Skip to main content
Download PDF

Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

BMC Microbiology volume 22, Article number: 275 (2022) Cite this article

Abstract

Background

Wickerhamomyces anomalus (W. anomalus) is a kind of non-Saccharomyces yeast that has a variety of unique physiological characteristics and metabolic features and is widely used in many fields, such as food preservation, biomass energy, and aquaculture feed protein production. However, the mechanism of W. anomalus response to ethanol stress is still unclear, which greatly limits its application in the production of ethanol beverages and ethanol fuels. Therefore, we checked the effects of ethanol stress on the morphology, the growth, and differentially expressed genes (DEGs) and metabolites (DEMs) of W. anomalus.

Results

High concentrations of ethanol (9% ethanol and 12% ethanol) remarkably inhibited the growth of W. anomalus. Energy metabolism, amino acid metabolism, fatty acids metabolism, and nucleic acid metabolism were significantly influenced when exposing to 9% ethanol and 12% ethanolstress, which maybe universal for W. anomalus to response to different concentrations of ethanol stressl Furthermore, extracellular addition of aspartate, glutamate, and arginine significantly abated ethanol damage and improved the survival rate of W. anomalus.

Conclusions

The results obtained in this study provide insights into the mechanisms involved in W. anomalus response to ethanol stress. Therefore, new strategies can be realized to improve the ethanol tolerance of W. anomalus through metabolic engineering.

Peer Review reports

Introduction

Wickerhamomyces anomalus (W. anomalus), formerly known as Pichia anomala, Hansenula anomala, and Candida pelliculosa, possesses a variety of unique physiological characteristics and metabolic features [1,2,3], such as tolerance to a wide range of extreme environmental conditions [4]. W. anomalus secrete many kinds of glycosidases to improve the flavor of foods, generating numerous volatile ester compounds with floral and fruity aromas, and these compounds are safe for people and livestock [5, 6]. W. anomalus has been widely applied in various industrial fields including in food preservation and processing [7], the energy and chemical industry [8], and aquaculture feed protein production [9]. However, the mechanism of the yeast cell response to ethanol stress has not been fully elucidated, which greatly limits its application in the production of ethanol beverages and ethanol fuels.

Studies have shown ethanol stress can trigger changes in a large number of genes and signal transduction pathways that are related to cell structure, substance metabolism and transport, and stress response [10, 11]. However, the underlining mechanism of the response to ethanol stress may be different for different yeast species. For example, Saccharomyces cerevisiae improved the stability of cell membrane proteins and the cell membrane by increasing the synthesis of ergosterol and proline transporters under ethanol stress [12, 13]. In contrast, Kluyveromyces marxianus stabilized its cell membrane structure by reducing the expression of some genes related to ergosterol and fatty acid synthesis [14, 15]. For W. anomalus, the specific genes and pathways influenced by ethanol stress remain unclear.

At the metabolic level, yeast cells can cope with ethanol stress by adjusting various intracellular metabolic reactions and the contents of metabolites. For S. cerevisiae, the energy demand of yeast cells was maintained by adjusting the intracellular glucose metabolism pathways (glycolysis and TCA cycle), which helped cells to relieve the damage caused by an adverse environment (ethanol stress) [16, 17]. In addition, fatty acid metabolism, amino acid metabolism, trehalose metabolism, and other metabolic pathways were regulated, cell membrane structure and components were reconstructed, endogenous protective substances (such as a variety of amino acids and trehalose) were recruited, the hydrophilic environment around protein molecules changed, and the aggregation and precipitation of proteins were reduced when S. cerevisiae encountered ethanol stress [18,19,20]. Thus, the structural stability of proteins and cells was maintained, and the damage caused by ethanol was reduced. Moreover, carbon metabolism, amino acid metabolism, trehalose metabolism, and ergosterol metabolism are known to be involved in the process of responding to ethanol stress for K. marxianus [21]. The metabolites involved in the response of different yeast species to ethanol stress may also be different. For example, glutamate was confirmed to be involved in the K. marxianus stress response to ethanol, but not in the S. cerevisiae response. Therefore, it is necessary to explore the metabolites of W. anomalus in response to ethanol stress.

In our previous study, an intense aroma-producing yeast strain, W. anomalus C11, was obtained from the spontaneous fermentation of Rosa roxburghii Tratt by the sniffing method [22]. This strain yeast tolerated 9% (v/v) ethanol treatment. In the present study, we combined RNA-seq and liquid chromatography technologies to explore the ethanol stress response and ethanol tolerance mechanisms of W. anomalus. To the best of our knowledge, this study represents the first integrated transcriptomic and metabolomics analysis to reveal the ethanol stress response mechanism of W. anomalus.

Results

Effects of ethanol stress on morphological characteristics of W. anomalus

As shown in Fig. 1A, W. anomalus cells were ellipsoidal, full, and smooth when no ethanol stress was induced (0% ethanol group). With the increase in ethanol concentration, the fullness decreased and the surface became uneven and deformed in many W. anomalus cells (Fig. 1B to 1D). The surface cracks and ruptured cells appeared when the ethanol concentration increased to 12% (Fig. 1E).

Fig. 1
figure 1

Morphological characteristics of W. anomalus under different ethanol treatments. A control (0% ethanol); B 3% ethanol treatment group; C 6% ethanol treatment group; D 9% ethanol treatment group; E) 12% ethanol treatment group. Bar = 5 μm

Full size image

Effects of ethanol stress on the growth of W. anomalus

The effects of ethanol stress on the growth of W. anomalus are exhibited in Fig. 2A. The 3% and 6% ethanol treatments mainly inhibited the growth of W. anomalus cells in the logarithmic growth phase, prolonging it, but these treatments had little effect on the yeast cells during the stationary phase. The 9% and 12% ethanol treatments strongly inhibited the growth of W. anomalus, shortening the logarithmic growth period and causing the yeast cells to quickly enter the stationary phase.

Fig. 2
figure 2

Effects of different ethanol treatments (0%, 3%, 6%, 9%, and 12% v/v) on the growth of W. anomalus. A Growth curves of W. anomalus; B Survival rate of W. anomalus; C Death rate of W. anomalus; D Biomass of W. anomalus after ethanol treatment. Values in the same column with different lowercase letters are significantly different (P < 0.05)

Full size image

The effects of ethanol stress on the survival of W. anomalus were monitored and depicted in Figs. 2B and 2C. Yeast growth of all the groups was similar before the stress treatment (0 h). The survival rate of yeast cells in the 3% and 6% ethanol treatment groups was lower than that of the control group (0% ethanol) after 6 h of ethanol treatment. The survival rate of the 9% ethanol treatment group was significantly lower than that of the control. Most of the cells in the 12% ethanol treatment group died, and the survival rate was much lower than in the control group. In the 3% and 6% ethanol treatment groups, 1%and 8.6% of cells were stained blue, respectively, indicating a low number of dead cells. In contrast, 27.54% and 66.33% of the cells were stained blue in the 9% and 12% ethanol treated groups, respectively (Fig. 2C, Fig. S1). Therefore, the survival of W. anomalus decreased with increasing ethanol concentration.

In addition, ethanol stress also reduced the biomass of W. anomalus, and the biomass value gradually decreased with increasing ethanol concentration. Among these treated groups, 12% ethanol treatment had the strongest inhibited impact on the biomass of W. anomalus, while the 3% ethanol treatment had the weakest inhibited impact (Fig. 2D). Collectively, a high concentration of ethanol (9% ethanol and 12% ethanol) remarkably inhibited the growth (survival rate and death rate) of W. anomalus. Therefore, 9% and 12% ethanol were selected as the treatment concentrations for gene expression profile (transcriptomics) and metabolic profile analysis (metabolomics).

Effects of ethanol stress on the expression of genes in W. anomalus

Here, 7,141,520,437, 6,897,852,543, and 6,664,489,895 raw bases were obtained in 0%, 9%, and 12% ethanol treatment groups, respectively. The 0%, 9%, and 12% ethanol treatment groups obtained 6,969,933,135, 6,735,048,208 and 6,501,374,566 clean bases after data processing, respectively. The error rate in these three groups was 0.02%, and the values of Q20 (%) and Q30 (%) were greater than 90% (Table 1). In addition, principal component analysis (PCA) results indicated each group was in a separate confidence circle (Fig. S2). The data suggest the sample quality was acceptable, and the sequencing data were accurate for this transcriptome sequencing.

Table 1 Quality analysis of transcriptome sequencing data of W. anomalus with ethanol stress
Full size table

To identify the differentially expressed genes (DEGs) of W. anomalus with ethanol stress, we compared gene expression levels among the ethanol-treated groups and the control group, with a filter condition set to P values < 0.05 and | log2 (FC) |> 2. There were 2227 DEGs found in the 9% ethanol treatment group compared to control group, of which 1,068 DEGs were upregulated and 1159 DEGs were downregulated (Figs. 3A and B, Table S1). In addition, 2274 DEGs were common between the 12% ethanol treatment group and the control group, of which 1153 DEGs were upregulated and 1121 DEGs were downregulated (Figs. 3A and C, Table S2). In addition, 345 DEGs were detected between 12 and 9% ethanol treated groups, 25 DEGs were upregulated and 320 DEGs were downregulated (Fig. 3D, Table S3).

Fig. 3
figure 3

Differentially expressed genes (DEGs) of W. anomalus under ethanol stress. A Bar chart of DEGs; B Volcano plot of DEGs between the 9% ethanol treatment group and the control group; C Volcano plot of DEGs between the 12% ethanol treatment group and the control group; D Volcano plot of DEGs between the 12% ethanol treatment group and 9% ethanol treatment group

Full size image

Moreover, the DEGs induced by ethanol stress were further analyzed via KEGG pathway enrichment, and the data shows the following pathways: oxidative phosphorylation, glycolysis/gluconeogenesis, ribosome, pyruvate metabolism, lysine biosynthesis, propanoate metabolism, fatty acid degradation, glycine, serine, and threonine metabolism, were significantly enriched. These pathways are related to energy metabolism (oxidative phosphorylation, glycolysis/gluconeogenesis, fructose, and mannose metabolism), amino acid metabolism (lysine biosynthesis, glycine, serine, and threonine metabolism), protein synthesis (ribosome), fatty acid metabolism (fatty acid degradation, biosynthesis of unsaturated fatty acids, steroid biosynthesis), and oxidative stress regulation (peroxisome) (Fig. 4). In addition, the numbers of enriched DEGs in some enrichment pathways were different between 9% treated group and 12% ethanol treated group. For axample, the number of enriched ribosme in 9% treated group was 77, and which was 82 in 12% treated group (Fig. 4A and B).

Fig. 4
figure 4

KEGG pathway enrichment of the DEGs of W. anomalus under (A) 9% and (B) 12% ethanol treatments

Full size image

Effects of ethanol stress on the metabolites of W. anomalus

The quality control analysis of the metabolome sequencing samples was carried out by PCA, and the results show sequencing samples of each group in positive and negative ion modes were densely clustered, and the repeatability of samples was stable (Fig. S3).

Differentially expressed metabolites (DEMs) were screened with the criteria of variable importance in the projection (VIP) > 1 and P < 0.05. There were 3845 ion peaks and 267 annotated DEMs detected between the 9% ethanol treatment group and control group in positive and negative ion modes. Among them, 162 DEMs were upregulated and 105 were downregulated (Figs. 5A–C, Table S4). In addition, 4,409 ion peaks and 296 annotated DEMs, including 146 upregulated DEMs and 150 downregulated DEMs, were obtained between the 12% ethanol treatment group and control group (Figs. 5A, 5B, and 5D, Table S5). There were totally 4037 ion peaks and 253 annotated DEMs were found comparing 12% ethanol treated group to 9% ethanol treated group, of which 79 DEMs were upregulated and 174 DEMs were downregulated (Figs. 5A and 5B, Table S6). Furthermore, most of these DEMs were identified as vitamins, amino acids, nucleic acids, and cofactors, by comparing them with the KEGG compound database. However, the identified numbers of cofactors, nucleotides, base, phospholipids and oligosaccharides were higher in 12% ethanol treated group than 9% ethanol treated group (Figs. 5E and F).

Fig. 5
figure 5

Differentially expressed metabolites (DEMs) of W. anomalus induced by ethanol stress. A) Comparison of the number of upregulated and downregulated ion peaks detected in positive and negative ion modes for samples treated with different percentages of ethanol; B) DEMs induced by 9% and 12% ethanol treatments; C) Volcano plot comparing the DEMs between the 9% ethanol treatment group and control group; D) Volcano plot comparing the DEMs between the 12% ethanol treatment group and control group; E) Identification of DEMs by KEGG database in the 9% ethanol treatment group and control group; F) Identification of DEMs by KEGG database in the 12% ethanol treatment group and control group; G) Identification of enriched DEMs by KEGG database in the 9% ethanol treatment group and control group; G) Identification of enriched DEMs by KEGG database in the 12% ethanol treatment group and control group

Full size image

We further enriched the DEMs using the KEGG database and found that most DEMs were enriched in the pathways related to yeast growth, such as cell cycle-yeast, longevity regulating pathway-multiple species, and meiosis-yeast (Figs. 5G and H). The following pathways: amino acid metabolism (arginine biosynthesis, alanine, aspartate and glutamate metabolism, arginine and proline metabolism), energy metabolism (oxidative phosphorylation, galactose metabolism, starch and sucrose metabolism), and nucleic acid metabolism (purine metabolism, pyrimidine metabolism), were also enriched. Therefore, we deduced ethanol stress had pleiotropic effects on the metabolic response of W. anomalus, including energy metabolism, protein biosynthesis, and nucleic acid metabolism.

Integrated transcriptomics and metabolomics analyses

Integrated analysis was performed to further identify the DEGs obtained from transcriptomics and DEMs obtained from metabolomics. The results are shown in Fig. 6. Most of the significantly enriched DEGs and DEMs were associated with oxidative phosphorylation and galactose metabolism. Metabolic pathways, such as aspartate and glutamate metabolism, starch and sucrose metabolism, and arginine biosynthesis, were also enriched. Sphingolipid metabolism, lysine biosynthesis, cystcine and methionine metabolism an pyrimidine metabolism were only enriched in 12% ethanol treatment group. Validation of differentially expressed genes and metabolites.

To quantitatively verify the reliability of the transcriptomics results, the gene expression in five upregulated and five downregulated genes were confirmed by q-PCR. As demonstrated in Fig. 6, Wa60613, Wa78611, Wa13980, Wa63188, and Wa46076 were proved to be upregulated, while Wa31465, Wa92106, Wa34481, Wa60885, and Wa26420 were downregulated (Fig. 6). These results are in good agreement with the transcriptomics data.

Fig. 6
figure 6

DEGs and DEMs identified in W. anomalus under ethanol stress by integrating transcriptomics and metabolomics analyses. A DEGs and DEMs between the 9% ethanol treatment group and control group; B DEGs and DEMs between the 12% ethanol treatment group and control group

Full size image

The transcriptomics and metabolomics data show aspartate, glutamate, and arginine were involved in regulating the response of W. anomalus to ethanol stress (Fig. 7). To further confirm the roles of aspartate, glutamate, and arginine in the response of W. anomalus to ethanol stress, the cells were analyzed after the exogenous addition of these amino acids. As demonstrated in Fig. 8, the yeast growth was constant at 0 h under all treatments. However, the yeast growth was significantly inhibited by 9% and 12% ethanol treatments at 6 h. In contrast, the effects of the ethanol treatments were partly mitigated by the addition of exogenous aspartate, glutamate, and arginine, and the cell survival rate of W. anomalus increased. Therefore, the extracellular addition of aspartate, glutamate, and arginine can abate the damage due to ethanol stress and improve the survival of W. anomalus.

Fig. 7
figure 7

PCR validations of RNA-seq data for genes A Wa60613; B Wa78611; C Wa13980; D Wa63188; E Wa46076; F Wa31465; G Wa92106; H Wa34481; I Wa60885; J Wa26420

Full size image
Fig. 8
figure 8

Exogenous supplementation of aspartate, glutamate, and arginine improved the survival rate of W. anomalus. A Survival of yeast cells in 9% ethanol; B Survival of yeast cells in 12% ethanol

Full size image

Discussion

Studies have shown that ethanol stress has pleiotropic effects on yeast cells, including the regulation of gene expression and signal transduction pathways [23]. The response of yeast cells to ethanol stress is complex and involves multiple genes, components, and metabolic pathways. Therefore, the integration of transcriptomics and metabolomics techniques enhances the understanding of the molecular mechanisms and yeast cell response to ethanol stress.

Transcriptomics is a powerful technique that can elucidate gene transcriptional regulation by monitoring the expression profiles of all genes in a certain physiological state. It has been widely used to explore the mechanism of various stress responses in cells [24, 25]. Li et al. identified 937 DEGs in the strain of S. cerevisiae Sc131 upon exposure to ethanol stress [12, 26]. Most of the significantly upregulated DEGs were filamentous growth-related genes (such as OPI1, MIT1, and GLK1), mitochondrial energy metabolism-related genes (such as COX1, COX2, and COX3), amino acid metabolism-related genes, as well as protein folding- and protein degradation-related genes. In contrast, ribosome biosynthesis-related genes were significantly downregulated. For K. marxianus, the expression of heat shock proteins significantly increased, while the expression of genes related to carbon metabolism, ribosome biosynthesis, unsaturated fatty acid, and sterol biosynthesis remarkably decreased under ethanol treatment. Therefore, energy metabolism, amino acid metabolism, nucleic acid metabolism, and fatty acid metabolism pathways may be the common target of ethanol attack for yeasts, including S. cerevisiae, K. marxianus, and W. anomalus [14]. It is worth noting that the expression of trehalose metabolism-related genes was upregulated at low concentrations of ethanol rather than high concentrations of ethanol in K. marxianus [14, 15], but these genes were not found changed in S. cerevisiae or W. anomalus subjected to ethanol stress.

In a recent study, a metabolomics approach, using gas chromatography coupled with mass spectrometry, was performed to investigate the metabolic changes of S. cerevisiae under ethanol stress. The authors found the profiles of fatty acids changed significantly [27]. In a study conducted by Li et al., unsaturated fatty acids played a positive role in the response of S. cerevisiae to ethanol stress [28]. In the present study, we also observed fatty acids and fatty acid metabolism-related genes changed dramatically in W. anomalus. The functions of fatty acids in the response to ethanol stress can be further confirmed in future research.

Microorganisms have developed different strategies to minimize the adverse effects of ethanol stress, and several of them are connected to amino acid metabolism. For instance, the intracellular accumulation of L-proline protected S. cerevisiae from damage caused by ethanol stress, which supports the ethanol tolerances observed in yeast cells [19, 29]. Enrichment analysis also revealed the prominent role of amino acid metabolism in ethanol stress for W. anomalus (Fig. 7). Therefore, aspartate, glutamate, and arginine were supplemented to further enhance the survival rate of yeast cells under ethanol stress. It was reported that arginine could protect S. cerevisiae cells from ethanol damage [30]. In the present study, we found arginine also played a protective role in W. anomalus cells. However, the role of glutamate in ethanol stress may differ for different yeast species. Glutamate was proved to be involved in the ethanol stress response of K. marxianus [21] and W. anomalus in this study, but it has not been confirmed in S. cerevisiae. Glutamate is required for synthesis of protective compound glutathione and indeed the increased content of glutathione was observed during the ethanol stress (data were not shown). But, further investigation of the underlying mechanisms of these amino acids roles in the ethanol response of W. anomalus is needed.

Conclusions

To the best of our knowledge, this was the first study to systematically analyze the effect of ethanol stress on the morphological characteristics and growth of W. anomalus, and the response mechanism of ethanol stress, based on transcriptomics and metabolomics approaches. High concentrations of ethanol (9% and 12%, v/v) remarkably inhibited the growth of W. anomalus. Energy metabolism, amino acid metabolism, fatty acids metabolism, and nucleic acid metabolism changed significantly in response to ethanol stress. Thus, the results obtained in this study could provide insights into the mechanisms involved in the response of W. anomalus to ethanol stress, enabling future metabolic engineering approaches to improve its ethanol tolerance.

Materials and methods

Yeast strains and culture conditions

The W. anomalus C11 strain was separated from the fruit spontaneous fermentation broth of R. roxburghii and cultured with yeast extract peptone dextrose medium (YEPD) at 28 °C for 72 h. The culture was kept at 4 °C for later use.

Scanning electron microscopy (SEM) analysis

W. anomalus cells were cultured with a YEPD medium at 28 °C for 8 h. Different concentrations of ethanol (3%, 6%, 9%, and 12%, v/v) were added, and the cells were cultured for 6 h to implement ethanol stress. Cells cultured without ethanol (0% Ethanol) were used as the control for this experiment and other experiments in this study. Yeast cells were centrifuged at 4, 000 × g for 10 min and washed three times with physiological saline. Then, the cells were re-suspended in 2.5% glutaraldehyde for 4 h at 4 °C and washed three times with 0.1 M PBS (pH 7.4). Subsequently, the cells were eluted with a gradient concentration of ethanol solutions (0%, 50%, 70%, 85%, 95%, and 100%) for 15 min each. Yeast cells were dried with a critical point dryer (Hitachi, Japan), coated with a gold/palladium alloy, and observed with an FEI Quanta FEG 450 scanning electron microscopy system (FEI, USA).

Growth analysis of W. anomalus under ethanol treatment

For the growth curve determination, W. anomalus cells were inoculated into YEPD liquid medium and incubated at 28 °C with 180 rpm shaking for 8 h. The cells were treated with 3%, 6%, 9% or 12% (v/v) concentrations of ethanol, respectively. The optical density (OD) of the cultures was measured at 600 nm every 4 h. Each experiment was repeated three times.

For the survival analysis, W. anomalus cells were treated with 3%, 6%, 9%, or 12% (v/v) concentrations of ethanol for 6 h. Yeast cells were harvested, washed twice with distilled water, and re-suspended in distilled water at a same cell concentration (OD600 = 1). The cell suspension was diluted to 100, 10−2, and 10−4, and 2 μL of each diluent was spotted onto YEPD solid plates. The cells were incubated at 28 °C for 36 h. Colonies were observed and captured using a microscope (Olympus, Japan).

The yeast cells under ethanol stress were monitored for 6 h, using methylene blue staining, and the death rate was calculated by counting the number of dead cells in a random location on the plate. Ten locations were observed for each sample.

Biomass was detected by measuring the dry weight of W. anomalus cells under different ethanol treatments. Briefly, 30 mL of W. anomalus cells were collected by centrifugation at the speed of 4, 000 × g for 10 min. The cells were dried at 65 °C until a constant weight was achieved, and the weight was measured using an analytical balance (Lichen, China).

Transcriptomics analysis

W. anomalus cells were treated with 9% or 12% (v/v) concentrations of ethanol for 6 h, collected via centrifugation at the speed of 4, 000 × g for 10 min, frozen quickly using liquid nitrogen, and stored at − 80 °C for transcriptomics and metabolomics analyses.

Total RNA was extracted from the yeast cells using Trizol reagent (Invitrogen, USA) according to the manufacturer’s instructions, and genomic DNA was removed using DNase I (Takara, Japan). An RNA-seq transcriptome library was prepared using a TruSeqTM RNA sample preparation Kit from Illumina (San Diego, USA) and 1 μg of total RNA. Libraries were size-selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose, followed by PCR amplification using Phusion DNA polymerase (NEB, USA) for 15 PCR cycles. The paired-end RNA-seq sequencing library was sequenced with the Illumina HiSeq xten/NovaSeq 6000 sequencer (Illumina, USA).

The raw paired-end reads were trimmed and controlled by SeqPrep (https://github.com/jstjohn/SeqPrep) and Sickle (https://github.com/najoshi/sickle) with default parameters. Then, clean reads were separately aligned to reference genomes with orientation mode using HISAT2 (http://ccb.jhu.edu/software/hisat2/index.shtml) software. The mapped reads of each sample were assembled by StringTie (https://ccb.jhu.edu/software/stringtie/index.shtml? t = example) in a reference-based approach. To identify differentially expressed genes (DEGs), the expression level of each transcript was calculated according to the transcripts per million reads method. RSEM (http://deweylab.biostat.wisc.edu/rsem/) was used to quantify gene abundances.

In addition, KEGG pathway analysis was carried out by Goatools (https://github.com/tanghaibao/Goatools) and KOBAS (http://kobas.cbi.pku.edu.cn/home.do) [31,32,33].

Non-targeted metabolomics

Liquid chromatography-mass spectrometry (LC–MS)-based metabolomics was conducted by Majorbio Biotech (Shanghai, China). Briefly, 50 mg of the ethanol-treated samples or control sample were accurately weighed, and the metabolites were extracted using 400 µL of methanol:water (4:1, v/v) solution, containing 0.02 mg/mL of L-2-chlorophenylalanine as the internal standard. The mixture was allowed to settle at − 20 °C, and then it was vortexed for 30 s and ultrasonicated at 40 kHz for 30 min at 5 °C. The supernatant was carefully prepared for LC–MS analysis after centrifugation at 13,000 × g for 15 min. The chromatographic separation and mass spectrometric conditions were from a previously published method by Chen [34]. The raw data were imported into Progenesis QI 2.3 (Waters, USA) for peak detection and alignment. The preprocessing results generated a data matrix comprised of retention time, mass-to-charge ratio values, and peak intensity. Annotation of the metabolites and differential metabolites between ethanol-treated groups and the control group was performed on the Majorbio Cloud Platform (https://cloud.majorbio.com).

Real-time quantitative PCR (q-PCR) analysis

Total RNA was extracted from the ethanol-treated groups and control group using Trizol reagent (Sangon Biotech, China), according to the manufacturer's instructions. cDNA was transcribed using the PrimerScript RT Reagent Kit (Takara). Real-time PCR was performed using the Light Cycler 96 detection system (Roche, Germany). The primers used for q-PCR in this study are shown in Table S7. Data were normalized to the housekeeping gene actin and analyzed using the 2−ΔΔCT method.  

Growth analysis of W. anomalus by exogenous addition of amino acids

W. anomalus cells were cultured at 28 °C for 8 h and then divided into ten groups. For the control group and ethanol treatment groups, no ethanol and 9% or 12% (v/v) ethanol were added, respectively. For the amino acid supplementary groups, 9% or 12% (v/v) ethanol was added, along with 5 mM of exogenous aspartate, glutamate, or arginine. All the treatment groups were further cultured at 28 °C for 8 h. The cultures were diluted to 100, 10−2, or 10−4, spread on YEPD solid medium and incubated at 28 °C for 36 h.

Statistics

The results are expressed as the mean ± standard deviation. Univariate analysis of variance (ANOVA) of the data and the determination of the significance of the difference were performed using SPSS 21.0 software. P < 0.05 was considered statistically significant.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Abbreviations

W. anomalus:

Wickerhamomyces anomalus

K. marxianus:

Kluyveromyces marxianus

S. cerevisiae:

Saccharomyces cerevisiae

DEGs:

Differentially expressed genes

DEMs:

Differentially expressed metabolites

TCA cycle:

Tricarboxylic acid cycle

RNA-seq:

RNA-sequencing

VIP:

Variable importance in the projection

PBS:

Phosphatic buffer solution

OD:

Optical density

q-PCR:

Quantitative PCR

References

  1. Wang W, Fan G, Li X, Fu Z, Liang X, Sun B. Application of Wickerhamomyces anomalus in simulated solid-state fermentation for baijiu production: changes of microbial community structure and flavor metabolism. Front Microbiol. 2020;11: 598758.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Junges DSB, Delabeneta MF, Rosseto LRB, Nascimento BL, Paris AP, Persel C, et al. Antibiotic activity of Wickerhamomyces anomalus mycocins on multidrug-resistant acinetobacter baumannii. Microb Ecol. 2020;80:278–85.

    Article  CAS  PubMed  Google Scholar 

  3. Cecarini V, Cuccioloni M, Bonfili L, Ricciutelli M, Valzano M, Cappelli A, et al. Identification of a killer Toxin from Wickerhamomyces anomalus with β-Glucanase activity. Toxins. 2019;11:568.

    Article  CAS  PubMed Central  Google Scholar 

  4. Cunha AC, Santos RACD, Riaño-Pachon DM, Squina FM, Oliveira JVC, Goldman GH, et al. Draft genome sequence of Wickerhamomyces anomalus LBCM1105, isolated from cachaça fermentation. Genet Mol Biol. 2020;43: e20190122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. de Fernández UM, Bulacios GA, Navarro SA, Lanza L, Mendoza LM, Chalón MC. The killer yeast Wickerhamomyces anomalus Cf20 exerts a broad anti-Candida activity through the production of killer toxins and volatile compounds. Med Mycol. 2020;58:1102–13.

    Article  Google Scholar 

  6. Comitini F, Agarbati A, Canonico L, Galli E, Ciani M. Purification and characterization of WA18, a new mycocin produced by Wickerhamomyces anomalus active in wine against Brettanomyces bruxellensis spoilage yeasts. Microorganisms. 2020;9:56.

    Article  PubMed Central  Google Scholar 

  7. Padilla B, Gil JV, Manzanares P. Challenges of the non-conventional yeast Wickerhamomyces anomalus in winemaking. Fermentation. 2018;4:1–14.

    Article  Google Scholar 

  8. Zha Y, Hossain AH, Tobola F, Sedee N, Havekes M, Punt PJ. Pichia anomala 29X: A resistant strain for lignocellulosic biomass hydrolysate fermentation. FEMS Yeast Res. 2013;13:609–17.

    Article  CAS  PubMed  Google Scholar 

  9. Huyben D, Nyman A, Vidakovic A, Passoth V, Moccia R, Kiessling A, et al. Effects of dietary inclusion of the yeasts Saccharomyces cerevisiae and Wickerhamomyces anomalus on gut microbiota of rainbow trout. Aquaculture. 2017;473:528–37.

    Article  CAS  Google Scholar 

  10. Snoek T, Verstrepen KJ, Voordeckers K. How do yeast cells become tolerant to high ethanol concentrations? Curr Genet. 2016;62:475–80.

    Article  CAS  PubMed  Google Scholar 

  11. Udom N, Chansongkrow P, Charoensawan V, Auesukaree C. Coordination of the cell wall integrity and high-osmolarity glycerol pathways in response to ethanol stress in Saccharomyces cerevisiae. Appl Environ Microbiol. 2019;85:e00551-e619.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Li R, Xiong G, Yuan S, Wu Z, Miao Y, Weng P. Investigating the underlying mechanism of Saccharomyces cerevisiae in response to ethanol stress employing RNA-seq analysis. World J Microbiol Biotechnol. 2017;33:206.

    Article  PubMed  Google Scholar 

  13. Kasavi C, Eraslan S, Oner ET, Kirdar B. An integrative analysis of transcriptomic response of ethanol tolerant strains to ethanol in Saccharomyces cerevisiae. Mol Biosyst. 2016;12:464–76.

    Article  CAS  PubMed  Google Scholar 

  14. Diniz RHS, Villada JC, Alvim MCT, Vidigal PMP, Vieira NM, Lamas-Maceiras M, et al. Transcriptome analysis of the thermotolerant yeast Kluyveromyces marxianus CCT 7735 under ethanol stress. Appl Microbiol Biotechnol. 2017;101:6969–80.

    Article  CAS  PubMed  Google Scholar 

  15. Fu X, Li P, Zhang L, Li S. Understanding the stress responses of Kluyveromyces marxianus after an arrest during high-temperature ethanol fermentation based on integration of RNA-Seq and metabolite data. Appl Microbiol Biotechnol. 2019;103:2715–29.

    Article  CAS  PubMed  Google Scholar 

  16. Saini P, Beniwal A, Kokkiligadda A, Vij S. Response and tolerance of yeast to changing environmental stress during ethanol fermentation. Process Biochem. 2018;72:1–12.

    Article  CAS  Google Scholar 

  17. Tian J, Zhang SX, Li H. Changes in intracellular metabolism underlying the adaptation of Saccharomyces cerevisiae strains to ethanol stress. Ann Microbiol. 2017;67:195–202.

    Article  CAS  Google Scholar 

  18. Vanegas JM, Contreras MF, Faller R, Longo ML. Role of unsaturated lipid and ergosterol in ethanol tolerance of model yeast biomembranes. Biophys J. 2012;102:507–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Takagi H, Taguchi J, Kaino T. Proline accumulation protects Saccharomyces cerevisiae cells in stationary phase from ethanol stress by reducing reactive oxygen species levels. Yeast. 2016;33:355–63.

    Article  CAS  PubMed  Google Scholar 

  20. Divate NR, Chen GH, Wang PM, Ou BR, Chung YC. Engineering Saccharomyces cerevisiae for improvement in ethanol tolerance by accumulation of trehalose. Bioengineered. 2016;7:445–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Alvim MCT, Vital CE, Barros E, Vieira NM, da Silveira FA, Balbino TR, et al. Ethanol stress responses of Kluyveromyces marxianus CCT 7735 revealed by proteomic and metabolomic analyses. Anton Leeuw Int J G. 2019;112:827–45.

    Article  CAS  Google Scholar 

  22. Liu XZ, Li YF, Zhao HB, Yu ZH, Huang MZ. Identification and fermentative properties of an indigenous strain of Wickerhamomyces anomalus isolated from Rosa roxburghii Tratt. Brit Food J. 2021;123:4069–81.

    Article  Google Scholar 

  23. Stanley D, Bandara A, Fraser S, Xiao S, Li C, Lin R, et al. The ethanol stress response and ethanol tolerance of Saccharomyces cerevisiae. J Appl Microbial. 2010;109:13–24.

    Article  CAS  Google Scholar 

  24. Dong ZC, Chen Y. Transcriptomics: advances and approaches. Sci China Life Sci. 2013;56:960–7.

    Article  CAS  PubMed  Google Scholar 

  25. McGettigan PA. Transcriptomics in the RNA-seq era. Curr Opin Chem Biol. 2013;17:4–11.

    Article  CAS  PubMed  Google Scholar 

  26. Li R, Miao Y, Yuan S, Li Y, Wu Z, Weng P. Integrated transcriptomic and proteomic analysis of the ethanol stress response in Saccharomyces cerevisiae Sc131. J Proteomics. 2019;203: 103377.

    Article  CAS  PubMed  Google Scholar 

  27. Ming M, Wang X, Lian L, Zhang H, Gao W, Zhu B, et al. Metabolic responses of Saccharomyces cerevisiae to ethanol stress using gas chromatography-mass spectrometry. Mol Omics. 2019;15:216–21.

    Article  CAS  PubMed  Google Scholar 

  28. Li H, Ma M, Luo S, Zhang R, Han P, Hu W. Metabolic responses to ethanol in Saccharomyces cerevisiae using a gas chromatography tandem mass spectrometry-based metabolomics approach. Int J Biochem Cell Biol. 2012;44:1087–96.

    Article  CAS  PubMed  Google Scholar 

  29. Takagi H, Takaoka M, Kawaguchi A, Kubo Y. Effect of L-proline on sake brewing and ethanol stress in Saccharomyces cerevisiae. Appl Environ Microbiol. 2005;71:8656–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cheng Y, Du Z, Zhu H, Guo X, He X. Protective effects of arginine on Saccharomyces cerevisiae against ethanol stress. Sci Rep. 2016;6:1–12.

    Google Scholar 

  31. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28:27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28:1947–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49:D545–51.

    Article  CAS  PubMed  Google Scholar 

  34. Chen Z, Lin Y, Zhou Q, Xiao S, Li C, Lin R, et al. Ginsenoside Rg1 mitigates morphine dependence via regulation of gut microbiota, tryptophan metabolism, and serotonergic system function. Biomed Pharmacother. 2022;150: 112935.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (32160557), and Open Fund of Chongqing Key Laboratory of Industrial Fermentation Microorganism (Chongqing University of Science and Technology) (GYFJWSW-02).

Author information

Authors and Affiliations

  1. Guizhou Institute of Technology, Guiyang, 550000, People’s Republic of China

    Yinfeng Li, Hua Long, Guilan Jiang, Xun Gong, Zhihai Yu, Mingzheng Huang & Xiaozhu Liu

  2. Chongqing University of Science and Technology, Chongqing, 404100, People’s Republic of China

    Tianbing Guan & Xiaozhu Liu

  3. Henan Institute of Science and Technology, Xinxiang, 453000, People’s Republic of China

    Yuanyuan Guan

Authors
  1. Yinfeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guilan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xun Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhihai Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mingzheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tianbing Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yuanyuan Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaozhu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y. L and X. L wrote the original draft manuscript; H. L, G. J conducted the experiments; X. G, Z. Y., M. H, T. G and Y. G analyzed the data of transcriptomics and metabolomics; X. L conceive and designed the experiments. All authors contributed to the article and approved the submitted version. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Xiaozhu Liu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that there are no competing interests among them.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

FigureS1. Cells death determination under different concentrations of ethanoltreatment by methylene blue staining. A,0% ethanol treatment group; B, 3% ethanol treatment group; C, 6% ethanoltreatment group; D, 9% ethanol treatment group; E, 12% ethanol treatment group.Bar=100 μm.Figure S2.  Results of principalcomponent analysis (PCA) of the samples for transcriptome sequencing. FigureS3.  PCA score plots of the samples for metabolomicsanalysis in positive and negative ion modes. A, Positive ion mode; B, Negativeion mode. Table S7. Primersused in this study for real-time quantitative PCR detection.

Additional file 2:

 Table S1.

Additional file 3: Table S2.

Additional file 4: Table S3.

Additional file 5: Table S4.

Additional file 6: Table S5.

Additional file 7: Table S6.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Long, H., Jiang, G. et al. Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches. BMC Microbiol 22, 275 (2022). https://doi.org/10.1186/s12866-022-02691-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12866-022-02691-y

Keywords

BMC Microbiology

ISSN: 1471-2180

Contact us