Ethanol production from lignocellulosic biomass holds promise as an alternative fuel. However, industrial stresses, including ethanol stress, limit microbial fermentation and thus prevent cost competitiveness with fossil fuels. To identify novel engineering targets for increased ethanol tolerance, we took advantage of natural diversity in wild Saccharomyces cerevisiae strains. We previously showed that an S288c-derived lab strain cannot acquire higher ethanol tolerance after a mild ethanol pretreatment, which is distinct from other stresses. Here, we measured acquired ethanol tolerance in a large panel of wild strains and show that most strains can acquire higher tolerance after pretreatment. We exploited this major phenotypic difference to address the mechanism of acquired ethanol tolerance, by comparing the global gene expression response to 5% ethanol in S288c and two wild strains. Hundreds of genes showed variation in ethanol-dependent gene expression across strains. Computational analysis identified several transcription factor modules and known coregulated genes as differentially expressed, implicating genetic variation in the ethanol signaling pathway. We used this information to identify genes required for acquisition of ethanol tolerance in wild strains, including new genes and processes not previously linked to ethanol tolerance, and four genes that increase ethanol tolerance when overexpressed. Our approach shows that comparative genomics across natural isolates can quickly identify genes for industrial engineering while expanding our understanding of natural diversity.
CELLULOSIC materials are an attractive source for biofuel production, given the availability of agricultural residues that do not directly compete with food sources (Solomon 2010). However, fermentation of cellulosic biomass is problematic. Stressful by-products generated during preprocessing, coupled with the unique composition of pentose and hexose sugars, limit microbial ethanol production. Significant attention is therefore being dedicated toward engineering stress-tolerance microbes for cellulosic fermentation.
Saccharomyces cerevisiae has been the organism of choice for ethanol production, because of its inherent ethanol tolerance. However, high ethanol levels can still inhibit viability and fermentation, and engineering greater ethanol resistance has led to improved bioethanol production (Alper et al. 2006). Ethanol affects many cellular processes, including membrane fluidity, protein stability, and energy status (reviewed recently in Stanley et al. 2010). Recent genetic screens have implicated additional genes important for ethanol tolerance, including those involved in vacuolar, peroxisomal, and vesicular transport, mitochondrial function, protein sorting, and aromatic amino acid metabolism (Kubota et al. 2004; Fujita et al. 2006; Van Voorst et al. 2006; Teixeira et al. 2009; Yoshikawa et al. 2009). Yet despite the attention to the mechanism of ethanol tolerance, significant gaps in our knowledge remain.
Several studies have also investigated the global gene expression response to ethanol (Alexandre et al. 2001; Chandler et al. 2004; Fujita et al. 2004; Hirasawa et al. 2007). However, mutational analysis shows that most genes upregulated by ethanol are not required for ethanol tolerance (Yoshikawa et al. 2009). Thus, gene expression responses in a single strain are poor predictors of genes important for tolerance of the initial stressor. Instead, we have argued that the role of stress-dependent gene expression changes is not to survive the initial stress, but rather to protect cells against impending stress in a phenomenon known as acquired stress resistance (Berry and Gasch 2008). When cells are pretreated with a mild stress, they often acquire tolerance to what would otherwise be a lethal dose of the same or other stresses. Consistently, the gene expression response triggered by a single stress treatment has no impact on surviving the initial stress, but instead is critical for the increased resistance to subsequent stress (Berry and Gasch 2008). However, it remains true that relatively few of the expression changes are important for subsequent tolerance of a particular stress. Thus, identifying the important genes remains a challenge.
Our understanding of the physiological and transcriptional response to ethanol has been further narrowed since most studies focus on laboratory-derived strains. While ethanol tolerance and adaptation have been explored in sake, wine, and industrial yeast strains (Rossignol et al. 2003; Wu et al. 2006), we have only recently begun to appreciate the physiological diversity of natural yeast isolates. Wild yeast isolates from diverse environments have widely varying phenotypes under various conditions, and many of these phenotypes may be related to variation in gene expression (Cavalieri et al. 2000; Fay et al. 2004; Kvitek et al. 2008). Here we exploited strain-specific differences in the physiological and transcriptional response to ethanol. We compared strains with and without the ability to acquire increased ethanol tolerance after ethanol pretreatment, then identified corresponding gene expression differences across strains. This rapidly revealed genes that were involved in acquired ethanol tolerance and identified several new genes that increase ethanol tolerance when overexpressed. By applying systems biology approaches to the analysis of phenotypic diversity, we have generated a new understanding of the transcriptional response to ethanol and have identified novel genes involved in its tolerance.
MATERIALS AND METHODS
Strains, culture media, and growth conditions:
Strains used are listed in supporting information, File S1. All chemicals were purchased from Sigma (St. Louis, MO). Gene deletions were created by homologous recombination that replaced the gene-coding sequence with KanMX3 drug resistance cassettes. The HO gene was replaced with the HygMX3 cassette to generate a haploid YPS163 upon dissection, and this was used as the background in all YPS163 strain knockouts. The haploid strain behaved similarly to the diploid strain in all ethanol resistance assays (compare Figure 1D [diploid] and Figure S3 [haploid]). All mutations were confirmed by diagnostic PCR.
Ethanol resistance assays:
Acquired ethanol resistance was assayed as in Berry and Gasch (2008). Briefly, cultures were grown in YPD (1% yeast extract, 2% peptone, 2% glucose) for at least eight generations to an optical density (OD600) of 0.3. Each culture was split into two cultures and received either a single dose of 5% (v/v) ethanol or 5% water as a mock control. Mock-treated cells were thereafter handled identically. Cells were exposed to a panel of severe ethanol doses (ranging from 5 to 25% v/v depending on the experiment) in YPD for 2 hr in 96-well plates. A 50-fold dilution of each culture was spotted onto YPD agar plates and grown for 48 hr, after which viability at each dose was scored by visual inspection using a four-point scale to score 100%, 50–100%, 10–50%, or 0% survival compared with the no stress (YPD) control. An overall ethanol tolerance score was calculated as the sum of scores over 11 doses of stress.
Cycloheximide experiments were performed as above, except that 10 μg/ml cycloheximide was added to the culture 20 min before and throughout the ethanol pretreatment. A mock-treated culture received inhibitor treatment but no primary stress. Long-term ethanol tolerance was scored by plating cells on YPD + 8% (v/v) ethanol. Growth was scored after 3 days (or 2 days in the case of controls).
To measure the effects of gene overexpression, BY4741 cells harboring galactose-inducible, GST-tagged constructs (Open Biosystems, Huntsville, AL; Sopko et al. 2006) were grown overnight on SC -Ura containing 2% dextrose, and then subcultured for at least 8 generations in SC -Ura containing 2% galactose to induce overexpression before exposure to ethanol as described above. All overexpression strains were compared to the isogenic BY4741 containing the vector only control (pEGH). Ethanol tolerance was scored using both the spot assay described above and flow cytometry. For flow cytometry, viability was assayed using the LIVE/DEAD FungaLight yeast viability kit (Invitrogen, Carlsbad, CA) on a Guava EasyCyte flow cytometer (Millipore, Billerica, MA) according to both manufacturers' instructions. Briefly, mock and ethanol-treated cells were diluted 10-fold into 10 mm HEPES–NaOH (pH 7.2 at 25°) supplemented with 2% dextrose and the viability dye reagents (SYTO 9 and propidium iodide). The proportion of prodium iodide negative cells was reported as percentage of viable cells.
Array hybridization and analysis:
Cells were grown overnight for at least eight generations to an OD600 of 0.3–0.6. A sample of cells was collected (time 0), and ethanol was added to a final concentration of 5% (v/v). Cells were collected at 15, 30, 45, and 60 min post-ethanol addition. A single biological replicate was collected for each strain during the time course. For detailed analysis of the 30-min time point, biological triplicates were collected using a paired experimental design. Cell collection, RNA isolation, and microarray labeling were performed as described (Gasch 2002), using cyanine dyes (Flownamics, Madison, WI), Superscript III (Invitrogen, Carlsbad, CA), and amino-allyl-dUTP (Ambion, Austin, TX). Microarrays were spotted in house using 70mer oligonucleotides representing each of the yeast ORFs (Qiagen, Chatsworth, CA). We previously showed that <5% of measured expression differences could be affected by hybridization defects due to polymorphism (Kvitek et al. 2008). Arrays were scanned using a scanning laser (GenePix 4000B) from Molecular Devices (Sunnyvale, CA). Inverse dye labeling was used in replicates to control for dye-specific effects. Data were filtered (retaining unflagged spots with R2 > 0.1) and normalized by regional mean centering (Lyne et al. 2003). Genes with significant expression differences in response to ethanol were identified separately for each strain by performing a t-test using the BioConductor package Limma v. 2.9.8 (Smyth 2004) and FDR correction (Storey and Tibshirani 2003) (see File S3 for the Limma output). Expression differences in YPS163 or M22 relative to S288c, both with and without ethanol treatment, were identified in a similar manner. Gene clustering was done in Cluster 3.0 (http://bonsai.ims/u-tokyo.ac.jp/∼mdehoon/software) using hierarchical clustering and uncentered Pearson correlation as the metric (Eisen et al. 1998). Arrays were weighted using a cutoff value of 0.4 and an exponent value of 1. Enrichment of gene ontology (GO) functional categories was performed using GO-TermFinder (http://go.princeton.edu/cgi-bin/GOTermFinder) hosted by the Lewis–Sigler Institute for Integrative Genomics (Boyle et al. 2004), with Bonferroni-corrected P-values < 0.01 taken as significant. Clusters were analyzed for enrichment of known transcription factor ChIP-Chip targets (Harbison et al. 2004) using Fisher's exact test and by upstream motif identification using multiple em for motif elicitation (MEME; Bailey and Elkan 1994). MEME parameters were model, tcm; minimum width, 6; maximum width, 12; maximum number of motifs, 3. All microarray data are available through the NIH Gene Expression Omnibus (GEO) database under accession number GSE22904.
Lipidomic gas chromatography mass spectrometry:
Cells were grown in synthetic complete (SC; Sherman 2002) medium for at least 8 generations to an OD600 of 0.3–0.6. Acquired ethanol tolerance was similar in SC vs. YPD (data not shown). Two technical replicate samples were collected for each biological sample; biological triplicates were collected. Cells were collected immediately prior to the addition of 5% (v/v) ethanol (time 0) and at 60 min after the ethanol addition. For the collections, 2 ml of cells were added directly to 200 μl concentrated HCl (final concentration of 1.1 m) and heated to 95 ° for 1 hr. Total lipids were then extracted by the method of Bligh and Dyer (1959). Preparation of fatty acid methyl esters (FAMEs) was performed using the method of Christie(1989). FAMEs were analyzed by gas chromatography mass spectrometry (GC–MS) using a Pegasus 4D GCxGC-TOF gas chromatograph–mass spectrometer (Leco Corp. St. Joseph, MI) fitted with a Rx1-5MS column (30-m, 0.25-mm ID, 0.25u df; Restek, Inc., Bellefonte, PA). Instrument parameters were: He carrier gas flow rate: 1 ml/min; split ratio: 5:1; injector temperature: 250°, GC oven: 50° for 1 min initially, increased at 20°/min to 330°, and held at 330° for 5 min.
Natural variation in acquired ethanol tolerance in diverse yeast strains:
We previously showed that an S288c-derived lab strain, pretreated with individual mild stressors, can acquire increased tolerance to either the same or different stresses (Berry and Gasch 2008). However, ethanol was the only pretreatment that did not increase resistance to subsequent stresses, including ethanol itself (Figure 1A and Berry and Gasch 2008). This raised the question of whether ethanol was unique as a stressor, or whether the S288c laboratory strain was anomalous. To test this, we performed acquired ethanol tolerance assays on 47 diverse strains from vineyards, oak exudate, sake and wine fermentations, clinical settings, and other natural environments. Cells were exposed to 5% ethanol for 60 min, then exposed to a panel of 11 high doses of ethanol (Figure 1 and materials and methods). Intriguingly, most (but not all) strains tested could acquire further ethanol tolerance after mild pretreatment (Figure 1B). The major progenitor strain of S288c, EM93 (Mortimer and Johnston 1986), showed some acquisition of ethanol tolerance after a pretreatment (Figure 1B and File S1), suggesting that S288c lost this ability relatively recently. We subsequently focused on two wild strains—oak-soil strain YPS163 and the vineyard strain M22—to probe the physiology of acquired ethanol resistance.
Variation in the genomic expression response to ethanol:
Acquired resistance to several stresses requires nascent protein synthesis during the mild-stress pretreatment (Berry and Gasch 2008). Consistently, we found that acquired ethanol resistance in wild strains also requires protein synthesis during pretreatment (Figure 1, C and D). We therefore suspected that S288c may have an altered genomic expression response to ethanol. We used whole-genome DNA microarrays to measure the gene-expression response of S288c, YPS163, and M22 responding to 5% ethanol over a 60-min time course (Figure 2A). To identify statistically significant differences between strains, we performed biological triplicates before and at 30 min after ethanol treatment, which encompassed the peak response.
As expected, ethanol induced a dramatic remodeling of the yeast transcriptome. Over half of the genome (3941 genes, false discovery rate, FDR, of 0.01) was significantly affected by ethanol in any of the three strains, with similar kinetics (Figure S1). Genes induced more than threefold were enriched for certain functional categories, including vacuolar catabolic processes, response to temperature stimulus, glucose metabolism, alcohol catabolism, and metabolism of energy reserves including glycogen and trehalose (Bonferroni-corrected P < 0.01 in all cases). The genes significantly repressed more than threefold by ethanol were strongly enriched for ribosome biogenesis and protein synthesis. Together, these results are consistent with activation of the yeast environmental stress response (Gasch et al. 2000) and largely agree with the previous literature (Alexandre et al. 2001; Chandler et al. 2004; Fujita et al. 2004; Hirasawa et al. 2007).
We next identified genes with larger ethanol-responsive induction in wild strains compared to S288c, reasoning that they may account for the phenotypic difference in acquired ethanol tolerance. We therefore identified expression differences between each wild strain compared directly to S288c (FDR < 0.05). There were 1555 genes (25%) and 1662 genes (27%) differentially expressed in response to ethanol in M22 and YPS163, respectively, compared to S288c—875 of these genes were common to both comparisons (Figure 2B). In contrast, the two wild strains compared to each other showed differential ethanol response at only 735 genes, revealing a large fraction of S288c-specific differences. A fraction (38–45%) of the 875 ethanol-responsive genes that distinguish S288c from the wild strains also showed underlying differences in basal gene expression (393/875 in M22 compared to S288c and 329/875 in YPS163 vs. S288c). However, there was little overlap in functional groups enriched in genes with basal expression differences compared to genes with variation in ethanol response (File S2). Together, this indicates significant variation in the gene expression response to ethanol.
Network analysis implicates transcription factors underlying expression differences:
To identify patterns in the data set, we hierarchically clustered ∼2300 genes with ethanol-dependent expression differences in either strain compared to S288c (FDR < 0.05, Figure 2A). We systematically scored enrichment of GO functional categories for each cluster (File S2). Several gene clusters with higher induction in both wild strains were enriched for functional categories, including vacuolar protein catabolism, trehalose biosynthesis, response to oxidative stress, alcohol metabolism, and proteolysis (cluster J), and transposition (clusters L and M). Several gene clusters actually showed higher induction in S288c, such as oxidative phosphorylation and cellular respiration (cluster H) and protein folding (cluster I). These may represent processes that are more strongly affected by ethanol in the S288c background.
The results of the clustering analysis suggested upstream differences in physiology and/or ethanol signaling that affected many genes in trans. We sought to implicate transcription factors required for a robust ethanol response and to examine whether variability in transcription factor function was responsible for S288c's inability to mount a proper response to ethanol.
We first ruled out a known polymorphism in the S288c HAP1 gene (Gaisne et al. 1999), which encodes a transcription factor involved in heme and oxygen sensing (Figure S2 and Figure S3). Clustering analysis and transcription factor-target and motif enrichment (see materials and methods) implicated three additional transcriptional regulators: Rpn4, which regulates proteasome genes, the oxidative-stress transcription factor Yap1, and the stress-activated factor Msn2. The targets of Rpn4 and Yap1 showed weaker induction in S288c compared to both wild strains, indicating variation in their responsiveness to ethanol. However, neither Rpn4 nor Yap1 had an effect on acquired ethanol tolerance, as mutants lacking either gene acquired ethanol resistance at wild-type levels (Figure S4).
In contrast, deletion of msn2 in YPS163 impaired both acquired ethanol resistance and gene expression. The YPS163 msn2Δ mutant showed reduced acquisition of ethanol tolerance after pretreatment but no difference in basal ethanol tolerance (Figures 3A and 4A). Transcriptional profiling of the YPS163 msn2Δ mutant responding to 5% ethanol identified 244 genes with attenuated gene induction (FDR < 0.01; File S3), confirming involvement of Msn2 in the ethanol response. Of the 239 Msn2-regulated genes, 106 (44%, p = 4x10−9, Fisher's exact test) also had significantly lower induction in S288c responding to ethanol compared to YPS163 (Figure 3B). This suggests that Msn2 activation by ethanol is partially defective in S288c (see discussion), and implicates one or more Msn2 targets as likely direct effectors of acquired ethanol tolerance.
Identifying mutants with defects in acquired ethanol resistance:
To identify additional effectors of acquired ethanol resistance, we generated deletion mutants of 20 manually chosen genes, implicated by their reduced induction in S288c (see materials and methods). Strikingly, over 50% of the genes interrogated (Table 1) were required for normal acquisition of ethanol tolerance, indicating that our method produced a “hit rate” significantly higher than that of other studies (3–6%) (Kubota et al. 2004; Fujita et al. 2006; Van Voorst et al. 2006; Teixeira et al. 2009; Yoshikawa et al. 2009). We identified eight genes (in addition to MSN2) that were necessary for acquired ethanol resistance (ELO1, SLA1, AIP1, TPS1, EDE1, GPB2, PEP4, and OAC1; Figure 4A). These genes participate in a variety of cellular functions including RAS signaling (GPB2), cytoskeleton/endocytosis (SLA1, AIP1, EDE1), vacuolar protein degradation (PEP4), mitochondrial transport (OAC1), fatty acid lipid elongation (ELO1), and trehalose biosynthesis (TPS1). Several of these genes (ELO1, EDE1, PEP4, and OAC1) had never been linked to ethanol resistance before and showed no discernable ethanol sensitivity in prior ethanol screens (see discussion).
To further characterize the behavior of the mutant strains, we analyzed long-term growth on agar plates containing ethanol (Figure 4B). Three of the strains (YPS163 gpb2Δ, pep4Δ, and tps1Δ mutants) were unable to grow in the presence of ethanol, and two additional strains had weak defects (sla1Δ, sod2Δ). However, the remaining mutants (amounting to 50%) had no discernable defect in surviving a single dose of ethanol (Figure 4B). Thus, these genes play a specific role in acquired ethanol tolerance, highlighting that the mechanism is overlapping with but distinct from the mechanism of basal ethanol tolerance.
We hypothesized that genes involved in acquired ethanol resistance are potential engineering targets for improving ethanol resistance. We used galactose-inducible overexpression constructs in S288c to test for increased basal ethanol tolerance. Growth in galactose increased basal ethanol tolerance in even the control strain. However, overexpression of MSN2 in S288c further increased ethanol resistance beyond that of the control strain, as was expected (Watanabe et al. 2009). In fact, half of the other overexpressing constructs tested (TPS1, EDE1, and ELO1) significantly increased basal ethanol resistance compared to the control (Table 1 and Figure 4, C and D). This indicates that effectors of acquired ethanol resistance are productive targets for directed engineering.
Strain-specific differences in lipid composition:
The requirement for fatty acid elongase I (Elo1) raised the possibility that S288c may not properly remodel its plasma membrane in response to the fluidizing effects of ethanol. We therefore performed GC–MS analysis of the total membrane fatty acids from S288c, YPS163, and the YPS163 elo1Δ strain, in either the presence or the absence of 5% ethanol.
In response to ethanol, YPS163 increased the proportion of oleic acid (18:1) in the membrane, with a commensurate decrease in palmitic acid (16:0) (Figure 5). Indeed, higher levels of oleic acid are known to correlate with higher ethanol tolerance (You et al. 2003). The membrane lipid profile of S288c contrasted with YPS163, since basal levels of palmitic acid were higher while oleic acid was lower in S288c. Upon ethanol treatment, S288c was able to increase its oleic acid content but not to levels seen in YPS163 (Figure 5). Thus, the difference in lipid content in S288c correlates with its inability to acquire ethanol resistance after a mild pretreatment.
Given that the YPS163 strain lacking ELO1 had a defect in acquired ethanol tolerance, we expected it would have lower levels of long-chain fatty acids, and specifically oleic acid (C18:1). Starting levels of 14:0 were slightly higher than the YPS163 parent, similar to the S288c strain (Figure 5). However, following ethanol treatment the membrane lipid profile of the YPS163 elo1Δ strain did not differ measurably from wild-type YPS163. The effect of Elo1 on lipid profiles may be obscured by technical limitations of our study, since we were unable to observe subclasses of these lipids. Nonetheless, this result shows that the effect of Elo1 is more complicated than simply elongating fatty acid chains (see discussion).
By taking advantage of the phenotypic diversity in yeast ethanol responses, we have identified new genes and processes required for ethanol tolerance while providing a glimpse into natural variation in S. cerevisiae. A key feature of our approach is to compare and contrast strains with unique phenotypes, to quickly implicate the genetic basis of the phenotypic difference. We identified hundreds of gene expression differences in S288c that correlate with its inability to acquire ethanol tolerance after a mild pretreatment. Strikingly, over 50% of the genes interrogated in our pilot knock-out study were important for the phenomenon of acquired ethanol resistance, a far higher proportion than that of library screens for basal ethanol resistance mutants (3–6%) (Kubota et al. 2004; Fujita et al. 2006; Van Voorst et al. 2006; Teixeira et al. 2009; Yoshikawa et al. 2009). This dramatic improvement in identifying relevant genes resulted not only from the cross-strain comparison, but also from assaying the phenotype most dependent on gene expression changes—that of acquired, rather than basal, stress resistance. Our results highlight the promise of this approach in rapidly identifying new targets for biofuels engineering, particularly for engineering increased microbial tolerance to relevant stresses.
We also generated new insights into the mechanism of ethanol defense by identifying both downstream and upstream effectors. We identified several downstream processes as important for acquired ethanol tolerance, including trehalose metabolism, vacuolar function, actin cytoskeleton, endocytosis, transport, and fatty acid elongation/metabolism. While some of these were previously implicated in ethanol tolerance, their precise roles in acquired ethanol tolerance are not entirely clear. However, many of these processes are likely to affect the cellular membrane. We identified several genes that affect endocytosis, which may be important in remodeling the membrane following ethanol exposure, and is known to be inhibited by ethanol (Meaden et al. 1999). Vacuolar processes, including protein degradation by Pep4, may help to turn over internalized membrane and other proteins. We confirmed trehalose as an important player, since the YPS163 tps1Δ strain had the strongest defect of all the mutants tested, and overexpression of Tps1 conferred higher ethanol resistance. Intriguingly, trehalose can also protect endocytosis from the inhibitory effects of ethanol (Lucero et al. 2000), in addition to its traditional role in protein and membrane stabilization (Singer and Lindquist 1998).
Our lipidomic analysis suggests a complicated role for membrane fatty acid metabolism in ethanol tolerance. While fatty acid desaturation has been previously implicated in ethanol resistance (You et al. 2003), we identified a role for fatty acid elongase in the acclimation to ethanol. ELO1 was induced to higher levels in wild strains responding to ethanol and was required for normal acquisition of ethanol resistance. That ELO1 was not required for basal ethanol tolerance is consistent with a specific role in acquired ethanol tolerance. Despite the implication of Elo1 in this response, the YPS163 elo1Δ strain did not show a discernable defect in oleic acid accumulation (C18:1) compared to its wild-type parent. This may be due to Elo1's effect on lipid subclasses, which we were unable to measure here. Indeed, variations in lipid polar head groups or membrane sterol composition has been shown to affect ethanol tolerance (Walker-Caprioglio et al. 1990; Sajbidor et al. 1995). Future detailed studies of Elo1 will increase our understanding of the role of fatty acid elongation in the acclimation to ethanol stress.
We have also identified upstream regulators of the adaptive response to ethanol, including Msn2. The increased ethanol tolerance afforded by Msn2 overexpression confirms its importance in ethanol resistance. However, the msn2Δ strain had a relatively mild defect in acquired ethanol resistance, suggesting that other regulators play a role. Interestingly, another gene we identified—GPB2—is a negative regulator of the RAS pathway, which suppresses Msn2 activity (Lu and Hirsch 2005). Notably, the gpb2Δ mutation produced a stronger defect in both basal and acquired ethanol resistance compared to the msn2Δ mutation, suggesting that Gpb2 and/or PKA signaling play an additional, Msn2-independent role in ethanol resistance.
Our results have also reflected upon the diversity in S. cerevisiae strains, in terms of stress tolerance, gene expression, and membrane lipid content. Several modules of transcription-factor targets, including genes regulated by Rpn4, Yap1, and Msn2, show variable responses across strains. Most of these genes show no difference in expression in the absence of stress and are revealed only as variably affected upon environmental shift. Although future studies will be required to dissect the precise genetic basis for these expression differences, this work demonstrates the substantial variation in or above regulatory networks that coordinate environmental responses. This, in turn, further underscores the importance of considering multiple strain backgrounds to identify the mechanisms of stress resistance.
Author contributions: J.A.L. and A.P.G designed research; J.A.L. and I.M.E. performed experiments; M.A.M. and A.J.H. performed lipidomic analysis; J.A.L. and A.P.G analyzed data; and J.A.L. and A.P.G wrote the article. This work was funded by the Department of Energy (DOE) Great Lakes Bioenergy Research Center (DOE Office of Biological and Environmental Research Office of Science DE-FC02-07ER64494).
Supporting information is available online at http://www.genetics.org/cgi/content/full/genetics.110.121871/DC1.
Communicating editor: F. Winston
- Received August 5, 2010.
- Accepted September 18, 2010.
- Copyright © 2010 by the Genetics Society of America