate and incubated at 28°C for 48 h: a pure S. cerevisiae strain (Lalvin® BA11), a pure S. uvarum strain (CBS 7001), and hybrid of S. cerevisiae x S. uvarum (Lalvin® S6U, formerly referred to as a S. cerevisiae x S. bayanus hybrid) (S2B Fig). DNA was extracted from each yeast isolate by performing a water DNA extraction, as described previously [7].

Multiplex PCR and fragment analysis

S. uvarum strain typing was performed as previously described [15] using a multiplex PCR reaction targeting 11 microsatellite loci that had been selected from two previous studies: L1, L2, L3, L4, L7, L8, L9 [41], NB1, NB4, NB8, and NB9 [28]. Briefly, multiplex PCR was performed on the extracted DNA from each presumed S. uvarum isolate and submitted to Fragment Analysis and DNA Sequencing Services at the University of British Columbia (Kelowna, BC, Canada) for fragment analysis on a 3130xl DNA sequencer (Applied Biosystems, Foster City, CA, USA). GeneMapper 4.0 software (Applied Biosystems, Foster City, CA, USA) was used to determine the fragment size at each locus, and the resulting multilocus genotype (MLG) of each isolate was compared to that of the others using Bruvo’s genetic distance [42]. Yeast isolates obtained in this study were compared to a database containing 150 S. uvarum strains identified during the 2015 vintage at the same winery [15], as well as 12 international S. uvarum strains (Table 2).

Table 2. Names, geographical origins, and sources of international Saccharomyces uvarum strains used for comparison in this study.
Name Geographical Origin Source
CBS 395 The Netherlands Centraalbureau voor Schimmelcultures (CBS)
CBS 7001 Spain
CBS 8690 Moldova
CBS 8696 California (USA) Westerdijk Fungal Biodiversity Institute (Utrecht, The Netherlands)
CBS 8711 France
PYCC 6860 Hornby Island (Canada) Portuguese Yeast Culture Collection (PYCC)
PYCC 6861 Hornby Island (Canada)
PYCC 6862 Japan
PYCC 6871 Portugal Universidade Nova de Lisboa (Caparica, Portugal)
PYCC 6901 Oregon (USA)
PYCC 6902 Missouri (USA)
Velluto BMV58® Spain Commercial strain from Lallemand®

Bruvo distance was calculated using an algorithm that takes into account stepwise mutations, making it appropriate for use with microsatellite data. Bruvo distance is calculated on experimental data and allows the user to collapse MLGs with slight differences in allele size into a single strain category, based on similarity at a threshold value from 0 to 1. An applied threshold of 0 results in every unique MLG being classified as a different strain, and an applied threshold of 1 results in all the MLGs in a dataset being collapsed into a single strain. Bruvo distance was calculated in R (version 3.5.1) using the ‘poppr’ package (version 2.8.1) [43], and applying a genetic distance threshold of 0.3 for strain classification. A histogram of pairwise genetic distances was created in R (version 3.5.1) using the ‘poppr’ package (S3 Fig). Because the histogram was not bimodal, the largest gap between putative thresholds was determined visually as 0.3 [44]. Additionally, 0.3 was chosen as the cut-off threshold in order to remain consistent with the threshold used in a previous study of this same population [15]. This was particularly important because of the novelty of the high genetic diversity observed in this yeast population. Using a low cut-off threshold for distinguishing strains could overestimate this diversity, and artificially inflate the significance of our results; by using a conservative threshold, we are able to make more confident statements regarding the diversity of this population.

Isolates that only partially amplified were re-run and were subsequently excluded from analysis upon a second failure. Isolates that did not amplify were considered to be non-S. uvarum yeasts and were also excluded from analysis. Although 47 yeast isolates were originally selected from each sample, not all belonged to S. uvarum and some were presumed to be S. cerevisiae based on WLN. After strain identification, each sample was rarefied to 32 S. uvarum isolates.

Presumed S. cerevisiae isolates were also strain-typed as described previously [15] in order to confirm their identities, but were not included in the analysis of this study.

RFLP analysis for hybrid investigation

Restriction fragment length polymorphism (RFLP) analysis was performed on 50 yeast isolates from this study, representing the most abundant 50 strains identified. A pure S. cerevisiae strain (Fermol® Mediterranée), a pure S. uvarum strain (CBS 7001), and a known S. cerevisiae x S. uvarum hybrid strain (Lalvin® S6U) were included in this analysis as reference strains. PCR-RFLP was performed via amplification of the ITS1 region of the rRNA gene using primers ITS1 and ITS4, followed by digestion using the restriction enzyme HaeIII (Product no. ER0151, Thermo Fisher, Waltham, MA, USA), as described previously [45]. The products were run on a 1.8% agarose gel and the resulting patterns of each of the 50 strains were compared to those of the reference strains (S4 Fig).

High-throughput amplicon sequencing

Sample treatment and DNA extraction

Samples for high-throughput amplicon sequencing (Illumina MiSeq) were taken from grapes in the vineyard (grapes), as well as from four stages of fermentation in the winery (cold settling, early, mid, and late).

Samples (previously frozen at -80°C) were thawed on ice, and then washed before total DNA was extracted following a modified protocol from a previously-published study [46]. Samples were pelleted by centrifugation at 13,200 rpm for 5 min. The supernatant was discarded and the pellet was re-suspended in 1 mL chilled 1x phosphate-buffered saline (Sigma-Aldrich, St. Louis, MO, USA), then centrifuged again at 13,200 rpm for 5 min. The supernatant was discarded and the pellet was re-suspended in 500 μL of 50 mM ethylenediaminetetraacetic acid (EDTA, pH 8.0) (Invitrogen, Grand Island, NY, USA). Samples were mixed by pipetting up and down five times, and the entire sample was transferred to a FastPrep tube (MP Biomedicals, Santa Ana, CA, USA) containing 200 mg of 0.5 mm glass disruptor beads (Scientific Industries, Bohemia, NY, USA). The FastPrep tubes were placed into a Vortex-Genie 2 Digital bead beater (Scientific Industries, Bohemia, NY, USA) for two rounds of 2.5 min (30/s), separated by 1 min on ice. Aliquots of 500 μL Nuclei Lysis solution (Fisher, Hampton, VA, USA) were added to the FastPrep tubes and lysed in the bead beater for 1 min (30/s). Samples were then incubated for 10 min at 95°C and then centrifuged at 13,200 rpm for 5 min. To an autoclaved 2 mL microcentrifuge tube (VWR, Radnor, PA, USA), 500 μL of the supernatant was added, followed by 250 μL Protein Precipitation solution (Fisher, Hampton, VA, USA). Samples were then vortexed lightly and kept at room temperature (22°C) for 15 min. Samples were then centrifuged at 13,200 rpm for 5 min, and 500 μL of the supernatant was transferred to new 2 mL microcentrifuge tubes containing 75 μL 20% (v/v) polyvinylpyrrolidone (PVP) solution (Sigma-Aldrich, St. Louis, MO, USA). Samples were pulse-vortexed for 10–20 sec and centrifuged at 13,200 rpm for 10 min. Supernatant (500 μL) was transferred to a new 2 mL microcentrifuge tube containing 300 μL chilled 2-propanol (Sigma-Aldrich, St. Louis, MO, USA). Tubes were inverted to mix several times and left to sit at room temperature for 15 min. Samples were then centrifuged at 13,200 rpm for 2 min and the supernatant was discarded. The pellet was re-suspended in 1 mL chilled 100% ethanol (Commercial Alcohols, Brampton, ON, Canada), centrifuged at 13,200 for 2 min, and the entire supernatant was carefully discarded. Samples were left open in a biosafety cabinet for a maximum of 30 minutes to ensure complete evaporation of the ethanol. The samples were re-suspended in 50 μL of 10 mM TE buffer (Invitrogen, Grand Island, NY, USA), and frozen at -80°C. A positive control, containing pure S. cerevisiae cells, and a negative control, containing only molecular-grade water, were also subjected to the same DNA extraction protocol, and were included during library preparation and Illumina sequencing.

Illumina MiSeq library preparation

Sample library preparation used a two-step PCR procedure consisting of ‘amplicon’ and ‘index’ PCR reactions, as described previously [47]. Amplicon PCR was performed by amplifying the ITS1 region of the rRNA gene using BITS and B58S3 primers [48] with CS1 and CS2 linker sequences, respectively. Index PCR primers contained Illumina MiSeq adapter sequences, unique eight nucleotide barcodes, 9–12 bp heterogeneity spacers, and CS1/CS2 linker sequences. After both PCR reactions, samples were submitted to the IBEST Genomics Resources Core at the University of Idaho (Moscow, ID, USA) for quantification, normalization, pooling, and sequencing. Paired-end sequencing (300 bp) was performed on an Illumina MiSeq Desktop Sequencer (Illumina Inc., San Diego, CA, USA).

Illumina MiSeq data processing

Illumina MiSeq data processing was performed using both R (version 3.5.1) and the open-source bioinformatics pipeline Quantitative Insights Into Microbial Ecology (QIIME2 version 2018.11) [49]. In R, sequences were denoised using the ‘dada2’ package (version 1.8) [50], as well as the ‘ShortRead’ (version 1.36.1) [51] and ‘Biostrings’ (version 2.46.0) packages. All forward and reverse primer sequences had been removed from the 5’ end of the sequences by IBEST at the University of Idaho before being returned, but because some ITS sequences are likely to be shorter than 300 bp, it is possible that these sequences contain nucleotides from the opposite primer, which required removal before further processing using Cutadapt [52]. After all primer sequences had been removed, sequences were filtered and trimmed using the “filterandTrim” function in the ‘dada2’ package. Any sequence containing an N was removed, as well as any sequence shorter than 50 bp. The maximum number of expected errors allowed in any sequence was set to 2. Because the ITS1 region in fungi is highly variable [53, 54], trimming all sequences to the same length can reduce the diversity of the identified community and can even remove sequences with true lengths shorter than the specified truncation length. For this reason, sequences were not trimmed to a consistent length. Forward and reverse reads were merged using the “mergePairs” function, a sequence table was constructed with the “makeSequenceTable,” and chimeras were removed with the “removeBimeraDenovo” function. The representative sequence table was converted to a Fasta file before being transferred from R to the QIIME2 pipeline to complete analysis.

In QIIME2, sequences underwent paired-end alignment using MAFFT [55], and a phylogenetic tree with a mid-point root was created using FastTree 2 [56]. Sequence variants were classified to the species level (if possible) using a dynamic (97–99%) threshold classifier made with the UNITE (version 8.0) database [57]. Sequence variants that could not be classified to the order level or lower and those that appeared with a total frequency of < 100 sequences were excluded from analysis. Samples were rarefied to 20,000 sequences before being exported from QIIME2 for statistical analysis and visualization. Two samples did not meet the applied threshold of 20,000 sequences, and were therefore removed from analysis: one sample was a cold settling sample from the Vineyard 8 fermentations, and the other was a grape sample from Vineyard 2. In the UNITE (version 8.0) database, S. uvarum is incorrectly classified as S. bayanus, because in the past both species were considered synonymous. Based on our culture-dependent data, we are confident that sequences classified in the UNITE database as S. bayanus belong to S. uvarum, and we have accordingly re-named all our sequences identified as S. bayanus to S. uvarum.

Statistical analysis

All statistical analyses in this study, unless otherwise specified, were performed in R (version 3.5.1) using RStudio software, and all statistical tests assume a significance level of α = 0.05. The chemical parameters of the grape must and wine from each vineyard were statistically compared by performing a one-way analysis of variance (ANOVA), using the “aov” function. Each parameter was evaluated separately. Normality was assessed visually. Not all of the chemical parameters met the assumption of normal distribution, but as ANOVA are robust to departures from normality [58], no data transformations were performed. The assumption of homogeneity of variance was tested using the “leveneTest” function in the ‘Rcmdr’ package (version 2.5–1). Levene’s test indicated no violation in the assumption of homogeneity of variance in any chemical parameter (S1 Table).

Rarefaction curves of both species richness (for the fungal community) and strain richness (for the S. uvarum population) were created using the “rarecurve” function in the ‘vegan’ package (version 2.5–1) (S5 Fig) [59]. The fungal community was rarefied to 20,000 sequences per sample; species richness reached a plateau at this sequencing depth for all the samples that were retained after rarefying (S5A Fig). The S. uvarum population did not reach terminal sampling depth before the rarefication point of 32 isolates per sample (S5B Fig); however, this number was chosen because it was the highest number of S. uvarum isolates that allowed every sample to be retained for analysis.

Simpson’s Index of Diversity (1 − D) and Shannon’s Diversity Index (H) were calculated using the “diversity” function in the ‘vegan’ package (version 2.5–1) and reported ± the standard error of the mean (SEM). Differences in diversity between the two vineyard treatments were assessed by performing a one-way ANOVA on grape samples using the “aov” function, and by performing one-way repeated-measures ANOVA on both the fungal communities and the S. uvarum populations throughout alcoholic fermentation, using the “Anova” function in the ‘car’ package (version 3.0–2). Grape samples were analyzed separately from the fermentation samples because they represent a different sample type, and cold settling samples were excluded from analysis because Vineyard 8 contained only two replicates at that stage. Residuals and histograms were plotted to test the assumptions of the model.

The relative abundance of fungal species and S. uvarum strains was visualized by creating stacked bar charts using GraphPad Prism (version 8.2.1) software (La Jolla, CA, USA). Differences in composition between vineyard treatments were assessed by performing permutational analysis of variance (PERMANOVA) tests using the “adonis” function (vegan package), and using Bray-Curtis dissimilarity matrices, which were calculated on untransformed abundance data using the “vegdist” function (vegan package). The assumption of homogeneity of multivariate group dispersions (PERMDISP) was analyzed using the “betadisper” and “permutest” functions (vegan package), using Bray-Curtis dissimilarity and calculating deviation from centroid. Test statistics (F values for PERMDISP and Pseudo-F values for PERMANOVA) were calculated based on 999 permutations of raw data. No violation of the assumption of homogeneity of multivariate group dispersions was observed for the grape samples (F(1,9) = 0.65, p = 0.51) or the S. uvarum population during alcoholic fermentation (F(1,16) = 1.9, p = 0.19). The fungal community during alcoholic fermentation did violate this assumption (F(1,16) = 10.4, p = 0.001), but PERMANOVA tests are considered robust to unequal variances among treatments [60], so no data transformations were made. With regards to the fungal community, the grape samples were analyzed separately from the fermentation samples because they represent a different sample type, and the cold settling samples were excluded from analysis because Vineyard 8 contained only two biological replicates at that stage.

A principal coordinates analysis (PCoA) was generated using the “wcmdscale” and “ordihull” functions (vegan package), using Bray-Curtis dissimilarity, in order to visualise the spatial distribution of the S. uvarum population among samples and treatments. The design of this study involved repeated measures, meaning some data were not independent, which could potentially lead to an overestimation of treatment differences as a result of the PERMANOVA tests, which cannot account for repeated measures. Therefore, the PCoA ordination was used to visualize distances between samples of different treatments [9, 61]. Finally, we note that because the fermentations from both vineyards did not occur simultaneously (Vineyard 8 was harvested 14 days after Vineyard 2), and because the Vineyard 8 must spent longer in the large settling tank, it is possible that the differences in fungal communities and S. uvarum populations observed in this study are not solely a result of differences in vineyard geography.

S. uvarum population structure was assessed on the 102 indigenous strains identified in this study (36 unique to 2017, 66 found in both 2015 and 2017) [15], as well as 12 international S. uvarum strains from ten geographic locations, isolated from winery and natural environments around the world (Table 2), for a total of 114 strains. Population structure was assessed by performing Bayesian clustering in InStruct [62], using the admixture model with a burn-in of 500,000 and a total run of 100,000 iterations with 5 chains per cluster (K), from K = 1 to K = 12. The 12 clusters were made up of the two vintages analyzed at this winery as well as the 10 geographic origins of the 12 international strains. The Deviance Information Criterion (DIC) method, outlined previously [63], was used to determine the optimal number of subpopulations, which was found to be K = 11. A plot of DIC at the last chain of 5, according to K revealed an additional, minor minimum plateau at K = 5, thus K = 5 was also included and passed onto CLUMPP for alignment of the chains. Five InStruct chains at K = 11 and K = 5 were aligned using the LargeKGreedy algorithm in CLUMPP (version 1.1.2) [64], with 10,000 random input orders. The highest H value derived from the CLUMPP population alignment was 0.51 for K = 11 and 0.99 for K = 5. The higher H value for the K = 5 clusters suggested better CLUMPP-alignment of the InStruct output than for K = 11, although the DIC value recommended the optimal number of clusters was 11. Therefore, inferred ancestry profiles were visualized for both K = 11 and K = 5 subpopulations. The CLUMPP-aligned ancestry profiles were visualized using DISTRUCT (version 1.1) [65], which provides a stacked bar plot for each strain, with strains (bars) partitioned into coloured segments that correspond to membership coefficients of inferred subpopulations. ObStruct [66] was used to determine significance of the InStruct-inferred population structure. GenAlEx (version 6.5) [67, 68] was used to calculate the Probability of Identity (PI) and fixation indices (FST and FIS) for the S. uvarum population observed in this study, and to estimate heterozygosity.

An unrooted, neighbour-joining phylogenetic tree was generated to compare the genetic relatedness of the indigenous S. uvarum strains and the 12 international strains. The phylogenetic tree was generated using the ‘ape’ package (version 5.2) [69], while clustering of strains was accomplished by using the Bayesian clustering output of InStruct (K = 5 clusters) for statistical grouping of subpopulations in the tree. A dominant ancestor was identified if the inferred coefficient was equal to or higher than 0.75 (75%), based on the metric used previously for a similar population analysis [41]. Strains were coloured according to their dominant inferred ancestor. Bootstrap values were obtained using the “bruvo.boot” function in the ‘poppr’ package (version 2.8.1) [43], which randomly sampled loci 1000 times to recalculate the percent support of the tree success. Only branches with 50% support or higher were indicated on the tree.

All raw data and R scripts used in the preparation of this manuscript can be viewed at https://osf.io/j7rx8/.

Results and discussion

Fermentation progression and wine chemistry

The total time from harvest of the Chardonnay grapes to the end of alcoholic fermentation was similar for the must from both vineyards: the must from Vineyard 2 completed fermentation 40 days after harvest, and the must from Vineyard 8 completed fermentation 37 days after harvest. However, the time the must spent in the settling tank before being transferred to barrels for fermentation differed between the two vineyard treatments. Must from Vineyard 2 spent only two days in the large stainless steel settling tank, while must from Vineyard 8 spent eight days, due to a higher proportion of solid material that required a longer settling time.

Sugar concentration at crush was similar between the two vineyards (Table 3). Although a significant difference was found between the vineyards for sugar concentration (°Brix), this is likely because of the low variation observed within each treatment. It is unlikely that a difference of 0.3°Brix would have any biologically relevant effects on the microbial composition of the wine. The pH was higher in the must from Vineyard 2, while yeast assimilable nitrogen, titratable acidity, and malic acid were all significantly higher in the must from Vineyard 8.

Table 3. Chemical parameters of stainless steel barrel-fermented Chardonnay wines sourced from two vineyards.

Samples were taken at the cold settling stage (prior to the start of alcoholic fermentation) and at the late stage (towards the end of alcoholic fermentation). Values are the mean ± SEM (n = 3 barrels per vineyard treatment). An asterisk next to the chemical parameter indicates a significant difference (p ≤ 0.05) between the two vineyards. Each chemical parameter was evaluated separately within each stage.

Stage Chemical Parameter Vineyard 2 Vineyard 8
Cold settling pH* 3.37 ± 0.006 3.31 ± 0.003
Residual sugar (°Brix)* 22.3 ± 0.0 22.0 ± 0.03
Yeast assimilable nitrogen (mg/L)* 77.7 ± 5.6 110.7 ± 3.9
Titratable acidity (g/L)* 5.07 ± 0.03 7.20 ± 0.0
Malic acid (g/L)* 2.20 ± 0.0 3.47 ± 0.03
Late pH 3.37 ± 0.01 3.41 ± 0.01
Titratable acidity (g/L)* 7.20 ± 0.2 7.83 ± 0.03
Malic acid (g/L)* 1.97 ± 0.03 2.40 ± 0.06
Volatile acidity (g/L)* 0.47 ± 0.003 0.38 ± 0.001
Ethanol (% v/v) 11.7 ± 0.1 11.3 ± 0.1
Glucose (g/L) 1.07 ± 0.1 1.37 ± 0.3
Fructose(g/L) 28.9 ± 1 27.1 ± 2

By the late stage of alcoholic fermentation, there was no significant difference between the wines from the two vineyards in terms of pH, ethanol content, or glucose and fructose concentration (Table 3). Very little glucose remained in the wine by the end of alcoholic fermentation (< 2 g/L), while close to 30 g/L fructose remained unfermented.

By the late stage, titratable acidity and malic acid were still significantly higher in the wines from Vineyard 8 than the wines from Vineyard 2 (Table 3). However, the change in malic acid from the cold settling to the late stage was also much greater in the wines from Vineyard 8. In the wines from Vineyard 2, the malic acid concentration decreased from 2.20 g/L to 1.97 g/L, a decrease of approximately 10%. Meanwhile, in the wines from Vineyard 8, the malic acid concentration changed from 3.47 g/L to 2.40 g/L, a decrease of approximately 30%. This could suggest a more significant presence of malic acid-degrading bacteria in the must from Vineyard 8. Indeed, a previous study conducted at this same winery with grapes from Vineyard 8 identified Tatumella spp. in barrels that did not receive SO2 [15]. These bacteria were thought to originate in the vineyard, as they were present in vineyard samples, but seemed to be sensitive to SO2; the bacteria were not able to persist in treatments that received 40 mg/L SO2 at crush. When they did persist, however, the malic acid was almost completely degraded by the end of alcoholic fermentation. It is possible that the addition in this current study of only 20 mg/L SO2 allowed a portion of these bacteria to survive in the must and perform a partial degradation of malic acid. Unfortunately, we did not identify the bacterial community in this current study, so more research is needed to confirm this hypothesis.

Volatile acidity (estimated as acetic acid) was found to be significantly higher in the wines from Vineyard 2 (Table 3). However, neither vineyard produced wines with unacceptable levels of volatile acidity, and all the wines in this study contained volatile acidity levels that were below its sensory detection threshold of 0.7 g/L [70].

Fungal communities

Fungal community diversity

Fungal species diversity was highest in the grape and cold settling samples for both vineyards, and decreased for the early, mid, and late stages of fermentation (Table 4). A decrease in the diversity of fungal taxa is expected at the onset of alcoholic fermentation, because most yeasts and fungi present on grapes are non-fermentative, and are either killed by the presence of ethanol or absence of oxygen, or out-competed for space and nutrients by the more dominant yeasts [39, 71]. Diversity did not change throughout the three alcoholic fermentation stages (early, mid, and late) within each vineyard treatment, but the wines from Vineyard 8 had a consistently higher diversity than the wines from Vineyard 2, regardless of the diversity index used.

Table 4. Fungal species diversity, measured as Simpson’s Index of Diversity (1 − D) and Shannon’s Diversity Index (H), of stainless steel barrel-fermented Chardonnay sourced from two different vineyards.

Diversity ± SEM was measured from grape samples taken in the vineyard (n = 5 for Vineyard 2, n = 6 for Vineyard 8), as well as at four stages in the winery: cold settling (n = 3 for Vineyard 2, n = 2 for Vineyard 8), early, mid, and late (n = 3 for both vineyards at all three fermentation stages). For each diversity index, a one-way repeated-measures ANOVA was performed to compare the differences between vineyards across the three fermentation stages (cold settling samples were not included because Vineyard 8 contained only two replicates). Grape samples were analyzed separately by performing one-way ANOVA, because they constituted a different sample type. The p-values for each index are indicated in the appropriate columns, and any significant differences (p ≤ 0.05) are in bold.

Simpson’s diversity (1−D) Shannon’s diversity (H)
Sample Vineyard 2 Vineyard 8 p = Vineyard 2 Vineyard 8 p =
Grapes 0.85 ± 0.03 0.69 ± 0.05 0.02 2.51 ± 0.20 0.69 ± 0.13 0.005
Cold settling 0.64 ± 0.03 0.87 ± 0.02 1.65 ± 0.14 2.29 ± 0.15
Early 0.06 ± 0.01 0.21 ± 0.09 0.01 0.17 ± 0.01 0.38 ± 0.14 0.006
Mid 0.06 ± 0.01 0.31 ± 0.04 0.14 ± 0.02 0.61 ± 0.08
Late 0.06 ± 0.01 0.30 ± 0.10 0.15 ± 0.02 0.57 ± 0.20

Fungal community composition

In total, 194 fungal species were identified in this study, of which 11 achieved ≥ 10% relative abundance in at least two samples (S2 Table). These 194 species belonged to 19 different classes and 37 different orders; however, only four classes (Dothideomycetes, Eurotiomycetes, Leotiomycetes, Saccharomycetes), and six orders (Capnodiales, Dothideales, Pleosporales, Eurotiales, Erysiphales, Saccharomycetales), were represented in the top 90% of all identified sequences. For a list of all species identified in this study along with their taxonomic classifications, please visit https://osf.io/j7rx8/.

A PERMANOVA was performed to test the differences in community composition between grape (vineyard) samples, which were found to be significantly different (F(1,9) = 8.3, R2 = 0.48, p = 0.002). Interestingly, the fungal communities of the grape and cold settling samples within each vineyard treatment were also different from each other (Fig 1). The cold settling samples were taken after the grapes had been harvested, crushed, and processed, so at least some of the differences observed between these stages can be attributed to contact with winery equipment. In the Vineyard 2 treatment, the most abundant fungi in the grape samples were Alternaria sp., Mycosphaerella tassiana, and Epicoccum nigrum, while the most abundant fungi in the cold settling samples were Aspergillus niger, Aureobasidium pullulans, and Penicillium sp. In the Vineyard 8 treatment, the most abundant fungi in the grape samples were Erysiphe necator and Mycosphaerella tassiana, while the most abundant fungi in the cold settling samples were Candida sp., Penicillium sp., S. uvarum, and Hanseniaspora osmophila.

Fig 1. Fungal species abundance.

Fig 1

Relative abundance of the dominant fungi present in grape samples taken in the vineyard (grapes), as well as at four stages of fermentation in the winery (cold settling, early, mid, and late), of Chardonnay sourced from (A) Vineyard 2 or (B) Vineyard 8. Vineyard 2 grape sample values are the means of five replicates, and Vineyard 8 grape sample values are the means of 6 replicates. All winery fermentation stages have three reported replicates, with the exception of the cold settling stage from the Vineyard 8 fermentations, which contained two. Relative abundance was calculated from 20,000 sequences per sample. Any fungal taxa that did not achieve at least 10% relative abundance in at least two samples were grouped into the Minor Fungi category, with one exception: S. cerevisiae did not achieve 10% in any one sample, but because of its importance during alcoholic fermentation it has been included here. For variation among samples please see S2 Table.

A. pullulans is a ubiquitous environmental yeast-like fungus that is commonly associated with grapes and vineyards [47, 72, 73], and was also the most common fungal species identified at the cold settling stage in must from Vineyard 8 two years prior to this current study [15]. E. nigrum is an endophytic fungus that has been previously identified in wine regions such as Italy, Portugal, and Spain [7476]. Because both A. pullulans and E. nigrum are typically associated with vineyard environments, they have been proposed as potential biological control agents against grapevine pathogens [77]. Alternaria sp. are plant pathogens that are also commonly identified on grapes and in grape must [47, 78, 79]. M. tassiana, also known as Davidiella tassiana, is a common grape symbiont that has been previously isolated from vineyards [15, 47, 80, 81], and has been identified as the most abundant fungal species isolated on grapes in one study [79]. The presence of Aspergillus and Penicillium spp. in food, including grape must, has been observed previously [15, 47, 82]. These fungi have the potential to produce mycotoxins, which can be dangerous if consumed in large quantities [83, 84]. However, low levels of mycotoxins are common in commercial wines [84], and Canadian wines typically have a lower concentration of mycotoxins than wines produced elsewhere in the world [85]. Furthermore, the process of alcoholic fermentation has been shown to reduce the presence of mycotoxins, through enzymatic conversion to a less toxic form and/or the adsorption to lees and subsequent removal from the wine [83, 86]. E. necator (also called Uncinula necator) is a grapevine pathogen responsible for powdery mildew, and is found in all regions of the world where grapes are grown [87]. The dominant presence of this fungus in the grape samples of Vineyard 8 is of potential concern, because the presence of even small amounts can have a negative effect on the overall sensory profile of the wine [88]. However, the grapes sampled in this study were not visibly infected with powdery mildew, and the presence of E. necator seems to have been eliminated by the time the cold settling sample was taken (Fig 1B). In the Vineyard 8 cold settling sample we observed the presence of yeasts with fermentative potential such as Candida sp., H. osmophila, and S. uvarum. These fermentative yeasts were not identified in the Vineyard 2 cold settling sample, likely because the must from Vineyard 8 spent an extended amount of time in the settling tank before being transferred to barrels (where the cold settling samples were taken), and therefore was more susceptible to early winery-resident yeast exposure. Although Candida and Hanseniaspora spp. have been found in association with grapes in the vineyard [89], in this current study it seems more likely that the origin of these yeasts is the winery equipment that the grapes came into contact with, because neither Candida nor Hanseniaspora spp. were identified in the grape samples from Vineyard 8 (S2 Table). The grape samples from Vineyard 2 had a greater presence of Saccharomyces spp. than the grape samples from Vineyard 8: S. cerevisiae was present at 1.61 ± 1.3% and S. uvarum at 2.61 ± 2.6% in Vineyard 2, and at 0.053 ± 0.03% and 0.027 ± 0.02%, respectively, in Vineyard 8 (S2 Table). The abundance of Saccharomyces yeasts on healthy grapes in vineyards had been estimated at approximately 1/1000 yeast isolates [40], which is more in line with the results observed in Vineyard 8. The increased abundance of Saccharomyces yeasts in Vineyard 2 should be further investigated.

A PERMANOVA was performed to test the differences in fungal community composition throughout the three stages of alcoholic fermentation; a significant difference was observed between the two vineyard treatments (F(1,16) = 18.1, R2 = 0.53, p = 0.001). S. uvarum dominated the early, mid, and late stages of alcoholic fermentation in both vineyard treatments. In the Vineyard 2 fermentations, S. uvarum made up 96.81 ± 0.2% of the relative abundance of these three stages, and S. cerevisiae made up 2.91 ± 0.2% (Fig 1A). In the Vineyard 8 fermentations, while S. uvarum still dominated with 82.88 ± 3.5% of the relative abundance and S. cerevisiae was present at 0.89 ± 0.2%, a third yeast, H. osmophila, was also present, maintaining 14.56 ± 3.2% relative abundance through to the end of alcoholic fermentation (Fig 1B). Originally, it was thought that Hanseniaspora spp. could not survive during the later stages of alcoholic fermentation due to low ethanol tolerance, because these yeasts could not be isolated from later stages using culture-dependent methods [8, 90, 91]. However, H. osmophila has been shown to have an ethanol tolerance of at least 9% [92], and the advent of culture-independent identification techniques such as high-throughput amplicon sequencing have allowed for the identification of most microorganisms present in wine fermentations, including those in a viable but not culturable (VBNC) state. Indeed, other studies that have employed similar culture-independent techniques to ours have identified Hanseniaspora spp. through to the end of alcoholic fermentation [47, 71, 9395]. H. osmophila has been characterized as a glucophilic yeast [92, 96, 97], and may have contributed to the alcoholic fermentation in the fermentations from Vineyard 8. However, it should be noted that the method of identifying the yeast community in this study involved analyzing DNA, not live cells, so it is possible that the DNA identified here as belonging to H. osmophila may not have come from living cells.

S. uvarum was also found dominating Chardonnay fermentations at this same winery two years previously [15], suggesting that this is a winery-resident population, capable of overwintering and entering fermentations year after year. Although it is possible that the S. uvarum population in this study was brought in from the vineyards with the grapes, we consider it more likely that the majority of these yeasts have established themselves as winery residents as opposed to vineyard residents. As mentioned above, Saccharomyces yeasts are typically rare on healthy grapes in the vineyard [40], and when Saccharomyces strains are identified in the vineyard, their presence is inconsistent between vintages [38, 98], and they are not necessarily the strains that conduct alcoholic fermentation in the winery [99, 100]. The presence of winery-resident yeast populations and communities is well-established in the literature [72, 91, 101103], and these yeasts are capable of over-wintering in the winery and entering fermentations to which they have not been inoculated [4, 6, 7, 104]. S. uvarum is usually found in association with white wine fermentations in cool-climate wine regions. It has been identified dominating such fermentations in wineries across Europe, including France [12, 13], Hungary [14, 34], Italy [105], and Slovakia [14]. S. uvarum is known to be cryotolerant [18, 27], explaining its preference for low-temperature fermentations and cool-climate wine regions. The cellar in which the fermentations from this study were conducted is temperature-controlled and kept at 12°C. This temperature is within a desirable growth range for S. uvarum [105, 106], but is too low for S. cerevisiae to grow optimally [34, 105]. Additionally, S. uvarum is a glucophilic yeast [33], and the residual fructose observed in the late stage of fermentation suggests that this yeast is less adept at fermenting fructose than S. cerevisiae. This result is supported by previous research [107].

To our knowledge, there is currently only one other winery (located in Alsace, France) that has been reported to have a local S. uvarum population dominating uninoculated fermentations across multiple vintages [12]. Interestingly, the Alsace cellar was also kept at 12°C. It is possible that this temperature provides the optimal over-wintering conditions for S. uvarum, allowing it to out-compete S. cerevisiae, but more research is needed to investigate this.

Saccharomyces uvarum strains

Investigating the potential for S. uvarum hybrids

Because both S. cerevisiae and S. uvarum were identified in these fermentations, we acknowledge the potential for S. cerevisiae x S. uvarum hybrids in this community. Originally, 47 yeast colonies were isolated for strain identification; they were plated on WLN media in order to differentiate between presumed S. cerevisiae and presumed S. uvarum isolates (Fig 2). Additionally, in order to determine the potential colour of a hybrid strain on WLN plates, a pure S. cerevisiae strain (Lalvin BA11), a pure S. uvarum strain (CBS 7001), and a known S. cerevisiae x S. uvarum hybrid strain (Lalvin S6U) were all plated onto a WLN plate and incubated. The S. cerevisiae strain appeared as a cream-coloured colony, the S. uvarum strain appeared as a green colony, and the hybrid strain appeared as a cream-coloured colony (S2B Fig). Therefore, we conclude at least some hybrid strains may appear more similar to their S. cerevisiae parent when plated on WLN.

Fig 2. Relative abundance of presumed S. cerevisiae and S. uvarum.

Fig 2

Relative abundance was determined based on phenotypical characteristics of presumed S. cerevisiae and presumed S. uvarum isolates present in three stages of fermentation (early, mid, and late) of Chardonnay sourced from two different vineyards (n = 3). Relative abundance was calculated from 47 yeast isolates per sample plated on Wallerstein Nutrient agar and presented ± SEM.

All but four of the presumed S. cerevisiae isolates were able to be strain-typed using primers that targeted S. cerevisiae microsatellite loci (for S. cerevisiae strain data see https://osf.io/j7rx8/). However, of the presumed S. uvarum isolates, a small proportion of the isolates only partially amplified when using primers that targeted S. uvarum microsatellite loci. These isolates were eventually removed from the analysis, and the S. uvarum population of each sample was rarefied to 32 S. uvarum strains, which represented the common denominator. The partial amplification of these isolates that were dropped from the analysis could suggest the presence of a small population of hybrid yeasts, as has been shown previously [41]. Another study conducted by Le Jeune et al. [108] characterized natural S. cerevisiae x S. uvarum hybrids where all loci amplified with primer sets from both species; however, only four S. uvarum microsatellite loci were targeted in that study.

To definitively determine if our S. uvarum strains were hybrids with S. cerevisiae, we conducted PCR-RFLP analysis on 50 isolates from this current study, representing the 50 most abundant strains identified. We amplified the ITS1 sequence of the rRNA gene, followed by digestion with the restriction enzyme HaeIII, which results in DNA fragments of different sizes for S. uvarum (180 bp, 230 bp, and 500 bp) and S. cerevisiae (180 bp, 230 bp, and 320 bp). All 50 of our isolates matched the restriction pattern of the reference S. uvarum strain (CBS 7001) (S4 Fig). The reference S. cerevisiae strain (Fermol Mediterranée) had a different restriction pattern, while the reference hybrid strain (Lalvin S6U) had a pattern that combined the patterns of the two pure species (S4 Fig). Therefore, we are confident that the strains included in the analysis of this study constitute pure S. uvarum strains, and not hybrids. Future studies specifically targeting S. uvarum hybrids will help determine if there is a significant hybrid population in the Okanagan wine region.

S. uvarum strain diversity

S. uvarum isolates were strain-typed using 11 microsatellite loci. Prior to strain-typing, presumed S. uvarum colonies were distinguished from presumed S. cerevisiae colonies by plating them on WLN media (S2 Fig). Similar to the Illumina fungal community results above, we observed an increase in the relative abundance of presumed S. cerevisiae isolates in the fermentations from Vineyard 2 (Fig 2).

S. uvarum strain diversity was similarly high in the fermentations from both vineyards (Table 5), and this high diversity was maintained throughout alcoholic fermentation. No significant difference in strain diversity was observed between the two vineyards. The study conducted in 2015 at this same winery also reported high S. uvarum strain diversity that was established early and was maintained throughout alcoholic fermentation [15]. This result has also been observed with regards to S. cerevisiae strain diversity in uninoculated commercial fermentations [6, 109].

Table 5. Saccharomyces uvarum strain diversity, measured as Simpson’s Index of Diversity (1 − D) and Shannon’s Diversity Index (H), of stainless steel barrel-fermented Chardonnay sourced from two different vineyards.

Diversity ± SEM was measured from three stages of fermentation in the winery: early, mid, and late (n = 3). For each diversity index, a one-way repeated-measures ANOVA was performed to compare the differences between vineyards across all fermentation stages. The p-values for each index are indicated in the appropriate columns, and any significant differences (p ≤ 0.05) are in bold.

Simpson’s Index (1−D) Shannon’s Index (H)
Sample Vineyard 2 Vineyard 8 p = Vineyard 2 Vineyard 8 p =
Early 0.88 ± 0.02 0.92 ± 0.009 0.19 2.46 ± 0.16 2.75 ± 0.08 0.09
Mid 0.89 ± 0.01 0.90 ± 0.001 2.51 ± 0.04 2.59 ± 0.07
Late 0.85 ± 0.03 0.88 ± 0.04 2.30 ± 0.23 2.57 ± 0.26

S. uvarum strain composition

A total of 106 unique S. uvarum strains were identified across the six fermentations sampled in this study (S3 Table). Two years previously, 150 unique S. uvarum strains were identified in fermentations at this same winery [15]; 66 of these strains (44–62% of all strains) were identified in both vintages. The previous study strain-typed 1,860 yeast isolates, in comparison to the 576 isolates strain-typed in this current study, so it is expected that fewer strains would be identified here. This large overlap in strains identified across vintages further suggests that most of these strains are winery residents: previous research investigating vineyard-derived S. cerevisiae strains found only a 9–13% overlap in successive vintages [38]. Other studies conducted to investigate S. uvarum strain abundance have identified far fewer strains (a maximum of 89 unique strains isolated from grapes, wine, and other environments), although it should be noted that fewer isolates (up to 114) were strain-typed in those studies, potentially explaining this discrepancy [1214, 22, 28, 41, 105, 110].

Of the 106 strains identified in this study, only four were able to achieve ≥ 10% relative abundance in at least two samples (Fig 3). The other 102 strains, termed ‘minor strains,’ accounted for ~ 50% of the relative abundance in the Vineyard 2 fermentations, and ~ 65% of the relative abundance in the Vineyard 8 fermentations. The two most abundant strains in both vineyard treatments (‘2015 Strain 2’ and ‘2015 Strain 3’) were first identified as dominant strains at the same winery two years previously, during the 2015 vintage [15]. The reoccurrence of ‘2015 Strain 2’ and ‘2015 Strain 3’ after two years suggests that these strains have established themselves as persistent winery residents, capable of entering and dominating fermentations in multiple vintages. The other two dominant strains identified in these fermentations (‘2017 Strain 151’ and ‘2017 Strain 182’) were not isolated during the 2015 vintage, and were also not evenly distributed among the fermentations from the two vineyards (Fig 3). The ‘2017 Strain 151’ represented ~ 7–17% of the relative abundance in the Vineyard 2 fermentations, and was only identified at ~ 1% in a single sample in the Vineyard 8 fermentations. Meanwhile, the ‘2017 Strain 182’ represented ~ 2–12% of the relative abundance in the Vineyard 8 fermentations, and was not identified at all in the Vineyard 2 fermentations. This difference in dominant strains could be attributed to a number of causes, including differences in acidity levels in the musts from the two different vineyards, or simply changes in the winery environment over time, since these fermentations did occur approximately two weeks apart. A significant correlation between grape must acidity and the dominance of specific S. cerevisiae strains has been previously observed [111], so it is understandable that a similar result might be expected with regards to S. uvarum strains. It is also possible that these two differentially-abundant strains are predominantly vineyard-residents, with ‘2017 Strain 151’ present in (or originating from) Vineyard 2, and ‘2017 Strain 182’ present in (or originating from) Vineyard 8; this could explain their increased presence in one vineyard treatment over the other, but more research is needed in order to determine if this is the case.

Fig 3. S. uvarum strain abundance.

Fig 3

Relative abundance of the dominant S. uvarum strains present in three stages of fermentation (early, mid, and late) of Chardonnay sourced from two different vineyards (n = 3). Relative abundance was calculated from 32 S. uvarum yeast isolates per sample. Any strains that did not achieve ≥ 10% relative abundance in at least two samples were grouped into the Minor Strains category. For variation among samples please see S4 Table.

A PERMANOVA was performed to test the differences in S. uvarum population composition throughout the three stages of alcoholic fermentation; a significant difference was observed between the two vineyard treatments (F(1,16) = 3.7, R2 = 0.19, p = 0.001). A principal coordinates analysis (PCoA) was also generated in order to visualize the differences in S. uvarum strain composition between the fermentations from the two vineyards while including all 106 strains (Fig 4). The PCoA ordination showed a clear separation between samples taken from the two vineyards. This result highlights the importance of analyzing not only the diversity of a sample but also the composition. In this study, as in previous studies of a similar nature [15, 47], treatments have been found to have near-identical diversity values but completely distinct compositions. This is because composition considers not only the relative abundance of different strains, but also the identities of those strains, and can provide a more accurate summary of the differences observed among treatments. Previous research has identified that different strains of S. uvarum can produce wines with different sensory-active metabolite profiles, especially when fermented at lower temperatures [32, 33], and it is therefore expected that different S. uvarum populations would contribute differently to the production of wine sensory attributes. However, this is still a new topic of investigation, and more research is needed in this area to determine whether the differences in secondary metabolite composition among wines fermented with different S. uvarum strains translate into detectable differences in the sensory profiles of the wines.

Fig 4. S. uvarum strain composition.

Fig 4

Principal coordinates analysis (PCoA) ordination of the S. uvarum strain composition in Chardonnay wines sourced from two vineyards: Vineyard 2 (black) and Vineyard 8 (grey). Individual data points represent the composition of S. uvarum strains in a single sample (based on 32 yeast isolates per sample). Samples were taken at three stages of alcoholic fermentation, and each vineyard treatment contained three biological replicates, for a total of nine samples per vineyard. Dimension 1 (Dim1) explains 20.98% of variance, and Dimension 2 (Dim2) explains 13.07% of variance.

S. uvarum genetic diversity

Of the 106 strains identified in this study, 66 were also identified in the 2015 vintage at the same winery, including all four of the dominant strains from the 2015 vintage [15]. Additionally, we noted that 32 strains were unique to the Vineyard 2 fermentations, 40 strains were unique to the Vineyard 8 fermentations, and 34 strains were found in both the Vineyard 2 and Vineyard 8 fermentations. Although some minor strains were found to be unique to specific stages of fermentation, these strains were found in very low abundance: of the top 20 strains with the highest overall abundance, 18 were identified in all three stages of fermentation, suggesting that ethanol tolerance is not a major contributor to any differences in strain abundance observed here.

Four minor strains were found to bear genetic similarity to international strains (within a Bruvo distance of 0.3). Seven isolates from the Vineyard 2 fermentations were genetically similar to the commercial strain Velluto BMV58®, despite this strain not being sold in Canada at the time of this study. Additionally, three isolates were genetically similar to the previously-sequenced Spanish strain CBS 7001, one isolate was genetically similar to the French strain CBS 8711, and one isolate was genetically similar to strain PYCC 6860, which was isolated from oak trees on Hornby Island, in the same province of Canada as the winery from this study (although separated by hundreds of km). Local strains bearing genetic relatedness to international strains has been previously observed; some S. uvarum strains isolated in New Zealand were also found to bear significant nucleotide similarity with CBS 7001 [28].

The probability that two unrelated individuals would have identical multilocus genotypes (MLGs) was calculated to be PI = 6.7 × 10−8. However, this probability does not take into account the Bruvo distance used in this study; individuals in this study could have slightly different MLGs and still be grouped into the same strain classification. Fixation indices were also calculated for this S. uvarum population, comparing the subpopulation identified in the Vineyard 2 fermentations with the subpopulation identified in the Vineyard 8 fermentations. The proportion of genetic variance contained within each vineyard treatment subpopulation relative to the entire population (FST) was calculated to be 0.014 ± 0.003, indicating that the two subpopulations share a high degree of genetic material and suggesting a high level of inter-population breeding. The inbreeding coefficient (FIS) was calculated to be 0.74 ± 0.04, indicating a considerable degree of inbreeding.

The occurrence of heterozygosity in this study was higher than was observed at the same winery two years previously: 51.9% of the strains in this study contained at least one heterozygous locus, as compared to 42.7% in 2015 [15]. This is the highest incidence of heterozygosity observed in an S. uvarum population to-date: previous studies have found heterozygous loci in 28.8% [41], 23.1% [28], and 0% [22] of S. uvarum strains isolated from grapes and wine. However, these studies did contain fewer isolates and analyzed fewer hypervariable microsatellite loci, which may explain some of the differences observed. The L9 microsatellite locus was the most variable by far, containing 18 different alleles in this study. The second most variable locus was L2, containing eight different alleles. The other loci contained either five alleles (L7 and L8), four alleles (L1, L3, L4, and NB9), or three alleles (NB1, NB4, and NB8). While the rate of heterozygosity was higher in this population than has been previously observed, the observed heterozygosity rate for each locus was between 2 and 16 times lower than the unbiased expected heterozygosity rate for each locus, suggesting that this population still has a high selfing rate (for supporting data, visit https://osf.io/j7rx8/).

Population structure was assessed by performing Bayesian clustering on the 102 indigenous S. uvarum strains identified in the 2017 winery fermentations, as compared to 12 international strains (Table 2), using InStruct [62]. This method, which assigns strains membership coefficients to different inferred ancestors and takes into account inbreeding rates, has been previously used to assess the structure of both S. cerevisiae [9, 38, 112, 113] and S. uvarum [41] populations. Both K = 11 and K = 5 clusters were identified depending on the analysis method, and both were plotted to visualize the inferred ancestry of each strain using DISTRUCT (Fig 5).

Fig 5. DISTRUCT plots of S. uvarum population structure by inferred ancestry.

Fig 5

Each column represents a single S. uvarum strain, and different colours correspond to different inferred ancestors: (A) K1 to K5 inferred ancestry clusters, and (B) K1 to K11 inferred ancestry clusters. Strains included in this analysis include 12 international strains, as well as 102 indigenous strains isolated at an Okanagan winery during the 2017 vintage. Strains in the Okanagan (2015) section were originally identified at the same winery during the 2015 vintage, and were isolated again in 2017. Strains in the Okanagan (2017) section were unique to the 2017 vintage. The four dominant strains identified in this study are indicated.

A dominant inferred ancestor was identified if the inferred coefficient was equal to or higher than 0.75 (75%) of the total ancestry; a similar metric has been used previously to infer dominant ancestry in S. uvarum populations [41]. For the K = 11 analysis (Fig 5B), no strain had a single ancestor, and most strains were not dominated by any one subpopulation; instead, the strains appeared to have several ancestors, suggesting significant admixture between subpopulations. For the K = 5 analysis, most of the strains contained one dominant ancestor, although all strains contained some contribution from multiple inferred ancestors (Fig 5A). Many of the Okanagan strains identified in this study had a dominant inferred ancestor that was distinct from the international strains. Furthermore, there was a stark distinction in inferred ancestry profiles between the strains originally identified in 2015 and those identified for the first time in 2017, suggesting a strong vintage-to-vintage variability in at least a portion of the S. uvarum population. Indeed, an analysis using ObStruct [66] found that both the geographic origin and the vintage of origin (in the case of the Okanagan strains identified in this study) influenced population structure (R2 = 0.28, p < 0.001); interestingly the output of the ObStruct analysis also suggested that the predefined population of Okanagan S. uvarum strains identified for the first time in 2017 was the greatest driver of population structure (https://osf.io/j7rx8/).

Twenty of the strains originally identified in 2015 had K5 (orange) as the dominant inferred ancestor; none of the international strains had this structure, and only one of the 2017 strains had K5 (orange) as their dominant inferred ancestor (Fig 5A). Additionally, 10 of the 2015 strains had K2 (grey) as the dominant inferred ancestor, which was the case for just one 2017 strain and one international strain (CBS 8711 from France). For the strains unique to the 2017 vintage, more than half had K3 (green) as the dominant inferred ancestor; no international strains nor any 2015 strains had K3 (green) as a dominant inferred ancestor, making this result unique and specific to the strains from that vintage. Indeed, K3 (green) comprises on average less than 5% of the ancestry profiles of the international and 2015 strains. Additionally, seven of the 2017 strains had K4 (purple) as a dominant inferred ancestor. While none of the 2015 strains had this result, three international strains also had K4 (purple) as their dominant inferred ancestor, all from the west coast of North America: CBS 8696 (California), PYCC 6901 (Oregon), and PYCC 6861 (Hornby Island, British Columbia, Canada). Finally, a number of strains, both of international original and from this study, had K1 (blue) as the dominant inferred ancestor: these included one strain from 2017, 17 strains from 2015, and four international strains, from Portugal (PYCC 6871), Spain (CBS 7001 and BMV58), and Moldova (CBS 8690). The four dominant strains from this study all had different inferred ancestry. ‘2017 Strain 151’ had K4 (purple) as its dominant inferred ancestor, ‘2017 Strain 182’ had K3 (green) as its dominant inferred ancestor, and ‘2015 Strain 2’ had K5 (orange) as its dominant inferred ancestor; for each of these strains, the dominant inferred ancestor represented almost 100% of the ancestry profile (coefficients 0.979, 0.976, and 0.971, respectively). Contrastingly, ‘2015 Strain 3’ did not contain a dominant inferred ancestor, and its inferred ancestry profile was split between K2 (grey), K3 (green), and K4 (purple) (coefficients 0.596, 0.110, and 0.260, respectively). The strain with the closest inferred ancestry profile to ‘2015 Strain 3’ was strain CBS 395 from The Netherlands (coefficients K2 = 0.284, K3 = 0.205, K4 = 0.402.

The dominant strains from this study had a similar structure using both the K = 5 and the K = 11 clustering methods. In both cases, ‘2017 Strain 151’ had K = 4 (purple) as a majority ancestor, ‘2017 Strain 182’ had a K = 3 (green) as a majority ancestor, and ‘2015 Strain 2’ had K = 5 (orange) as a majority ancestor, and ‘2015 Strain 3’ comprised multiple inferred ancestors, with none dominating.

A number of the Okanagan strains identified in this study (including one of the dominant strains), along with the west coast international strains from California, Oregon, and Hornby Island, contained K4 (purple) as a dominant or significant inferred ancestor. This suggests that these strains may have a shared geographical origin. Aside from these west coast strains, most of the Okanagan strains from both vintages had different majority inferred ancestors from the international strains. In particular, 41 of the 102 Okanagan strains had a K3 (green) or K5 (orange) dominant inferred ancestor, neither of which appeared in significant proportions in the inferred ancestry profiles of the international strains (Fig 5A). This demonstrates that a significant majority proportion of the Okanagan strains have unique inferred ancestry not seen elsewhere in the world, further bolstering the idea of the presence of Okanagan-specific S. uvarum population.

A phylogenetic tree was also created in order to visualize the genetic relatedness of the 102 indigenous Okanagan S. uvarum strains compared to the 12 international S. uvarum strains (Fig 6). These strains were differentiated into six sub-populations using the same K = 5 clustering output from InStruct that was used to visualize population structure in the DISTRUCT plots, which used Bayesian clustering to find inferred ancestry of the strains. Strains were coloured based on dominant inferred ancestry clustering, defined as representing at least 75% of the inferred ancestry profile based on the InStruct analysis for K = 5 clusters. Strains without a dominant inferred ancestor were left uncoloured (black). This clustering method identified two ancestors that appear unique to the Okanagan, and to each vintage: K3 (green) is unique to 2017, while K5 (orange) is unique to 2015. Additionally, the inferred ancestor K4 (purple) was identified as a Pacific west coast ancestor, because it contained Okanagan strains as well as strains from other Pacific west coast locations. The strains with K5 (orange) as their dominant inferred ancestor grouped together on the phylogenetic tree (Fig 6). Interestingly, the strains with K3 (green) or K4 (purple) as their dominant inferred ancestor grouped closely together, suggesting that these strains (most of which were identified in 2017) are genetically similar, and that these two inferred ancestors may have been closely related. The strains with K1 (blue), as their dominant inferred ancestor clustered together, separate from the other strains. This group contained Okanagan strains first identified during the 2015 vintage as well as international strains from Europe (Spain, Portugal, and Moldova). Finally, the strains with K2 (grey) as their dominant inferred ancestor grouped closely with the strains that had no dominant ancestor, and were spread over multiple branches of the phylogenetic tree. These strains had K2 (grey) as a dominant ancestor only in the K = 5 clustering method (Fig 5): in the K = 11 clustering method, these strains all contained multiple minor inferred ancestors (none of them dominant), which can explain why they are seen clustering with the strains that have no dominant inferred ancestry in this tree. The four dominant strains from this study (in bold and marked with an asterisk in Fig 6) all belonged to separate clusters, as was expected based on their inferred ancestry profiles (Fig 5).

Fig 6. Phylogenetic tree of Okanagan and international S. uvarum strains.

Fig 6

An unrooted, neighbour-joining phylogenetic tree using Bruvo distance, comparing the genetic relatedness of the S. uvarum strains identified in Chardonnay fermentations in the Okanagan Valley (Canada) during the 2017 vintage, as well as 12 selected S. uvarum strains isolated from around the world (see Table 2 for strain origins). The international strains are shown in bold, and the four dominant strains identified in this study are shown with an asterisk. Strains are coloured based on dominant inferred ancestry clustering, defined as representing at least 75% of the inferred ancestry profile based on an InStruct analysis for K = 5 clusters. Strains without a dominant inferred ancestor were left uncoloured. A Bruvo distance of 0.05 is shown for scale. Bootstrap values above 0.5 are indicated.

The only commercial S. uvarum strain included in the construction of our phylogenetic tree was Velluto® BMV58 (Lallemand, Montreal, QC, Canada). Recently, one additional S. uvarum strain and one hybrid S. uvarum × S. cerevisiae strain have been commercially released: VitiFermTM Sauvage BIO and EnartisFerm® ES U42, respectively. Some enological properties of S. uvarum have been studied, but more research is needed to allow winemakers to make informed decisions when selecting these non-traditional yeast strains for inoculation. S. uvarum produces lower levels of ethanol, acetic acid, and acetaldehyde, and higher levels of glycerol, succinic acid, malic acid, isoamyl alcohol, isobutanol, and ethyl acetate, as compared to S. cerevisiae [3134]. Furthermore, due to its relatively lower production of ethanol, S. uvarum has been suggested as a potential means of mitigating the effects of climate change on winemaking [114]. With a warming climate, many winemaking regions are beginning to produce very ripe grapes with high sugar contents, which, with traditional fermentation techniques, can result in wines with very high ethanol contents. If this trend continues, many wines produced in the future could contain alcohol concentrations above the legal regulations of some countries, since diluting grape must with water is not permitted in wine production. Using non-traditional yeasts such as S. uvarum or non-Saccharomyces species, which can metabolize grape sugars into compounds other than ethanol (such as glycerol), may help mitigate this issue by keeping ethanol production within permitted levels. The recent increase in the availability of commercial S. uvarum strains indicates that this is clearly an area of interest for winemakers, and our changing climate and consumer preferences require investigation into new and creative methods of wine production, including the use of non-traditional yeasts such as S. uvarum, either alone or in combination with S. cerevisiae.

Conclusions

This study investigated the fungal communities and S. uvarum populations present in uninoculated commercial Chardonnay fermentations of grapes that originated from two different vineyards. Differences in fungal community composition were observed, with H. osmophila representing a larger proportion of the fungal community in the fermentations from one vineyard over the other. However, in all of the fermentations, S. uvarum was the dominant yeast during the early, mid, and late stages of alcoholic fermentation. An investigation into the genetic diversity of the S. uvarum strains present in this study was conducted, and this population was found to be both highly diverse and genetically distinct from S. uvarum strains identified in other regions of the world. A total of 106 S. uvarum strains were identified in this study, but only four strains played a dominant role in fermentation; two of these dominant strains were also identified as dominant strains at this same winery two years previously. The presence of persistent non-commercial strains, as well as the population structure analysis generated to compare Okanagan and international strains, provides evidence for an indigenous S. uvarum population with unique genotypes in the Okanagan Valley of British Columbia, Canada.

Supporting information

S1 Fig. Vineyard sampling map.

Sampling layouts for (A) Vineyard 2 and (B) Vineyard 8. Each of the two conjoined squares represent the generated site of collection for one sample, with the sample number also given at each sampling site. One sampling site contains approximately 15 vines, and two grape clusters were taken from each vine (one on either side of the row), for a total of 30 clusters per sampling site. The geographic orientation of each vineyard is indicated in the bottom right corner of each sampling map.

(DOCX)

S2 Fig. Yeast isolate plating on Wallerstein Nutrient (WLN) media.

(A) Forty-seven yeast colonies per barrel, per sampling stage, were isolated and plated onto WLN media in order to distinguish between presumed S. cerevisiae and presumed S. uvarum isolates prior to strain-typing. On each plate, an S. cerevisiae control (Lalvin BA11) and an S. uvarum control (CBS 7001) were used to help aid the differentiation between the two species. Presumed S. cerevisiae isolates appeared cream-coloured, while presumed S. uvarum isolates appeared green. (B) Comparison of colony colour of a pure S. cerevisiae strain (Lalvin BA11), a pure S. uvarum strain (CBS 7001), and a S. cerevisiae x S. uvarum hybrid (Lalvin S6U), plated on WLN media.

(DOCX)

S3 Fig. Histogram of pairwise genetic distances of multilocus genotypes analyzed in poppr (R package).

The plot shows genetic distance cutoff as a function of the number of multilocus lineages, depending on the clustering method used.

(DOCX)

S4 Fig. Restriction analysis of yeast DNA using HaeIII.

Restriction digest profiles of 50 yeast isolates, representing the 50 most abundant strains found in this study, as compared to reference strains: a pure S. cerevisiae strain (Fermol Mediterranée), a pure S. uvarum strain (CBS 7001), and a S. cerevisiae x S. uvarum hybrid strain (Lalvin S6U). (A) First 25 strains, as well as a ladder and all three reference strains. (B) Second 25 strains, as well as a ladder and all three reference strains.

(DOCX)

S5 Fig. Rarefaction curves.

Rarefaction curves featuring (A) species richness in the fungal community, and (B) strain richness in the S. uvarum community, at different sampling depths.

(DOCX)

S1 Table. Results of Levene’s test for equal variance among groups for all chemical parameters measured.

Any significant differences (p ≤ 0.05) are in bold.

(DOCX)

S2 Table. Fungal community composition of Chardonnay grapes, must, and fermenting wine sourced from two different vineyards, based on 20,000 sequences per sample and represented as percent (%) relative abundance.

Samples were taken from grapes in the vineyard (G), and at four stages of fermentation in the winey: cold settling (C), early (E), mid (M), and late (L). Vineyard 2 grape sample values are the means ± SEM of five replicates, and Vineyard 8 grape sample values are the means ± SEM of 6 replicates. All winery fermentation stages have three reported replicates, with the exception of the cold settling stage from the Vineyard 8 fermentations, which contained two. Sequences were identified to the species level unless otherwise indicated. Fungal species that represented less than 10% of the relative abundance in at least two samples were grouped into the Minor Fungi category. One exception was Saccharomyces cerevisiae, which never reached 10% relative abundance in any sample but is included in this table because of its importance during alcoholic fermentation. The last two columns indicate positive (Pos) and negative (Neg) controls. For the raw data containing all the fungi identified in this study (including minor fungi), please visit https://osf.io/j7rx8/.

(DOCX)

S3 Table. Microsatellite identities of the representative multilocus genotypes (MLGs) of Saccharomyces uvarum strains isolated from stainless steel barrel-fermented Chardonnay at Canadian winery during the 2017 vintage.

Allele sizes for allele 1 (A1) and allele 2 (A2) are shown for each of the 11 microsatellite loci analyzed. Strains with the prefix “2017” were isolated exclusively during the 2017 vintage. Strains with the prefix “2015” were previously isolated and characterized during the 2015 vintage at the same winery, and were also isolated during the 2017 vintage. Strains without a vintage prefix are those that belong to global yeast databases (see S5 Table).

(DOCX)

S4 Table. Saccharomyces uvarum population composition of Chardonnay grapes, must, and fermenting wine sourced from two different vineyards, based on 32 yeast isolates per sample.

Samples were taken at three stages of fermentation: early (E), mid (M), and late (L). Values are the means ± SEM (n = 3). Strains that represented less than 10% of the relative abundance in at least two samples were grouped into the Minor Strains category. For the raw data, containing all the S. uvarum strains identified in this study (including minor strains), please visit https://osf.io/j7rx8/.

(DOCX)

S5 Table. Microsatellite identities of the multilocus genotypes (MLGs) of Saccharomyces uvarum strains obtained from global yeast databases.

Allele sizes for allele 1 (A1) and allele 2 (A2) are shown for each of the 11 microsatellite loci.

(DOCX)

Acknowledgments

The authors would like to thank the winemakers Darryl Brooker, Corrie Krehbiel, and Alexandra Haselich of Mission Hill Family Estate Winery for their assistance, guidance, and donation of fermentation samples. We also thank Britney Johnston and Mehrbod Estaki of the University of British Columbia for technical support and high-throughput amplicon sequencing data analysis support, respectively.

Data Availability

All raw data, R scripts, software packages, versions, parameters, and primer sequences used in this study can be viewed at https://osf.io/j7rx8/. The QIIME2 artifact can be viewed at https://view.qiime2.org. For raw Illumina sequence data, please visit https://qiita.ucsd.edu and search for Study 12837.

Funding Statement

This work was supported by a Natural Sciences and Engineerig Research Council of Canada (NSERC) Discovery Grant (RGPIN-2016-04261) to V.M., and an NSERC Collaborative Research and Development Grant (CRDPJ 514045-17) to D.M.D. and V.M.

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Decision Letter 0

Cecile Fairhead

28 Apr 2020

PONE-D-19-30860

An indigenous Saccharomyces uvarum population with high genetic diversity dominates uninoculated Chardonnay fermentations at a Canadian winery

PLOS ONE

Dear Dr. Morgan,

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Reviewer #1: In this manuscript McCarthy et al. present the characterization of a unique yeast population from a Canadian winery. Interestingly, this winery is inhabited by a S. uvarum population, which makes it one of the few example of yeast microflora not containing Saccharomyces cerevisiae. It presents a comprehensive analysis of the fungal population from the vineyard to the vats, and an analysis of S. uvarum population in the winery and the vineyard.

This original, well led and well written study is an interesting case study of alternative micro-flora under cold climate. This case was known for cool climate vineyards, but until now, this is the first study presenting this situation.

Despite the quality of the manuscript, I feel one main lack and few missing piece of information or analyses.

1 – The authors have selected theirs strains on Wallerstein nutrient media. This is not a robust species identification method, and authors have not explored for the presence of S. cerevisiae x S. uvarum interspecic hybrids that may participate to the ecosystem. Doing so they may have omitted one component of the fermentation microflore. This had been described in one of the first winery containing S. uvarum strains (Demuyter et al. 2014 J. Applied Microbiol , LeJeune et al. 2007 FEMS Yeast Research), and I personally have detected many others in cold cellars. As a consequence, it is necessary to check for the presence of hybrids among the “S. uvarum” isolates (that microsatellite considered as S. uvarum) or among the S. cerevisiae. An overview of the frequencies of the counts of S. uvarum and S. cerevisiae in the different samples is missing

2 – When analyzing their population structure, with the package adegenet, the authors mention that the topology of the tree was not matching the clustering obtained with the “find.cluster function of the adegenet package. This function is very efficient for the identification of groups, but less for the identification of admixed populations. Alternative populations structure software enable such inferences : i.e. InStruct, or Structure softwares take into account admixture, making the assumption of an inbred population (described as the case in manuscripts of other groups) or maximizing the likelihood of Hardy Weinberg equilibrium.

3 – Some minors point

Line 127-147 : what is the total weight of the grape samples?

Line 364: the distance used for the estimation of dissimilarity is not indicated: the function “vegdist” of the vegan package uses several dissimilarity distances (i.e : Bray-Curtis, Jaccard…)

Line 424 : the unit in which volatile acidity is expressed is not indiccated : is this g/l of acetic acid, or H2SO4? This threshold is very high and caused by the choice of the analytical method. Other methods (i.e. enzymatic kits, or distillation) provide much higher sensitivity.

Line 439-449: Table 4 : Why only Simpson’s Index is given. Other indices such as the popular Shannon index provide complementary information on the diversity isolated in the samples. A rarefaction method should be used in order to avoid variations of sample size (several tools/package enable such inferences i.e. EstimateS…)

Line 523 : how far is vineyard 2 from the cellar (or from any other) in comparison to vineyard 1?

Line 567-72 Table 5 : same comment as for table 4.

Line 615-31: same as above : which dissimilarity distance has been used for PCoA

Line 654 : “genetically similar to the commercial strain Velluto BMV58” . Could the authors estimate a likelihood to obtain the same genotype by chance from the frequencies of each allele in this dataset ?

Line 663-667 : more than the number of heterozygote loci per strains , or heterozygote strains, I think that it is more relevant to infer a selfing rate. It takes into account the allelic richness of the loci and make the comparison easier for different datasets.

Line 703-707 : Some admixing events is far from a panmictic population. And indeed as for S. cerevisiae, S. uvarum has been described as an inbred species. The extent of admixture should be evaluated. See comment above about the interest of methods inferring admixed populations. The addition of a figure presenting this population structure may also explain uneven clustering of strains in the tree according to clusters.

The tree does not contain bootstrap value, which may also explain this pattern . The robustness of these nodes could be evaluated by a jacknife or a bootstrap procedure.

Reviewer #2: Review for POND-D-19-30860

This is an interesting study looking at microbial diversity in Canadian wine systems. The main issue with the study as it stands is that appropriate analyse have not been conducted on the data and thus some conclusions are not supported. Specifically, the authors make the following statements in the conclusion section that are not supported by analyses:

Differences in fungal community composition were observed, with H. osmophila representing a significant proportion of the fungal community in the fermentations from one vineyard, but not the other.

>no significance is shown

The presence of persistent non-commercial strains, as well as the phylogenetic tree generated to compare Okanagan and international strains, provides evidence for an indigenous winery-resident S. uvarum population in the Okanagan Valley of Canada

>no evidence for indigenous winery-resident S. uvarum population is provided.

The study deserves publication after these, and other less major issues listed below, are dealt with.

Other comments

Line 53-54 “aggressively competitive towards indigenous yeasts” – a couple of points here: 1) I think it is the massive inoculation size of commercial yeast that allows them to outcompete indigenous yeasts – not some other inherent advantage; 2) ‘competitive towards’ is not grammatically correct ‘more competitive than..’ would work.

The authors cite RStudio for analyses – I appreciate they used this (as do I) but the R platform was used for analyses – Rstudio is just an interface to R.

How many tanks were each vineyard put into? Just one? If so it isn’t really appropriate to use the sub-samples from this mixed tank as replicates – these are pseudo samples – one would need to sample multiple tanks. Either way, given the low sample numbers and non-normality of the chemical data, isn’t it more conservative and thus appropriate to use a Kruskal-Wallis text rather than an ANOVA?

I think we need clarification of whether any YAN or sulphur additions were made to the juices and if so what these were.

The logic to not perform inferential statistics on the fungal community data – due to different times of harvest and fermentation as well as location is not sound. The statistical tests are the only way to test if these communities differ – if they do one cannot determine whether location or time (or both) drove this difference – but this does not mean this should not be tested… Both between tank and within tank statistical analyses of fungal communities are absolutely required to support any statements of difference and change. Phrases such as ‘very different’ are not valid unless the are supported by an analyses. This is the core of science – not subjective interpretation but by means of objective analyses. Such analysis needs to be applied to the whole of the ‘Fugal communities’ section on the results. If not, this entire section is simply an observation and no conclusions may be drawn or made.

Some basic presentation of the fungal community data is desirable: how many reads and ASVs (species) were there? What classes, orders etc did these encapsulate?

“S. uvarum, which dominated the fermentations from both vineyards, is a glucophilic

yeast [29], and the residual fructose observed in the late stage of fermentation suggests that this yeast is less adept at fermenting fructose than S. cerevisiae. This result is supported by previous research [55]” and other discussion about yeasts in lines 410-413 - the authors have not yet shown what species dominated or are present the ferments they analysed – it would be better to move these statements until after they have.

‘two of the dominant strains were previously identified as dominant strains

39 in uninoculated Chardonnay fermentations at the same winery two years earlier,

40 providing evidence for a winery-resident population of indigenous S. uvarum’

and

Line 590 “The reoccurrence of ‘2015 Strain 2’ and ‘2015 Strain 3’ suggests that these strains have established themselves as persistent winery residents, capable of entering and dominating fermentations in multiple vintages.”

I don’t see why this conclusion is drawn and what support it? Why is this conclusion draw over the other option – that these strains reside in the vineyards the fruit derived from – that is the reason they reoccur… there is good evidence that a large genetic variability of Saccharomyces yeasts reside in various vineyard habitats (soil, bark) and that these are regionally distinct populations – what direct evidence is there that live Saccharomyces overwinter in wineries?

Since yeast are sexual (i.e. loci recombine – the authors show heterozygosity in their data) is there a reason the authors did not analyse the microsatellite data with a method that accounts for this – some network analyses for example? The comparison of individual stains is not that powerful or appropriate – it is the degree to which the populations are genetically related and structured by analysing the alleles they share that is the better approach. Why was a ‘Structure’ analyses or similar not conducted?

Why is a Bruvo distance of 0.3 ‘genetically similar’ – what does this mean? Can the authors quantify this in some way?

**********

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Reviewer #1: Yes: JL Legras

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PLoS One. 2021 Feb 4;16(2):e0225615. doi: 10.1371/journal.pone.0225615.r002

Author response to Decision Letter 0


15 Jun 2020

Reviewer and Editor comments:

*Please note that references to line numbers in the manuscript refer to the cleaned and highlighted version, not the track-changes version.

Associate Editor

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process, in particular the following major points raised by referees: 1- S. cerevisiae x S. uvarum hybrid detection, 2- the use of another software than adegenet for the detection of admixture, 3- statistical issues raised by second referee, such as Kruskal-Wallis text vs ANOVA, and statistical evaluation of communities, 4- taking into account heterozygosity at microsatellite loci. All minor points raised by referees should be addressed as well.

1. S. cerevisiae x S. uvarum hybrids:

a. A discussion of the potential for S. cerevisiae x S. uvarum hybrids in this population has been added on Lines 892-916. In this manuscript, we only present strains for which all 11 microsatellite loci amplify with S. uvarum primers, whereas typically hybrid strains have a reduced success rate of microsatellite amplification with a primer set for one particular species. In a previous study conducted in 2015 at the same winery (Morgan et al. 2019, DOI: 10.1093/femsyr/foz049), yeast isolates were initially presumed to belong to S. cerevisiae, but no amplification was detected with S. cerevisiae microsatellite primers, which is what initially led us to test S. uvarum primers on these isolates. Therefore, we know conclusively that the 66 strains identified in both the current study and the 2015 study are not hybrids with S. cerevisiae. Future studies targeting the presence of hybrid strains will help us determine if they do indeed constitute an important component of the yeast community at this winery, but investigating hybrid strains was not an objective in this study.

b. The Wallerstein agar was determined previously to show a definitive colour difference between these two species (Morgan et al. 2020, DOI: 10.5344/ajev.2020.19072; also see Figure S2, newly added to this manuscript). Therefore, we chose this method to differentiate between presumed S. cerevisiae and presumed S. uvarum isolates prior to identification via microsatellite analysis.

c. We are currently conducting whole genome sequencing of five of the strains identified in this study. We will be mapping the sequencing reads against the S. uvarum reference strain CBS 7001 in the coming month, which will allow us to conclusively determine how much of the genomes of these strains maps to S. uvarum, and what introgressions there may be from other Saccharomyces species. However, the whole genome sequencing project is beyond the scope of this current study.

2. At the request of both reviewers, we have added an analysis of population structure of the strains of significance as compared to international strains, using InStruct software and visualized with DISTRUCT plots. Related discussions can be viewed on Lines 808-848, and the plot is presented in a new figure (Fig 5).

3. Statistical analysis of the fungal communities and S. uvarum populations have been added. Details of the statistical tests are described in the completely-revised Statistical Analysis section of the Materials and Methods (Lines 337-429), while a discussion of statistical results has been added throughout the Results and Discussion section (Lines 496-499; 525-527; 595-598; 663-664; 729-732; and Tables 3, 4, and 5). We have chosen not to perform non-parametric tests such as Kruskal-Wallis tests, and instead to keep the parametric ANOVA tests, because our data met the assumption of homogeneity of variance, and ANOVA are robust to deviations from normality. We have explained our reasoning on Lines 342-344.

4. We have expanded our discussion of the genetic diversity and population structure of this yeast population, including fixation indices and observed vs. expected heterozygosity, on Lines 780-791; 803-807; 808-848.

Reviewer #1

In this manuscript McCarthy et al. present the characterization of a unique yeast population from a Canadian winery. Interestingly, this winery is inhabited by a S. uvarum population, which makes it one of the few example of yeast microflora not containing Saccharomyces cerevisiae. It presents a comprehensive analysis of the fungal population from the vineyard to the vats, and an analysis of S. uvarum population in the winery and the vineyard.

This original, well led and well written study is an interesting case study of alternative micro-flora under cold climate. This case was known for cool climate vineyards, but until now, this is the first study presenting this situation.

Despite the quality of the manuscript, I feel one main lack and few missing piece of information or analyses.

1 – The authors have selected theirs strains on Wallerstein nutrient media. This is not a robust species identification method, and authors have not explored for the presence of S. cerevisiae x S. uvarum interspecific hybrids that may participate to the ecosystem. Doing so they may have omitted one component of the fermentation microflore. This had been described in one of the first winery containing S. uvarum strains (Demuyter et al. 2014 J. Applied Microbiol , LeJeune et al. 2007 FEMS Yeast Research), and I personally have detected many others in cold cellars. As a consequence, it is necessary to check for the presence of hybrids among the “S. uvarum” isolates (that microsatellite considered as S. uvarum) or among the S. cerevisiae. An overview of the frequencies of the counts of S. uvarum and S. cerevisiae in the different samples is missing.

• The Wallerstein agar was determined previously to show a definitive colour difference between these two species (Morgan et al. 2020, DOI: 10.5344/ajev.2020.19072; also see Figure S2, newly added to this manuscript). Therefore, we chose this method to differentiate between presumed S. cerevisiae and presumed S. uvarum isolates prior to identification via microsatellite analysis.

• We have added a discussion of the potential for S. cerevisiae x S. uvarum hybrids in this population on Lines 892-916. In this manuscript, we only present strains for which all 11 microsatellite loci amplify with S. uvarum primers, whereas typically hybrid strains have a reduced success rate of microsatellite amplification with a primer set for one particular species. In a previous study conducted in 2015 at the same winery (Morgan et al. 2019, DOI: 10.1093/femsyr/foz049), yeast isolates were initially presumed to belong to S. cerevisiae, but no amplification was detected with S. cerevisiae microsatellite primers, which is what initially led us to test S. uvarum primers on these isolates. Therefore, we know that the 66 strains identified in both the current study and the 2015 study are not hybrids with S. cerevisiae. We did not test the isolates that only partially amplified using S. uvarum primers with S. cerevisiae primers; future studies targeting the presence of hybrid strains will help us determine if they do indeed constitute an important component of the yeast community at this winery. However, the targeting of hybrids was not an objective of this study.

• We have added a discussion of the LeJeune et al. 2007 paper that looked at hybrids (Lines 905-907), and we believe the Demuyter et al. 2014 paper mentioned was a typo, and refers to the Demuyter et al. 2004 paper, which is referenced throughout the manuscript.

• To acknowledge that there is the potential for hybrid or mosaic genomes, we have used the phrase ‘presumed S. cerevisiae’ and ‘presumed S. uvarum’ when referring to pre-strain typed yeast isolates. We have included two new figures (Fig 2 and Fig S2) that show the relative proportion of presumed S. cerevisiae and presumed S. uvarum strains in the fermentations, as estimated by visual differentiation on WLN media. The presumed S. cerevisiae isolates were in fact strain-typed (strain identities available on OSF: https://osf.io/j7rx8/). A discussion of these results has been added to Lines 648-652.

2 – When analyzing their population structure, with the package adegenet, the authors mention that the topology of the tree was not matching the clustering obtained with the “find.cluster function of the adegenet package. This function is very efficient for the identification of groups, but less for the identification of admixed populations. Alternative populations structure software enable such inferences : i.e. InStruct, or Structure softwares take into account admixture, making the assumption of an inbred population (described as the case in manuscripts of other groups) or maximizing the likelihood of Hardy Weinberg equilibrium.

• We have added an analysis of population structure of the strains of significance as compared to international strains, using InStruct software and visualized with DISTRUCT plots. Related discussions can be viewed on Lines 808-848, and the plot can be seen in Fig 5.

• We have also removed any statements regarding admixed or panmictic populations in reference to the tree (Fig 6), which has been updated with bootstrap values.

3 – Some minors point

Line 127-147 : what is the total weight of the grape samples?

• The total weight of the grape samples was 2-3 kg (Line 142).

Line 364: the distance used for the estimation of dissimilarity is not indicated: the function “vegdist” of the vegan package uses several dissimilarity distances (i.e : Bray-Curtis, Jaccard…)

• We apologize for this unintended omission. We have re-written the entire Statistical Analysis section, and have included this information. We used a Bray-Curtis dissimilarity matrix when performing PERMANOVA/PERMDISP tests (Lines 372, 376) and generating the PCoA ordination (Line 389).

Line 424 : the unit in which volatile acidity is expressed is not indicated : is this g/L of acetic acid, or H2SO4? This threshold is very high and caused by the choice of the analytical method. Other methods (i.e. enzymatic kits, or distillation) provide much higher sensitivity.

• Estimated as g/L acetic acid (added to Line 485).

• In this context, ‘detection threshold’ refers to the sensory (tasting) threshold, not an analytical detection threshold. This has been clarified in text (Line 488).

Line 439-449: Table 4 : Why only Simpson’s Index is given. Other indices such as the popular Shannon index provide complementary information on the diversity isolated in the samples. A rarefaction method should be used in order to avoid variations of sample size (several tools/package enable such inferences i.e. EstimateS…)

• We have added Shannon’s Index to Table 4 and Table 5. We initially chose Simpson’s index because it places more emphasis on dominant species/strains, which is what we were most interested in.

• We have created rarefaction curves for both the fungal species and S. uvarum strains (Lines 348-356; Fig S3). However, our data did not include variations of sample size during analysis, because all samples were rarefied prior to analysis.

Line 523 : how far is vineyard 2 from the cellar (or from any other) in comparison to vineyard 1?

• The two vineyards are directly south of the winery and separated from each other by approximately 30 km. This information has been added to Lines 119-121.

Line 567-72 Table 5 : same comment as for table 4.

• We have added Shannon’s Index to Table 4 and Table 5.

Line 615-31: same as above : which dissimilarity distance has been used for PCoA

• We used a Bray-Curtis dissimilarity matrix when performing PERMANOVA/PERMDISP tests (Lines 372, 376) and generating the PCoA ordination (Line 389).

Line 654 : “genetically similar to the commercial strain Velluto BMV58” . Could the authors estimate a likelihood to obtain the same genotype by chance from the frequencies of each allele in this dataset ?

• We have used GenAlEx to calculate probability of identity (PI) - the probability that two unrelated individuals would have identical multilocus genotypes. PI was calculated to be 6.7 � 10-8. However, this probability does not take into account the Bruvo distance used in this study; individuals in this study could have slightly different MLGs and still be grouped into the same strain classification. This discussion has been added to Lines 780-784.

Line 663-667 : more than the number of heterozygote loci per strains , or heterozygote strains, I think that it is more relevant to infer a selfing rate. It takes into account the allelic richness of the loci and make the comparison easier for different datasets.

• We have used GenAlEx to calculate fixation indices (FST and FIS) which estimate the variance in allele frequency between subpopulations and inbreeding coefficients, respectively. FST was calculated to be 0.014 ± 0.003, indicating that the two subpopulations (Vineyard 2 and Vineyard 8) share a high degree of genetic material, and suggestings a high level of breeding between the subpopulations. FIS was calculated to be 0.74 ± 0.04, indicating a considerable degree of inbreeding. This information has been added to Lines 784-791.

• We have also used GenAlEx to compare the observed heterozygosity rate, and found it to be 2 to 16 times lower than the unbiased expected heterozygosity rate for each locus, suggesting that this population has a high selfing rate (added to Lines 803-807).

Line 703-707 : Some admixing events is far from a panmictic population. And indeed as for S. cerevisiae, S. uvarum has been described as an inbred species. The extent of admixture should be evaluated. See comment above about the interest of methods inferring admixed populations. The addition of a figure presenting this population structure may also explain uneven clustering of strains in the tree according to clusters.

The tree does not contain bootstrap value, which may also explain this pattern. The robustness of these nodes could be evaluated by a jacknife or a bootstrap procedure.

• We have added an analysis of population structure of the strains of significance as compared to international strains, using InStruct software and visualized with DISTRUCT plots. Related discussions can be viewed on Lines 808-848, and the plot can be seen in Fig 5.

• We have also removed any statements regarding admixed or panmictic populations in reference to the tree, and have added bootstrap values above 0.5 to Fig 6.

Reviewer #2

This is an interesting study looking at microbial diversity in Canadian wine systems. The main issue with the study as it stands is that appropriate analyse have not been conducted on the data and thus some conclusions are not supported. Specifically, the authors make the following statements in the conclusion section that are not supported by analyses:

Differences in fungal community composition were observed, with H. osmophila representing a significant proportion of the fungal community in the fermentations from one vineyard, but not the other.

>no significance is shown

• We have completely re-written the Statistical Analysis section, and have added statistical analyses of the fungal communities and S. uvarum populations in this study, described on Lines 337-429. A discussion of statistical results has been added throughout the Results and Discussion section (Lines 496-499; 525-527; 595-598; 663-664; 729-732; and Tables 3, 4, and 5).

• Data analyses conducted in the revised manuscript include: (1) statistical comparisons of chemical parameters, alpha diversity (Simpson’s and Shannon’s Indices), and beta diversity (composition, based on Bray-Curtis dissimilarity); (2) rarefaction curves of species/strain richness; (3) S. uvarum population structure using InStruct; (4) unrooted tree of S. uvarum strains using k-means clustering with added bootstrap values; (5) estimations of selfing, (in)breeding, and probability of identity within the S. uvarum population.

• We have removed the term ‘significant’ when discussing the proportion of H. osmophila in the conclusion. However, we do not believe statistics are necessary in order to discuss the differential abundance of this species in the two vineyard treatments, which can be visually distinguished in Fig 1, and which is a small point of discussion.

The presence of persistent non-commercial strains, as well as the phylogenetic tree generated to compare Okanagan and international strains, provides evidence for an indigenous winery-resident S. uvarum population in the Okanagan Valley of Canada

>no evidence for indigenous winery-resident S. uvarum population is provided.

• We agree that we do not have conclusive proof for an indigenous winery-resident population. However, we did detect an overlap of 66 strains in Chardonnay fermentations at the same winery two years apart. This overlap is much higher than has been detected from studies analyzing vineyard yeast populations from successive vintages. Therefore, we suspect that the majority of strains identified in this study reside in the winery, but the non-overlapping strains (especially those with differential abundance in the fermentations from one vineyard over another) could be derived from the vineyard. We have softened the language of the Abstract and Conclusion sections (Lines 40; 956-957) and added a discussion of winery- versus vineyard-resident yeasts, taking into account both our data and previous research on this topic (Lines 618-628; 687-690; 717-720).

The study deserves publication after these, and other less major issues listed below, are dealt with.

Other comments

Line 53-54 “aggressively competitive towards indigenous yeasts” – a couple of points here: 1) I think it is the massive inoculation size of commercial yeast that allows them to outcompete indigenous yeasts – not some other inherent advantage; 2) ‘competitive towards’ is not grammatically correct ‘more competitive than..’ would work.

• We have changed the wording of this statement (Line 53). However, previous research supports the idea that commercial yeast strains used previously at a winery may out-compete indigenous strains, even when they are not inoculated. This discussion has been added to Lines 54-56.

The authors cite RStudio for analyses – I appreciate they used this (as do I) but the R platform was used for analyses – Rstudio is just an interface to R.

• We have amended this wording (Lines 225; 338).

How many tanks were each vineyard put into? Just one? If so it isn’t really appropriate to use the sub-samples from this mixed tank as replicates – these are pseudo samples – one would need to sample multiple tanks. Either way, given the low sample numbers and non-normality of the chemical data, isn’t it more conservative and thus appropriate to use a Kruskal-Wallis text rather than an ANOVA?

• Must from each vineyard was first transferred temporarily to a large stainless steel settling tank, before being transferred to stainless steel barrels. Three stainless steel barrels per vineyard were analyzed in this study, and the cold settling samples for microbial and chemical analysis were all taken from the stainless steel barrels. We believe our use of ‘cold settling tank’ has resulted in confusion, and have changed the name of the tank in the manuscript text to simply a ‘settling tank’ (Lines 156; 158; 159). In this study, ‘cold settling’ refers to the period of time before alcoholic fermentation started.

• We have chosen not to perform non-parametric tests such as Kruskal-Wallis tests, and instead to keep the parametric ANOVA tests, because our data met the assumption of homogeneity of variance, and ANOVA are considered robust to deviations from normality. We have explained and cited our reasoning on Lines 342-344.

I think we need clarification of whether any YAN or sulphur additions were made to the juices and if so what these were.

• This information is present on Lines 169-173.

The logic to not perform inferential statistics on the fungal community data – due to different times of harvest and fermentation as well as location is not sound. The statistical tests are the only way to test if these communities differ – if they do one cannot determine whether location or time (or both) drove this difference – but this does not mean this should not be tested… Both between tank and within tank statistical analyses of fungal communities are absolutely required to support any statements of difference and change. Phrases such as ‘very different’ are not valid unless the are supported by an analyses. This is the core of science – not subjective interpretation but by means of objective analyses. Such analysis needs to be applied to the whole of the ‘Fugal communities’ section on the results. If not, this entire section is simply an observation and no conclusions may be drawn or made.

• We have completely re-written the Statistical Analysis section, and have added statistical analyses of the fungal communities and S. uvarum populations in this study, described on Lines 337-429. A discussion of statistical results has been added throughout the Results and Discussion section (Lines 496-499; 525-527; 595-598; 663-664; 729-732; and Tables 3, 4, and 5).

• Data analyses conducted in the revised manuscript include: (1) statistical comparisons of chemical parameters, alpha diversity (Simpson’s and Shannon’s Indices), and beta diversity (composition, based on Bray-Curtis dissimilarity); (2) rarefaction curves of species/strain richness; (3) S. uvarum population structure using InStruct; (4) unrooted tree of S. uvarum strains using k-means clustering with added bootstrap values; (5) estimations of selfing, (in)breeding, and probability of identity within the S. uvarum population.

Some basic presentation of the fungal community data is desirable: how many reads and ASVs (species) were there? What classes, orders etc. did these encapsulate?

• We have added a basic presentation of the fungal community data on Lines 517-524. Our fungal community data were rarefied to 20,000 sequences per sample and presented as relative abundance.

“S. uvarum, which dominated the fermentations from both vineyards, is a glucophilic

yeast [29], and the residual fructose observed in the late stage of fermentation suggests that this yeast is less adept at fermenting fructose than S. cerevisiae. This result is supported by previous research [55]” and other discussion about yeasts in lines 410-413 - the authors have not yet shown what species dominated or are present the ferments they analysed – it would be better to move these statements until after they have.

• We have moved these statements to another section (moved to Lines 636-639).

‘two of the dominant strains were previously identified as dominant strains

39 in uninoculated Chardonnay fermentations at the same winery two years earlier,

40 providing evidence for a winery-resident population of indigenous S. uvarum’

and

Line 590 “The reoccurrence of ‘2015 Strain 2’ and ‘2015 Strain 3’ suggests that these strains have established themselves as persistent winery residents, capable of entering and dominating fermentations in multiple vintages.”

I don’t see why this conclusion is drawn and what support it? Why is this conclusion draw over the other option – that these strains reside in the vineyards the fruit derived from – that is the reason they reoccur… there is good evidence that a large genetic variability of Saccharomyces yeasts reside in various vineyard habitats (soil, bark) and that these are regionally distinct populations – what direct evidence is there that live Saccharomyces overwinter in wineries?

• As described above, we based our conclusions on previous research showing very little overlap between vineyard S. cerevisiae populations from vintage to vintage (Martiniuk et al. 2016; DOI: 10.1371/journal.pone.0160259). We have softened the language of the Abstract and Conclusion sections (Lines 40; 956-957) and added a discussion of winery- versus vineyard-resident yeasts, taking into account both our data and previous research on this topic (Lines 618-628; 687-690; 717-720).

• There is a body of evidence that Saccharomyces and other yeasts overwinter in wineries (discussion added on Lines 625-628), and the presence of certain dominant yeast strains in fermentations from vineyards that are separated by more than 30 km (and did not enter the winery at the same time) suggests that many of these yeast strains are winery-residents. However, it is possible that some of these yeasts may live in the vineyard (and it is of course likely that this population originated in a nearby natural environment), and we have added statements to this effect (Lines 618-620; 717-720.

Since yeast are sexual (i.e. loci recombine – the authors show heterozygosity in their data) is there a reason the authors did not analyse the microsatellite data with a method that accounts for this – some network analyses for example? The comparison of individual stains is not that powerful or appropriate – it is the degree to which the populations are genetically related and structured by analysing the alleles they share that is the better approach. Why was a ‘Structure’ analyses or similar not conducted?

• We have added an analysis of population structure of the strains of significance as compared to international strains, using InStruct software and visualized with DISTRUCT plots. Related discussions can be viewed on Lines ##-##, and the plot can be seen in Fig 5.

Why is a Bruvo distance of 0.3 ‘genetically similar’ – what does this mean? Can the authors quantify this in some way?

• Bruvo distance is calculated using an algorithm that takes into account stepwise mutations, making it appropriate for use with microsatellite data. This distance is calculated on experimental data and allows the user to collapse MLGs with slight differences in allele size into a single strain category, based on similarity at a threshold value from 0 to 1. An applied threshold of 0 results in every unique MLG being classified as a different strain, and an applied threshold of 1 results in all the MLGs in a dataset being collapsed into a single strain. This information has been added to the manuscript on Lines 218-224.

Attachment

Submitted filename: Response to Reviewers.docx

Decision Letter 1

Cecile Fairhead

20 Jul 2020

PONE-D-19-30860R1

An indigenous Saccharomyces uvarum population with high genetic diversity dominates uninoculated Chardonnay fermentations at a Canadian winery

PLOS ONE

Dear Dr. Morgan,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Both reviewers felt that the ms has been greatly improved, but they have made several additional comments that all need to be addressed. Reviewer 1 mentions additional experiments. You may choose not to do these experiments, but in that case, you should modify your text to take into account the possibility mentioned by the reviewer that you may have missed the observation of hybrids.

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Cecile Fairhead, Ph.D.

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This new version of the manuscript of Morgan et al. is clearly improved in many aspects, and I appreciated the new statistical analyses that were provided. Nevertheless, I have still several comments to make.

1. About the potential hybrid status of the strains, I disagree with the authors on several points:

“a. In this manuscript, we only present strains for which all 11 microsatellite loci amplify with S. uvarum primers, whereas typically hybrid strains have a reduced success rate of microsatellite amplification with a primer set for one particular species.”

This statement is not right. Some hybrids have indeed uneven contribution of each parental genome in their genome, but often the contribution of each parent remain significant and similar (i.e. Le Jeune al. 2007 for microsatellite typing of both species)

The authors have characterized 66 strains in 2015 which share their genotypes with some actual isolates. The same genotypes from the S. uvarum genome side does not mean that all isolates are S. uvarum only. Indeed, in Lejeune et al. 2007, a hybrid strain and a potential parental strain were found with the same genotype. In addition, we can see from the tree in figure 6 (that contain 31 of these strains) that these genotypes are not spread evenly in the S. uvarum tree: some 2015 genotypes belong to specific clusters of strains, so that some clusters might not be explored.

“b. The Wallerstein agar was determined previously to show a definitive color difference between these two species (Morgan et al. 2020, DOI:10.5344/ajev.2020.19072; also see Figure S2, newly added to this manuscript)”

This is an interesting point, but we do not know what would the color of the colony of a hybrid on this media: closer to one of the other parent or between the two colors.…

“c. We are currently conducting whole genome sequencing of five of the strains identified in this study. We will be mapping the sequencing reads against the S. uvarum reference strain CBS 7001 in the coming month…” This an interesting point, but one can wonder what is the position of these sequenced strains among the groups of strains that have been genotyped.

As a summary, and even if the presence of hybrids is not the focus of that paper, these 3 answers are not really solid enough in order to put aside the presence of hybrids in this winery. Because they may combine the cold temperature fermentation ability and the performance of S. cerevisiae, they have the potential for a better fitness in such an environment, especially in a cellar. I am reluctant to ask for more experimental work, but ITS amplification (from cell suspension obtained from colonies) + digestion with HaeIII is a very simple control to perform that would provide a relevant and solid information about the presence of hybrids, and their potential ability to colonize the winery.

Some minor comments :

Page 19 line 120 : “Vineyard 8 is located approximately 90 km south of the winery; the two wineries are” : this should be the two vineyards…

Page 23 line 220-226 : The explanation of the meaning of a threshold is clear, but the criteria leading to the choice of 0.3 are not : why 0.3 is better than 0.1 or .01. This has may be been inferred from a bimodal distribution of the values of the pairwise distance matrix …. Please explain your choice (see reviewer 2)

Page 30, line 371: a reference should be given for the PERMANOVA method

Page 31, line 712-720 : The authors cannot rule-out the presence of specific populations in the fermentation from different vineyards as the result of the import of two different S. uvarum population.

Page 32, line 422-3 : this k-means clustering is a tool developed with the DAPC method (Discriminant analysis of principal components), described in Jombart et al. 2010 BMC Bioinformatics, 11:94, that should be cited.

Page 33, line 769 -779 : It is really difficult to conclude to a relatedness from an arbitrary threshold of 0.3 As mentioned earlier if we had an idea of the distribution of the pairwise distances obtained with this metric, that would enable to figure out the distribution of distances which might give more weight. The authors should have in mind the amazing high relatedness found among strains from the Holartic group for this species (See Almeida et al. 2014 Nature Comm). I think it might be useful to highlight in the tree may be the presence of natural isolates from Canada or USA.

Page 50, line 850- 874: This paragraph is interesting but raises questions. I had not fully seen the problem of sub-setting the data for this analysis in the first version of the manuscript.

1- I think that the number of cluster inferred by InStruct is amazingly high: especially as most strains appear as highly admixed. There might have been a problem to reach convergence of the MCMC chain or InStruct had difficulties to reach a convergence because of the data. This could come from the small number of individuals given the information contained in the dataset: too small to infer ancestry.

2- In addition, for some data sets, InStruct infers higher values of K but at the cost of much more variable of DIC. I would recommend plotting DIC according to K. A first plateau with little variance of DIC might appear before the lowest value of DIC. A higher variance among chains should be visible from the 5 chains

The authors should check for a possible solution with a lower number of ancestral population. The barplot suggests indeed the presence of 6 major groups (K1, K2, K3, K10, K4, K8). At least the solution K=5 should be drawn in order to permit comparison with the clustering obtained with DAPC.

3- In the same manner, the clustering with DAPC, has apparently been performed on a subset of strains including 12 from other origins.

There is no reason to perform a tree, an ancestry analysis and a clustering with a subset of “relevant strains". What may look technologically relevant may not be ecologically relevant. In addition, given the high number of strains obtained, the frequency of many genotypes may be underestimated while others are overestimated so why choose 1%. A tree containing 106 + 12 extra strains should be easy to draw and color with the "ggtree" R package and the "groupOTU" function. This tree might also show as well how related are the different strains.

Nevertheless, the identification of the specificities of the Okanagan population is indeed amazing, and should appear.

Data: the microsatellite dataset should be made available

Reviewer #2: Review of PONE-D-19-30860R1

I commend the authors for a serious revision of the manuscript in light if review comments – it is nice to see serious revisions rather than minimal changes.

I think this represents a solid study, and have only very minor further suggestions.

Can the R^2 values be included when PermANOVA results are presented?

Can 95% confidence limits be included with the estimation of proportions of different species (lines 599-604)

Lines 608 – 610 “identification techniques such as high-throughput amplicon sequencing have allowed for the identification of all microorganisms present in wine fermentations…”

Even very deep sequencing will unlikely get ‘all’ species; this should be changed to ‘most’ or some equivalent.

Lines 600 - 615

The authors need to appreciate that this method analyses DNA – not live cells – from samples: the presence of H. osmophila DNA does not mean these cells are alive or even intact…

On that note – did the ITS1 ASVs you are using to count H. osmophila allow you to identity to species level with such certainly? Usually ITS1 can get to genus robustly, but not always species level? The same comment for other species identified with ITS1 sequences.

**********

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Reviewer #1: No

Reviewer #2: Yes: Matthew R Goddard

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PLoS One. 2021 Feb 4;16(2):e0225615. doi: 10.1371/journal.pone.0225615.r004

Author response to Decision Letter 1


31 Oct 2020

Response to Reviewers

*Please note that references to line numbers in the manuscript refer to the cleaned version, not the track-changes version.

Reviewer #1:

This new version of the manuscript of Morgan et al. is clearly improved in many aspects, and I appreciated the new statistical analyses that were provided. Nevertheless, I have still several comments to make.

1. About the potential hybrid status of the strains, I disagree with the authors on several points:

“a. In this manuscript, we only present strains for which all 11 microsatellite loci amplify with S. uvarum primers, whereas typically hybrid strains have a reduced success rate of microsatellite amplification with a primer set for one particular species.”

This statement is not right. Some hybrids have indeed uneven contribution of each parental genome in their genome, but often the contribution of each parent remain significant and similar (i.e. Le Jeune al. 2007 for microsatellite typing of both species)

• We discussed the Le Jeune et al. 2007 paper on lines 702-705 and have clarified our viewpoint. We acknowledge that some hybrids have an equal contribution of genomes from each parent species, as is the case of the strains in the Le Jeune 2007 paper. However, based on our analysis of the ITS1 region of the rRNA gene by RFLP using HaeIII (described below in more detail under 1c), we have confirmed that the strains we have included in the analysis of our study are S. uvarum and not hybrids of S. uvarum and S. cerevisiae.

The authors have characterized 66 strains in 2015 which share their genotypes with some actual isolates. The same genotypes from the S. uvarum genome side does not mean that all isolates are S. uvarum only. Indeed, in Lejeune et al. 2007, a hybrid strain and a potential parental strain were found with the same genotype. In addition, we can see from the tree in figure 6 (that contain 31 of these strains) that these genotypes are not spread evenly in the S. uvarum tree: some 2015 genotypes belong to specific clusters of strains, so that some clusters might not be explored.

• At the request of the reviewer, we have amplified the ITS1 region of the rRNA gene on 50 of the most commonly identified strains and carried out a restriction enzyme digest with HaeIII. The results of this restriction digest are presented on lines 706-718 and Figure S4. (More detailed discussion of these results are presented below under 1c).

“b. The Wallerstein agar was determined previously to show a definitive color difference between these two species (Morgan et al. 2020, DOI:10.5344/ajev.2020.19072; also see Figure S2, newly added to this manuscript)”

This is an interesting point, but we do not know what would the color of the colony of a hybrid on this media: closer to one of the other parent or between the two colors.…

• We plated a known pure S. uvarum strain (CBS 7001), a known pure S. cerevisiae strain (Lalvin BA11), and a known S. uvarum x S. cerevisiae hybrid strain (Lalvin S6U, formerly referred to as a S. bayanus x S. cerevisiae hybrid) on WLN to determine the colour of the hybrid colony on this media. A picture of this plate has been added in Figure S2, and shows the S. uvarum strain as a green colony, the S. cerevisiae strain as a cream-coloured colony, and the hybrid strain as a cream-coloured colony. This suggests that WLN media is able to distinguish between pure S. uvarum strains and hybrid strains, but not between pure S. cerevisiae strains and hybrid strains (i.e. the hybrid colony appeared similar to the S. cerevisiae colony). The methods have been updated to reflect this addition on lines 206-211 and the discussion has been updated with this information on lines 686-693.

“c. We are currently conducting whole genome sequencing of five of the strains identified in this study. We will be mapping the sequencing reads against the S. uvarum reference strain CBS 7001 in the coming month…” This an interesting point, but one can wonder what is the position of these sequenced strains among the groups of strains that have been genotyped.

• The strains undergoing whole genome sequencing are as follows: 2015 Strain 1 (dominant in 2015), 2015 Strain 3 (dominant in 2015 and 2017), 2017 Strain 151 (dominant in 2017), 2015 Strain 163 (non-dominant), and 2017 Strain 097 (non-dominant). However, we have removed any references to whole genome sequencing because it is outside the scope of this current study.

As a summary, and even if the presence of hybrids is not the focus of that paper, these 3 answers are not really solid enough in order to put aside the presence of hybrids in this winery. Because they may combine the cold temperature fermentation ability and the performance of S. cerevisiae, they have the potential for a better fitness in such an environment, especially in a cellar. I am reluctant to ask for more experimental work, but ITS amplification (from cell suspension obtained from colonies) + digestion with HaeIII is a very simple control to perform that would provide a relevant and solid information about the presence of hybrids, and their potential ability to colonize the winery.

• At the request of the reviewer, we have conducted a digestion of the ITS1 region of the rRNA gene using the restriction enzyme HaeIII on 50 isolates from this study, representing the 50 most abundant strains. We have updated the Methods section to include this additional experiment (lines 259-269), and the results of this restriction digest are presented on lines 706-718 and Figure S4. These results show that the restriction patterns of the isolates match that of a pure S. uvarum strain, while the restriction patterns of the pure S. cerevisiae strain and the hybrid S. uvarum x S. cerevisiae strain used as control strains have distinct DNA fragment sizes. While we only had the resources to conduct this additional experiment on 50 of the 576 isolates analyzed in this study, these 50 isolates do represent the 50 most abundant strains. When combined with other evidence, such as the fact that in 2015 none of the isolates demonstrated amplification with S. cerevisiae microsatellite primers as well as the WLN agar colour selection, we are confident that the isolates analyzed in this study are pure S. uvarum strains, not hybrids.

• We acknowledge that there may be a low-level presence of hybrids in the winery environment, because we excluded a small number of isolates from our analysis when one or more loci consistently failed to amplify (described on lines 250-252 and discussed on lines 696-702). Further research specifically targeting hybrid populations will be able to determine if this winery, or the Okanagan valley in general, is host to a significant hybrid population, but this is outside the scope of our current study (lines 716-718).

Page 19 line 120 : “Vineyard 8 is located approximately 90 km south of the winery; the two wineries are” : this should be the two vineyards…

• This has been corrected (line 120).

Page 23 line 220-226 : The explanation of the meaning of a threshold is clear, but the criteria leading to the choice of 0.3 are not : why 0.3 is better than 0.1 or .01. This has may be been inferred from a bimodal distribution of the values of the pairwise distance matrix …. Please explain your choice (see reviewer 2)

• This explanation has been updated on lines 227-244. Based on poppr guidelines for selecting cut-off thresholds (https://grunwaldlab.github.io/poppr/articles/mlg.html#choosing-a-threshold), two methods may be used. The first, as the reviewer mentioned, is to select the threshold at the low point between a smaller peak and a larger peak, when the data have a bimodal distribution. In our case, the data are not bimodally distributed (see new Figure S3), so we chose the second method, which involves identifying ‘the largest gap between all putative thresholds.’ Based on Figure S3 we identified this cut-off to be approximately 0.3.

• Additionally, we chose this threshold to be consistent with the threshold used in a previous study of this same population (Morgan et al. 2019), because we were using strain identities determined in that previous study to strain-type isolates in this current study. Finally, because of the novelty of the high genetic diversity of the yeast population in this study (no other S. uvarum population has been found to be this genetically diverse to-date), it was important to us not to overestimate this diversity by using a lower threshold cut-off, which could artificially inflate the significance of our results. By using a conservative threshold, we are able to make more confident statements regarding the diversity of this population.

Page 30, line 371: a reference should be given for the PERMANOVA method

• We have included the citation for the ‘vegan’ package (which includes the PERMANVOA method ‘adonis’ that was used in this study) the first time it is mentioned, on line 376.

Page 31, line 712-720 : The authors cannot rule-out the presence of specific populations in the fermentation from different vineyards as the result of the import of two different S. uvarum population.

• We agree that we cannot rule out the presence of vineyard-derived yeast strains contributing to the observed populations, and have clarified our discussion of this topic on lines 789-793.

Page 32, line 422-3 : this k-means clustering is a tool developed with the DAPC method (Discriminant analysis of principal components), described in Jombart et al. 2010 BMC Bioinformatics, 11:94, that should be cited.

• We have removed k-means clustering from this manuscript and replaced with the clustering output from InStruct (Methods updated on lines 455-459).

Page 33, line 769 -779 : It is really difficult to conclude to a relatedness from an arbitrary threshold of 0.3. As mentioned earlier if we had an idea of the distribution of the pairwise distances obtained with this metric, that would enable to figure out the distribution of distances which might give more weight. The authors should have in mind the amazing high relatedness found among strains from the Holartic group for this species (See Almeida et al. 2014 Nature Comm). I

• We have explained our reasoning for selecting a cutoff of 0.3 above and on lines 227-244 and Figure S3 of the manuscript. Additionally, we note that due to the extraordinarily high number of multilocus genotypes that were identified in this study, the cutoff threshold chosen could help reduce noise or potential errors in the genotyping method, which is a function of the poppr package.

Page 50, line 850- 874: This paragraph is interesting but raises questions. I had not fully seen the problem of sub-setting the data for this analysis in the first version of the manuscript.

1- I think that the number of cluster inferred by InStruct is amazingly high: especially as most strains appear as highly admixed. There might have been a problem to reach convergence of the MCMC chain or InStruct had difficulties to reach a convergence because of the data. This could come from the small number of individuals given the information contained in the dataset: too small to infer ancestry.

• We have re-run the InStruct structure analysis (lines 426-451) and re-created the DISTRUCT plot (Figure 5) to include all strains identified in this study, as well as the international strains. According to the DIC, K = 11 was found to be the optimal number of clusters. A very small minimum was also seen at K = 5, so we ran both levels of clustering through CLUMPP and visualized the output of both clustering thresholds using DISTRUCT (Figure 5A and 5B).

2- In addition, for some data sets, InStruct infers higher values of K but at the cost of much more variable of DIC. I would recommend plotting DIC according to K. A first plateau with little variance of DIC might appear before the lowest value of DIC. A higher variance among chains should be visible from the 5 chains

The authors should check for a possible solution with a lower number of ancestral population. The barplot suggests indeed the presence of 6 major groups (K1, K2, K3, K10, K4, K8). At least the solution K=5 should be drawn in order to permit comparison with the clustering obtained with DAPC.

• We included a solution for K = 5 clusters based on a DIC plot: the CLUMPP alignment was nearly optimal for K = 5 clusters ( H = 0.99) and was much lower for K = 11 (H = 0.60), so we accordingly focused most of our discussion on the K = 5 clustering method (lines 881-950).

3- In the same manner, the clustering with DAPC, has apparently been performed on a subset of strains including 12 from other origins.

There is no reason to perform a tree, an ancestry analysis and a clustering with a subset of “relevant strains". What may look technologically relevant may not be ecologically relevant. In addition, given the high number of strains obtained, the frequency of many genotypes may be underestimated while others are overestimated so why choose 1%. A tree containing 106 + 12 extra strains should be easy to draw and color with the "ggtree" R package and the "groupOTU" function. This tree might also show as well how related are the different strains.

Nevertheless, the identification of the specificities of the Okanagan population is indeed amazing, and should appear.

• We have re-created the phylogenetic tree (Figure 6) using all of the strains identified in this study, along with the 12 international strains, and clustered the tree according to the K = 5 output from the InStruct analysis described above. Strains with a dominant ancestor (defined as having a single inferred ancestor represent at least 75% of their total ancestry, determined by InStruct) were coloured in the tree accordingly. This has been updated in the Methods (lines 452-462) and Discussion (lines 963-991). We were able to identify Okanagan-specific clusters that were genetically determined by Bruvo distance to be different than the international strains.

Data: the microsatellite dataset should be made available

• The microsatellite genotypes of the strains identified at the winery in this study are in Table S3.

• The full microsatellite dataset is available at https://osf.io/j7rx8/. At this link, there is an Excel document titled ‘manuscript-data’ and within that document are two tabs containing microsatellite data: one tab contains only the relevant multilocus genotypes (MLGs) for this study (‘uvarum-mlgs-study’), and the other contains all the MLGs within our database (‘uvarum-database’).

Reviewer #2:

I commend the authors for a serious revision of the manuscript in light of review comments – it is nice to see serious revisions rather than minimal changes. I think this represents a solid study, and have only very minor further suggestions.

Can the R^2 values be included when PermANOVA results are presented?

• The R^2 values have been added to the PERMANOVA results (lines 556, 627, and 804).

Can 95% confidence limits be included with the estimation of proportions of different species (lines 599-604)

• All approximations/estimated proportions of species have been updated and are now represented as mean +/- SEM (to be consistent with values presented in Table S2) on lines 619-620 and 630-634.

Lines 608 – 610 “identification techniques such as high-throughput amplicon sequencing have allowed for the identification of all microorganisms present in wine fermentations…”

Even very deep sequencing will unlikely get ‘all’ species; this should be changed to ‘most’ or some equivalent.

• We have changed the wording of this statement to “most microorganisms” increase its accuracy (line 640).

Lines 600 - 615

The authors need to appreciate that this method analyses DNA – not live cells – from samples: the presence of H. osmophila DNA does not mean these cells are alive or even intact…

• We have updated this discussion to acknowledge the possibility that the DNA identified as belonging to H. osmophila may not have come from living cells (lines 646-649).

On that note – did the ITS1 ASVs you are using to count H. osmophila allow you to identity to species level with such certainly? Usually ITS1 can get to genus robustly, but not always species level? The same comment for other species identified with ITS1 sequences.

• ITS1 (fungi) has been generally very successful with getting to species-level identification, unlike 16S (bacteria), which (currently) can rarely identify ASVs past the genus level. Classifiers, including the UNITE classifier used in this study, are rather conservative, and will usually not give a species-level identification unless confident in that assignment. Due to the methodology used in this study (paired-end sequencing for longer read lengths, ITS-specific denoising that does not trim all sequences to the same length, and the use of a dynamic classifier that can improve the accuracy of taxonomic assignment), many of the ASVs in this study were identified to the species-level with >97% confidence. We note that there were other Hanseniaspora spp. also identified to the species-level that were present in lower relative abundance in this study, including H. uvarum, H. guilliermondii, and H. valbyensis (see ‘fungal community’ tab in the ‘manuscript-data’ Excel file on OSF - https://osf.io/j7rx8/). Based on the reviewer’s inquiry, we also confirmed the species identity of the ASVs identified as H. osmophila via NCBI BLAST. Furthermore, we note that there were a number of ASVs that could not be confidently identified to the species level using our classifier - those ASVs (such as Penicillium sp., Candida sp., and others) were identified to the most accurate taxonomic level available. Based on this question, we have added a taxonomy.qzv file to our supplemental information (https://osf.io/j7rx8/), which contains more information regarding the ASV sequences in this study, their taxonomic classification, and the confidence of the taxonomic assignments. To view this document, simply visit view.qiime2.org and drag and drop the taxonomy.qzv file into the box.

Decision Letter 2

Luca Cocolin

29 Dec 2020

An indigenous Saccharomyces uvarum population with high genetic diversity dominates uninoculated Chardonnay fermentations at a Canadian winery

PONE-D-19-30860R2

Dear Dr. Morgan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Luca Cocolin

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This new version answers to my concerns in a satisfactory manner. I think that this is a nice study.

**********

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Reviewer #1: Yes: Jean-Luc Legras

Acceptance letter

Luca Cocolin

25 Jan 2021

PONE-D-19-30860R2

An indigenous Saccharomyces uvarum population with high genetic diversity dominates uninoculated Chardonnay fermentations at a Canadian winery

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Vineyard sampling map.

    Sampling layouts for (A) Vineyard 2 and (B) Vineyard 8. Each of the two conjoined squares represent the generated site of collection for one sample, with the sample number also given at each sampling site. One sampling site contains approximately 15 vines, and two grape clusters were taken from each vine (one on either side of the row), for a total of 30 clusters per sampling site. The geographic orientation of each vineyard is indicated in the bottom right corner of each sampling map.

    (DOCX)

    S2 Fig. Yeast isolate plating on Wallerstein Nutrient (WLN) media.

    (A) Forty-seven yeast colonies per barrel, per sampling stage, were isolated and plated onto WLN media in order to distinguish between presumed S. cerevisiae and presumed S. uvarum isolates prior to strain-typing. On each plate, an S. cerevisiae control (Lalvin BA11) and an S. uvarum control (CBS 7001) were used to help aid the differentiation between the two species. Presumed S. cerevisiae isolates appeared cream-coloured, while presumed S. uvarum isolates appeared green. (B) Comparison of colony colour of a pure S. cerevisiae strain (Lalvin BA11), a pure S. uvarum strain (CBS 7001), and a S. cerevisiae x S. uvarum hybrid (Lalvin S6U), plated on WLN media.

    (DOCX)

    S3 Fig. Histogram of pairwise genetic distances of multilocus genotypes analyzed in poppr (R package).

    The plot shows genetic distance cutoff as a function of the number of multilocus lineages, depending on the clustering method used.

    (DOCX)

    S4 Fig. Restriction analysis of yeast DNA using HaeIII.

    Restriction digest profiles of 50 yeast isolates, representing the 50 most abundant strains found in this study, as compared to reference strains: a pure S. cerevisiae strain (Fermol Mediterranée), a pure S. uvarum strain (CBS 7001), and a S. cerevisiae x S. uvarum hybrid strain (Lalvin S6U). (A) First 25 strains, as well as a ladder and all three reference strains. (B) Second 25 strains, as well as a ladder and all three reference strains.

    (DOCX)

    S5 Fig. Rarefaction curves.

    Rarefaction curves featuring (A) species richness in the fungal community, and (B) strain richness in the S. uvarum community, at different sampling depths.

    (DOCX)

    S1 Table. Results of Levene’s test for equal variance among groups for all chemical parameters measured.

    Any significant differences (p ≤ 0.05) are in bold.

    (DOCX)

    S2 Table. Fungal community composition of Chardonnay grapes, must, and fermenting wine sourced from two different vineyards, based on 20,000 sequences per sample and represented as percent (%) relative abundance.

    Samples were taken from grapes in the vineyard (G), and at four stages of fermentation in the winey: cold settling (C), early (E), mid (M), and late (L). Vineyard 2 grape sample values are the means ± SEM of five replicates, and Vineyard 8 grape sample values are the means ± SEM of 6 replicates. All winery fermentation stages have three reported replicates, with the exception of the cold settling stage from the Vineyard 8 fermentations, which contained two. Sequences were identified to the species level unless otherwise indicated. Fungal species that represented less than 10% of the relative abundance in at least two samples were grouped into the Minor Fungi category. One exception was Saccharomyces cerevisiae, which never reached 10% relative abundance in any sample but is included in this table because of its importance during alcoholic fermentation. The last two columns indicate positive (Pos) and negative (Neg) controls. For the raw data containing all the fungi identified in this study (including minor fungi), please visit https://osf.io/j7rx8/.

    (DOCX)

    S3 Table. Microsatellite identities of the representative multilocus genotypes (MLGs) of Saccharomyces uvarum strains isolated from stainless steel barrel-fermented Chardonnay at Canadian winery during the 2017 vintage.

    Allele sizes for allele 1 (A1) and allele 2 (A2) are shown for each of the 11 microsatellite loci analyzed. Strains with the prefix “2017” were isolated exclusively during the 2017 vintage. Strains with the prefix “2015” were previously isolated and characterized during the 2015 vintage at the same winery, and were also isolated during the 2017 vintage. Strains without a vintage prefix are those that belong to global yeast databases (see S5 Table).

    (DOCX)

    S4 Table. Saccharomyces uvarum population composition of Chardonnay grapes, must, and fermenting wine sourced from two different vineyards, based on 32 yeast isolates per sample.

    Samples were taken at three stages of fermentation: early (E), mid (M), and late (L). Values are the means ± SEM (n = 3). Strains that represented less than 10% of the relative abundance in at least two samples were grouped into the Minor Strains category. For the raw data, containing all the S. uvarum strains identified in this study (including minor strains), please visit https://osf.io/j7rx8/.

    (DOCX)

    S5 Table. Microsatellite identities of the multilocus genotypes (MLGs) of Saccharomyces uvarum strains obtained from global yeast databases.

    Allele sizes for allele 1 (A1) and allele 2 (A2) are shown for each of the 11 microsatellite loci.

    (DOCX)

    Attachment

    Submitted filename: Response to Reviewers.docx

    Data Availability Statement

    All raw data, R scripts, software packages, versions, parameters, and primer sequences used in this study can be viewed at https://osf.io/j7rx8/. The QIIME2 artifact can be viewed at https://view.qiime2.org. For raw Illumina sequence data, please visit https://qiita.ucsd.edu and search for Study 12837.


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