Yeast isolation

Samples were collected and processed as previously reported [23,34] from bark samples from 106 Coigüe (Nothofagus dombeyi) trees from three different localities in coastal Patagonia: Chiloé, Reserva Costera Valdiviana, and San Jose de la Mariquina (Table A in S1 Table). Saccharomyces identification was performed as previously reported [23]. First, the internal transcribed spacer (ITS) was amplified and sequenced using primers ITS1 and ITS4 [35] to discriminate between Saccharomyces and non-Saccharomyces colonies. Subsequently, differentiation between Saccharomyces species was performed using the polymorphic marker RIP1 by amplification, enzyme restriction, and Sanger sequencing as previously reported [23,25]. Furthermore, we used 42 strains reported as S. uvarum from our yeast collection [23] to reveal the complete diversity landscape of Saccharomyces isolates from Patagonia (Table B in S1 Table). Representative sequences for ITS and RIP1 can be found under the code PP618688-PP618690 and PP578963-PP578965.

Whole-genome sequencing and variant calling

Genomic DNA was extracted using the Qiagen Genomic-tip 20/G kit (Qiagen, Hilden, Germany) as previously described [23] and sequenced using an Illumina NextSeq 500 system. Raw reads quality was checked using FastQC [36]. Reads were processed with fastp 0.23.2 (-q 20 -F 15 -f 15) [37]. Trimmed reads were aligned against the S. uvarum CBS7001^T [38] using BWA-mem (option: -M -R) [39]. Mapping quality and summary statistics were obtained using Qualimap v.2.2.2-dev [40]. SAM files were sorted and transformed to BAM format using Samtools 1.14 [41]. BAM files were tagged for duplicates using MarkDuplicates of Picard tools 2.27.2 [42]. Variant calling and filtering were done using GATK 4.2.3.0 [43]. Specifically, variants were called per chromosome per sample using HaplotypeCaller (default settings). Next, a variant database was built using GenomicsDBImport. Genotypes per chromosome were called using GenotypeGVCFs (-G StandardAnnotation). Genome-wide genotype for each sample was merged using MergeVcfs. We applied recommended filters for coverage (>10 mapping reads = “FORMAT/DP>10”) and quality (–minQ 30). Multisample VCF was further filtered when needed with vcftools 0.1.16. We only considered SNPs without missing data using–max-missing 1 [43]. Statistics from the final VCF file were obtained using bcftools stats command [41].

To obtain individual consensus genomes for each strain, we used the VCF data together with the CBS7001^T genome. First, we used the vcf-subset mode of vcftools 0.1.16 to extract individual SNPs from each strain. Then, we indexed each VCF with IndexFeature mode from GATK 4.2.3.0. Finally, using FastaAlternateReferenceMaker from GATK 4.2.3.0 we obtained consensus fasta genome per strain.

Phylogenomic reconstruction

To perform the phylogenomic analysis using our biallelic SNP dataset, we transformed a VCF file containing 1,504,923 SNPs into phylip format using vcf2phylip [44]. This was used as input for the IQ-TREE [45] to generate a maximum likelihood phylogeny with the ultrafast bootstrap option and ascertain bias correction (-st DNA -o CL815 -m GTR+ASC -nt 8 -bb 1000) [46]. The number of parsimony informative sites was 833,534. Trees were visualized on the iTOL website [47].

Phylogenomic analysis using orthologues genes was done using OrthoFinder V2.5.5 [48]. Specifically, the complete set of ORFs (in peptide sequence) was extracted from the Augustus output [49], and then used as input for OrthoFinder V2.5.5 [48]. Visualization of the tree was done with iTOL website [47].

Population genetic analyses

First, a VCF file’s thinned version was generated with vcftools 0.1.16 (—thin 500) [41]. Ancestry estimation was explored by running ADMIXTURE [50] using k = 2 to k = 10 five times per k using different seed numbers. Cross-validation errors of each run were used to choose the best number of populations. Results were visualized using Pong [51]. Additionally, to perform a clustering analysis using SMARTPCA [52], we kept only those individuals with no evidence of admixture in the ADMIXTURE analysis. Using the same data set of phased individuals with Beagle 3.0.4 [53], we performed a fineSTRUCTURE analysis as previously reported [54] considering a constant recombination rate between consecutive SNPs based on the S. cerevisiae average recombination rate (0.4 cM/kbp) [55]. Pairwise nucleotide diversity (π) and Fixiation index (F_st) were estimated using the R package PopGenome [56]. To predict the number of generations since the most recent common ancestor of any pair of lineages we used the mutation parameter [57], θ_w = 2N_θμ, in which N_θ represents the number of generations. Considering that the single-base mutation has been reported similar for multiples yeast species [58,59], we used the mutation rate μ = 1.84 x 10⁻¹⁰ (mutations per generation) previously reported in S. cerevisiae for calculations [60].

Species delineation analysis

To delineate species boundaries based on genetic differences, we used the average nucleotide identity (ANI) as a main criterion [61]. All ANI values were calculated against the S. uvarum reference genome, CBS7001^T, by using OrthoANI algorithm [17]. As described for yeast, we used the 95–96% ANI value as cut off for species delinitation [15,16].

Spore viability analysis of interpopulation hybrids

To test spore viability between crosses from different lineages, diploid cells were sporulated on 2% potassium acetate agar plates (2% agar) for at least 7 days at 25°C. Meiotic segregants were obtained by dissecting tetrad ascospores treated with 10 μL Zymolyase 100T (50 mg/mL) on YPD agar plates with a SporePlay micromanipulator. Spores were crossed against the S. uvarum haploid CBS7001^T strain derivate (HOL, HO::NatMx) [62]. Hybrid candidates were picked from selective media [62] and confirmed by PCR amplification of the MAT locus (hybrids were Mat-a/Mat-α, HO::NatMx). The hybrids were then sporulated as previously mentioned. At least 96 ascospores per hybrid were dissected in YPD plates and incubated at 25°C for two days. Spore viability was calculated using the formula (n° of viable spores/n° of dissected spores) × 100 [21,63]. Additionally, spores from hybrids with low reproductive success were sporulated and dissected for a F2 collection.

Nanopore sequencing, de novo assembly and genome annotation

For nanopore sequencing EXP-NBD104 barcodes and SQK-LSK109 adapters were ligated, and library was loaded on R9.4.1 flowcells; sequencing was performed on a MiniIon device (Nanopore, Oxford, UK). Raw fast5files were converted for fastq files using Guppy, followed by Porechop for the removal of adapters and barcodes [64]. Canu v2.2 [65] with default setting was used for assembly, followed by two rounds of both racon v1.4.3 [66] and pilon v1.2.4 [67] for long and short read polishing. Genome assemblies were annotated using the pipeline LRSDAY [68] using the complete genome of the CBS7001T type strain as training input for AUGUSTUS [49]. Gene function was predicted using as reference the S. cerevisiae S288c genome, all the other parameters were set as default. Genome completeness was assessed by using BUSCO 5.5.0 [69]. Assemblies were compared with CBS7001^T using nucmer [70], and structural variants (SVs) were called using MUM&Co [71]. Calls on the ribosomal DNA were not considered. Divergence was re-estimated from whole genome alignment by using Mummer4 dnadiff command [70].

Introgression identification

Introgressions from S. uvarum were identified using a combination of SNP density analysis and mapping mean coverage within 1 kb non-overlapping windows [28]. To achieve this, we mapped one representative strain from each S. uvarum lineage to a de novo assembly and called variants as previously described [23]. Regions exhibiting zero SNP density and more than 20x mapping coverage were selected as potential introgressions.

Phenotypic diversity among isolates

The different isolates were subjected to morphological, physiological, and biochemical characterization under solid media using the replica-plating technique. This physiological and biochemical characterization was done using the standard methods described by Kurtzman et al., with a few modifications [72]. The growth of the yeast strains was tested on YNB plates containing 2% of each carbon source. For cell morphology analysis, observations were made using a Zeiss AXIO Imager M2 microscope equipped with differential interference contrast optics (Axiocam 506 mono) after 3 days of growth in YPD broth, incubated at 25°C. Ascospore production was conducted on 2% potassium acetate agar plates (2% agar) for at least 7 days at 25°C. Pseudohyphae and true hyphae formation were detected using the Dalmau plate culture method, as described by Kurtzman et al [72].

To determine the phenotypic diversity of the different isolates, we estimate the growth and biomass production in several environmental conditions that are representative of different yeast habitats, either in nature or in industrial settings, as previously described [73]. For this, we performed a high-throughput phenotyping assay in 96-well microculture plates. Briefly, cells were pre-cultivated in 200 μL of a YNB medium supplemented with 2% glucose for 48h at 25°C. Then, strains were inoculated to an optical density (OD) of 0.03–0.1 (wavelength 620 nm) in 200 μL of media and incubated without agitation at 20°C for 24 h (YNB control) and 48–96 h under other conditions. OD was measured every 30 min using a 620 nm filter. Each experiment was performed in triplicates. The conditions considered were Yeast Nitrogen Base (YNB) supplemented with 2% at 25°C; 2% glycerol, 2% glucose 2% NaCl; 2% maltose; 2% Fructose; 2% Sucrose; 2% Galactose and 2% glucose + 5% ethanol. Area under the curve (AUC) was estimated with the R package growthcurver [74].

Statistical analysis

All statistical analyses were performed using biological triplicates. The difference was considered statistically significant at p-value < 0.05. The comparison of The Area Under the Curve (AUC) between strains/lineages was performed using a one-way ANOVA. The heat map was created using R package, ComplexHeatmap [75].

A highly divergent Saccharomyces lineage discovered in coastal Patagonia

To identify the Saccharomyces lineages in the Coastal Patagonian region [23,73], we conducted a survey targeting N. dombeyi forests along the South Chilean Pacific coast (Fig 1A). We collected bark samples from different sampling points in the Chiloe island and the Valdivian coastal area, isolating 106 Saccharomyces strains (Table A in S1 Table). Subsequently, we performed genotyping on these isolates by sequencing the highly polymorphic RIP1 marker, enabling the discrimination between S. uvarum and S. eubayanus species. Interestingly, our genotyping assay unveiled three distinct genotypes for the RIP1 marker: two patterns corresponding to S. uvarum and S. eubayanus, while a third, matched to a high divergent S. uvarum lineage (AUS from Almeida 2014), identified in 43 isolates (S1A Fig and Table A in S1 Table). Further Sanger sequencing of these isolates’ ITS, LSU, and SSU regions confirmed a 100% sequence identity with S. uvarum, strongly suggesting that these isolates likely belong to this species (S1B and S1C Fig and Table A in S1 Table).

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Fig 1. Geographic distribution and population structure of Coastal Patagonian isolates.

(A) World map illustrating the distribution of sequenced genomes of S. uvarum globally (black circles), with specific locations in Chile in Andean (purple) and Coastal (blue) Patagonia where new strains were isolated. (B) Unrooted maximum likelihood tree inferred from an alignment of 100 strains. Red lines indicate the average nucleotide distance between lineages using Illumina reads. The tree contains five main lineages: South America (SA-A, SA-B and SA-C), Australia (AUS) and Holarctic (HOL). Tree scale is substitutions per site (total SNPs across the whole genome size). (C) Non-scaled Maximum likelihood (ML) phylogenetic tree and population analysis using ADMIXTURE. The optimum of k = 7 groups is shown. Lineages identified are: South America (SA-A (green), SA-B1 (blue), SA-B2 (yellow), SA-B3 (orange) and SA-C (purple)), Australia (AUS, red) and Holarctic (HOL, brown). S. eubayanus CL815 was used as outgroup. (D) Population differentiation (F_ST) and nucleotide diversity (π) of the seven lineages. Circle size indicates π value. Dashed circles represent the F_ST value between groups. (E) Distribution of genomic variation in 100 strains based on the first two components from a PCA analysis. Color codes match the clusters presented in Panel C. Each dot on the graph represents an individual strain. https://github.com/ropensci/rnaturalearth/blob/master/data/df_layers_physical.rda.

https://doi.org/10.1371/journal.pgen.1011396.g001

To unravel the phylogenetic relationships of the coastal Patagonia strains within the S. uvarum phylogeny, we obtained short-read whole-genome sequences from 19 isolates representing various localities in Coastal Patagonia. Additionally, we sequenced 37 S. uvarum isolates previously obtained by our group from diverse locations in Andean Patagonia (Table B in S1 Table and Fig 1A), and we included 43 S. uvarum genomes from previous reports (Table C in S1 Table) [28,76], along with one representative S. eubayanus isolate (Table B in S1 Table) [23]. In total, we analyzed 100 strains: 17 strains isolated from Argentina, 56 from Chile, 5 from Australia, and 19 from the Holarctic regions (Europe, North America, and Asia) (Tables A and B in S2 Table). The maximum-likelihood phylogenetic tree, employing S. eubayanus as an outgroup, reaffirmed earlier observations: the AUS isolates exhibited the greatest genetic divergence from the SA-A and SA-B South American populations (Fig 1B). Notably, the isolates from coastal Patagonia formed a distinct lineage, closely clustering with AUS, strongly suggesting the presence of a unique independent lineage.

We further utilized the ADMIXTURE software to delve into the population structure of Saccharomyces isolates, using from k 2 to 10 (S2 Fig). The analysis suggests 7 populations as the best-fitting model (S3 Fig), which revealed distinct lineages predominantly corresponding to the geographical origin of the isolates. This analysis sheds light on a different lineage encompassing the coastal Patagonian isolates (hereafter termed SA-C), closely clustering with the AUS population. Moreover, multiple S. uvarum lineages were identified within South America (SA-B1, SA-B2, and SA-B3) (Fig 1C). This topology was consistently supported by both F_st and PCA analyses (Fig 1D and 1E and S3 Table), demonstrating substantial genetic differentiation between the AUS and SA-C lineages compared to the SA-A and SA-B Andean Patagonian lineages. FineSTRUCTURE analysis echoed these findings, highlighting an equivalent number of distinct lineages (S4 Fig). Estimating genetic diversity (π) revealed in the SA-C and AUS populations a mean π = 0.0032 and 0.0042, respectively, surpassing the genetic diversity observed in other S. uvarum populations (Fig 1D and S4 Table). Patterns of pairwise nucleotide variation using Illumina reads showed that AUS and SA-C were, on average 3.69% and 4.06% divergent from the S. uvarum reference genome (CBS 7001^T, HOL), respectively (S5 Table and S5 Fig). Interestingly, the average pairwise nucleotide divergence between the AUS and SA-C lineages was, on average, 1.03%, suggesting a genetic differentiation between these two lineages (S5 Table). The high genetic divergence between AUS and SA-C relative to S. uvarum suggests that these groups of strains could be reproductively isolated and represent a distinct evolutionary lineage.

Low spore viability between SA-C and other S. uvarum lineages

To assess the impact of nucleotide sequence divergence on offspring viability, we crossed spores from SA-C and AUS isolates with the S. uvarum CBS7001^T reference strain, estimating spore viability. Crosses involving representative SA-C and AUS strains (CL1604 and ZP966 strains, respectively) exhibited markedly lower spore viability (6.02% and 5.09%, respectively, S6 Table and Fig 2). The resulting spores were sterile, and several did not undergo tetrad formation. On the contrary, crosses between representative strains from different S. uvarum populations with CBS7001^T had higher spore viability, ranging between 50% and 81%, consistent with their lower genetic divergence (S6 Table and Fig 2). This reproductive isolation suggests distinct barriers within the SA-C and AUS lineages compared to other S. uvarum populations.

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Fig 2. Offspring viability against S. uvarum.

Plot depicting spore viability between lineages and genetic divergence (measured as nucleotide identity). Each dot compares one representative strain mated against the S. uvarum reference strain CBS7001^T. A linear regression model was applied, with an Adjusted R-squared of 0.8891 and a p-value of 0.001. 95% confidence intervals are in gray.

https://doi.org/10.1371/journal.pgen.1011396.g002

We crossed representative SA-C and AUS strains (CL1604 and ZP966 strains, respectively) to explore potential lineage relationships between SA-C and AUS, revealing a 25.7% spore viability (S6 Table). This indicates genetic differences beyond nucleotide divergence, likely involving additional genetic variants, such as structural variants (SVs). Interestingly, diploid SA-C and AUS strains exhibited normal spore viabilities. In addition, we identified a full HO gene version within these strain’s genomes, suggesting that they are homothallic and ruling out inherent reproductive issues (S6 Table). These results suggest reproductive isolation between SA-C, AUS, and other S. uvarum populations, possibly due to nucleotide divergence. Additionally, SA-C and AUS lineages may exhibit additional genetic variants impacting their reproductive status. Considering the nucleotide divergence and reproductive isolation, we propose classifying the SA-C and AUS lineages as a distinct species, named Saccharomyces chiloensis sp. nov. for their most frequent isolation in the Chiloe island.

Coastal Patagonian lineages exhibit large genomic rearrangements

To identify SVs that could shed light on the low spore viability between the SA-C and AUS S. chiloensis sp. nov lineages, we obtained de novo assemblies using ONT long-read sequences from a subset of 5 representative strains from both S. chiloensis sp. nov lineages (S7 Table). Using the assemblies, we identified SVs with Mum&Co of at least 50 bp relative to the S. uvarum and S. eubayanus reference genomes (CBS7001^T and CBS12357^T, respectively). To ensure accuracy and avoid false positives stemming from assembly errors, we filtered the dataset for SVs size (> 1kb), resulting in the identification, on average, of 72.6 and 85.8 SVs at the lineage level between the SA-C S. chiloensis sp. nov. lineage and S. eubayanus and S. uvarum, respectively (S6 Fig and S8 Table). Interestingly, we identified five large rearrangements across all SA-C S. chiloensis sp. nov. strains: one reciprocal translocation (ChrVI-ChrX^t, ChrX-ChrVI^t), two non-reciprocal translocation between three chromosomes (ChrXV-ChrII^t and ChrII-ChrVII^t), one inversion (ChrXIII^I) and a deletion on the right arm of ChrVII relative to S. uvarum (Fig 3A). The translocated segments spanned approximately 54 (ChrVI-ChrX^t), 318 (ChrX-ChrVI^t), 433 (ChrXV-ChrII_t) and 143 (ChrII-ChrVII^t) kb, and comprised 19, 145, 212, and 64 genes, respectively. The inversion spanned approximately 487 kb, covering 225 genes (S9 Table). A similar comparison between the AUS lineage and S. uvarum revealed the presence of the reciprocal translocation previously described between ChrVI-ChrX^t (Fig 3B), indicating that this reciprocal translocation would be shared across S. chiloensis sp. nov. However, not all SVs are conserved across S. chiloensis sp. nov lineages (Fig 3C). In this sense, the two non-reciprocal translocations (ChrXV-ChrII^t and ChrII-ChrVII^t) along with the ChrXIII inversion and Chrm VII deletion are exclusively found in the SA-C lineage, likely arisen following the split of the AUS and SA-C lineages along the SA-C branch (Fig 3C and S9 Table).

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Fig 3. Structural variants identification between Saccharomyces chiloensis sp. nov. and other Saccharomyces species.

(A) Genome synteny analysis of the S. uvarum reference strain CBS7001^T, and (A) S. chiloensis sp. nov. CL1206 strain (SA-C) and (B) ZP966 strain (AUS). The dot plot representation depicts the DNA sequence identity between the genomes. Translocation and inversions are highlighted in red circles and grey rectangles indicate the chromosomes involved. Translocations were mapped in all the S. chiloensis sp. nov. strains sequenced in this study. (C) Genome synteny analysis of the S. chiloensis sp. nov. CL1206 strain (SA-C) and ZP966 strain (AUS) strains.

https://doi.org/10.1371/journal.pgen.1011396.g003

Since many genes are affected by SVs, we decided to explore how gene content varies between Saccharomyces species. We first annotated all the de novo assembled genomes and then constructed a phylogeny considering both S. chiloensis sp. nov lineages and the different Saccharomyces reference genomes (Fig 4A). Interestingly, we reveal the apparent greater genetic distance between SA-C and AUS relative to S. uvarum compared to other lineages within the same species (Fig 4A and S10 Table). We used the Ortho Average Nucleotide Identity (OANI) tool to explore the mean nucleotide identity between S. chiloensis sp. nov and S. uvarum lineages. The SA-C vs. S. uvarum comparison showed 92.9% identity, while the AUS vs. S. uvarum comparison showed 93.3%. These are lower values than any of the other comparisons between the remaining S. uvarum lineages revealed (Fig 4B and S10 Table). When we performed a similar analysis using highly divergent lineages in S. paradoxus [77] and S. kudriavzevii (nucleotide divergence ~ 4.7%), the comparisons denoted values near the 95% species delineation limit (S10 Table), demonstrating that the SA-C and AUS lineages are more divergent than others in the genus (Fig 4C).

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Fig 4. Phylogenetic relationship across different Saccharomyces species.

(A) Maximum likelihood (ML) phylogenetic tree using different Saccharomyces species and lineages where long-reads are available. The tree was built considering 5,772 orthologues genes using OrthoFinder. Branch lengths represent the average number of substitutions per site (tree scale). Representative lineages from each species were selected, and annotated genomes were obtained from previous studies using publicly available data. (B) OANI values were estimated using each genome from the ONT assemblies across the different lineages against S. uvarum reference genome (CBST7001^T). Dashed red and orange lines denote the 95% and 96% species delineation thresholds. (C) Plot depicting OANI values and offspring viabilities comparing different sister Saccharomyces species (blue dots: S. cerevisiae x S. paradoxus [55,78]; S. jurei x S. mikatae [21] and S. eubayanus x S. uvarum), Saccharomyces lineages within the same species (red dots, intraspecies crosses in S. cerevisiae [78], S. kudriavzevii [78], S. paradoxus [78] and S. uvarum), and S. chiloensis sp. nov. x S. uvarum crosses (green dots). NRT = Non-Reciprocal Translocations and RT = Reciprocal Translocation. Spore viabilities from other crosses were obtained from Liti et al, 2006 [78]. (D) Estimation of the divergence time since the last common ancestor between lineages. Time between (1) S. eubayanus and S. uvarum/S. chiloensis sp. nov. lineages; (2) S. uvarum and S. chiloensis sp. nov.; (3) the SA-C and AUS S. chiloensis sp. nov. lineages. Timeline is shown in Thousand’s years (Ky).

https://doi.org/10.1371/journal.pgen.1011396.g004

In addition, we assessed the presence of nuclear genome contributions from S. uvarum in S. chiloensis sp. nov. We detected introgression signals ranging from 2–79 kb on chromosomes V (~70 kb), XI (~49 kb) and XVI (~19 kb) (S7 Fig and S11 Table). These findings conclusively establish the existence of a distinct lineage of Saccharomyces strains in coastal Patagonia and Tasmania, exhibiting significant divergence from the established S. uvarum lineages. The notable nucleotide divergence and OrthoANI values strongly suggest potential reproductive isolation, emphasizing the likelihood of these lineages representing a separate species.

Finally, we estimated the divergence time since the most recent common ancestor between lineages using the population genomic data as previously described in yeast [57,79]. The divergence time between S. uvarum and the SA-C and AUS lineages corresponds to approximately 473–59 KYA (Fig 4D), likely dating back to the Middle Pleistocene. On the other hand, the divergence between AUS and SA-C was dated to approximately 171–21 KYA, likely during the late Pleistocene (Fig 4D).

Our results indicate that S. chiloensis sp. nov. would represent a distinct Saccharomyces species. Additionally, the lack of many SVs between the AUS lineage and S. uvarum strongly supports that nucleotide divergence, rather than SVs, is responsible for the reduced spore viability between S. chiloensis sp. nov. and S. uvarum. In addition, the significant SV count between SA-C and AUS reinforces our observations of the lower offspring viability among these S. chiloensis sp. nov. lineages.

Phenotypic diversity across S. chiloensis sp. nov. populations

Description of Saccharomyces chiloensis sp. nov.

Standard description of Saccharomyces chiloensis T. A. Peña, P. Villarreal, N. Agier, M. de Chiara, T. Barría, K. Urbina, C. A. Villarroel, A. R. O. Santos, C.A. Rosa, R. F. Nespolo, G. Liti, G. Fischer, and F. A. Cubillos sp. nov.

Etymology: Saccharomyces chiloensis (chi.lo.en´sis. N.L. f. adj. chiloensis of or pertaining to the Chiloé island (Chile), where this yeast was found).

On YM agar after 3 days at 25°C, the cells are globose, ovoid or elongate, 2.8–6.3 X 4.1–7.8 μm, and occur singly or in small clusters (Fig 5A). In Dalmau plates after 2 weeks on cornmeal agar, pseudohyphae are either not formed or are rudimentary. Budding cells are transformed directly into persistent asci containing one to four globose ascospores formed after 11 days on YM agar at 25°C (Fig 5B). Fermentation of glucose is positive. Assimilation of carbon compounds: positive for glucose, galactose, maltose (variable, weak/slow), sucrose, melibiose (weak/slow), raffinose, and D-manitol (variable, weak/slow). Negative for L-sorbose, cellobiose, trehalose, lactose, melezitose, inulin, soluble starch, D-xylose, L-arabinose, D-arabinose, D-ribose, L-rhamnose, ethanol, glycerol, erythritol, ribitol, galactitol, D-glucitol, salicin, DL-lactate, succinate, citrate, myo-inositol, methanol, hexadecane, xylitol, acetone, ethylacetate, 2-propanol, D-gluconate, and N-acetyl-D-glucosamine. Assimilation of nitrogen compounds: negative for lysine, nitrate, and nitrite. Growth in amino-acid-free medium is positive. Growth at 37°C is negative. Growth in 1% acetic acid is negative. Growth on YM agar with 10% sodium chloride is negative. Growth in 50% glucose/yeast extract (0.5%) is negative. Acid production is positive. Starch-like compounds are not produced. In 100 μg cycloheximide ml^-1 growth is negative. Urease activity is negative. Diazonium Blue B reaction is negative (S12 Table). The CL1206 strain was designated as the type strain. The holotype of S. chiloensis sp. nov., strain CBS 18620^T, is preserved in a metabolically inactive state at the CBS Yeast Collection of the Westerdijk Fungal Diversity Institute in Utrecht, the Netherlands. Additionally, two isotypes of S. chiloensis sp. nov., were deposited as strain RGM 3578, and PYCC 10014 in the Chilean Culture Collection of Microbial Genetic Resources (CChRGM) at the Agricultural Research Institute (INIA) in Chile, and the Portuguese Yeast Culture Collection in Portugal, respectively.

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Fig 5. Phenotypic diversity in S. chiloensis sp. nov.

(A) Differential interference contrast micrograph of budding cells of S. chiloensis sp. nov. grown in YPD broth after three days at 25°C. Bars: 10 μm. This image was obtained using differential interference contrast (DIC) microscopy. (B) Budding cells and asci with ascospores on Yeast extract—Malt extract agar (YM) after three days at 25°C using 40X microscopy. (C) Heatmap depicting the phenotypic diversity in S. chiloensis sp. nov. obtained from 8 different environmental conditions. Strains are grouped by hierarchical clustering. The colours indicate the species and S. chiloensis sp. nov. (SA-C in purple, and AUS in red), S. uvarum (brown) and S. eubayanus (calypso). The heatmaps were obtained from the Area Under the Curve (AUC) data and normalized using Z-score per column (D) The Area Under the Curve (AUC) across all environmental conditions grouped by species, lineages and locality. Different letters reflect statistical differences between strains with a P-value < 0.05, one-way analysis of variance (ANOVA). Glc = glucose, EtOh = Ethanol.

https://doi.org/10.1371/journal.pgen.1011396.g005

Next, we explored phenotypic differences between S. chiloensis sp. nov. and S. uvarum. Through measurements of microbial growth in microcultures, we calculated the area under the curve (AUC) of growth curves under eight conditions that included different carbon sources, stresses, and environmental parameters (S13 Table). Hierarchical clustering of the AUC data revealed two clades, clearly distinguishing S. uvarum and S. eubayanus strains from S. chiloensis sp. nov (Fig 5C). Notably, specific traits were identified as characteristic to S. chiloensis sp. nov., establishing differences from its sister species. We observed a significantly lower AUC in S. chiloensis sp. nov. strains grown under maltose and ethanol 5% relative to S. uvarum (p-value < 0,05, ANOVA, S13 Table), suggesting a lower ethanol tolerance and capacity to assimilate carbon sources other than glucose and fructose. These findings highlight a distinct phenotypic profile in S. chiloensis sp. nov. relative to its sister species, S. uvarum.

Next, we sought to determine whether S. chiloensis sp. nov. exhibited intraspecies phenotypic variation. To investigate this, we compared the AUC among strains obtained from three different locations. Chiloé (SA-C-C), Reserva Costera Valdiviana (SA-C-RCV) and Australia (AUS). Interestingly, the AUS strains exhibited the lowest fitness across conditions compared to the other locations (p-value < 0,05, ANOVA, (S13 Table and Fig 5D), particularly under galactose and higher maltose concentrations. These results indicate a separation of the isolate’s phenotypic profile according to their geographic origin, either from Patagonia or Australia, demonstrating substantial intraspecies phenotypic variation (Fig 5C).