|
|
Mol Biol Cell. 2000 December; 11(12): 4241–4257. | PMCID: PMC15070 |
Copyright © 2000, The American Society for Cell Biology Genomic Expression Programs in the Response of Yeast Cells to
Environmental Changes  Audrey P. Gasch,*¶ Paul T. Spellman,†¶ Camilla M. Kao,*¶ Orna Carmel-Harel,‡ Michael B. Eisen,§ Gisela Storz,‡ David Botstein,† and Patrick O. Brown*‖ *Departments of Biochemistry and †Genetics, Stanford University School of Medicine, Stanford, CA 94305-5428; ‡Cell Biology and Metabolism Branch, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-5430; §Lawrence Berkeley National Labs and Department of Molecular and Cellular Biology, University of California Berkeley, Berkeley, CA 94720; and ‖Howard Hughes Medical Institute, Stanford, CA Pamela A. Silver, Monitoring Editor Received July 11, 2000; Revised September 19, 2000; Accepted October 11, 2000. 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. 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. (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
(OD 600 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
H 2O 2 (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 H 2O 2
were maintained throughout the experiment. The
H 2O 2 concentration was
monitored every 3 min using a horseradish-peroxidase based assay ( Green
and Hill, 1984 ), which showed that the concentration of
H 2O 2 was maintained at 0.32
+/−0.03 mM H 2O 2 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/). 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 ×
10 5 quantitative measurements of transcript
levels was organized by hierarchical clustering and displayed as
previously described ( Eisen et al., 1998 ) (Figure
). 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 . 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 is that, with the exception of adaptation to starvation
conditions, the global changes in transcript abundance were largely
transient (Figure , 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 ). 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
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 ). 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
). 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 ). 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 A), 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 F). 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
A 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 , 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 C). 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
H 2O 2 treatment, relative to
the isogenic wild-type, and the genes affected varied in their
dependence on Msn2/Msn4p (Figure ). 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 H 2O 2, 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
H 2O 2 treatment was
unaffected by deletion of the factors (Figure
). 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
H 2O 2 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
H 2O 2. 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
), 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 H 2O 2 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
H 2O 2 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 H 2O 2 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). 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. 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. 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. 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. | | dithiothrietol (DTT) | environmental stress response (ESR), hydrogen peroxide
(H2O2), Msn2p and/or Msn4p (Msn2/Msn4p), stress
response element (STRE) |
- Bailey TL, Elkan C. Conference on Intelligent systems for Molecular Biology. Menlo Park, CA: AAAl Press; 1994. Fitting a mixture model by expectation maximization to discover motifs in biopolymers; pp. 28–36.
- Ball CA, Dolinski K, Dwight SS, Harris MA, Issel-Tarver L, Kasarskis A, Scafe CR, Sherlock G, Binkley G, Jin H, Kaloper M, Orr SD, Schroeder M, Weng S, Zhu Y, Botstein D, Cherry JM. Integrating functional genomic information into the Saccharomyces genome database. Nucleic Acids Res. 2000;28:77–80. [PubMed]
- Boy-Marcotte E, Perrot M, Bussereau F, Boucherie H, Jacquet M. Msn2p and Msn4p control a large number of genes induced at the diauxic transition which are repressed by cyclic AMP in Saccharomyces cerevisiae. J Bacteriol. 1998;180:1044–1052. [PubMed]
- Cappellaro C, Mrsa V, Tanner W. New potential cell wall glucanases of Saccharomyces cerevisiae and their involvement in mating. J Bacteriol. 1998;180:5030–5037. [PubMed]
- Cox JS, Shamu CE, Walter P. Transcriptional induction of genes encoding endoplasmic reticulum resident proteins requires a transmembrane protein kinase. Cell. 1993;73:1197–1206. [PubMed]
- DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science. 1997;278:680–686. [PubMed]
- Dey NB, Bounelis P, Fritz TA, Bedwell DM, Marchase RB. The glycosylation of phosphoglucomutase is modulated by carbon source and heat shock in Saccharomyces cerevisiae. J Biol Chem. 1994;269:27143–27148. [PubMed]
- Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998;95:14863–14868. [PubMed]
- Fernandes L, Rodrigues-Pousada C, Struhl K. Yap, a novel family of eight bZIP proteins in Saccharomyces cerevisiae with distinct biological functions. Mol Cell Biol. 1997;17:6982–6993. [PubMed]
- Gorner W, Durchschlag E, Martinez-Pastor MT, Estruch F, Ammerer G, Hamilton B, Ruis H, Schuller C. Nuclear localization of the C2H2 zinc finger protein Msn2p is regulated by stress and protein kinase A activity. Genes Dev. 1998;12:586–597. [PubMed]
- Green MJ, Hill HA. Chemistry of dioxygen. Meth Enzymol. 1984;105:3–22. [PubMed]
- Hardie DG. Roles of the AMP-activated/SNF1 protein kinase family in the response to cellular stress. Biochem Soc Symp. 1999;64:13–27. [PubMed]
- Hardie DG, Carling D. The AMP-activated protein kinase: fuel gauge of the mammalian cell? Eur J Biochem. 1997;246:259–273. [PubMed]
- Hohmann S, Mager WH. Yeast stress responses. New York, NY: Chapman & Hall,; 1997.
- Hounsa CG, Brandt EV, Thevelein J, Hohmann S, Prior BA. Role of trehalose in survival of Saccharomyces cerevisiae under osmotic stress. Microbiology. 1998;144:671–680. [PubMed]
- Huang D, Moffat J, Wilson WA, Moore L, Cheng C, Roach PJ, Andrews B. Cyclin partners determine Pho85 protein kinase substrate specificity in vitro and in vivo: control of glycogen biosynthesis by Pcl8 and Pcl10. Mol Cell Biol. 1998;18:3289–3299. [PubMed]
- Hughes JD, Estep PW, Tavazoie S, Church GM. Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol. 2000;296:1205–1214. [PubMed]
- Hwang PK, Tugendreich S, Fletterick RJ. Molecular analysis of GPH1, the gene encoding glycogen phosphorylase in Saccharomyces cerevisiae. Mol Cell Biol. 1989;9:1659–1666. [PubMed]
- Jamieson DJ. Oxidative stress responses of the yeast Saccharomyces cerevisiae. Yeast. 1998;14:1511–1527. [PubMed]
- Jamsa E, Simonen M, Makarow M. Selective retention of secretory proteins in the yeast endoplasmic reticulum by treatment of cells with a reducing agent. Yeast. 1994;10:355–370. [PubMed]
- Klein C, Struhl K. Protein kinase A mediates growth-regulated expression of yeast ribosomal protein genes by modulating RAP1 transcriptional activity. Mol Cell Biol. 1994;14:1920–1928. [PubMed]
- Kobayashi N, McEntee K. Identification of cis and trans components of a novel heat shock stress regulatory pathway in Saccharomyces cerevisiae. Mol Cell Biol. 1993;13:248–256. [PubMed]
- Li B, Nierras CR, Warner JR. Transcriptional elements involved in the repression of ribosomal protein synthesis. Mol Cell Biol. 1999;19:5393–5404. [PubMed]
- Mager WH, De Kruijff AJ. Stress-induced transcriptional activation. Microbiol Rev. 1995;59:506–531. [PubMed]
- Marchase RB, Bounelis P, Brumley LM, Dey N, Browne B, Auger D, Fritz TA, Kulesza P, Bedwell DM. Phosphoglucomutase in Saccharomyces cerevisiae is a cytoplasmic glycoprotein and the acceptor for a Glc-phosphotransferase. J Biol Chem. 1993;268:8341–8349. [PubMed]
- Marchler G, Schuller C, Adam G, Ruis H. A Saccharomyces cerevisiae UAS element controlled by protein kinase A activates transcription in response to a variety of stress conditions. EMBO J. 1993;12:1997–2003. [PubMed]
- Martinez-Pastor MT, Marchler G, Schuller C, Marchler-Bauer A, Ruis H, Estruch F. The Saccharomyces cerevisiae zinc finger proteins Msn2p and Msn4p are required for transcriptional induction through the stress response element (S.T.R.E). EMBO J. 1996;15:2227–2235. [PubMed]
- Moehle CM, Hinnebusch AG. Association of RAP1 binding sites with stringent control of ribosomal protein gene transcription in Saccharomyces cerevisiae. Mol Cell Biol. 1991;11:2723–2735. [PubMed]
- Moskvina E, Schuller C, Maurer CT, Mager WH, Ruis H. A search in the genome of Saccharomyces cerevisiae for genes regulated via stress response elements. Yeast. 1998;14:1041–1050. [PubMed]
- Mumberg D, Muller R, Funk M. Regulatable promoters of Saccharomyces cerevisiae: comparison of transcriptional activity and their use for heterologous expression. Nucleic Acids Res. 1994;22:5767–5768. [PubMed]
- Neuman-Silberberg FS, Bhattacharya S, Broach JR. Nutrient availability and the RAS/cyclic AMP pathway both induce expression of ribosomal protein genes in Saccharomyces cerevisiae but by different mechanisms. Mol Cell Biol. 1995;15:3187–3196. [PubMed]
- Nierras CR, Warner JR. Protein kinase C enables the regulatory circuit that connects membrane synthesis to ribosome synthesis in Saccharomyces cerevisiae. J Biol Chem. 1999;274:13235–13241. [PubMed]
- Norbeck J, Blomberg A. Metabolic and regulatory changes associated with growth of Saccharomyces cerevisiae in 1.4 M NaCl. Evidence for osmotic induction of glycerol dissimilation via the dihydroxyacetone pathway. J Biol Chem. 1997;272:5544–5554. [PubMed]
- Parrou JL, Enjalbert B, Plourde L, Bauche A, Gonzalez B, Francois J. Dynamic responses of reserve carbohydrate metabolism under carbon and nitrogen limitations in Saccharomyces cerevisiae. Yeast. 1999;15:191–203. [PubMed]
- Parrou JL, Teste MA, Francois J. Effects of various types of stress on the metabolism of reserve carbohydrates in Saccharomyces cerevisiae: genetic evidence for a stress-induced recycling of glycogen and trehalose. Microbiology. 1997;143:1891–1900. [PubMed]
- Rep M, Krantz M, Thevelein JM, Hohmann S. The transcriptional response of Saccharomyces cerevisiae to osmotic shock. Hot1p and Msn2p/Msn4p are required for the induction of subsets of high osmolarity glycerol pathway-dependent genes. J Biol Chem. 2000;275:8290–8300. [PubMed]
- Rep M, Reiser V, Gartner U, Thevelein JM, Hohmann S, Ammerer G, Ruis H. Osmotic stress-induced gene expression in Saccharomyces cerevisiae requires Msn1p and the novel nuclear factor Hot1p. Mol Cell Biol. 1999;19:5474–5485. [PubMed]
- Ruis H, Schuller C. Stress signaling in yeast. Bioessays. 1995;17:959–965. [PubMed]
- Schnell N, Krems B, Entian KD. The PAR1 (YAP1/SNQ3) gene of Saccharomyces cerevisiae, a c-jun homologue, is involved in oxygen metabolism. Curr Genet. 1992;21:269–273. [PubMed]
- Shalon D, Smith SJ, Brown PO. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 1996;6:639–645. [PubMed]
- Sherman F. Getting started with yeast. In: Fink GR, editor. Guide to Yeast Genetics and Molecular Biology. San Diego, CA: Academic Press; 1991. pp. 3–21.
- Singer MA, Lindquist S. Multiple effects of trehalose on protein folding in vitro and in vivo. Mol Cell. 1998;1:639–648. [PubMed]
- Smith A, Ward MP, Garrett S. Yeast PKA represses Msn2p/Msn4p-dependent gene expression to regulate growth, stress response and glycogen accumulation. EMBO J. 1998;17:3556–3564. [PubMed]
- Stephen DW, Rivers SL, Jamieson DJ. The role of the YAP1 and YAP2 genes in the regulation of the adaptive oxidative stress responses of Saccharomyces cerevisiae. Mol Microbiol. 1995;16:415–423. [PubMed]
- Travers KJ, Patil CK, Wodicka L, Lockhart DJ, Weissman JS, Walter P. Functional and genomic analyses reveal an essential coordination between the unfolded protein response and ER-associated degradation. Cell. 2000;101:249–258. [PubMed]
- Warner JR. The economics of ribosome biosynthesis in yeast. Trends Biochem Sci. 1999;24:437–440. [PubMed]
- Zahringer H, Burgert M, Holzer H, Nwaka S. Neutral trehalase Nth1p of Saccharomyces cerevisiae encoded by the NTH1 gene is a multiple stress responsive protein. FEBS Lett. 1997;412:615–620. [PubMed]
|
This article has been 
![[filled triangle]](/corehtml/pmc/pmcents/utrif.gif)
) and cells
transferred from 37°C to 25°C
