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Molecular and Cellular Biology, November 2000, p. 8157-8167, Vol. 20, No. 21
0270-7306/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Regulatory Networks Revealed by Transcriptional
Profiling of Damaged Saccharomyces cerevisiae Cells: Rpn4
Links Base Excision Repair with Proteasomes
Scott A.
Jelinsky,1
Preston
Estep,2
George M.
Church,2 and
Leona D.
Samson1,*
Cancer Cell Biology, Harvard School of Public
Health,1 and Department of Genetics,
Harvard Medical School,2 Boston, Massachusetts
02115
Received 31 May 2000/Returned for modification 13 July
2000/Accepted 4 August 2000
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ABSTRACT |
Exposure to carcinogenic alkylating agents, oxidizing agents, and
ionizing radiation modulates transcript levels for over one third of
Saccharomyces cerevisiae's 6,200 genes. Computational analysis delineates groups of coregulated genes whose upstream regions
bear known and novel regulatory sequence motifs. One group of
coregulated genes contain a number of DNA excision repair genes (including the MAG1 3-methyladenine DNA glycosylase gene)
and a large selection of protein degradation genes. Moreover,
transcription of these genes is modulated by the proteasome-associated
protein Rpn4, most likely via its binding to MAG1 upstream
repressor sequence 2-like elements, that turn out to be almost
identical to the recently identified proteasome-associated control
element (G. Mannhaupt, R. Schnall, V. Karpov, I. Vetter, and H. Feldmann, FEBS Lett. 450:27-34, 1999). We have identified a large
number of genes whose transcription is influenced by Rpn4p.
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INTRODUCTION |
Biological processes depend upon the
structural integrity of the molecules that comprise living organisms.
The structural integrity of the genome is particularly important
because molecular alterations in the genetic material, usually DNA, can
lead to permanent inheritable changes, i.e., mutations. However, the
structural integrity of other cellular molecules, such as proteins,
RNA, carbohydrates, and lipids, is also important, because the precise three-dimensional shape and the detailed chemistry of these molecules orchestrate the biochemical processes vital for life. Most biomolecules are inherently reactive, and as such their structural integrity is
constantly challenged by reactive chemical and physical agents in the
environment. It should therefore come as no surprise that all cells can
sense and respond to unfavorable molecular alterations. Indeed, it is
well known that cells sense and respond to damaged DNA and proteins,
and such responses are exemplified by the SOS and heat shock responses
that have been well characterized in Escherichia coli and
other organisms (11, 12, 28).
Here we explore the transcriptional response of Saccharomyces
cerevisiae to a wide range of chemical and physical damaging agents. Specifically, we explore how transcript levels for every S. cerevisiae gene and open reading frame (ORF) respond when
cellular molecules are damaged by a selection of environmentally and
clinically relevant chemical and physical carcinogens. The global
transcriptional response of this budding yeast to these damaging agents
turns out to be far more extensive than anticipated. However,
computational analysis of almost 200,000 data points reveals patterns
in the data that allow us to define novel regulatory networks. We find that the responses of S. cerevisiae to each of six damaging
agents are markedly different and that, for at least one agent, the
response is dramatically affected by the cell's position in the cell
cycle at the time of exposure. Computational clustering of the data and
subsequent searching for common sequence motifs in promoter regions
reveal nine such motifs, only five of which have known binding factors.
Furthermore, we find that a large number of protein degradation genes
and a selection of base excision and nucleotide excision DNA repair
genes are linked in a transcriptional regulon controlled by Rpn4p, a
proteasome-associated protein (22).
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MATERIALS AND METHODS |
Strains, media, growth conditions, and damaging-agent
exposure.
Log-phase S. cerevisiae strain DBY747
(MATa his3-
1 leu2-3,112
ura3-52 trp1-289a gal2 can1 CUP1s) or BY4740 and its
rpn4 derivative (MATa ura30 lys20 leu20 rpn4) was grown to a density of 5 × 106 cells per ml in 1% yeast extract-2% peptone-2%
glucose. Cultures were split in two, and methyl methanesulfonate (MMS)
was added to 0.1% to one half. Incubation was continued for 10, 30, or
60 min. For one experiment, MMS was added to 0.05, 0.1 or 0.2% and incubated for 1 h. For the other agents, log-phase cells were grown to a density of 5 × 106 cells per ml in 1%
yeast extract-2% peptone-2% glucose. Cultures were split, and
N-methyl-N'-nitro-N-nitrosoguanidine
(MNNG) (6.7 or 27 µg/ml), mitomycin C (MMC) (2 µg/ml),
4-nitroquinoline n-oxide (4NQO) (2 or 8 µg/ml),
1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU) (200 µM), or
tert-butyl hydroperoxide (t-BuOOH) (5 mM) was
added, and incubation was continued for 60 min. For cell synrochony, log-phase cells were arrested in G1 by 
-factor (3 µM
for 120 min), in S phase by hydroxyurea (0.1 M for 210 min), and in
G2 by nocodazole (15 µg/ml for 120 min).
Stationary-phase cells were harvested after 3 days of growth (5 × 108 cells/ml). Arrested cells were confirmed by microscopy
and by fluorescence-activated cell sorting analysis (data not shown) and split in two, and 0.1% MMS was added to half the cultures for
1 h before RNA isolation.
GeneChip hybridizations.
RNA isolation and purification and
cRNA labeling were done as described (17). Hybridizations
with a set of four oligonucleotide arrays (GeneChip Ye6100 arrays;
Affymetrix, Santa Clara, Calif.) containing probes for 6,218 yeast ORFs
were done at 45°C for 16 h with constant mixing in 200 µl of
MES buffer (100 mM MES [morpholimeethanesulfonic acid], 1 M
Na+, 20 mM EDTA, 0.01% Tween 20) with 10 µg of labeled
cRNA. After hybridization, arrays were washed in nonstringent wash A
buffer (6× SSPE [1× SSPE is 0.18 M NaCl, 10 mM NaH2
PO4, and 1 mM EDTA (pH 7.7)], 0.01% Tween 20, 0.005%
antifoam, 25°C) followed by stringent wash B buffer (100 mM MES, 0.1 M Na+, 0.01% Tween 20, 50°C). Arrays were then stained
with strepavidin-phycoerythrin (30 min, 25°C; Molecular Probes),
followed by rinsing with wash A buffer. Arrays were stained with
R-phycoerythrin-streptavidin (10 µg/ml; Molecular Probes) in 100 mM
MES-1.0 M Na+-0.01% Tween 20 at 25°C. All washes were
automated on a fluidics station (Affymetrix). Arrays were scanned using
a specialized confocal laser scanning microscope (Hewlett Packard or
Molecular Dynamics) and analyzed using the GeneChip analysis suite,
version 3.1. All arrays were scaled so that the average of the average intensity difference of the perfect match probes minus the mismatch probes was 300. This scaling allowed all the arrays to be directly compared with each other. Integrity of the sample was determined by
measuring the intensity of probes derived from both the 3' and 5' ends
of actin and TATA-binding protein. The signal from probes corresponding
to the 5' end was not less then twofold of the intensity of probes
derived from the 3' end of the gene in any sample. These measurements
suggest that the mRNA is not more degraded in the treated samples than
in the controls.
Determination of fold change.
Three control untreated
log-phase samples were hybridized to three different sets of GeneChip
arrays. A baseline value was determined by averaging the hybridization
intensity from the three control experiments. Each gene has
approximately 20 pairs of oligonucleotide probes; within each pair, one
is a perfect match to the gene and one has a mismatch. Hybridization
intensity was determined by calculating the average intensity
difference between the perfect match signal and the mismatch signal
across the 20 pairs of probes. Fold changes were calculated by dividing
the average intensity difference values from experimental samples by
the baseline values. Note that in our original report we arbitrarily
chose fourfold as the cutoff for induction and threefold as the cutoff
for repression (17). Given an improved algorithm for
calculating hybridization intensities, we now adopt threefold as the
cutoff for both induction and repression. Accordingly, the number of
genes categorized as responsive in this study has increased compared to
our previous study.
Cluster analysis.
All genes showing a change of 3.0-fold or
more in at least one experimental condition were included in the
analysis. Three control untreated experiments were performed. The
baseline intensity value was calculated as the average of the three
average difference values. For each treatment, the fold change was
determined by dividing the average intensity difference from the
experimental sample by the baseline intensity. The average difference
was determined using GeneChip analysis suite 3.1 software (Affymetrix).
Although some genes have less than the ideal number of probes, they
were still included in the analysis. Cluster analysis was performed using one of three methods: Euclidean distances (34),
hierarchical (10), or self-organized maps (SOMs)
(33). For Euclidean distance measurements, log-transformed
fold changes was arbitrarily clustered into groups of genes having
similar expression profiles. Hierarchical clustering was done as
described (10). The third method was based on SOMs using
Genecluster 1.0 (33). A. filter was used to eliminate genes
which had a relative change of less than 3.0-fold and whose expression
level were less than 60 across all treatments. Expression levels were
normalized to have a mean of 0 and a variance of 1, which forced genes
to be grouped based on the shape of their expression pattern rather
than on their absolute values (33). The number of clusters
was chosen to give the largest number of fundamentally different patterns.
On the arrays, many genes are represented by more than one set of
probes. In order to accurately determine the distribution of functional
categories present, only one set of probes was used for each gene. When
more than one set was present, sets containing less than the ideal
number of probes were eliminated. In other cases the probe set with the
higher signal was included.
Statistical calculation.
To determine if fold changes were
statistically significant between the triplicate experiments, a Student
t test was performed in Microsoft Excel using a two-tailed
distribution. To determine if SOMs produced distinct clusters,
correlation coefficients were determined from the mean of each group.
Correlation coefficients p(x,y) were
determined using Microsoft Excel with the formula
where
x,
y represents any pair of clusters,
xi or
yi equals the value
of treatment
i in cluster
x or
y,
µ
x and µ
y equal the
means of the normalized intensities across the 26 treatments
in
clusters
x and
y, and
x
and
y equal the
standard deviations of the
normalized intensities across the 26
treatments in clusters
x and
y,
respectively.
Only four groups showed significant similarity. They included clusters
1 and 4 (0.86), clusters 14 and 17 (0.83), clusters
17 and 15 (0.78),
and clusters 9 and 12 (0.79). Hypergeometric
distribution
[
P(
x)] was used to determine the chance
probability
of observing the number of genes of a particular function
category
or with a particular upstream motif within each cluster, as
described
(
34); calculations were determined using the
formula
where
x is the number of genes in a functional
category or with a motif in a defined cluster,
n is the
total number of genes
in the cluster,
M is the total number
of genes in a functional
category or with a particular motif in the
dataset, and
N is the
total number of genes in the
dataset.
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RESULTS AND DISCUSSION |
Reproducibility of transcriptional profiling by oligonucleotide DNA
microarray analysis.
We previously used Affymetrix GeneChip
oligonucleotide arrays to characterize the global transcriptional
response of S. cerevisiae upon exposure to a mildly
toxic dose of a monofunctional SN2 alkylating agent,
MMS (17). MMS is typical of a large class of reactive chemicals present in the air we breathe and the food we eat, as well as being representative of some normal cellular metabolites (23). To our surprise, transcript levels for roughly 400 of S. cerevisiae's ~6,200 genes were responsive to MMS
exposure; ~300 genes were induced by 4- to 250-fold, and ~100 genes
were repressed by 3- to 18-fold (17). Before undertaking a
much larger study, we assessed the reproducibility of the
transcriptional responsiveness measured by GeneChip analysis. Six
separate S. cerevisiae cultures were grown to mid-log
phase; three were exposed to 0.1% MMS for 60 min, and three remained
untreated. cRNA prepared from each culture was hybridized to the
GeneChip arrays as described above (17, 38). Figure 1a
displays the range of hybridization intensities obtained for a
representative selection of 69 MMS-inducible and 31 MMS-repressible
genes; note that in virtually no instance do the error bars
(representing standard deviations) for treated and untreated come close
to overlapping. Figures 1b and c display individual hybridization
intensities for the 693 genes whose transcripts changed by
threefold or more upon MMS treatment (the identities of these
genes and the raw data can be found at
www.hsph.harvard.edu/geneexpression.) Individual
values for the three untreated cultures are plotted against their
averages in Fig. 1b, and individual values for the three MMS-treated
cultures are plotted against their averages in Fig. 1c; this provides a
graphic representation of the variation between experiments, viewing
the untreated and treated groups separately. Figure 1d displays the
average hybridization intensities for the 693 responsive genes, but
here the average values for the MMS-treated cultures are plotted
against those for the untreated cultures to provide a graphic
representation of where the 693 responsive genes fall in the 3- to
217-fold range of MMS responsiveness. The data in Fig.
1 confirm that, in our hands, transcript
levels measured by GeneChip analysis are highly reproducible and show that transcript levels observed in MMS-treated cells are likewise reproducible. Furthermore, the changes in transcript level are also reproducible, and 648 of the 693 responsive genes (94%) showed a
statistically significant change at a 90% confidence level. We are
therefore confident that our estimate of such a surprisingly large
number of MMS-responsive genes is reliable.
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FIG. 1.
Reproducibility of mRNA profiling by GeneChip analysis.
Hybridization intensities for transcripts from three untreated
log-phase samples and three 0.1% MMS-treated samples. (a)
Hybridization intensities for 100 MMS-responsive genes; symbols
represent basal transcript intensity, averaged from three untreated
cultures, ± standard deviation. Green and red symbols represent values
for the three MMS-treated cultures, with green for induced and red for
repressed. (b) Intensity of hybridization signals for 693 MMS-responsive genes from three independent untreated cultures versus
the average of their intensity values. (c) Intensity of hybridization
signals for 693 MMS-responsive genes from three independent MMS-treated
cultures versus the average of their intensity values. In panels b and
c, red lines represent twofold changes from the average. (d) Average
intensity value of the untreated cultures versus the average intensity
value for the MMS-treated cultures for the 693 MMS-responsive genes.
Red lines represent 2-fold, 5-fold, and 10-fold differences in
transcript levels.
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Having established that mRNA profiling using the Affymetrix
oligonucleotide chips was reproducible, we adopted an experimental
strategy first suggested by Eisen et al. (
10), that it is
much
more informative to establish mRNA profiles for a wide variety
of
conditions than to make repeat observations on identical conditions.
We
therefore committed our available resources to monitoring changes
in
transcript levels induced by numerous different MMS exposures
and
induced by roughly equitoxic exposures to numerous carcinogens.
Note
that by using a 90% confidence level to determine significance,
we may
increase the number of false-positive results while increasing
the
number of responsive genes. We chose to be more inclusive
with our
data, relying on the clustering algorithms to determine
interesting
patterns that would be unaffected by a few false-positive
results.
Kinetics of the MMS-induced transcriptional response.
The
collection of MMS-responsive S. cerevisiae genes observed
after 60 min of exposure to 0.1% MMS (17) (Fig. 1)
represents a simple snapshot of the transcriptional response of this
eukaryote to alkylation damage. This raises the possibility that if one could monitor transcriptional responses as a continuum, even more genes
might be counted as MMS responsive. In an attempt to gain insight into
this continuum, we monitored transcriptionally responsive genes as a
function of time in 0.1% MMS and as a function of MMS dose. Figures 2a
and b provide a diagrammatic
representation of the results (the identities of the genes and the raw
data can be found at www.hsph.harvard /geneexpression).
Represented in Fig. 2a and b are genes whose transcript levels
either increased (green) or decreased (red) by threefold or more for at
least one of the treatments. In addition, the responsive genes are
clustered into groups that show similar kinetics using the hierarchical clustering program developed by Eisen et al. (10). It is
immediately apparent that many more than 400 genes are MMS responsive,
and the total number of genes represented in Fig. 2a and b is 969 and
1,863, respectively. The set of transcriptionally responsive genes is
quite different at early versus late times and at low versus high
alkylation levels. For the temporal response, there appear to be
distinct groups of early-, middle-, and late-responsive genes, clusters
IV, III, and II, respectively, in Fig. 2a. In addition, the response of
several sets of genes appears to be transient, in that their
responsiveness is seen at only one or two time points (e.g., clusters
I, V, and VI). For the dose response (Fig. 2b), it is clear that with
increasing alkylation levels, the number of responsive genes as well as
the degree of responsiveness increases in a cumulative way. At the
highest dose, we monitored 1,426 responsive genes, with 999 upregulated
and 427 downregulated; this represents over 20% of the S. cerevisiae genome. Why such a large fraction of the yeast genome
should be MMS responsive is discussed below.
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FIG. 2.
Expression patterns with different MMS exposures. (a)
Cells were exposed to 0.1% MMS for the indicated times. The 969 genes
that showed a transcript level change of threefold or higher were
grouped by similar patterns of expression using hierarchical clustering
and visualized graphically using colors to represent the direction and
extent of change. Increases in mRNA expression are represented by
shades of green, and decreases are shown by shades of red. (b) Cells
were exposed to the indicated MMS dose for 60 min. The expression
pattern for 1,863 responsive genes is shown as described above. The
results in panels a and b were derived independently using different
datasets.
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MMS-induced transcriptional response as a function of cell cycle
position.
As eukaryotic cells move through the cell cycle,
specific sets of genes are transcriptionally activated and inactivated,
although transcript levels for the vast majority of genes do not change (4, 32). Moreover, responses to DNA-damaging agents are
known to vary throughout the cell cycle; e.g., G1 cells
that experience DNA damage activate a G1/S checkpoint,
those in S phase activate an S-phase delay, and those in G2
or M activate a G2/M checkpoint (16, 21, 30,
37). For these reasons we monitored how S. cerevisiae responds to MMS-induced alkylation damage as a function of cell cycle. (Note that in our initial dataset [17]
very few of the MMS-responsive genes turned out to be cell cycle
regulated genes.) Cells arrested in G1, S, G2,
or stationary phase were exposed to 0.1% MMS for 60 min; the
MMS-induced transcriptional profiles for each synchronized population
are diagrammed in Fig. 3 and presented
numerically at www.hsph.harvard.edu/geneexpression. Cell cycle stage had a profound effect on the MMS-induced
transcriptional profiles. Numerous genes appear to be cell cycle
specific in that they were only scored as weakly responsive or
nonresponsive in MMS-treated log-phase cultures, but were scored as
clearly responsive in a synchronized culture. Among them, 199 genes
appear responsive only if cells experience damage in G1
(clusters II, V, and VI); 84 genes are only responsive in S phase
(clusters IV and VII); 94 are only responsive in G2
(clusters III and IX); and 229 are only responsive in stationary phase
(clusters I, VIII, and X). Fewer than 20% of these 614 genes were
previously shown to have cell cycle-dependent expression (4,
32).
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FIG. 3.
Effect of cell cycle on MMS-induced mRNA profiles. (a)
Cells were arrested in G1 with -factor, in S with
hydroxyurea, or in G2 with nocodazole or allowed to grow to
stationary (stat) phase. Transcript levels in each population were
measured with and without 0.1% MMS exposure for 60 minutes. The data
were divided into 40 clusters using a K-means algorithm
(34). The centroids of the 40 clusters were grouped by hierarchical
clustering to group clusters with similar profiles together. These
1,876 genes were responsive by more than 3.0-fold. (b) Hierarchical
clustering (10) of transcript changes in MMS-treated log-phase cells,
untreated stationary-phase cells (compared to untreated log-phase
cells), and MMS-treated stationary-phase cells. Genes with more than a
three- fold change in any one condition are shown. Fold changes are
presented as described above. Clusters I to X represent cell
cycle-specific responsive clusters.
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It turns out that a large fraction of the genes that are responsive to
MMS in log-phase cycling cells are also responsive
to simply being held
in stationary phase, independent of MMS exposure.
This is shown clearly
in Fig.
3b, where the transcript level changes
for MMS-treated
log-phase cells and those for stationary-phase
versus log-phase cells
are reclustered and shown alongside each
other. The MMS exposure (0.1%
for 60 min) to some extent appears
to mimic the arrest of cells in
stationary phase, at least in
terms of transcriptional profile. At
first it seemed that fewer
genes are MMS responsive in stationary-phase
cells than in cells
in other parts of the cell cycle (Fig.
3a and b).
However, this
may be explained by the fact that 335 transcripts that
ordinarily
respond to MMS are already up- or downregulated in
stationary-phase
cells prior to MMS exposure (Fig.
3b, clusters I and
II, respectively)
and respond no further upon alkylation exposure.
There appears
to be an overlap of responsive genes by two different
stressful
conditions, MMS exposure and stationary growth. This may
reflect
a general stress response pathway, although we do not yet know
whether these are a primary, secondary or tertiary response to
stress.
Transcriptional responses to other damaging agents.
One of our
major goals is to understand exactly how cells respond to a range of
carcinogenic alkylating agents representative of those present in our
environment and those used in the cancer clinic. We therefore set out
to compare the transcriptional response of S. cerevisiae to
various candidate alkylating agents, including the SN2
alkylating agent MMS, the SN1 alkylating agent MNNG and the
chemotherapeutic alkylating agent BCNU. In addition, we wished to
determine which aspects of the responses are alkylating agent specific,
and so for comparison we determined the transcriptional profile of
cells exposed to three other types of damaging agent: 
-irradiation,
4NQO, and the oxidizing agent t-BuOOH (12). Cells were exposed to roughly equitoxic doses of each agent, as measured by
colony formation, and the resulting profiles are shown in Fig. 4 (shown numerically at
www.hsph.harvard.edu/geneexpression). Doses were
relatively nontoxic, resulting in 75 to 100% survival. Very few genes
were responsive to all of the agents; indeed, among the hundreds of
responsive genes, the transcript levels for only 12 turned out to be
induced by all treatments, and transcripts for only 9 were repressed by
all treatments. These 21 genes do not include any DNA repair genes, and
since they were determined independently of clustering, they are
detailed at http://www.hsph.harvard.edu/geneexpression for easy access. Furthermore, there were surprisingly extensive differences between the transcriptional profiles induced by each of the
six damaging agents. Even the closely related methylating agents MMS
and MNNG induce quite distinct transcriptional profiles at roughly
equitoxic doses (Fig. 4, lane 4 versus 6). At a higher MNNG dose (24%
survival), the profile begins to overlap more with the MMS-induced
profile, although each profile still remains quite distinct (lane 4 versus 7). It is also notable that the profiles produced by
equitoxic exposure to -rays and the oxidizing agent t-BuOOH are dramatically different. Since it has been
estimated that following ionizing radiation, ~65% of the damage to
DNA occurs by base oxidation and only ~35% occurs directly by
ionization (12, 36), one might have expected more overlap.
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FIG. 4.
Global transcriptional profiles in response to different
damaging agents. Log-phase cells were exposed to the indicated agents;
exposure was limited to 1 h and resulted in the percent survival
(as determined by colony-forming ability) indicated in parentheses.
Expression profiles for 2,324 responsive genes were grouped by a
K-means clustering algorithm into 30 clusters and are
represented by colors as described in the legend to Fig. 2. Six
clusters containing members whose transcript levels appear to be
responsive to only a particular agent are indicated. Treatments were as
follows: 200 µM BCNU, 30-kilorad -ray, 5 mM t-BuOOH, 0.1% MMS,
4NQO at 2 (low) or 8 (high) µg/ml, and MNNG 8 at (low) or 27 (high)
µg/ml. Clusters I to IV represent DNA-damaging agent-specific
clusters.
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In addition, there appear to be groups genes that are specifically
responsive to each damaging agent (clusters I to VI), and
these may
turn out to represent unique signatures for each agent.
It should be
noted once again that these profiles represent snapshots
of
transcriptional responses, and upon further kinetic analysis,
genes
that appear to be agent specific in Fig.
4 may turn up in
response to
the other agents. In fact, only 30% of the genes that
appear to be
agent specific in Fig.
4 (excluding the MMS-specific
cluster IV) can be
found among the numerous MMS-induced profiles
described in this study.
However, a more extensive kinetic analysis
will be needed to establish
if there are certain responsive genes
that are truly specific for a
particular agent or class of agent.
Finally, it is clear from Fig.
4
that the volume of genes responding
to exposure to a damaging agent is
not a good predictor of toxicity.
For example, the most toxic treatment
(4NQO, producing 10% survival)
influenced the expression of far fewer
genes than did the least
toxic treatment (BCNU, producing 100%
survival).
Over one third of S. cerevisiae genes respond to
cellular damage.
Taken as a whole, this study shows that damaging
cells by physical and chemical carcinogens elicits significant changes
in transcript level for more than 2,500 of S. cerevisiae's
~6,200 genes. These transcriptionally responsive genes can be
categorized by functional category (as defined by the S. cerevisiae genome database [1]) and are
summarized in Table A at
http: //www.harvard.edu/geneexpression. The number of induced
genes is listed in green, the number of repressed genes is listed in
red, and each number is linked to its corresponding list of genes
and to a numerical representation of transcript levels and
fold induction values. By far the largest category of responsive
genes is genes of unknown function, and the next largest categories
include those for protein and mRNA metabolism. Surprisingly, DNA
repair, DNA replication, and cell cycle progression genes are
only modestly represented in the dataset.
SOMs of the responsive genes in 26 transcriptional profiles.
A
powerful computational method for seeking meaningful patterns in large
datasets can now be applied to transcriptional profiling data
(33). The organization of data into SOMs places genes into clusters that behave similarly across multiple conditions. Using this
algorithm, we organized 26 transcriptional profiles into 18 such SOMs
(Fig. 5A); the 26 conditions are listed
in the figure legend, and at the website each box links to a list of
the genes in that particular SOM. Note that for 3,600 genes, either the transcript levels did not change significantly for any of the 26 treatments or they were expressed at very low levels and were eliminated from the dataset; the remaining 2,610 genes are apportioned to the 18 SOMs. For this analysis, transcript levels are compared across all 26 conditions, and clusters are created based on whether or
not the transcript levels go up or down; the analysis does not weigh
the actual fold differences in transcript levels, but instead notes the
trend. Put another way, genes whose transcript levels change by up to
10-fold in any one of the 26 conditions may be clustered with those
that change up to 100-fold, provided the up and down trend is similar
across all 26 conditions. Such computational organization of
transcriptionally responsive genes is designed to cluster together
genes that respond to some of the same signal transduction events, and
thus genes whose expression may be controlled by the same regulatory
proteins. In other words, it is hoped that such clustering will
identify individual regulons and their regulators.
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FIG. 5.
SOMs of transcript levels for 2,610 genes that
change by threefold or more across 26 exposure conditions. The 26 exposure conditions are as follows: untreated no. 1, untreated no. 2, untreated no. 3, 30 kilorads of irradiation, G1 arrest,
0.1% MMS for 10 min, stationary-phase growth plus 0.1% MMS for 60 min, stationary-phase growth, G1 phase arrest plus 0.1%
MMS for 60 min, 0.1% MMS for 30 min, 0.2% MMS for 60 min, S-phase
arrest plus 0.1% MMS for 60 min, G2 phase arrest plus
0.1% MMS for 60 min, 0.1% MMS for 60 min no. 1, 0.1% MMS for 60 min
no. 2, 0.05% MMS for 60 min, 0.1% MMS for 60 min no. 3, 5 mM
t-BuOOH, 200 µM BCNU, 4NQO at 8 µg/ml, MNNG at 27 µg/ml, MNNG at 6.7 µg/ml, S-phase arrest, 4NQO at 2 µg/ml,
G2 arrest, and MMC at 2 µg/ml. (A) A total of 2,610 genes
were grouped into 18 clusters as described in the text, and the average
expression profile for the genes in each cluster is represented; the
number of genes in each cluster is indicated. The cluster containing
the MAG1 gene is highlighted. The 18 clusters are numbered
left to right and top to bottom. (B) Data from SOMs are converted to
color representation as described in the legend to Fig. 3, and fold
changes are compared to the average transcript levels in three
untreated cultures. Lane labels indicate cell cycle phase
(G1, G2, S, or stationary [STAT]), agent,
concentration, and time (in minutes). The treatments are clustered
based on hierarchical methods to group treatments that produce similar
profiles together, and their correlation is represented by the
dendogram. (C) Distribution of genes containing a MAG1
URS2-like element among the 18 clusters.
|
|
Figure
5B presents a diagrammatic representation of the 18 SOMs, but
this time the fold changes in transcript levels are presented
as they
relate to the average transcript levels observed in the
three untreated
log-phase cultures (see Fig.
1). The individual
transcriptional
profiles are also arranged so that those that
are most similar to each
other lie next to each other, and the
extent of their relatedness is
indicated by the dendogram above
the figure. Accordingly, almost all of
the MMS-induced profiles
lie together, with the exception that
the untreated stationary-phase
profile sorts with this group, as
mentioned above (Fig.
3b). Note
that the profiles induced by the
two agents that, like MMS, are
strong inducers of protein
degradation and amino acid metabolism
genes (BCNU and
t-BuOOH; see Table A at
www.hsph.harvard.edu/geneexpression)
sort next to
the MMS-induced profiles. Again, it is surprising
that the profile
induced upon exposure to the oxidizing agent
t-BuOOH sorts
so far away from that induced by an equitoxic
-radiation
dose, given
that
-irradiation-induced toxicity is thought to
derive in large
part from a flux of oxidative damage (
12,
36).
The SOMs produced a distinct organization of genes. Of the 162 possible
pairwise comparisons of the patterns within each cluster
(Fig.
5A),
only four showed significant similarity (with a correlation
coefficient
greater than 0.75), while the vast majority did not.
This indicates
that for the most part, this analysis divided the
responsive genes into
distinctive groups. For some of the SOMs
derived from this diverse
array of damage-inducible profiles,
it is quite apparent that genes
encoding functionally related
proteins become grouped together. As
examples, SOM1 (Fig.
5A and
B) contains 130 of the 212 responsive
protein synthesis genes;
SOM3 contains 47 of the 62 responsive genes
involved in energy
metabolism; SOM5 contains 13 of the 19 responsive
genes involved
in mating; and SOM13 contains 29 of the 96 responsive
amino acid
metabolism genes. Such grouping of functionally related
genes
agrees well with the results of Eisen et al. (
10), who
first
proposed that clustering the combined data from transcriptional
profiles generated by a large number of treatments would allow
genes to be sorted into functional
groups.
Identification of an MMS-responsive regulon that includes the
MAG1 3-methyladenine DNA glycosylase gene and protein
degradation genes.
Several S. cerevisiae DNA repair and
DNA metabolism genes have long been known to be induced upon MMS
exposure, among them the MAG1 3-methyladenine DNA
glycosylase gene, known for its important role in base excision repair
and in alkylation resistance (2, 3, 31, 39). Indeed, it was
the fact that MMS-responsive genes like MAG1 are important
for protecting cells against carcinogenic alkylating agents that
prompted us to seek the identity of all MMS-responsive S. cerevisiae genes, on the premise that some of these genes may also
be important for alkylation resistance. We were therefore particularly
interested to determine which genes cluster with MAG1 across
the 26 conditions shown in Fig. 5. MAG1 turns out to cluster
with 213 other genes in cluster 14, as highlighted in Fig. 5A and
detailed in Table 1 (and presented
numerically at the website). To our surprise, the largest category of
known genes to cluster with MAG1 were the protein
degradation genes, and only four other DNA repair genes were present in
the cluster. In our initial report (17), we noted that a
large fraction of protein degradation genes were induced by MMS, along
with an equally high fraction of amino acid metabolism genes. We
inferred from these data that MMS exposure might signal the induction
of a program to eliminate and replace alkylated proteins. Here, SOM
analysis indicates a correlation between the regulation of
MAG1 and nearly 50% of the responsive protein degradation
genes (most amino acid metabolism genes cluster elsewhere). This
observation led us to search for common regulatory motifs upstream of
MAG1, upstream of the protein degradation genes, and
upstream of the other genes in cluster 14.
Several years ago we identified an upstream repressor sequence
(URS), called URS2, in the
MAG1 promoter region with
the sequence
GGTGGCGA (
31,
39). Using the
AlignACE and ScanACE programs
developed by Roth et al. (
25),
we now find that sequence motifs
similar to the
MAG1 URS2
can be found upstream of 56 of the 214
genes in cluster 14 and that 33 of these 56 genes are protein
degradation and ubiquitination related.
In total, 68 responsive
genes are involved in protein degradation, and
almost 50% (
33)
are found in this cluster. In order to show
the significance of
this finding, Fig.
5C displays the distribution of
genes with
MAG1 URS2-like elements among the 18 SOMs;
clearly this element
is overrepresented (
P = <10
300) in cluster 14 containing the
MAG1 and
protein degradation
genes.
We and others have pointed out that sequence motifs similar to the
MAG1 URS2 element are found upstream of over a dozen DNA
repair and metabolism genes (
27,
31,
39). These elements
have been referred to as damage repair consensus elements
(
27),
and many but not all genes bearing such elements are
damage responsive.
More recently, a similar putative regulatory
sequence was identified
for numerous genes encoding proteins involved
in ubiquitin-mediated
protein degradation; this was named the
proteasome-associated
control element (PACE) (
22). It is now
clear that damage repair
consensus elements can be separated into two
different sequence
motif groups, one of which is
indistinguishable from the PACE
sequence motif group and which
includes the
MAG1 URS2 element
(P. Estep G. Church,
unpublished data). A protein that binds specifically
to the PACE
sequence motif was identified by one-hybrid analysis
as Rpn4
(
22), a protein thought to be associated with
proteasomes
(
13,
14). It appears that Rpn4p binds the PACE
sequence to
serve as a transcriptional activator (
22).
Here we characterize an
rpn4 deletion strain for its ability
to induce
MAG1 transcript levels in response to MMS. Figure
6a
shows the dramatic loss of
MAG1 MMS inducibility in the
rpn4 deletion
strain, and as a result the
rpn4 deletion strain turns out
to
be MMS sensitive, although not as sensitive as a
mag1
deletion
strain (Fig.
6b). That the
MAG1 URS2 element
behaves as a repressor
binding site (
31) does not
necessarily exclude Rpn4p's behaving
as an activator at this site; our
current model predicts that
Rpn4p and a putative repressor compete for
binding at the GGTGGCGA
sequence. We also monitored the MMS
inducibility of two other
genes that contain the
MAG1 URS2
sequence motif. The loss of Rpn4p
caused a dramatic loss of
inducibility for the
RAD23 nucleotide
excision repair gene
and attenuated the inducibility of the
PRE2 proteasome
subunit gene. Rpn4p thus influences the regulation
of genes in at least
three different pathways, namely, base excision
repair, nucleotide
excision repair, and protein degradation. Note
that two other
MMS-inducible genes, neither of which localized
to cluster 14, are
totally unaffected by the absence of the Rpn4
transcriptional activator
(Fig.
6a).
View larger version (47K):
[in this window]
[in a new window]
|
FIG. 6.
Characterization of an rpn4 deletion strain.
(a) Northern analysis of wild-type (WT) and rpn4 cells
treated with 0.1% MMS for the indicated times. Blots were probed for
expression with MAG1-, RAD23-, PRE2-,
RNR3-, and SNZ1-derived probes. (b)
Colony-forming ability of wild-type, mag1, and
rpn4 cells was measured after 1 h of exposure to
0.1, 0.2 and or 0.3% MMS. (c) SOM clustering of transcript changes in
a rpn4 deletion strain versus wild (WT), in wild-type
cells treated with 0.1% MMS, and in rpn4 cells treated
with. 0.1% MMS. (d) Relative transcript levels of the 44 genes
containing a MAG1 URS2-like sequence in wild-type (WT) and
rpn4 cells with and without 0.1% MMS for 1 h. Black
lines show 21 genes from cluster 3, blue lines show 12 genes from
cluster 6, and orange lines show 11 genes from other clusters.
|
|
Rpn4p influences the basal and damage-responsive expression of many
genes.
We find that S. cerevisiae transcriptional
profiles change dramatically in the absence of Rpn4p, both with and
without MMS exposure. Lane 1 in Fig. 6c displays transcript level
changes in the untreated rpn4 deletion strain compared to
its untreated wild-type parent. A total of 350 genes are downregulated,
suggesting that Rpn4p affects transcriptional activation, and an even
larger group of genes, 389, are upregulated, suggesting that Rpn4p
affects transcriptional repression. Lanes 2 and 3 in Fig. 6c depict
MMS-responsive genes in wild-type and rpn4-deleted cells,
respectively, treated with 0.1% MMS for 60 min. Extensive differences
between the two profiles are quite apparent, and both the upregulation
and downregulation of transcripts are affected by the loss of Rpn4. The
data in Fig. 6c were organized into 12 SOMs, and the multiple effects
of losing the Rpn4 regulatory protein can be summarized as follows. (i) A total of 230 genes that are not MMS responsive in wild-type cells
become susceptible to repression by MMS (cluster 2) and 85 become
susceptible to induction by MMS (cluster 5); (ii) 461 genes become
refractory to MMS induction (clusters 3, 8, and 10); (iii) 333 genes
become more sensitive to MMS repression (clusters 4 and 7) and 455 become more sensitive to MMS induction (clusters 9 and 12); and (iv)
660 genes show little difference in their response to MMS despite the
fact that their basal-level expression changed in
rpn4-deleted cells (clusters 1, 6, and 11).
For the group of 213 genes that clustered with
MAG1 (cluster
14, Fig.
5A), 56 had an upstream
MAG1 URS2-1ike sequence; 44
of these 56 appear in the profiles shown in Fig.
6c because their
expression was affected by Rpn4p. The relative expression of all
44 genes is shown in Fig.
6d, and the genes are grouped into three
categories based on their distribution in Fig.
6c. Shown in black
are
21 genes from cluster 3 (which contains
MAG 1), in blue are
12 genes from cluster 6, and in orange are the remaining 11 genes
from
four separate clusters. The following conclusions can be
made: on
average, their basal expression is lower in the
rpn4 deletion strain than in the wild-type strain, and on average,
the
absence of Rpn4p renders these genes less MMS inducible. Presumably
the
constellation of other transcription factors at each promoter
determines how Rpn4p influences
transcription.
Finally, two DNA nucleotide excision repair genes,
RAD23 and
SSL2, display another intriguing link to the
ubiquitin-mediated
proteasome degradation pathway. First,
RAD23 and
SSL2 are coregulated
with
MAG1 and protein degradation genes. Second, Rad23p has an
N-terminal ubiquitin-like domain that interacts with the 26S proteasome
and a C-terminal domain that interacts with Rad4p (
29). The
Rad23p-Rad4p complex in turn interacts with TFIIH (which contains
Ssl2p), the transcription initiation factor known to be required
for
nucleotide excision repair (
15). Thus, just as regulation
of
RAD23, SSL2, and the proteasome genes is transcriptionally
linked, their products are physically linked via protein-protein
interactions. Moreover, recent in vitro and in vivo evidence
demonstrates
that such protein interactions are important for optimal
nucleotide
excision repair activity (
26). Since the
transcription of three
DNA glycosylase genes (
MAG1, NTG1,
and
NTG2) is also coregulated
with proteasome genes, it is
tempting to speculate that optimal
base excision repair is connected in
a similar way to proteasome
function.
Identification of several known and several putative control
elements upstream of damage-responsive genes.
The promoter regions
for the genes in each of the 18 clusters identified by SOMs in Fig. 5A
were analyzed by the AlignACE program (25) for common
sequence motifs, and Table 2 lists the
consensus sequence for each motif with a MAP score of >10. This score
is an internal metric used to determine the significance of an
alignment. AlignACE searches in unaligned sequences for conserved DNA
motifs and scores each motif based on the alignment and on the
frequency of occurrences in intergenic regions. Motifs were considered
significant if their MAP score was greater than 10 and if their
distribution was significantly enriched in a particular cluster. Nine
significant sequence motifs were identified, of which five are bound by
known factors.
Five of the sequence motifs have known binding factors, namely, Rpn4p
(discussed above), Rap1p (
19), Hap2/3/4/5p (
24),
Abf1p (
7), and Ste12p (
40). Rap1p regulates
ribosomal protein
gene transcription (
19), and accordingly
Rap1 binding sites
were found upstream of 45% of the genes in cluster
1 (Fig.
5A),
where most of the ribosomal protein genes sort. Indeed, a
consensus
Rap1p binding sequence was recently determined by a
systematic
search of all upstream ribosomal protein transcription start
sites
(
18) that turns out to be identical to the motif
identified
here by the blind AlignACE search of our dataset. The
HAP2/3/4/5p
binding complex is important for the transcription of many
mitochondrial
proteins (
20); 10% of the genes in cluster 3 contain a HAP2/3/4/5p
binding site, more than half of which turn out to
be involved
in mitochondrial functions, including ATP synthesis,
oxidative
phosphorylation, and respiration. Abf1p binds to a
sequence motif
present in replication origins, promoters of rRNA genes,
and other
genes involved in translation and glycoylsis and at mating
type
silencing sequences (
6); cluster 4 contains a
concentration
of RNA metabolism genes and translation genes. Ste12p is
a transcription
factor for yeast mating genes and associated cell cycle
regulation
genes (
40), and these sites are found in 16% of
the cluster
5 genes; most of the mating genes sort to cluster 5. The
factors
that bind the remaining four motifs (if any) remain to be
identified.
Concluding comments.
Exploring transcriptional profiles is
inherently descriptive. For S. cerevisiae, most of the
transcriptional profiling carried out to date describes changes that
occur upon specific alterations in growth conditions or upon specific
alterations in genotype (4, 5, 9, 17, 18, 32, 35). Here we
describe changes in transcriptional profiles that take place when cells
are exposed to a reactive chemical or physical agent, such that
virtually every molecule in the cell is at risk of being altered in
some way. In retrospect, we should perhaps not have been surprised by
the fact that over one third of S. cerevisiae's entire gene repertoire can respond to the deluge of damage. Nevertheless, the
results were surprising, and they challenge us to determine what roles,
if any, such a myriad of transcriptional changes play in protecting
cells against inevitable exposures to carcinogenic agents.
One way to make sense of global transcriptional responses is to break
them down into smaller components, by identifying individual
regulons
and their regulators. Ultimately, manipulating each regulon
to alter
the response component by component should help to reveal
their in vivo
roles. Despite the complexity and the sheer volume
of information
contained in global transcriptional profiles, elegant
computational
methods can unveil patterns in the data. In this
study, these patterns
led us to genetically define a novel MMS-responsive
regulon that is
controlled, at least in part, by the proteasome-associated
protein
Rpn4p. Moreover, the Rpn4p binding site was only one of
nine sequence
motifs identified upstream of the damage-responsive
genes. Among the
remaining eight motifs, four are known to be
bound by previously
characterized transcription factors, and four
warrant further
investigation. In this way, it may be ultimately
possible to
systematically study each component of the complex
transcriptional
response of eukaryotes to carcinogenic agents.
Perhaps more important
will be subsequent determination of the
relative importance of each
component in protecting against the
cytotoxic, mutagenic, and thus
carcinogenic effects of the kinds
of damaging agents used in this
study.
It is important to note that the transcriptional profiles from all the
diverse exposures were required in order to generate
the SOMs that link
the proteasome and the DNA repair genes. If
some of the profiles are
omitted, the apparent connection between
protein degradation and DNA
repair is lost. This underscores the
power of combining the information
from numerous diverse treatments
in order to generate informative
patterns in the
data.
The presentation of enormous datasets associated with transcriptional
profiling in conventional publications must of necessity
be limited to
describing patterns and trends in the data, rather
than discussing the
identity of every transcript whose expression
is affected. Such
patterns and trends hold the promise of identifying
novel biological
pathways, elucidating how pathways are regulated,
assigning to genes of
unknown function a known or probable function,
and ultimately (in
conjunction with proteomics and other emerging
techniques) elucidating
how all the molecular components of cells
integrate to make a living
organism. However, in order to understand
the final integrated picture,
the identity of each gene whose
changing expression produces the
patterns and trends must ultimately
be considered. It is therefore
important that the information
be scrutinized by experts in many
different areas of molecular
biology. Here we have inspected our
dataset from the perspective
of DNA repair in general and DNA
alkylation repair in particular.
We hope that others will inspect our
data (at
http://www.hsph.harvard.edu/geneexpression)
from
the perspective of their own highly specialized areas of
expertise.
| |
ACKNOWLEDGMENTS |
This work was supported by National Institutes of Health
grant RO1 CA5502 to L.D.S. and ONR grant N00014-97-1-0865 to
G.M.C. S.A.J. was supported by National Institutes of Health
training grant CA09078 and NRSA grant CA81744. The Affymetrix academic user program was supported in part by National Institutes of Health grant PO1-HG0132. L.S. was a Burroughs Wellcome Toxicology Scholar.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Cancer Cell
Biology, Harvard School of Public Health, 665 Huntington Avenue,
Boston, MA 02115. Phone: (617) 432-1085. Fax: (617) 432-0400. E-mail: lsamson{at}hsph.harvard.edu.
| |
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