Much attention has been given to the ways in which organisms evolve new
phenotypes and the influence of the environment on this process. A major focus
of study is defining the genetic basis for phenotypes important for organismal
fitness. As a first step toward this goal, we surveyed phenotypic variation in
diverse yeast strains collected from different environments by characterizing
variations in stress resistance and genomic expression. We uncovered many
phenotypic differences across yeast strains, both in stress tolerance and gene
expression. The similarities and differences of the strains analyzed uncovered
phenotypes shared by strains that live in similar environments, suggesting
common features of yeast niches as well as mechanisms that different strains use
to thrive in those conditions. We provide evidence that some characteristics of
strains isolated from oak tree soil have been selected for, perhaps because of
the shared selective pressures imposed by their environment. One theme emerging
from our studies is that the laboratory strain of yeast, long used as a model
for yeast physiology and basic biology, is aberrant compared to all other
strains. This result raises caution about making general conclusions about yeast
biology based on a single strain with a specific genetic makeup.
Interactions between an organism and its environment can significantly influence
phenotypic evolution. A first step toward understanding this process is to
characterize phenotypic diversity within and between populations. We explored
the phenotypic variation in stress sensitivity and genomic expression in a large
panel of Saccharomyces strains collected from diverse
environments. We measured the sensitivity of 52 strains to 14 environmental
conditions, compared genomic expression in 18 strains, and identified gene
copy-number variations in six of these isolates. Our results demonstrate a large
degree of phenotypic variation in stress sensitivity and gene expression.
Analysis of these datasets reveals relationships between strains from similar
niches, suggests common and unique features of yeast habitats, and implicates
genes whose variable expression is linked to stress resistance. Using a simple
metric to suggest cases of selection, we found that strains collected from oak
exudates are phenotypically more similar than expected based on their genetic
diversity, while sake and vineyard isolates display more diverse phenotypes than
expected under a neutral model. We also show that the laboratory strain S288c is
phenotypically distinct from all of the other strains studied here, in terms of
stress sensitivity, gene expression, Ty copy number, mitochondrial content, and
gene-dosage control. These results highlight the value of understanding the
genetic basis of phenotypic variation and raise caution about using laboratory
strains for comparative genomics.
IntroductionA major focus of genetic study is to elucidate the effects of genetic variation on
phenotypic diversity. The evolution of phenotypes is often driven by environmental
factors and the interactions between each organism and its environment. Recently,
there has been a renewed interest in characterizing the diversity and ecology of
organisms long used in the laboratory as models for biological study. Yeast, worms,
flies, and mice have been studied on a molecular level for decades and have provided
many insights into basic biology. However, most of our knowledge base exists for
only a handful of domesticated lines. Little is known about the natural ecology of
these organisms or the degree to which individuals of each species vary within and
between natural populations.The budding yeast Saccharomyces cerevisiae exists in diverse niches
across the world and can be found in natural habitats associated with fruits, tree
soil, and insects, in connection with human societies (namely through brewing and
baking), and in facultative infections of immuno-compromised individuals [1]. These
yeasts are transported by insect vectors and likely through association with human
societies. Recent population-genetic studies have begun to explore the genetic
diversity of S. cerevisiae strains [2]–[5]. These
studies have demonstrated little geographic structure in natural yeast populations
and relatively low sequence diversity, particularly within vineyard strains. It has
been proposed that low sequence diversity in this species may be due to a more
recent common ancestor compared to other yeasts [6]. Genomic comparisons
also suggest low rates of outcrossing between strains [7], which may limit the
fixation of genetic differences under selection by reducing effective population
sizes [8].Although the genetic diversity of S. cerevisiae populations is
emerging from large-scale sequencing projects, the phenotypic diversity within and
between yeast populations has been less systematically studied. Myriad studies have
characterized strain-specific differences in specific phenotypes to identify the
genetic basis for phenotypes of interest (for example, those related to wine making
[9],
thermotolerance [10]–[12], sporulation efficiency
[13]–[16], drug sensitivity [17]–[19], and others [20]–[25]). The degree to which
these phenotypes vary across diverse strains has not been systematically explored.
Other genomic studies have investigated variation in genomic expression across
strains, with the goal of investigating the mode and consequence of gene-expression
evolution [26]–[30]. These studies
demonstrated significant variation in gene expression between strains, and in some
cases pointed to the genetic basis for those differences [27], [31]–[35].
However, each study investigated only a few strains, typically vineyard strains. The
broader phenotypic variation across diverse yeast strains and populations,
particularly natural isolates, is largely uncharacterized.Here we investigated the variation in stress sensitivity and genomic expression in a
large panel of Saccharomyces strains. We quantified the sensitivity
of 52 strains collected from diverse niches to 14 environmental conditions and
measured genomic expression in 18 of these strains growing in standard medium. We
observe a large amount of phenotypic variation, both in terms of stress sensitivity
and gene expression. Associations among phenotypes revealed relationships between
environmental conditions and among yeast strains. One case in particular suggests
that genetically diverse strains collected from oak soil have undergone selection
for growth in a common niche. This study provides a representative description of
expression variation and stress sensitivity within and across yeast populations,
particularly non-laboratory strains, setting the stage for elucidating the genetic
basis of this variation.ResultsVariation in Environmental Sensitivity in a Large Panel of
Saccharomyces StrainsFay and Benavides conducted a population-genetic study of 81
Saccharomyces strains by analyzing ∼7 kb of coding
and non-coding sequence from each isolate [2]. We characterized the
phenotypic diversity of 52 of these strains, shown in Figure 1. This set included natural isolates
from European vineyards, yeasts collected from African palm-wine fermentations,
commercial wine- and sake-producing strains, clinical yeasts, natural isolates
collected from African and Asian fruit substrates, strains from oak-tree soil
and exudates from the Northeastern United States, three common lab strains, and
other isolates (see Table S1 and [2] for references). We
also characterized two haploid S. cerevisiae strains (RM11-1a
and YJM789) and three other Saccharomyces species (S.
paradoxus, S. mikatae, and S.
bayanus) for which whole-genome sequence is available [36],[37]. Each strain was grown under 31 different
conditions representing 14 unique environments, chosen to provoke diverse
physiological responses. These environments varied in nutrient composition,
growth temperature, and presence of toxic drugs, heavy metals, oxidizing agents,
and osmotic/ionic stress. Cells were grown on solid medium in the presence of
each environmental variable, and viability was scored relative to a no-stress
control for each strain (see Materials and
Methods for details).10.1371/journal.pgen.1000223.g001Figure 1Phylogeny of Saccharomyces strains used in this
study.The phylogeny was inferred from 13,061 bp of coding and non-coding
sequence generated by [2] and this study, using the program
MrBayes [73]. Nodes with a posterior
probability<0.9 are collapsed. Strains are color coded according
to the niche from which they were originally isolated, as shown in the
key at the bottom of the figure.The results reveal a tremendous amount of phenotypic diversity in environmental
sensitivity (Figure 2).
Although there were similarities between strains, no two strains were exactly
alike in phenotypic profile. Each displayed a propensity for growth under at
least one environment and sensitivity to one or more conditions. Some strains
were generally tolerant to stressful environments across the board. For example,
strain Y2, originally collected from a Trinidadian rum distillery, and clinical
isolates YJM454 and YJM440 were tolerant of most of these conditions, while the
S. bayanus strain used in our study was sensitive to nearly
all stresses tested. Several strains, including commercial sake-producing
strains, showed a wide standard deviation of growth scores across the stresses,
reflecting that they were either highly sensitive or highly resistant to
different stresses. In contrast, most vineyard isolates grew moderately well in
most of the environments examined (see Discussion).10.1371/journal.pgen.1000223.g002Figure 2Phenotypic variation in diverse Saccharomyces
strains.The viability of 52 Saccharomyces strains and species
grown under 14 different environmental conditions was measured. Strains
were grown in at least duplicate on solid agar plates containing
1–3 doses of each environmental variable, as described in
Materials and Methods. Each row
on the plot represents a different strain and each column indicates a
given environment. Colored boxes represent the average growth score of
each strain grown in each environment, according to the key shown at the
lower right. Strains and conditions were organized by hierarchical
clustering using the Pearson correlation as a similarity metric.Exploration of the range of strain sensitivities measured for each environment
also suggested common and unique features of
Saccharomyces' habitats. Collectively, this set of
strains showed the greatest variation in copper sulfate tolerance, sodium
chloride resistance, and freeze-thaw survival, implicating these as
niche-specific features not generally experienced by yeast. In contrast, strains
showed the least variation (but some variability nonetheless) for growth on
non-fermentable acetate, in minimal medium lacking supplemental amino acids, and
at 37°C. Presumably, defects in respiration, prototrophy, and growth at
physiological temperature represent a significant selective disadvantage,
regardless of the particular niche.Strains from Similar Niches Display Similar Profiles of Stress SensitivityHierarchical clustering of the phenotype data revealed interesting relationships
between groups of strains. In particular, several groups of strains displayed
similar profiles of stress sensitivity across the environments tested (Figure 2). As a group, the
sake-producing strains were extremely resistant to lithium chloride but
sensitive to copper sulfate, calcium chloride, cadmium chloride, and SDS
detergent (p<0.005 based on 10,000 permutations, see Materials and Methods); indeed, this group was slightly more
sensitive to stress in general. Many of the vineyard strains shared specific
phenotypes, including resistance to copper sulfate, as previously noted for
other vineyard strains [26],[38],[29]. The
group of laboratory strains was also highly resistant to copper sulfate as well
as sodium and lithium chloride. In contrast, strains collected from oak soil
were particularly sensitive to copper sulfate and sodium chloride but highly
resistant to freeze-thaw stress (p<0.005, 10,000 permutations).The similarities in phenotypic profiles could arise through selection (either
directional or purifying) due to shared selective pressures across strains
living in the same environment. Alternatively, phenotypic similarity could
result simply if the strains are genetically related due to a recent common
ancestor. For example, many of the lab strains are closely related, since a
large fraction of their genomes is derived from a common progenitor [39],[40]. We wished to
distinguish between these possibilities for other strain groups. Natural
selection can be inferred by comparing the population genetic structure
(FST) to an analogous measure of phenotypic structure
(QST) [41],[42]. A deviation from
unity suggests that either divergent (QST/FST>1) or
purifying (QST/FST<1) selection has occurred across
populations. We wished to analyze each subpopulation separately, and therefore
we devised a simple alternative approach to identify deviations from neutral
phenotypic variation. We calculated the average pairwise phenotypic distance
over the average pairwise genetic distance for pairs of strains collected from
the same environment (‘sake’,
‘vineyard’, ‘oak’,
‘clinical’, ‘natural’ or
‘other fermentation’). This ratio was compared to the ratio
of distances calculated for pairs of strains between niche groups, generating
the parameter P/G. A P/G ratio = 1 is expected
under neutrality, where the phenotypic to genetic distance is equal for
within-group versus between-group comparisons. In contrast, a value of
P/G<1 suggests that the strains within the group are more similar in
phenotype than would be expected under the neutral model, whereas a ratio
>1 indicates that the strains are phenotypically more variable than
expected based on their genetic relatedness.The results provide evidence of both selection and shared ancestry for different
groups of strains. First, the P/G ratio did not deviate significantly from unity
for strains in the ‘clinical’,
‘natural’, or ‘other fermentation’
groups (average
P/G = 1.02+/−0.22), nor did
it deviate significantly for randomized simulations (data not shown). In
contrast, P/G was 4.2 and 3.0 for sake strains and vineyard strains,
respectively. Thus, the similarity in their phenotypes likely arises due to
their recent divergence from a common ancestor. Interestingly, these P/G values
were significantly higher than expected by chance (p<0.0001 from 10,000
permutations), suggesting that the strains show more phenotypic
variation than expected. This could arise if strains have experienced
diversifying selection for disparate phenotypes, although it could also result
if genetic distances are underrepresented or skewed due to limited sequence
data.In contrast, strains collected from oak-tree exudates and soil are phenotypically
more similar than would be expected under a neutral model. We observed a P/G
ratio of 0.31 (p = 0.0013 from 10,000
permutations), indicating that phenotypic variation within this group is lower
than expected based on the strains' genetic relatedness. This suggests
that the strains have undergone selection for growth in a common environment
(see Discussion). Consistent with this model, the S. paradoxus
strain YPS125, also collected from Northeastern oak flux [6], is
phenotypically more similar to S. cerevisiae strains collected
from that environment (pairwise R of 0.61, 0.66, and 0.77 to YPS1000, YPS1009,
and YPS163, respectively) than to the other S. paradoxus strain
in our collection (R = 0.51). At least some of
the phenotypes shared by these strains are likely important for their ability to
thrive in their niche (see Discussion).Extensive Variation in Genomic Expression in Non-Laboratory StrainsNumerous studies have characterized differences in genomic expression between
individual strains of yeast, typically vineyard and lab strains [13],
[26]–[31],[34],[43]. To more broadly
survey the variation in genomic expression across populations, we measured
whole-genome expression in 17 non-laboratory strains compared to that in the
diploid S288c-derived strain DBY8268, using 70mer oligonucleotide arrays
designed against the S288c genome. The long oligos used to probe each gene
minimize hybridization defects due to sequence differences from S288c. We
verified this by hybridizing genomic DNA from 6 strains of varying genetic
distance from S288c: indeed, fewer than 5% of the observed gene
expression differences described below could be explained by defective
hybridization to the arrays (see Materials and
Methods). Therefore the vast majority of measured expression
differences are due to differences in transcript abundance.A striking number of yeast genes showed differential expression from the
laboratory strain in at least one other strain (Figure 3A). Of the ∼5,700 predicted
S. cerevisiae open reading frames, 2680
(∼47%) were statistically significantly altered in expression
(false discovery rate, FDR = 0.01) in at least
one non-laboratory strain compared to S288c, with an average of 480 genes per
strain. At an FDR of 0.05, over 70% of genes were significantly
altered in expression in at least one non-lab strain (Table 1). The number of expression
differences is comparable to that observed by Brem et al., who reported over
half of yeast genes differentially expressed between the vineyard strain RM11-1a
and S288c [27].10.1371/journal.pgen.1000223.g003Figure 3Variation in gene expression in S. cerevisiae
isolates.The diagrams show the average log2 expression differences measured in the
denoted strains. Each row represents a given gene and each column
represents a different strain, color-coded as described in Figure 1. (A)
Expression patterns of 2,680 genes that varied significantly
(FDR = 0.01, paired t-test) in at least
one strain compared to S288c. (B) Expression patterns of 953 genes that
varied significantly in at least one strain compared to strain YPS163
(FDR = 0.01, unpaired t-test). For (A)
and (B), a red color indicates higher expression and a green color
represents lower expression in the denoted strain compared to S288c,
according to the key. (C) Expression patterns of 1,330 genes that varied
significantly (FDR = 0.01, paired
t-test) in at least one strain compared to the mean expression of all 17
strains. Here, red and green correspond to higher and lower expression,
respectively, compared to the mean expression of that gene in all
strains. Genes were organized independently in each plot by hierarchical
clustering.10.1371/journal.pgen.1000223.t001Table 1Number of differentially expressed genes in 17 non-laboratory
strains.StrainExpression Differences from S288cFDR 0.01Expression Differences from MeanExpression Differences from YPS163B198 a,
176 b (4.6)33, 18 (0.085)84, 25 (1.8)I14112, 260 (6.2)14, 17 (0.5)15, 14 (0.5)K1174, 239 (6.9)59, 32 (1.5)63, 22 (1.4)K9232, 212 (7.5)70, 33 (1.7)100, 22 (2.1)M22220, 550 (15)103, 245 (6.8)75, 69 (2.8)M8133, 311 (7.5)10, 80 (1.5)13, 18 (0.5)PR160, 271 (7.4)9, 73 (1.4)11, 13 (0.4)RM11-1a326, 253 (11.4)191, 116 (6.1)151, 53 (4)SB131, 272 (6.9)24, 31 (0.9)63, 21 (1.4)Y1185, 189 (6.4)92, 14 (1.8)92, 18 (1.9)Y10120, 263 (6.5)74, 20 (1.6)59, 11 (1.2)Y12162, 195 (5.9)36, 14 (0.8)46, 9 (0.9)YJM269285, 255 (8.9)148, 53 (3.3)132, 43 (2.9)YJM308364, 288 (11)153, 34 (3.1)142, 40 (3.1)YJM789669, 329 (19.7)452, 163 (12.1)338, 105 (8.7)YPS1009189, 402 (10.2)31, 88 (2)3, 35 (0.7)YPS163186, 297 (8.4)11, 41 (0.9)StrainExpression Differences from S288cFDR 0.05Expression Differences from MeanExpression Differences from YPS163B1178a,
260 b (7.3)64, 33 (1.6)126, 42 (2.8)I14177, 371 (9.1)29, 39 (1.1)32, 42 (1.2)K1288, 366 (11)104, 57 (2.7)101, 48 (2.5)K9414, 409 (13.8)158, 93 (4.2)166, 73 (4)M22360, 903 (24.7)149, 437 (11.4)120, 187 (6)M8240, 486 (12.3)25, 112 (2.3)36, 50 (1.5)PR266, 352 (10.6)31, 98 (2.2)34, 27 (1)RM11-1a667, 418 (21.4)375, 206 (11.5)288, 130 (8.3)SB219, 402 (10.7)65, 87 (2.6)97, 52 (2.6)Y1363, 309 (11.4)189, 48 (4)135, 38 (2.9)Y10237, 456 (11.8)115, 57 (2.9)95, 30 (2.1)Y12282, 303 (9.6)58, 25 (1.4)78, 24 (1.7)YJM269453, 428 (14.5)247, 105 (5.8)214, 101 (5.2)YJM308627, 568 (20.1)264, 111 (6.3)238, 76 (5.3)YJM789995, 555 (30.5)774, 323 (21.6)581, 201 (15.4)YPS1009365, 658 (17.6)76, 192 (4.6)18, 54 (1.2)YPS163322, 496 (14.3)26, 82 (1.9)Expression differences relative to S288c, the mean of 17 strains, or
strain YPS163 were defined at a false discovery rate (FDR) of 0.01
or 0.05. Values represent the number of genes expressed a
higher or b lower than the designated reference. The
percent of yeast genes affected in each strain at each significance
threshold is shown in parentheses.However, closer inspection revealed that many of these expression differences
were common to all of the non-laboratory strains (Figure 3A), revealing that these expression
patterns were unique to S288c. This group was enriched for functionally related
genes, including those involved in ergosterol synthesis, mitochondrial function,
respiration, cell wall synthesis, transposition, and other functions (Table 2). Many of these
functional groups were also reported by Brem et al., who noted that multiple
categories (including ergosterol synthesis and mitochondrial function) can be
linked to a known polymorphism in the Hap1p transcription factor [44].
Indeed, the expression differences specific to S288c were enriched for targets
of Hap1p (p<10−11, hypergeometric distribution) as
well as targets of Hap4p (p<10−6) [45],
which regulates genes involved in respiration. Hence, many of the observed
expression differences may result because of S288c-specific physiology (see
Discussion).10.1371/journal.pgen.1000223.t002Table 2Functional enrichment in genes differentially expressed in
S288c.Higher expression in S288c
p valuePhosphate metabolism a
7/33 b
1×10−06
Cell wall6/383×10−05
Cytokinesis4/54×10−07
Transposable element genes71/901×10−80
Extracellular proteins11/841×10−07
HELICc Domain14/772×10−11
DEXDc Domain13/805×10−10 Lower expression in S288c
Respiration20/881×10−18
Mitochondrion35/3661×10−19
Carbon Utilization78/2201×10−103
Sterol biosynthesis10/256×10−13
Functional enrichment was calculated using the hypergeometric
distribution with Bonferroni correction in the program FunSpec [82] on genes called differentially
expressed (FDR 0.05) in 70% of all strains compared to
S288c.aFunctional group with statistically significant enrichment.bNumber of genes in selected group compared to total number of genes
in the genome with that annotation.For a more representative description of expression variation in non-laboratory
strains, we sought to represent the expression differences in a way that was not
obscured by S288c. First, we identified genes whose expression varied
significantly from the oak strain YPS163. Second, we identified transcripts
whose abundance varied from the mean of all non-laboratory strains (see Materials and Methods). Although the mean
expression value of each gene is merely an arbitrary reference point, this data
transformation serves to remove the effect of S288c from each array while
maintaining the statistical power to identify expression differences.Roughly 1330 (23%) of yeast genes varied in expression in at least one
non-laboratory strain relative to the mean of all strains, while 953
(17%) of genes varied significantly from YPS163
(FDR = 0.01). In both cases, two thirds of
significant expression differences were specific to only one strain (Figure 3B and 3C). The number
of genes with statistically significant expression differences from the mean
ranged from 30 (in vineyard strain I14) to nearly 600 (in clinical isolate
YJM789), with a median of 88 expression differences per strain. The number of
expression differences did not correlate strongly with the genetic distances of
the strains (R2 = 0.16). However,
this is not surprising since many of the observed expression differences are
likely linked in trans to the same genetic loci [27],[31],[34],[35],[43]. Consistent with this
interpretation, we found that the genes affected in each strain were enriched
for specific functional categories (Table S4), revealing that altered expression
of pathways of genes was a common occurrence in our study.We noticed that some functional categories were repeatedly affected in different
strains. To further explore this, we identified individual genes whose
expression differed from the mean in at least 3 of the 17 non-laboratory
strains. This group of 219 genes was strongly enriched for genes involved in
amino acid metabolism (p<10−14), sulfur metabolism
(p<10−14), and transposition
(p<10−47), revealing that genes involved in
these functions had a higher frequency of expression variation. Differential
expression of some of these categories was also observed for a different set of
vineyard strains [26],[28], and the genetic
basis for differential expression of amino acid biosynthetic genes in one
vineyard strain has recently been linked to a polymorphism in an amino acid
sensory protein [35]. We also noted that the 1330 genes with
statistically variable expression in at least one non-laboratory strain were
enriched for genes that contained upstream TATA elements [46]
(p = 10−16) and genes
with paralogs (p = 10−6)
but under-enriched for essential genes [47]
(p = 10−25). The
trends and statistical significance were similar using 953 genes that varied
significantly from YPS163. Thus, genes with specific functional and regulatory
features are more likely to vary in expression under the conditions examined
here, consistent with reports of other recent studies [30],[43],[48],[49] (see
Discussion).Influence of Copy Number Variation on Gene Expression VariationExpression from transposable Ty elements was highly variable across strains.
However, Ty copy number is known to vary widely in different genetic backgrounds
[50],[51], suggesting that
these and other observed expression differences could be due to copy number
variations in particular strains. Indeed, numerous expression differences could
be linked to known gene amplifications in S288c, such as ASP3,
ENA1, CUP1, and hexose transporters [52],[51]. We quantified the
contribution of increased copy number to the observed increases in gene
expression relative to S288c in 6 of our strains. In general,
∼2–5% of expression differences could be wholly or
partially explained by differences in gene copy number (see Materials and Methods). YPS1009 was an exception to the
trend, since nearly 20% of genes with higher expression could be
attributed to increased copy number - most of these genes reside on Chromosome
XII. In fact, more than 80% of genes on Chromosome XII met our
criteria for increased copy number (Figure S1A), indicating that the entire
chromosome is duplicated in this strain. Another example of chromosomal
aneuploidy is evident in strain K9, for which Chromosome IX appears amplified
(Figure
S1B). Whole-chromosome aneuploidy has been frequently observed in strains
growing under severe selective pressure (for example [53]–[56].
Interestingly, the majority of genes on these duplicated chromosomes do not show
elevated transcript abundance in the respective strains.In fact, only ∼25% of genes with increased copy number in each
strain showed elevated expression (defined at
FDR = 0.01 or as genes whose expression is
>1.5× over S288c). This is in stark contrast to previous
studies demonstrating little dosage compensation in S288c in response to gene
amplification and chromosomal aneuploidy, leading to the conclusion that yeast
does not have a mechanism for dosage compensation. [53],[54],[57]. Instead, our
results suggest that some form of feedback control acts to normalize the dosage
of most genes in non-laboratory yeast strains. The remaining quarter of
amplified genes may be inherently exempt from this feedback mechanism.
Alternatively, relaxed feedback may occur for specific amplifications if the
resulting transcript increase provides a selective advantage to the strain in
question. Indeed, 15–40% (depending on the strain) of genes
lacking feedback control show at least 1.5× higher expression beyond
what can be accounted for by gene amplification alone, indicating that the
expression differences are affected by both gene dosage and regulatory
variation. These genes are excellent candidates for future studies of adaptive
changes.As observed for gene expression, we found that some genomic amplifications were
common across all 6 strains compared to S288c. All strains showed decreased Ty1
copy number, ranging from 2–15× lower than S288c. This is
consistent with previous studies that showed higher Ty1 copy number (including
active and partial Ty elements) in S288c compared to wine strains and natural
isolates [50],[51],[58]. Most strains also
showed even lower Ty1 transcript abundance, beyond what could be explained by
copy number variations. Thus, in addition to a higher Ty content, S288c also
shows higher expression from Ty genes, perhaps reflecting elevated rates of
retrotransposition under the conditions studied here. In contrast, all strains
showed higher copy number of the mitochondrial genome compared to S288c,
typically elevated 2–3× but nearly 7× higher in
clinical strain YJM789. The most likely explanation is that these strains harbor
more mitochondria than S288c, a fact confirmed in vineyard strain RM11-1a by
mitochondrial staining [25].Correlations between Altered Gene Expression and Environmental SensitivityIn addition to revealing phenotypic diversity within and between yeast
populations, natural variation can also uncover new insights into the effects of
each environment on cellular physiology. For example, we noted correlations
between environments based on the distribution of strain-sensitivity scores. The
most likely explanation is that these stresses have similar effects on cellular
function, and thus strains display similar sensitivities to them. Resistance to
sodium chloride and lithium chloride or tolerance of ethanol and elevated
temperature were highly correlated (R = 0.66 at
p<0.0001 and R = 0.51 at
p<0.0006, respectively, based on 10,000 permutations), consistent with
the known effects of these stress pairs on ion concentrations or membrane
fluidity/protein structure, respectively. Other relationships were not
previously known, including the correlation between sensitivity to SDS detergent
and the heavy metal cadmium (R = 0.64,
p<0.0001) and between ethanol and caffeine tolerance
(R = 0.59, p<0.0001). In contrast,
resistance to freeze-thaw stress was anticorrelated to sodium chloride
resistance (R = −0.35,
p = 0.006), suggesting antagonistic outcomes of
the same underlying physiology. These relationships point to commonalities in
the cellular consequences inflicted by these environments that will be the
subject of future investigations of stress-defense mechanisms.We also conducted an associative study to identify gene expression patterns
correlated with environmental sensitivity across the 17 non-laboratory strains
(see Materials and Methods for details). As
basal expression differences could significantly contribute to the inherent
ability of cells to survive a sudden dose of stress, the results point to genes
whose expression is related to, and perhaps causes, the phenotypes in question.
Among the top genes associated with copper sulfate resistance was the
metallotheionein CUP1, important for copper resistance and
known to have undergone tandem duplications in copper-resistant strains [59],[60]. Of the genes whose
expression was correlated to sodium chloride tolerance, nearly 20%
are known to function in Na+ homeostasis and/or osmolarity maintenance
(including RHR2, COS3, SIS2
identified through genetic studies [61]–[63] and
JHD2, SRO7, YML079W,
YOL159C, TPO4, UTH1
implicated in high-throughput fitness experiments in S288c [64]). Thus, these and
likely other genes whose expression is highly correlated with each
stress-sensitivity profile play a functional role in surviving that condition.Other correlations were not expected. Ethanol and caffeine tolerance were both
correlated to the expression of genes encoding transmembrane proteins
(p<0.003, hypergeometric distribution), perhaps related to the effect of
these drugs on membrane fluidity. Sensitivity to the cell-wall damaging drug
Congo Red was significantly correlated to the expression of genes involved in
mitochondrial function and translation, respiration, and ATP synthesis
(p<10−13), revealing a link between
mitochondria/respiration and the cell wall. Although these connections will
require further characterization, they demonstrate the power of using natural
diversity to uncover previously unknown relationships between stresses and
cellular processes.DiscussionThis study demonstrates the vast amount of phenotypic variation in
Saccharomyces strains collected from diverse natural habitats,
used in industrial processes, and associated with human illness. Considering the
phenotypic responses to the conditions studied here provides insights into the
relationships between specific strains and their niches. For example, the wide
variance in growth scores of sake-producing strains indicates that they are either
highly resistant or sensitive to the different environments studied here, suggesting
that they may be specialized for growth in the defined conditions of sake
fermentation. In contrast, many of the vineyard isolates survived relatively well in
most of the conditions tested. This may reflect their ability to thrive in more
variable, natural environments and may also have facilitated their dispersal into
new environments in a manner associated with human interactions [5].
Geographic dispersal might also explain the higher-than-expected phenotypic
diversity of vineyard strains, which might be driven by diversifying selection
(suggested by our analysis) due to unique pressures imposed after expansion into new
environments.Although many of the phenotypic differences we observed are probably neutral,
providing no benefit or disadvantage to the strains in question, some are likely to
provide a selective advantage. Copper-sulfate resistance in European vineyard
strains may have arisen through positive selection, since copper has long been used
as an antimicrobial agent in vineyards and orchards [1],[65]. Another example may
apply to the oak strains studied here. Our simple metric comparing phenotypic to
genetic diversity in strains collected from similar environments suggests that oak
strains are phenotypically more similar than expected based on their genetic
relationship. Formally, this could arise if multiple traits are evolving neutrally
(but slower than the genetic drift represented by the sequences used here) since the
strains diverged from a distant, common progenitor. However, the fact that
S. paradoxus oak isolate YPS125 is phenotypically more similar
to S. cerevisiae oak strains than the other S.
paradoxus isolate in our analysis instead supports that these strains have
undergone selection for growth in a common environment. One intriguing phenotype is
freeze-thaw resistance, which may be important to survive the wintry niche from
where these strains were collected. Consistent with this hypothesis, we have
recently isolated numerous Sacharomycete strains (including
S. cerevisiae) from Wisconsin oak exudates, of which
86% (19/22) are freeze-thaw tolerant (DJK and APG, unpublished data).
Ongoing studies in our lab are dissecting the genetic basis for this phenotypic
difference.In addition to stress sensitivity, gene expression also varies significantly across
yeast populations. More than a quarter of yeast genes varied in expression in at
least one non-laboratory strain under the conditions studied here. Consistent with
other recent reports [30],[48],[49],[66], we find that genes with specific structural or
functional characteristics (including nonessential genes and those with upstream
TATA elements and paralogs) show higher levels of expression variation across
strains. This has previously been interpreted as a higher rate of regulatory
divergence for genes with these features, either in response to selection [48] or
mutation accumulation [49]. However, these features are also common to genes
whose expression is highly variable within the S288c lab strain grown under
different conditions ([67] and data not shown), particularly those induced
by stressful conditions [46],[68]. It is also notable that genes with TATA elements
show higher ‘noise’ in gene expression within cultures of the
same strain [69],[70]. Thus, an alternative, but not necessarily
mutually exclusive, hypothesis is that the expression of these genes is more
responsive to environmental or genetic perturbations, again consistent with previous
studies [66],[30],[48],[49]. We have conducted our experiments under
‘common garden’ lab conditions in attempt to minimize
environmental contributions to expression phenotypes. However, because each strain
may have evolved for growth in a unique environment, each may in fact respond
differently to the same growth conditions used here. Indeed, this may explain the
prevalence of metabolic genes in our set of genes showing variable expression in
multiple strains, since many of these strains have not evolved for growth in highly
artificial laboratory media.Emerging from our analysis is the fact that S288c is phenotypically distinct from the
other non-laboratory strains studied here. This strain displays extreme resistance
to specific stresses, harbors fewer mitochondria, contains more transposable
elements, and shows unique expression of many genes compared to all other strains
investigated (a direct comparison of the number of differentially expressed genes in
S288c is difficult due to the different statistical power in calling these genes).
We have also found that this strain has an aberrant response to ethanol, since it is
unable to acquire alcohol tolerance after a mild ethanol pretreatment, unlike
natural strains [71]. It is likely that additional responses found in
natural strains have been lost or altered in this domesticated line. The progenitor
of S288c was originally isolated from a fallen fig in Merced, California, and
sequence analysis indicates that S288c is genetically similar to other natural
isolates [1]–[3]. A recent study by Ronald et
al. counters the proposal that S288c has undergone accelerated divergence during its
time in the laboratory [72]. Instead, our results suggest that the strain has
evolved unique characteristics through inadvertent selection for specific traits
(such as growth on artificial media) and population bottlenecks. Thus, the
laboratory strain of yeast may not present an accurate depiction of natural yeast
physiology. Indeed, no single strain can be used to accurately represent the
species, a note especially important for comparing phenotypes across species.
Complete exploration of an organism's biology necessitates the study of
multiple genetic backgrounds to survey physiology across populations.Despite its limitations, the lab strain offers nearly a century of detailed
characterization, along with powerful genetic and genomic tools. A useful approach
is to complement studies on laboratory strains with investigations of natural
variation. By characterizing stress sensitivity in a large set of strains, we have
leveraged the power of natural diversity to uncover new relationships between
stresses and to reveal previously unknown connections between genes, stresses, and
cellular processes. These connections lead to hypotheses about stress defense
mechanisms that can often be dissected using the valuable tools provided by the lab
strain. Application of genomic techniques to characterize natural yeast strains will
foster such studies while revealing additional insights into genetic and phenotypic
variation in Saccharomyces.Materials and MethodsStrains and Sequence AnalysisStrains used in this study and references are found in Table S1.
In addition to sequence data from [2], an additional 5,305 bp of noncoding DNA was
sequenced for 41 S. cerevisiae strains over 8 intergenic
sequences (GENBANK accession numbers EU845779 - EU846095) for a total of 13,016
bp over 13 loci. Phylogenetic analysis shown in Figure 1 was performed on the combined
sequence set using the program MrBayes [73]. Evolutionary
distances were estimated using the Jukes-Cantor (JC) model based on 2,056 bp
noncoding sequence data present in all strains; results and significance were
very similar when the distance was based on 9,334 bp of noncoding sequence
excluding only pairwise-deletion data [74]. Strains with
evolutionary distances equal to zero over this subsequence (but clearly non-zero
when all sequence was assayed) were set to 0.00001 to facilitate permutation
calculations. Paralogs were defined as genes with a BLAST E-value score
<10−100.Phenotyping and AnalysisYeast strains were grown in YPD medium at 30°C to an optical density of
∼0.3 in 96-well plates. Three 10-fold serial dilutions were spotted onto
YPD agar plates containing the appropriate stress, as well as a YPD plate for a
no-stress control. Cells were also plated onto minimal medium [75] or
YP-acetate. In the case of freeze-thaw stress, 200 µl cells was frozen
in a dry ice/ethanol bath for two hours or left on ice as a control before
spotting onto YPD plates. Cells were grown for 2–3 days at
30°C unless otherwise noted, and viability of each dilution was scored
relative to the no-stress control for each strain. All experiments were done in
at least duplicate over 2–3 doses of most stresses (see Table S2
for raw data and stress doses). Final resistance scores were summed over the 3
serial dilutions then averaged over replicates and stress doses, providing a
single score ranging from 0 (no growth) to 6 (complete growth) for each strain
and each stress condition.For Figure 2, strains were
clustered based on phenotypes using the Pearson correlation and UPGMA clustering
[76]. Correlations between stresses were calculated
based on the Pearson correlation between strains, excluding 14 strains of highly
similar genetic distance (JC<0.0008). Phenotypes specific to groups of
strains collected from similar environments (see Table S1
for groupings) were calculated based on the median growth score of strains in
that group. Significance was estimated by 10,000 permutations of strain-group
labels, scoring the frequency of observing a median growth score equal to or
greater than that observed.A parameter, P/G, was calculated to compare the similarity in phenotype to the
similarity in genotype for strains within and between niche groups. The average
pairwise phenotypic distance, taken as the Pearson distance (1 –
Pearson correlation) between phenotype vectors, was divided by the average
pairwise JC distance for strains within a niche group. This value was divided by
the same ratio calculated for all pairs of strains between niche groups (see
Table
S1 for niche groupings). Significance was estimated based on 10,000
random permutations of strain-group labels. The distribution of P/G ratios from
randomized trials was centered on 0.99; furthermore P/G was ∼1.0 for
strains in the ‘clinical’, ‘natural’,
and ‘other fermentation’ groups, reflecting either neutral
drift for these groups or that these strains were inappropriately grouped
together into somewhat amorphous categories.Gene Expression AnalysisSeventeen strains (including B1, I14, M22, M8, PR, RM11-1a, K1, K9, YJM308,
YJM789, YJM269, Y12, SB, Y1, Y10, YPS1009, and YPS163) were chosen for
whole-genome expression analysis. Cells were grown 2–3 doublings in
YPD medium to early log-phase in at least biological triplicate. Cell
collection, RNA isolation, and microarray labeling and scanning were done as
previously described [77], using cyanine dyes (Flownamics, Madison, WI)
and spotted DNA microarrays consisting of 70mer oligos representing each yeast
ORF (Qiagen). For all arrays, RNA collected from the denoted strain was compared
directly to that collected from the diploid S288c lab strain DBY8268, with
inverse dye labeling used in replicates to control for dye-specific effects. At
least three biological replicates were performed for all comparisons. Data were
filtered (retaining unflagged spots with R2>0.1) and
normalized by regional mean-centering [78]. Genes with
significant expression differences (compared to the S288c control, strain
YPS163, or the mean expression across all strains) were identified separately
for each strain with a paired t-test (or unpaired t-test in reference to YPS163)
using the BioConductor package Limma v. 2.9.8 [79] and FDR correction
[80], taking p<0.01 as significant unless
otherwise noted (see Table S3 for limma output and Figure S2
for a comparison of the statistical power for each strain). All microarray data
are available through the NIH Gene Expression Omnibus (GEO) database under
accession number GSE10269.Comparative Genomic HybridizationsArray-based comparative genomic hybridization (aCGH) was performed in duplicate
on six strains (K9, M22, RM11-1a, Y10, YJM789, and YPS1009) relative to the
DBY8268 control as previously described [81], using amino-allyl
dUTP (Ambion), Klenow exo-polymerase (New England Biolabs), and random hexamers.
Post-synthesis coupling to cyanine dyes (Flownamics) was performed using inverse
dye labeling in replicate experiments. Technical variation in hybridization was
defined as the mean+2 standard deviations (a log2 value of 0.3) of all
spot ratios, based on triplicate comparisons of DBY8268 to DBY8268 genomic DNA.
For non-lab strains compared to DBY8268, genes with negative aCGH ratios outside
the range of technical variation on both duplicates were defined as those
affected by copy number and/or hybridization defects. Transcript levels within
0.45 (3 standard deviations of technical variation) of the aCGH ratio were
identified as those largely explained by copy number and/or hybridization
defects – on average, fewer than 5% of genes with
statistically significant (FDR = 0.01)
differential expression compared to DBY8268 fell into this class. Genes with a
positive aCGH ratio >0.7 in log2 space were defined as genes with
increased copy number in each non-lab strain. All microarray data are available
through the NIH Gene Expression Omnibus (GEO) database under accession number
GSE10269.Associations between Phenotype and Gene Expression VectorsA vector of relative phenotype scores was generated by dividing scores from Figure 2 by the score measured
for DBY8268. The Pearson correlation between this vector and the measured
expression vector for each strain relative to DBY8268 was calculated for all
genes in the dataset. Genes whose expression was correlated above or below what
was expected by chance (p<0.01) were defined based on
100 permutations of each of the ∼6,000 expression vectors.Supporting InformationFigure S1Chromosomal aneuploidy in specific S. cerevisiae strains.
Log2 ratios of copy number variations in (A) YPS1009 and (B) K9 compared to
S288c are shown for each of the 16 yeast chromosomes. Each red bar indicates
an elevated aCGH ratio measured at a given yeast gene, while each green bar
indicates a decreased aCGH ratio compared to S288c. The height of each bar
is proportional to the aCGH ratio measured on the arrays and represents the
average of duplicate hybridizations.(1.04 MB TIF)Click here for additional data file.Figure S2GEL50 plots representing statistical power. The fraction of genes called
statistically significant at FDR 0.01 is plotted against the log2 value of
relative gene expression. Genes were binned over 0.3 increments in gene
expression and smoothed using a running average over 3 adjacent bins. The
median GEL50, the log2 value at which 50% of measurements were
called statistically significant, was 1.4.(0.68 MB TIF)Click here for additional data file.Table S1Strains used in this study.(0.03 MB XLS)Click here for additional data file.Table S2Raw phenotype scores, conditions, and stress doses used to make Figure 2.(0.05 MB XLS)Click here for additional data file.Table S3limma output for uncentered and mean-centered expression data.(8.22 MB ZIP)Click here for additional data file.Table S4Functional GO enrichment.(0.20 MB TXT)Click here for additional data file.
Genetics and Genomics/Comparative Genomics Genetics and Genomics/Functional Genomics Genetics and Genomics/Gene Expression Genetics and Genomics/Genomics Genetics and Genomics/Microbial Evolution and Genomics