Gene expression variation is extensive in nature, and is hypothesized to play a major role in shaping phenotypic diversity. However, connecting differences in gene expression across individuals to higher-order organismal traits is not trivial. In many cases, gene expression variation may be evolutionarily neutral, and in other cases expression variation may only affect phenotype under specific conditions. To understand connections between gene expression variation and stress defense phenotypes, we have been leveraging extensive natural variation in the gene expression response to acute ethanol in laboratory and wild Saccharomyces cerevisiae strains. Previous work found that the genetic architecture underlying these expression differences included dozens of “hotspot” loci that affected many transcripts in trans. In the present study, we provide new evidence that one of these expression QTL hotspot loci affects natural variation in one particular stress defense phenotype—ethanol-induced cross protection against severe doses of H2O2. A major causative polymorphism is in the heme-activated transcription factor Hap1p, which we show directly impacts cross protection, but not the basal H2O2 resistance of unstressed cells. This provides further support that distinct cellular mechanisms underlie basal and acquired stress resistance. We also show that Hap1p-dependent cross protection relies on novel regulation of cytosolic catalase T (Ctt1p) during ethanol stress in a wild oak strain. Because ethanol accumulation precedes aerobic respiration and accompanying reactive oxygen species formation, wild strains with the ability to anticipate impending oxidative stress would likely be at an advantage. This study highlights how strategically chosen traits that better correlate with gene expression changes can improve our power to identify novel connections between gene expression variation and higher-order organismal phenotypes.
A major goal in genetics is to understand how individuals with different genetic makeups respond to their environment. Understanding these “gene-environment interactions” is important for the development of personalized medicine. For example, gene-environment interactions can explain why some people are more sensitive to certain drugs or are more likely to get certain cancers. While the underlying causes of gene-environment interactions are unclear, one possibility is that differences in gene expression across individuals are responsible. In this study, we examined that possibility using baker’s yeast as a model. We were interested in a phenomenon called acquired stress resistance, where cells exposed to a mild dose of one stress can become resistant to an otherwise lethal dose of severe stress. This response is observed in diverse organisms ranging from bacteria to humans, though the specific mechanisms governing acquisition of higher stress resistance are poorly understood. To understand the differences between yeast strains with and without the ability to acquire further stress resistance, we employed genetic mapping. We found that part of the variation in acquired stress resistance was due to sequence differences in a key regulatory protein, thus providing new insight into how different individuals respond to acute environmental change.
Citation: Stuecker TN, Scholes AN, Lewis JA (2018) Linkage mapping of yeast cross protection connects gene expression variation to a higher-order organismal trait. PLoS Genet 14(4): e1007335. https://doi.org/10.1371/journal.pgen.1007335
Editor: Aimee M. Dudley, Pacific Northwest Research Institute, UNITED STATES
Received: June 30, 2017; Accepted: March 27, 2018; Published: April 12, 2018
Copyright: © 2018 Stuecker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This material is based upon work supported by National Science Foundation Grant No. IOS-1656602 (JAL) (URL = https://www.nsf.gov), startup funds provided by the University of Arkansas (JAL) (URL = http://www.uark.edu), the Arkansas Biosciences Institute (Arkansas Settlement Proceeds Act of 2000) (JAL) (URL = http://arbiosciences.org), and a Research Assistantship provided through the University of Arkansas Cell and Molecular Biology Graduate Program (ANS) (URL = http://cell.uark.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
A fundamental question in genetics is how individuals with extremely similar genetic makeups can have dramatically different characteristics. One hypothesis is that a small number of regulatory polymorphisms can have large effects on gene expression, leading to the extensive phenotypic variation we see across individuals. In fact, gene expression variation is hypothesized to underlie the extensive phenotypic differences we see between humans and chimpanzees despite >98% DNA sequence identity [1, 2]. This hypothesis is supported by numerous examples of gene expression variation affecting higher-order organismal traits.
For example, human genome-wide association studies (GWAS) have found that a substantial fraction of disease-associated variants are concentrated in non-coding regulatory DNA regions [3–8]. Further examples include gene expression variation being linked to differences in metabolism [9–11], physiology [12–16], morphology [17–23], and behavior [24–27].
While gene expression variation is pervasive, there is often a lack of obvious phenotypic change associated with differentially expressed genes. This can occur for a variety of reasons. First, a large fraction of expression variation has been postulated to be evolutionarily neutral with no effect on organismal fitness [28–30]. Second, co-regulation of genes that share the same upstream signaling network and transcription factors can lead to genes whose expression differences correlate with phenotype but are not truly causative. Finally, some gene expression differences may truly affect phenotype, but only under specific conditions. For example, the predictive power of expression quantitative trait loci (eQTL) mapping studies on higher-order phenotypes can be poor unless multiple environments are considered . Similarly, tissue-restricted eQTLs are more likely to map to known disease-associated loci identified from GWAS than non-tissue-restricted eQTLs [32, 33].
Thus, a major challenge for connecting gene expression variation to downstream effects on higher-order traits is the choice of which conditions and traits to examine. To this end, we have been leveraging natural variation in the model eukaryote Saccharomyces cerevisiae, and a phenotype called acquired stress resistance. Many studies have shown a poor correlation between genes that respond to stress and their importance for surviving stress [34–43]. Thus, we and others have argued that the role of stress-activated gene expression is not to survive the initial insult, but instead protects cells from impending severe stress through a phenomenon called acquired stress resistance [44, 45]. Acquired stress resistance (sometimes referred to as “induced tolerance” or the “adaptive response”) occurs when cells pretreated with a mild dose of stress gain the ability to survive an otherwise lethal dose of severe stress. Notably, acquired stress resistance can occur when the mild and severe stresses are the same (same-stress protection) or across pairs of different stresses (cross protection). This phenomenon has been observed in diverse organisms ranging from bacteria to higher eukaryotes including humans [44–50]. The specific mechanisms governing acquisition of higher stress resistance are poorly understood, but there are wide reaching implications. In humans, ischemic preconditioning (transient ischemia followed by reperfusion—i.e. mild stress pretreatment followed by severe stress) may improve outcomes of cardiovascular surgery [51–54], while transient ischemic attacks (“mini-strokes”) may protect the brain during massive ischemic stroke [55–57]. Thus, understanding the genetic basis of acquired stress resistance in model organisms holds promise for mitigating the effects of stress in humans.
A previous study found that a commonly used S288c lab strain is unable to acquire further ethanol resistance when pretreated with a mild dose of ethanol . We found this phenotype to be surprising, considering the unique role ethanol plays in the life history of Saccharomyces yeast, where the evolution of aerobic fermentation gave yeast an advantage over ethanol-sensitive competitors . Because ethanol is a self-imposed stress that induces a robust stress response [59–63], we expected that ethanol should provoke acquired stress resistance in wild yeast strains. Indeed, this turned out to be the case, with the majority of tested wild strains acquiring resistance to severe ethanol following a mild ethanol treatment . Furthermore, this phenotype correlated with extensive differences in the transcriptional response to acute ethanol stress in the lab strain when compared to a wild vineyard (M22) and wild oak (YPS163) strain (>28% of S288c genes were differentially expressed at an FDR of 0.01) [45, 64]. We performed linkage mapping of S288c crossed to a wild vineyard strain (M22) and wild oak strain (YPS163), and observed numerous “hotspots” where the same eQTL loci affect the expression of a large number of transcripts (anywhere from 10–500 transcripts per hotspot) .
In the present study, we provide new evidence that one of these eQTL hotspot loci affects natural variation in acquired stress resistance, namely the ability of ethanol to cross protect against oxidative stress in the form of hydrogen peroxide. The causative polymorphism is in the heme-activated transcription factor Hap1p, which we show directly impacts cross protection, but not the basal resistance of unstressed cells. Finally, we show that the Hap1p effect is mediated through novel regulation of cytosolic catalase T (Ctt1p) during ethanol stress in wild strains. This study highlights how strategically chosen traits that are better correlated with gene expression changes can improve our power to identify novel connections between gene expression variation and higher-order organismal phenotypes.
The genetic basis of natural variation in yeast cross protection
We previously found that an S288c-derived lab strain was unable to acquire further ethanol resistance when pretreated with a mild dose of ethanol, in contrast to the vast majority of ~50 diverse yeast strains . In addition to the S288c strain’s acquired ethanol resistance defect, ethanol also failed to cross protect against other subsequent stresses [44, 65]. In nature, wild yeast cells ferment sugars to ethanol, and then shift to a respiratory metabolism that generates endogenous reactive oxygen species [66–68]. Thus, we hypothesized that ethanol might cross protect against oxidative stress in wild yeast strains. We tested this hypothesis by assessing whether mild ethanol treatment would protect a wild oak strain (YPS163) from severe oxidative stress in the form of hydrogen peroxide (H2O2). Cross protection assays were performed by exposing cells to a mild, sublethal dose of ethanol (5% v/v) for 60 min, followed by exposure to a panel of 11 increasingly severe doses of H2O2 (see Materials and Methods). Confirming the observations of Berry and Gasch , ethanol failed to cross protect against H2O2 in S288c, and in fact slightly exacerbated H2O2 toxicity (Fig 1). In contrast, ethanol strongly cross protected against H2O2 in YPS163 (Fig 1).
(A) A representative acquired H2O2 resistance assay is shown. S288c (lab strain–DBY8268) and YPS163 (wild oak strain) were exposed to 5% ethanol or mock (5% water) pretreatment for 60 min, washed, exposed to 11 doses of severe H2O2 for 2 hr, and then plated to score viability. (B) A single survival score was calculated from the viability at all H2O2 doses (see Materials and Methods). Each plot shows the mean and standard deviation of 4 independent biological replicates. The replicates for mock-treated YPS163 all had the same tolerance score and thus zero standard deviation (see S1 Table for raw numerical data). Asterisks represent resistance that was significantly different from mock-treated cells (*** P < 0.001, t-test).
The inability of ethanol to induce acquired stress resistance in S288c correlates with thousands of differences in ethanol-dependent gene expression in comparison to wild strains that can acquire ethanol resistance [45, 64]. In light of this observation, and the known dependency of cross protection on stress-activated gene expression changes , we hypothesized that differences in cross protection against H2O2 by ethanol may be linked to differential gene expression. To test this, we performed quantitative trait loci (QTL) mapping using the same mapping population as our original eQTL study that mapped the genetic architecture of ethanol-responsive gene expression . Specifically, we conducted QTL mapping of both basal and acquired H2O2 resistance in 43 F2 progeny of S288c crossed with YPS163 (see Materials and Methods). While we found no significant QTLs for basal H2O2 resistance, we did find a significant QTL peak on chromosome XII that explained 38% of the variation in cross protection (Fig 2). It is unlikely that our failure to detect a chromosome XII QTL for basal H2O2 resistance was due to a lack of statistical power, because two independent basal H2O2 resistance QTL studies using millions of S288c x YPS163 F2 segregants also found no significant associations at this locus [69, 70]. Additionally, we estimated the heritability of phenotypic variation in basal resistance to be 0.79, which is slightly above the median value estimated by Bloom and colleagues for 46 yeast traits , and is only moderately lower than the heritability for cross protection (0.92). Lastly, the shape of the distribution of phenotypes in the F2 were markedly different between basal and acquired H2O2 resistance, with basal resistance showing a transgressive segregation pattern and acquired resistance showing a continuous distribution (S1 Fig). Altogether, these results suggest that the genetic basis of natural variation in acquired stress resistance is distinct from the basal resistance of unstressed cells (see Discussion).
Linkage mapping of the S288c x YPS163 cross identified no significant QTLs for basal H2O2 resistance (top panel), but did identify a major QTL on chromosome XII for ethanol-induced cross protection against H2O2 (bottom panel). The red horizontal line denotes the LOD threshold for significance (1% FDR).
The significant QTL for cross protection was located near a known polymorphism in HAP1, a heme-dependent transcription factor that controls genes involved in aerobic respiration [72–74], sterol biosynthesis [75–77], and interestingly, oxidative stress [77, 78]. S288c harbors a known defect in HAP1, where a Ty1 transposon insertion in the 3’ end of the gene’s coding region has been shown to reduce its function . In fact, we previously hypothesized that the defective HAP1 allele was responsible for the inability of S288c to acquire further resistance to ethanol. However, a YPS163 hap1Δ strain was still fully able to acquire ethanol resistance, despite notable differences in the gene expression response to ethanol in the mutant . Likewise, despite previous studies implicating Hap1p as a regulator of oxidative stress defense genes [77, 78], HAP1 is apparently dispensable for same-stress acquired H2O2 resistance . These observations suggest that the molecular mechanisms underlying various acquired stress resistance phenotypes can differ, even when the identity of the secondary stress is the same.
A role for HAP1 in ethanol-induced cross protection against severe H2O2
Because we previously implicated HAP1 as a major ethanol-responsive eQTL hotspot affecting over 100 genes, we hypothesized that ethanol-induced cross protection against H2O2 may depend upon Hap1p-regulated genes. However, it was formally possible that HAP1 was merely linked to the truly causal polymorphism. To distinguish between these possibilities, we generated deletion mutations in the YPS163 background for every non-essential gene within the 1.5-LOD support interval of the QTL peak (encompassing IFH1 –YCS4). Of the 36 mutants tested, two showed significantly and highly diminished acquired H2O2 resistance (Fig 3 and S2 Fig), hap1Δ and top3Δ (encoding DNA topoisomerase III). To determine whether different alleles of HAP1 and/or TOP3 were responsible for natural variation in acquired H2O2 resistance, we applied an approach called reciprocal hemizygosity analysis , where the TOP3 and HAP1 alleles were analyzed in an otherwise isogenic S288c-YPS163 hybrid background (see Fig 4A for a schematic). In each of the two reciprocal strains, one allele of the candidate gene was deleted, producing a hybrid strain containing either the S288c or YPS163 allele in single copy (i.e. hemizygous for TOP3 or HAP1). While we found only mild allelic effects for TOP3, the effects of different HAP1 alleles were striking (Fig 4B and 4C). The hybrid strain containing the HAP1YPS163 allele showed full cross protection, while the strain containing the HAP1S288c allele showed none. Thus, we examined the effects of HAP1 on acquired H2O2 resistance further. Intriguingly, we found that the YPS163 hap1Δ mutant was unaffected for acquired H2O2 resistance when mild H2O2 or mild NaCl were used as mild stress pretreatments (Fig 5), suggesting that Hap1p plays a distinct role in ethanol-induced cross protection (see Discussion).
Deletions of all non-essential genes within the 1.5-LOD support interval of the chromosome XII QTL peak were constructed in JL111 (YPS163 MATa haploid) background and tested for defects in acquired H2O2 resistance. Each plot shows the mean and standard deviation of 2 independent biological replicates, with the exception of the JL111 control (35 replicates). The replicates for several strains all had the same tolerance score and thus zero standard deviation (see S1 Table for raw numerical data). Asterisks represent acquired H2O2 resistance that was significantly lower than wild-type YPS163 (* P < 0.001, one-way ANOVA).
(A) Schematic of reciprocal hemizygosity analysis. Each block represents a gene, and each hybrid strain contains a single-copy deletion of hap1 or top3, and a single copy of the respective S288c (lab) or YPS163 (oak) allele. (B) Representative acquired H2O2 resistance assays for wild-type YPS163, the YPS163-S288c hybrid, and the reciprocal hemizygotes. (C) Each survival score plot shows the mean and standard deviation of biological triplicates. Asterisks represent significant differences in acquired resistance between denoted strains (** P < 0.01, *** P < 0.001, ns = not significant (P > 0.05), t-test).
Cultures of wild-type YPS163 and the YPS163 hap1Δ mutant were split and exposed to either 0.4 mM H2O2, 0.4 M NaCl, or a mock (media only) treatment for 60 min, washed, exposed to 11 doses of severe H2O2 for 2 hr, and then plated to score viability. The survival scores across each of the 11 doses are plotted as the mean and standard deviation of biological triplicates.
Finally, we performed allele swap experiments to examine the effects of the different HAP1 alleles in the original parental backgrounds. We introduced only the Ty element from HAP1S288c into the YPS163 HAP1 gene, and observed a loss of acquired H2O2 resistance similar to the YPS163 hap1Δ strain (Fig 6). We next tested whether repair of the defective hap1 allele in S288c could restore cross protection. Surprisingly, S288c repaired with HAP1 YPS163 was largely unable to acquire further H2O2 resistance (Fig 6). This additional layer of genetic complexity suggests that S288c harbors additional polymorphisms that affect cross protection. To determine whether this was due to allelic variation in TOP3, the only other locus showing a difference in acquired H2O2 resistance, we genotyped each of the segregants at both the HAP1 and TOP3 loci. We identified two segregants with both the HAP1 YPS163 and TOP3YPS163 alleles that were nonetheless unable to acquire further resistance (S3 Fig, S1 Table). These data, along with the continuous distribution of F2 phenotypes (S1 Fig), is consistent with other loci outside of the chromosome XII QTL peak contributing to variation in acquired H2O2 resistance. Moreover, the causative alleles at these loci are apparently masked in YPS163-S288c hybrids that fully acquire H2O2 resistance, suggesting that they are recessive (see Discussion). We also noted during the genotyping that a small number of segregants contained the HAP1 S288c (or TOP3S288c) allele but were still able to acquire further H2O2 resistance (S3 Fig and S1 Table), suggesting that HAP1 function is conditionally necessary in certain genetic backgrounds. To determine whether this was due to a unique genetic background for YPS163, we deleted HAP1 in three additional wild strains. A wild oak (YPS1000) and wild vineyard (M22) strain showed defects in acquired H2O2 resistance similar to that of the YPS163 hap1Δ strain, while a wild coconut (Y10) strain showed a very slight defect (S4 Fig). Altogether, these results are consistent with HAP1 being necessary for ethanol-induced cross protection against H2O2 in some genetic backgrounds, including those of several wild strains, but not others (see Discussion).
(A) Representative acquired H2O2 resistance assays for wild-type YPS163 (oak), YPS163 hap1Δ mutant, YPS163 HAP1S288c, and S288c HAP1YPS163. (B) Each survival score plot shows the mean and standard deviation of at least biological triplicates. The replicates for YPS163 HAP1S288c all had the same tolerance score and thus zero standard deviation (see S1 Table for raw numerical data). Asterisks represent significant differences in acquired resistance between denoted strains (** P < 0.01, *** P < 0.001, ns = not significant (P > 0.05), t-test).
HAP1 affects catalase expression and peroxidase activity during ethanol stress
Because Hap1p is a transcription factor, we hypothesized that acquired H2O2 resistance relied on Hap1p-dependent expression of a stress protectant protein. We reasoned that the putative stress protectant protein should have the following properties: i) a biological function consistent with H2O2 detoxification or damage repair, ii) reduced ethanol-responsive expression in S288c versus YPS163, iii) be a target gene of the HAP1 eQTL hotspot, and iv) possess evidence of regulation by Hap1p.
We first looked for overlap between our previously identified HAP1 eQTL hotspot (encompassing 376 genes) and genes with significantly reduced ethanol-responsive induction in S288c versus YPS163 (309 genes) . Thirty-four genes overlapped for both criteria, including several that directly defend against reactive oxygen species (TSA2 encoding thioredoxin peroxidase, SOD2 encoding mitochondrial manganese superoxide dismutase, CTT1 encoding cytosolic catalase T, and GSH1 encoding γ-glutamylcysteine synthetase (Fig 7A and S1 Table)). Of those 34 genes, 8 also had direct evidence of Hap1p binding to their promoters  (Fig 7B and S1 Table), including CTT1 and GSH1 (though both TSA2 and SOD2 have indirect evidence of regulation by Hap1p [82, 83]).
(A) Overlap between genes that were HAP1 eQTL hotspot targets from , genes with defective induction in S288c vs. YPS163 from , and direct targets of HAP1 identified via ChIP experiments compiled from . (B) Descriptions of the eight genes that overlapped for all three criteria. (C) Previous eQTL mapping of the yeast ethanol response (newly plotted here using data described in ), implicated HAP1 as causative for natural variation in CTT1 induction levels during ethanol stress.
We first focused on CTT1, since it is both necessary for NaCl-induced cross protection against H2O2 in S288c , and sufficient to increase H2O2 resistance when exogenously overexpressed in S288c . We deleted CTT1 in the YPS163 background, and found that ethanol-induced cross protection against H2O2 was completely eliminated (Fig 8). The complete lack of cross protection in the ctt1Δ mutant suggests that other peroxidases cannot compensate for the lack of catalase activity under this condition. Next, because CTT1 was part of the HAP1 eQTL hotspot (Fig 7C, plotted using the data described in ), we tested whether the S288c HAP1 allele reduced CTT1 expression during ethanol stress. To do this, we performed qPCR to measure CTT1 mRNA induction following a 30-minute ethanol treatment (i.e. the peak ethanol response ). Consistent with our previous microarray data [45, 64], we saw lower induction of CTT1 by ethanol in S288c relative to YPS163 (Fig 9A). Moreover, we saw dramatically reduced induction of CTT1 in a YPS163 hap1Δ mutant compared to the wild-type YPS163 control (Fig 9A). Further support that HAP1 is causative for reduced CTT1 expression was provided by performing qPCR in the HAP1 reciprocal hemizygotes, where we found that the HAP1S288c allele resulted in significantly reduced CTT1 induction compared to the HAP1YPS163 allele (Fig 9A).
(A) Representative acquired H2O2 resistance assays for wild-type YPS163 and the YPS163 ctt1Δ mutant. (B) Survival score plots indicating the mean and standard deviation of biological triplicates. Asterisks represent significant differences in acquired resistance between denoted strains (*** P < 0.001, t-test).
(A) Fold induction of CTT1 mRNA in indicated strains following 30 min ethanol stress compared to unstressed cells, assessed by qPCR. (B) Peroxidase activity measured in cell-free extracts in either mock-treated or ethanol-stressed cells. The plots indicate the mean and standard deviation of biological triplicates (mRNA) or quadruplicates (peroxidase activity). Asterisks represent significant differences in CTT1 mRNA induction or peroxidase activity between denoted strains (* P < 0.05, ** P < 0.01, paired t-test).
To determine whether the differences in CTT1 induction across strain backgrounds also manifested as differences in each strain’s ability to detoxify H2O2, we measured in vitro peroxidase activity in cell-free extracts. We compared in vitro peroxidase activity in extracts from unstressed cells and cells exposed to ethanol stress for 60 minutes (i.e. the same pre-treatment time that induces acquired H2O2 resistance (see Materials and Methods)). For wild-type YPS163, ethanol strongly induced peroxidase activity, and this induction was completely dependent upon CTT1 (Fig 9B). Mirroring CTT1 gene expression patterns, the induction of peroxidase activity was reduced in a YPS163 hap1Δ mutant. Additionally, reciprocal hemizygosity analysis provided further support that lack of HAP1 function results in decreased peroxidase activity, as the hybrid containing the HAP1S288c allele showed significantly reduced peroxidase activity following ethanol stress compared to the hybrid containing the HAP1YPS163 allele (Fig 9B). Notably, the hybrid containing the HAP1YPS163 allele had lower CTT1 induction and in vitro peroxidase activity following ethanol shock than wild-type YPS163, despite equivalent levels of acquired H2O2 resistance in the strains. These results suggest that HAP1 may play additional roles in acquired H2O2 resistance beyond H2O2 detoxification, depending upon the genetic background (see Discussion). Interestingly, S288c showed no induction of peroxidase activity upon ethanol treatment, despite modest induction of the CTT1 transcript. This result is reminiscent of Ctt1p regulation during heat shock in the S288c background, where mRNA levels increase without a concomitant increase in protein levels . Thus, in addition to strain-specific differences in CTT1 regulation at the RNA level, there are likely differences in regulation at the level of translation and/or protein stability.
In this study, we leveraged extensive natural variation in the yeast ethanol response to understand potential connections between gene expression variation and higher-order organismal traits. Previous screens of gene deletion libraries have found surprisingly little overlap between the genes necessary for surviving stress and genes that are induced by stress. [34–43]. Instead, gene induction may be a better predictor of a gene’s requirement for acquired stress resistance . Thus, we hypothesized that phenotypic variation in acquired stress resistance may be linked to natural variation in stress-activated gene expression. Our results provide a compelling case study in support of this notion—namely that a polymorphism in the HAP1 transcription factor affects natural variation in acquired H2O2 resistance, but not the basal H2O2 resistance of unstressed cells. Forward genetic screens have shown that the genes necessary for basal and acquired resistance are largely non-overlapping [34, 36, 84], suggesting that mechanisms underlying basal and acquired stress resistance are distinct. We provide further genetic evidence to support this model. YPS163 hap1Δ mutants and the hybrid carrying the HAP1S288c allele had strong acquired H2O2 defects, but no differences in their basal H2O2 resistance (Figs 4 and 6). Moreover, the YPS163 hap1Δ mutant was affected only when ethanol was the mild pretreatment, and was able to fully acquire H2O2 resistance following mild H2O2 or mild NaCl (Fig 5). These results suggest that the mechanisms underlying acquired resistance differ depending upon the mild stress that provokes the response. Further dissection of the mechanisms underlying acquired stress resistance will provide a more integrated view of eukaryotic stress biology.
Our results reveal a new role for Hap1p in cross protection against H2O2 that has been lost in the S288c lab strain. We propose that a major mechanism underlying ethanol-induced cross protection against H2O2 is the induction of cytosolic catalase T (Ctt1p), and that in the YPS163 background, Hap1p is necessary for proper induction of CTT1 during ethanol stress. We based this mechanism on the following observations. First, over-expression of CTT1 in S288c is sufficient to induce high H2O2 resistance . Second, a YPS163 ctt1Δ mutant cannot acquire any further H2O2 resistance following ethanol pre-treatment (Fig 8), suggesting that no other antioxidant defenses are able to compensate under this condition. Lastly, the defect in cross protection for the YPS163 hap1Δ mutant correlates with reduced CTT1 expression and peroxidase activity during ethanol stress (compare Figs 6 and 9). How Hap1p is involved in the regulation of CTT1 during ethanol stress remains an open question, but we offer some possibilities. Hap1p is activated by heme, thus promoting transcription of genes involved in respiration, ergosterol biosynthesis, and oxidative stress defense including CTT1 [75, 76, 78, 82]. Because heme biosynthesis requires oxygen, Hap1p is an indirect oxygen sensor and regulator of aerobically expressed genes [74, 75, 86]. There is currently no evidence that heme levels are affected by ethanol stress, nor is there evidence that Hap1p is “super-activating” under certain conditions. Thus, we disfavor a mechanism of induction caused solely by Hap1p activation. Instead, we favor a mechanism where Hap1p interacts with other transcription factors at the CTT1 promoter during ethanol stress, leading to full CTT1 induction. One possibility that we favor is recruitment of the general stress transcription factor Msn2p, which plays a known role in acquired stress resistance [44, 45]. We previously showed that a YPS163 msn2Δ mutant had no induction of CTT1 mRNA during ethanol stress , suggesting that Msn2p was an essential activator for CTT1 under this condition. The CTT1 promoter region contains three Msn2p DNA-binding sites, two of which are ~100-bp away from the Hap1p binding site. Hap1p binding to the CTT1 promoter could help recruit Msn2p during ethanol stress, possibly through chromatin remodeling that increases accessibility of the Msn2p binding sites as proposed by Elfving and colleagues .
What is the physiological role of Hap1p-dependent induction of CTT1 during ethanol stress? One possibility is that regulation tied to the heme- and oxygen-sensing role of Hap1p ensures that CTT1 induction only occurs under environmental conditions where reactive oxygen species (ROS) are most likely to be encountered—namely stressful conditions that are also aerobic. In the context of ethanol stress, aerobic fermentation would lead to subsequent respiration of the produced ethanol and simultaneous ROS production. Under these conditions, CTT1 induction leading to ethanol-mediated cross protection against ROS would likely confer a fitness advantage. On the other hand, during stressful yet anoxic conditions, Ctt1p and other ROS-scavenging proteins are likely unnecessary. Furthermore, because heme is not synthesized during anoxic conditions , Hap1p would fail to induce CTT1 and other genes encoding non-essential heme-containing proteins. This may improve fitness by conserving energy used for biosynthesis and by redirecting limited heme to more essential heme-containing proteins.
The S288c lab strain has long been known to possess a defective HAP1 allele . Apparently, the defective allele arose relatively recently, as only S288c contains a HAP1 Ty1 insertion out of over 100 sequenced strains [88, 89]. The lack of HAP1 function in S288c could be due to relaxation of selective constraint, though others have argued in favor of positive selection for reduced ergosterol biosynthetic gene expression [90, 91]. Regardless, the loss of ethanol-induced acquired H2O2 resistance is likely a secondary effect of the loss of Hap1p function. Intriguingly, we did find that two (non-S288c) domesticated yeast strains also lack ethanol-induced cross protection against H2O2 (S5 Fig), suggesting that phenotypic differences in acquired stress resistance may differentiate domesticated versus wild yeast. Because environmental stresses are likely encountered in combination or sequentially , acquired stress resistance is likely an important phenotype in certain natural ecological settings. Future studies directed at understanding differences in acquired stress resistance phenotypes in diverse wild yeast strains may provide unique insights into the ecology of yeast.
While our QTL mapping identified HAP1 as the major effector of cross protection, we note that additional complexity remains unexplained. Notably, despite the strong cross protection defect in the YPS163 hap1Δ mutant, some residual cross protection persists that is absent in S288c (Fig 6). Intriguingly, the residual cross protection is also absent in the hybrid carrying the HAP1S288c allele, suggesting the involvement of other genes depending upon the genetic background (Fig 4B and 4C). It is known that yeast strains with respiratory defects have increased ROS sensitivity [93, 94], potentially due to increased programmed cell death . It is possible that reduced respiratory activity and concomitant ROS sensitivity in strains lacking HAP1 is exacerbated by genetic interactions with other alleles.
The lack of cross protection in S288c and the HAP1S288c hybrid correlates with the lack of inducible peroxidase activity following ethanol pretreatment in those strains. The lack of inducible peroxidase activity in S288c despite modest induction of CTT1 mRNA could be due to translational regulation, which is supported by the observation that while mild heat shock induces CTT1 mRNA, protein levels remain nearly undetectable . Strikingly, the hybrid carrying the HAP1YPS163 allele still cross protects despite levels of CTT1 mRNA induction and peroxidase activity that are lower than in the YPS163 hap1Δ strain that is unable to acquire further resistance (Fig 9). These data suggest that HAP1 plays an additional role in ethanol-induced cross protection beyond H2O2 detoxification by Ctt1p. Moreover, the continuous distribution of the cross protection phenotype in the segregants (S1 Fig) and the results of allele swap experiments (Fig 6) strongly implicate other genes and processes in this complex trait. Specifically, the lack of complementation by the HAP1YPS163 allele in the S288c background suggests that additional loci in S288c render HAP1 necessary but not sufficient for cross protection in this background. Moreover, our genotyping of the segregants at HAP1 revealed a small number that still possessed cross protection in the absence of functional HAP1 (S3 Fig and S1 Table), suggesting that HAP1 is dispensable in certain genetic backgrounds. We examined the effects of hap1Δ mutations in other wild strain backgrounds and found two additional strains with a strong HAP1 requirement and a third strain with at most a mild HAP1 effect (S4 Fig). This result, as well as those from other recent studies [96–98], suggests that these types of genetic background effects are likely the rule rather than the exception. Future high resolution mapping experiments will be necessary to identify and characterize the source of these genetic background effects.
Gene expression variation is extensive in nature and is hypothesized to be a major driver of higher-order phenotypic variation. However, there are inherent challenges to connecting gene expression variation to higher-order organismal traits. Hundreds to thousands of genes are often differentially expressed across individuals, so identifying which particular transcripts exert effects on fitness is difficult. By studying acquired stress resistance—a phenotype better correlated with stress-activated gene expression changes—we were able to uncover a novel connection between gene expression variation and an organismal trait.
Materials and methods
Strains and growth conditions
Strains and primers used in this study are listed in S2 and S3 Tables, respectively. The parental strains for QTL mapping were YPS163 (oak strain) and the S288c-derived DBY8268 (lab strain; referred to throughout the text as S288c). The construction of the S288c x YPS163 QTL mapping strain panel (44 F2 progeny) is described in  (kindly provided by Justin Fay). Genotypes for the strain panel are listed in S4 Table. During the course of analyzing HAP1 genotypes, we found one segregant (YS.15.2) to be a mixed population, so it was removed from subsequent analyses. Deletions in the BY4741 (S288c) background were obtained from Open Biosystems (now GE Dharmacon), with the exception of hap1 (whose construction is described in ). Deletions were moved into haploid MATa derivatives of DBY8268, M22, and YPS163 by homologous recombination with the deletion::KanMX cassette amplified from the appropriate yeast knockout strain . Homozygous hap1Δ strains of YPS1000 and Y10 were generated by moving the hap1Δ::KanMX allele from the BY4741 background into the strains, followed by sporulation and tetrad dissection. All deletions were verified by diagnostic PCR. DBY8268 containing a wild-type HAP1 allele from YPS163 was constructed in two steps. First, the MX cassette from the hap1Δ::KanMX deletion was replaced with a URA3MX cassette, selecting for uracil prototrophy. Then, URA3 was replaced with wild-type HAP1 from YPS163 (amplified using primers 498-bp upstream and 1572-bp downstream of the HAP1 ORF), while selecting for loss of URA3 on 5-fluoroorotic acid (5-FOA) plates. Deletions and repair of HAP1 were confirmed by diagnostic PCR (see S3 Table for primer sequences). YPS163 containing a HAP1S288c allele was constructed by first inserting a KanMX cassette into S288c 117-bp downstream of the Ty element to create JL1032. We then amplified and transformed the Ty element into YPS163 using primers that annealed 103-bp upstream of the Ty element and 177-bp downstream of the KanMX cassette, generating JL1069. Diploid strains for HAP1 and TOP3 reciprocal hemizygosity analysis were generated as follows. The hemizygote containing the wild-type S228c HAP1 allele (JL580) was generated by mating JL140 (YPS163 MATa hoΔ::HygMX hap1Δ::KanMX) to JL506 (DBY8268 MATα ho ura3 hap1). The hemizygote containing the wild-type YPS163 allele (JL581) was generated by mating JL112 (YPS163 MATα hoΔ::HygMX HAP1) to JL533 (DBY8268 MATa ho ura3 hap1Δ::KanMX). The hemizygote containing the wild-type S288c TOP3 allele (JL1107) was created by mating JL1066 (YPS163 MATa hoΔ::HygMX top3Δ::KanMX) to BY4742 (MATα TOP3). The hemizygote containing the wild-type YPS163 allele (JL1106) was created by mating JL1121 (BY4741 MATa top3Δ::KanMX) to JL112 (YPS163 MATα hoΔ::HygMX TOP3). All strains were grown in batch culture in YPD (1% yeast extract, 2% peptone, 2% dextrose) at 30°C with orbital shaking (270 rpm).
HAP1 and TOP3 genotyping
To identify possible promoter polymorphisms, the HAP1 promoters of the DBY8268 (JL505), YPS163 (JL111), and S288c HAP1YPS163 (JL975) strains were amplified using primers that anneal 1091-bp upstream and 134-bp downstream of the HAP1 start codon. PCR products were purified with a PureLink PCR cleanup kit (Invitrogen) and sequenced by Sanger Sequencing (Eurofins Genomics) using a primer that anneals 498-bp upstream of the HAP1 start codon. Sequences were aligned to the S288c and YPS163 reference sequences using SnapGene v4.1 (GSL Biotech). This verified the presence of a 1-bp indel within a poly-A stretch that differs between S288c and YPS163. The S288c HAP1YPS163 (JL975) strain contains the YPS163 HAP1 promoter sequence. Additionally, the YPS163 strain containing the HAP1S288c was constructed to only contain the Ty element and not the S288c promoter polymorphism.
The HAP1 allele of each segregant for the QTL mapping panel was genotyped by differential PCR analysis where the same forward primer (HAP1 int 3’ F) was paired with two different reverse primers. One primer (Ty R) anneals specifically to the Ty element, yielding an 856-bp product when amplifying the S288c allele. The second primer (HAP1 3’ end R) anneals 3’ to the Ty element of HAP1S288c, yielding a 570-bp product for HAP1YPS163 and a 6.5-kb product for HAP1S288c. Each segregant was genotyped using both sets of primer pairs, and only one segregant (YS.15.2) appeared to contain both HAP1 alleles. Subsequent analysis of multiple colonies verified that YS.15.2 was a mixed population, and thus it was removed it from all subsequent analyses.
The TOP3 alleles of S288c and YPS163 contain two non-synonymous SNPs at nucleotide positions 1,398 and 1,422. Segregant genotypes at TOP3 were determined by analyzing restriction fragment length polymorphisms. TOP3 was amplified using primers (TOP3 up F and TOP3 down R) that anneal ~500-bp upstream and downstream of the open reading frame, generating a 2.9-kb product. PCR products were digested with either 1) PstI, which cuts at position 1,248 only within the TOP3YPS163 ORF allele yielding 1.7- and 1.2-kb products, or (2) KflI, which cuts at position 1,155 only within the TOP3S288c yielding 1.6- and 1.3-kb products. Genotypes for HAP1 and TOP3 are listed in S1 Table.
Cross protection assays
Cross-protection assays were performed as described in  with slight modifications. Briefly, 3–4 freshly streaked isolated colonies (<1 week old) were grown overnight to saturation, sub-cultured into 6 ml fresh media, and then grown for at least 8 generations (>12 h) to mid-exponential phase (OD600 of 0.3–0.6) to reset any cellular memory of acquired stress resistance . Each culture was split into two cultures and pretreated with YPD media containing either a single mild “primary” dose or the same concentration of water as a mock-pretreatment control. Primary doses consisted of 5% v/v ethanol, 0.4 M NaCl, or 0.4 mM H2O2. Thereafter, mock and primary-treated cells were handled identically. Following 1-hour pretreatment at 30°C with orbital shaking (270 rpm), cells were collected by mild centrifugation at 1,500 x g for 3 min. Pelleted cells were resuspended in fresh medium to an OD600 of 0.6, then diluted 3-fold into a microtiter plate containing a panel of severe “secondary” H2O2 doses ranging from 0.5–5.5 mM (0.5 mM increments; 150 μl total volume). Microtiter plates were sealed with air-permeable Rayon films (VWR), and cells were exposed to secondary stress for 2 hours at 30°C with 800 rpm shaking in a VWR symphony Incubating Microplate Shaker. Four μl of a 50-fold dilution was spotted onto YPD agar plates and grown 48 h at 30°C. Viability at each dose was scored using a 4-point semi-quantitative scale to score survival compared to a no-secondary stress (YPD only) control: 100% = 3 pts, 50–90% = 2 pts, 10–50% = 1 pt, or 0% (3 or less colonies) = 0 pts. An overall H2O2 tolerance score was calculated as the sum of scores over the 11 doses of secondary stress. Raw phenotypes for all acquired stress resistance assays can be found in S1 Table. A fully detailed acquired stress protocol has been deposited to protocols.io under doi dx.doi.org/10.17504/protocols.io.g7sbzne. Statistical analyses were performed using Prism 7 (GraphPad Software).
QTL mapping and heritability estimates
Phenotyping of the QTL mapping strain panel for basal and acquired H2O2 resistance was performed in biological duplicate. Because cross-protection assays on the entire strain panel could not all be performed at the same time, we sought to minimize day-to-day variability. We found that minor differences in temperature and shaking speed affected H2O2 resistance; as a result, we used a digital thermometer and tachometer to ensure standardization across experiments. Moreover, we found that differences in handling time were a critical determinant of experimental variability. To minimize this source of variability, all cell dilutions were performed quickly using multichannel pipettes, and no more than two microtiter plates were assayed during a single experiment. To ensure that replicates on a given day were reproducible, we always included the YPS163 wild-type parent as a reference.
Single mapping scans were performed using Haley-Knott regression  implemented through the R/QTL software package . Genotype probabilities were estimated at every cM across the genome using the calc.genoprob function. Significant LOD scores were determined by 100,000 permutations that randomly shuffled phenotype data (i.e. strain labels) relative to the genotype data. The maximum LOD scores for the permuted scans were sorted, and the 99th percentile was used to set the genome-wide FDR at 1%. This resulted in LOD cutoffs of 3.07 for QTL mapping of basal H2O2 resistance, and 4.24 for acquired H2O2 resistance.
Broad-sense heritability (H2) was estimated from the segregant data as described in  using a random-effects ANOVA model implemented through the lmer function in the lme4 R package . H2 was estimated using the equation , where represents the genetic variance due to the effects of segregrant, and represents the residual (error or environmental) variance. The proportion of variance explained by a QTL was estimated using the equation , where n represents the number of segregants.
Quantitative PCR of CTT1 expression and cellular peroxidase assays
Induction of CTT1 by ethanol was assessed by real-time quantitative PCR (qPCR) using the Maxima SYBR q-PCR Master Mix (Thermo Fisher Scientific) and a Bio-Rad CFX96 Touch Real-Time PCR Detection System, according to the manufacturers’ instructions. Cells were grown to mid-exponential phase (OD600 of 0.3–0.6) as described for the cross-protection assays. Cells were collected by centrifugation at 1,500 x g for 3 minutes immediately prior to the addition of 5% v/v ethanol (unstressed sample) and 30 minutes post-ethanol treatment, which encompasses the peak of global expression changes to acute ethanol stress . Cell pellets were flash frozen in liquid nitrogen and stored at -80°C until processed. Total RNA was recovered by hot phenol extraction as previously described , and then purified with a Quick-RNA MiniPrep Plus Kit (Zymo Research) including on-column DNase I treatment. cDNA synthesis was performed as described , using 10 μg total RNA, 3 μg anchored oligo-dT (T20VN), and SuperScript III (Thermo Fisher Scientific). One ng cDNA was used as template for qPCR with the following parameters: initial denaturation at 95°C for 3 minutes followed by 40 cycles of 95°C for 15 seconds and 55°C annealing and elongation for 1 minute. Cq was determined using regression analysis, with baseline subtraction via curve fit. The presence of a single amplicon for each reaction was validated by melt curve analysis. The average of two technical replicates were used to determine relative CTT1 mRNA abundance via the ΔΔCq method , by normalizing to an internal control gene (ERV25) whose expression is unaffected by ethanol stress and does not vary in expression between S288c and YPS163 . Primers for CTT1 and ERV25 were designed to span ~200 bp in the 3’ region of each ORF (to decrease the likelihood of artifacts due to premature termination during cDNA synthesis), and for gene regions free of polymorphisms between S288c and YPS163 (see S3 Table for primer sequences). Three biological replicates were performed and statistical significance was assessed via a paired t-test using Prism 7 (GraphPad Software).
For peroxidase activity assays, mid-exponential phase cells were collected immediately prior to and 60 minutes post-ethanol treatment, to assess peroxidase activity levels during the induction of cross protection. Cells were collected by centrifugation at 1,500 x g for 3 minutes, washed twice in 50 mM potassium phosphate buffer, pH 7.0 (KPi), flash frozen in liquid nitrogen, and then stored at -80°C until processed. For preparation of whole cell extracts, cells were thawed on ice, resuspended in 1 ml KPi buffer, and then transferred to 2-ml screw-cap tubes for bead beating. An equal volume (1 ml) of acid-washed glass beads (425–600 micron, Sigma-Aldrich) was added to each tube. Cells were lysed by four 30-second cycles of bead beating in a BioSpec Mini-Beadbeater-24 (3,500 oscillations/minute, 2 minutes on ice between cycles). Cellular debris was removed by centrifugation at 21,000 x g for 30 minutes at 4°C. The protein concentration of each lysate was measured by Bradford assay (Bio-Rad) using bovine serum albumin (BSA) as a standard . Peroxidase activity in cellular lysates was monitored as described , with slight modifications. Briefly, 50 μg of cell free extract was added to 1 ml of 15 mM H2O2 in KPi buffer. H2O2 decomposition was monitored continuously for 10 minutes in Quartz cuvettes (Starna Cells, Inc.) at 240 nm (ε240 = 43.6 M-1 cm-1) using a SpectraMax Plus Spectrophotometer (Molecular Devices). One unit of catalase activity catalyzed the decomposition of 1 μmol of H2O2 per minute. For each sample, results represent the average of technical duplicates. To assess statistical significance, four biological replicates were performed and significance was assessed via a paired t-test using Prism 7 (GraphPad Software).
S1 Fig. Distribution of phenotypes in the F2 segregants.
Survival score plots indicating the mean of biological duplicates for (A) basal and (B) acquired H2O2 resistance.
S2 Fig. Representative acquired H2O2 resistance assays for candidate genes under the chromosome XII QTL peaks.
Representative acquired H2O2 resistance assays for wild-type YPS163 and each of 36 mutants generated for candidates falling within the 1.5-LOD support interval of the chromosome XII QTL peak.
S3 Fig. Effect plots for HAP1 and TOP3 alleles.
Boxplots and raw data points depict the distribution of segregant phenotypes depending on their alleles for either HAP1 or TOP3 (see methods for genotyping details).
S4 Fig. HAP1 is necessary for acquired H2O2 resistance in some wild strains.
Survival score plots indicating the mean and standard deviation of at least biological triplicates. The replicates for mock-treated Y10 all had the same tolerance score and thus zero standard deviation (see S1 Table for raw numerical data). Asterisks represent significant differences in acquired resistance between denoted strains (* P < 0.05, ** P < 0.01, *** P < 0.001, ns = not significant (P > 0.05), t-test).
S5 Fig. Other non-S288c-derived yeast isolates lack ethanol-induced cross protection against H2O2.
(A) Representative acquired H2O2 resistance assays for wild-type YPS163, YJM627, and YJM1129. (B) Survival score plots indicating the mean and standard deviation of biological duplicates. The replicates for ethanol-treated YJM627 all had the same tolerance score and thus zero standard deviation (see S1 Table for raw numerical data).
S1 Table. Raw data used to generate each figure.
S4 Table. Genotypes for S288c x YPS163 QTL mapping strain panel.
The “Strain” heading for column 1 denotes strain labels for the parental strains (Y = YPS163, S = S288c) and each segregant. Subsequent columns represent genotypes at each marker (Row heading 1 = marker name; Row heading 2 = marker chromosome; Row heading 3 = marker position in cM). Genotypes at each marker are denoted as having the S288c allele (S), YPS163 allele (W), or missing data (NA).
We thank Audrey Gasch and Justin Fay for strains, Andy Alverson and Christian Tipsmark for the use of their equipment, and members of the Lewis lab for helpful conversations.
- 1. King MC, Wilson AC. Evolution at two levels in humans and chimpanzees. Science. 1975;188(4184):107–116. pmid:1090005
- 2. Enard W, Khaitovich P, Klose J, Zollner S, Heissig F, Giavalisco P, et al. Intra- and interspecific variation in primate gene expression patterns. Science. 2002;296(5566):340–343. pmid:11951044
- 3. Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M. Linking disease associations with regulatory information in the human genome. Genome Res. 2012;22(9):1748–1759. pmid:22955986
- 4. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337(6099):1190–1195. pmid:22955828
- 5. Nica AC, Montgomery SB, Dimas AS, Stranger BE, Beazley C, Barroso I, et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 2010;6(4):e1000895. pmid:20369022
- 6. Zhang F, Lupski JR. Non-coding genetic variants in human disease. Human Mol Genet. 2015;24(R1):R102–R110. pmid:26152199
- 7. Li MJ, Wang P, Liu X, Lim EL, Wang Z, Yeager M, et al. GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2012;40(D1):D1047–D1054. pmid:22139925
- 8. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009;106(23):9362–9367. pmid:19474294
- 9. Oleksiak MF, Churchill GA, Crawford DL. Variation in gene expression within and among natural populations. Nat Genet. 2002;32(2):261–266. pmid:12219088
- 10. Ferea TL, Botstein D, Brown PO, Rosenzweig RF. Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc Natl Acad Sci U S A. 1999;96(17):9721–9726. pmid:10449761
- 11. Crawford DL, Powers DA. Molecular basis of evolutionary adaptation at the lactate dehydrogenase-B locus in the fish Fundulus heteroclitus. Proc Natl Acad Sci U S A. 1989;86(23):9365–9369. pmid:2594773
- 12. Krebs RA, Feder ME. Natural variation in the expression of the heat-shock protein HSP70 in a population of Drosophila melanogaster and its correlation with tolerance of ecologically relevant thermal stress. Evolution. 1997;51(1):173. pmid:28568793
- 13. Fay JC, McCullough HL, Sniegowski PD, Eisen MB. Population genetic variation in gene expression is associated with phenotypic variation in Saccharomyces cerevisiae. Genome Biol. 2004;5(R26. pmid:15059259
- 14. Wen G, Mahata SK, Cadman P, Mahata M, Ghosh S, Mahapatra NR, et al. Both rare and common polymorphisms contribute functional variation at CHGA, a regulator of catecholamine physiology. Am J Hum Genet. 2004;74(2):197–207. pmid:14740315
- 15. MacLaren EJ, Sikela JM. Cerebellar gene expression profiling and eQTL analysis in inbred mouse strains selected for ethanol sensitivity. Alcoholism: Clinical and Experimental Research. 2005;29(9):1568–1579. pmid:16205357
- 16. Nogami S, Ohya Y, Yvert G. Genetic complexity and quantitative trait loci mapping of yeast morphological traits. PLoS Genet. 2007;3(2):e31. pmid:17319748
- 17. Sicard A, Thamm A, Marona C, Lee YW, Wahl V, Stinchcombe JR, et al. Repeated evolutionary changes of leaf morphology caused by mutations to a homeobox gene. Curr Biol. 2014;24(16):1880–1886. pmid:25127212
- 18. Chapman MA, Hiscock SJ, Filatov DA. The genomic bases of morphological divergence and reproductive isolation driven by ecological speciation in Senecio (Asteraceae). J of Evol Biol. 2016;29(1):98–113. pmid:26414668
- 19. Ranjan A, Budke JM, Rowland SD, Chitwood DH, Kumar R, Carriedo L, et al. eQTL regulating transcript levels associated with diverse biological processes in tomato. Plant Physiol. 2016;172(1):328–340. pmid:27418589
- 20. Boell L, Pallares LF, Brodski C, Chen Y, Christian JL, Kousa YA, et al. Exploring the effects of gene dosage on mandible shape in mice as a model for studying the genetic basis of natural variation. Dev Genes Evol. 2013;223(5):279–87. pmid:23563729
- 21. Indjeian Vahan B, Kingman Garrett A, Jones Felicity C, Guenther Catherine A, Grimwood J, Schmutz J, et al. Evolving new skeletal traits by cis-regulatory changes in bone morphogenetic proteins. Cell. 2016;164(1–2):45–56. pmid:26774823
- 22. McGregor AP, Orgogozo V, Delon I, Zanet J, Srinivasan DG, Payre F, et al. Morphological evolution through multiple cis-regulatory mutations at a single gene. Nature. 2007;448(7153):587–590. pmid:17632547
- 23. Abzhanov A, Protas M, Grant BR, Grant PR, Tabin CJ. Bmp4 and morphological variation of beaks in Darwin's finches. Science. 2004;305(5689):1462–5. pmid:15353802
- 24. Ziebarth JD, Cook MN, Wang X, Williams RW, Lu L, Cui Y. Treatment- and population-dependent activity patterns of behavioral and expression QTLs. PLoS One. 2012;7(2):e31805. pmid:22359631
- 25. Whitfield CW, Cziko AM, Robinson GE. Gene expression profiles in the brain predict behavior in individual honey bees. Science. 2003;302(5643):296–299. pmid:14551438
- 26. Wang J, Ross KG, Keller L. Genome-wide expression patterns and the genetic architecture of a fundamental social trait. PLoS Genet. 2008;4(7):e1000127. pmid:18636101
- 27. Chesler EJ, Lu L, Shou S, Qu Y, Gu J, Wang J, et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genet. 2005;37(3):233–242. pmid:15711545
- 28. Khaitovich P, Paabo S, Weiss G. Toward a neutral evolutionary model of gene expression. Genetics. 2005;170(2):929–939. pmid:15834146
- 29. Helantera H, Uller T. Neutral and adaptive explanations for an association between caste-biased gene expression and rate of sequence evolution. Front Genet. 2014;5(297. pmid:25221570
- 30. Yang JR, Maclean CJ, Park C, Zhao H, Zhang J. Intra- and inter-specific variations of gene expression levels in yeast are largely neutral. Mol Biol Evol. 2017; pmid:28575451
- 31. Gagneur J, Stegle O, Zhu C, Jakob P, Tekkedil MM, Aiyar RS, et al. Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype. PLoS Genet. 2013;9(e1003803. pmid:24068968
- 32. Grundberg E, Small KS, Hedman AK, Nica AC, Buil A, Keildson S, et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet. 2012;44(10):1084–1089. pmid:22941192
- 33. Hauberg ME, Zhang W, Giambartolomei C, Franzen O, Morris DL, Vyse TJ, et al. Large-scale identification of common trait and disease variants affecting gene expression. Am J Hum Genet. 2017;100(6):885–894. pmid:28552197
- 34. Gibney PA, Lu C, Caudy AA, Hess DC, Botstein D. Yeast metabolic and signaling genes are required for heat-shock survival and have little overlap with the heat-induced genes. Proc Natl Acad Sci U S A. 2013;110(46):E4393–402. pmid:24167267
- 35. Warringer J, Ericson E, Fernandez L, Nerman O, Blomberg A. High-resolution yeast phenomics resolves different physiological features in the saline response. Proc Natl Acad Sci U S A. 2003;100(26):15724–15729. pmid:14676322
- 36. Jarolim S, Ayer A, Pillay B, Gee AC, Phrakaysone A, Perrone GG, et al. Saccharomyces cerevisiae genes involved in survival of heat shock. G3 (Bethesda). 2013;3(12):2321–2333. pmid:24142923
- 37. Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002;418(6896):387–391. pmid:12140549
- 38. Birrell GW, Brown JA, Wu HI, Giaever G, Chu AM, Davis RW, et al. Transcriptional response of Saccharomyces cerevisiae to DNA-damaging agents does not identify the genes that protect against these agents. Proc Natl Acad Sci U S A. 2002;99(13):8778–8783. pmid:12077312
- 39. van Voorst F, Houghton-Larsen J, Jonson L, Kielland-Brandt MC, Brandt A. Genome-wide identification of genes required for growth of Saccharomyces cerevisiae under ethanol stress. Yeast. 2006;23(5):351–359. pmid:16598687
- 40. Teixeira MC, Raposo LR, Mira NP, Lourenco AB, Sa-Correia I. Genome-wide identification of Saccharomyces cerevisiae genes required for maximal tolerance to ethanol. Appl Environ Microbiol. 2009;75(18):5761–5772. pmid:19633105
- 41. Yoshikawa K, Tanaka T, Furusawa C, Nagahisa K, Hirasawa T, Shimizu H. Comprehensive phenotypic analysis for identification of genes affecting growth under ethanol stress in Saccharomyces cerevisiae. FEMS Yeast Res. 2009;9(32–44. pmid:19054128
- 42. Kubota S, Takeo I, Kume K, Kanai M, Shitamukai A, Mizunuma M, et al. Effect of ethanol on cell growth of budding yeast: genes that are important for cell growth in the presence of ethanol. Biosci Biotechnol Biochem. 2004;68(4):968–972. pmid:15118337
- 43. Fujita K, Matsuyama A, Kobayashi Y, Iwahashi H. The genome-wide screening of yeast deletion mutants to identify the genes required for tolerance to ethanol and other alcohols. FEMS Yeast Res. 2006;6(5):744–750. pmid:16879425
- 44. Berry DB, Gasch AP. Stress-activated genomic expression changes serve a preparative role for impending stress in yeast. Mol Biol Cell. 2008;19(11):4580–4587. pmid:18753408
- 45. Lewis JA, Elkon IM, McGee MA, Higbee AJ, Gasch AP. Exploiting natural variation in Saccharomyces cerevisiae to identify genes for increased ethanol resistance. Genetics. 2010;186(4):1197–1205. pmid:20855568
- 46. Trent JD, Gabrielsen M, Jensen B, Neuhard J, Olsen J. Acquired thermotolerance and heat shock proteins in thermophiles from the three phylogenetic domains. J Bacteriol. 1994;176(19):6148–6152. pmid:7928981
- 47. Collinson LP, Dawes IW. Inducibility of the response of yeast cells to peroxide stress. J Gen Microbiol. 1992;138(2):329–335. pmid:1564443
- 48. Dahlgaard J, Loeschcke V, Michalak P, Justesen J. Induced thermotolerance and associated expression of the heat-shock protein Hsp70 in adult Drosophila melanogaster. Funct Ecol. 1998;12(5):786–793.
- 49. Raffaghello L, Lee C, Safdie FM, Wei M, Madia F, Bianchi G, et al. Starvation-dependent differential stress resistance protects normal but not cancer cells against high-dose chemotherapy. Proc Natl Acad Sci U S A. 2008;105(24):8215–8220. pmid:18378900
- 50. Hong SW, Vierling E. Mutants of Arabidopsis thaliana defective in the acquisition of tolerance to high temperature stress. Proc Natl Acad Sci U S A. 2000;97(8):4392–4397. pmid:10760305
- 51. Ali ZA, Callaghan CJ, Lim E, Ali AA, Nouraei SA, Akthar AM, et al. Remote ischemic preconditioning reduces myocardial and renal injury after elective abdominal aortic aneurysm repair: a randomized controlled trial. Circulation. 2007;116(11 Suppl):I98–105. pmid:17846333
- 52. Cheung MM, Kharbanda RK, Konstantinov IE, Shimizu M, Frndova H, Li J, et al. Randomized controlled trial of the effects of remote ischemic preconditioning on children undergoing cardiac surgery: first clinical application in humans. J Am Coll Cardiol. 2006;47(11):2277–2282. pmid:16750696
- 53. Hausenloy DJ, Mwamure PK, Venugopal V, Harris J, Barnard M, Grundy E, et al. Effect of remote ischaemic preconditioning on myocardial injury in patients undergoing coronary artery bypass graft surgery: a randomised controlled trial. Lancet. 2007;370(9587):575–579. pmid:17707752
- 54. Thielmann M, Kottenberg E, Kleinbongard P, Wendt D, Gedik N, Pasa S, et al. Cardioprotective and prognostic effects of remote ischaemic preconditioning in patients undergoing coronary artery bypass surgery: a single-centre randomised, double-blind, controlled trial. Lancet. 2013;382(9892):597–604. pmid:23953384
- 55. Wegener S, Gottschalk B, Jovanovic V, Knab R, Fiebach JB, Schellinger PD, et al. Transient ischemic attacks before ischemic stroke: preconditioning the human brain? A multicenter magnetic resonance imaging study. Stroke. 2004;35(3):616–621. pmid:14963288
- 56. Huang HL, Wang N, Zhou H, Yu CY. Study on influence of transient ischemic attack on subsequent cerebral infarction. Eur Rev Med Pharmacol Sci. 2016;20(24):5164–5167. pmid:28051252
- 57. Moncayo J, de Freitas GR, Bogousslavsky J, Altieri M, van Melle G. Do transient ischemic attacks have a neuroprotective effect? Neurology. 2000;54(11):2089–94. pmid:10851368
- 58. Piskur J, Rozpedowska E, Polakova S, Merico A, Compagno C. How did Saccharomyces evolve to become a good brewer? Trends Genet. 2006;22(4):183–6. pmid:16499989
- 59. Chandler M, Stanley GA, Rodgers P, Chambers P. A genomic approach to defining the ethanol stress response in the yeast Saccharomyces cerevisiae. Ann Microbiol. 2004;54(4):427–545.
- 60. Fujita K, Matsuyama A, Kobayashi Y, Iwahashi H. Comprehensive gene expression analysis of the response to straight-chain alcohols in Saccharomyces cerevisiae using cDNA microarray. J Appl Microbiol. 2004;97(1):57–67. pmid:15186442
- 61. Hirasawa T, Yoshikawa K, Nakakura Y, Nagahisa K, Furusawa C, Katakura Y, et al. Identification of target genes conferring ethanol stress tolerance to Saccharomyces cerevisiae based on DNA microarray data analysis. J Biotechnol. 2007;131(1):34–44. pmid:17604866
- 62. Piper PW, Talreja K, Panaretou B, Moradas-Ferreira P, Byrne K, Praekelt UM, et al. Induction of major heat-shock proteins of Saccharomyces cerevisiae, including plasma membrane Hsp30, by ethanol levels above a critical threshold. Microbiology. 1994;140 (Pt 11)(3031–3038. pmid:7812443
- 63. Alexandre H, Ansanay-Galeote V, Dequin S, Blondin B. Global gene expression during short-term ethanol stress in Saccharomyces cerevisiae. FEBS Lett. 2001;498(1):98–103. pmid:11389906
- 64. Lewis JA, Broman AT, Will J, Gasch AP. Genetic architecture of ethanol-responsive transcriptome variation in Saccharomyces cerevisiae strains. Genetics. 2014;198(1):369–382. pmid:24970865
- 65. Semchyshyn HM. Hormetic concentrations of hydrogen peroxide but not ethanol induce cross-adaptation to different stresses in budding yeast. Int J Microbiol. 2014;2014(485792. pmid:24669223
- 66. Cadenas E, Boveris A, Ragan CI, Stoppani AO. Production of superoxide radicals and hydrogen peroxide by NADH-ubiquinone reductase and ubiquinol-cytochrome c reductase from beef-heart mitochondria. Arch Biochem Biophys. 1977;180(2):248–257. pmid:195520
- 67. Drakulic T, Temple MD, Guido R, Jarolim S, Breitenbach M, Attfield PV, et al. Involvement of oxidative stress response genes in redox homeostasis, the level of reactive oxygen species, and ageing in Saccharomyces cerevisiae. FEMS Yeast Res. 2005;5(12):1215–1228. pmid:16087409
- 68. Pérez-Gallardo RV, Briones LS, Díaz-Pérez AL, Gutiérrez S, Rodríguez-Zavala JS, Campos-García J. Reactive oxygen species production induced by ethanol in Saccharomyces cerevisiae increases because of a dysfunctional mitochondrial iron-sulfur cluster assembly system. FEMS Yeast Research. 2013;13(8):804–819. pmid:24028658
- 69. Ehrenreich IM, Bloom J, Torabi N, Wang X, Jia Y, Kruglyak L. Genetic architecture of highly complex chemical resistance traits across four yeast strains. PLoS Genet. 2012;8(3):e1002570. pmid:22438822
- 70. Linder RA, Seidl F, Ha K, Ehrenreich IM. The complex genetic and molecular basis of a model quantitative trait. Mol Biol Cell. 2016;27(1):209–218. pmid:26510497
- 71. Bloom JS, Ehrenreich IM, Loo WT, Lite TL, Kruglyak L. Finding the sources of missing heritability in a yeast cross. Nature. 2013;494(7436):234–237. pmid:23376951
- 72. Creusot F, Verdiere J, Gaisne M, Slonimski PP. CYP1 (HAP1) regulator of oxygen-dependent gene expression in yeast. I. Overall organization of the protein sequence displays several novel structural domains. J Mol Biol. 1988;204(2):263–276. pmid:2851658
- 73. Pfeifer K, Kim KS, Kogan S, Guarente L. Functional dissection and sequence of yeast HAP1 activator. Cell. 1989;56(2):291–301. pmid:2643482
- 74. Zhang L, Hach A. Molecular mechanism of heme signaling in yeast: the transcriptional activator Hap1 serves as the key mediator. Cell Mol Life Sci. 1999;56(5–6):415–426. pmid:11212295
- 75. Hickman MJ, Winston F. Heme levels switch the function of Hap1 of Saccharomyces cerevisiae between transcriptional activator and transcriptional repressor. Mol Cell Biol. 2007;27(21):7414–7424. pmid:17785431
- 76. Tamura K, Gu Y, Wang Q, Yamada T, Ito K, Shimoi H. A hap1 mutation in a laboratory strain of Saccharomyces cerevisiae results in decreased expression of ergosterol-related genes and cellular ergosterol content compared to sake yeast. J Biosci Bioeng. 2004;98(159–166. pmid:16233684
- 77. Ter Linde JJ, Steensma HY. A microarray-assisted screen for potential Hap1 and Rox1 target genes in Saccharomyces cerevisiae. Yeast. 2002;19(10):825–840. pmid:12112237
- 78. Winkler H, Adam G, Mattes E, Schanz M, Hartig A, Ruis H. Co-ordinate control of synthesis of mitochondrial and non-mitochondrial hemoproteins: a binding site for the HAP1 (CYP1) protein in the UAS region of the yeast catalase T gene (CTT1). EMBO J. 1988;7(6):1799–1804. pmid:2844525
- 79. Gaisne M, Becam AM, Verdiere J, Herbert CJ. A 'natural' mutation in Saccharomyces cerevisiae strains derived from S288c affects the complex regulatory gene HAP1 (CYP1). Curr Genet. 1999;36(195–200. pmid:10541856
- 80. Steinmetz LM, Sinha H, Richards DR, Spiegelman JI, Oefner PJ, McCusker JH, et al. Dissecting the architecture of a quantitative trait locus in yeast. Nature. 2002;416(6878):326–330. pmid:11907579
- 81. Abdulrehman D, Monteiro PT, Teixeira MC, Mira NP, Lourenco AB, dos Santos SC, et al. YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface. Nucleic Acids Res. 2011;39(Database issue):D136–140. pmid:20972212
- 82. Pinkham JL, Wang Z, Alsina J. Heme regulates SOD2 transcription by activation and repression in Saccharomyces cerevisiae. Curr Genet. 1997;31(4):281–291. pmid:9108135
- 83. Wong CM, Ching YP, Zhou Y, Kung HF, Jin DY. Transcriptional regulation of yeast peroxiredoxin gene TSA2 through Hap1p, Rox1p, and Hap2/3/5p. Free Radic Biol Med. 2003;34(5):585–597. pmid:12614847
- 84. Berry DB, Guan Q, Hose J, Haroon S, Gebbia M, Heisler LE, et al. Multiple means to the same end: the genetic basis of acquired stress resistance in yeast. PLoS Genet. 2011;7(11):e1002353. pmid:22102822
- 85. Guan Q, Haroon S, Bravo DG, Will JL, Gasch AP. Cellular memory of acquired stress resistance in Saccharomyces cerevisiae. Genetics. 2012;192(2):495–505. pmid:22851651
- 86. Hon T, Dodd A, Dirmeier R, Gorman N, Sinclair PR, Zhang L, et al. A mechanism of oxygen sensing in yeast. Multiple oxygen-responsive steps in the heme biosynthetic pathway affect Hap1 activity. J Biol Chem. 2003;278(50):50771–50780. pmid:14512429
- 87. Elfving N, Chereji RV, Bharatula V, Bjorklund S, Morozov AV, Broach JR. A dynamic interplay of nucleosome and Msn2 binding regulates kinetics of gene activation and repression following stress. Nucleic Acids Res. 2014;42(9):5468–5482. pmid:24598258
- 88. Strope PK, Skelly DA, Kozmin SG, Mahadevan G, Stone EA, Magwene PM, et al. The 100-genomes strains, an S. cerevisiae resource that illuminates its natural phenotypic and genotypic variation and emergence as an opportunistic pathogen. Genome Res. 2015;25(5):762–774. pmid:25840857
- 89. Liti G, Carter DM, Moses AM, Warringer J, Parts L, James SA, et al. Population genomics of domestic and wild yeasts. Nature. 2009;458(7236):337–341. pmid:19212322
- 90. Fraser HB, Moses AM, Schadt EE. Evidence for widespread adaptive evolution of gene expression in budding yeast. Proc Natl Acad Sci U S A. 2010;107(2977–2982. pmid:20133628
- 91. Chang J, Zhou Y, Hu X, Lam L, Henry C, Green EM, et al. The molecular mechanism of a cis-regulatory adaptation in yeast. PLoS Genet. 2013;9(e1003813. pmid:24068973
- 92. Mitchell A, Romano GH, Groisman B, Yona A, Dekel E, Kupiec M, et al. Adaptive prediction of environmental changes by microorganisms. Nature. 2009;460(7252):220–224. pmid:19536156
- 93. Grant CM, MacIver FH, Dawes IW. Mitochondrial function is required for resistance to oxidative stress in the yeast Saccharomyces cerevisiae. FEBS Lett. 1997;410(2–3):219–222. pmid:9237633
- 94. Jamieson DJ. Saccharomyces cerevisiae has distinct adaptive responses to both hydrogen peroxide and menadione. J Bacteriol. 1992;174(20):6678–6781. pmid:1400218
- 95. Wang CQ, Li X, Wang MQ, Qian J, Zheng K, Bian HW, et al. Protective effects of ETC complex III and cytochrome c against hydrogen peroxide-induced apoptosis in yeast. Free Radic Res. 2014;48(4):435–444. pmid:24437935
- 96. Chandler CH, Chari S, Kowalski A, Choi L, Tack D, DeNieu M, et al. How well do you know your mutation? Complex effects of genetic background on expressivity, complementation, and ordering of allelic effects. PLoS Genet. 2017;13(11):e1007075. pmid:29166655
- 97. Sardi M, Paithane V, Place M, Robinson E, Hose J, Wohlbach DJ, et al. Genome-wide association across Saccharomyces cerevisiae strains reveals substantial variation in underlying gene requirements for toxin tolerance. PLoS Genet. 2018;14(2):e1007217. pmid:29474395
- 98. Ziv N, Shuster BM, Siegal ML, Gresham D. Resolving the complex genetic basis of phenotypic variation and variability of cellular growth. Genetics. 2017;206(3):1645–1657. pmid:28495957
- 99. Kim HS, Fay JC. Genetic variation in the cysteine biosynthesis pathway causes sensitivity to pharmacological compounds. Proc Natl Acad Sci U S A. 2007;104(49):19387–19391. pmid:18042712
- 100. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, Andre B, et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science. 1999;285(5429):901–906. pmid:10436161
- 101. Haley CS, Knott SA. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity (Edinb). 1992;69(4):315–24. pmid:16718932
- 102. Broman KW, Wu H, Sen S, Churchill GA. R/qtl: QTL mapping in experimental crosses. Bioinformatics. 2003;19(7):889–890. pmid:12724300
- 103. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear mixed-effects models using lme4. J of Stat Softw. 2015;67(1):
- 104. Gasch AP. Yeast genomic expression studies using DNA microarrays. Methods Enzymol. 2002;350(393–414. pmid:12073326
- 105. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 2001;25(4):402–408. pmid:11846609
- 106. Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976;72(248–54. pmid:942051
- 107. Beers RF Jr., Sizer IW. A spectrophotometric method for measuring the breakdown of hydrogen peroxide by catalase. J Biol Chem 1952;195(1):133–140. pmid:14938361