Received 6 July 2001/Returned for modification 17 August
2001/Accepted 10 September 2001
In an effort to identify sets of yeast genes that are coregulated
across various cellular transitions, gene expression data sets derived
from yeast cells progressing through the cell cycle, sporulation, and
diauxic shift were analyzed. A partitioning algorithm was used to
divide each data set into 24 clusters of similar expression profiles,
and the membership of the clusters was compared across the three
experiments. A single cluster of 189 genes from the cell cycle
experiment was found to share 65 genes with a cluster of 159 genes from
the sporulation data set. Many of these genes were found to be
clustered in the diauxic-shift experiment as well. The overlapping set
was enriched for genes required for rRNA biosynthesis and included
genes encoding RNA helicases, subunits of RNA polymerases I and III,
and rRNA processing factors. A subset of the 65 genes was tested for
expression by a quantitative-relative reverse transcriptase PCR
technique, and they were found to be coregulated after release from
alpha factor arrest, heat shock, and tunicamycin treatment.
Promoter scanning analysis revealed that the 65 genes within this
ribosome and rRNA biosynthesis (RRB) regulon were enriched for two
motifs: the 13-base GCGATGAGATGAG and the 11-base TGAAAAATTTT consensus
sequences. Both motifs were found to be important for promoting gene
expression after release from alpha factor arrest in a test rRNA
processing gene (EBP2), which suggests that these
consensus sequences may function broadly in the regulation of a set of
genes required for ribosome and rRNA biosynthesis.
 |
INTRODUCTION |
Ribosome biosynthesis is a complex
and demanding process that depends directly upon multiple metabolic
pathways, including the activities of three different RNA polymerases
(reviewed in references 21 and 27). In
Saccharomyces cerevisiae there are 137 ribosomal protein
genes (RP genes), and they are transcribed by RNA polymerase II to
yield 78 ribosomal proteins. Because the RP genes are transcribed at
such a high level, together they account for nearly 50% of the total
RNA polymerase II-mediated transcription initiation events
(18). The 25S, 18S, and 5.8S rRNAs are synthesized by RNA
polymerase I, first as a large 35S transcript that subsequently gets
processed into the three smaller, mature species. Synthesis of the 5S
rRNA is distinct from the other rRNAs and is carried out by RNA
polymerase III. In order to achieve the high levels of rRNA production
that are required during rapid cell division, yeast cells contain
roughly 150 repeats of the rRNA genes in a tandem array on chromosome
XII. Together, these repeats represent 10% of the genome, and rRNA
production alone accounts for some 60% of the total cellular transcription.
Ribosome biogenesis also depends upon the activities of a large number
of protein and RNA molecules that are not themselves components of the
final ribosome. The complex processing pathway that converts the 35S
precursor rRNA into the mature 25S, 18S, and 5.8S rRNA species requires
a multitude of factors, including RNA endonucleases, exonucleases, RNA
helicases, base modification enzymes, and small nucleolar RNAs
(24). Many of these processing factors are nucleolar
proteins that were identified through the characterization of mutants
that exhibit defects in ribosome biosynthesis. For example, Ebp2p is an
essential, nucleolar protein that is required for processing of the 27S
pre-rRNA (13). Temperature-sensitive ebp2-1
mutants become depleted of the mature 25S and 5.8S rRNAs at the
restrictive temperature, and this diminution leads to a decline in
ribosome production and the cessation of cell division. Similarly,
there are dozens of other genes whose essential functions relate to the
roles they play in rRNA biosynthesis.
Given the importance of ribosome biogenesis to the total economy of
cellular metabolism, it is perhaps not surprising that cells have
evolved mechanisms to regulate this process. Yeast cells can modulate
ribosome production in response to nutrient availability, heat shock,
and defects in the secretory pathway (27). The major
mechanism whereby cells effect this regulation is through
transcriptional control, and both heat shock and secretory defects
cause a rapid repression of rRNA and RP gene transcription (18,
19). Most RP gene promoters contain two Rap1p binding sites
(17) and Rap1p can act both as an activator and as a
silencer of transcription (20). Although promoter swap
experiments have demonstrated that the Rap1p binding sequences from the
RPL30 promoter are sufficient to confer the repression
response when placed upstream of the ACT1 gene, they are not
the only cis-acting elements involved in this response
(18).
The recent development of microarray hybridization technologies has
provided the opportunity to investigate the regulation of gene
expression on a genome-wide scale (10). By this approach researchers have been able to compile very large data sets that include
transcription profiles for nearly all of the known yeast genes. These
data sets include expression profiles derived from cells proceeding
through the cell cycle, as well as expression responses to external
stresses and drug treatments (5, 7, 11, 23). Analysis of
these data sets has identified clusters of genes that exhibit similar
expression profiles. The members of these clusters could potentially
represent common targets of a particular transcriptional control
pathway, such as genes containing the MCB element in their promoters.
Alternatively, the clusters could represent sets of genes that are
regulated by different mechanisms to achieve a coordinated response to
a particular cellular requirement or stimulus. In either case, the
identification and characterization of these clusters provide insights
into how cells coordinate and regulate gene expression networks in
response to diverse stimuli.
In this study we have used microarray expression data (7, 9,
23) to identify a novel cluster of coregulated genes. This
regulon is enriched for genes that encode for proteins that have been
implicated to play a role in RNA metabolism and rRNA processing.
Promoter analysis reveals that this set of genes is highly enriched for
two sequence motifs that are preferentially located between 50 and 200 bp upstream of their respective translation initiation sites.
Site-directed mutagenesis was used to demonstrate that both promoter
elements were important in vivo for regulating the expression of the
rRNA processing-related EBP2 gene after release from alpha
factor arrest.
| |
MATERIALS AND METHODS |
Strains and media.
The yeast strains and plasmids used in
this study are described in Table 1.
Standard yeast genetic and molecular biology techniques were used
throughout (1, 12), and a list of the oligonucleotides
used here can be found in Table 2.
Cluster analysis.
The microarray time-series data was
analyzed by using the Partitioning Around Medoids (PAM) algorithm
provided by the SPLUS statistical analysis software package
(22). PAM is a variant of the well known "k-means"
cluster algorithm for grouping multidimensional data. The relative
expression measurements at T time points for each gene on
the microarray correspond to a single point in a T-dimensional space. Nearby genes in this
T-dimensional space will have similar expression profiles
over time. The goal of the cluster algorithm is to identify the groups
of points (genes) that are close together but far from other groups.
The user specifies the number, k, of groups or clusters to
be found and then the program automatically optimizes the membership of
the clusters to minimize the distance between members of the cluster
and to maximize the distance from other clusters (see reference
15 for details.).
Before application of the algorithm, the large group of genes that
remain relatively constant across each experiment were removed. The
cutoff level was chosen so that ca. one-half of the 6,000 genes would
be included in the final cluster analysis. This cutoff varied from the
requirement that the relative expression in at least one time point
change by 1.65-fold for the alpha-pheromone experiments to a 2-fold
change in the sporulation experiments.
The PAM algorithm was then applied to the (ca. 3,000) gene time-series.
The "distance" between genes was determined by the Euclidean metric
applied to the log-ratio time series. The log ratio was defined as the
log2(gene expression value at a given time
point/control gene expression value). We searched for k = 24 clusters so that each cluster would contain roughly 100 to 150 genes. For each experiment the clusters were graphically characterized by the time series of the most representative gene (the "medoid") of the cluster, and a list of genes in each cluster was generated along
with a quantitative measure of the "strength" of their membership. A complete list of the cluster membership can be obtained from the
authors or at the ribosome and rRNA biosynthesis (RRB) regulon website
(http://mmcalear.web.wesleyan.edu/rrb-regulon/).
Statistical analysis of clusters across experiments.
To
estimate the numbers of overlapping genes from a purely random sampling
of N 
3,000 genes, we first computed the
probability that any particular set of m genes found in a
cluster in the first experiment would be found in a cluster of size
n2 in the second experiment:
|
(1)
|
However, since there are
different ways of choosing a particular set of m
genes from the n1 genes in the
original cluster in the first experiment, the probability that a
cluster with n1 genes in the first
experiment shares m genes with a cluster of size
n2 in the second experiment can be
estimated by the equation:
|
(2)
|
Note that equation 2 is an overestimate, since it double counts
the occurrence of the same m + 1, m + 2, ... genes in both clusters. As a consequence, this estimate erroneously
gives a probability of >1 when m is small. Finally, since
there are nc = 24 clusters in the
first experiment that can be compared with any of the
nc = 24 clusters in the second
experiment, P2(N,
n1, n2, m) should be multiplied
by a factor of nc2 to
estimate the odds of finding m genes in common between any two clusters in the two experiments by chance.
By using this very conservative estimate for the random probability
that two clusters from two different experiments share m
genes, we can assess whether an observed overlap could have occurred by
chance or indicates a systematic relationship among the common genes.
For example, equation 2 predicts that the random probability of the
overlap of m = 65 genes between two clusters of size
n1 n2 125 sampled out of ca. 3,000 genes is 1.2 × 10
62 which is much, much
smaller than 1/nc2
2 × 103. These estimates indicate
that only an overlap of ca. 16 genes could be attributed to chance.
Alpha factor arrest, heat shock, and tunicamycin treatment.
Yeast strains were grown to a density of 3 × 107 cells/ml in either yeast
extract-peptone-dextrose (YPD; pH 5.5) (yMM13 and yMM177) or
synthetic complete (SC)-Leu liquid medium (yMM354, yMM355, and yMM373)
at 30°C. The cultures were arrested by the addition of alpha factor
for 2 h at a final concentration of 5 µg/ml. The cells were
washed free of the alpha factor and released into fresh medium at
30°C. At the indicated time intervals, 10-ml aliquots of the cultures
were collected on ice, harvested with a table-top centrifuge, and
flash-frozen in a dry ice-ethanol bath.
The heat shock experiment was performed by growing strains in either
YPD (yMM13 and yMM177) or SC-Leu liquid medium (yMM354, yMM355, and
yMM373) at 25°C to ca. 3 × 107
cells/ml. The cells were harvested by centrifugation and then resuspended in YPD or SC-Leu medium at 37°C. Samples were incubated at 37°C, and aliquots were collected every 10 min and then
flash-frozen in dry ice and ethanol.
For the tunicamycin treatment, cells were grown in liquid YPD medium to
a concentration of 3 × 107 cells/ml, and
then tunicamycin was added to a concentration of 1 µg/ml. Cultures
were incubated at 30°C, and samples were collected as outlined above.
RNA preparation and relative quantitative RT-PCR.
Total RNA
was prepared from the samples by using a hot acidic phenol extraction
protocol (1). The concentrations of the RNA preparations
were determined by spectrophotometry, and aliquots were tested by
electrophoresis on 1.2% agarose-2.2 M formaldehyde gels. The samples
were treated with DNase I (Ambion Co.) and tested by PCR to confirm
that there was no contaminating DNA. Then, 2 µg of RNA from each
sample was incubated with reverse transcriptase (RT) to yield cDNA as
per the instructions in the Ambion RETROscript kit. PCRs were then
performed as described by Ambion's QuantumRNA Universal 18S
rRNA Internal Standards Kit. Briefly, for each gene, PCRs were
performed to determine the linear range of amplification that would
permit a quantitative assessment of expression levels (typically
between 14 and 24 amplification cycles). We then empirically determined
the 18S rRNA primer-to-competimer ratio that would allow for an
appropriate level of amplification for the 18S rRNA internal control.
PCRs containing two pairs of oligonucleotides (one for the 18S rRNA
control and one for the gene of interest) were performed in the
presence of [
-32P]dATP, and the fragments
were separated by gel electrophoresis. The PCR products were quantified
by PhosphorImager analysis and ImageQuant software. For each time point
of each gene series the number of counts in the PCR product of interest
were divided by the number of counts in the internal 18S control.
Expression levels across a time course were then normalized to a mean
of expression level of 1.
Construction of the motif 1 and motif 2 promoter element
substitution strains.
The two promoter motif elements located
upstream of the EBP2
N62 allele were altered by a
PCR-based substitution strategy. Briefly, pairs of oligonucleotides
were used to amplify separate DNA fragments that flanked the motif
sequences within plasmid pMM147. Each of these fragments contained
altered sequences including an AatII site at one end in
place of the normal motif sequences. The two DNA fragments were
digested with either AatII and NcoI or
AatII and HindIII to produce compatible
sticky ends. These two inserts were then ligated with an
NcoI and HindIII fragment from plasmid pMM147
and transformed into Escherichia coli. The resulting motif 1 and motif 2 substitution plasmids (pMM322 and pMM341, respectively)
were recovered, sequenced, and transformed into yeast strain yMM13.
| |
RESULTS |
Clustering of microarray expression profiles.
Genome-wide
changes in gene expression have been monitored by microarray analysis
as yeast cells progress through a variety of conditions, including
traversal through the cell cycle (23), through sporulation
(7), and through diauxic shift (9). Each of
these studies produced large data sets containing time course
expression profiles for more than 6,000 yeast genes. In the effort to
identify groups of genes that share similar transcription patterns,
these data sets were independently analyzed by using a partitioning
cluster algorithm (described in Materials and Methods). This algorithm
is designed to divide the data set into groups of genes that are
similar to one another and yet distinct from other groups. As initial
parameters for this partitioning, we limited the analysis to the
expression profiles that varied the most within each data set (ca.
3,000 genes in each case), and we set the number of clusters to be 24. Although somewhat arbitrary, these parameters were chosen to exclude
those genes that varied the least over the respective time courses and
to allow for the potential discrimination of many different expression
profiles. The resulting clusters typically contained 75 to 175 genes
each, and the relative "strengths" of membership for each gene
within a cluster could be assigned based on the similarity of their
profiles to the most representative or medoid gene.
Analysis of the expression profiles and memberships of the clusters
revealed that several sets overlapped with groups of genes that were
previously identified to have distinct time courses in the
transcriptional programs for the cell cycle, sporulation, and diauxic
shift (Fig. 1). For example, the 24 clusters derived from the alpha factor arrest and release experiment
(23) included seven clusters (totaling 523 genes) that
clearly oscillated through two full cell cycles. With a few exceptions,
the memberships of these clusters corresponded to those previously
identified as being maximally expressed within the
G1, S, G2, M, and
M/G1 stages of the yeast cell cycle
(23). One such cluster contained the histone genes, and
they exhibited the characteristic expression profile of genes that peak
in S phase. The remaining 17 clusters contained genes that varied by at
least 1.65-fold at at least one time point but did not exhibit a strong
cell cycle dependence.
View larger version (44K):
[in this window]
[in a new window]
|
FIG. 1.
Yeast gene expression profiles derived from microarray
experiments were analyzed by a partitioning algorithm and grouped into
24 clusters. One gene expression data set came from cells after release
from alpha factor arrest (23) (A), and another was derived
from cells progressing through sporulation (7) (B).
Expression profiles of two clusters from each set of 24 groups are
depicted, including the number of genes within the cluster and the
expression profiles of representative members. The gene expression
profile corresponding to the medoid of each cluster is indicated by
"M."
|
|
Similarly, the grouping of the sporulation and diauxic-shift data sets
identified clusters of genes that have previously been described as
being important for the execution of these pathways. Our sporulation
cluster number 24 contained 34 genes, and it included members of the
Early I gene set recognized (7) as being important for
chromsome synapsis or recombination (i.e., the ZIP1,
DMC1, HOP1, and IME2 genes). In
addition to these previously identified gene sets, there were numerous
gene clusters that have not previously been characterized.
Progression through the cell cycle, sporulation, and diauxic shift all
involve significant changes in the metabolic activities of the cell. In
order to identify sets of gene that could potentially be important for
more than one of these transitions, we investigated whether any of the
clusters from one experiment shared an unusual number of genes with a
cluster from another experiment. We reasoned that this approach could
potentially identify gene sets whose regulation is important for
general aspects of yeast cell metabolism. In the case where there was
the largest degree of overlap, a single cell cycle cluster of 189 genes
shared 65 genes with a sporulation cluster of 159 genes. In both cases,
the clusters were characterized by expression profiles that varied
transiently and did so at the beginning of their respective metabolic
transitions. The large degree of overlap between these two gene sets
was striking, since for all of the other cluster pairs, no two groups
had more than 24 genes in common. The probability that these two
clusters from two different experiments could have 65 genes in common
by chance, estimated by using equation 2 (see Materials and Methods) is
P(3,000, 189, 159, 65) 2 × 1038.
In addition, many of these 65 genes were also found in two similar
clusters of genes in the diauxic-shift experiments that showed a sharp
decrease in expression at the start of the diauxic shift
(9). The original cluster of 189 genes from the alpha factor release experiment shared 62 genes with the two diauxic-shift clusters with a combined total of 211 genes. The probability of this
large overlap occurring by chance is estimated to be
P(3,000, 189, 211, 62) 2 × 1025. Therefore, the overlapping set of 65 genes appear to be coregulated across different growth conditions.
A set of rRNA biosynthesis genes are coregulated.
Since the
theoretical calculations indicated that the set of 65 genes were highly
unlikely to have been cogrouped into two distinct clusters by chance,
we investigated this subset further to ascertain whether there was a
physiologically relevant basis for their apparent coregulation. To do
this, we first scanned the gene set to determine whether the members
have been implicated to function in common areas of metabolism. There
is little known about nearly half of the 65 genes in this set; however,
for those genes that have reported activities, many encode proteins
that function in aspects of rRNA metabolism (Table
3). There are six known or putative RNA
helicases, three subunits of either RNA polymerase I or III, and twelve
other proteins that have been indicated to function in rRNA processing
or ribosome biogenesis. This observation is significant, since in a
random selection, less than 10% of the characterized genes would be
expected to have functions related to rRNA biosynthesis. Of particular
interest to us was the coinclusion of the EBP2 and
RRS1 genes in this gene set. Ebp2p and Rrs1p interact, and
both are essential proteins that function in the rRNA processing
pathway (13, 25). Because this set of 65 coregulated genes
is enriched for genes that are required for ribosome and rRNA
production, we suggest that they may represent members of an RRB
regulon. It is worth noting that this set does not include any of the
RP genes themselves.
Although microarray analysis is a powerful technique for measuring gene
expression levels of large sets of genes, this approach is subject to
experimental variations within and between individual hybridizations.
In order to independently measure gene expression levels, we used a
relative-quantitative RT-PCR technique (see Materials and Methods).
Briefly, in this approach RNAs are first converted to cDNAs, and then
gene-specific oligonucleotides are used to quantitatively amplify the
cDNA fragments (reviewed in reference 4). When care is
taken to ensure that the genomic DNA is removed prior to the RT step
and that the amplification reactions fall within a linear range, one
can obtain a representative expression profile (Fig.
2). To account for differences between samples in a time course experiment, an internal 18S rRNA-derived fragment is coamplified for each reaction. Because the consistently high levels of rRNA within the RNA preparations would overwhelm the
amplification reactions, nonamplifying rRNA competimer oligonucleotides are used to dampen the rRNA signal to a level that is comparable to the
mRNA of interest. Once the appropriate competimer ratio and
amplification cycles are determined for a given gene, the relative
transcript abundance can be calculated in relation to the 18S rRNA
levels. It should be noted that the relative mRNA expression levels, as
detected by this analysis, are influenced both by changes in
transcription, as well as mRNA turnover rates.
View larger version (21K):
[in this window]
[in a new window]
|
FIG. 2.
Gene expression levels can be assessed by a
relative-quantitative RT-PCR technique. (A) RT-PCRs with primers
specific to the HHT2 gene were performed on a total RNA
sample derived from a logarithmically growing culture of strain yMM177.
The products were labeled with [-32P]dATP, separated
by gel electrophoresis, and quantified by phosphorimaging analysis. In
order to determine the linear range of the assay, levels of
[-32P]dATP incorporation were measured for up to 26 amplification cycles. (B) RT-PCR assays were performed simultaneously
with primers sets specific to both the HHT2 and 18S rRNA
sequences. Increasing amounts of nonamplifying competimer 18S primers
were added to dampen the 18S amplification reactions down to a level
comparable to the HHT2 reaction.
|
|
The RT-PCR technique was used to assess the expression profiles of a
subset of genes from within and outside of the RRB cluster. Cells were
first arrested with alpha factor, and then samples were collected every
10 min after release into the cell cycle. The efficiency of the arrest
and release protocol was confirmed by monitoring the cells for the
appearance of schmoos and new buds, respectively (data not shown). In
order to validate the RT-PCR technique for obtaining relative mRNA
transcript profiles, we used it to monitor histone (HHT2)
gene expression levels in three independent experiments (Fig.
3). In each case, the
profiles matched the expected pattern for an S-phase-expressed gene,
and they correlated very well with the HHT2 microarray
derived expression profiles. Similarly, we observed that our
measurements of the SWI4 transcript levels were consistent
with microarray data, although, by comparison, our profiles appeared to
be delayed by 5 to 10 min.
View larger version (23K):
[in this window]
[in a new window]
|
FIG. 3.
Gene expression levels change as cells progress through
the cell cycle. RT-PCR assays were performed on RNA samples derived
from yMM13 cells after release from alpha factor arrest. Relative gene
expression levels were determined as a ratio of the expression values
for the gene of interest divided by the 18S values. Expression profiles
for the HHT2 (A), SWI4 (B), and the
EBP2, RRS1, ROK1,
NOP2, and YML093W genes (C) are indicated. Expression
profiles as determined by microarray hybridization studies were
converted to a similar scale and are depicted as indicated for the
HHT2 and SWI4 genes (array).
|
|
Having determined that the RT-PCR technique could reproducibly
corroborate the microarray profiles, we then investigated a sampling of
genes from the RRB cluster. Five genes were chosen for this analysis
(EBP2, RRS1, ROK1, NOP1,
and YML093W), and as expected from this cluster analysis,
they exhibited similar expression profiles (Fig. 3C). Although the five
genes exhibited a transient peak of expression 10 min after release
from arrest, the RT-PCR expression profile was unlike the microarray
data. The microarray profiles indicated that, rather than increase, the
expression levels of these genes transiently declined after release
from arrest. Differences in strain handling procedures could
potentially be responsible for this discrepancy, particularly if one
set of cell harvesting conditions triggers a stress response (see
Discussion). In this regard, the microarray profiles for the RRB genes
in the alpha factor release experiment (23) were similar
to profiles observed when cells respond to stressful conditions
(11). In any case, the sample of RRB cluster members did
exhibit similar profiles, and they were unlike the expression patterns
for the nonmember gene controls.
If the genes of the RRB cluster are members of a regulon, one might
expect them to be coregulated under a variety of conditions. To test
this hypothesis, we monitored gene expression levels following a shift
from 25 to 37°C. Since heat shock has previously been shown to
temporarily repress transcription of the rRNA and RP genes, we reasoned
that it could also impact upon the transcription of the rRNA processing
factors. RNA samples were collected from cells after a shift from 25 to
37°C, and transcript levels were determined by the RT-PCR technique
(Fig. 4A). As a group, the RRB cluster
subset exhibited similar profiles, with a transient drop in expression
that recovered by 30 min after the heat shock. These profiles were
similar to those that have been reported for these genes in microarray
experiments (11) and were unlike the expression profile
determined for the HHT2 gene.
View larger version (30K):
[in this window]
[in a new window]
|
FIG. 4.
RRB cluster members are coregulated following heat shock
and tunicamycin treatment. RT-PCR assays were performed on RNA samples
derived from yMM13 cells after a shift from 25 to 37°C (A) or after
treatment with 1 µg of the drug tunicamycin/ml (B). Heat shock
expression profiles of the HHT2 and EBP2
genes as determined by microarray hybridizations (11)
(array) are also indicated.
|
|
We also tested whether the RRB sample subset was affected by the drug
tunicamycin. Tunicamycin is known to interfere with the yeast secretory
pathway, and this stress subsequently leads to an inhibition of rRNA
biogenesis (19, 25, 26). Gene expression levels were
monitored after cells were treated with tunicamycin, and again the RRB
cluster subset of genes yielded similar profiles. After an initial
rise, there was a gradual decline in expression levels that continued
for at least two more hours (Fig. 4B and data not shown). Since this
expression pattern was not observed for either the SWI6 or
HHT2 genes, it does not represent a general transcriptional
response to tunicamycin treatment.
RRB gene promoters share common sequence motifs.
One mechanism
by which the RRB cluster of genes could be regulated is through their
sharing of common regulatory sequences within their promoters. To
identify any such sequences, we used the MEME program (2)
to scan through the 500 bp of sequence that are immediately upstream of
the coding sequences for each gene member. This program identified two
enriched sequence motifs, each of which was present in at least 41 of
the 65 RRB cluster promoters (Fig. 5 and
Table 3). Motif 1 is a 13-base G(C/A)GATGAG(A/C)TGA(G/A) consensus
sequence that is found 50 times (upstream of 44 genes) within the RRB
cluster promoters. The corresponding P values for each of
these occurrences range from 2.3 × 106 to
3 × 109. Within motif 1 one can recognize
the 11-base GATGAGATGAG tandem repeat sequence. There are 67 perfect
matches to this 11-base sequence within the entire yeast genome, and
remarkably, 21 of these sequences can be found within the 65 RRB
cluster promoters (i.e., upstream of only 1% of the total number of
yeast genes). Typically, motif 1 sequences are located between 50 and
150 bases upstream and on the same strand (60%) of their respective
initiator codons. Motif 2 is the 11-base TGAAAA(A/T)TTTT sequence. It
is present 48 times (upstream of 41 genes) within the RRB
promoters, and it is usually found on the same strand as the ATG codon
(67% of the time). Of the 65 RRB promoters, 29 contain both motif 1 and motif 2 sequences, with a typical arrangement of motif 2 occurring some 15 to 50 bp upstream of motif 1.
View larger version (18K):
[in this window]
[in a new window]
|
FIG. 5.
RRB cluster promoters are enriched for two sequence
motifs that cluster to positions between 50 to 200 bp upstream of their
respective genes. (A) The MEME program was used to identify sequence
motifs that are highly represented within the 500 bp of sequence
immediately upstream of the RRB cluster genes. The consensus sequences
of the two identified motifs are indicated, along with the two examples
of the promoter occurrence set that correspond to the highest and
lowest P values, as defined by the MEME program. (B) The
relative positions of the two motif sequences are represented with
respect to the initiator ATG codons of their respective genes.
|
|
Motifs 1 and 2 are important for the regulation of
EBP2 gene expression in vivo
Given
the high representation of sequence motifs 1 and 2 within the RRB
cluster gene promoters, we sought to determine whether they played a
role in transcriptional regulation in vivo. Since the promoter of the
EBP2 gene contains both of these consensus sequences, we
chose it as a reporter gene. To test the elements, we constructed a
yeast strain that would allow us to simultaneously monitor expression
levels in EBP2 alleles that both contained and that
lacked the consensus sequences (Fig.
6). In this way, our experiments
would contain an internal control consisting of the integrated
wild-type EBP2 gene and promoter. To do this, we constructed a CEN ARS LEU2 plasmid that contained a
62-codon N-terminal EBP2 deletion construct that was
driven by the natural EBP2 promoter. This allele
produced a truncated but functional Ebp2 protein. By choosing an
appropriate single pair of oligonucleotides, we could amplify and
distinguish RT-PCR fragments derived from transcripts of the normal,
integrated EBP2 allele and from the truncated, plasmid-borne EBP2N62 construct (443 and 257 bp,
respectively).
View larger version (25K):
[in this window]
[in a new window]
|
FIG. 6.
A plasmid-borne EBP2 construct can be
used to simultaneously monitor gene expression levels in two
EBP2 alleles. A plasmid-borne EBP2N62
allele was created that lacked the first 62 codons of the
EBP2 gene but retained the natural EBP2
promoter (pEBP2, represented by a boldface line). The
EBP2 primer set used for the RT-PCRs could
simultaneously amplify fragments of different sizes (dashed lines) that
were derived from both the integrated EBP2 and the
plasmid-derived EBP2N62 transcripts. This plasmid was
subsequently modified, replacing the EBP2 promoter
sequences within motifs 1 and 2 (boxed) with heterologous sequences
containing an AatII restriction site (underlined).
|
|
To test whether the plasmid-borne EBP2N62 allele
exhibited a transcription profile that was similar to the wild-type
EBP2 allele, we arrested yeast strain yMM354 with alpha
factor and released it into fresh medium. The transcription profile of
the EBP2N62 construct did very closely match the profile
of the wild-type allele, although the overall normalized levels of the
plasmid-derived transcripts were consistently higher (by ca. 60%) than
those of the integrated allele (Fig. 7A).
This elevated expression of plasmid versus integrated EBP2
alleles could be due to the plasmid being present on average in more
than one copy per cell.
View larger version (39K):
[in this window]
[in a new window]
|
FIG. 7.
Motifs 1 and 2 are important for the regulated
expression of the EBP2 gene after release from alpha
factor arrest. Yeast strains containing either the
EBP2N62, the p-M1 EBP2N62, or the
p-M2 EBP2N62 plasmid-borne alleles were arrested with
alpha factor and released into the cell cycle (A) or else subjected to
heat shock (B). RNA samples were prepared for RT-PCR analysis and the
amplification products derived from the 18S rRNA, the integrated
EBP2, and the EBP2N62 alleles were
separated and quantitated. Each expression time point value represents
the mean expression level derived from at least two experiments.
|
|
Next, we mutated the deletion construct promoter, replacing the motif 1 element with a heterologous sequence that included an AatII
restriction site. This plasmid was reintroduced into yeast creating
strain yMM355, and expression levels after release from alpha factor
arrest were monitored again. In contrast to what was observed for the
natural EBP2 promoter plasmid, the p-M1 EBP2N62 plasmid allele did not exhibit the characteristic
EBP2 expression profile. We observed in three separate
experiments that the transient peak in expression that occurs 10 min
after release from alpha factor arrest was much less pronounced with this allele. We also altered motif 2 sequences and tested that allele
(p-M2 EBP2N62) for expression levels in strain yMM373. Again, motif 2 sequences were found to be important for the
characteristic peak of expression that normally occurs soon after
release from alpha factor arrest. Also, the overall levels of
expression were lower for the p-M2 EBP2N62 allele. The
transcription profiles of the integrated, wild-type EBP2
control allele remained consistent across the three different yeast
strains (i.e., compare the EBP2 profiles across the three
panels of Fig. 7A). Thus, motif 1 and motif 2 sequences are highly
enriched, positionally biased, and functionally important for the
regulated expression of at least one member of the RRB regulon
(EBP2).
In order to investigate the extent to which motif 1 and motif 2 sequences contribute to the regulated expression of the EBP2 gene, the same set of alleles were monitored for expression levels after heat shock treatment (Fig. 7B). In the case of heat shock, the
transcription profile of the EBP2N62 allele did not match as closely the expression profile of the integrated EBP2
allele. The decline in expression after heat shock was less pronounced for the plasmid-borne allele, and the same general expression profiles
were observed for the plasmid alleles lacking motif 1 and 2 sequences.
However, as seen for the alpha factor release experiment, the allele
that lacked motif 2 sequences exhibited a lower overall level of
expression. Therefore, within the context of the alleles tested here,
motif 1 and 2 sequences were found to be most important for promoting
EBP2 expression soon after release from alpha factor arrest.
It is worth considering how the RRB regulon, as defined by this study,
relates to other groups of genes that are coordinately regulated after
changes in environmental conditions. The environmental stress response
group of genes includes subsets of genes that are either induced or
repressed after exposure to a wide variety of conditions, including
osmotic shock, heat shock, nutrient deprivation, hydrogen peroxide, and
diamide or dithiothreitol treatment (11). The large set of
600 genes that are repressed after these stresses include genes
involved in growth related processes, such as nucleotide biosynthesis,
RNA metabolism, protein synthesis, and secretion. The RRB regulon
appears to be a subset of the environmental stress response (ESR)
repression group. Interestingly, most of the RP genes are also found
within the ESR set, although as a group, they exhibit an expression
profile that is distinct from the RRB set under a number of conditions,
including heat shock, diauxic shift, and diamide or dithiothreitol
treatment (Fig. 8). The fact that the
clustering strategy that we used did not result in any RP genes being
included in the RRB gene set also suggests that the regulation of the
RP and RRB genes is distinguishable. However, the overall
transcriptional patterns are consistent between these two sets of genes
and can be understood in terms of strategies that cells use to regulate
cell growth and division in response to varied conditions. It would be
appropriate for cells to increase rRNA and RP synthesis at the onset of
progression through the cell division cycle (i.e., after alpha factor
arrest), as it would be to scale back these processes at the onset of
sporulation or diauxic shift. It will be of interest to determine what
factors regulate this set of genes and how the respective signaling
pathways are coordinated to effect these rapid and genome-wide changes in gene expression.
View larger version (16K):
[in this window]
[in a new window]
|
FIG. 8.
The RRB and RP gene set expression profiles are similar
and yet distinguishable. The average expression profiles for a set of
59 RRB and 128 RP genes are depicted as determined by microarray
hybridization analysis (11) after heat shock (A) or
diamide treatment (B).
|
|
| |
DISCUSSION |
In this study we have used computational analysis of gene
expression profiles to identify a set of coregulated genes that function in various aspects of rRNA metabolism. However, because the
strategy used to define this gene set relied upon both experimental data and computational assumptions, it is likely that additional RRB
genes exist and that some of the listed 65 genes may not have rRNA-related functions. For example, the original expression profile data sets were compiled from single microarray hybridizations on over
6,000 genes for each time point. The inevitable experimental variations
that arise within or between hybridizations could significantly affect
the apparent expression profile for a given gene. Because the RRB
cluster profiles were typified by early and transient gene expression
changes, measurement errors in one or two early expression time points
would be particularly significant. Additionally, by changing the
parameters of the clustering algorithm, one would tend to either merge
(for low k values) or split (for larger k values)
sets of dissimilar expression profiles. The choice to divide the
expression data sets into k = 24 clusters was somewhat arbitrary, and it did produce clusters that contained similar expression profiles. Therefore, rather than being a definitive membership list for the RRB regulon, the identified set of 65 coregulated genes from the overlap of a single cell cycle cluster and a
single sporulation cluster is perhaps best considered as representatives of the RRB regulon.
Even though the exact membership of the RRB regulon as defined in this
study is likely to be incomplete, the identification of this class of
genes is useful. One prediction is that the large fraction of
uncharacterized genes within the RRB cluster may also have rRNA-related
functions. Likewise, it is possible that even the genes reported to
have functions in other aspects of cellular metabolism could also play
a role in rRNA metabolism. For example, the Cbf5 protein, originally
identified as a centromere-binding factor, is now believed to be
involved in rRNA pseudouridylation (16). Similarly, since
translation initiation factor 6 (eIF6) has recently been shown to be
involved in pre-rRNA processing (3), eIF3 could have a
similar role.
One can also investigate other known ribosome and rRNA
metabolism-related genes and determine whether they, too, exhibit
expression profiles characteristic of the RRB cluster members. An
investigation of 24 ribosome and rRNA-related genes that were not among
the original 65 RRB genes revealed that several (i.e.,
DBP8, DIM1, NOP8,
SPB1, NIP7, MTR4, SQT1, and
NMD3) exhibited alpha factor release and sporulation
microarray expression profiles that were similar to the RRB cluster
members. Two genes (XRN1 and RSA1) exhibited
profiles that were unlike the RRB members, and the remaining 14 genes
(RNT1, FAL1, RRP3,
DBP6, NOP4, MAK5, DRS1,
DBP3, RRP5, RAT1, DBP10,
SPB4, RRP7, and NOP3) had expression
profiles that were somewhat similar to the RRB genes. One must be
careful with these categorizations, however, because the microarray
profile for any given gene may contain either widely varying or missing data points that could significantly alter the shape of the expression profile. Although these individual variations may not be as problematic when one considers multiple, large data sets covering thousands of
genes, they are relevant to single gene investigations. In this regard,
the RT-PCR approach can be a useful, independent method for monitoring
changes in mRNA levels.
Although for the most part, we observed that the gene expression
profiles as determined by RT-PCR assays were very similar to those
reported in microarray studies, there was one notable exception. We
consistently found that the expression profiles of the RRB cluster
members peaked 10 min after release from alpha factor arrest. In
contrast, the expression profiles for the same gene set, as determined
by a microarray hybridization method (23), exhibited a
transient decline in expression levels. This discrepancy could reflect
the underlying fundamental differences in the way that the relative
mRNA levels are measured in the two different methodologies, or it
could be due to variations in strains or experimental conditions.
Support for the latter explanation comes from the fact that 59 of the
65 RRB cluster members were identified as being part of the set of 600 genes known collectively as the ESR repression subgroup
(11). This group of genes covers a range of biological
pathways and exhibits expression profiles that declined transiently
upon stressful environmental conditions (see below). Therefore,
depending on the methods used to release cells from alpha factor arrest
(i.e., sample harvesting, medium changes, etc.), one could potentially
trigger a stress response. We suggest that it would be reasonable to
expect cells to increase expression of rRNA metabolism related genes as
they entered the cell cycle. In either case, it is significant that
both the microarray and RT-PCR assays indicated that the expression
profiles of the RRB cluster gene members were similar to one another.
Our results indicate that motifs 1 and 2 are strong candidates for
promoting expression of members of the RRB regulon after release from
alpha factor arrest. We also noted that these two motif sequences are
frequently located adjacent to one another and within 200 bp upstream
of their respective start codons. Sequences related to these two motifs
have been described previously. The G(C/A)GATGAGAT sequence was
recognized as being one of 17 positionally biased and over-represented
sequences in a study designed to identify DNA-binding motifs for novel
yeast transcription factors (14). That same study
identified a subsequence of motif 2, TGAAAA(A/T)TTT (named RRPE),
that was preferentially associated with genes involved in rRNA
processing. Likewise, similar sequences were recognized as potential
regulatory sequences within a group of genes that share a common
response to environmental changes (5), as well as within
the ESR gene set (11). The motif 1 G(C/A)GATGAG(A/C)TGA(G/A) consensus contains a tandem overlapping
repeat of the GATGAG sequence. The related TG(C/A)GATGAG sequence was
previously named the PAC element because it was frequently found
upstream of subunits of RNA polymerase A (I) and C (III)
(8). However, the specific functions of these motifs were
not previously defined, nor have their presumptive binding factors been identified.
In the effort to determine whether motif 1 could stimulate gene
expression within the context of a heterologous promoter, the
corresponding sequences were inserted into a reporter construct bearing
the lacZ gene under control of the minimal CYC1
promoter (6). lacZ expression levels were
monitored by liquid 
-galactosidase assays (1), and it
was observed that motif 1 sequences did not stimulate expression either
in logarithmically growing cells or in cells released from alpha factor
arrest (data not shown). While this result may not provide much insight
into the function of motif 1 (since there are many reasons why motif 1 might not be active in this artificial context), it does suggest that
the one-hybrid approach could potentially be used to identify the putative factor that binds to motif 1 sequences. If this putative factor could bind to motif 1 and act as a transcriptional activator on
its own, one would not be able to select for such an activity from a
one-hybrid fusion library.
The dual-allele relative quantitative RT-PCR strategy that we have
developed should be generally useful for testing putative promoter
elements for their activities under a variety of conditions. The
advantage of this system is that it allows for the simultaneous assessment of expression levels for both a wild-type control and a
mutated allele of a given gene at the same time. The mutated allele
could be integrated instead of plasmid-borne and could contain
deletions, insertions, or even novel restriction sites that would allow
its RT-PCR product to be distinguished from the wild-type product. Once
the appropriate alleles and strains were generated, one could test
expression levels under a variety of conditions. Given the increasing
potential for mapping out genetic networks based on genome-wide
expression and promoter sequence analysis data (5, 10, 28,
29), it will be useful to have a reliable, internally
controlled, and flexible experimental approach to test putative
promoter elements for their functions in individual test genes in vivo.
We thank Susan Baserga, David Stillman, and members of the
McAlear lab for helpful discussions.
This study was supported by a National Science Foundation (NSF) Career
Grant (MCB-9875283) to M.A.M. and by NSF grant PHY-9900746 to
R.V.J. C.W. was supported in part by a Research Experiences for
Undergraduates Grant from the NSF.
| 1.
|
Ausubel, F. M.,
R. Brent,
R. E. Kingston,
D. D. Moore,
J. G. Seidman,
J. A. Smith, and K. Struhl (ed.).
1993.
Current protocols in molecular biology.
Wiley Interscience, New York, N.Y.
|
| 2.
|
Bailey, T. L., and C. Elkan.
1994.
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
Proc. Int. Conf. Intell. Syst. Mol. Biol.
2:28-36[Medline].
|
| 3.
|
Basu, U.,
K. Si,
J. R. Warner, and U. Maitra.
2001.
The Saccharomyces cerevisiae TIF6 gene encoding translation initiation factor 6 is required for 60S ribosomal subunit biogenesis.
Mol. Cell. Biol.
21:1453-1462[Abstract/Free Full Text].
|
| 4.
|
Bustin, S. A.
2000.
Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays.
J. Mol. Endocrinol.
25:169-193[Abstract].
|
| 5.
|
Causton, H. C.,
B. Ren,
S. S. Koh,
C. T. Harbison,
E. Kanin,
E. G. Jennings,
T. I. Lee,
H. L. True,
E. S. Lander, and R. A. Young.
2001.
Remodeling of yeast genome expression in response to environmental changes.
Mol. Biol. Cell
12:323-337[Abstract/Free Full Text].
|
| 6.
|
Chang, Y. C., and W. E. Timberlake.
1993.
Identification of Aspergillus brlA response elements (BREs) by genetic selection in yeast.
Genetics
133:29-38[Abstract].
|
| 7.
|
Chu, S.,
J. DeRisi,
M. Eisen,
J. Mulholland,
D. Botstein,
P. O. Brown, and I. Herskowitz.
1998.
The transcriptional program of sporulation in budding yeast.
Science
282:699-705[Abstract/Free Full Text].
|
| 8.
|
Dequard-Chablat, M.,
M. Riva,
C. Carles, and A. Sentenac.
1991.
RPC19, the gene for a subunit common to yeast RNA polymerases A (I) and C (III).
J. Biol. Chem.
266:15300-15307[Abstract/Free Full Text].
|
| 9.
|
DeRisi, J. L.,
V. R. Iyer, and P. O. Brown.
1997.
Exploring the metabolic and genetic control of gene expression on a genomic scale.
Science
278:680-686[Abstract/Free Full Text].
|
| 10.
|
Futcher, B.
2000.
Microarrays and cell cycle transcription in yeast.
Curr. Opin. Cell Biol.
12:710-715[CrossRef][Medline].
|
| 11.
|
Gasch, A. P.,
P. T. Spellman,
C. M. Kao,
O. Carmel-Harel,
M. B. Eisen,
G. Storz,
D. Botstein, and P. O. Brown.
2000.
Genomic expression programs in the response of yeast cells to environmental changes.
Mol. Biol. Cell
11:4241-4257[Abstract/Free Full Text].
|
| 12.
|
Guthrie, C., and G. R. Fink (ed.).
1991.
Methods in enzymology, vol. 194. , p. 305.
. Guide to yeast genetics and molecular biology. Academic Press, Inc., San Diego, Calif.
|
| 13.
|
Huber, M. D.,
J. H. Dworet,
K. Shire,
L. Frappier, and M. A. McAlear.
2000.
The budding yeast homolog of the human EBNA1-binding protein 2 (Ebp2p) is an essential nucleolar protein required for pre-rRNA processing.
J. Biol. Chem.
275:28764-28773[Abstract/Free Full Text].
|
| 14.
|
Hughes, J. D.,
P. W. Estep,
S. Tavazoie, and G. M. Church.
2000.
Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae.
J. Mol. Biol.
296:1205-1214[CrossRef][Medline].
|
| 15.
|
Kaufman, L., and P. J. Rousseauw.
1990.
Finding groups in data: an introduction to cluster analysis.
Wiley Interscience, New York, N.Y.
|
| 16.
|
Kendall, A.,
M. W. Hull,
E. Bertrand,
P. D. Good,
R. H. Singer, and D. R. Engelke.
2000.
A CBF5 mutation that disrupts nucleolar localization of early tRNA biosynthesis in yeast also suppresses tRNA gene-mediated transcriptional silencing.
Proc. Natl. Acad. Sci. USA
97:13108-13113[Abstract/Free Full Text].
|
| 17.
|
Lascaris, R. F.,
W. H. Mager, and R. J. Planta.
1999.
DNA-binding requirements of the yeast protein Rap1p as selected in silico from ribosomal protein gene promoter sequences.
Bioinformatics
15:267-277[Abstract/Free Full Text].
|
| 18.
|
Li, B.,
C. R. Nierras, and J. R. Warner.
1999.
Transcriptional elements involved in the repression of ribosomal protein synthesis.
Mol. Cell. Biol.
19:5393-5404[Abstract/Free Full Text].
|
| 19.
|
Mizuta, K., and J. R. Warner.
1994.
Continued functioning of the secretory pathway is essential for ribosome synthesis.
Mol. Cell. Biol.
14:2493-2502[Abstract/Free Full Text].
|
| 20.
|
Moehle, C. M., and A. G. Hinnebusch.
1991.
Association of RAP1 binding sites with stringent control of ribosomal protein gene transcription in Saccharomyces cerevisiae.
Mol. Cell. Biol.
11:2723-2735[Abstract/Free Full Text].
|
| 21.
|
Planta, R. J.
1997.
Regulation of ribosome synthesis in yeast.
Yeast
13:1505-1518[CrossRef][Medline].
|
| 22.
|
Ripley, B. D., and W. N. Venables.
1997.
Applied statistics with S-Plus.
Springer-Verlag, New York, N.Y.
|
| 23.
|
Spellman, P. T.,
G. Sherlock,
M. Q. Zhang,
V. R. Iyer,
K. Anders,
M. B. Eisen,
P. O. Brown,
D. Botstein, and B. Futcher.
1998.
Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.
Mol. Biol. Cell
9:3273-3297[Abstract/Free Full Text].
|
| 24.
|
Tollervey, D.
1996.
trans-Acting factors in ribosome synthesis.
Exp. Cell Res.
229:226-232[CrossRef][Medline].
|
| 25.
|
Tsuno, A.,
K. Miyoshi,
R. Tsujii,
T. Miyakawa, and K. Mizuta.
2000.
RRS1, a conserved essential gene, encodes a novel regulatory protein required for ribosome biogenesis in Saccharomyces cerevisiae.
Mol. Cell. Biol.
20:2066-2074[Abstract/Free Full Text].
|
| 26.
|
Vai, M.,
L. Popolo, and L. Alberghina.
1987.
Effect of tunicamycin on cell cycle progression in budding yeast.
Exp. Cell Res.
171:448-459[CrossRef][Medline].
|
| 27.
|
Warner, J. R.
1999.
The economics of ribosome biosynthesis in yeast.
Trends Biochem. Sci.
24:437-440[CrossRef][Medline].
|
| 28.
|
Wolfsberg, T. G.,
A. E. Gabrielian,
M. J. Campbell,
R. J. Cho,
J. L. Spouge, and D. Landsman.
1999.
Candidate regulatory sequence elements for cell cycle-dependent transcription in Saccharomyces cerevisiae.
Genome Res.
9:775-792[Abstract/Free Full Text].
|
| 29.
|
Zhang, M. Q.
1999.
Promoter analysis of coregulated genes in the yeast genome.
Comput. Chem.
23:233-250[CrossRef][Medline].
|
![[Feedback]](/icons/banner/feedbackACT.gif){kind=icon}
![[For Subscribers]](/icons/banner/subscriberACT.gif){kind=icon}
![[Archive]](/icons/banner/archiveACT.gif){kind=icon}
![[Search]](/icons/banner/searchACT.gif){kind=icon}
![[Contents]](/icons/banner/tocACT.gif){kind=icon}
Top
Abstract
Introduction
