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Article

Transcriptomic and Physiological Meta-Analysis of Multiple Stress-Resistant Saccharomyces cerevisiae Strains

by
Abdulkadir Özel
1,2,
Alican Topaloğlu
1,2,
Ömer Esen
1,2,
Can Holyavkin
1,2,
Mehmet Baysan
3,* and
Zeynep Petek Çakar
1,2,*
1
Department of Molecular Biology & Genetics, Faculty of Science & Letters, Istanbul Technical University, 34469 Istanbul, Turkey
2
Dr. Orhan Öcalgiray Molecular Biology, Biotechnology and Genetics Research Center (ITU-MOBGAM), Istanbul Technical University, 34469 Istanbul, Turkey
3
Department of Computer Engineering, Istanbul Technical University, 34469 Istanbul, Turkey
*
Authors to whom correspondence should be addressed.
Stresses 2024, 4(4), 714-733; https://doi.org/10.3390/stresses4040046
Submission received: 26 September 2024 / Revised: 24 October 2024 / Accepted: 28 October 2024 / Published: 1 November 2024
(This article belongs to the Collection Feature Papers in Human and Animal Stresses)
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Abstract

:
Meta-analysis is a beneficial approach to reevaluating the outcomes of independent previous studies in the same scope. Saccharomyces cerevisiae, or the baker’s yeast, is a commonly used unicellular and eukaryotic model organism. In this study, 12 evolved S. cerevisiae strains that became resistant to diverse stress conditions (boron, caffeine, caloric restriction, cobalt, coniferyl aldehyde, ethanol, iron, nickel, oxidative stress, 2-phenylethanol, and silver stress) by adaptive laboratory evolution were reassessed to reveal the correlated stress/stressor clusters based on their transcriptomic and stress–cross-resistance data. Principal Component Analysis (PCA) with k-means clustering was performed. Five clusters for the transcriptomic data of strains and six clusters for cross-resistance stressors were identified. Through statistical evaluations, critical genes pertinent to each cluster were elucidated. The pathways associated with these genes were investigated using the KEGG database. The findings demonstrated that caffeine and coniferyl aldehyde stressors exhibit clear distinctions from other stressors in terms of both physiological stress-cross-resistance responses and transcriptomic profiles. Pathway analysis showed that ribosome biogenesis was downregulated, and starch and sucrose metabolism was upregulated across all clusters. Gene and pathway analyses have shown that stressors lead to distinct changes in yeast gene expression, and these alterations have been systematically documented for each cluster. Several of the highlighted genes are pivotal for further exploration and could potentially clarify new aspects of stress response mechanisms and multiple stress resistance in yeast.

Graphical Abstract

1. Introduction

Saccharomyces cerevisiae is a unicellular, eukaryotic, and well-studied model organism. The high conservation rate of molecular processes across eukaryotes [1], ease of manipulation in the laboratory, and having individual databases such as the Saccharomyces Genome Database (SGD) [2] are some of the remarkable specialties of S. cerevisiae. Due to its “Generally Recognized as Safe” (GRAS) status [3], highly efficient fermentation capacity, and extensive research knowledge, S. cerevisiae is utilized in many industrial applications such as food and beverage [4]. Topaloğlu et al. (2023) recently reviewed the significant role of S. cerevisiae in the bioethanol industry. The introduction of pentose pathway reactions through metabolic engineering enhances the yield of ethanol by the conversion of pentose sugars like xylose and arabinose, which are abundant in lignocellulosic wastes [5].
Industrial applications cause S. cerevisiae to encounter harsh environmental conditions such as high temperature, oxidation, drying, and freezing/thawing [6]. In addition, the stressors that affect the higher eukaryotes, i.e., humans, and the responses against stressors are studied using S. cerevisiae as a eukaryotic model organism. For instance, Terhorst et al. (2023) studied a cell cycle-arrested S. cerevisiae mutant, which could be used with nutrient deprivation or chemotherapeutic agents to investigate which cellular mechanisms enable cells to endure prolonged cell cycle arrest [7]. The study showed that the ribosome content in the cells was reduced, and the Environmental Stress Response (ESR) was activated. Resistant yeast strains should be engineered to withstand multiple stresses, which can enhance process yields in industrial applications and enable studies on stress responses in higher eukaryotes [8].
Adaptive laboratory evolution (ALE), also known as evolutionary engineering, is an effective approach for improving microbial strains. It includes systematic continuous evolutionary processes with selective environmental pressure that contributes to the desired phenotype of evolved microbial strains either directly or indirectly [9,10,11,12,13]. A variety of genetically stable S. cerevisiae strains resistant to various stressors were successfully obtained in previous studies conducted by Çakar’s laboratory, including those that were resistant to multi-stress [10], ethanol [14], boron [15], caffeine [16], cobalt [17], coniferyl aldehyde (a lignocellulosic inhibitor) [18], iron [19], nickel [20], 2-phenylethanol [21], silver [22], oxidative stress [23], and caloric restriction (starvation) which resulted in increased chronological life span of the evolved strain [24].
Moreover, during these comprehensive studies, the evolved yeast strains that developed resistance to specific types of stress also exhibited resistance or sensitivity to a range of several secondary stress factors. Considering the complexity and multigenic nature of stress-resistance mechanisms [10], a detailed analysis of the relationship between resistance and sensitivity to various stressors correlated with transcriptomic data can significantly enhance our understanding of these mechanisms.
This study aimed to identify common mechanisms underlying stress resistance by analyzing transcriptomic data and cross-resistance profiles of stress-resistant yeast strains against various stressors based on transcriptomic and metabolic pathway analyses. For this purpose, differential gene expression (DGE) analysis was performed on 12 evolved yeast strains obtained by evolutionary engineering. Principal Component Analysis (PCA) was applied to the normalized gene expression data to cluster the transcriptomic profiles of the strains. The cross-resistance profiles of evolved yeast strains against 26 stress factors were clustered based on the pairwise calculated Spearman’s rank correlation coefficients. The coefficients were visualized by cluster heatmap with dendrograms. For each stress factor, groups were detected according to the cross-resistance profiles of the strains based on the spot assay experiments for DGE analysis. Differentially expressed genes (DEG) for each stressor were utilized in PCA to reveal clusters by k-means clustering. All clusters detected throughout the study were compared with each other and significant genes and pathways were identified for the clusters. Through these statistical analyses, this research contributed significantly to the elucidation of the complex molecular mechanisms underlying stress resistance.

2. Results and Discussion

2.1. Clustering Stress-Resistant Yeast Strains Based on the Transcriptomic Data

The normalized whole transcriptomics data of the evolved yeast strains (Table 1) were analyzed by the PCA method (Section 3.5). The results are shown in Figure 1. The data were clustered into five clusters with the k-means clustering method (Section 3.6) of the evolved yeast strains. The optimum cluster number was determined by the Elbow method (Section 3.6) located on the upper left of PCA. The transcriptomic profiles of the 12 evolved yeast strains were heterogeneously spread on the PCA plot. The multigenic nature of the stress-resistance mechanisms of S. cerevisiae may cause these separations.
Among the evolved strains, the B2-ethanol stress-resistant strain had a similar profile to the reference strain. According to Table 1, the strain with the least number of differentially expressed genes was the B2 strain. Each strain represents three consistent biological replicates as microarray data and exhibited the reliability of the independent microarray experiments. Additionally, after DGE analysis of the evolved yeast strains compared with the reference strain, significant DE genes were filtered using p-value < 0.05, and clustering was performed by hierarchical cluster heatmap in Figure S1. When compared with PCA, the strains were consistently clustered in both plots. This revealed that genes with significant differential expression are the determinants of cluster formation. Genes were identified as being specific to clusters or significantly upregulated or downregulated compared to other clusters (Section 3.8). A small number of the cluster-specific, upregulated, and downregulated genes for the five clusters are shown in Table 2 with their categories, open reading frame (ORF) of the genes, gene name, fold-change values, and pathway annotation with KEGG. The results of all inter-cluster comparisons are shown in Table S1.
According to the PCA plot, Cluster 1 consisted of silver-resistant, oxidative stress-resistant, and long-lived mutant strains. Silver stress causes an increase in oxidative stress [22,25]. Starvation stress in the long-lived strain upregulated genes responsible for the oxidative stress response [24]. Although the oxidative-stress-resistant strain has a more distinct transcriptomic profile than the other strains in Cluster 1, 33 cluster-specific genes were detected as significant only for Cluster 1. In addition, as a result of the comparisons, 284 upregulated and 392 downregulated genes were detected as significant. SOD1, VPS34, TSA1, CCP1, and GRX1 are among those genes associated with cellular response to oxidative stress-related GO term (Table 2).
Two ethanol-resistant strains (B2 and B8) and the cobalt-resistant strain constitute Cluster 2. High levels of ethanol result in increased membrane fluidity, protein denaturation, and reactive oxygen species accumulation [26]. Cobalt stress affects DNA replication, metal ion homeostasis, and proteins that use the cobalt ions [17,27,28]. Interestingly, no unique genes were identified for Cluster 2; however, analysis revealed four significantly upregulated genes and 18 significantly downregulated genes. Among the upregulated genes, OPT1 and OPT2 are involved in oligopeptide transport across membranes and play a crucial role in peptide uptake and cellular homeostasis [29]. Another notable upregulated gene is DPB2, a subunit of DNA Polymerase ε, essential for DNA replication [30]. Downregulated genes are primarily related to carbon metabolism, protein processing, and MAPK signaling pathways. Notably, the fold changes in the downregulated genes in the resistant strains with respect to the reference strain were relatively high (Table 2). These genes were categorized as downregulated genes because the fold changes for these genes were significantly lower than those in the other clusters.
Cluster 3 is composed of iron and nickel-resistant strains. Both iron and nickel stress lead to ROS production and general stress response and affect iron homeostasis [20]. Four genes (2 upregulated and 2 downregulated) that were significantly DE for this cluster but not in other strains were detected. PCL5 is an autophagy-related upregulated cluster-specific gene. SDT1 is another upregulated cluster-specific gene that encodes a pyrimidine nucleotidase [31]. MPP6 is a cluster-specific downregulated gene that is involved in RNA degradation. According to the comparisons among the clusters, 15 significantly upregulated and 8 significantly downregulated genes were found. ATG1 is an upregulated gene that is involved in the general stress response (Table 2).
Transcriptomic data of boron and 2-phenylethanol-resistant yeasts are grouped in Cluster 4. Oxidative stress and disruption of membrane integrity are common negative outcomes of boron and 2-phenylethanol stress [32,33,34]. Ten cluster-specific significantly DE genes (7 upregulated and 3 downregulated) were detected compared to other evolved strains. PKP1, which encodes a mitochondrial kinase, is the most significant DE gene in the cluster-specific category. PGS1 is involved in glycerophospholipid synthesis as a catalyzer. Based on the inter-cluster comparison, 36 upregulated and 14 downregulated genes were found for Cluster 4. Among these, 17 upregulated membrane-related genes were identified, such as CWP2 and YAK1. Six downregulated genes were associated with membrane function, such as SUL1 and YLR126C (Table 2).
Caffeine and coniferyl aldehyde-resistant strains formed Cluster 5, which is located differently from all other clusters, based on the PC2 axis (Figure 1). Caffeine and coniferyl aldehyde stresses influence the energy pathways and pleiotropic drug resistance [16,18]. The number of genes observed as cluster-specific was 155. According to the inter-cluster comparison, 235 upregulated and 137 downregulated genes were identified as significantly DE. Classifying this group was difficult due to the presence of many genes and the heterogeneity of pathway enrichment categories. This implies that caffeine and coniferyl aldehyde stress affect a variety of pathways in the cell. These results are parallel with those found in the evolutionary engineering studies in which the caffeine and coniferyl aldehyde-resistant yeast strains were obtained [16,18]. Based on GO terms, genes were mostly associated with general molecular functions such as cytoplasm, membrane, protein binding, and integral components of the membrane.
Pathway enrichment analyses with DEG of evolved yeast strains against reference strains are shown in Table S2. Accordingly, the ribosome biogenesis pathway was downregulated, and the starch and sucrose metabolism pathways were upregulated for all strains. Apart from these, 63 more different pathways were affected by various evolved yeast strains. The galactose metabolism pathway was upregulated for all clusters except Cluster 1. For Cluster 1, the oxidative phosphorylation pathway was upregulated, which can be associated with oxidative stress.

2.2. Clustering Stress-Resistant Yeast Strains Based on the Physiological Stress-Cross-Resistance Data

The stress–cross-resistance matrix of the evolved yeast strains (Section 3.3) was evaluated to reveal clusters based on the cross-resistance by measuring Spearman’s rank correlation coefficients (Figure 2). Essentially, the correlation coefficients elucidate the correlation between stress responses in stress-resistant, evolved yeast strains and various secondary stressors. These data can provide information about the similarity of molecular pathways that change in the process of evolutionary engineering of yeast for resistance against various stressors. Based on the measured coefficients, some clusters could be defined with high correlation: ethanol-resistant strains (B2–B8), caffeine- and coniferyl aldehyde-resistant strains, cobalt- and oxidative-stress-resistant strains, and iron- and nickel-resistant strains. The long-lived strain and the silver stress-resistant strain did not show a strong correlation with each other or with any other stress-resistant strain. Compared to Figure 1, common cluster definitions could be made except for Cluster 1. The mutual groupings observed in the transcriptomic data may indicate a relationship between the cross-resistance abilities of yeast strains against stress factors and the corresponding changes in their transcriptomic profiles.

2.3. Clustering the Stress Factors Used in the Spot Assay, Based on the Physiologically Grouped Transcriptomic Data

The cross-resistance of evolved yeast strains against 26 different stress factors was determined using the spot assay method and is shown as a stress-cross-resistance matrix (Section 3.3). Based on the chemical properties of the stress factors or their effects on cells, several categories have been defined in Table 3, as supported by the literature. This categorization has been established to facilitate explanation and to provide a summary of the results.
The evolved yeast strains exhibited diversified resistance or sensitivity profiles against various stress factors. DGE analysis was performed between the resistant and sensitive strain groups for each stress factor to investigate the effect of the stressors at the gene level. PCA was performed to reveal clusters of the stress factors based on the gene expression levels (Figure 3). The colors in Figure 3 are grouped according to the known characteristics of the stress factors, which are categorized in Table 3. Clustering was performed using the k-means clustering method. The optimal number of clusters and sub-clusters was found with the Elbow method (Figure S2). The significant genes (p-value < 0.05) were utilized in pathway enrichment analysis (Section 3.10). The NES values of the enriched pathways that belong to the stressors are shown in Table S3.
According to the PCA results in Figure 3, the clusters consist of stress factors from many categories, except for Cluster B. The transcriptomic effects of stress response and resistance involve a significant number of shared genes [23,50,51]. Different stressors can upregulate or downregulate the same or similar genes/gene groups. As a result, even with physiologically distinct stress resistances, the overlap in some transcriptomic data may lead to the formation of mixed groups. This indicates that the studied stress factors may potentially influence a number of common metabolic processes within the cell. For instance, it is known that most heavy metals also have the potential to cause oxidative stress [28]. Accordingly, metals and stress factors that resulted in oxidative stress are grouped in all clusters except for Cluster B. Some of the pleiotropic drugs that can affect various cellular pathways are separated from all other stress factors and they formed Cluster B homogeneously. Moreover, clustering was performed based on the PCC values calculated pairwise over the logFC values of significantly DE genes (p-value < 0.05 and |logFC| > 1) as the hierarchical clustered heatmap in Figure S3. Clustering obtained by the two methods was consistent when compared with PCA, indicating that significantly DE genes were the determinants for clustering.
Cluster-specific significantly DE genes, upregulated and downregulated genes were identified for each cluster, using significant DE gene lists, through various comparisons (Section 3.8). Key results are shown in Table 4, and Table S4 contains all results. In the case of Clusters C and F, no significant DE genes were found compared to other clusters. It may be the case that they are in the middle of the PCA plot, which could mean that they are not very distinct from other clusters in terms of their stress-cross-resistance profile. In addition, as a result of the investigation of Clusters C and F in Table S3, no significant common pathway was found for Clusters C and F. Although different stress factors may result in similar transcriptomic profiles, the pathways through which they affect cells may be different. This may be a reason for the observed results regarding Clusters C and F.
Methanol, magnesium chloride, and acetic acid stress constituted Cluster A. Methanol stress has an impact on cell walls and membranes [49]. Acetic acid toxicity causes ROS production and cell death induction in S. cerevisiae [47]. Six upregulated and eight downregulated genes were identified as cluster-specific for Cluster A. Some of the cluster-specific upregulated genes were associated with the proteasome, cysteine, and methionine metabolism, or the pentose-phosphate pathway (Table 4). Some of the cluster-specific downregulated genes were associated with ribosome and pyrimidine metabolism, and genes associated with membrane functions were also found. A total of 187 genes were identified as significantly upregulated compared to other clusters. Among the upregulated genes, MAPK signaling has been associated with a number of metabolic pathways, particularly meiosis. MAPK signaling and proteasome pathways were also upregulated according to Table S3 for this cluster. Compared to other clusters, 62 genes were found to be significantly downregulated. Most of these genes were associated with ribosome, purine, pyrimidine, RNA, and various amino acid biosyntheses. These pathways were also downregulated, according to Table S3. These effects are consistent with the observed pathways.
Cluster B is composed of transcriptomic data related to coniferyl aldehyde, rapamycin, and caffeine stressors. Coniferyl aldehyde, caffeine, and rapamycin stress can be grouped as pleiotropic drug stress that leads to a wide range of biological effects [16,18,36]. Thus, a number of genes with various pathways were detected. Eleven upregulated and 24 downregulated genes were identified that are specific to Cluster B. Some of the specific upregulated genes were associated with steroid and purine metabolism, TCA cycle, and aminoacyl-tRNA pathways (Table 4). Many of the downregulated genes in the cluster-specific group were also associated with the ribosome and autophagy. Among the clusters, the majority of the 68 significantly upregulated genes were associated with the metabolism of various amino acids and glycolysis/gluconeogenesis. Among the clusters, 227 significantly downregulated genes were related to oxidative phosphorylation, the ribosome, ABC transporters, protein-related (proteasome and protein processing), and RNA-related (RNA degradation, RNA polymerase, and Spliceosome) pathways. According to Table S3, energy production (aminoacyl-tRNA), amino acid and nucleotide metabolism (purine metabolism and alanine, aspartate, and glutamate metabolism), and metabolic and environmental responses (biosynthesis of secondary metabolites, one carbon pool by folate and 2-oxocarboxylic acid pathways) were the upregulated pathways. Oxidative phosphorylation and protein processing in ER pathways were the downregulated pathways for this cluster.
Cluster D consists of close transcriptomic profiles of copper and sodium chloride stresses. Highly expressed copper metallothionein proteins in response to sodium chloride stress may be effective in the formation of this cluster [52]. Copper-induced oxidative stress and sodium chloride stress cause osmotic and ionic stress [53,54]. Only 18 cluster-specific upregulated and 41 cluster-specific downregulated genes were detected for Cluster D. Cluster-specific upregulated genes associated with the ribosome, biosynthesis of amino acids, RNA polymerase, and base excision repair pathways (Table 4). TAH18 is an upregulated gene in this cluster that is involved in nitric oxide biosynthesis [31]. Nitric oxide is an antioxidant and provides the cells with tolerance against oxidative stress [55]. Cluster-specific downregulated genes were found to be related to oxidative phosphorylation, TCA cycle, galactose, and various metabolic pathways. Twenty-six common pathways with the same upregulation and downregulation patterns were detected according to Table S3.
Metals (manganese, iron, and chromium), boron that is a metalloid, two ethanol group stressors (ethanol and 2-phenylethanol), and sodium acetate stress contributed to Cluster E. Metal groups cause ROS formation in the cell, disrupt membrane functionality and affect proteins, as they disturb ion homeostasis [56,57]. The osmotic pressure caused by sodium acetate stress also affects the membrane [40]. Ethanol groups lead to oxidative stress and membrane disruption [21,48]. In Cluster E, 26 significantly upregulated and nine significantly downregulated genes were found, compared to other clusters. Upregulated genes were generally associated with membrane, mitochondrial, protein processing, and antioxidant GO terms. The pathways related to these genes were oxidative phosphorylation, protein processing in ER, proteasome, fructose and mannose metabolism, meiosis, and arginine biosynthesis (Table 4). CUP1-1 and CUP1-2 are upregulated genes in this cluster, which encode a metallothionein involved in the detoxification of metal ions in the cell as antioxidants [31]. Downregulated genes were also associated with the membrane GO terms. For pathway annotations, purine metabolism, cysteine and methionine metabolism, and thiamine metabolism were associated with downregulated genes (Table 4). The collective effect of all these stressors was observed in genes that are significant for this cluster. As shown in Table S3, the oxidative phosphorylation pathway was upregulated for all stressors in this cluster.
In this study, the aim was to investigate the stress-resistance mechanisms in yeast by utilizing the transcriptomic data obtained from various stress-resistant, evolved yeast strains and stress factors categorized within the stress–cross-resistance matrix, such as metal ion stress, pleiotropic drug stress, oxidative stress, and osmotic stress. To achieve this aim, several clusters were created using transcriptomic data associated with the stress factors (Figure 3). In a previous study by our group, transcriptomic data obtained from some of the evolved yeast strains used in the present study (strains that are resistant to ethanol, caffeine, coniferyl aldehyde, iron, nickel, 2-phenylethanol, and silver stress) were analyzed using a genome-scale metabolic model, to elucidate how yeast adapts to these diverse stressors [58]. The close similarity found between the in silico flux values of the coniferyl aldehyde- and caffeine-resistant strains, compared to the other strains, a metric associated with cross-resistance outcomes, corresponds with the separation of the pleiotropic drug group in our study, as shown in Figure 1 and Figure 3. According to the genome-scale metabolic model in our previous study, aldehyde dehydrogenase activity was found to increase in the coniferyl aldehyde- and caffeine-resistant strains [58]. Similarly, in our current study, the gene encoding aldehyde dehydrogenase was also significantly upregulated in Cluster B, which includes pleiotropic drug stress factors, compared to other clusters (Table S4). In another study, a yeast isolate (BT0510) resistant to multiple stresses was used to investigate the transcriptional stress response to osmotic, oxidative, and glucose withdrawal stresses using the RNA-sequencing method [8]. As a result of the study, the pentose-phosphate pathway, peroxisome activity, and oxidative stress response were highlighted as key processes in responding to the applied stresses. The study identified SYM1, STF2, and HSP as prominent, along with ADR1 and USV1, encoding key transcription factors. In our study, STF1 was identified as a significantly upregulated gene for Cluster 1 and STF2 for Cluster 5 (Figure 1). In addition, HSP genes (HSP60 and HSP10) and genes interacting as co-chaperones with HSPs (e.g., HLJ1, CDC37, CNS1, UTP21) emerged as prominent genes across multiple clusters. Hosiner et al. (2014) conducted acute toxicity tests of ten metals on S. cerevisiae [28]. They reported the stress response of S. cerevisiae against the tested metals by microarray analysis. Oxidative stress response, iron homeostasis and metal scavengers, protein catabolism, and repression of protein synthesis were identified as the affected mechanisms in S. cerevisiae [28]. Our analyses conducted with cobalt- and nickel-resistant yeast strains in this study showed that, in addition to common protein synthesis repression, RNA polymerase and nucleotide metabolism were negatively impacted (Table S2). Jin et al. (2008) investigated the genomic effects of exposing S. cerevisiae to various metals (some of Group IB, IIB, VIA, and VB elements) through deletome and transcriptomic analyses [59]. Common metal-responsive (CMR) genes were identified as a prominent group and were found to be associated with various biological processes. Induced CMR genes were related to metal ion homeostasis, ROS detoxification, polyamine transport, RNA polymerase II transcription, and carbohydrate and fatty acid metabolism. Repressed CMR genes, however, were associated with G-protein signaling, polysaccharide biosynthesis, protein targeting, and transport processes [59]. According to the pathway enrichment analysis results of our study with metal-resistant yeast strains (silver-, iron-, nickel-, and cobalt-resistant strains), starch and sucrose metabolism, glycerolipid metabolism, secondary metabolite biosynthesis, carbon metabolism, glycolysis/gluconeogenesis metabolism, TCA cycle pathways were found to be enriched among the upregulated genes of the metal-resistant yeast strains (Table S2). It is evident from both studies that during the interaction of yeast cells with metals, carbohydrate and fatty acid metabolism are positively enriched, either directly or indirectly. On the other hand, nucleotide metabolism and RNA polymerase were enriched among downregulated genes (Table S2).
The existing studies in the literature have typically investigated either the transcriptomic stress response of a wild-type yeast strain under acute stress with a specific category, e.g., metals/heavy metals stress, or the stress-resistance mechanisms of a yeast isolate with multi-stress resistance [8,28,59]. In our study, however, transcriptomic data from 12 yeast strains—evolved through evolutionary engineering from a single S. cerevisiae reference strain and resistant to 11 industrially important stressors—were analyzed, along with the data from a cross-resistance matrix involving 26 stress factors, both separately and together. Based on these data, clusters of evolved yeast strains were formed from both a transcriptomic and physiological perspective. These clusters were compared, and specific genes that were significantly expressed within the stress groups were identified (Tables S1 and S4). To the best of our knowledge, this study is the first one to evaluate yeast strains that evolved under multiple individual stress conditions and investigate the cross-resistance mechanisms of evolved yeast strains.
Analyses and evaluations showed that by comparing and grouping evolved yeast strains classified by their cross-resistance against various stress factors using the spot assay method. We could identify critical genes (Table S4). These genes are crucial for understanding how evolved yeast strains develop resistance to the stress factors that contribute to cluster formation. It is noteworthy that, in addition to the genes studied in the literature, many uncharacterized genes are also present in the cluster-specific gene lists. Yet, while this finding encourages further functional genomic studies, it also clearly shows the existence of multigenic responses against different stressors.

3. Materials and Methods

3.1. Evolved Strains and Evolutionary Engineering Procedure

Twelve evolutionary engineered S. cerevisiae strains which were obtained previously from Çakar’s laboratory were analyzed in this study: the two ethanol-resistant [14], the boron-resistant [15], the caffeine-resistant [16], the cobalt-resistant [17], the coniferyl aldehyde-resistant strain [18], the iron-resistant strain [19], the nickel-resistant strain [20], the 2-phenylethanol-resistant strain [21], the silver-resistant strain [22], the oxidative stress-resistant [23] and long-lived strain [24].
The strains used in this study (Table 1) were evolved from the S. cerevisiae CEN. PK 113-7D (MATa, MAL2-8c, SUC2) reference strain using evolutionary engineering strategy. Basically, random mutations were generated first by using a chemical mutagen ethyl methane sulfonate (EMS) on the background reference strain, as explained previously [60], except for the caffeine-resistant strain [16], which evolved without EMS mutagenesis. This initial population was then subjected to selection pressure via repetitive batch cultivations in synthetic media (0.67% w/v yeast nitrogen base w/o amino acids, 2% w/v glucose) supplemented with different stressors. In each passage, survival rates were calculated, and the stress level was gradually increased until lethal doses were reached. Following the completion of the selection step, evolved yeast mutants with the desired resistance were isolated for further physiological and molecular characterization.
The growth physiological analyses of the evolved strains were performed both spectrophotometrically and by the measurement of biomass production using cell dry weight analysis. High-performance liquid chromatography (HPLC) was used to quantify the residual glucose, glycerol, acetate, and ethanol, as described previously [20].

3.2. Whole Genome Transcriptomic Analysis

Whole genome transcriptomic analysis was applied to the evolved strains using the Agilent yeast DNA microarray system. Basically, precultures of the evolved strains were cultivated in Yeast Minimal Medium (YMM) (0.67% w/v yeast nitrogen base w/o amino acids, 2% w/v glucose) at 30 °C and 150 rpm until reaching early-exponential phase (~107 cells/mL) and transcriptomic material was isolated using the RNeasy Mini Kit (Qiagen, Germantown, MD, USA) according to the manufacturer’s manual. The RNA Integrity Numbers (RIN) were determined, and RNA samples with RIN numbers at least higher than 7 were used for microarray analysis. The microarray analysis was performed using a one-color Agilent yeast DNA microarray system as described previously [24] with three biological replicates. Detailed information about the evolved strains and the Gene Expression Omnibus (GEO) entry codes for their whole genome transcriptomic data are listed in Table 1.

3.3. Stress-Resistance Estimation by Spot Assay and Formation of the Stress–Cross-Resistance Matrix

To determine the cross-resistance of the evolved strains against various stressors spot assay was performed. For each evolved strain, the overnight precultures were inoculated into 10 mL YMM in 50 mL culture tubes starting from 0.2 OD600 (~2.8 × 106 cells/mL) and cultivated until reaching early-exponential phase (~107 cells/mL) at 30 °C and 150 rpm. 4 OD600/mL (~5.6 × 107 cells/mL) of cells were collected by centrifugation (10,000× g for 5 min.) and resuspended in dH2O. The cell suspensions were serially 10-fold diluted and 5 µL samples were spotted onto YMM plates supplemented with various chemicals (0.5 M sodium acetate, 3 mM cobalt(II) chloride (CoCl2), 0.25 mM copper(II) sulfate (CuSO4), 0.5 mM nickel(II) chloride (NiCl2), 10% v/v ethanol, 0.5 mM hydrogen peroxide, 2 mL/L 2-phenylethanol, 7.5 mM aluminum chloride (AlCl3), 15 mM caffeine, 250 μg/mL propolis, 1 mM coniferyl aldehyde, 40 ng/mL rapamycin, 50 mM ammonium iron (II) sulfate (NH4FeSO4), 0.75 mM sodium chloride (NaCl), 50 °C pulse heat stress, 20 mM manganese(II) chloride (MnCl2), 4 mM vanillin, 1.5 mM sorbitol, 2.5 mM chromium(III) chloride (CrCl3), 1.25 mM magnesium chloride (MgCl2), 50 mM boric acid (H3BO3), 2 cycle (120 min. −20 °C/10 min. +30 °C) freeze–thaw, 10% v/v methanol, 750 μM silver nitrate (AgNO3), 1.75 M potassium chloride (KCl), 0.5% v/v acetic acid, and 10 mM chlorpheniramine). The plates were incubated at 30 °C for 72 h. Representative spot assay results are shown in Figure 4, for rapamycin and iron stress. Cross-resistance results of the evolved yeast strains against various stress types are summarized in the stress–cross-resistance matrix shown in Table 5.

3.4. Differential Gene Expression (DGE) Analysis

The transcriptomic data of the evolved strains were compared with those of the reference yeast strain to conduct the DGE analysis using R (ver. 4.2.2). Initially, Biobase [61], limma [62], and GEOquery [63] packages were imported. Raw microarray data from three replicates, each of evolved and reference strains, were imported and normalized using quantile normalization. A design matrix that distinguishes the evolved strain replicates and the reference strain replicates was created. The contrast matrix was constructed for pairwise comparisons of the evolved strains against the reference strain, based on the design matrix. Linear models for gene expression were fitted using this contrast matrix. The fitted models were subjected to the empirical Bayes method for differential expression evaluation and reducing the bias of the data, and the Benjamini-Hochberg method was used for multiple testing correction and false discovery rate (FDR) control [64]. These steps were applied for each evolved strain listed in Table 1. In addition, the evolved strains were grouped based on their cross-resistance abilities (resistance or sensitivity) for each stress factor. DGE analysis was also performed between these groups of evolved strains.

3.5. Principal Component Analysis (PCA)

Principle component analysis (PCA) is a statistical method that reduces high-dimensional data into a lower-dimensional form while preserving as much variability as possible [65,66]. The principal components (PCs) were calculated as linear combinations of the original data with complex mathematical formulas [67]. The prcomp function of the Stats package (ver. 4.2.2) was used in the R environment to determine the PCs. The first and second PCs were used for plotting the PCA with ggplot2 [68] (ver. 3.5.1).

3.6. K-Means Clustering and the Elbow Method

The data for the PCA plots were clustered using the k-means clustering technique, a widely used and simple clustering algorithm. In k-means clustering, a dataset with “n” objects is divided into “K” distinct subsets or clusters. The process begins by randomly selecting “k” objects as initial cluster centers. Each object was allocated to the nearest cluster until every object was assigned to a cluster. The center of the clusters was revised based on the mean of the assigned objects. This process of reassignment and center updating continues until it results in “k” distinct clusters with significant separation [69]. In R, the k-means clustering was performed using the k-means function from the stats package (version 3.6.2), combined with PCA to optimize cluster separations [70]. The Elbow method was used for determining the ideal number of clusters for the dataset in k-means clustering. The sum of square errors (SSE) for varying cluster numbers was analyzed. The point that the SSE value significantly decreased was detected as resembling an elbow on the plot. This point represents the ideal cluster number for the relevant data set and indicates the trade-off between low SSE and avoiding overfitting [71].

3.7. Gene-Based Clustering by Pairwise Pearson’s Correlation Coefficients (PCC) Calculation

PCC for two objects with paired attributes is calculated by adding up the differences between the means of objects and dividing that total by the product of the squared differences between the means [72]. The pairwise correlation coefficients of logarithmic fold-change (logFC) values of genes with significant differential expression (p-value < 0.05 and |logFC| > 1) were calculated using PCC. Samples were clustered according to their pairwise correlated coefficient values. Groupings were visualized with the clustered heatmap method with the seaborn [73] (ver 0.1.2) library.

3.8. Comparison of the logFC Values of the Genes Between Clusters and Their Pathway Annotation

Comparisons were made between clusters created based on both transcriptomic and stress-cross-resistance profiles. First, significantly differentially expressed (DE) genes (p-value < 0.05) resulting from DGE analysis were identified and merged for all clusters. Genes that were not significantly DE for any sample in the merged gene set were assigned 0. Genes that were DE only in one cluster and not DE in any other sample were reported as “cluster-specific genes” for that cluster. For non-cluster-specific genes, before comparing the clusters, it was checked whether the relevant gene was normally distributed both within the cluster and in the remaining clusters. If the logFC values of the relevant gene were normally distributed, an unpaired t-test was applied. If it was not normally distributed, the Mann–Whitney U test was selected and applied between the relevant cluster and the remaining samples. As a result of these tests, the significant DE genes (p-value < 0.05) were reported as significantly upregulated or downregulated for that cluster. This procedure was applied for all clusters and all genes in the Python (ver. 3.10) environment using Pandas [74] (ver. 1.5.2), numpy [75] (version 1.23.5), and scipy.stats [76] (ver. 1.10.1) (ttest_ind, mannwhitneyu, shapiro packages). The ORF names of the genes that were specific or significant for each cluster were converted to gene names with the biomart package in the Python environment. Pathway annotation of genes was performed from the database available for S. cerevisiae using the Kyoto Encyclopedia of Genes and Genomes (KEGG) [77] package from the Bioservices library in the Python environment.

3.9. Spearman’s Rank Correlation Test

Spearman’s rank correlation is a statistical method that non-parametrically measures the direction and strength of the relationship between two ranked variables [78]. Briefly, Spearman’s rank correlation coefficients were separately calculated to assess the correlations of both the cross-resistance abilities of evolved strains and the cumulative effects of stress factors on the evolved strains. For this purpose, the stress–cross-resistance matrix (Table 5) was imported into a Python environment with the Pandas library [74] (ver. 1.5.2) as a Pandas data frame for use in coefficient calculations. The Spearmanr function from the Scipy library [76] (ver. 1.10.1) was preferred for the pairwise calculation of the correlation coefficients. The correlation matrix, consisting of the calculated correlation coefficients for all pair combinations of the evolved strains, was visualized as a clustered heatmap using seaborn [73] (ver. 0.1.2). To calculate the correlation coefficients between stress factors, the transposition of the imported cross-resistance matrix was used. The same steps as for evolved strains were applied. The clustered heatmap containing the correlations of stress factors was also visualized separately.

3.10. Pathway Enrichment Analysis

Pathway enrichment tests were performed with the significant DE genes resulting from DGE analysis between grouped evolved strains based on cross-resistance against each stressor. The significant DE genes were selected by their p values of <0.05. ClusterProfiler package [79] in R/Bioconductor was applied, and the normalized enrichment scores (NES) of the related pathways for each sample were detected.

4. Conclusions

In conclusion, we have analyzed in this study transcriptomic data from 12 evolved yeast strains and their cross-resistance profiles against 26 different stress factors using spot assay. By clustering the strains and stress factors based on transcriptomic and pathway data, common genes and pathways were identified that significantly contribute to elucidating stress-resistance mechanisms. Clusters were created using PCA and k-means clustering methods. The results showed that stressors such as caffeine and coniferyl aldehyde were clearly different from other stressors based on both physiological and transcriptomic evidence. Pathway analysis results indicated common downregulation in ribosome biogenesis and upregulation of starch and sucrose metabolism across clusters. A variety of genes impacting distinct pathways were identified as significant for each cluster. These were reported individually per cluster, and their associations with stress factors were established. As a future study, the mentioned multigenic results can be tested in vivo as a further effort, and functional analyses can be conducted, including the analysis of transcription factors that regulate genes in individual clusters.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/stresses4040046/s1, Figure S1: Hierarchical cluster heatmap of DEG of transcriptomic data that belong to the evolved strains.; Figure S2: Applied Elbow methods for stress factors to detect optimal cluster numbers.; Figure S3: Heatmap for stress factors based on DEG genes for transcriptomic data based on the cross-resistance of the evolved strains against each stressor.; Table S1: The entire table for cluster-significant genes for transcriptomic data of the evolved strains.; Table S2: Pathway enrichment results for transcriptomic data of the evolved strains.; Table S3: Pathway enrichment results for transcriptomic data belonging to stressors.; Table S4: The entire table for cluster-significant genes for transcriptomic data of stressors.

Author Contributions

Z.P.Ç. and M.B.: project administration, supervision, review, and editing, A.Ö.: formal analysis, software, investigation, visualization, writing—original draft preparation, A.T. and Ö.E.: data curation, editing, C.H.: methodology. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Raw microarray data are available in GEO, accessible through accession numbers indicated in Table 4. Repository of all codes used in the analysis process: https://github.com/AbdulkadirOzel/cakarslab (accessed on 20 September 2024).

Acknowledgments

We thank Mehmet Arif Ergün and Hayrun Nisa Özel for critical discussions on our study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Stresses 04 00046 g001
Figure 1. PCA of normalized transcriptomic data of stress-resistant evolved yeast strains clustered by k-means clustering. The optimum cluster number was determined by the Elbow method, as shown in the upper left graph. Colors represent strains, and shapes represent clusters. Boron-, cobalt-, iron-, nickel-, oxidative stress-, and silver-resistant strains were obtained in the presence of H3BO3, CoCl2, FeCl2, NiCl2, H2O2 and AgNO3, respectively.
Figure 1. PCA of normalized transcriptomic data of stress-resistant evolved yeast strains clustered by k-means clustering. The optimum cluster number was determined by the Elbow method, as shown in the upper left graph. Colors represent strains, and shapes represent clusters. Boron-, cobalt-, iron-, nickel-, oxidative stress-, and silver-resistant strains were obtained in the presence of H3BO3, CoCl2, FeCl2, NiCl2, H2O2 and AgNO3, respectively.
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Figure 2. Clustered heatmap of Spearman’s rank correlation coefficients of the stress–cross-resistance matrix (Section 3.3) of the evolved strains against 26 different stress factors. Boron-, cobalt-, iron-, nickel-, oxidative stress-, and silver-resistant strains were obtained in the presence of H3BO3, CoCl2, FeCl2, NiCl2, H2O2 and AgNO3, respectively.
Figure 2. Clustered heatmap of Spearman’s rank correlation coefficients of the stress–cross-resistance matrix (Section 3.3) of the evolved strains against 26 different stress factors. Boron-, cobalt-, iron-, nickel-, oxidative stress-, and silver-resistant strains were obtained in the presence of H3BO3, CoCl2, FeCl2, NiCl2, H2O2 and AgNO3, respectively.
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Figure 3. PCA of the transcriptomic data obtained as a result of comparing the strains that are resistant and sensitive to each stressor used in the spot assay for cross-resistance analysis. Colors indicate the group to which the stressors belong, and shapes indicate clusters. Boron-, cobalt-, iron-, nickel-, oxidative stress-, and silver-resistant strains were obtained in the presence of H3BO3, CoCl2, FeCl2, NiCl2, H2O2 and AgNO3, respectively.
Figure 3. PCA of the transcriptomic data obtained as a result of comparing the strains that are resistant and sensitive to each stressor used in the spot assay for cross-resistance analysis. Colors indicate the group to which the stressors belong, and shapes indicate clusters. Boron-, cobalt-, iron-, nickel-, oxidative stress-, and silver-resistant strains were obtained in the presence of H3BO3, CoCl2, FeCl2, NiCl2, H2O2 and AgNO3, respectively.
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Figure 4. Cross-resistance test results of the reference and the evolved yeast strains against (a) 40 ng/mL rapamycin and (b) 50 mM iron ((NH4)2 Fe (SO4)2) stress. The results are based on growth on solid YMM plates at 30 °C for 72 h with serial dilutions shown from 100 to 10−5, from left to right.
Figure 4. Cross-resistance test results of the reference and the evolved yeast strains against (a) 40 ng/mL rapamycin and (b) 50 mM iron ((NH4)2 Fe (SO4)2) stress. The results are based on growth on solid YMM plates at 30 °C for 72 h with serial dilutions shown from 100 to 10−5, from left to right.
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Table 1. Brief description and the number of differentially expressed (DE) genes of the evolved yeast strains analyzed in this study.
Table 1. Brief description and the number of differentially expressed (DE) genes of the evolved yeast strains analyzed in this study.
Strain NameStressNumber of DE GenesGEO Accession QueryReferences
UpDownTotal
BA8Boron (H3BO3)5354681003GSE224764[15]
CAF905-2Caffeine97010071977GSE124452[16]
CI25ECobalt (CoCl2)20699305GSE39185[17]
BH13Coniferyl aldehyde8869321818GSE119240[18]
B2Ethanol8373156GSE78759[14]
B8Ethanol14996245GSE78759[14]
8CIron (FeCl2)306315621GSE61317[19]
SRM11Starvation *8538891742GSE99041[24]
M9Nickel (NiCl2)443420863GSE50985[20]
H7Oxidative (H2O2)109811942292GSE184952[23]
C92-Phenylethanol7978171614GSE59353[21]
2ESilver (AgNO3)8778861763GSE143335[22]
* Starvation stress caused a longer chronological life span for this strain; thus, it is mentioned as the “long-lived mutant” in the text.
Table 2. Some of the cluster-specific important genes for the five clusters based on the transcriptomic data of the stress-resistant evolved yeast strains. CS stands for “cluster-specific”, referring to genes that are significant for a particular cluster but not for the others. “Up and “Down” refer to upregulated and downregulated genes, respectively. These genes were statistically compared with other clusters and identified as either upregulated or downregulated.
Table 2. Some of the cluster-specific important genes for the five clusters based on the transcriptomic data of the stress-resistant evolved yeast strains. CS stands for “cluster-specific”, referring to genes that are significant for a particular cluster but not for the others. “Up and “Down” refer to upregulated and downregulated genes, respectively. These genes were statistically compared with other clusters and identified as either upregulated or downregulated.
CategoryORFGene NameFold ChangePathwayCategoryORFGene NameFold ChangePathway
Cluster 1 (Long-lived strain and Silver-resistant strain)
CS. UpYJR104CSOD12.29PeroxisomeUpYLR142WPUT18.05Arginine and proline
YDL142CCRD12.42GlycerophospholipidYER020WGPA22.87Meiosis
YLR240WVPS342.25Inositol
phosphate		YLR102CAPC92.56Cell cycle
YDL215CGDH22.67Arginine biosynthesis
YER170WADK22.3PurineYLR356WATG333.52Mitophagy
YIL098CFMC12.5MitophagyYIL007CNAS22.97Proteasome
YBR018CGAL74.73GalactoseDownYJL148WRPA340.22RNA polymerase
YDL004WATP162.64Oxidative
phosphorylation		YHL001WRPL14B0.36Ribosome
YLR344WPRL26A0.4Ribosome
CS. DownYGR119CNUP570.42Nucleocytoplasmic transportYLR448WRPL6B0.33Ribosome
YDR395WSXM10.38YER171WRAD30.4Basal transcription factors
Cluster 2 (Two ethanol-resistant (B2 and B8) strains and Cobalt-resistant strain)
UpYPR194COPT27.1-DownYFR053CHXK14.78Glycolysis/Gluconeogenesis
YJL212COPT12.85-YGR248WSOL42.93Pentose-phosphate
YHR214W-A-11.79-YDR258CHSP783.91Longevity-regulating
YAR068W 10.29-YML100WTSL14.27Starch and sucrose
Cluster 3 (Iron-resistant strain and Nickel-resistant strain)
CS. UpYHR071WPCL53.77AutophagyUpYJR130CSTR23.36Cysteine and methionine
YGL224CSDT12.51Nicotinate and
nicotinamide		YCL030CHIS42.42Histidine
DownYKL068WNUP1000.43Nucleocytoplasmic transport
CS. DownYNR024WMPP60.41RNA degradationYOR143CTHI800.43Thiamine metabolism
YNL050C-0.43-YDR339CFCF10.44Ribosome biogenesis
UpYGL180WATG12.95Autophagy
Cluster 4 (Boron-resistant strain and 2-phenylethanol-resistant strain)
CS. UpYIL042CPKP12.33-UpYKL096W-ACWP23.37-
YDL023C-2.62-YGL088W 8.43-
YML099W-A-2.40-YJL141CYAK12.37-
YCL004WPGS12.09GlycerophospholipidYDR173CARG822.56Inositol phosphate
YMR069WBUD192.28-YCL057C-A 2.30-
YJL188CNAT42.17-DownYKL218CSRY10.43-
YNL179C-2.41-YJR057WCDC80.41Pyrimidine
CS. DownYJL202C-0.49-YOL014W 0.29-
YCR102C-0.40-YOL064CMET220.44Sulfur
YLR126C-0.45-YBR294WSUL10.15-
Cluster 5 (Caffeine-resistant strain and Coniferyl aldehyde-resistant strain)
CS. UpYMR161WHIJ12.32Protein processing in ERUpYPR094WRDS38.82Spliceosome
YGR281WYOR14.21ABC transportersYOR153WPDR514.81ABC transporters
YMR158WMRPS82.83RibosomeYKL006C-ASFT16.78SNARE interactions in
vesicular transport
YMR230WRPS10B2.8RibosomeYDR377WATP176.95Oxidative phosphorylation
YOR103COST23.85N-Glycan biosynthesisYDR011WSNQ26.88ABC transporters
CS. DownYLR059CREX20.48Ribosome biogenesisDownYFR034CPHO40.2Cell cycle
YDL138WRGT20.41MeiosisYPL058CPDR120.35ABC transporters
YNL220WADE120.42Purine metabolismYGR108WCLB10.13MAPK signaling pathway
YHL004WMRP40.46RibosomeYDL205CHEM30.46Porphyrin metabolism
YDL084WSUB20.43Nucleocytoplasmic transportYDR352WYPQ20.37Efferocytosis
Table 3. Categorization based on the chemical characteristics of stress factors and their effects on cells.
Table 3. Categorization based on the chemical characteristics of stress factors and their effects on cells.
CategoryStress FactorReference
Metals/Heavy metalsCobalt(II) chloride (CoCl2)[28]
Nickel(II) chloride (NiCl2)
Silver nitrate (AgNO3)
Aluminum chloride (AlCl3)
Manganese(II) chloride (MnCl2)
Chromium(III) chloride (CrCl3)[35]
Boric acid (boron is a metalloid) (H3BO3)
Magnesium chloride (MgCl2)
Ammonium iron(II) sulfate
Copper(II) sulfate (CuSO4)
Pleiotropic drugCaffeine[36]
Rapamycin[37]
Coniferyl aldehyde[18]
Propolis[38]
Vanillin[39]
Osmotic stressSodium acetate[40]
Sodium chloride[41]
Potassium chloride[42]
Sorbitol[43]
Oxidative stressHydrogen peroxide[44]
Heat stress[45]
Freeze–thaw stress[46]
Acetic acid[47]
Ethanol[48]
Methanol[49]
2-Phenylethanol[21]
Table 4. Some of the important genes from the clusters formed based on transcriptomic data obtained by grouping the physiological data of the strains and performing DGE analysis. CS denotes cluster-specific. Up and Down denote upregulated and downregulated, respectively.
Table 4. Some of the important genes from the clusters formed based on transcriptomic data obtained by grouping the physiological data of the strains and performing DGE analysis. CS denotes cluster-specific. Up and Down denote upregulated and downregulated, respectively.
CategoryORFGene NameFold ChangePathwayCategoryORFGene NameFold ChangePathway
Cluster A (Methanol, Acetic acid, Magnesium chloride)
CS. UpYJR148WBAT22.09Cysteine and methionineUpYHR200WRPN102.2Proteasome
YNL241CZWF12.15Pentose-phosphateYOR185CGSP22.66Ribosome biogenesis
YOL038WPRE62.02ProteasomeYNL333WSNZ23.11Vitamin B6 metabolism
YER143WDDI12.62-YFL059WSNZ33.06Vitamin B6 metabolism
YGL126WSCS32.58-YPR193CHPA22.63D-Amino acid metabolism
CS. DownYDR020CDAS20.43Pyrimidine metabolismDownYDR321WASP10.42Ala, Asp & Glu metabolism
YDR382WRPP2B0.48RibosomeYLR060WFRS10.45Aminoacyl-tRNA biosynt *
YBR189WRPS9B0.42RibosomeYKL106WAAT10.32Arginine biosynthesis
YBR048WRPS11B0.45RibosomeYOL140WARG80.40Arginine biosynthesis
YIL104CSHQ10.47-YJL088WARG30.44Arginine biosynthesis
Cluster B (Caffeine, Rapamycin, Coniferyl aldehyde)
CS. UpYNL073WMSK12.24Aminoacyl-tRNA biosynt *UpYER073WALD52.6Glycolysis/Gluconeogenesis
YIL125WKGD12.11Citrate (TCA) cycleYDR298CATP52.63Oxidative phosphorylation
YOR142WLSC12.31Citrate (TCA) cycleYAR015WADE12.31Purine metabolism
YNL220WADE122.22Purine metabolismYLR432WIMD32.73Purine metabolism
YGR060WERG252.58Steroid metabolismYFR034CPHO43.62Cell cycle
CS. DownYKL073WLHS10.40Protein processing in ERDownYKL079WSMY10.33Endocytosis
YGL070CRPB90.35RNA polymeraseYJL034WKAR20.41Protein export
YNL006WLST80.44AutophagyYBR101CFES10.36Protein processing in ER
YER117WRPL23B0.33RibosomeYOR234CRPL33B0.39Ribosome
YOR096WRPS7A0.37RibosomeYOR159CSME10.40Spliceosome
Cluster D (Copper, Sodium Chloride)
CS UpYJL071WARG22.12Arginine biosynthesisCS DownYKL141WSDH30.39Citrate (TCA) cycle
YJL148WRPA342.6RNA polymeraseYDR178WSDH40.4Citrate (TCA) cycle
YDR450WRPS18A2.18RibosomeYLR284CECI10.41Nucleocytoplasmic transport
YMR093WUTP152.45Ribosome biogenesisYBR018CGAL70.27Galactose metabolism
YML060WOGG12.42Base excision repair-YIL162WSUC20.30Galactose metabolism
Cluster E (Manganase(II) chloride, Ammonium iron(II) sulfate, Chromium(III) chloride, Boric acid, Ethanol, 2-phenylethanol, Sodium acetate)
UpYDR322C-ATIM112.69Oxidative phosphorylationDownYHL028WWSC40.26-
YLR080WEMP462.60Protein processing in ERYDR222W-0.33-
YDR363W-ASEM12.75ProteasomeYIR032CDAL30.35Purine metabolism
YEL070WDSF13.06Fructose and mannose
metabolism		YFR055WIRC70.33Cysteine and methionine
metabolism
YCR097WHMRA12.64MeiosisYBR092CPHO30.37Thiamine metabolism
Note 1: Biosynt * means biosynthesis. Note 2: In Clusters C and F, no significantly differentially expressed genes were found compared to other clusters; therefore, they are not included in the table.
Table 5. Stress-cross-resistance matrix of the evolved strains based on the physiological spot assay results. By comparing the growth or survival performance of an evolved strain with the reference strain during the spot assay, it was shown that they had resistance (1) or sensitivity (−1) against the tested stressors. Strains showing the same growth/survival performance as the reference strain are indicated with (0) in the matrix.
Table 5. Stress-cross-resistance matrix of the evolved strains based on the physiological spot assay results. By comparing the growth or survival performance of an evolved strain with the reference strain during the spot assay, it was shown that they had resistance (1) or sensitivity (−1) against the tested stressors. Strains showing the same growth/survival performance as the reference strain are indicated with (0) in the matrix.
Strains2E8CBA8BH13B2B8C9CI25ECAF905-2H7M9SRM11
Stress Factor
Acetic acid0000110−1−10−10
Aluminum chloride (AlCl3)00−1−100−1−1−1−1−11
Ammonium iron(II) sulfate−111−111−11−1110
Boric acid (H3BO3)−101−111−1−1−1001
Caffeine−1001000−110−10
Chromium(II) chloride−110−11001−11−10
Cobalt(II) chloride (CoCl2)110011−110110
Coniferyl aldehyde−1−111000010−10
Copper(II) sulfate (CuSO4)100−1−1000−11−11
Ethanol−101−1111−1−10−11
Freeze–thaw1010111001−11
Hydrogen peroxide (H2O2)001−1001−1−11−1−1
Magnesium chloride (MgCl2)−1000110−1−10−10
Manganese(II) chloride (MnCl2)−111−11111−1110
Methanol−100−1110−1−10−10
Nickel(II) chloride (NiCl2)−111110011110
2-Phenylethanol−111−11111−10−11
Potassium chloride (KCl)0−11−1−1−1−1−1−10−10
Propolis0111111111−1−1
Heat stress0−11−1−1−111−11−11
Rapamycin0001000−110−10
Silver nitrate (AgNO3)100−101−1−100−10
Sodium acetate−110−111−10−11−10
Sodium chloride (NaCl)−10−1−1−1−1−1−1−11−1−1
Sorbitol0101110−1−11−10
Vanillin0000110−100−10
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Özel, A.; Topaloğlu, A.; Esen, Ö.; Holyavkin, C.; Baysan, M.; Çakar, Z.P. Transcriptomic and Physiological Meta-Analysis of Multiple Stress-Resistant Saccharomyces cerevisiae Strains. Stresses 2024, 4, 714-733. https://doi.org/10.3390/stresses4040046

AMA Style

Özel A, Topaloğlu A, Esen Ö, Holyavkin C, Baysan M, Çakar ZP. Transcriptomic and Physiological Meta-Analysis of Multiple Stress-Resistant Saccharomyces cerevisiae Strains. Stresses. 2024; 4(4):714-733. https://doi.org/10.3390/stresses4040046

Chicago/Turabian Style

Özel, Abdulkadir, Alican Topaloğlu, Ömer Esen, Can Holyavkin, Mehmet Baysan, and Zeynep Petek Çakar. 2024. "Transcriptomic and Physiological Meta-Analysis of Multiple Stress-Resistant Saccharomyces cerevisiae Strains" Stresses 4, no. 4: 714-733. https://doi.org/10.3390/stresses4040046

APA Style

Özel, A., Topaloğlu, A., Esen, Ö., Holyavkin, C., Baysan, M., & Çakar, Z. P. (2024). Transcriptomic and Physiological Meta-Analysis of Multiple Stress-Resistant Saccharomyces cerevisiae Strains. Stresses, 4(4), 714-733. https://doi.org/10.3390/stresses4040046

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