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Article

Preliminary Genetic and Physiological Characterization of Starmerella magnoliae from Spontaneous Mead Fermentation in Patagonia

by
Victoria Kleinjan
1,
Melisa González Flores
1,2,
María Eugenia Rodriguez
1,3 and
Christian Ariel Lopes
1,2,*
1
PROBIEN (CONICET-UNCO), City of Neuquén 8300, Neuquén, Argentina
2
Faculty of Agronomic Sciences, University of Comahue, Cinco Saltos 8303, Río Negro, Argentina
3
Faculty of Medical Sciences, University of Comahue, Cipolletti 8324, Río Negro, Argentina
*
Author to whom correspondence should be addressed.
Fermentation 2025, 11(9), 494; https://doi.org/10.3390/fermentation11090494
Submission received: 26 June 2025 / Revised: 9 August 2025 / Accepted: 21 August 2025 / Published: 24 August 2025
(This article belongs to the Special Issue Yeast Fermentation, 2nd Edition)
Browse Figures
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Abstract

Honey possesses unique properties, characterized by its high sugar concentration and the synergistic interaction among nectar, pollen, bees, and yeasts. These features render it an exceptional substrate for exploring microbial diversity for bioprospecting purposes. In this study, we characterized fermentative yeast populations from 19 honey samples collected in Northern Patagonia, Argentina. A total of 380 yeast isolates were obtained, identifying eight yeast species. Starmerella magnoliae emerged as the dominant species, found in 76% of samples and representing 63% of total isolates. Intraspecific diversity analysis, using mtDNA-RFLP and sequencing of nuclear genes (FSY1 and FFZ1), revealed the presence of two distinct phylogeographic populations. Phenotypic assays indicated that most S. magnoliae strains tolerate high sulfite and ethanol concentrations, alongside exhibiting broad temperature tolerance, with some strains thriving even at 37 °C. Despite the fact that none of the strains completed the fermentation, microfermentation trials confirmed the fructophilic nature of this species and highlighted intraspecific variability in glycerol and acetic acid production. These findings underscore S. magnoliae as a promising non-Saccharomyces yeast for the fermented beverage industry.

1. Introduction

Honey is a natural substance produced by honeybees, primarily Apis mellifera, through the biochemical transformation of floral nectar or plant exudates [1]. Its chemical composition is highly complex, consisting primarily of simple carbohydrates, along with a diverse array of bioactive compounds, including enzymes, amino acids, organic acids, minerals, aromatic compounds, pigments, waxes, and pollen grains [2]. The predominant carbohydrates in honey are fructose (28–44%) and glucose (22–40%), while other sugars, such as sucrose and maltose, are present in lower concentrations (<16%) [3,4]. In addition, honey contains 14–23% water and has an acidic pH, ranging from 4.1 to 4.6 [3,4,5].
These physicochemical properties, particularly honey’s high osmolarity and low water activity, create a selective microbial environment. While bacterial growth is largely inhibited, osmotolerant yeasts are capable of persisting in honey either in a latent state or as viable but non-culturable cells [6,7,8]. Several studies have documented the presence of diverse yeast genera in honey, including Rhodotorula, Starmerella, Candida, Tausonia, Lachancea, Torulaspora, Saccharomyces, and Zygosaccharomyces [9,10,11].
Yeast species isolated from honey have been associated with novel applications in the food, pharmaceutical, and agricultural industries. Some species produce high-value compounds such as kynurenic acid, erythritol, mannitol, and citric acid [12]. Others exhibit biocontrol activity against phytopathogenic molds in crops such as citrus fruits, peanuts, corn, and sugarcane [11]. In addition, several yeasts isolated from honey play a key role in mead production [13,14,15], an alcoholic beverage obtained by fermenting honey diluted in potable water [16,17]. Mead fermentation can occur spontaneously, driven by indigenous yeasts present in honey, fruits, or the surrounding environment, or it can be initiated by inoculating commercial yeast strains [3]. The final characteristics of the product vary depending on fermentation conditions and residual sugar content, classifying mead into categories such as sweet (high sugar content, between 54 g/L and 90 g/L), dry (low sugar content, less than 36 g/L), sparkling (naturally effervescent), or carbonated (artificially infused with CO2) [3].
Despite increasing interest in honey-associated microbiota due to its potential biotechnological applications, the diversity of yeasts in honey from ecologically unique regions, such as Patagonia, remains largely unexplored. Patagonia is characterized by extreme climatic conditions, including low temperatures, strong winds, and endemic flora—factors that may select for yeasts with unique metabolic and physiological adaptations. These microorganisms may exhibit enhanced stress tolerance, a trait that is potentially valuable for industrial applications.
In this study, yeast strains were obtained from spontaneously fermented mead elaborated from honey from different locations in North Patagonia. Genetic and physiological characterization of the most frequently detected yeast species Starmerella magnoliae was also performed. The findings will contribute not only to ecological and microbial knowledge but also to the identification of yeasts with potential applications in the fermented beverage industry.

2. Materials and Methods

2.1. Sampling

Nineteen honey samples from twelve different locations in the provinces of Neuquén (8 locations), Río Negro (1 location), and Chubut (3 locations) were analyzed (Table 1). The floral origin of the honey was determined by means of a palynological study [18].

2.2. Mead Production and Yeast Isolation

Two must formulations were prepared for each honey sample to produce sweet mead (SM: 420 g/L honey, 0.6 g/L ammonium phosphate and 0.2 g/L tartaric acid) and dry mead (DM: 320 g/L honey, 0.5 g/L ammonium phosphate and 0.2 g/L tartaric acid) [3]. The non-sterilized musts were incubated without agitation at 25 °C in flasks of 50 mL containing 35 mL of the respective must. Each fermentation was carried out in duplicate. After 30 days, yeast isolation was performed on GPY-agar plates (% w/v: glucose 2, peptone 0.5, yeast extract 0.5, agar 2) supplemented with chloramphenicol (50 mg/L). Ten yeast colonies were isolated from each plate based on cell and colony morphology and frequency of appearance.

2.3. Yeast Identification

Total genomic DNA was extracted according to the protocol described by Lopez et al. (2001) [19]. Yeast species were identified by sequencing the D1/D2 domain of the 26S rDNA gene following the methodology of Kurtzman and Robnett (2011) [20]. PCR products were submitted to a sequencing service (Macrogen, Gangnam District, Seoul, South Korea) and compared using BLASTn with type strain sequences available in the NCBI database.

2.4. Intraspecific Characterization of Starmerella magnoliae Isolates

Mitochondrial DNA restriction analysis (mtDNA-RFLP) was used for the molecular characterization of the 240 isolates identified as S. magnoliae, according to the methodology described by Lopez et al. (2001) [19].

2.5. Phylogenetic Analysis

The nuclear genes FSY1 and FFZ1 were sequenced for all S. magnoliae isolates. Primers were designed using PRIMER3 (https://www.primer3plus.com/index.html, accessed on 9 August 2022) (FSY1fwd: ATGCTCATGACCCCACTGAA; FSY1rev: CCAGCGTCTTGTCCTTTGTC; FFZ1fwd: AATTGCGCCACTATTCTTGG; FFZ1rev: GCATACTGGCTTCCCACATT). PCR products were purified using the AccuPrep PCR Purification Kit (Bioneer, Inc., Oackland, CA, USA) and submitted to a sequencing service (Macrogen, Gangnam District, Seoul, South Korea). Sequences of FSY1 and FFZ1 were deposited in the NCBI database under accession numbers PV130388–PV130417 and PV130418–PV130447, respectively. Homologous gene sequences from S. magnoliae strains MDK2023, PYCC2903T, and JH110 were included for comparison (no additional homologous sequences were available in the NCBI database).
Sequences were aligned with ClustalW [21] and concatenated using Sequences Matrix 1.7.6 [22]. The best evolutionary model was selected with JModelTest 2.1.10 [23], identifying the HKY85 model [24] as the best fit. Phylogenetic trees were reconstructed using the Maximum Likelihood method [24] with 1000 bootstrap replicates in MEGA7 [25].
Bayesian clustering of individuals was performed using STRUCTURE 2.3.4 [26] to investigate population structure with admixture in S. magnoliae. STRUCTURE analysis was conducted for 32 S. magnoliae sequences with 10 independent runs for each K value (1 to 5), using 500,000 MCMC iterations after a burn-in of 50,000 steps. The optimal K was determined using the Evanno method [27] and visualized with the pophelper R package. (version R 3.0.2) [28].
For phylogeographic analysis, haplotype classification was carried out in DnaSP v5 [29]. Median-joining (MJ) networks were constructed for the concatenated nuclear gene sequences using Network 4.5 [30]. Tajima’s neutrality test [31] was used to calculate nucleotide diversity (π) and pairwise differences between clusters (Dij).

2.6. Stress Tolerance Assays

2.6.1. Sulfite Tolerance

Sulfite tolerance was assessed using a drop test on YEPD-TA agar plates supplemented with 0, 1, 2, 3, and 4 mM Na2S2O5 [32]. Plates were inoculated with a 5 μL drop of yeast suspension (1 × 106 cells/mL) and four serial dilutions (1:5). Plates were incubated at 25 °C and monitored daily for five days.

2.6.2. Temperature Tolerance

Temperature tolerance was assessed on GPY agar plates following the drop test as described above. The plates were incubated at 8, 13, 20, 25, 30, and 37 °C for five days.

2.6.3. Ethanol Tolerance

Ethanol tolerance was evaluated in 96-well microtiter plates containing 200 μL of Yeast Nitrogen Base (YNB) supplemented with 0%, 3%, 5%, and 10% (v/v) ethanol. Each well was inoculated with a pure culture of each yeast at a final concentration of 2.5 × 106 cell/mL. Growth was monitored by measuring optical density (OD) at 630 nm using a Rayto RT-2100 C microplate reader (Nanshan, Shenzhen, China). Assays were performed in triplicate with randomized plate positions and incubated at 25 °C for six days.

2.7. Microfermentation of Mead with S. magnoliae

A representative strain from each mtDNA-RFLP profile was selected to evaluate fermentative capacity in a standard honey must. S. cerevisiae NPCC1634, isolated in this study, was used as a control. Microfermentation was conducted in 100 mL flasks filled with 80 mL of steam-flushed must (100 °C, 40 min). The must (25 °Brix) was prepared with commercial honey diluted in water and supplemented with 0.5 g/L ammonium phosphate and 0.2 g/L tartaric acid. Sugar composition was glucose 129.3 g/L, fructose 121.0 g/L, and sucrose 37.6 g/L. Each flask was inoculated with 2 × 106 cells/mL and incubated at 25 °C. Fermentation progress was monitored by daily measurement of °Brix using a refractometer and CO2 release was quantified through weight loss by daily weighing of each flask using an analytical balance (±0.0001 g precision). Each assay was performed in duplicate for each must.

2.8. Physicochemical Parameters

Ethanol, glycerol, acetic acid, lactic acid, succinic acid, citric acid, malic acid, and methanol concentrations were determined by HPLC using an Agilent 1260 system (Quat Pump VL, ALS, TCC), following Gonzalez Flores et al. (2017) [33]. Glucose, fructose, and sucrose concentrations were determined with the Sac/D-Glucose/D-Fructose enzymatic kit (Megazyme Ltd.®, Wicklow, Ireland, 2023). Both HPLC and enzymatic sugar measurements were performed at the end of the fermentation carried out by the control strain S. cerevisiae.

2.9. Statistical Analysis

Yeast growth in ethanol tolerance assays were modeled using the modified Gompertz equation [34].
y = A exp − e x p µ max e A λ − t + 1
where y is the ln(Nt/ N0), N0 is the initial OD630nm and Nt is the culture OD630nm measured at time t (h); A = ln(N∞/N0) is the maximum population value reached with N∞ as the asymptotic maximum of OD630nm, µmax is the maximum OD630nm specific increase rate, λ is the length of the lag phase period (h), and e is the base of the natural logarithm (e ≈ 2.71828).
Modeling was performed using RStudio, (version R 3.0.2) [28].
Kinetic and physicochemical data were analyzed by ANOVA and Fisher’s LSD test (α = 0.05) using RStudio, (version R 3.0.2) [28].
Heatmaps of ethanol assay parameters were generated using MeV (version 3.2.1) with Euclidean distance and average linkage clustering. The kinetic parameter values for each strain under ethanol stress conditions were normalized by dividing them by the value obtained for the same strain under the 0% ethanol condition.
Principal Component Analysis (PCA) was performed for the physicochemical parameters evaluated in microfermentation. This analysis was performed with RStudio (version R 3.0.2) [28] employing the base R function prcomp.

3. Results

3.1. Yeast Diversity in Mead

Nineteen honey samples obtained from 12 locations in North Patagonia were used to produce sweet and dry mead by spontaneous fermentation. After 30 days of incubation, all fermentation samples showed both a loss of weight caused by CO2 release and an increase in turbidity, indicating yeast growth.
Ten colonies were isolated from each fermentation by plating serial dilutions on GPY agar and identified by sequencing the D1/D2 domain of the 26S rDNA gene. Eight different species were identified among 380 isolates (Table 1). Starmerella magnoliae was the predominant species, detected in 76% of the fermentation and representing 63.15% of the total isolates. Additionally, other non-Saccharomyces species such as Zygosaccharomyces rouxii, Pichia membranifaciens, and Rhodotorula mucilaginosa were detected, though at lower frequencies. Regarding the Saccharomyces genus, two species—Saccharomyces cerevisiae and Saccharomyces uvarum—were recovered from six dry mead samples. In Huinganco dry mead, 100% of the isolates corresponded to Saccharomyces species, whereas in the remaining dry mead, they represented only 20% of the isolates (Table 1).

3.2. Intraspecific Characterization of S. magnoliae

A total of 240 S. magnoliae isolates were characterized at the intraspecific level using mtDNA-RFLP analysis. Four different profiles were detected and designated as profiles A, B, C and D (Table 2 and Figure 1A). Profile A was the most prevalent, representing 64.6% of the total isolates, and was found in six of the twelve locations analyzed. This profile was particularly abundant in the central sampling zone (Figure 1B,C). Profile B, the second most frequent, accounted for 34.2% of the isolates and was distributed across ten locations, including the northern, eastern, and southern zones. This profile exhibited a broader geographical distribution than profile A. In contrast, profiles C and D collectively represented just over 1% of the isolates and were restricted to single locations: Junín de los Andes and Cholila, respectively (Table 2 and Figure 1A).

3.3. Phylogenetic and Phylogeographic Analysis of S. magnoliae

To extend the phylogenetic analysis of the previously characterized strains, a representative strain from each mtDNA-RFLP profile, location, or mead type (sweet or dry) was selected. A total of 29 S. magnoliae strains were analyzed by sequencing the FSY1 and FFZ1 genes, both encoding species-specific fructose transporters. Sequences from the type strain PYCC2903T (originally isolated from magnolia flowers in The Netherlands) and from strains JH110 and MDK2023 (isolated from honeycombs in Korea) were included for comparison using publicly available data.
Phylogenetic analysis based on the concatenated sequences of the two genes (1625 bp) revealed two well-defined clades. Clade A included strains with mtDNA-RFLP profiles A, C, and D, while clade B included only strains with profile B and the three reference strains (Figure 2A).
Clade A exhibited higher nucleotide diversity (μA = 0.0031 vs. μB = 0.0025). Notably, the nucleotide distance (Dij) between the most genetically distant reference strains, PYCC2903T and JH110, was shorter (Dij = 0.008) than the distance between two Patagonian strains, NPCC1801 (profile A) and NPCC1795 (profile B), isolated from the same honey sample in Aluminé (Dij = 0.011).
Despite the tree showing two distinct clades, two strains (NPCC1803 and PYCC2903T) occupied intermediate positions, diverging from the main branches. This suggests the possible existence of hybridization or admixed populations. To investigate this, population structure analysis was performed. Although constrained by the dataset size and uncertainty regarding marker polymorphism, this analysis identified two distinct populations (K = 2), with some strains exhibiting varying degrees of admixture (Figure 2A). The most notable case was the S. magnoliae type strain PYCC2903T, which displayed 48.5% admixture with population 1 (K = 1, clade A) and 51.5% with population 2 (K = 2, clade B).
To assess phylogeographic structure, a haplotype network was constructed from the same sequence dataset (Figure 2B). This confirmed two populations in Patagonia. Population A, corresponding to clade A in the phylogenetic tree, was the most diverse, with haplotype H5—formed by strains from six locations—occupying a central position. Strains with profiles C (H1) and D (H9) were located at more distant nodes. The type strain PYCC2903T grouped closer to population A. Population B included strains from six locations and featured a central haplotype (H13) identical to that of strain MDK2023 (Korea), indicating closer genetic relatedness between these and the Korean strain than to coexisting Patagonian strains.

3.4. Starmerella Magnoliae Growth Under Different Stress Conditions

A total of 69% of the isolates were able to grow at sulfite concentrations up to 4 mM (equivalent to 500 mg/L total SO2) (Appendix A Figure A1). However, the reference S. cerevisiae strain showed superior growth at 4 mM Na2S2O5 compared to S. magnoliae isolates.
No direct correlation was observed between the isolation origin and sulfite tolerance; however, isolates from clade A, identified through phylogenetic analysis, exhibited the highest sulfite resistance.
Regarding temperature tolerance, seven of the 29 isolates analyzed in this study were able to grow at 37 °C (Appendix A Figure A1), four of them belonged to phylogenetic clade B, and the remaining three isolates to phylogenetic clade A.
Ethanol tolerance was evaluated in all 29 isolates. All strains were able to grow in the presence of at least 10% (v/v) ethanol. S. cerevisiae, used as a reference, exhibited greater ethanol tolerance—reflected in higher μmax values and lower λ—than the S. magnoliae strains. Our results revealed intraspecific variability in ethanol response, based on both μmax (maximum growth rate) and λ (lag phase) parameters. These parameters were obtained by modeling 360 growth curves using the modified Gompertz equation and are represented in a heatmap (Figure 3). The μmax and λ values for the different S. magnoliae strains at various ethanol concentrations, along with the corresponding analysis of variance, are provided in Appendix A Table A1.
Based on μmax values, two main groups of strains were identified (Figure 3a): Groups I and II. Group II was further subdivided into IIa and IIb. Strains in Groups I and IIa showed lower susceptibility to increasing ethanol concentrations, maintaining higher μmax values. Notably, some strains within these groups even exhibited relatively higher μmax values compared to the 0% ethanol condition. In contrast, strains in Group IIb were more sensitive to ethanol, with a progressive decrease in μmax as ethanol concentration increased. Strains from phylogenetic clade A were distributed across both Groups I and II, suggesting greater variability in ethanol tolerance. In contrast, strains from clade B were predominantly found in Group I (75%), indicating higher ethanol resistance. Regarding the λ parameter (lag phase), two major groups—Groups I and II—were again identified (Figure 3b), with Group II further divided into IIa and IIb. Strains in Groups I, IIa, and some from IIb showed increased sensitivity to ethanol, as evidenced by longer lag phases with rising ethanol concentrations. In contrast, other strains in Group IIb exhibited stable lag phases regardless of ethanol concentration, suggesting that ethanol did not significantly affect their lag phase. These strains originated from different locations and displayed diverse mitochondrial profiles.

3.5. Microfermentation of Mead with S. magnoliae

None of the S. magnoliae strains completed fermentation within 45 days, leaving more than 100 g/L of residual sugars. The evaluation of metabolite concentrations by HPLC and enzymatic sugar measurements was performed at the end of each fermentation. All S. magnoliae strains showed a clear preference for fructose over glucose. However, intraspecific differences were observed: strains NPCC1806 and NPCC1782 exhibited a more pronounced fructophilic phenotype than the others (Table 3). Regarding sucrose, all strains consumed it to varying extents. In contrast, none of the strains effectively fermented glucose. By comparison, the S. cerevisiae strain consumed all sugars within 16 days (Table 3).
Principal Component Analysis (PCA), performed using chemical characteristics of the fermented products, segregated the strains into three distinct clusters (Figure 4A). Cluster I, comprising only the mead fermented with S. cerevisiae NPCC1634, exhibited the highest ethanol production, with statistically significant differences (Table 3). Cluster II, formed by strain NPCC1817, showed higher concentrations of residual fructose and sucrose. This strain produced more ethanol than the other S. magnoliae strains but lower concentrations of glycerol and acetic acid (Table 3). Finally, Cluster III included mead fermented with strains NPCC1782, NPCC1785, and NPCC1806. These mead samples were characterized by elevated glycerol and acetic acid content (Figure 4A,B).

4. Discussion

A high level of interspecific yeast diversity was observed in spontaneously fermented mead produced in 12 different locations across North Patagonia. Starmerella magnoliae was notably the most frequently isolated yeast species in the majority of samples. The significant biotechnological potential of this species, combined with the limited global data available, prompted us to focus on its genomic and physiological characterization.
S. magnoliae is consistently associated with pollen, flowers, bees, honey, and mead [35,36,37]. Its dominance in these habitats is likely due to its osmotolerance [38] and fructophilic behavior [39,40,41], traits that confer a competitive advantage in high-stress environments.
Sequencing analysis of the FSY1 and FFZ1 genes revealed that the nucleotide distance between PYCC2903T and JH110—the most genetically divergent strains with publicly available sequences, isolated from different countries and ecological niches—was shorter than the distances observed among Patagonian strains collected from the same location. This finding suggests remarkable genetic diversity of S. magnoliae in North Patagonia. Notably, population structure analysis showed that Patagonian strains exhibited low levels of admixture, even when strains from different populations coexisted in the same location. Similar population dynamics have been reported in Patagonia for other yeast species, such as Saccharomyces eubayanus [42] and Saccharomyces uvarum [43,44]. In both cases, Patagonia harbors the highest global genomic diversity, with two well-differentiated populations—South America A and B in S. uvarum, and Patagonian A and B in S. eubayanus—coexisting at the same sites. In fact, the nucleotide distance calculated between the two Patagonian populations of S. magnoliae (0.0111) is comparable to the value observed for South America B (SA-B) and Europe (Hol) populations of S. uvarum (0.01048), based on ten nuclear genes analyzed in Gonzalez Flores et al. (2020) [44]. Notably, in the case of S. uvarum, a larger genomic matrix was used, suggesting that the genetic diversity detected within S. magnoliae populations in Patagonia is consistent with diversity levels reported for other yeast species.
Alongside the high intraspecific diversity observed in S. magnoliae, notable physiological traits were also identified. The species exhibited high sulfite tolerance, with strains capable of growing in culture media containing up to 500 mg/L total SO2. This contrasts with findings from Fiore et al. (2005) [45], who reported sulfite tolerance up to 300 mg/L in a S. magnoliae strain isolated from mezcal. Additionally, Englezos et al. (2014) [46] found that only 11% of Starmerella bacillaris strains from grapes tolerated SO2 concentrations between 100 and 150 mg/L. These data suggest that S. magnoliae exhibits markedly higher sulfite tolerance than its close relative S. bacillaris.
Although no direct correlation was observed between the isolation source and sulfite tolerance, isolates from clade A identified through phylogenetic analysis exhibited the highest resistance to sulfites. The high sulfite resistance observed in these yeast species appears contradictory, as sulfites are absent in floral nectar and honey. It may instead reflect adaptation to other environmental stressors such as plant-derived antimicrobials or the osmotic stress of high-sugar substrates. High sulfite tolerance is relevant to the fermented beverage industry, where sulfites are commonly added to suppress microbial growth. In particular, current regulations allow SO2 concentrations in mead ranging from 150 to 300 mg/L [4,16,47], levels tolerated by all strains analyzed in this study.
Previous studies have shown that S. magnoliae exhibits a broad temperature growth range, thriving between 8 °C and 25 °C, with some strains growing at 37 °C [35]. In our study, all strains grew at 25 °C, and several also grew at 37 °C. No clear relationship was observed between isolation site and growth temperature. However, all isolates capable of growing at 37 °C coexisted at the same sites with isolates that could not grow at this temperature—specifically Junín de los Andes, Peñas Blancas, Confluencia, Maitén, and Huinganco. This suggests that variability in temperature tolerance may function as an adaptive mechanism, enabling the coexistence of genetically diverse strains within the same environment. Interestingly, a similar phenomenon has been reported in strains of S. cerevisiae and the cryotolerant species Saccharomyces kudriavzevii, which coexist in Spanish oaks [48]. A comparable pattern was also observed in a study analyzing the sympatric yeast species S. cerevisiae and Saccharomyces paradoxus, isolated from tree bark in Portugal. While both species showed a preference for higher temperatures, S. cerevisiae was able to grow at slightly higher temperatures than S. paradoxus [49].
Regarding ethanol tolerance, our results were consistent with those observed by Desai et al., 2012 [50], who reported the growth of Starmerella strains, particularly S. magnoliae isolated from apple fruits, at concentrations higher than 10% v/v ethanol. Our results also revealed intraspecific variability in ethanol tolerance. As expected, all strains capable of growing at 37 °C also exhibited enhanced ethanol tolerance. It is well known that the ability to withstand elevated temperatures is closely linked to ethanol tolerance. This correlation arises because the two environmental stressors have the plasma membrane as their primary target; moreover, ethanol exposure induces the production of heat shock proteins, which confer resistance to elevated and potentially lethal temperatures [51,52,53]. Furthermore, strains belonging to phylogenetic clade A showed greater variability in ethanol tolerance. Notably, this finding aligns with the results of the phylogenetic analysis, as this clade showed higher nucleotide diversity in both genes analyzed. No relation was observed between either the isolation location or the phylogenetic origin and the ethanol tolerance. This is consistent with the findings of Englezos et al. (2014) [46], who evaluated the growth of S. bacillaris strains isolated from grapes under different ethanol concentrations and across various sampling sites. Determining ethanol tolerance in yeasts is crucial to determine the biotechnological potential, as it directly impacts various applications, including bioethanol production, brewing, and industrial fermentation processes.
Both phylogenetic and physiological analyses were useful for selecting representative strains (those showing different mtDNA-RFLP profiles that also showed differential response to sulfite, temperature and ethanol) for evaluating its potential in mead elaboration. Fermentation carried out with these selected strains evidenced that all S. magnoliae strains left a substantial amount of residual glucose, i.e., they were not able to complete the fermentation. This is consistent with previous observations in S. magnoliae, where an accumulation of glucose and fructose was reported at the start of fermentation due to sucrose hydrolysis, likely driven by high invertase activity in honey-isolated strains [38]. All S. magnoliae strains showed a clear preference for fructose over glucose, consistent with their fructophilic nature [38,39,40]. However, intraspecific differences were observed in this work, with NPCC1806 and NPCC1782 strains exhibiting a more pronounced fructophilic character than the others.
Variations in ethanol, glycerol and acetic acid concentrations were observed among strains. In particular, strains NPCC1782, NPCC1785, and NPCC1806 were characterized by significantly elevated glycerol production, a trait of considerable relevance to the fermented beverage industry, as glycerol contributes to specific sensory attributes of the final product [54]. A high production of glycerol—even higher than that produced by S. cerevisiae—has also been reported for Starmerella bacillaris (previously described as Candida stellata), a species phylogenetically related to S. magnoliae. This has been attributed to higher activity of glycerol-3-phosphate dehydrogenase [55,56]. Additionally, the same three strains produced high amounts of acetic acid (around 1 g/L), which could impart undesirable flavors to the final product. Artisanal mead has been reported to contain lower concentrations than those reported. In fact, Sánchez-Moro (2021) [57] demonstrated that mixed fermentation of S. bacillaris and S. cerevisiae significantly reduced acetic acid production in wines while increasing glycerol concentration.
The preferences for fructose over glucose, glycerol production, sulfite resistance, ethanol tolerance, and growth across a wide temperature range confer high biotechnological potential to the species S. magnoliae. However, its low fermentative performance suggests that pure cultures of S. magnoliae are not suitable for mead fermentation. Despite these promising traits, intraspecific diversity within S. magnoliae remains largely unexplored. To the best of our knowledge, this is the first study to evaluate such diversity over an extensive area, providing a valuable starting point for future research on the population dynamics and bioprospecting of this species.

5. Conclusions

This study represents the first large-scale analysis of S. magnoliae isolated from spontaneously fermented mead in Patagonia. Our findings revealed that S. magnoliae was the dominant species across diverse locations and exhibited remarkable genetic and physiological diversity. Two well-defined phylogeographic populations were identified, with evidence of possible admixture events.
From a biotechnological point of view, several strains demonstrated favorable traits for application in the fermented beverage industry, including high glycerol production, fructophilic metabolism, and robust stress tolerance. However, the low fermentative capacity of S. magnoliae indicates that they are unsuitable for mead fermentation in pure cultures.
The combination of ecological dominance, stress resilience, and metabolite production highlights S. magnoliae as a promising non-Saccharomyces yeast for industrial applications. Future studies employing whole-genome sequencing and pilot-scale fermentation are needed to further explore the evolutionary history and enological potential of this species.

Author Contributions

V.K. carried out the experimental work and wrote the initial version of the manuscript. M.G.F. performed the evolutionary analyses and contributed to writing and reviewing the manuscript. M.E.R. critically reviewed the manuscript. C.A.L. supervised the experimental design and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Agency for the Promotion of Research, Technological Development and Innovation (Agencia I+D+i) through projects PICT-StartUp-2019-00034 and PICT 2019-1727, and PICT 2021-GRF-TII00417, and the University of Comahue project 04/A143.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Consent to submit has been received explicitly from all co-authors, as well as from the responsible authorities—tacitly or explicitly—at the PROBIEN institute where the work was carried out, before submission.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Acknowledgments

We would like to thank the Yanina Maidana for her contribution to yeast isolation and species characterization. We also thank Olga Aplabaza for her help in providing honey samples from the different sampling sites. V.K. gratefully acknowledges CONICET for her fellowship.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Fermentation 11 00494 g0a1
Figure A1. Stress tolerance assays of Starmerella magnoliae. Drop test of representative S. magnoliae isolates. Each row represents a different strain, listed on the left side of the figure. The first two columns show growth on GPY agar at 25 °C and 37 °C. The last three columns display growth on YEPD-TA agar with increasing concentrations of sodium metabisulfite. The colors of the strain names represent the mitochondrial profile: purple for profile A, yellow for B, light blue for C, and magenta for D.
Figure A1. Stress tolerance assays of Starmerella magnoliae. Drop test of representative S. magnoliae isolates. Each row represents a different strain, listed on the left side of the figure. The first two columns show growth on GPY agar at 25 °C and 37 °C. The last three columns display growth on YEPD-TA agar with increasing concentrations of sodium metabisulfite. The colors of the strain names represent the mitochondrial profile: purple for profile A, yellow for B, light blue for C, and magenta for D.
Fermentation 11 00494 g0a1
Table A1. Growth at increasing ethanol concentrations of Starmerella magnoliae.
Table A1. Growth at increasing ethanol concentrations of Starmerella magnoliae.
Appμmax (h−1)λ (h)
S.mangoliae0%3%5%10%0%3%5%10%
NPCC16380.051 ± 0.004 efghi0.128 ± 0.007 d0.093 ± 0.004 de0.098 ± 0.011 ab1.437 ± 0.002 efg1.264 ± 0.164 fg2.389 ± 0.304 hi3.525 ± 0.187 e
NPCC17770.049 ± 0.008 fghij0.090 ± 0.004 hij0.061 ± 0.004 fghi0.087 ± 0.004 bc1.320 ± 0.054 gh3.444 ± 0.295 b2.461 ± 0.051 ghi3.325 ± 0.291 e
NPCC17780.049 ± 0.003 fghij0.114 ± 0.002 def0.070 ± 0.002 fg0.107 ± 0.013 a1.959 ± 0.026 cd2.550 ± 0.129 cd1.248 ± 0.131 kl1.642 ± 0.117 h
NPCC17790.049 ± 0.003 ghijk0.109 ± 0.003 efg0.065 ± 0.007 fghi0.111 ± 0.021 a1.962 ± 0.024 cd2.545 ± 0.128 cd1.560 ± 0.282 jkl1.699 ± 0.160 h
NPCC17810.044 ± 0.002 ijk0.101 ± 0.011 fghij0.055 ± 0.002 ghijk0.073 ± 0.011 cd3.084 ± 0.013 a3.675 ± 0.238 ab7.728 ± 0.471 a3.942 ± 0.384 cd
NPCC17820.086 ± 0.002 c0.094 ± 0.009 ghij0.113 ± 0.001 bc0.116 ± 0.015 a2.873 ± 0.019 b2.379 ± 0.151 d1.151 ± 0.158 l1.534 ± 0.338 h
NPCC17830.048 ± 0.002 hijk0.106 ± 0.003 efgh0.069 ± 0.002 fgh0.104 ± 0.009 ab1.886 ± 0.090 cd2.536 ± 0.130 cd1.481 ± 0.391 jkl1.688 ± 0.166 h
NPCC17850.044 ± 0.002 jk0.086 ± 0.001 ij0.052 ± 0.001 hijk0.072 ± 0.011 cd3.004 ± 0.098 ab3.716 ± 0.043 a7.701 ± 0.626 a3.624 ± 0.103 de
NPCC17870.055 ± 0.001 ef0.186 ± 0.001 c0.132 ± 0.042 a0.051 ± 0.001 ef1.400 ± 0.081 fg1.482 ± 0.202 f1.595 ± 0.240 jkl2.605 ± 0.211 fg
NPCC17880.048 ± 0.001 ghijk0.107 ± 0.003 efg0.067 ± 0.006 fgh0.113 ± 0.020 a1.994 ± 0.013 cd2.546 ± 0.138 cd1.392 ± 0.069 jkl1.678 ± 0.153 h
NPCC17890.048 ± 0.001 ghijk0.107 ± 0.004 efg0.060 ± 0.007 fghi0.106 ± 0.021 a1.891 ± 0.071 cd2.558 ± 0.152 cd1.569 ± 0.325 jkl1.701 ± 0.151 h
NPCC17900.048 ± 0.002 hijk0.106 ± 0.004 efgh0.068 ± 0.002 fgh0.106 ± 0.011 a1.964 ± 0.160 cd2.567 ± 0.135 cd1.633 ± 0.239 jkl1.695 ± 0.143 h
NPCC17930.056 ± 0.001 e0.261 ± 0.041 b0.098 ± 0.011 cd0.115 ± 0.003 a1.576 ± 0.071 e2.509 ± 0.256 cd2.662 ± 0.297 ghi6.919 ± 0.166 b
NPCC17940.064 ± 0.002 d0.019 ± 0.001 mn0.018 ± 0.001 n0.023 ± 0.003 h1.041 ± 0.092 jk0.334 ± 0.068 i3.388 ± 0.210 ef1.162 ± 0.209 i
NPCC17950.017 ± 0.001 m0.043 ± 0.001 kl0.038 ± 0.001 klm0.040 ± 0.004 efgh1.184 ± 0.003 hij0.385 ± 0.041 i4.486 ± 0.324 c2.468 ± 0.382 fg
NPCC17960.050 ± 0.002 fghij0.100 ± 0.002 fghij0.060 ± 0.002 fghi0.079 ± 0.010 c0.584 ± 0.069 l0.935 ± 0.024 h2.907 ± 0.542 fg4.248 ± 0.220 c
NPCC17970.049 ± 0.001 fghij0.107 ± 0.005 efg0.069 ± 0.003 fg0.111 ± 0.014 a2.030 ± 0.038 c2.563 ± 0.130 cd1.434 ± 0.256 jkl1.657 ± 0.126 h
NPCC17980.042 ± 0.002 k0.086 ± 0.001 j0.056 ± 0.004 ghij0.074 ± 0.009 cd3.027 ± 0.112 ab3.830 ± 0.129 a7.319 ± 0.143 a3.628 ± 0.101 de
NPCC18000.051 ± 0.001 efghi0.120 ± 0.010 de0.076 ± 0.003 ef0.101 ± 0.008 ab1.466 ± 0.213 efg1.166 ± 0.155 gh2.744 ± 0.296 ghi2.340 ± 0.239 g
NPCC18010.047 ± 0.001 hijk0.035 ± 0.010 lm0.032 ± 0.002 lmn0.042 ± 0.005 efg1.040 ± 0.115 jk0.311 ± 0.031 i3.748 ± 0.516 de1.177 ± 0.149 i
NPCC18030.084 ± 0.004 c0.054 ± 0.008 k0.041 ± 0.002 jkl0.038 ± 0.006 fgh1.216 ± 0.190 hi2.631 ± 0.052 c1.806 ± 0.170 j0.752 ± 0.098 j
NPCC18040.084 ± 0.001 c0.052 ± 0.001 k0.049 ± 0.007 ijkl0.039 ± 0.009 fgh1.585 ± 0.270 e2.605 ± 0.065 cd1.708 ± 0.076 jk0.768 ± 0.068 j
NPCC18050.026 ± 0.013 l0.102 ± 0.013 fghi0.016 ± 0.003 n0.027 ± 0.006 gh0.637 ± 0.041 l0.306 ± 0.039 i2.870 ± 0.241 gh1.738 ± 0.115 h
NPCC18060.131 ± 0.010 a0.116 ± 0.008 def0.115 ± 0.018 ab0.113 ± 0.017 a0.541 ± 0.108 l1.882 ± 0.094 e5.862 ± 0.065 b2.760 ± 0.144 f
NPCC18070.108 ± 0.004 b0.011 ± 0.003 n0.113 ± 0.022 bc0.044 ± 0.007 efg0.537 ± 0.028 l0.414 ± 0.007 i7.798 ± 0.070 a8.267 ± 0.339 a
NPCC18080.055 ± 0.004 efg0.298 ± 0.015 a0.100 ± 0.012 bcd0.044 ± 0.007 efg1.557 ± 0.008 ef2.508 ± 0.262 cd2.705 ± 0.216 ghi1.134 ± 0.124 i
NPCC18100.052 ± 0.002 efgh0.041 ± 0.004 kl0.023 ± 0.007 mn0.057 ± 0.008 de1.113 ± 0.027 ij0.268 ± 0.029 i2.373 ± 0.316 i1.092 ± 0.151 ij
NPCC18110.048 ± 0.002 hijk0.108 ± 0.004 efg0.070 ± 0.002 fg0.110 ± 0.012 a1.864 ± 0.024 d2.557 ± 0.150 cd1.665 ± 0.281 jk1.749 ± 0.178 h
NPCC18170.050 ± 0.002 efghij0.043 ± 0.003 kl0.065 ± 0.003 fghi0.070 ± 0.015 cd0.916 ± 0.009 k0.307 ± 0.035 i3.966 ± 0.352 d1.675 ± 0.349 h
The growth at increasing ethanol concentrations was modeled by the modified Gompertz equation [34]. Superscript letters per row indicate differences between strains within each condition, Fisher’s LSD test p-value 0.02.

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Figure 1. Sampling sites and intraspecific characterization of Starmerella magnoliae. (A) Geographic distribution of S. magnoliae isolates. The rectangles indicate the grouping of different sampling zones. (B) Agarose gel (1%) showing the four mtDNA-RFLP profiles found in S. magnoliae. mm: Molecular marker PstI λ. Letters A to D represent each mitochondrial profile. (C) Histogram representing the proportion of strains of each mitochondrial profile, differentiated by sampling site.
Figure 1. Sampling sites and intraspecific characterization of Starmerella magnoliae. (A) Geographic distribution of S. magnoliae isolates. The rectangles indicate the grouping of different sampling zones. (B) Agarose gel (1%) showing the four mtDNA-RFLP profiles found in S. magnoliae. mm: Molecular marker PstI λ. Letters A to D represent each mitochondrial profile. (C) Histogram representing the proportion of strains of each mitochondrial profile, differentiated by sampling site.
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Figure 2. Phylogeny and phylogeography of Starmerella magnoliae. (A) Maximum Likelihood (ML) phylogenetic tree of 32 strains based on the concatenated sequences of two nuclear genes (FSY1 and FFZ1), comprising 1625 aligned positions in the final dataset. The tree was reconstructed using the HKY85 model [24], selected as the best-fitting model using JModelTest v2.1.10 [23]. ML analysis was performed with 1000 bootstrap replicates in MEGA7 [25], and bootstrap values are indicated on the branches. The two main clades are labelled A and B. Population structure analysis is shown to the right of the phylogenetic tree. The optimal number of genetic clusters (K = 2) was determined following the Evanno method [27]. Both panels to the right indicate the haplotype assignment and the sampling site of each strain. (B) Median-Joining (MJ) network reconstructed using the concatenated partial sequences of the same two nuclear genes (FSY1, FFZ1). H1 to H18 correspond to the haplotypes indicated in the left panel of Figure 2A. Circles represent individual haplotypes, and circle size is proportional to the number of isolates belonging to each haplotype.
Figure 2. Phylogeny and phylogeography of Starmerella magnoliae. (A) Maximum Likelihood (ML) phylogenetic tree of 32 strains based on the concatenated sequences of two nuclear genes (FSY1 and FFZ1), comprising 1625 aligned positions in the final dataset. The tree was reconstructed using the HKY85 model [24], selected as the best-fitting model using JModelTest v2.1.10 [23]. ML analysis was performed with 1000 bootstrap replicates in MEGA7 [25], and bootstrap values are indicated on the branches. The two main clades are labelled A and B. Population structure analysis is shown to the right of the phylogenetic tree. The optimal number of genetic clusters (K = 2) was determined following the Evanno method [27]. Both panels to the right indicate the haplotype assignment and the sampling site of each strain. (B) Median-Joining (MJ) network reconstructed using the concatenated partial sequences of the same two nuclear genes (FSY1, FFZ1). H1 to H18 correspond to the haplotypes indicated in the left panel of Figure 2A. Circles represent individual haplotypes, and circle size is proportional to the number of isolates belonging to each haplotype.
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Figure 3. Heatmap representation of growth in different ethanol concentrations. (a) μmax and (b) λ values of the analyzed strains at increasing ethanol concentrations. Rows represent individual strains, labeled on the right side of the figures, while columns represent different ethanol concentrations, labeled at the top. The color key bars at the top indicate the growth parameter values relative to the 0% v/v ethanol condition. In the heatmap, lighter colors indicate lower values, while darker colors indicate higher values for μmax and λ. Hierarchical clustering results are displayed on the left side of the figures. Mitochondrial profiles are color-coded circles as follows: purple for profile A, yellow for profile B, light blue for profile C, and magenta for profile D.
Figure 3. Heatmap representation of growth in different ethanol concentrations. (a) μmax and (b) λ values of the analyzed strains at increasing ethanol concentrations. Rows represent individual strains, labeled on the right side of the figures, while columns represent different ethanol concentrations, labeled at the top. The color key bars at the top indicate the growth parameter values relative to the 0% v/v ethanol condition. In the heatmap, lighter colors indicate lower values, while darker colors indicate higher values for μmax and λ. Hierarchical clustering results are displayed on the left side of the figures. Mitochondrial profiles are color-coded circles as follows: purple for profile A, yellow for profile B, light blue for profile C, and magenta for profile D.
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Fermentation 11 00494 g004aFermentation 11 00494 g004b
Figure 4. Principal Component analysis of microfermentation of mead with Starmerella magnoliae. (A) Principal Component Analysis (PCA) conducted using the physicochemical compounds detected in mead microfermentation performed with four S. magnoliae strains (NPCC1782, NPCC1785, NPCC1806, and NPCC1817, corresponding to mitochondrial profiles C, B, A, and D, respectively) and the control strain S. cerevisiae (NPCC1634). The values on the axes indicate the percentages of total variability explained by each Principal Component (PC). (B) Projection of the eigenvectors onto the principal plane PC1 and PC2; the size of each eigenvector is directly proportional to the percentage of variability explained in the two components. The list on the right details the variables used. The letter D indicates biological duplicates.
Figure 4. Principal Component analysis of microfermentation of mead with Starmerella magnoliae. (A) Principal Component Analysis (PCA) conducted using the physicochemical compounds detected in mead microfermentation performed with four S. magnoliae strains (NPCC1782, NPCC1785, NPCC1806, and NPCC1817, corresponding to mitochondrial profiles C, B, A, and D, respectively) and the control strain S. cerevisiae (NPCC1634). The values on the axes indicate the percentages of total variability explained by each Principal Component (PC). (B) Projection of the eigenvectors onto the principal plane PC1 and PC2; the size of each eigenvector is directly proportional to the percentage of variability explained in the two components. The list on the right details the variables used. The letter D indicates biological duplicates.
Fermentation 11 00494 g004aFermentation 11 00494 g004b
Table 1. Yeast species isolated from sweet and dry mead obtained from spontaneous fermentation.
Table 1. Yeast species isolated from sweet and dry mead obtained from spontaneous fermentation.
Sampling SiteSample a N°Plant Species bYeast Species Isolated from Mead c *
Sweet dDry e
Neuquén Province
Aluminé1Centaurea cyanusStarmerella magnoliae (100%)Starmerella magnoliae (80%)
Zygosaccharomyces rouxii (20%)
2Centaurea cyanusStarmerella magnoliae (100%)Zygosaccharomyces rouxii (100%)
Chos Malal3Centaurea solstitialis
Centaurea cyanus		Starmerella magnoliae (100%)Rhodotorula mucilaginosa (40%)
Saccharomyces cerevisiae (20%)
Starmerella magnoliae (40%)
4Centaurea cyanusRhodotorula mucilaginosa (100%)Starmerella magnoliae (100%)
Huinganco5UnidentifiedStarmerella magnoliae (100%)Saccharomyces cerevisiae (100%)
6UnidentifiedStarmerella magnoliae (100%)Saccharomyces cerevisiae (100%)
Junín de los Andes7UnidentifiedStarmerella magnoliae (100%)Starmerella magnoliae (100%)
8UnidentifiedStarmerella magnoliae (100%)Rhodotorula mucilaginosa (80%)
Saccharomyces cerevisiae (20%)
Neuquén9UnidentifiedStarmerella magnoliae (100%)Nakaseomyces glabratus (40%)
Saccharomyces uvarum (20%)
Starmerella magnoliae (40%)
10 Nakaseomyces glabratus (100%)Saccharomyces uvarum (20%)
Starmerella magnoliae (80%)
San Martín de los Andes11Lomatia hirsutaStarmerella magnoliae (100%)Zygosaccharomyces rouxii (20%)
Starmerella magnoliae (80%)
Tricao Malal12Centaurea cyanusPichia membranifaciens (20%)
Starmerella magnoliae (80%)		Starmerella magnoliae (100%)
Villa Nahueve13Azorella prolifera
Centaurea cyanus		Starmerella magnoliae (100%) Zygosaccharomyces rouxii (100%)
Río Negro Province
Peñas Blancas14Lotus corniculatus
Medicago sativa		Starmerella magnoliae (100%)Starmerella magnoliae (100%)
15Lotus corniculatus
Medicago sativa		Starmerella magnoliae (100%)Zygosaccharomyces rouxii (100%)
Chubut Province
Cholila16Lomatia hirsuta
Fabiana imbricata
Rhamnus lycioides
Salix humboldtiana
Brassica rapa
Taraxacum officinale		Starmerella magnoliae (60%)
Pichia membranifaciens (20%)
Zygosaccharomyces rouxii (20%)		Zygosaccharomyces rouxii (20%)
Starmerella magnoliae (80%)
17Lomatia hirsuta
Fabiana imbricata
Rhamnus lycioides		Starmerella magnoliae (100%)Starmerella magnoliae (100%)
Epuyen18Taraxacum officinale
Lomatia hirsuta
Discaria trinervis
Rhamnus lycioides
Trifolium		Starmerella magnoliae (20%)
Zygosaccharomyces rouxii (40%)
Starmerella bombi (40%)		Pichia membranifaciens (100%)
El Maiten19Carduus spp.
Erodium cicutarium
Chamaemelum nobile		Pichia membranifaciens (40%)
Zygosaccharomyces rouxii (40%)
Starmerella magnoliae (20%)		Pichia membranifaciens (40%)
Zygosaccharomyces rouxii (40%)
Starmerella magnoliae (20%)
a. Number of honey samples analyzed from the same sampling site. b. Plant species identified through palynological study, corresponding to the characterization of pollen found in each collected honey. c. Yeast species identified by sequences of D1/D2 domain of 26S rDNA region in mead produced with each honey. d. Mead made with a honey concentration of 420 g/L. e. Mead made with a honey concentration of 350 g/L. * The percentage of identity between the nucleotides sequence obtained for the isolated strain and the closest type species available in NCBI database was higher than 98% in all cases. Type strains used in this analysis: Starmerella magnoliae PYCC2903T, Zygosaccharomyces rouxii CBS732T, Rhodotorula mucilaginosa CBS316T, Saccharomyces cerevisiae EC1118T, Nakaseomyces glabratus CBS138T, Saccharomyces uvarum CBS7001T, Pichia membranifaciens CBS107T, Starmerella bombi CBS5836T.
Table 2. Sampling Sites and Intraspecific Characterisation of Starmerella magnoliae.
Table 2. Sampling Sites and Intraspecific Characterisation of Starmerella magnoliae.
Sampling SitemtDNA-RFLPRepresentative Strain *
Peñas Blancas-RNA (25 S1,S2,D1)NPCC1803 S1; NPCC1804 D1
B (5 S2)NPCC1794 S2;
Villa Nahuave-NQA (10 S1)NPCC1800 S1
Huinganco-NQB (20 S1,S2)NPCC1796 S1; NPCC1798 S2
Tricao Malal-NQA (18 S1,D1)NPCC1783 S1; NPCC1788 D1
Chos Malal-NQA (24 S1,D1,D2)NPCC1797 S1; NPCC1790 D1; NPCC1789 D2
Confluencia-NQA (22 S1,D1,D2)NPCC1777 D1; NPCC1778 D2; NPCC1779 S1
Aluminé-NQA (10 S1)NPCC1801 S1
B (18 S2,D1)NPCC1793 S2; NPCC1795 D1
Junín de los Andes-NQB (29 S1,S2,D1)NPCC1781 S1; NPCC1785 D1
C (1 S2)NPCC1782 S2
San Martín de los Andes-NQA (10 S1)NPCC1638 S1
B (8 D1)NPCC1787 D1
Maitén-CHA (2 S1)NPCC1805 S1
B (2 D1)NPCC1808 D1
Epuyén-CHA (2 S1)NPCC1806 S1
Cholila-CHA (32 S1,S2,D1,D2)NPCC1807 S1; NPCC1811 D1
D (2 S2,D2)NPCC1817 S2;
NPCC1810 D2
* Representative strain from each sampling site selected for genetic and physiological analysis. Province: RN: Río Negro; NQ: Neuquén; CH: Chubut. Superscripts ‘S’ and ‘D’ correspond to the sweet and dry honey must from which these strains were isolated, 1 and 2 correspond to biological duplicates.
Table 3. Chemical composition of microfermentation of honey must using different strains of Starmerella magnoliae and a Saccharomyces cerevisiae as control strain.
Table 3. Chemical composition of microfermentation of honey must using different strains of Starmerella magnoliae and a Saccharomyces cerevisiae as control strain.
ParametersS. magnoliaeS. cerevisiae
NPCC1782NPCC1785NPCC1806NPCC1817NPCC1634
Glycerol (g/L)8.26 ± 0.34 B8.59 ± 0.11 B8.83 ± 0.48 B7.47 ± 1.79 AB6.52 ± 1.07 A
Citric acid (g/L)0.26 ± 0.005 A0.26 ± 0.009 A0.26 ± 0.007 A0.25 ± 0.001 A0.46 ± 0.025 B
Malic acid (g/L)NDNDNDND0.47 ± 0.08
Acetic acid (g/L)1.01 ± 0.30 abC0.714 ± 0.11 abBC1.16 ± 0.04 bC0.57 ± 0.13 aA0.68 ± 0.01 B
Lactic acid (g/L)1.17 ± 0.18 aB1.89 ± 0.13 bcB1.35 ± 0.36 abB2.22 ± 0.31 cC0.17 ± 0.02 A
Succinic acid (g/L)0.35 ± 0.07 A0.39 ± 0.053 A0.27 ± 0.09 A0.31 ± 0.07 A0.72 ± 0.04 B
Ethanol (% v/v)3.99 ± 1.20 A4.36 ± 0.32 A4.05 ± 0.24 A4.39 ± 0.007 A9.29 ± 1.32 B
Methanol (% v/v)NDNDNDNDND
Residual sugars (g/L)Glucose125.47 ± 7.56 aB160.07 ± 2.06 bB124.94 ± 7.58 aB145.16 ± 6.12 bB1.81 ± 2.08 A
Sucrose0.01 ± 1 × 10−30.44 ± 0.630.79 ± 1.123.89 ± 0.070.55 ± 0.78
Fructose9.02 ± 0.06 abB17.44 ± 1.38 bC5.37 ± 3.45 aB34.12 ± 6.61 cC1.80 ± 9.4 × 10−3A
Superscript letters indicate significant differences between strains (p-value ≤ 0.05). Lowercase superscripts correspond to intra-specific ANOVA considering only S. magnoliae strains. Uppercase superscripts indicate significant differences based on ANOVA including all strains, S. magnoliae and S. cerevisiae. Rows without superscripts denote no statistically significant differences detected under the applied tests.
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Kleinjan, V.; González Flores, M.; Rodriguez, M.E.; Lopes, C.A. Preliminary Genetic and Physiological Characterization of Starmerella magnoliae from Spontaneous Mead Fermentation in Patagonia. Fermentation 2025, 11, 494. https://doi.org/10.3390/fermentation11090494

AMA Style

Kleinjan V, González Flores M, Rodriguez ME, Lopes CA. Preliminary Genetic and Physiological Characterization of Starmerella magnoliae from Spontaneous Mead Fermentation in Patagonia. Fermentation. 2025; 11(9):494. https://doi.org/10.3390/fermentation11090494

Chicago/Turabian Style

Kleinjan, Victoria, Melisa González Flores, María Eugenia Rodriguez, and Christian Ariel Lopes. 2025. "Preliminary Genetic and Physiological Characterization of Starmerella magnoliae from Spontaneous Mead Fermentation in Patagonia" Fermentation 11, no. 9: 494. https://doi.org/10.3390/fermentation11090494

APA Style

Kleinjan, V., González Flores, M., Rodriguez, M. E., & Lopes, C. A. (2025). Preliminary Genetic and Physiological Characterization of Starmerella magnoliae from Spontaneous Mead Fermentation in Patagonia. Fermentation, 11(9), 494. https://doi.org/10.3390/fermentation11090494

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