Next Article in Journal
The Effects of Fermentation Time and the Addition of Blueberry on the Texture Properties and In Vitro Digestion of Whey Protein Gel
Previous Article in Journal
Comprehensive Analysis and Production of Powdered Starter Using Saccharomyces cerevisiae YM45
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Fermentation
Volume 11
Issue 4
10.3390/fermentation11040204
fermentation-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Beyond Saccharomyces: Exploring the Bioethanol Potential of Wickerhamomyces anomalus and Diutina rugosa in Xylose and Glucose Co-Fermentation

by
Arthur Gasetta Batista
1,
Marcus Vinicius Astolfo da Costa
1,
Marita Vedovelli Cardozo
1,
Sarah Regina Vargas
2,
Marita Gimenez Pereira
2,
Vinícius de Abreu D’Ávila
3,
Janerson José Coelho
4 and
Caio Roberto Soares Bragança
1,*
1
Laboratory of Microorganism Physiology, Department of Biomedical Sciences and Health, UEMG, Rua Sabará, 164, Centro, Passos 37900-004, MG, Brazil
2
Laboratory of Applied Biotechnology, Department of Biomedical Sciences and Health, UEMG, Rua Sabará, 164, Centro, Passos 37900-004, MG, Brazil
3
Insect Biofactory, Department of Agricultural and Earth Sciences, UEMG, Rua Sabará, 164, Centro, Passos 37900-004, MG, Brazil
4
Department of Biological Sciences, Universidade Regional do Cariri (URCA), Rua Cel. Antônio Luíz, 1161, Crato 63105-000, CE, Brazil
*
Author to whom correspondence should be addressed.
Fermentation 2025, 11(4), 204; https://doi.org/10.3390/fermentation11040204
Submission received: 4 March 2025 / Revised: 5 April 2025 / Accepted: 5 April 2025 / Published: 9 April 2025
(This article belongs to the Section Microbial Metabolism, Physiology & Genetics)
Browse Figures
Review Reports Versions Notes

Abstract

:
Efficient co-fermentation of glucose and xylose remains a critical hurdle in second-generation bioethanol production. In this study, we evaluated two non-Saccharomyces yeasts—Wickerhamomyces anomalus UEMG-LF-Y2 and Diutina rugosa UEMG-LF-Y4—under mixed-sugar conditions. D. rugosa exhibited superior xylose metabolism and ethanol productivity, achieving a maximum volumetric productivity (QP) of 0.55 g/L·h in a medium containing 20 g/L glucose and 40 g/L xylose. Its highest ethanol yield (YP/S) reached 0.45 g EtOH/g sugar, comparable to results from engineered Saccharomyces cerevisiae strains. By contrast, W. anomalus displayed lower ethanol yields (0.24–0.34 g/g) and greater sensitivity to catabolite repression induced by 2-deoxyglucose (2-DG). Xylose consumption by D. rugosa exceeded 80% in high-xylose media, while W. anomalus left residual xylose under all tested conditions. A strong inverse correlation (r < −0.98) between ethanol accumulation and xylose uptake was observed, especially for W. anomalus, indicating ethanol-induced inhibition as a key challenge. These findings highlight the potential of D. rugosa as a robust non-Saccharomyces platform for lignocellulosic bioethanol processes, whereas W. anomalus may benefit from further metabolic or process optimizations. Future research should address ethanol tolerance, inhibitory byproducts, and large-scale feasibility to fully exploit these strains for second-generation bioethanol production.

1. Introduction

The increasing urgency to reduce greenhouse gas emissions and diversify global energy sources has led to a surge of interest in sustainable biofuels [1]. Bioethanol, in particular, offers an attractive alternative to fossil fuels due to its compatibility with existing distribution networks and potential to lower net carbon dioxide emissions [2,3]. However, first-generation bioethanol, derived predominantly from sugar- or starch-based feedstocks such as sugarcane juice and corn starch, has been criticized for competing with food resources and driving up commodity prices [4].
To address these concerns, second-generation bioethanol—produced from lignocellulosic biomass—has emerged as a more sustainable option [5,6]. Agricultural residues, forestry byproducts, and other cellulose-rich materials provide abundant, low-cost substrates with reduced environmental impacts [7,8]. Yet, a major challenge in lignocellulosic fermentation lies in the efficient utilization of pentoses like xylose, which can comprise a significant fraction of hemicellulose [9,10,11]. Many industrial yeasts, including Saccharomyces cerevisiae, struggle to metabolize xylose at rates necessary for economically viable ethanol production [10].
Over the past few decades, S. cerevisiae has undergone extensive metabolic engineering to enable pentose fermentation, involving the introduction of heterologous pathways and adaptive evolution strategies [12,13,14]. Although these approaches have led to improved xylose assimilation in engineered strains, they often demand complex genetic modifications and process optimizations [15,16]. Consequently, there is growing interest in nonconventional yeasts that inherently possess the capacity to ferment both glucose and xylose, thus reducing the reliance on genetic engineering.
Wickerhamomyces anomalus and Diutina rugosa are two such nonconventional yeasts that have drawn attention for their broad metabolic capabilities [17,18]. W. anomalus has been associated with the production of aroma compounds and enzymes in food fermentations [19,20,21], whereas D. rugosa (formerly Candida rugosa) is known for its wide substrate range and potential in bioconversion processes [22,23,24]. Despite sporadic reports describing these species in the context of biomass conversion, to date, no study has systematically evaluated their performance under lignocellulosic-like conditions, where the co-fermentation of glucose and xylose is essential for optimal ethanol yields.
Another critical aspect of mixed-sugar fermentations is catabolite repression [25,26], wherein the presence of glucose can suppress the uptake and metabolism of other sugars, notably xylose [27]. If a yeast strain exhibits lower sensitivity to glucose repression, it can simultaneously or sequentially co-ferment pentoses and hexoses more efficiently, leading to higher ethanol yields without the need for highly controlled feeding regimes [28]. Moreover, the accumulation of ethanol itself may inhibit further sugar consumption [29,30], emphasizing the importance of understanding how these yeasts respond to both glucose repression and ethanol inhibition.
Given the potential advantages of nonconventional yeasts, W. anomalus and D. rugosa warrant closer examination, especially considering their ecological origins—sugarcane residues and ruminal environments—rich in diverse carbohydrates. Such niches may have driven the evolution of versatile metabolic pathways and robust stress responses. However, empirical data comparing their fermentation profiles in lignocellulosic-like substrates remain limited, leaving a gap in our understanding of their true industrial potential [18].
Accordingly, this study evaluates the bioethanol potential of W. anomalus UEMG-LF-Y2 and D. rugosa UEMG-LF-Y4 under various glucose/xylose ratios. We examined growth kinetics, sugar consumption, and ethanol production, paying particular attention to catabolite repression and the inhibitory effect of ethanol on xylose utilization. The insights obtained here may also inform strategies to optimize mixed-sugar fermentations, potentially enhancing the economic viability and environmental sustainability of lignocellulosic ethanol production.

2. Materials and Methods

2.1. Yeast Strains and Maintenance

The yeast strains used in this study, Wickerhamomyces anomalus UEMG-LF-Y2 and Diutina rugosa UEMG-LF-Y4, were previously isolated by Costa et al. [18]. W. anomalus UEMG-LF-Y2 was obtained from sugarcane bagasse, while D. rugosa UEMG-LF-Y4 was isolated from bovine rumen samples. Each strain was cultivated at its optimal growth temperature: 25 °C for W. anomalus and 37 °C for D. rugosa. The cultures were maintained on solid Yeast extract Peptone Dextrose (YPD) medium (10 g/L yeast extract, 20 g/L peptone, 20 g/L glucose, and 15 g/L agar), with the pH adjusted to 6.5 ± 0.2. To prevent bacterial contamination, the medium was supplemented with 0.01% (w/v) chloramphenicol. For long-term storage, cultures were preserved at −80 °C in a cryoprotectant solution containing 80% (v/v) glycerol. For routine use, cultures were maintained on YPD-agar plates at 4 °C, with periodic subculturing to ensure viability and purity.

2.2. Culture Medium and Inoculum Preparation

Experiments were conducted using a selective Yeast Nitrogen Base (YNB) medium without amino acids, supplemented with 5.0 g/L ammonium sulfate (YNB-agar 0.67% (w/v); Sigma-Aldrich, St. Louis, MO, USA). Carbon sources were provided as separate media containing 2% (w/v) D-xylose (designated as YNB-X) or 2% (w/v) D-glucose (designated as YNB-G). D-xylose and D-glucose solutions were sterilized separately by autoclaving at 121 °C for 20 min and subsequently added to the sterile basal medium. Precultures were initiated from −80 °C glycerol stocks by inoculating cells into 125 mL Erlenmeyer flasks containing 10 mL of the appropriate medium. The cultures were incubated at the previously determined optimal temperature for each strain under 180 rpm agitation. After 48 h of incubation, cells were harvested by centrifugation at 3000× g for 5 min, washed twice with sterile distilled water, and used for subsequent inoculations.

2.3. Analysis of Glucose Repression Using 2-Deoxyglucose

Glucose repression was evaluated using 2-deoxyglucose (2-DG), a nonmetabolizable analog of glucose that inhibits the utilization of alternative carbon sources by triggering catabolite repression pathways. Yeast strains were first grown in 50 mL of YPD broth at their respective optimal temperatures with shaking at 150 rpm for 24–48 h. Cells were then harvested by centrifugation (3000× g, 5 min), washed twice with sterile 0.85% (w/v) saline solution, and resuspended to an OD600nm of 1.0. Serial tenfold dilutions (10−1 to 10−5) were prepared, and 10 µL of each dilution was spotted onto YNB-G agar (2% w/v glucose), serving as the growth control, and YNB-X agar (2% w/v xylose) supplemented with 0.1% (w/v) 2-DG. Plates were incubated for 48 h at the optimal growth temperature for each strain. Reduced or absent growth on YNB-X + 2-DG compared to robust growth on YNB-G indicated a higher sensitivity to catabolite repression. Conversely, strains exhibiting residual growth on YNB-X + 2-DG were considered more tolerant to glucose repression, suggesting they can better utilize alternative carbon sources even in the presence of a glucose analog.

2.4. Fermentation Setup

All fermentations were conducted in 125 mL Erlenmeyer flasks, each containing 10 mL of the basal medium described in Section 2.2. Six distinct glucose/xylose combinations, labeled A through F, were tested: (A) 20 g/L glucose + 5 g/L xylose, (B) 40 g/L glucose + 5 g/L xylose, (C) 20 g/L glucose + 10 g/L xylose, (D) 20 g/L glucose + 20 g/L xylose, (E) 5 g/L glucose + 40 g/L xylose, and (F) 20 g/L glucose + 40 g/L xylose, based on typical sugar compositions reported in lignocellulosic hydrolysates derived from feedstocks such as sugarcane bagasse and corn stover [9]. Each flask was inoculated at an initial OD600nm of 1.0 with cells from the preculture and incubated under constant shaking (180 rpm) at the previously determined optimal temperature for each yeast strain. Samples were collected at 0, 6, 12, 24, 36, and 48 h to quantify sugar consumption, ethanol production, and cell biomass. Individual fermentations using either glucose or xylose were conducted to determine the inherent substrate-specific performance of each strain. All experiments were carried out in triplicate, and results are presented as mean ± standard deviation.

2.5. Analytical Methods

2.5.1. Growth Measurement

Cell growth was monitored by OD600nm using a UV–Vis spectrophotometer (e.g., Multiskan SkyHigh, Thermo Fisher Scientific, Waltham, MA, USA). At each sampling point (0, 6, 12, 24, 36, and 48 h), a 1 mL aliquot of culture was withdrawn and measured against a blank of sterile medium in a 1 cm path-length cuvette. The cell dry weight (CDW) was determined by filtering 1 mL of cell suspension through a pre-weighed 0.45 µm membrane filter (Millipore, Burlington, MA, USA). After thorough washing with approximately 10 mL of sterile distilled water to remove residual medium, the filter was dried for 15 min in a microwave at 200–300 W, cooled in a desiccator, and reweighed. The net difference in mass was used to calculate CDW. A calibration curve was established by plotting OD600nm versus CDW (g/L) across a range of 0.1–1.0 OD units, enabling the conversion of routine OD600nm measurements into biomass concentrations throughout the fermentation. Cell dry weights were recorded as the mean of triplicate determinations.

2.5.2. Sugar and Ethanol Quantification

Glucose, xylose, and ethanol concentrations were measured using High-Performance Liquid Chromatography (HPLC). A 1 mL aliquot of culture was centrifuged at 3000× g for 5 min, and the supernatant was filtered through a 0.22 µm membrane prior to injection. Analyses were performed on a Shimadzu LC-2050C 3D LT Plus (Shimadzu Corporation, Kyoto, Japão) chromatograph equipped with a Refractive Index Detector (RID20A) and a CarboSep™ CHO 782 column maintained at 70 °C. The mobile phase consisted of ultrapure water delivered at a flow rate of 0.7 mL/min under isocratic conditions, and 10 µL of each sample was injected automatically. Quantification was carried out against external standard curves (0.5–50 g/L) for glucose, xylose, and ethanol. Chromatographic data were processed using Shimadzu LabSolutions software (Shimadzu Corporation, Kyoto, Japan), and all measurements were performed in triplicate.

2.6. Kinetic and Yield Parameters

Growth kinetics were evaluated by determining the specific growth rate (μ, h−1) from the exponential phase of the growth curve. For this purpose, μ was calculated using the following equation:
μ = ln ⁡ O D t 2 − l n ( O D t 1 ) t 2 − t 1
To further describe substrate utilization, the maximum specific growth rate (μmax) and the half-saturation constant (Ks) were estimated by fitting the growth data to the Monod model. Yield coefficients were calculated based on total sugar consumption and included the ethanol yield (Yp/s, g EtOH per g sugar consumed) and the biomass yield (YX/S, g biomass per g sugar consumed). The ethanol yield was determined using:
Y P / S = g r a m s   o f   e t h a n o l   p r o d u c e d g r a m s   o f s u g a r   c o n s u m e d
Volumetric ethanol productivity (QP, g/L·h) was calculated as:
Q p = g r a m s   o f   e t h a n o l   p r o d u c e d L   o f   m e d i u m · h
Similarly, the sugar consumption rate (QS, g/L·h) was determined by:
Q S = t o t a l   g r a m s   o f   s u g a r   c o n s u m e d L   o f   m e d i u m · h
Finally, the specific ethanol production rate (Qp/CDW, g EtOH per g cell dry weight per hour) was obtained by normalizing volumetric ethanol productivity to the cell dry weight, as follows:
Q P C D W = Q P g r a m s   o f   c e l l   d r y   w e i g h t
Pearson’s correlation analysis was applied to assess the inhibitory effect of ethanol on xylose consumption, using ethanol titer and residual xylose concentration data from each time point.

2.7. Statistical Analysis

Statistical analyses were conducted using GraphPad Prism (version 8, GraphPad Software, San Diego, CA, USA) and Python (SciPy library, version 1.10.1). Experimental data were obtained from triplicate assays, and results are presented as mean ± standard deviation (SD). A paired t-test was conducted to compare ethanol yield (YP/S) between W. anomalus UEMG-LF-Y2 and D. rugosa UEMG-LF-Y4, aiming to determine whether D. rugosa exhibited a statistically higher conversion efficiency. Additionally, a one-way analysis of variance (ANOVA) was applied to assess significant differences in ethanol yield, sugar consumption rate (QS), ethanol productivity (QP), and biomass yield (YX/S) across different fermentation conditions. For datasets exhibiting significant differences in ANOVA (p < 0.05), Tukey’s post hoc test was conducted to identify specific conditions with statistically distinct values. Pearson’s correlation coefficient (r) was calculated to evaluate the inhibitory effect of ethanol production on xylose consumption.

3. Results

3.1. Effect of 2-DG on Xylose Utilization

To assess glucose repression effects, the yeast strains W. anomalus UEMG-LF-Y2 and D. rugosa UEMG-LF-Y4 were cultivated in YNB medium supplemented with either glucose (YNB-G) or xylose (YNB-X), with 2-DG added only to the YNB-X medium. Growth was monitored under both conditions to evaluate the impact of 2-DG on xylose utilization (Figure 1).
Both W. anomalus and D. rugosa displayed robust growth in YNB-G. When cultured in YNB-X + 2-DG, W. anomalus exhibited a pronounced reduction in growth across all dilution levels, whereas D. rugosa maintained visible growth, particularly in the higher inoculum concentrations. These results indicate that D. rugosa exhibited greater tolerance to 2-DG, while W. anomalus was more affected under the same condition.

3.2. Growth Kinetics in Different Glucose/Xylose Ratios

The growth performance of W. anomalus Y2 and D. rugosa Y4 was initially evaluated in YNB medium containing increasing concentrations of either glucose or xylose (0.5–40 g/L). As shown in Figure 2, both strains exhibited higher specific growth rates (μ, h−1) in glucose than in xylose at all tested concentrations. D. rugosa Y4 consistently outperformed W. anomalus Y2, reaching maximal μ values of 0.347 h−1 (glucose) and 0.241 h−1 (xylose), whereas W. anomalus attained 0.299 h−1 and 0.164 h−1, respectively. Growth rates in both yeasts tended to plateau at substrate concentrations of 20 g/L or higher.
It is important to note that substrate concentration is not the sole factor influencing the specific growth rate. The intrinsic nature of the substrate also plays a critical role. Glucose, being a preferred carbon source, is metabolized more efficiently due to more direct entry into central metabolic pathways. In contrast, xylose requires additional metabolic conversion steps, which can limit its utilization rate. Consequently, the observed differences in specific growth rates are attributed both to the concentration of the substrates and to their inherent metabolic properties.
Additional batch fermentations were then performed with mixed glucose/xylose substrates (Conditions A–F) (Figure 3). Among these, Condition F (20 g/L glucose + 40 g/L xylose) (Figure 3F) led to the highest cell dry weight (CDW) at 48 h, reaching 0.88 g/L for W. anomalus Y2 and 0.95 g/L for D. rugosa Y4. D. rugosa Y4 consistently maintained higher CDW values throughout the fermentation, while W. anomalus Y2 showed a more pronounced decline after 24–36 h.

3.3. Substrate Consumption and Ethanol Production

Glucose and xylose consumption, as well as ethanol production, were monitored throughout the batch fermentations under different glucose/xylose ratios (Figure 3). Both strains exhibited distinct sugar consumption profiles depending on the substrate composition. In conditions with higher glucose concentrations (Figure 3B), W. anomalus Y2 and D. rugosa Y4 preferentially consumed glucose before initiating significant xylose metabolism. The highest ethanol titer was observed in Condition F (20 g/L glucose + 40 g/L xylose), reaching 12.77 g/L for W. anomalus Y2 and 26.26 g/L for D. rugosa Y4 after 48 h.
Figure 4 provides a comparative summary of total glucose and xylose consumption across the tested conditions. W. anomalus Y2 exhibited lower xylose utilization efficiency compared to D. rugosa Y4, with a residual xylose concentration detected in all conditions where xylose was present. In contrast, D. rugosa Y4 demonstrated a more efficient co-metabolism of glucose and xylose, particularly under Conditions E and F, where over 80% of the available xylose was consumed within 48 h. These data suggest a strain-dependent preference in sugar utilization and ethanol production efficiency.

3.4. Monod Model Fitting and Kinetic Parameters

The growth kinetics of W. anomalus Y2 and D. rugosa Y4 were further analyzed using the Monod model to estimate the maximum specific growth rate (µmax, h−1) and the half-saturation constant (KS) for glucose and xylose utilization (Figure 5). The model adequately described the experimental data, with both yeast strains displaying higher µmax values when utilizing glucose compared to xylose. Specifically, D. rugosa Y4 exhibited a higher µmax than W. anomalus Y2 for both substrates, with values of 0.347 h−1 and 0.299 h−1 for glucose and 0.241 h−1 and 0.164 h−1 for xylose, respectively.
The estimated half-saturation constants (KS) further support differences in substrate affinity between the two yeast strains. W. anomalus Y2 exhibited a lower KS for glucose (0.0994 g/L) compared to D. rugosa Y4 (0.2499 g/L), indicating a higher affinity for this substrate. Conversely, D. rugosa Y4 showed a lower KS for xylose (0.1231 g/L) than W. anomalus Y2 (0.3058 g/L), suggesting that D. rugosa Y4 was more efficient in utilizing xylose. The specific growth rate of both strains followed a saturation pattern, increasing with substrate concentration and reaching a plateau beyond 10–20 g/L.

3.5. Yield and Productivity Parameters

The fermentation efficiency of the yeast strains was assessed by analyzing ethanol yield (YP/S), biomass yield (YX/S), volumetric productivity (QP), sugar consumption rate (Qs), and specific ethanol production rate (QP/CDW). Table 1 summarizes the highest and lowest values observed for each parameter, while the complete dataset is available in the Supplementary Material (Table S1).
Table 1 shows that the highest ethanol yield (0.45 g EtOH/g sugar) was observed in Condition D (20 g/L glucose + 20 g/L xylose), while the lowest (0.22 g/g) occurred in Condition E (5 g/L glucose + 40 g/L xylose). The highest volumetric productivity (0.55 g/L·h) was recorded under condition F, nearly twofold higher than the lowest value (0.14 g/L·h) observed under condition A. Similarly, the highest sugar consumption rate (QS = 1.18 g/L·h) was found in condition F, whereas the lowest (0.48 g/L·h) was detected under condition A.

3.6. Inhibition of Xylose Consumption by Ethanol

Pearson’s correlation coefficients (r) were calculated to assess the relationship between ethanol accumulation and residual xylose under different fermentation conditions. As shown in Figure 6, all correlations were strongly negative, indicating an inverse relationship between ethanol concentration and xylose consumption. For W. anomalus UEMG-LF-Y2, the correlation ranged from −0.9998 to −0.9365, with the strongest inhibition observed in condition A (r = −0.9998). In contrast, D. rugosa UEMG-LF-Y4 exhibited values between −0.9984 and −0.9341, with the highest inhibition occurring in Condition B (r = −0.9984).

4. Discussion

The results of this study underscore the potential of W. anomalus UEMG-LF-Y2 and D. rugosa UEMG-LF-Y4 as promising candidates for lignocellulosic ethanol production. Both strains demonstrated measurable ethanol yields under mixed-sugar conditions, revealing distinctive strategies for glucose and xylose utilization. These observations add to the growing body of evidence that non-Saccharomyces yeasts can complement or even surpass traditional industrial strains when fermenting substrates rich in pentoses [31].
From an industrial and regulatory perspective, the safety of yeast strains is a critical consideration when evaluating their potential for bioethanol production. W. anomalus is widely recognized for its application in food preservation, biomass energy generation, and aquaculture feed and is generally considered safe due to its long-standing use in food-related fermentations [32,33]. Notably, the European Food Safety Authority (EFSA) classifies W. anomalus as a Biosafety Level 1 organism, indicating a low risk to human health, and several killer strains of this species have been applied as biocontrol agents against molds and bacteria in the agro-food sector [34]. In contrast, D. rugosa is not formally classified as Generally Recognized as Safe (GRAS), but it has long been used in industrial biotechnology, particularly in the sustainable production of lipases from agro-industrial residues. Despite the absence of a GRAS designation, D. rugosa has not been associated with pathogenicity under controlled fermentation conditions [35]. These considerations reinforce the suitability of both strains for ethanol production under non-food industrial settings.
In addition to the individual fermentation capabilities demonstrated by W. anomalus and D. rugosa, co-fermentation and co-culture strategies have been shown to significantly enhance overall process efficiency by promoting synergistic interactions among diverse microbial populations. These interactions can lead to improved substrate conversion, more balanced metabolic fluxes, and mitigation of inhibitory effects during fermentation. For example, in various biotechnological applications and wastewater treatment processes, mixed microbial cultures have achieved higher yields and increased robustness compared to monocultures [36,37]. Although our present study evaluated the strains individually, future work will investigate co-culture systems to explore the potential synergistic effects between these yeasts, which may further optimize mixed-sugar fermentations for industrial bioethanol production.
One key insight gleaned from these findings is the contrasting metabolic profiles of the two yeasts. D. rugosa, originally isolated from a ruminal environment, consistently exhibited robust growth and higher ethanol yields, reflecting a natural proficiency in pentose metabolism. Previous studies [38,39] have shown that microorganisms from rumen-like ecosystems often evolve efficient pathways for processing diverse sugars, which may explain the superior xylose utilization observed in D. rugosa. Meanwhile, W. anomalus, isolated from sugarcane bagasse, showed a more moderate capacity to co-ferment glucose and xylose, suggesting partial but less efficient adaptation to pentose-rich substrates.
Building on these metabolic distinctions, a notable finding was the differential response to 2-deoxyglucose (2-DG), which induces catabolite repression [40]. The inhibitory effect was particularly evident in W. anomalus, suggesting a higher sensitivity to glucose repression mechanisms, while D. rugosa maintained residual growth (Figure 1), indicating a comparatively lower susceptibility. Similar effects have been reported in S. cerevisiae SR8, where xylose consumption was severely inhibited by 2-DG, reinforcing its role as a competitive inhibitor that exacerbates glucose repression effects on alternative sugar metabolism pathways [41]. Such resilience to catabolite repression is crucial in industrial settings where multiple sugars coexist, and overcoming this inhibition remains a major challenge in optimizing second-generation bioethanol production.
It is plausible that W. anomalus, despite its higher sensitivity to catabolite repression, channels a greater proportion of the available carbon into ethanol synthesis under high-glucose conditions, thus achieving high ethanol titers.
Several yeasts, such as Scheffersomyces stipitis [42,43,44] and C. tropicalis [45,46,47,48], have been extensively studied for their inherent ability to ferment xylose efficiently. D. rugosa’s ability to maintain xylose consumption under conditions of glucose repression aligns with reports on S. stipitis, which possesses more flexible regulatory mechanisms that allow co-fermentation of mixed sugars [49,50]. On the other hand, the stronger repression observed in W. anomalus resembles C. tropicalis, which often exhibits preferential glucose consumption before shifting to xylose metabolism [46,51]. These findings suggest that, despite their ability to ferment pentoses, non-Saccharomyces yeasts display distinct regulatory strategies, with D. rugosa showing a more favorable metabolic profile for lignocellulosic ethanol production.
The differences in catabolite repression mechanisms among these yeasts also impacted their ethanol yields under various glucose/xylose conditions. D. rugosa consistently outperformed W. anomalus in ethanol production, with the highest yield (0.45 g EtOH/g sugar) observed in Condition D (20 g/L glucose + 20 g/L xylose) (Figure 3). This yield is comparable to those reported for engineered S. cerevisiae strains, such as strain F106-KR, which achieved an ethanol yield of 0.42 g/g total sugars, and its diploid variant, which reached 0.48 g/g [52]. Similarly, the engineered strain 6M-15 obtained 0.43 g/g when fermenting non-detoxified lignocellulosic hydrolysates [53]. These values reinforce the potential of D. rugosa as a strong candidate for second-generation bioethanol production, demonstrating ethanol yields on par with or even exceeding those of modified S. cerevisiae strains. In contrast, W. anomalus exhibited lower ethanol yields across all conditions, likely due to its limited ability to metabolize xylose efficiently. This pattern aligns with findings from other non-Saccharomyces yeasts, where strains with weaker glucose repression mechanisms tend to achieve higher ethanol titers in mixed-sugar fermentations [54]. These results suggest that the ability to mitigate catabolite repression plays a crucial role in determining the fermentation efficiency of pentose-rich substrates.
For instance, engineered S. cerevisiae strains have been reported to achieve ethanol yields ranging from 0.42 to 0.48 g/g under mixed-sugar fermentations [52,53], which is comparable to our maximum yield of 0.45 g/g observed with D. rugosa under Condition D. Similarly, nonconventional yeasts such as S. stipitis typically achieve yields around 0.40 g/g under similar conditions [42], underscoring that D. rugosa can perform at levels competitive with established yeast systems.
The observed differences in ethanol yield were closely linked to sugar consumption rates (Qs), further highlighting the metabolic distinctions between the two yeasts. D. rugosa consistently exhibited higher total sugar consumption, particularly in Conditions E and F, where over 80% of the available xylose was depleted within 48 h. This efficient sugar uptake contributed to its superior ethanol productivity. In contrast, W. anomalus showed a more pronounced glucose preference, leaving significant residual xylose in all tested conditions. This pattern is consistent with reports that various yeast species preferentially metabolize simpler sugars like glucose before switching to pentose utilization, often requiring adaptation or metabolic engineering to enhance xylose consumption [55,56,57,58].
Although our optimal ethanol yield of 0.45 g EtOH/g sugar was observed under Condition D (20 g/L glucose + 20 g/L xylose), real lignocellulosic hydrolysates typically exhibit a broader range of sugar concentrations, often with a total sugar content between 40 and 60 g/L. Studies have shown that feedstocks such as sugarcane bagasse, corn stover, and wheat straw—when subjected to appropriate pretreatment methods (e.g., dilute acid or enzymatic hydrolysis)—yield hydrolysates with such sugar compositions [5,7]. Based on our findings, we predict that D. rugosa will remain effective as long as the overall sugar concentration is maintained within this range, even if the individual glucose/xylose ratio deviates from 1:1. We recommend that industrial applications focus on optimizing pretreatment strategies to maximize both glucose and xylose release, thereby reducing the inhibitory effects of catabolite repression and enhancing ethanol productivity. Future pilot-scale studies are warranted to validate these predictions under industrial conditions.
One promising approach to overcome these limitations is the engineering of sugar transporters to enhance xylose uptake. Recent studies have identified a conserved protein motif (G-G/F-XXX-G) in sugar transporters, which can be modified to shift substrate preferences towards xylose, as demonstrated through saturation mutagenesis of multiple transporters [59]. Alternatively, long-term adaptation to specific carbon sources has been shown to drive significant metabolic rewiring in yeasts. For instance, S. cerevisiae populations evolved in galactose as the sole carbon source exhibited substantial fitness gains, with key mutations occurring outside the canonical GAL pathway, suggesting the involvement of alternative adaptive strategies [60]. A similar evolutionary approach could potentially be applied to enhance the xylose metabolism of W. anomalus, mitigating its preference for glucose and improving its efficiency in mixed-sugar fermentations.
Beyond metabolic engineering and adaptive strategies, another crucial aspect in determining the industrial feasibility of a yeast strain is its volumetric ethanol productivity (QP) [61,62]. While optimizing sugar transport and metabolism can enhance sugar uptake efficiency, high QP values are essential for ensuring competitive ethanol yields at scale [63,64]. In this study, D. rugosa exhibited the highest Qp, reaching 0.55 g/L·h in Condition F, a value higher than the 0.49 g/L·h reported for optimized co-cultures of S. stipitis and S. cerevisiae [49]. However, this value remains lower than the 1.9 g/L·h achieved in a continuous fixed-bed reactor system, where a baker’s yeast strain co-immobilized with xylose isomerase sustained ethanol production over a 7-day operation period [65]. While continuous systems maximize productivity over extended fermentations, batch processes [66] like those used in the present study may offer greater operational flexibility and adaptability to industrial settings.
While volumetric productivity underscores how rapidly ethanol is generated, biomass yield (YX/S) offers additional insight into how substrate carbon is distributed between cell growth and ethanol synthesis. In this study, both strains exhibited relatively low biomass yields (Table 1), suggesting a metabolic preference for ethanol production rather than extensive proliferation. W. anomalus appeared to channel more substrate carbon toward biomass, partially explaining its lower ethanol yield. Similar trade-offs have been reported in yeasts lacking specialized regulatory circuits favoring high ethanol output [67,68,69]. Adjusting culture parameters—such as aeration or nitrogen supplementation—could potentially shift the metabolic balance further toward ethanol, thus enhancing overall process efficiency [70,71].
From an industrial perspective, lower biomass yields are advantageous as they indicate that a larger proportion of the substrate’s carbon is converted into ethanol rather than being used for cell proliferation. This metabolic allocation is critical for maximizing product yield and process efficiency in large-scale fermentations [68,69].
Alongside considerations of how substrate carbon is allocated between biomass and ethanol, the accumulation of ethanol itself presents another challenge in mixed-sugar fermentations [72,73]. The strong negative correlation observed between ethanol accumulation and xylose uptake (Figure 6) indicates that rising ethanol titers can hamper xylose transporters and enzymatic pathways. This inhibition was more pronounced in W. anomalus, underscoring its heightened sensitivity to ethanol. Identifying strategies to alleviate ethanol-induced inhibition, whether through strain improvement or process modifications, could enhance overall fermentation efficiency.
Future studies should evaluate the performance of these strains in the presence of inhibitors commonly found in lignocellulosic hydrolysates, such as furfural, hydroxymethylfurfural (HMF), acetic acid, and phenolic compounds, which are typically generated during pretreatment processes. Assessing and enhancing the tolerance of W. anomalus and D. rugosa to these inhibitors is critical for improving ethanol productivity and scaling up the fermentation processes to industrial levels.
While overcoming ethanol-induced inhibition is pivotal for boosting fermentation efficiency, additional factors must be addressed before fully harnessing the potential of these non-Saccharomyces yeasts. This study illustrates the capacity of W. anomalus UEMG-LF-Y2 and D. rugosa UEMG-LF-Y4 to co-ferment glucose and xylose under laboratory conditions, yet their performance under industrial-scale stresses—such as high ethanol concentrations and inhibitory compounds—remains to be determined. Further improvements may involve adaptive laboratory evolution, targeted mutagenesis, or enhanced sugar transport systems, all of which could refine the metabolic capabilities of these strains. By tackling these challenges, the role of non-Saccharomyces yeasts in second-generation bioethanol production may expand significantly, offering a sustainable alternative to conventional S.-cerevisiae-based processes and paving the way for more robust, cost-effective lignocellulosic biorefineries.

5. Conclusions

In summary, W. anomalus UEMG-LF-Y2 and D. rugosa UEMG-LF-Y4 demonstrated promising capabilities for co-fermenting glucose and xylose, achieving competitive ethanol yields and productivities under laboratory conditions. While D. rugosa showed a stronger capacity to overcome glucose repression and efficiently metabolize xylose, W. anomalus may benefit from further optimization. Moving forward, adaptive laboratory evolution, metabolic engineering, and process enhancements can address current limitations—such as ethanol inhibition and tolerance to lignocellulosic inhibitors—ultimately strengthening the role of non-Saccharomyces yeasts in sustainable second-generation bioethanol production.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fermentation11040204/s1, Table S1: Comprehensive kinetic and productivity data for W. anomalus and D. rugosa under mixed-sugar fermentation.

Author Contributions

Conceptualization, C.R.S.B.; methodology, A.G.B. and M.V.A.d.C.; formal analysis, M.V.C.; investigation, A.G.B. and C.R.S.B.; resources, C.R.S.B.; data curation, S.R.V. and M.G.P.; writing—original draft preparation, A.G.B.; writing—review and editing, J.J.C. and M.V.C.; visualization, V.d.A.D.; supervision, M.V.C.; project administration, C.R.S.B.; funding acquisition, C.R.S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Research Support Foundation of the State of Minas Gerais (FAPEMIG), the Structuring Research and Extension Project (SREP/UEMG), and the Research Productivity Scholarships (PQ/UEMG).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

This work was conducted at the Laboratory of Microorganism Physiology, Department of Biomedical Sciences and Health, Minas Gerais State University (UEMG), Passos, Brazil. We thank all contributors for their efforts in this study and acknowledge the institutional and financial support provided to this research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Mehejabin, F.; Musharrat, A.; Ahmed, S.F.; Kabir, Z.; Khan, T.M.Y.; Saleel, C.A. Sustainable Biofuel Production Utilizing Nanotechnology: Challenges and Potential Solutions. GCB Bioenergy 2024, 16, e70001. [Google Scholar] [CrossRef]
  2. Blay-Roger, R.; Saif, M.; Bobadilla, L.F.; Ramirez-Reina, T.; Nawaz, M.A.; Odriozola, J.A. Embracing the Sustainable Horizons through Bioenergy Innovations: A Path to a Sustainable Energy Future. Front. Chem. 2024, 12, 1416102. [Google Scholar] [CrossRef] [PubMed]
  3. Anekwe, I.M.S.; Isa, Y.M.; Oboirien, B. Bioethanol as a Potential Eco-Friendlier Feedstock for Catalytic Production of Fuels and Petrochemicals. J. Chem. Technol. Biotechnol. 2023, 98, 2077–2094. [Google Scholar] [CrossRef]
  4. Russo, C.; Cirillo, V.; Pollaro, N.; Terribile, F.; Chiodini, A.; Maggio, A. The Global Energy Challenge: Second-Generation Feedstocks on Marginal Lands for a Sustainable Biofuel Production. Chem. Biol. Technol. Agric. 2025, 12, 10. [Google Scholar] [CrossRef]
  5. Woźniak, A.; Kuligowski, K.; Świerczek, L.; Cenian, A. Review of Lignocellulosic Biomass Pretreatment Using Physical, Thermal and Chemical Methods for Higher Yields in Bioethanol Production. Sustainability 2025, 17, 287. [Google Scholar] [CrossRef]
  6. Pereira, L.M.S.; Taveira, I.C.; Maués, D.B.; de Paula, R.G.; Silva, R.N. Advances in Fungal Sugar Transporters: Unlocking the Potential of Second-Generation Bioethanol Production. Appl. Microbiol. Biotechnol. 2025, 109, 19. [Google Scholar] [CrossRef]
  7. Afedzi, A.E.K.; Afrakomah, G.S.; Gyan, K.; Khan, J.; Seidu, R.; Baidoo, T.; Sultan, I.N.; Tareen, A.K.; Parakulsuksatid, P. Enhancing Economic and Environmental Sustainability in Lignocellulosic Bioethanol Production: Key Factors, Innovative Technologies, Policy Frameworks, and Social Considerations. Sustainability 2025, 17, 499. [Google Scholar] [CrossRef]
  8. Zielińska, M.; Bułkowska, K. Agricultural Wastes and Their By-Products for the Energy Market. Energies 2024, 17, 2099. [Google Scholar] [CrossRef]
  9. Subudhi, S.; Mudgil, D.; Saha, K.; Sarangi, P.K.; Pal, P. Yeast as a Cell Factory for Fermentative Production of Ethanol from Xylose. J. Taiwan Inst. Chem. Eng. 2024, 105616. [Google Scholar] [CrossRef]
  10. Taveira, I.C.; Carraro, C.B.; Nogueira, K.M.V.; Pereira, L.M.S.; Bueno, J.G.R.; Fiamenghi, M.B.; dos Santos, L.V.; Silva, R.N. Structural and Biochemical Insights of Xylose MFS and SWEET Transporters in Microbial Cell Factories: Challenges to Lignocellulosic Hydrolysates Fermentation. Front. Microbiol. 2024, 15, 1452240. [Google Scholar] [CrossRef]
  11. Tsegaye, K.N.; Alemnew, M.; Berhane, N. Saccharomyces Cerevisiae for Lignocellulosic Ethanol Production: A Look at Key Attributes and Genome Shuffling. Front. Bioeng. Biotechnol. 2024, 12, 1466644. [Google Scholar] [CrossRef] [PubMed]
  12. dos Santos, L.V.; Neitzel, T.; Lima, C.S.; de Carvalho, L.M.; de Lima, T.B.; Ienczak, J.L.; Corrêa, T.L.R.; Pereira, G.A.G. Engineering Cellular Redox Homeostasis to Optimize Ethanol Production in Xylose-Fermenting Saccharomyces Cerevisiae Strains. Microbiol. Res. 2025, 290, 127955. [Google Scholar] [CrossRef] [PubMed]
  13. Procópio, D.P.; Lee, J.W.; Shin, J.; Tramontina, R.; Ávila, P.F.; Brenelli, L.B.; Squina, F.M.; Damasio, A.; Rabelo, S.C.; Goldbeck, R.; et al. Metabolic Engineering of Saccharomyces Cerevisiae for Second-Generation Ethanol Production from Xylo-Oligosaccharides and Acetate. Sci. Rep. 2023, 13, 19182. [Google Scholar] [CrossRef] [PubMed]
  14. Choi, B.; Tafur Rangel, A.; Kerkhoven, E.J.; Nygård, Y. Engineering of Saccharomyces Cerevisiae for Enhanced Metabolic Robustness and L-Lactic Acid Production from Lignocellulosic Biomass. Metab. Eng. 2024, 84, 23–33. [Google Scholar] [CrossRef]
  15. Wagner, E.R.; Gasch, A.P. Advances in S. Cerevisiae Engineering for Xylose Fermentation and Biofuel Production: Balancing Growth, Metabolism, and Defense. J. Fungi 2023, 9, 786. [Google Scholar] [CrossRef]
  16. Duperray, M.; Delvenne, M.; François, J.M.; Delvigne, F.; Capp, J.P. Genomic and Metabolic Instability during Long-Term Fermentation of an Industrial Saccharomyces Cerevisiae Strain Engineered for C5 Sugar Utilization. Front. Bioeng. Biotechnol. 2024, 12, 1357671. [Google Scholar] [CrossRef]
  17. Fan, L.; Doucette, C.; McSweeney, M.B.; English, M.; Song, J.; Vinqvist-Tymchuk, M.; Kernaghan, G. Non-Traditional Yeasts from Cool-Climate Vineyards for Novel Low-Alcohol Wines. Plants People Planet 2024. [Google Scholar] [CrossRef]
  18. da Costa, M.V.A.; Bragança, C.R.S.; Sousa, A.C.R.; Batista, A.G.; Paula, F.E.G.M.; Cardozo, M.V.; Vargas, S.R.; Philippini, R.R. Innovative Pathways for Ethanol Production: Harnessing Xylose’s Bioenergy Potential Using Brazilian Wild Isolated Yeasts. Bioresour. Technol. 2024, 404, 130930. [Google Scholar] [CrossRef]
  19. Chen, L.; Li, K.; Chen, H.; Li, Z. Reviewing the Source, Physiological Characteristics, and Aroma Production Mechanisms of Aroma-Producing Yeasts. Foods 2023, 12, 3501. [Google Scholar] [CrossRef]
  20. Li, Q.; Du, B.; Chen, X.; Zhao, Y.; Zhu, L.; Ma, H.; Sun, B.; Hao, J.; Li, X. Microbial Community Dynamics and Spatial Distribution of Flavor Compound Metabolism during Solid-State Fermentation of Baijiu Enhanced by Wickerhamomyces Anomalus. Food Biosci. 2024, 59, 103909. [Google Scholar] [CrossRef]
  21. Wang, J.; Yan, J.; Gao, H.; Li, X.; Dong, Z.; Yan, S.; Shi, F. Comparative Metabolic Profiling of Cabernet Sauvignon Wines Reveals the Potential of Different Wickerhamomyces Anomalus Co-Fermented with Commercial Saccharomyces Cerevisiae. LWT 2023, 186, 115229. [Google Scholar] [CrossRef]
  22. Rahmayetty; Whulanza, Y.; Sukirno; Rahman, S.F.; Suyono, E.A.; Yohda, M.; Gozan, M. Use of Candida Rugosa Lipase as a Biocatalyst for L-Lactide Ring-Opening Polymerization and Polylactic Acid Production. Biocatal. Agric. Biotechnol. 2018, 16, 683–691. [Google Scholar] [CrossRef]
  23. Vanleeuw, E.; Winderickx, S.; Thevissen, K.; Lagrain, B.; Dusselier, M.; Cammue, B.P.A.; Sels, B.F. Substrate-Specificity of Candida Rugosa Lipase and Its Industrial Application. ACS Sustain. Chem. Eng. 2019, 7, 15828–15844. [Google Scholar] [CrossRef]
  24. Almulaiky, Y.Q.; Altalhi, T.; El-Shishtawy, R.M. Enhanced Catalytic Performance of Candida Rugosa Lipase through Immobilization on Zirconium-2-Methylimidazole: A Novel Biocatalyst Approach. Int. J. Biol. Macromol. 2024, 279, 135211. [Google Scholar] [CrossRef]
  25. Sierra-Ibarra, E.; Vargas-Tah, A.; Moss-Acosta, C.L.; Trujillo-Martínez, B.; Molina-Vázquez, E.R.; Rosas-Aburto, A.; Valdivia-López, Á.; Hernández-Luna, M.G.; Vivaldo-Lima, E.; Martínez, A. Co-Fermentation of Glucose–Xylose Mixtures from Agroindustrial Residues by Ethanologenic Escherichia Coli: A Study on the Lack of Carbon Catabolite Repression in Strain MS04. Molecules 2022, 27, 8941. [Google Scholar] [CrossRef]
  26. An, N.; Chen, X.; Sheng, H.; Wang, J.; Sun, X.; Yan, Y.; Shen, X.; Yuan, Q. Rewiring the Microbial Metabolic Network for Efficient Utilization of Mixed Carbon Sources. J. Ind. Microbiol. Biotechnol. 2021, 48, 40. [Google Scholar] [CrossRef]
  27. Kaplan, N.A.; Islam, K.N.; Kanis, F.C.; Verderber, J.R.; Wang, X.; Jones, J.A.; Koffas, M.A.G. Simultaneous Glucose and Xylose Utilization by an Escherichia Coli Catabolite Repression Mutant. Appl. Environ. Microbiol. 2024, 90, e0216923. [Google Scholar] [CrossRef]
  28. Dos Santos, V.C.; Bragança, C.R.S.; Passos, F.J.V.; Passos, F.M.L. Kinetics of Growth and Ethanol Formation from a Mix of Glucose/Xylose Substrate by Kluyveromyces Marxianus UFV-3. Antonie Van Leeuwenhoek Int. J. General. Mol. Microbiol. 2013, 103, 153–161. [Google Scholar] [CrossRef]
  29. Cristina Oliveira, A.; Fernanda Rosa, M.; Cabral, J.M.S.; Raquel Aires-Barros, M. Effect of Extraction and Enzymatic Esterification of Ethanol on Glucose Consumption by Two Saccharomyces Cerevisiae Strains: A Comparative Study. J. Chem. Technol. Biotechnol. 2001, 76, 285–290. [Google Scholar] [CrossRef]
  30. Slininger, P.J.; Thompson, S.R.; Weber, S.; Liu, Z.L.; Moon, J. Repression of Xylose-Specific Enzymes by Ethanol in Scheffersomyces (Pichia) Stipitis and Utility of Repitching Xylose-Grown Populations to Eliminate Diauxic Lag. Biotechnol. Bioeng. 2011, 108, 1801–1815. [Google Scholar] [CrossRef]
  31. Koh, H.G.; Yook, S.; Oh, H.; Rao, C.V.; Jin, Y.S. Toward Rapid and Efficient Utilization of Nonconventional Substrates by Nonconventional Yeast Strains. Curr. Opin. Biotechnol. 2024, 85, 103059. [Google Scholar] [CrossRef] [PubMed]
  32. Li, Y.; Long, H.; Jiang, G.; Gong, X.; Yu, Z.; Huang, M.; Guan, T.; Guan, Y.; Liu, X. Analysis of the Ethanol Stress Response Mechanism in Wickerhamomyces Anomalus Based on Transcriptomics and Metabolomics Approaches. BMC Microbiol. 2022, 22, 275. [Google Scholar] [CrossRef] [PubMed]
  33. Padilla, B.; Gil, J.V.; Manzanares, P. Challenges of the Non-Conventional Yeast Wickerhamomyces Anomalus in Winemaking. Fermentation 2018, 4, 68. [Google Scholar] [CrossRef]
  34. Cappelli, A.; Favia, G.; Ricci, I. Wickerhamomyces Anomalus in Mosquitoes: A Promising Yeast-Based Tool for the “Symbiotic Control” of Mosquito-Borne Diseases. Front. Microbiol. 2021, 11, 621605. [Google Scholar] [CrossRef]
  35. de Freitas, M.d.F.M.; Cavalcante, L.S.; Gudiña, E.J.; Silvério, S.C.; Rodrigues, S.; Rodrigues, L.R.; Gonçalves, L.R.B. Sustainable Lipase Production by Diutina Rugosa NRRL Y-95 Through a Combined Use of Agro-Industrial Residues as Feedstock. Appl. Biochem. Biotechnol. 2021, 193, 589–605. [Google Scholar] [CrossRef]
  36. Perera, I.A.; Abinandan, S.; Panneerselvan, L.; Subashchandrabose, S.R.; Venkateswarlu, K.; Naidu, R.; Megharaj, M. Co-Culturing of Microalgae and Bacteria in Real Wastewaters Alters Indigenous Bacterial Communities Enhancing Effluent Bioremediation. Algal Res. 2022, 64, 102705. [Google Scholar] [CrossRef]
  37. Farias, D.; Maugeri Filho, F. Co-Culture Strategy for Improved 2G Bioethanol Production Using a Mixture of Sugarcane Molasses and Bagasse Hydrolysate as Substrate. Biochem. Eng. J. 2019, 147, 29–38. [Google Scholar] [CrossRef]
  38. Gharechahi, J.; Vahidi, M.F.; Sharifi, G.; Ariaeenejad, S.; Ding, X.Z.; Han, J.L.; Salekdeh, G.H. Lignocellulose Degradation by Rumen Bacterial Communities: New Insights from Metagenome Analyses. Environ. Res. 2023, 229, 115925. [Google Scholar] [CrossRef]
  39. Jia, M.; Zhu, S.; Xue, M.Y.; Chen, H.; Xu, J.; Song, M.; Tang, Y.; Liu, X.; Tao, Y.; Zhang, T.; et al. Single-Cell Transcriptomics across 2,534 Microbial Species Reveals Functional Heterogeneity in the Rumen Microbiome. Nat. Microbiol. 2024, 9, 1884–1898. [Google Scholar] [CrossRef]
  40. Novak, S.; D’Amore, T.; Russell, I.; Stewart, G.G. Sugar Uptake in a 2-Deoxy-d-Glucose Resistant Mutant of Saccharomyces Cerevisiae. J. Ind. Microbiol. 1991, 7, 35–39. [Google Scholar] [CrossRef]
  41. Lane, S.; Xu, H.; Oh, E.J.; Kim, H.; Lesmana, A.; Jeong, D.; Zhang, G.; Tsai, C.S.; Jin, Y.S.; Kim, S.R. Glucose Repression Can Be Alleviated by Reducing Glucose Phosphorylation Rate in Saccharomyces Cerevisiae. Sci. Rep. 2018, 8, 2613. [Google Scholar] [CrossRef] [PubMed]
  42. Shin, M.; won Kim, J.; Ye, S.; Kim, S.; Jeong, D.; Lee, D.Y.; Kim, J.N.; Jin, Y.S.; Kim, K.H.; Kim, S.R. Comparative Global Metabolite Profiling of Xylose-Fermenting Saccharomyces Cerevisiae SR8 and Scheffersomyces Stipitis. Appl. Microbiol. Biotechnol. 2019, 103, 5435–5446. [Google Scholar] [CrossRef] [PubMed]
  43. Nakanishi, S.C.; Soares, L.B.; Biazi, L.E.; Nascimento, V.M.; Costa, A.C.; Rocha, G.J.M.; Ienczak, J.L. Fermentation Strategy for Second Generation Ethanol Production from Sugarcane Bagasse Hydrolyzate by Spathaspora Passalidarum and Scheffersomyces Stipitis. Biotechnol. Bioeng. 2017, 114, 2211–2221. [Google Scholar] [CrossRef]
  44. Acevedo, A.; Conejeros, R.; Aroca, G. Ethanol Production Improvement Driven by Genome-Scale Metabolic Modeling and Sensitivity Analysis in Scheffersomyces Stipitis. PLoS ONE 2017, 12, e0180074. [Google Scholar] [CrossRef]
  45. Phommachan, K.; Keo-Oudone, C.; Nurcholis, M.; Vongvilaisak, N.; Chanhming, M.; Savanhnaly, V.; Bounphanmy, S.; Matsutani, M.; Kosaka, T.; Limtong, S.; et al. Adaptive Laboratory Evolution for Multistress Tolerance, Including Fermentability at High Glucose Concentrations in Thermotolerant Candida Tropicalis. Energies 2022, 15, 561. [Google Scholar] [CrossRef]
  46. Zhang, L.; Chen, Z.; Wang, J.; Shen, W.; Li, Q.; Chen, X. Stepwise Metabolic Engineering of Candida Tropicalis for Efficient Xylitol Production from Xylose Mother Liquor. Microb. Cell Fact. 2021, 20, 105. [Google Scholar] [CrossRef]
  47. Singh, A.K.; Deeba, F.; Kumar, M.; Kumari, S.; Wani, S.A.; Paul, T.; Gaur, N.A. Development of Engineered Candida Tropicalis Strain for Efficient Corncob-Based Xylitol-Ethanol Biorefinery. Microb. Cell Fact. 2023, 22, 201. [Google Scholar] [CrossRef]
  48. Kim, S.; Lee, J.; Sung, B.H. Isolation and Characterization of the Stress-Tolerant Candida Tropicalis YHJ1 and Evaluation of Its Xylose Reductase for Xylitol Production from Acid Pretreatment Wastewater. Front. Bioeng. Biotechnol. 2019, 7, 464705. [Google Scholar] [CrossRef]
  49. Unrean, P.; Khajeeram, S. Model-Based Optimization of Scheffersomyces Stipitis and Saccharomyces Cerevisiae Co-Culture for Efficient Lignocellulosic Ethanol Production. Bioresour. Bioprocess. 2015, 2, 41. [Google Scholar] [CrossRef]
  50. Mastella, L.; Senatore, V.; Beltrani, T.; Branduardi, P. Scheffersomyces Stipitis Ability to Valorize Different Residual Biomasses for Vitamin B9 Production. Microb. Biotechnol. 2023, 16, 392–403. [Google Scholar] [CrossRef]
  51. Lohmeier-Vogel, E.M.; Bä, B.; Hahn-Hägerdal, B.; Hägerdal, H.; Vogel, H.J. Phosphorus-31 and Carbon-13 Nuclear Magnetic Resonance Study of Glucose and Xylose Metabolism in Agarose-Immobilized Candida Tropicalis. Appl. Environ. Microbiol. 1995, 61, 1420–1425. [Google Scholar] [CrossRef] [PubMed]
  52. Liu, L.; Jin, M.; Huang, M.; Zhu, Y.; Yuan, W.; Kang, Y.; Kong, M.; Ali, S.; Jia, Z.; Xu, Z.; et al. Engineered Polyploid Yeast Strains Enable Efficient Xylose Utilization and Ethanol Production in Corn Hydrolysates. Front. Bioeng. Biotechnol. 2021, 9, 655272. [Google Scholar] [CrossRef] [PubMed]
  53. Wei, F.; Li, M.; Wang, M.; Li, H.; Li, Z.; Qin, W.; Wei, T.; Zhao, J.; Bao, X. A C6/C5 Co-Fermenting Saccharomyces Cerevisiae Strain with the Alleviation of Antagonism between Xylose Utilization and Robustness. GCB Bioenergy 2021, 13, 83–97. [Google Scholar] [CrossRef]
  54. Kastner, J.R.; Jones, W.J.; Roberts, R.S. Ethanol Fermentation of Mixed-Sugars Using a Two-Phase, Fed-Batch Process: Method to Minimize D-Glucose Repression of Candida Shehatae D-Xylose Fermentations. J. Ind. Microbiol. Biotechnol. 1999, 22, 65–70. [Google Scholar] [CrossRef]
  55. Ruchala, J.; Sibirny, A.A. Pentose Metabolism and Conversion to Biofuels and High-Value Chemicals in Yeasts. FEMS Microbiol. Rev. 2021, 45, fuaa069. [Google Scholar] [CrossRef]
  56. Nijland, J.G.; Driessen, A.J.M. Engineering of Pentose Transport in Saccharomyces Cerevisiae for Biotechnological Applications. Front. Bioeng. Biotechnol. 2020, 7, 512089. [Google Scholar] [CrossRef]
  57. Chroumpi, T.; Peng, M.; Aguilar-Pontes, M.V.; Müller, A.; Wang, M.; Yan, J.; Lipzen, A.; Ng, V.; Grigoriev, I.V.; Mäkelä, M.R.; et al. Revisiting a ‘Simple’ Fungal Metabolic Pathway Reveals Redundancy, Complexity and Diversity. Microb. Biotechnol. 2021, 14, 2525–2537. [Google Scholar] [CrossRef]
  58. Nijland, J.G.; Shin, H.Y.; De Jong, R.M.; De Waal, P.P.; Klaassen, P.; Driessen, A.J.M. Engineering of an Endogenous Hexose Transporter into a Specific D-Xylose Transporter Facilitates Glucose-Xylose Co-Consumption in Saccharomyces Cerevisiae. Biotechnol. Biofuels 2014, 7, 168. [Google Scholar] [CrossRef]
  59. Young, E.M.; Tong, A.; Bui, H.; Spofford, C.; Alper, H.S. Rewiring Yeast Sugar Transporter Preference through Modifying a Conserved Protein Motif. Proc. Natl. Acad. Sci. USA 2014, 111, 131–136. [Google Scholar] [CrossRef]
  60. Martínez, A.A.; Conboy, A.; Buskirk, S.W.; Marad, D.A.; Lang, G.I. Long-Term Adaptation to Galactose as a Sole Carbon Source Selects for Mutations Outside the Canonical GAL Pathway. J. Mol. Evol. 2023, 91, 46–59. [Google Scholar] [CrossRef]
  61. Ma, X.Y.; Coleman, B.; Prabhu, P.; Yang, M.; Wen, F. Engineering Compositionally Uniform Yeast Whole-Cell Biocatalysts with Maximized Surface Enzyme Density for Cellulosic Biofuel Production. ACS Synth. Biol. 2024, 13, 1225–1236. [Google Scholar] [CrossRef] [PubMed]
  62. Fentahun, M.; Andualem, B. Optimization of Bioethanol Production Using Stress-Tolerant Yeast Strains Isolated from Household Alcoholic Beverages (Tella, Tej, and Areke) and Molasses (as Substrate). F1000Research 2024, 13, 286. [Google Scholar] [CrossRef]
  63. Patané, A.; Jansen, G.; Conca, P.; Carapezza, G.; Costanza, J.; Nicosia, G. Multi-Objective Optimization of Genome-Scale Metabolic Models: The Case of Ethanol Production. Ann. Oper. Res. 2019, 276, 211–227. [Google Scholar] [CrossRef]
  64. AbdElhafez, S.E.; Taha, T.; Mansy, A.E.; El-Desouky, E.; Abu-Saied, M.A.; Eltaher, K.; Hamdy, A.; El Fawal, G.; Gamal, A.; Hashim, A.M.; et al. Experimental Optimization with the Emphasis on Techno-Economic Analysis of Production and Purification of High Value-Added Bioethanol from Sustainable Corn Stover. Energies 2022, 15, 6131. [Google Scholar] [CrossRef]
  65. Milessi, T.S.; Silva, C.R.; Moraes, G.S.; Aquino, P.M.; Giordano, R.C.; Giordano, R.L.C.; Zangirolami, T.C. Continuous 2G Ethanol Production from Xylose in a Fixed-Bed Reactor by Native Saccharomyces Cerevisiae Strain Through Simultaneous Isomerization and Fermentation. Cellulose 2020, 27, 4429–4442. [Google Scholar] [CrossRef]
  66. Moimenta, A.R.; Troitiño-Jordedo, D.; Henriques, D.; Contreras-Ruíz, A.; Minebois, R.; Morard, M.; Barrio, E.; Querol, A.; Balsa-Canto, E.; Zhang, Y. An Integrated Multiphase Dynamic Genome-Scale Model Explains Batch Fermentations Led by Species of the Saccharomyces Genus. mSystems 2025, 10, e0161524. [Google Scholar] [CrossRef]
  67. Yang, P.; Jiang, S.; Lu, S.; Jiang, S.; Jiang, S.; Deng, Y.; Lu, J.; Wang, H.; Zhou, Y. Ethanol Yield Improvement in Saccharomyces Cerevisiae GPD2 Delta FPS1 Delta ADH2 Delta DLD3 Delta Mutant and Molecular Mechanism Exploration Based on the Metabolic Flux and Transcriptomics Approaches. Microb. Cell Fact. 2022, 21, 160. [Google Scholar] [CrossRef]
  68. Du, Q.; Liu, Y.; Song, Y.; Qin, Y. Creation of a Low-Alcohol-Production Yeast by a Mutated SPT15 Transcription Regulator Triggers Transcriptional and Metabolic Changes During Wine Fermentation. Front. Microbiol. 2020, 11, 597828. [Google Scholar] [CrossRef]
  69. Naghshbandi, M.P.; Tabatabaei, M.; Aghbashlo, M.; Gupta, V.K.; Sulaiman, A.; Karimi, K.; Moghimi, H.; Maleki, M. Progress toward Improving Ethanol Production through Decreased Glycerol Generation in Saccharomyces Cerevisiae by Metabolic and Genetic Engineering Approaches. Renew. Sustain. Energy Rev. 2019, 115, 109353. [Google Scholar] [CrossRef]
  70. Mesquita, T.J.B.; Sargo, C.R.; Fuzer, J.R.; Paredes, S.A.H.; Giordano, R.D.C.; Horta, A.C.L.; Zangirolami, T.C. Metabolic Fluxes-Oriented Control of Bioreactors: A Novel Approach to Tune Micro-Aeration and Substrate Feeding in Fermentations. Microb. Cell Fact. 2019, 18, 150. [Google Scholar] [CrossRef]
  71. Phukoetphim, N.; Chan-U-Tit, P.; Laopaiboon, P.; Laopaiboon, L. Improvement of Bioethanol Production from Sweet Sorghum Juice under Very High Gravity Fermentation: Effect of Nitrogen, Osmoprotectant, and Aeration. Energies 2019, 12, 3620. [Google Scholar] [CrossRef]
  72. Samakkarn, W.; Ratanakhanokchai, K.; Soontorngun, N. Reprogramming of the Ethanol Stress Response in Saccharomyces Cerevisiae by the Transcription Factor Znf1 and Its Effect on the Biosynthesis of Glycerol and Ethanol. Appl. Environ. Microbiol. 2021, 87, AEM0058821. [Google Scholar] [CrossRef] [PubMed]
  73. Udom, N.; Chansongkrow, P.; Charoensawan, V.; Auesukaree, C. Coordination of the Cell Wall Integrity and Highosmolarity Glycerol Pathways in Response to Ethanol Stress in Saccharomyces Cerevisiae. Appl. Environ. Microbiol. 2019, 85, e00551-19. [Google Scholar] [CrossRef] [PubMed]
Fermentation 11 00204 g001
Figure 1. Effect of 2-DG on xylose utilization by W. anomalus UEMG-LF-Y2 (Y2) and D. rugosa UEMG-LF-Y4 (Y4). Yeast strains were grown in YNB medium supplemented with glucose (YNB-G) or xylose (YNB-X), with 2-DG added only to YNB-X. Serial tenfold dilutions (10−1 to 10−5) were spotted onto agar plates.
Figure 1. Effect of 2-DG on xylose utilization by W. anomalus UEMG-LF-Y2 (Y2) and D. rugosa UEMG-LF-Y4 (Y4). Yeast strains were grown in YNB medium supplemented with glucose (YNB-G) or xylose (YNB-X), with 2-DG added only to YNB-X. Serial tenfold dilutions (10−1 to 10−5) were spotted onto agar plates.
Fermentation 11 00204 g001
Fermentation 11 00204 g002
Figure 2. Specific growth rates (μ, h−1) of W. anomalus Y2 and D. rugosa Y4 in YNB medium containing 0.5–40 g/L glucose or xylose. Each point represents the mean of triplicate assays (±standard deviation).
Figure 2. Specific growth rates (μ, h−1) of W. anomalus Y2 and D. rugosa Y4 in YNB medium containing 0.5–40 g/L glucose or xylose. Each point represents the mean of triplicate assays (±standard deviation).
Fermentation 11 00204 g002
Fermentation 11 00204 g003
Figure 3. Growth kinetics and product formation in batch fermentations under different glucose/xylose ratios (Conditions A–F). Cultures were incubated for 48 h, and cell dry weight (CDW) was measured at the indicated time points. Glucose and xylose consumption, as well as ethanol production, were quantified throughout fermentation. The symbols indicate glucose consumption (Glc cons), xylose consumption (Xyl cons), and ethanol concentration (EtOH).
Figure 3. Growth kinetics and product formation in batch fermentations under different glucose/xylose ratios (Conditions A–F). Cultures were incubated for 48 h, and cell dry weight (CDW) was measured at the indicated time points. Glucose and xylose consumption, as well as ethanol production, were quantified throughout fermentation. The symbols indicate glucose consumption (Glc cons), xylose consumption (Xyl cons), and ethanol concentration (EtOH).
Fermentation 11 00204 g003
Fermentation 11 00204 g004
Figure 4. Comparative analysis of glucose and xylose consumption and ethanol formation over 48 h. Each condition led to distinct sugar utilization patterns, with higher glucose concentrations promoting faster sugar depletion and increased ethanol titers. Xylose consumption varied depending on the initial sugar composition. Data points represent mean ± standard deviation of triplicate assays, as indicated by error bars.
Figure 4. Comparative analysis of glucose and xylose consumption and ethanol formation over 48 h. Each condition led to distinct sugar utilization patterns, with higher glucose concentrations promoting faster sugar depletion and increased ethanol titers. Xylose consumption varied depending on the initial sugar composition. Data points represent mean ± standard deviation of triplicate assays, as indicated by error bars.
Fermentation 11 00204 g004
Fermentation 11 00204 g005
Figure 5. Monod model fitting for the specific growth rate (µ, h−1) of W. anomalus Y2 and D. rugosa Y4 in media containing different concentrations of glucose and xylose. Symbols represent experimental data, while solid lines indicate the model fit. The kinetic parameters µmax and KS were estimated based on the best-fit curves for each sugar. The goodness of fit was assessed using the coefficient of determination (r2).
Figure 5. Monod model fitting for the specific growth rate (µ, h−1) of W. anomalus Y2 and D. rugosa Y4 in media containing different concentrations of glucose and xylose. Symbols represent experimental data, while solid lines indicate the model fit. The kinetic parameters µmax and KS were estimated based on the best-fit curves for each sugar. The goodness of fit was assessed using the coefficient of determination (r2).
Fermentation 11 00204 g005
Fermentation 11 00204 g006
Figure 6. Heatmap of Pearson’s correlation coefficients (r) between ethanol concentration and xylose consumption across different conditions. Negative values (closer to −1.0) indicate a strong inverse relationship, suggesting that increased ethanol production coincided with reduced xylose utilization.
Figure 6. Heatmap of Pearson’s correlation coefficients (r) between ethanol concentration and xylose consumption across different conditions. Negative values (closer to −1.0) indicate a strong inverse relationship, suggesting that increased ethanol production coincided with reduced xylose utilization.
Fermentation 11 00204 g006
Table 1. Summary of the highest (Best) and lowest (Worst) values observed across all conditions for each parameter: ethanol yield (Yp/s), volumetric productivity (Qp), sugar consumption rate (Qs), biomass yield (Yx/s), and specific ethanol production rate (Qp/CDW). The values shown do not necessarily originate from a single experimental condition but represent the extremes recorded in the study.
Table 1. Summary of the highest (Best) and lowest (Worst) values observed across all conditions for each parameter: ethanol yield (Yp/s), volumetric productivity (Qp), sugar consumption rate (Qs), biomass yield (Yx/s), and specific ethanol production rate (Qp/CDW). The values shown do not necessarily originate from a single experimental condition but represent the extremes recorded in the study.
Condition 1Ethanol Yield (YP/S) (g/g)Volumetric Productivity
(QP) (g/L·h)		Sugar Consumption Rate
(QS) (g/L·h)		Biomass Yield (YX/S) (g/g)Specific Ethanol Production Rate (QP/CDW) (g/g·h)
Best0.450.551.180.020.48
Worst0.220.140.480.010.19
1 Note: “Best” refers to the highest (most favorable) value observed for each parameter among all experimental conditions (A–F), while “Worst” corresponds to the lowest value. For example, the highest ethanol yield (0.45 g/g) was obtained under Condition D (20 g/L glucose + 20 g/L xylose), whereas the lowest yield (0.22 g/g) was observed under Condition E (5 g/L glucose + 40 g/L xylose).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Batista, A.G.; da Costa, M.V.A.; Cardozo, M.V.; Vargas, S.R.; Pereira, M.G.; D’Ávila, V.d.A.; Coelho, J.J.; Bragança, C.R.S. Beyond Saccharomyces: Exploring the Bioethanol Potential of Wickerhamomyces anomalus and Diutina rugosa in Xylose and Glucose Co-Fermentation. Fermentation 2025, 11, 204. https://doi.org/10.3390/fermentation11040204

AMA Style

Batista AG, da Costa MVA, Cardozo MV, Vargas SR, Pereira MG, D’Ávila VdA, Coelho JJ, Bragança CRS. Beyond Saccharomyces: Exploring the Bioethanol Potential of Wickerhamomyces anomalus and Diutina rugosa in Xylose and Glucose Co-Fermentation. Fermentation. 2025; 11(4):204. https://doi.org/10.3390/fermentation11040204

Chicago/Turabian Style

Batista, Arthur Gasetta, Marcus Vinicius Astolfo da Costa, Marita Vedovelli Cardozo, Sarah Regina Vargas, Marita Gimenez Pereira, Vinícius de Abreu D’Ávila, Janerson José Coelho, and Caio Roberto Soares Bragança. 2025. "Beyond Saccharomyces: Exploring the Bioethanol Potential of Wickerhamomyces anomalus and Diutina rugosa in Xylose and Glucose Co-Fermentation" Fermentation 11, no. 4: 204. https://doi.org/10.3390/fermentation11040204

APA Style

Batista, A. G., da Costa, M. V. A., Cardozo, M. V., Vargas, S. R., Pereira, M. G., D’Ávila, V. d. A., Coelho, J. J., & Bragança, C. R. S. (2025). Beyond Saccharomyces: Exploring the Bioethanol Potential of Wickerhamomyces anomalus and Diutina rugosa in Xylose and Glucose Co-Fermentation. Fermentation, 11(4), 204. https://doi.org/10.3390/fermentation11040204

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop