Copyright © 2000, The American Society for Cell Biology Pamela A. Silver, Monitoring Editor ¶Current address: Lawrence Berkeley National
Labs, Berkeley, CA 94720. ‡‡current address: Department
of Chemical Engineering, Stanford University, Stanford, CA 94305-5428. ‖Corresponding author. E-mail address:
pbrown/at/cmgm.stanford.edu. Received July 11, 2000; Revised September 19, 2000; Accepted October 11, 2000. This article has been cited by other articles in PMC. | ||||
Abstract We explored genomic expression patterns in the yeast
Saccharomyces cerevisiae responding to diverse
environmental transitions. DNA microarrays were used to measure changes
in transcript levels over time for almost every yeast gene, as cells
responded to temperature shocks, hydrogen peroxide, the
superoxide-generating drug menadione, the sulfhydryl-oxidizing agent
diamide, the disulfide-reducing agent dithiothreitol, hyper- and
hypo-osmotic shock, amino acid starvation, nitrogen source depletion,
and progression into stationary phase. A large set of genes (~900)
showed a similar drastic response to almost all of these environmental
changes. Additional features of the genomic responses were specialized
for specific conditions. Promoter analysis and subsequent
characterization of the responses of mutant strains implicated the
transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating
specific features of the transcriptional response, while the
identification of novel sequence elements provided clues to novel
regulators. Physiological themes in the genomic responses to specific
environmental stresses provided insights into the effects of those
stresses on the cell. | ||||
INTRODUCTION Cellular organisms require specific internal conditions for optimal growth and function. Myriad strategies have evolved to maintain these internal conditions in the face of variable and often harsh external environments. Whereas multicellular organisms can use specialized organs and tissues to provide a relatively stable and homogenous internal environment, unicellular organisms such as the yeast Saccharomyces cerevisiae have evolved autonomous mechanisms for adapting to drastic environmental changes. Yeasts regularly withstand fluctuations in the types and quantities of available nutrients, temperature, osmolarity and acidity of their environment, and the variable presence of noxious agents such as radiation and toxic chemicals. The genomic expression program required for maintenance of the optimal internal milieu in one environment may be far from optimal in a different environment. Thus, when environmental conditions change abruptly, the cell must rapidly adjust its genomic expression program to adapt to the new conditions. The complexity of the yeast cell's system for detecting and responding to environmental variation is only beginning to emerge. Genes whose transcription is responsive to a variety of stresses have been implicated in a general yeast response to stress (Mager and De Kruijff, 1995 ; Ruis and Schuller, 1995 ). Other gene expression responses appear to be specific to particular environmental conditions. Several regulatory systems have been implicated in modulating these responses, but the complete network of regulators of stress responses and the details of their actions, including the signals that activate them and the downstream targets they regulate, remain to be elucidated. We used DNA microarrays to analyze changes in transcript abundance in yeast cells responding to a panel of diverse environmental stresses. Our analysis of this large body of gene expression data allowed us to define stereotyped patterns of gene expression during the adaptation to stressful environments, and to compare and contrast the gene expression responses to different stresses. Here, we present three key results. First, we describe the global expression programs in response to a diverse set of stresses, including their specific features and a common response to all of the stressful conditions, termed the “environmental stress response” (ESR). Second, several sets of coregulated genes share promoter elements, which point to the involvement of specific transcription factors in the regulation of those genes. The roles of the transcription factor Yap1p and the related factors Msn2p and Msn4p are examined by analyzing the expression responses of strains deleted for or overproducing these factors. Third, we interpret the responses of genes with known functions to gain insights into the physiological effects of each of the stresses as well as the mechanisms that yeast cells use to cope with these stresses. The complete data set, as well as supplemental materials, is available at http://www-genome.stanford.edu/yeast_stress. | ||||
MATERIALS AND METHODS (Additional details, including descriptions of duplicated experiments and appropriate reference citations, can be found on the web supplement, at the address given above.) Strains and Growth Conditions The strains used in this study are listed in Table
1. Unless otherwise noted, cells were
grown in rich medium (YPD) (Sherman, 1991 ) at 30°C and shaken
at 250–300 rpm.
Sample Collection, Cell Lysis, and RNA Isolation In most cases, cells were grown to early log phase
(OD600 0.2 to 0.4), and an aliquot of
cells was collected to serve as the time-zero reference. Cells were
collected by centrifugation at 3000 ×g for 3 to 7 min at
room temperature. Each 50-ml cell pellet was resuspended in 3 to 10 ml
of lysis buffer (10 mM Tris-Cl pH 7.4, 10 mM EDTA, 0.5% SDS), and
stored at −80°C until RNA preparation. Total RNA was collected by
acid lysis similar to that previously described (Spellman
et al., 1998 ; see web supplement). Where indicated, mRNA was
purified using oligo-dT cellulose (Ambion, Austin, TX),
precipitated and resuspended in Tris-EDTA (TE) at a final
concentration of ~ 0.5–1 μg/μL.Probe Preparation, Microarray Hybridization, and Data Acquisition Probe preparation and microarray construction and analysis were
performed as previously described (Shalon et al., 1996 ;
DeRisi et al., 1997 ; Spellman et al., 1998 ; see
web supplement). Arrays were scanned using a commercially available
scanning laser microscope (GenePix 4000) from Axon Instruments (Foster
City, CA). Full details on using the GenePix 4000 can be obtained from
Axon. All arrays were analyzed using the program ScanAlyze (available
from http://rana.stanford.edu/), as described in the manual.Heat Shock from 25°C to 37°C Cells grown continuously at 25°C were collected by
centrifugation, resuspended in an equal volume of 37°C medium, and
returned to 37°C for growth. Samples were collected at 5, 15, 30, and
60 min. For array analysis, each Cy5-labeled sample was compared with a
Cy3-labeled reference pool, consisting of an equal mass of all of the
RNA samples. Following data acquisition and clustering analysis, the
data were mathematically “zero transformed” for visualization by
dividing the expression ratios for each gene measured on a given array
by the corresponding ratios measured for the unshocked, time-zero
cells. Therefore, in all figures, the ratios represent the expression
level at each time point relative to the expression level in the
unshocked, time-zero sample.Heat Shock from Various Temperatures to 37°C and Steady-State
Temperature Growth Six cultures were grown continuously at 17°, 21°, 25°,
29°, 33°, or 37°C for ~20 h. Half of each culture was collected
to serve as the unstressed reference, and the remainder of each culture
was collected by centrifugation and immediately resuspended in 37°C
medium. After 20 min at 37°C, the cells were harvested, and total RNA
was isolated.To measure steady-state expression at each temperature, RNA collected from cells grown continuously at each temperature was also compared directly to RNA from cells grown at 33°C. Temperature Shift from 37°C to 25°C Cells grown at 37°C for ~20 h were collected by
centrifugation, resuspended in two volumes of 25°C medium, and
returned to 25°C for growth. Samples were collected at 5, 15, 30, 45,
60, and 90 min, and total RNA was collected. Gene expression in cells
growing continuously at 37°C was also compared directly to expression
in cells growing at 25°C.Mild Heat Shock at Variable Osmolarity To compare the effects of mild heat shock at different
osmolarities, three experiments were performed. In the first, a YPD
culture of DBY7286 was grown at 29°C to OD600
0.3. Cells were collected by centrifugation, the culture was
resuspended in 33°C medium, and samples were collected at 5, 15, 30
min after resuspention. A second time series was performed nearly
identically, except that cells were grown in YPD supplemented with 1 M
sorbitol throughout the experiment. In the third experiment, cells
growing in YPD with 1 M sorbitol at 29°C were collected and
resuspended in YPD without sorbitol at 33°C, and serial samples were
collected. Total RNA was isolated for array analysis.Response of Mutant Cells to Heat Shock Wild-type and mutant strains were exposed to heat shock in
triplicate experiments. Wild-type, yap1, and msn2
msn4 cultures, grown at 30°C, were collected and
resuspended in an equal volume of medium preheated to 37°C. After 20
min at 37°C the cells were collected, and total RNA was isolated.Hydrogen Peroxide Treatment Cells were grown to early-log phase at which point
H2O2 (Sigma, St. Louis, MO)
was added for a final concentration of 0.30 mM. Samples were collected
at 10, 20, 30, 40, 50, 60, 80, 100, and 120 min. The culture volume and
the concentration of H2O2
were maintained throughout the experiment. The
H2O2 concentration was
monitored every 3 min using a horseradish-peroxidase based assay (Green
and Hill, 1984 ), which showed that the concentration of
H2O2 was maintained at 0.32
+/−0.03 mM H2O2 over the
course of the experiment (data not shown).Response of Mutant Cells to H2O2 Exposure Wild-type, yap1, and msn2 msn4 cultures
were exposed to 0.3 mM H2O2
in duplicate experiments. A single dose of
H2O2 was added to 0.3 mM of
each culture, and after 20 min, the cultures were collected, and mRNA
was isolated.Menadione Exposure Menadione bisulfite (Sigma) was suspended immediately before use
in water at a concentration of 1 M and was filter-sterilized. Menadione
bisulfite was added to the culture at a concentration of 1 mM, samples
were removed at 10, 20, 30, 40, 50, 60, 80, 105, and 120 min, and mRNA
was isolated.Diamide Treatment 1.5 mM diamide (Sigma) was added to the culture, and samples
were recovered at 5, 10, 20, 30, 40, 60, 90 min. Polyadenylated RNA was
isolated for array analysis.DTT Exposure Cells were grown at 25°C and dithiothrietol (DTT) (Boeringer
Manheim, Indianapolis, IN) was added for a final concentration of 2.5
mM. Samples were removed at 15, 30, 60, 120, 240, 480 min. Total RNA
recovered from each time point, as well as the unstressed sample, was
labeled with Cy5-dUTP and compared with a reference pool, consisting of
equal mass of total RNA from each sample that was labeled with
Cy3-dUTP. The array data were “zero-transformed” subsequent to
clustering analysis.Hyper-osmotic Shock A YPD culture was inoculated and grown to
OD600 0.6. One volume of 30°C YPD supplemented
with 2 M sorbitol was added to the culture for a final concentration of
1 M sorbitol. Samples were collected at 5, 15, 30, 45, 60, 90, and 120
min, and mRNA was isolated.Hypo-osmotic Shock Cells were grown for ~20 h in YPD supplemented with 1 M
sorbitol. The cells were grown to OD600 0.15,
collected by centrifugation, and resuspended in YPD without sorbitol.
Samples were collected at 5, 15, 30, 60 min, and total RNA was isolated
for array analysis.Amino Acid Starvation Cells were grown in complete minimal medium (SCD) to early-log
phase. Cells were collected by centrifugation and resuspended in an
equal volume of minimal medium lacking amino acids and adenine
(YNB−AA, 2% glucose, 20 mg/L uracil) and allowed to grow. Samples
were then harvested after 0.5 h, 1 h, 2 h, 4 h, and
6 h, and total RNA was collected.Nitrogen Depletion Cells were grown in SCD medium, collected by centrifugation and
resuspended in an equal volume of minimal medium without amino acids or
adenine and with limiting concentrations of ammonium sulfate
(YNB−AA−AS, 2% glucose, 20 mg/L uracil, 0.025% ammonium sulfate)
and returned to the 30°C shaker. Samples were subsequently harvested
after 0.5 h, 1 h, 2 h, 4 h, 8 h, 12 h,
1 d, 2 d, 3 d, and 5 d of culture incubation, and
mRNA was isolated.Stationary Phase A YPD culture was grown to OD600 0.3, at
which point a sample was collected to serve as the time-zero reference.
Samples were recovered at 2 h, 4 h, 6 h, 8 h,
10 h, 12 h, 1 d, 2 d, 3 d, and 5 d of
culture incubation. Total RNA was isolated for array analysis.Steady-state Growth on Alternative Carbon Sources Cells were grown continuously in YP media supplemented with 2%
weight to volume of glucose, galactose, raffinose, fructose, sucrose,
or ethanol as a carbon source. Total RNA harvested from each of the
samples was labeled with Cy5-dUTP and compared with a reference pool,
consisting of an equal mass of RNA from each sample that was labeled
with Cy3-dUTP. The data were mathematically transformed subsequent to
clustering analysis by dividing the expression ratios for each gene
measured on a given array by the corresponding ratios measured for the
cells grown in glucose.Overexpression Studies Overexpression constructs pRS-MSN2 and
pRS-MSN4, as well as the parent vector pRS416 (Mumberg
et al., 1994 ), were received from Tae Bum Shin (postdoctoral
fellow in Brown lab). Wild-type DBY7286 cells harboring each
plasmid were grown in SCD medium supplemented with 2% galactose
for ~ 6 h. Total RNA collected from cells harboring pTS1
(MSN2 vector) and pTS2 (MSN4 vector) was compared
directly to RNA collected from cells containing pRS416.Hierarchical Clustering Hierarchical clustering of the data was performed as previously
described (Eisen et al., 1998 ) using the program Cluster
(available at http://rana.stanford.edu). Data from 142 microarray
analyses of RNA samples isolated from wild-type cells under various
conditions were clustered, along with previously-published data (DeRisi
et al., 1997 ). The cluster analysis was performed without
the “time-zero” mathematical transformation of the data from
experiments in which a reference pool was used. The data from each
array experiment were weighted by the program Cluster (available at
http://rana.stanford.edu/software) according to the overall similarity
of each array to others in the data set, which served to under-weight
arrays that were highly similar. The resulting cluster was visualized
using the program TreeView (available at
http://rana.stanford.edu/software/).Promoter Analysis For coregulated genes, either 600 bp or 1000 bp, as indicated,
upstream of each gene start site was recovered using Yeast Tools
(http://copan.cifn.unam.mx/~jvanheld/rsa-tools). Sequence motifs
common to the upstream sequences were identified by the MEME algorithm
(http://www.sdsc.edu/MEME/meme/website/meme.html [Bailey and Elkan,
1994 ]). Upstream sequences were searched for specific sequence motifs
using Yeast Tools. | ||||
RESULTS Overview We characterized genomic expression programs in yeast responding
to environmental changes in three ways. First, we characterized the
temporal program of gene expression in the response of cells to heat
shock, hydrogen peroxide, superoxide generated by menadione, a
sulfhydryl oxidizing agent (diamide), and a disulfide reducing agent
(dithiothreitol), hyper-osmotic shock, amino acid starvation, nitrogen
source depletion, and progression into stationary phase. The severity
of each condition was calibrated to preserve more than 80% cell
viability, so that we could observe the expression programs in viable
cells adapting successfully to a changing environment. For most of the
environmental changes we studied, samples were collected over the
course of 2–3 h; in our investigation of the responses to nitrogen
depletion and stationary phase, samples were collected over a period of
5 d. Second, we examined the dose response to heat shock in a
series of experiments in which cells were subjected to temperature
shifts of variable magnitude. Third, we compared the genomic expression
programs in cells already adapted to steady-state growth at different
temperatures and on alternative carbon sources.In our initial experiments, 142 different mRNA samples were analyzed by whole-genome microarray hybridization. Each microarray used in this study contained ~ 6,200 known or predicted yeast genes that had been identified at the time of our analysis (Ball et al., 2000 ). The resulting table of ~ 9 × 105 quantitative measurements of transcript levels was organized by hierarchical clustering and displayed as previously described (Eisen et al., 1998 ) (Figure 1). Briefly, the clustering algorithm arranges genes according to their similarity in expression profiles across all of the array experiments, such that genes with similar expression patterns are clustered together. The data are graphically displayed in tabular format in which each row of colored boxes represents the variation in transcript abundance for each gene, and each column represents the variation in transcript levels of every gene in a given mRNA sample, as detected on one array. The variations in transcript abundance for each gene are depicted by means of a color scale, in which shades of red represent increases and shades of green represent decreases in mRNA levels, relative to the unstressed culture, and the saturation of the color corresponds to the magnitude of the differences. A black color indicates an undetectable change in transcript level, and a gray color represents missing data. A dendrogram constructed during the clustering process depicts the relationships between genes: the branch lengths represent the degree of similarity between genes based on their expression profiles. Genes that display similar patterns of gene expression over multiple experiments are thus grouped together on a common branch of the dendrogram and can also be recognized by an obvious pattern of contiguous patches of color in the cluster diagram. Several general features in the global expression pattern can be recognized from the results of hierarchical clustering (Eisen et al., 1998 ). First, genes that are coregulated under the conditions examined will correspondingly cluster together, and analysis of their promoters often identifies common sequence motifs, in some cases suggesting regulation by known transcription factors and in others identifying novel promoter elements (Eisen, Derisi, Brown - personal communication, and unpublished data). Second, because genes involved in the same cellular processes are usually similarly expressed, the functions of characterized genes in a given cluster can suggest hypothetical functions for uncharacterized genes in the same cluster. Third, the choreography of expression of the various gene clusters can be related to the series of events occurring during each experiment, suggesting links between specific sets of genes and specific features of the experimental conditions. Finally, in many cases, a physiological picture of the cellular response can be sketched by considering the expression of genes of known function and regulation, in turn suggesting specific effects of each condition on the cell. An overview of the microarray results is presented in Figure 1. The large-scale features of the expression programs visible in this display vividly illustrate the massive and rapid genome-wide changes in gene expression in response to each environmental shift. Some sets of genes responded in a stereotypical manner to many different environmental changes, whereas the response of other sets of genes was unique to specific conditions. Although there were shared features between the responses to different conditions, no two expression programs were identical in terms of the genes affected, the magnitude of expression alteration, and the choreography of expression. The uniqueness of each program highlights the precision with which yeast respond to changes in their environment. One of the remarkable features of the genomic expression programs shown in Figure 1 is that, with the exception of adaptation to starvation conditions, the global changes in transcript abundance were largely transient (Figure 2, A and C). Immediately after most of the environmental shifts, the cells responded with large changes in the transcript levels of hundreds of genes. However, genomic expression adapted over time to new steady-state transcript levels, with far smaller differences in transcript abundance between the steady-state programs at each condition. The duration and amplitude of the transient changes in transcript levels varied with the magnitude of the environmental change. Furthermore, the magnitude of differences in the corresponding steady-state gene expression programs also correlated with the magnitude of environmental shift. This trend was evident in a series of experiments in which cells subjected to temperature shifts of varying magnitude responded with correspondingly graded transcriptional changes (Figure 2). Cells subjected to a larger shift in temperature responded with larger and more prolonged alterations in gene expression before adapting to their new steady-state expression levels, relative to cells exposed to smaller temperature changes. The Environmental Stress Response A striking feature of the expression programs displayed in Figure
1 is the large fraction of the genome that responded in a stereotypical
manner to each of the stressful conditions we tested. Two large
clusters of genes, one consisting of repressed genes and one consisting
of induced genes, displayed reciprocal but otherwise nearly-identical
temporal profiles (Figure 3). These
clusters amounted to ~ 900 genes, more than 14% of the
currently-predicted genes in the yeast genome (Ball et al.,
2000 ). This stereotypical response shared features with the
previously-recognized general response to stress, which typically
refers to the response of a set of ~ 50 genes induced by a
variety of stresses through the stress response element (STRE) promoter
sequence, recognized by the transcription factors Msn2p and Msn4p
(Kobayashi and McEntee, 1993 ; Marchler et al., 1993 ;
Martinez-Pastor et al., 1996 ). Our results reveal that,
although genes in this large program showed a similar response to the
conditions tested here, the regulation of their expression is not
general, but is instead dependent on many different signaling systems
that act in a condition-specific and gene-specific manner (see below).
Therefore, while it is important to recognize the similarities between
this program and the previously-described general stress response, to
avoid confusion we refer to the stereotyped response of this entire set
of induced and repressed genes as the environmental stress response
(ESR).
Genes Repressed in the ESR Within the large cluster
of ~ 600 genes that were repressed in the ESR, two clusters with
distinct expression profiles are evident (see web supplement for
details). The first cluster consists of genes involved in
growth-related processes, various aspects of RNA metabolism (such as
RNA processing and splicing, translation initiation and elongation,
tRNA synthesis and processing), nucleotide biosynthesis, secretion, and
other metabolic processes. These genes appeared to be coregulated, and
promoter analysis revealed the presence of two novel and conserved
motifs in the upstream elements of these genes (see web supplement for
details), one of which was similar to a site identified in the
promoters of RNA processing genes by Hughes et al.
(2000) . The second cluster is distinguished from the first by a slight
delay in the decline in transcript levels, and it consists almost
entirely of genes encoding ribosomal proteins. The repression of
ribosomal protein genes has previously been observed during multiple
stress responses (Warner, 1999 ) and is known to be regulated by the
transcription factor Rap1p (Moehle and Hinnebusch, 1991 ; Li et
al., 1999 ). Our results show that the repression of the
ribosomal genes, along with the large set of genes involved in RNA
metabolism, protein synthesis, and aspects of cell growth, is a general
feature of the ESR.Genes Induced in the ESR Approximately 300 genes, of
which nearly 60% are completely uncharacterized, were induced in the
ESR (see web supplement for details). The functional themes represented
by these genes are likely to provide many clues to the ways cells
fortify themselves for survival in inhospitable environments. The genes
in this group with known molecular functions are involved in a wide
variety of processes, including carbohydrate metabolism, detoxification
of reactive oxygen species, cellular redox reactions, cell wall
modification, protein folding and degradation, DNA damage repair, fatty
acid metabolism, metabolite transport, vacuolar and mitochondrial
functions, autophagy, and intracellular signaling (Figure
4). Many of the genes induced in the ESR
have previously been proposed to offer cellular protection during
stressful conditions, such as oxidative stress, heat shock, osmotic
shock, and starvation (Hohmann and Mager, 1997 ; Mager and De Kruijff,
1995 ). More than half of the 50 genes that were previously reported to
be STRE-regulated (see Moskvina et al., 1998 and Yeast
Protein Database for references) were induced in the ESR.
One notable feature of the ESR was the differential expression of isozymes. For example, various enzymes involved in carbon metabolism, protein folding, and defense against reactive oxygen species were specifically induced in the ESR, while their counterparts were not (Figure 5). This result confirms and expands previous observations that isozymes involved in carbohydrate metabolism are differentially expressed in response to osmotic shock (Norbeck and Blomberg, 1997 ; Rep et al., 2000 ). One possible explanation for this divergence in regulation is that the putative isozymes might possess different properties, including biochemical function, substrate specificity, and physical location, that make one isozyme optimized to the ESR and the other to more specific conditions. Alternatively, the existence of differentially-regulated isozymes possessing similar properties might allow ESR isozymes to be regulated as part of this more general program, while their related counterparts are regulated by specialized signals. Among the genes induced in the ESR were many whose products play reciprocal metabolic roles. An example is provided by genes involved in the metabolism of trehalose and glycogen, whose roles in stress responses have been linked to storage of energy reserves, protein stabilization, and osmolyte balance (Hounsa et al., 1998 ; Singer and Lindquist, 1998 ). Genes encoding enzymes that synthesize trehalose, glycogen, and their precursors, as well as genes that encode catabolic enzymes for degrading these carbohydrates, are jointly induced in the ESR (Figure 4A), consistent with the previously-observed induction of many of these genes in response to numerous stresses (Parrou et al., 1999 ; Parrou et al., 1997 ; Rep et al., 2000 ; Zahringer et al., 1997 ). The simultaneous induction of both synthetic and catabolic enzymes seems paradoxical. However the activity of many of these enzymes is sensitively controlled at the posttranslational level (Hwang et al., 1989 ; Marchase et al., 1993 ; Dey et al., 1994 ; Huang et al., 1998 ; Parrou et al., 1999 ). We propose that the coinduction of these genes renders the cell poised to rapidly and sensitively modulate the activity of the corresponding enzymes and thereby increases the cell's capacity for regulated flux of carbohydrates into and out of its glycogen and trehalose stores. Induction of these genes presumably enhances the cell's ability to rapidly buffer and manage osmotic instability and energy reserves. The induction of genes encoding reciprocally-related functions is also evident in regulatory networks, including those that may be involved in regulation of the ESR itself. Components of the PKA pathway, including both positive effectors (SRA3, PKA3) and negative regulators (PDE2, SRA1) of PKA signaling, were coordinately induced in the ESR (Figure 4F). Induction of PKA signaling components in the ESR is particularly noteworthy, as activity of the pathway inhibits Msn2p and Msn4p activity by triggering relocalization of the factors to the cytosol (Gorner et al., 1998 ; Smith et al., 1998 ). In fact, induction of the PKA components in the ESR was dependent on Msn2p and Msn4p (see more below). The cell may concomitantly increase protein levels of positive and negative regulators of PKA signaling to allow sensitive posttranslational control of signaling through the pathway. Among the likely effects of this adaptation would be an enhancement of the cell's ability to rapidly and precisely modulate expression of genes in the ESR. What Triggers the ESR? Given the universal induction
of the ESR in our initial experiments, we hypothesized that the ESR
might be initiated in response to any abrupt change in the cells'
environment. According to this hypothesis, the response would be
triggered by transferring cells in either direction between two
environments. To test this, we examined the pattern of ESR expression
following an abrupt shift in temperature from 37°C to 25°C (Figure
6A and 6C). The response to this shift
was fundamentally different from the reverse shift from 25°C to
37°C in two ways. First, in response to the 37°C to 25°C
temperature shift, the cells responded with reciprocal changes in the
expression of ESR genes relative to the response to a 25°C to 37°C
heat shock, reflecting the suppression, rather than initiation, of the
ESR. Second, unlike the response to a 25°C to 37°C heat shock,
which elicited massive and transient changes in ESR expression, the
transition from 37°C to 25°C resulted in a simple, rapid transition
to the gene expression program characteristic of steady-state growth at
25°C, with essentially no transient features. A similar result was
observed when cells were transferred between medium of standard
osmolarity and medium supplemented with 1 M sorbitol: when cells were
transferred to hyperosmolar medium, they initiated the ESR with
transient changes in expression, whereas when cells adapted to growth
in 1 M sorbitol were transferred to medium of standard osmolarity, they
suppressed the ESR with only subtle transient features (Figure 6, B and
D). These results reveal that the ESR is not initiated in response to
all environmental changes and that the large, transient changes in
expression that are characteristic of the ESR are only seen when this
response is initiated and not in the reciprocal response to diminished
environmental stress.
We also characterized the response of cells shifted between two different environments that were equally stressful, in the sense that steady-state expression of genes in the ESR was comparable at each of the conditions. Cells adapted to growth at 29°C in the presence of 1 M sorbitol were shifted to 33°C medium lacking sorbitol, resulting in a mild temperature shift in combination with a shift to lower osmolarity. The resulting expression of ESR genes closely approximated the sum of the individual responses to heat shock and a transition to lower osmolarity, with only a few exceptions (see web for supplemental details). This result suggests that the cell responds independently to the unique features of each of the environmental transitions, i.e. the effects of temperature shift and the consequences of osmolarity change, resulting in additive effects on gene expression. Regulation of the ESR Because the ESR unfolds in a
stereotypical manner in response to diverse environmental stresses, it
might be supposed that the response is governed by one all-purpose
regulatory system. However, several lines of evidence suggest that the
ESR is not controlled by a single system but by different regulatory
systems evoked under different environmental conditions.Numerous subclusters of genes within the large cluster of induced ESR genes showed subtly different expression patterns, suggesting differences in the regulation of those genes. For example, genes in the TRX2 cluster were induced in the ESR but were super-induced relative to other ESR genes in response to agents that alter the cellular redox potential. Similarly, a group of protein folding chaperones induced in the ESR were super-induced in response to heat shock, relative to other stresses (Figure 4C). These results suggest that subsets of genes within the ESR are governed by condition-specific regulatory mechanisms. The expression of some of the genes induced in the ESR has previously been shown to be governed by Msn2p and/or Msn4p (Msn2/Msn4p) in response to stressful conditions (see Moskvina et al., 1998 and Yeast Protein Database for review and references). We characterized genomic expression in cells lacking these factors, identifying additional targets of Msn2/Msn4p and providing evidence for alternative regulators of ESR gene expression. The expression of roughly 180 genes was affected in an msn2 msn4 double deletion strain responding to heat shock or H2O2 treatment, relative to the isogenic wild-type, and the genes affected varied in their dependence on Msn2/Msn4p (Figure 7). One large class of genes, including previously known targets such as CTT1, HSP12, and carbohydrate metabolism genes (Martinez-Pastor et al., 1996 ; Schmitt et al., 1996; Boy-Marcotte et al., 1998 ; Moskvina et al., 1998 ), was largely dependent on Msn2/Msn4p in response to both heat shock and H2O2, while the induction of a second class of genes was partially dependent on these factors in response to both conditions. A third class of genes was dependent on Msn2/Msn4p in response to one of the two conditions, but not the other. For example, the induction of genes in the TRX2 cluster was dependent on Msn2/Msn4p in response to heat shock, but the expression of these genes in response to H2O2 treatment was unaffected by deletion of the factors (Figure 8). Because the genes in this group contained within their promoters the consensus binding site for the transcription factor Yap1p (Fernandes et al., 1997 ), we reasoned that Yap1p may also play a role in governing their expression. Characterization of gene expression in a yap1 deletion strain revealed that the induction of genes in the TRX2 cluster was dependent on Yap1p in response to H2O2 treatment, but their expression in response to heat shock was unaffected by deletion of YAP1. These data reveal that genes in the TRX2 cluster were induced through Msn2/Msn4p in response to heat shock but were induced through Yap1p in response to H2O2. Thus, genes in the ESR can be regulated by different transcription factors depending on the specific environmental shock. This conclusion confirms and expands that of a recent study by Rep et al. (1999) , in which the induction of the ESR genes GPD1, HSP12, and CTT1 was shown to be governed by different transcription factors, namely Msn1p, Msn2p, Msn4p, or Hot1p, depending on the exact environmental conditions (Rep et al., 1999 ). More than 90% of the genes whose expression was dependent on Msn2/Msn4p in response to heat shock or H2O2 exposure were also induced by overexpression of MSN2 or MSN4 (Figure 7), but significantly more ESR genes were affected by overexpression of the factors than by their deletion. Approximately 80 additional ESR genes were induced by MSN2 or MSN4 overexpression, as were other genes that do not participate in the ESR, whose expression in response to either heat shock or H2O2 treatment was unaffected by deletion of the factors. Some of these genes may be induced through indirect effects of MSN2 or MSN4 overexpression, but many may be legitimate targets of the factors. That more putative Msn2/4p targets were affected by overexpression of the factors than by their deletion, under the conditions we examined, is consistent with the hypothesis that, in response to stressful environmental changes, the dependence of gene induction on Msn2/4p is condition-specific. Deletion of these factors therefore reveals only the subset of their gene targets whose activation is Msn2/4p-dependent under the specific conditions examined. A substantial fraction of the genes in the ESR were unaffected by overexpression or deletion of MSN2 or MSN4 and did not contain the STRE promoter element recognized by the factors, further implicating additional regulators of ESR expression. Specific Responses to Specific Environmental Changes In addition to the common ESR, many of the gene expression
responses to different environmental changes were specific to
individual conditions. Thus, the global expression response to each of
the environmental transitions was unique. The physiological themes
represented by the gene expression changes in each global response
sketched a picture of the physiological effects of each condition and
suggested directions for future investigation of the molecular
adaptation to these conditions. We present a brief synopsis of each
genomic response, and we encourage readers to visit the companion
website to explore the complete data set and view supplemental details.Heat Sudden heat shock elicited massive and rapid
alterations in genomic expression. The ESR was initiated within minutes
of a temperature shift, and numerous specialized responses were also
triggered. Most notably, the concurrent induction of protein folding
chaperones localized to the cytoplasm, mitochondria, and ER supports
the notion that one of the primary effects of heat shock is protein
unfolding. In addition, the genomic response to heat shock was
strikingly similar to that triggered by stationary phase, including the
induction of genes involved in respiration and alternative carbon
source utilization. Because extracellular glucose concentrations did
not change during the course of the heat shock experiment (data not
shown), we propose that chaperone-dependent protein folding in the
immediate aftermath of heat shock causes a sudden decrease in cellular
ATP concentrations. A shift in the ATP:AMP ratio might then lead to the
observed expression alterations in central energy metabolism genes,
similar to the response seen in mammalian cells (Hardie and Carling,
1997 ; Hardie, 1999 ).H2O2 and Menadione The gene
expression programs following H2O2 and
menadione treatment were largely identical, despite the fact that these
agents are thought to generate different reactive oxygen species within
the cell. The responses to both agents were characterized by the strong
induction of genes known to be involved in the detoxification of both
H2O2 and superoxide (such as superoxide
dismutases, glutathione peroxidases, and thiol-specific antioxidants),
as well as genes involved in oxidative and reductive reactions within
the cell (thioredoxin, thioredoxin reductases, glutaredoxin, and
glutathione reductase). Many of the genes most strongly induced in
response to H2O2 and menadione were dependent
on the transcription factor Yap1p for their induction (Schnell
et al., 1992 ; Stephen et al., 1995 ;
Jamieson, 1998 ) (see web for supplemental details).DTT The transcriptional profile of the DTT response
was quite distinct from the responses to other stresses, particularly
in its temporal pattern. The initial induction response, which occurred
within 30 min of DTT exposure, included protein disulfide isomerases
and protein folding chaperones localized to the ER and genes implicated
in the response to alterations in the cellular redox potential. These
observations are consistent with the hypothesis that DTT-dependent
reduction inhibits protein folding in the ER, triggering the unfolded
protein response (Cox et al., 1993 ; Jamsa et
al., 1994 ; Travers et al., 2000 ). Surprisingly,
initiation of the ESR did not occur until hours after DTT exposure,
suggesting that secondary effects of DTT treatment eventually triggered
this response. Indeed, the late induction of genes involved in cell
wall synthesis, concomitant with the induction of signaling systems
involved in the response to cell wall damage, suggests that the
accumulation of cell wall defects ultimately initiated the ESR and
that, in response to DTT treatment, ESR expression may be governed by
regulatory systems specific to cell wall perturbations. Cell wall
defects may result from prolonged impairment of secretion, and they may
be exacerbated by direct effects of DTT on cell wall disulfide linkages
(Cappellaro et al., 1998 ).Diamide The expression response elicited by the
sulfhydryl-oxidant diamide resembled a composite of the responses to
heat shock, H2O2 and menadione, and DTT. For
example, genes involved in protein folding and respiration were induced
by diamide in a manner similar to heat shock. Genes whose products are
implicated in the response to altered cellular redox potential and
defense against reactive oxygen species were also strongly induced, as
they were during H2O2 and menadione treatment.
Finally, like DTT treatment, diamide induced many putative cell wall
biosynthesis genes, as well as genes involved in protein secretion and
processing in the ER. These observations suggest that diamide has
pleiotropic effects, including protein unfolding following oxidation of
protein sulfhydryl groups, oxidative stress resulting from the
sulfhydryl modification, and defects in secretion and, ultimately, cell
wall damage due to improper disulfide bond formation in the ER.Hyperosmotic Shock The genomic expression response
to sorbitol osmotic shock included only a few genes whose expression
was specifically affected by this condition, but there were two unique
features to this response. First, the global expression response to
sorbitol was extremely transient, perhaps indicative of the relatively
minor cellular changes required for adaptation to hyperosmolarity.
Second, numerous genes that were generally induced in the ESR appeared
to be super-induced in response to sorbitol, pointing to systems that
were selectively called into play in this response. Among the earliest
and strongest responses was the induction of ESR genes involved in the
synthesis and regulation of critical internal osmolytes, including
glycerol and trehalose. Interestingly, other ESR genes, including
oxidoreductases and cytosolic catalase, were superinduced in response
to sorbitol, for reasons that are not understood.Starvation Carbon and nitrogen starvation
elicited dramatic global changes in the gene expression program. A more
extensive discussion of genomic responses to starvation will be
presented elsewhere (Kao et al., unpublished
data). While many of the metabolic changes during starvation have been
described previously, thousands of genes that are known to participate
in other cellular processes or have completely unknown functions showed
significant, and previously unrecognized, expression changes during the
response to starvation. Many of the starvation-specific expression
alterations may be rationalized by the fact that starvation involves a
transition from active growth to growth arrest, in contrast to the
response to other stresses in which cells resume growth after adapting
to the new conditions. Furthermore, gradual nutrient starvation also
involves changes in multiple environmental parameters over time, such
as cell density, pH, and the successive depletion of various nutrients,
which contribute to the complex temporal pattern of gene expression
during starvation (Kao et al., unpublished data). | ||||
DISCUSSION To survive in natural environments, microorganisms must be able to respond swiftly and appropriately to sudden environmental changes, adapting to the unique features of each environment. The genomic expression programs characterized in this study reveal that yeast cells respond to environmental changes by altering the expression of thousands of genes, creating a genomic expression program that is customized for each environment. These genomic programs include features that are specific to each stress, reflecting gene products specifically called into play under those conditions. In addition, a remarkable fraction of the genome responds in a stereotypical manner following environmental stress, as part of a program we refer to as the ESR. Role of the ESR The ESR is initiated not only by conditions known to threaten
cellular viability (data not shown), but also by small environmental
changes that do not detectably impair viability and growth.
Nonetheless, the response appears to be specific to transitions to
environments less optimal for growth and survival. It is not triggered,
for example, when cells adapted to growth at elevated temperatures or
hyperosmolarity are suddenly shifted to standard growth conditions.
Based on these observations, we propose that the ESR is a general
adaptive response to suboptimal environments. We hypothesize that, when
a cell is shifted to an environment for which its physiological systems
are not optimized, the specific cellular consequences resulting from
the shift can lead to a series of secondary instabilities within the
cell, potentially threatening many key physiological systems. Thus, the
genome has evolved to initiate the ESR to protect and maintain critical
features of the yeast cell's internal system in response to diverse
signs of potential trouble.The functions of the characterized genes in the ESR provide clues to cellular features that are protected under stressful conditions. The requirement to conserve energy is likely an important feature of all stress responses, and the ESR presumably aids this effort by rapidly repressing hundreds of genes involved in protein synthesis and cellular growth. The characterized genes induced in the ESR participate in a diverse range of cellular processes, including energy generation and storage, defense against reactive oxygen species, synthesis of internal osmolytes, protein folding and turnover, and DNA repair, and together these may represent physiological systems that must be protected under any circumstance. Indeed, the broad protection of these systems by the ESR probably accounts for the observed cross-resistance to various stresses, in which cells exposed to a low dose of one stress become resistant to an otherwise low dose of a second, unrelated stress (Hohmann and Mager, 1997 ). The ESR is a graded response. The magnitude of the changes in gene expression, as well as the duration and amplitude of the transient expression changes seen when the response is initiated, is graded to the severity of the environmental stress (this work and data not shown). This correlation suggests that the ESR responds in proportion to the deviation of key physiological systems from a homeostatic set-point. The signals from different pathways that respond to distinct physiological perturbations appear to be integrated in transducing an overall measure of this deviation from homeostasis. Thus, initiation of the ESR may provide a useful operational definition of suboptimal environments, and expression of the program can therefore serve as a molecular gauge of the level of stress experienced by the cell. Regulation of the Environmental Stress Response While the ESR displays stereotypical expression changes under
diverse types of environmental shifts, we have shown that its
regulation is both gene-specific and condition-specific. The expression
of genes in the ESR is regulated by different transcription factors
depending on the conditions, and the response is governed by several
different upstream signaling pathways. For example, the repression of
genes encoding ribosomal proteins, and the induction of some of the
genes we find induced in the ESR, have previously been shown to be
regulated by the PKA pathway in response to nutritional signals and by
the PKC pathway following inhibition of secretion (Klein and Struhl,
1994 ; Neuman-Silberberg et al., 1995 ; Nierras and Warner,
1999 ), suggesting that the PKA pathway may govern the entire ESR in
response to nutritional signals, while the PKC pathway plays a key role
in ESR initiation when secretion is impaired. The induction of many
genes we find induced in the ESR was also shown to be dependent on the
high osmolarity glycerol (HOG) pathway in response to osmotic
stress (Rep et al., 2000 ), suggesting the involvement of the
HOG pathway in ESR regulation under those conditions. In response to
DNA-damaging agents, the ESR is governed by the DNA damage-specific
Mec1 pathway; the Mec1 pathway appears to play no role in ESR
regulation in response to heat shock, suggesting the specific
involvement of this pathway following DNA damage (Gasch, Huang,
Botstein, Elledge, Brown; manuscript in preparation). In addition to
regulating the ESR, each of these signaling systems has also been
implicated in regulating more specialized gene expression responses.
Thus, these pathways simultaneously regulate the expression of both the
ESR and specialized responses specific to the stimuli that activate the
pathways.Our results suggest that, in response to each environmental change, yeast cells simultaneously yet independently detect many distinct cellular signals and create a genomic expression program that integrates the individual responses to each of these signals. Evidence for composite expression programs is provided by the response of cells subjected to a shift to lower osmolarity in combination with mild temperature shock, which can be closely approximated as the sum of the individual responses. The additive response to multiple signals of physiological stress may allow the cell to customize its response to the specific features of the new environment. An example of such emergent genomic expression programs is provided by the response to diamide, which shares specific features of the responses to several other stresses, and which suggests that the pleiotropic effects of this agent trigger specific responses to misfolded proteins, redox stress, and secretion and cell wall defects. Accounting for the Large Transient Changes in Genomic Expression
following Environmental Changes Immediately following stressful environmental changes, the cell
responds with rapid and dramatic alterations in global gene expression,
but as the cell adapts to growth at the new conditions, the gene
expression program adjusts to a new steady-state that may be only
slightly altered from the program seen before the environmental change.
We consider two models for the physiological role of the large,
transient changes in gene expression. One possibility is that the gene
products affected play important roles mainly during the transient
period of adaptation to the new conditions. In this model, transient
changes in transcript levels would be accompanied by transient changes
in the corresponding protein levels. We favor an alternative model, in
which the large, transient changes in transcript levels serve as a
loading dose, providing rapid, but relatively small, alterations in the
corresponding protein levels to the new steady-state concentrations
appropriate to the new environment. After the new optimal protein
concentrations are achieved, only subtle differences in transcript
levels are required to maintain those subtly-altered protein
concentrations. The latter model is supported by the observation that,
in response to heat shock, the transient changes in transcripts
encoding protein folding chaperones do not lead to transient changes in
the corresponding protein levels, but rather result in a steady
increase in the levels of chaperones until they reach the appropriate
steady-state levels (S. Lindquist, personal communication). Future
experiments evaluating the changes in the levels of protein products of
genes regulated by environmental stress will further test the validity
of this model. | ||||
CONCLUSION The detailed characterization of global expression programs triggered by environmental stress is a first step toward defining the role of each gene and each physiological system in cellular adaptation to environmental change. This study suggests hypotheses for the mechanisms yeast employ to survive environmental stress, and raises many questions regarding the role and regulation of the observed genomic expression responses. How initiation of the ESR contributes to cellular resistance to various stresses is an important question in understanding the role of this program in the yeast life cycle. This work has provided a partial sketch of the complex regulation of this critical physiological program. More complete identification and mapping of the regulatory circuits that govern the ESR and the more specialized genomic responses to stress will help us understand the remarkable ability of yeast and other organisms to recognize and survive stressful and unstable environments. | ||||
Online References The following references are cited in the supplemental material available online at: http://www.genome.stanford.edu/yeast_stress. Jung, U.S., and Levin, D.E. (1999). Genome-wide analysis of gene expression regulated by the yeast cell wall integrity signaling pathway [In Process Citation]. Mol. Microbiol. 34, 1049–1057. Kawahara, T., Yanagi, H., Yura, T., and Mori, K. (1997). Endoplasmic reticulum stress-induced mRNA splicing permits synthesis of transcription factor Hac1p/Ern4p that activates the unfolded protein response. Mol. Biol. Cell 8, 1845–1862. Liu, X.D., Morano, K.A., and Thiele, D.J. (1999). The yeast Hsp110 family member, Sse1, is an Hsp90 cochaperone. J. Biol. Chem. 274, 26654–26660. Mattison, C.P., Spencer, S.S., Kresge, K.A., Lee, J., and Ota, I.M. (1999). Differential regulation of the cell wall integrity mitogen-activated protein kinase pathway in budding yeast by the protein tyrosine phosphatases Ptp2 and Ptp3. Mol. Cell. Biol. 19, 7651–7660. Morano, K.A., Santoro, N., Koch, K.A., and Thiele, D.J. (1999). A trans-activation domain in yeast heat shock transcription factor is essential for cell cycle progression during stress. Mol. Cell. Biol. 19, 402–411 Mori, K., Ogawa, N., Kawahara, T., Yanagi, H., and Yura, T. (1998). Palindrome with spacer of one nucleotide is characteristic of the cis- acting unfolded protein response element in Saccharomyces cerevisiae. J Biol Chem. 273, 9912–9920. Payne, W.E., and Garrels, J.I. (1997). Yeast protein database (YPD): a database for the complete proteome of Saccharomyces cerevisiae. Nucleic. Acids. Res. 25, 57–62. Schmitt, A.P., and McEntee, K. (1996). Msn2p, a zinc finger DNA-binding protein, is the transcriptional activator of the multistress response in Saccharomyces cerevisiae. Proc Natl Acad Sci USA. 93, 5777–5782. Tamai, K.T., Liu, X., Silar, P., Sosinowski, T., and Thiele, D.J. (1994). Heat shock transcription factor activates yeast metallothionein gene expression in response to heat and glucose starvation via distinct signaling pathways. Mol. Cell. Biol.14, 8155–8165. Winzeler, E.A., Shoemaker, D.D., Astromoff, A., Liang, H., Anderson, K., Andre, B., Bangham, R., Benito, R., Boeke, J.D., Bussey, H., and et al. (1999). Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906. | ||||
ACKNOWLEDGMENTS We thank Joe DeRisi for the original msn2 msn4 double-deletion strain and Tae Bum Shin for overexpression constructs. Special thanks to Christian Rees, Gavin Sherlock, and especially Ash Alizadeh for invaluable help with construction of the companion website, and Mark Schroeder, Gavin Sherlock, and the curators of Saccharomyces Genome Database (SGD) for computer support. We thank Susan Lindquist, Sean O'Rourke, Ira Herskowitz, Jonathan Warner, Anders Blomberg, Judith Frydman, Jim Garrels, Christoph Schuller, Max Diehn, Ash Alizadeh, Jennifer Boldrick, Oliver Rando, and members of the Brown and Botstein labs for helpful discussions. Much of the analysis presented here was possible due to genome databases, in particular SGD and the Yeast Protein Database (YPD). This work was supported by grants from the National Institutes of Health (HG-00450 and HG-00983) and by the Howard Hughes Medical Institute. P.O.B is an associate investigator of the Howard Hughes Medical Institute. | ||||
Abbreviations: | ||||
Footnotes Online version of this article contains data set
material, and is available at www.molbiolcell.org. | ||||
REFERENCES
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