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Vol. 9, Issue 12, 3273-3297, December 1998





*Department of Genetics, Stanford University Medical Center,
Stanford, California 94306-5120;
Cold Spring Harbor
Laboratory, Cold Spring Harbor, New York 11724-2209;
§Department of Biochemistry, Stanford University Medical
Center, Stanford, California 94306-5428; and
Howard
Hughes Medical Institute, Stanford, California 94305-5428
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ABSTRACT |
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We sought to create a comprehensive catalog of yeast genes whose
transcript levels vary periodically within the cell cycle. To this end,
we used DNA microarrays and samples from yeast cultures synchronized by
three independent methods:
factor arrest, elutriation, and arrest
of a cdc15 temperature-sensitive mutant. Using
periodicity and correlation algorithms, we identified 800 genes that
meet an objective minimum criterion for cell cycle regulation. In
separate experiments, designed to examine the effects of inducing
either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or
both of these cyclins. Furthermore, we analyzed our set of cell
cycle-regulated genes for known and new promoter elements and show
that several known elements (or variations thereof) contain information
predictive of cell cycle regulation. A full description and complete
data sets are available at http://cellcycle-www.stanford.edu
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INTRODUCTION |
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In 1981 Hereford and coworkers discovered that yeast histone mRNAs
oscillate in abundance during the cell division cycle (Hereford et al., 1981
). To date 104 messages that are cell cycle
regulated have been identified using traditional methods, and it was
estimated that some 250 cell cycle-regulated genes might exist (Price
et al., 1991
). There are several reasons why genes might be
regulated in a periodic manner coincident with the cell cycle. Such
regulation might be required for the proper functioning of mechanisms
that maintain order during cell division. Alternatively, regulation of
these genes could simply allow conservation of resources. Much of the
literature has focused on the posttranscriptional mechanisms that
control the basic timing of the cell cycle. However, there is also
clear evidence that trans-acting factors play a critical role in the regulation of the abundance of many cell cycle-regulated transcripts.
Most identified cell cycle controls that exert influence over mRNA
levels do so at the level of transcription. Three major types of cell
cycle transcription factors are known in yeast, the MBF and SBF
factors, Mcm1p-containing factors, and Swi5p/Ace2p (Table
1). Many genes expressed at about the
G1/S transition contain MCB or SCB elements in their promoters
to which MBF and SBF bind respectively (for review, see Koch and
Nasmyth, 1994
). It is now apparent that SBF is not as specific for SCBs
as was originally thought but, rather, can bind, at least in some
cases, to motifs more closely matching the MCB consensus (Partridge
et al., 1997
). MBF and SBF are activated posttranslationally
by Cln3p-Cdc28p, and SBF, at least, is inactivated by Clb2p-Cdc28p
(Amon et al., 1993
). It is this cyclin-dependent activation
and inactivation that causes MBF- and SBF-mediated transcription to be
cell cycle regulated. Mcm1p can bind with other DNA binding proteins to
mediate a specific biological effect. In cooperation with Ste12p, Mcm1p directs the cell cycle expression of some genes in early G1 phase (Oehlen et al., 1996
). In cooperation with an uncloned
factor called "Swi five factor" (SFF), it induces the expression of
CLB1, CLB2, BUD4, and
SWI5 in M (Lydall et al., 1991
; Sanders
and Herskowitz, 1996
). Finally, possibly acting without a partner, it
induces transcription of CLN3, SWI4, and
CDC6 at the M/G1 boundary (McInerny et al.,
1997
). The Mcm1p + SFF combination is interesting, because it is
somehow activated by Clb2p-Cdc28p, and Mcm1p + SFF then induces further
transcription of CLB2. Thus, Mcm1p is part of a positive
feedback loop for CLB2 transcription. Finally, Swi5p and
Ace2p, which are transcriptionally controlled by Mcm1p and SFF, are
responsible for the expression of many genes in M and M/G1 (Kovacech
et al., 1996
). Some of these genes are responsible for
inactivating Clb2p and promoting cytokinesis, thus allowing exit from
mitosis, and allowing the cycle to begin anew.
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Many cell cycle-regulated genes are involved in processes that occur only once per cell cycle. Such processes include DNA synthesis, budding, and cytokinesis. Additionally many of these genes are involved in controlling the cell cycle itself, although in most cases it is unclear whether their regulated transcription is absolutely required. The cell division cycle is thus a complex self-regulating program, such that many genes involved in aspects of the cell cycle are also controlled by it.
We present the results of a comprehensive series of experiments aimed
at objectively identifying all protein-encoding transcripts in the
genome of Saccharomyces cerevisiae that are cell cycle regulated. We used DNA microarrays to analyze mRNA levels in cell cultures that had been synchronized by three independent methods. These
data were analyzed by deriving a numerical score based on a Fourier
algorithm (testing periodicity) and by a correlation function that
identified genes whose RNA levels were similar to the RNA levels of
genes already known to be regulated by the cell cycle. This protocol
allowed us to include data from a previously published study (Cho
et al., 1998
). We find that ~800 genes are cell cycle
regulated, which constitutes >10% of all protein-coding genes in the
genome. We also find that about one-half of these genes can be
controlled by the G1 cyclin CLN3 and/or the mitotic cyclin
CLB2. The primary data presented in this article, tools for
examining them, and supporting analyses can be found at
http://cellcycle-www.stanford.edu.
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MATERIALS AND METHODS |
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Strains
Strains used in this study are shown in Table 2.
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Media and Growth Conditions
YEP medium (Sherman, 1991
) was used in all experiments,
supplemented with an appropriate carbon source. Carbon sources are indicated in the descriptions of each experiment and were used at a
final concentration of 2% (wt/vol), unless otherwise noted. The pH of
the YEP for the
factor experiment was adjusted to 5.5 before use.
The medium used for the elutriation was first passed through Whatman #1
filter paper. Cultures of yeast were shaken at 250-300 rpm, in a
volume no more than 25% of the vessel maximum at the temperature
specified in the description of each experiment.
Microarray Manufacture
Yeast ORFs were amplified using gene PAIRS primers (Research Genetics, Huntsville, AL). One hundred-microliter PCR reactions were performed in 96-well PCR plates using each primer pair with the following reagents: 1 µM each primer, 200 µM each dATP, dCTP, dTTP, and dGTP, 1× PCR buffer (Perkin Elmer-Cetus, Norwalk, CT), 2 mM MgCl2, and 2 U of Taq DNA polymerase (Perkin Elmer-Cetus). Thermalcycling was performed in Perkin Elmer-Cetus 9600 thermalcyclers with a 5-min denaturation step at 94°C, followed by 30 cycles with melting, annealing, and extension temperatures and times of 94°C, 30 s; 54°C, 45 s; and 72°C, 3 min 30 s, respectively. Production of the correct PCR product was verified by gel electrophoresis. Products deemed to have failed were reamplified either by repeating the PCR reaction with the gene PAIRS primers, ordering custom primers, or using the yeast ORF DNA (Research Genetics) as a template. Reamplification of failed PCRs used the same protocol as initial amplification.
DNAs were prepared and printed onto microarrays as described previously
(Shalon et al., 1996
; DeRisi et al., 1997
[http://cmgm.stanford.edu/pbrown/]; Eisen and Brown, 1999
)
with 190-µm spacing between the centers of each element. Each
microarray was visually inspected, and all microarrays used in this
study were estimated to be missing <1% of all elements except for
arrays used in the cdc15 experiments, which were missing ~3% of all elements.
Cell Density and Size Measurements
All cell size measurements were made using a Coulter Counter
Multisizer (Coulter Electronics, Hialeah, FL) or a Beckman FACScan workstation (Beckman Instruments, Fullerton, CA). The Coulter Counter
was also used to measure the cell density for elutriation. Cell
densities in the
factor experiment were measured at
OD600 using a Pharmacia Ultrospec III spectrophotometer
(Pharmacia, Piscataway, NJ).
Budding Index Calculations
Each sample was sonicated for 30 s with a Virsonic 300 (Virtis, Gardiner, NY) microprobe equipped sonicator at 50% power to separate divided cells. At least 200 cells were counted and scored for the presence of a bud.
DNA Content Determination
Samples were prepared as described previously (Futcher, 1993
),
and DNA content was measured using a Beckman FACScan workstation.
Nuclear Staining
Cells were washed in water and resuspended in water containing DAPI at 1 µg/ml. Cells were then placed on a glass slide and visualized by fluorescence microscopy, using a Zeiss Axioplan microscope (Carl Zeiss, Thornwood, NY).
Factor-based Synchronization
Yeast strain DBY8724 was grown to an OD600 of 0.2 in
YEP glucose, an asynchronous sample was taken, and
factor (PAN
facility, Beckman Center, Stanford University) was added to a
concentration of 12 ng/ml. After 120 min the
factor was removed by
pelleting the cells for 5 min in a Sorvall (Newtown, CT) S34
rotor at 3000 rpm and decanting the supernatant. The arrested cells
were resuspended in fresh YEP glucose to an OD600 of 0.18. Every 7 min, for the next 140 min, 25-ml samples were taken for RNA,
and 5-ml samples were taken for FACS analysis. At 91 min after release
the OD600 of the culture was reduced to ~0.2 from ~0.4
by addition of fresh medium.
Size-based Synchronization
Nine liters of yeast strain DBY7286 were grown in YEP ethanol (2%, vol/vol) at 25°C to a cell density of 1.5 × 107 cells/ml. Cells were pelleted in a Beckman JA-10 rotor for 10 min. The supernatant was saved and is referred to as clarified medium. Cells were resuspended in 300 ml of the clarified medium and sonicated for 2 min with a Virsonic 300 equipped with a microprobe at 50% power. This volume was loaded into a dual-chamber elutriation chamber (Beckman Instruments, Fullerton, CA; catalog numbers 356940 and 356941) in a Beckman J-6 M/E centrifuge equipped with a JE-5.0 elutriation rotor. The elutriator was run with clarified medium at 25°C. Unbudded daughter cells (400 ml at 2.3 × 107 cells/ml) were collected at a modal cell volume of 17.7 fl and grown at 25°C. Samples were take every 30 min for the next 6.5 h with independent samples for DAPI staining (1 ml), FACS analysis (2 ml), budding index (1 ml), and RNA preparation (25 ml). After harvesting, the samples for DAPI, FACS, and budding index were immediately chilled on ice.
Cdc15-based Synchronization
The cdc15-2 (DBY8728) strain was grown to 2.5 × 106 cells/ml in YEP glucose medium at 23°C. The culture was then shifted to an air incubator at 37°C and held at that temperature for 3.5 h. By this time, cell density had reached 6.6 × 106 cells/ml, and 96% of the cells were large dumbbells characteristic of a cdc15 arrest. The cells were then released from the cdc15 arrest by shifting the culture to a 23°C water bath. Samples were taken every 10 min for 300 min, starting at the time of the shift to the 23°C water bath. By 300 min after shift, cell density had reached 4 × 107 cells/ml. Part of the same original culture was grown at 23°C to 1 × 107 cells/ml, and cells were harvested for extraction of the control mRNA. Progress through the cell cycle was monitored by the appearance of new buds.
Because cdc15-2 cells do not quantitatively complete cell separation after a release from a 37°C arrest, FACS analysis is difficult to interpret. We therefore followed the progress of the cdc15-2 cells through the cycle by monitoring the appearance of new buds. The first new buds appeared 50 min after the release to 23°C, when 12% of the dumbbells had small buds (usually, two small buds, one on each half of the dumbbell). The percentage of dumbbells with small buds was 52% at 60 min, 76% at 70 min, 96% at 80 min, and virtually 100% at 90 min, at which time almost all the dumbbells had not just one bud, but two, one on each half of the dumbbell. The second round of small buds appeared at 150 min, when 3% of the cells had small buds. The percentage was 9.7% at 160 min, 32% at 170 min, 68% at 180 min, and 81% at 190 min. The third round of small buds appeared at 270 min, although by this time synchrony was decaying.
Cln3 and Clb2 Experiments
For the Cln3 experiment strain 31 (DBY8725) was grown in YEP raffinose/galactose (1% each) at 23°C to a density of 1 × 107 cells/ml. The cells were then filtered and washed with 2 vol of YEP and resuspended in YEP raffinose at 23°C. These cells arrested because of lack of Cln activity after incubating for 3 h. Cdc34p was then inactivated by shifting the culture to 37°C for 2.5 h. The culture was then split, and galactose was added to one-half at a final concentration of 2% (wt/vol). Cells from this culture were harvested every 10 min for 40 min for RNA. The entire control culture was harvested at time 0. The experiment was performed twice (once for each hybridization in our data set). Data from the 40-min (first experiment) and 30-min (second experiment) postgalactose samples are shown.
For the Clb2 experiment strain 245 (DBY8726) was grown to a density of 5 × 106 at 30°C in YEP raffinose/galactose (1% each) and then centrifuged, and the cells were washed with 2 vol of YEP and then resuspended in YEP raffinose. After 6 h DMSO was added to a final concentration of 1%, and nocodazole was added to a final concentration of 15 µg/ml. The culture was then split, and galactose was added to one-half at a final concentration of 2% (wt/vol). Cells from this culture were harvested every 10 min for 40 min for RNA. The entire control culture was harvested at time 0. The experiment was performed twice (once for each hybridization in our data set). Data from the two 40 min postgalactose samples are shown.
To control the Cln3 and Clb2 experiments for the effects of galactose addition, strain W303a (DBY8727) was grown in 250 ml of YEP raffinose at 30°C to a cell density of 1 × 107 cells/ml. The culture was split in two, and to one-half (the experimental culture) was added galactose to a final concentration of 2% (wt/vol). Forty minutes later both cultures were harvested for preparation of mRNA. The data for this experiment are available on our web site.
RNA Preparation
Samples for RNA isolation were taken by pipetting culture
directly into 50-ml Falcon (Lincoln Park, NJ) tubes containing ~20 g
of ice to quickly chill the cells. Cells were collected by spinning for
3 min in a tabletop centrifuge and then frozen by immersion in liquid
nitrogen and stored at
80°C until RNA was prepared. RNA was
prepared by adding 10 ml of water-saturated phenol, 10 ml of sodium
acetate buffer (50 mM sodium acetate, 10 mM EDTA, pH 5.0), and 1 ml of
10% SDS (all prewarmed to 65°C) to each frozen cell pellet. Each
mixture was incubated at 65°C for 10 min, vortexing vigorously every
1 min for 10 s. After spinning at 1500 × g for 10 min, the aqueous phase was transferred to another 50-ml conical tube
containing 10 ml of water-saturated phenol. Samples were vortexed for
30 s, and the spin was repeated. Aqueous phases were again
transferred to a new 50-ml conical tube and 10 ml of phenol:chloroform (1:1) were added, followed by a 15-min spin. RNA was precipitated by
adding the aqueous phase to an equal volume of isopropanol and 0.1 vol
of 3 M sodium acetate. Samples were spun for 30 min at 1500 × g to pellet the RNA. Pellets were washed with 70% ethanol and dried at room temperature. RNA pellets were dissolved in TE (10 mM
Tris, 1 mM EDTA, pH 8.0) to ~2.5 mg/ml.
Probe Preparation
Total RNA (15 µg) and 6 µg of oligo-dT were combined in a total volume of 15 µl. RNA oligo-dT mixtures were heated to 70°C for 1 min and then cooled on ice. Three microliters of 25 mM Cy3- or Cy5-conjugated dUTP (Amersham, Arlington Heights, IL), 3 µl of 1 M DTT, 6 µl of first-strand buffer (Stratagene, La Jolla, CA), 0.6 µl of dNTPs (25 mM each dATP, dCTP, and dGTP and 15 mM dTTP), and 2 µl of Superscript II (Stratagene) were added. Each sample was then incubated at 42°C for 2 h to generate Cy-labeled cDNA. Starting RNA was degraded by addition of 1.5 µl of stop solution (1 N NaOH, 0.1 M EDTA) and incubation at 70°C for 10 min. Samples were neutralized by addition of 15 µl of 0.1 N HCl and 400 µl of TE (10 mM Tris, 1 mM EDTA, pH 7.4). Labeled cDNA was concentrated and separated from unbound fluor by separation in a Centricon-30 (Amicon, Danvers, MA) until no further fluor was visible in the flow through, and the probe was concentrated to <4 µl.
Microarray Hybridizations
A probe mixture (12 µl) consisting of Cy3- and Cy5-labeled cDNAs, 3× SSC, 0.3% SDS, and 1.8 µg/µl yeast tRNA was applied to each microarray. The microarray was covered by a 22-mm-square coverslip (Fisher Scientific, Pittsburgh, PA) and placed in a custom-manu-factured hybridization chamber (see http://cmgm.stanford.edu/pbrown/). Ten microliters of water were placed inside the hybridization chamber before sealing, and the chamber was placed in a 65°C water bath. The microarrays were allowed to hybridize 4-6 h. Microarrays were removed from the chambers and placed in standard histochemistry slide holders where they were washed by plunging 30 times in each of the following solutions, respectively: 2× SSC, 0.2% SDS; 0.4× SSC; and 0.2× SSC.
Data Acquisition and Processing
Microarrays were scanned using a custom-built scanning laser microscope. Separate 2 × 2-cm images were acquired for each fluor at a resolution of 20 µm/pixel. Data were extracted by manually superimposing a grid of boxes over the combined Cy3-Cy5 image so that each box contained a single array spot. The average fluorescence intensity for each fluor within each box was recorded. The local background was estimated by averaging the intensities of the weakest 12% of the pixels in each box. Fluorescence ratios were computed based on the background corrected values. Spots of poor quality (as assayed by visual inspection) were removed from further consideration. As a measure of the internal consistency of the data for each spot, the pixel-by-pixel correlation coefficient between the Cy3 and Cy5 intensities was computed; spots with low correlation values (i.e., <0.4) were excluded from further analysis.
Identification of mRNAs Regulated in a Cell Cycle-dependent Manner
Data for each gene in the
factor time series were extracted
from the database and were normalized so that the average
log2(ratio) over the course of the experiments was equal to
0. A Fourier transform (Eq. 1-3) was applied to the data
series for each gene, and the resulting vector (C) was stored for each
gene, where
is the period of the cell cycle, t is the time,
is the phase offset, and ratio(t) is the ratio measurement at time
t. We found that the magnitude of the Fourier transform (D, Eq.
4) was unstable for small variations of
, so we averaged
the vectors of the transform over a range of 40 values, which were
evenly spaced around the estimated division t ime for the experiment
(66 ± 11). We initially set the value of
to 0.
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(1) |
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(2) |
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(3) |
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(4) |
The above process was repeated for the cdc15 experiment (
varying between 60 and 80) and for the cdc28 data (
varying between 80 and 100) from Cho et al. (1998)
. The
cdc28 data set was first converted to ratio style
measurements by dividing each measurement by the average value of the
measurements for that gene. Before this step it was necessary to
exclude some data points that appeared to be aberrant. Any data value
where the two values on either side were threefold different in the
same direction were excluded. Each gene thus had three vector scores
(one for each of the three analyzed data series).
To generate a single vector for each gene, we added the vectors for
each experiment together. However, the value of
for the three
experiments should not be the same, because the experiments start at
different points in the cell cycle. Therefore, before combining the
vectors from the three experiments, constants,
cdc15 and
cdc28
(relative to the
factor experiment), were calculated for the
cdc15 and cdc28 experiments, respectively, that
maximized, for the known genes, the average magnitude of the summed
vectors. The elutriation data were not included, because it was not
possible to calculate a
that maximized the values of more than a
handful of the known genes. The
factor and cdc15 vectors were
multiplied by 0.7, so that they would not unduly contribute to the
final "aggregate CDC score," which was calculated by taking the
magnitude of this final vector.
Genes were ranked by their aggregate CDC scores, and the list was examined to identify the positions of known cell cycle genes within it. We selected a threshold CDC score that was exceeded by 91% of known cell cycle-regulated genes. Altogether 800 genes met or exceeded this CDC score.
Promoter Analysis
Motifs were identified in the 700 bp upstream of the start codon
using a Gibbs sampling strategy. Such a strategy was originally developed by Lawrence et al. (1993)
to find patterns in
protein sequences and later modified by Neuwald et al.
(1995)
to take into account the possibility of a repeated motif. We
have modified these Gibbs sampling algorithms to allow pattern searches
of DNA (Zhang, unpublished data), for which functions such as
double-strand search, palindrome symmetry, and submotif inclusion and
exclusion were included. Once motifs were established for a group or
cluster, we tested their predictive value by searching for the motif
consensus (with specified mismatches) in the 700 bp upstream of the ATG for all groups, as well as for a control set of non-cell
cycle-regulated genes, and compared the distribution of these sites in
different groups.
TAQman Assay
The TAQman assay was performed on the same
factor samples
that were used in the microarray hybridization experiments. For each
sample, 500 ng of total RNA were incubated for 15 min with 0.1 U/µl
DNase I (amplification grade; Life Technologies, Grand Island, NY) in 2 mM MgCl2, 50 mM KCl, 20 mM Tris-HCl (pH 8.4). The reaction
was stopped by adding EDTA to 2.5 mM and incubating at 65°C for 10 min. The RNA was reverse transcribed using TAQman reverse transcription
reagents (PE Applied Biosystems, Foster City, CA) consisting of
2.5 µM oligo-dT 16 mer, 1.25 U/µl MultiScribe reverse
transcriptase, 0.5 mM dGTP, dATP, dTTP, and dCTP, 0.4 U/µl RNase
inhibitor, 50 mM KCl, 10 mM Tris-HCl (pH 8.3). The reaction was
incubated at 25°C for 10 min, 48°C for 30 min, and then 95°C for
5 min. The resulting cDNA served as a template for real-time
quantitative PCR as follows, in which a fluorescent reporter dye
(6-carboxy-fluorescein [FAM]) was released and quantitated during
each specific replication of the template (Heid et al., 1996
). The cDNA was mixed with 2× TAQman universal PCR master mix (PE
Applied Biosystems) and then split into separate reaction tubes
containing gene-specific forward and reverse primers (900 nM each) and
dye-labeled oligonucleotide probes (200 nM). Each resultant PCR (25 µl) contained cDNA generated from 5 ng of RNA. The sequences of the
primers and probes were the following: TUB1 primers:
forward, 5'-AAAGCCGAAGGGAGGAGAAG-3'; reverse,
5'-CCCTTGGAACGAACTTACCGT-3'; TUB1 probe:
5'(FAM)-CTCCACGTTTTTCCATGAAACCGGC-(6-carboxy-tetramethylrhodamine [TAMRA])p3'; TUB2 primers: forward,
5'-TTGTCCCATTCCCACGTTTAC-3'; reverse, 5'-GATTGAGAGCCAATTGCCGT-3';
TUB2 probe: 5'(FAM)-TTCTTCATGGTCGGCTACGCTCCATT-(TAMRA)p3'; TUB3 primers: forward,
5'-CCTGCGCCTCAATTGTCTACT-3'; reverse, 5'-TTCCAGGGTGGTATGCGTG-3'; TUB3 probe:
5'(FAM)-CGTCGTGGAACCTTACAACACGGTTTTAA-(TAMRA)p3'; PPA1
primers: forward, 5'-TGTCGGTG-CTTCCAATTTGAT-3'; reverse, 5'-CATCGGAAATGGCAGCAGT-3'; and PPA1 probe:
5'(FAM)-CCGGTGATACCGACAGCGATACCA-(TAMRA)p3'. Each gene-specific PCR was
done in triplicate or quadruplicate. The tubes were placed in a PE
Applied Biosystems Prism 7700 sequence detection system and were
incubated with the following parameters: 50°C for 2 min, 95°C for
10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min. The computer program Sequence Detector version 1.6.3 (PE Applied
Biosystems) provided output, which allowed the average quantities of
TUB mRNA relative to PPA1 mRNA to be determined
for each RNA sample. The TUB:PPA1 ratio in the
asynchronous sample A1 was arbitrarily set at 1, and the results from
the other samples were adjusted accordingly.
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RESULTS |
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Experimental Overview
We wished to identify the genes whose RNA levels varied
periodically during the cell cycle. We initially obtained microarray data from synchronized cells and suitable controls and analyzed the
>400,000 measurements to obtain objective scores based on a Fourier
algorithm (which assesses periodicity) and a correlation measurement
(which compared our data with those of previously identified cell
cycle-regulated genes). We compared scores among the previously known
and total gene sets to find a threshold value for deciding the
significance of the apparent cell cycle regulation. For completeness,
we also reanalyzed the published data of Cho et al. (1998)
.
Using all the data, we arrived at a threshold CDC value above which
91% (95 of 104) of the genes previously shown to be cell cycle
regulated are included. This procedure identified a total of 800 yeast
genes as being periodically regulated.
Synchronized Cultures
We measured the relative levels of mRNA as a function of time in
cell cultures that had been synchronized in three independent ways.
First we used
pheromone to arrest MATa cells in G1.
Second, we used centrifugal elutriation to obtain small G1 cells.
Finally, we used a temperature-sensitive mutation, cdc15-2, which, at the restrictive temperature, arrests cells late in mitosis. We used three methods because each introduces characteristic artifacts. For instance, use of pheromone has regulatory consequences
characteristic of mating, whereas use of temperature-sensitive mutants
can cause heat shock.
The synchronization experiments differed in major ways. First, they
were performed using different carbon sources and at different temperatures, with the consequence that the cells grew at different rates. Second, two different yeast strain backgrounds were used (S288C
and W303), and finally, cells were synchronized at different points
during the cell cycle. Each method produced significant cell cycle
synchrony through one cell cycle (elutriation), two cycles (
pheromone), or three cycles (cdc15), as established by at
least one of the following methods for each experiment: bud count, DNA
content analysis (FACS) and nuclear staining (DAPI), as described in
MATERIALS AND METHODS.
RNA was extracted from each of the samples collected, as well as from a
control sample (asynchronous cultures of the same cells growing
exponentially at the same temperature in the same medium).
Fluorescently labeled cDNA was synthesized using Cy3 ("green") for
all controls and Cy5 ("red") for all experimental samples. Mixtures
of labeled control and experimental cDNAs were competitively hybridized
to individual microarrays containing essentially all yeast genes
(DeRisi et al., 1997
). The ratio of experimental (red) to
control (green) cDNA was measured by scanning laser microscopy (Shalon
et al., 1996
).
Transcription in Response to the Cyclins Cln3p and Clb2p
To gain mechanistic insight into the control of the observed
cell cycle regulation, we identified genes whose mRNA levels responded
to the induction of two well-characterized cell cycle regulators, Cln3p
and Clb2p (see Nasmyth, 1993
). Late in G1 phase, the Cln3p-Cdc28p
protein kinase complex activates two transcription factors, MBF and
SBF, and these in turn promote the transcription of a number of genes
important for budding and DNA synthesis (Cross, 1995
). Later in the
cell cycle, the Clb2p-Cdc28p complex represses the activity of SBF,
returning the expression of SBF-regulated genes to low levels (Amon
et al., 1993
). Furthermore, Clb2p-Cdc28p is known to
activate expression of at least four genes, CLB1, CLB2, SWI5, and BUD4 (Althoefer
et al., 1995
; Sanders and Herskowitz, 1996
).
To identify other genes controlled by Cln3p and Clb2p, we arrested
cln
or clb
cells in
late G1 with cdc34-2 for the CLN3
experiment and in M with nocodazole for the CLB2 experiment.
We then induced expression of CLN3 or CLB2
without inducing cell cycle progression. RNA from the G1-phase cells
expressing Cln3p (labeled red) was compared with control RNA (labeled
green) from the G1-phase cells arrested in the absence of Cln3p.
Similarly, for the CLB2 experiment, RNA from M-phase cells
expressing Clb2p (labeled red) was compared with control RNA (labeled
green) from M-phase cells arrested in the absence of Clb2p. In each
case, mRNA levels were quantitatively measured by microarray
hybridization. In addition, we performed an experiment to test the
effects of galactose to an asynchronous culture with no inducible
cyclin (see MATERIALS AND METHODS). Genes identified as strongly
affected by galactose addition were not considered further in the Gal
cyclin experiments.
Data Analysis and Availability
The total data we collected comprise ~400,000 individual ratio measurements. The quality and reliability of the data can only be assessed by unrestricted access to all data in forms suitable for further query or computer analysis. Therefore, in addition to the summary printed here, we provide primary data from two locations on the Internet. The numerical data are provided in a table of the actual ratios measured for each gene, on each array. They can be downloaded as a tab-delimited text file from the journal web site (http://www.molbiolcell.org) or from a server at Stanford (http://cellcycle-www.stanford.edu). The Stanford web site also provides images of the arrays, accessory data, and the capability to browse and search the complete data set. Raw data are also available from the authors upon request.
The comprehensive nature of this work has another consequence: in what follows we refer by name to as many as 400 genes. It is impractical to provide detailed literature documentation for each gene every time it appears. Instead, we have provided references selectively, and we encourage readers to use the hyperlinks to the Saccharomyces Genome Database (http://genome-www.stanford.edu/Saccharomyces) and the Yeast Protein Database (http://quest7.proteome.com/YPDhome.html) that will be provided at both the Molecular Biology of the Cell and Stanford web sites.
Identification of Cell Cycle-regulated Transcripts
Combining the data from the synchronization experiments, we were able to identify 800 genes whose expression is cell cycle regulated. We did this by the combination of a Fourier algorithm and a correlation algorithm as described in MATERIALS AND METHODS. This resulted in a score for each gene that we refer to as the aggregate CDC score. To illustrate this, Table 3 provides some summary statistics and examples of the kinds of scores obtained for several genes (including specific examples that are and are not cell cycle regulated).
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In setting the threshold for the aggregate CDC score by our empirical method, we intended to minimize false-positive assessments while including the vast majority of previously characterized genes that are known to have periodic mRNA levels. Many additional genes showed indications of cell cycle regulation (by visual inspection of the data and by quantitation using our algorithm), although we could not objectively distinguish this behavior from noise.
We estimated the false-positive rate in two ways. First, we randomized the data from each experiment (both by gene and by time point) and performed all of the analyses described above. The randomized data produced 24 "genes" (of nearly 6200) with CDC scores that exceed the threshold we used to classify genes as cell cycle regulated. We assume that this represents a reasonable estimate of the false-positive rate (i.e., ~3% of all genes identified would be false positives). In a second, more conservative test, we randomized the data set only within genes. The number of genes that had scores above our threshold was about three times higher (75 genes) when we randomly shuffled the data in this way. Thus, the number of false positives (of the 800 genes identified as cell cycle regulated) is likely <10% and perhaps as low as 3%.
Classifying the Cell Cycle-regulated Genes by Pattern of Expression
We used two distinct methods to classify genes by their pattern of
expression, which we refer to as "phasing" (by time of peak
expression) and "clustering" (by similarity of expression across
the experiments, which is described below). There is no simple
relationship between these two methods, although there are common
features in the results. "Phase groups" were created by determining
the time of peak expression for each gene (calculated from the Fourier
algorithm) and ordering all genes by this time. We divided this ordered
set into five (somewhat arbitrary) groups termed G1, S, G2, M, and M/G1
that approximate those commonly used in the literature. To this end we
used the published timing of gene expression for the known genes in
determining which genes belonged in which phase group. Figure
1A displays the 800 genes that we
identified, sorted according to the phase of expression. Each column
represents a time point in an experiment, and each row represents a
gene that we identified as cell cycle regulated. The ratio of
expression that we measured for each gene in each time point is color
coded, reflecting the magnitude of the ratio of expression relative to
the average of that gene, with shades of red indicating an
increase (on) and shades of green indicating a decrease (off). This
display is based on the paradigm of Eisen et al.
(1998)
. Genes expressed during each part of the cell cycle are
indicated by the color bar (and phase) on the side, and temporal progress through the cell cycle is indicated on the top.
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By phasing there were 300 G1 genes (e.g., CLN2, RNR1, CDC9, RAD27, SMC3, and MNN1), 71 S genes (e.g., the histones), 121 G2 genes (e.g., CLB4, WHI3, and CIS3), 195 M genes (e.g., DBF2, CLB2, CDC5, CDC20, and SWI5), and 113 M/G1 genes (e.g., ASH1, SIC1, CDC6, and EGT2). This is a crude classification with many disadvantages (e.g., the last gene in the G2 group and the first gene in the M group are expressed at virtually the same time yet are in different groups), but nevertheless it is useful for discussing the results.
Identification of DNA Binding Sites
We searched through the 700 bp immediately upstream of the start codon of each of the 800 genes in our list to identify potential binding sites for known or novel factors that might control expression during the cell cycle. We found that the majority of the genes have good matches to known cell cycle transcription factor binding sites relevant to the time of peak expression. Furthermore, we examined the distribution of these elements within the upstream sequences, and found that both the site and its position relative to the ATG contain information that is predictive of the phase group of the gene. Figure 2 shows the frequency of six sites in promoters of the G1, S, G2, M, and M/G1 phase groups and a control set of non-cell cycle-regulated genes. These sites are the previously published SCB and MCB as well as four extensions and modifications of published sites (MCM1 + SFF, extended SWI5, SCB variant, and degenerate MCB). Full results of all promoter searches are available on our web site.
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Clusters and Their Regulation
Clusters were established using the clustering
algorithm of Eisen et al. (1999)
. This algorithm
sorts through all the data to find the pairs of genes that behave most
similarly in each experiment and then progressively adds other genes to
the initial pairs to form clusters of apparently coregulated genes. As
will be discussed below, the clustering algorithm successfully
identifies coregulated genes, because analysis of the 5' regions of the
genes in a cluster shows that such genes share common promoter
elements, many of which are identifiable based on the published
literature. Thus, these clusters provide a foundation for understanding
the transcriptional mechanisms of cell cycle regulation. Figure 1B shows the entire clustergram of our cell cycle-regulated genes; a
larger version with gene names attached is available at our web site.
The same color-coded presentation is used, with the addition, on the extreme left, of the similarity tree (dendrogram) calculated by the clustering algorithm. Many portions of the
clustergram (subclusters) are described below, and those that we
discuss are summarized in Table 4. The
locations of these subclusters in the main cluster are indicated on
Figure 1B.
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The G1 Clusters
The "CLN2" cluster is the largest subcluster and
contains 76 genes. Genes in this cluster include CLN1,
CLN2, CLB6, RNR1, CDC9,
CDC21, CDC45, POL12, POL30,
SWE1, and many other genes involved in DNA replication. A
portion of this cluster is shown in Figure 3A. The key features of these genes are
that expression is strongly cell cycle regulated (i.e., large
peak-to-trough ratios); peak expression occurs in mid-G1 phase (~10
min before budding in the cdc15 experiment); and they are
strongly induced by GAL-CLN3 but are strongly repressed by
GAL-CLB2. Fifty-eight percent of the 5' regions of these
genes had at least one copy of the motif ACGCGT (vs. 6% of control
genes), which is a perfect MCB element. Fifty-two percent had at least
one copy of CRCGAAA (vs. 13% of control genes), a degenerate SCB
element. In addition, 16 had the motif AGAAGAAA, which is similar to a
functionally important sequence found upstream of CLN3
(AAGAAAAA) (Parviz et al., 1998
). Finally, 17 had the motif
CCACAK, which we do not recognize. Outside the core of this cluster are
at least 43 additional genes that are less tightly clustered but
nevertheless appear to be coregulated with the CLN2 cluster
(119 total genes).
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The "Y'" cluster (Figure 3B) contains 31 ORFs that all share DNA sequence similarity. There are 38 ORFs that share this similarity in the genome and we identify 36 of them as cell cycle regulated. All of these 38 ORFs are found in Y' elements, located at chromosomes ends. It should be noted that these results may not represent 36 independent observations, because the cDNAs corresponding to these ORFs are almost certain to cross-hybridize on the microarrays. We do not know how these ORFs are regulated or the functional significance.
There is a set of 92 genes, containing ALG7,
FKS1, GAS1, GOG5, PMT1, and
PMI40, as well as other genes involved in cell wall synthesis (Klis, 1994
), that are not a cluster on the clustergram but
that are substantially coregulated. These genes can be seen on our web
site as Figure 3C. Expression is strongly cell cycle regulated, and
peak expression is nearly coincident with budding (~10 min later than
the CLN2 cluster in the cdc15 experiment). These
genes are induced by GAL-CLN3 and repressed by
GAL-CLB2. The majority of these genes had the motif ACRMSAAA
(where R is A or G, M is A or C, and S is C or G), which may be an
extension and variation of the SCB motif (CACGAAA). Comparison of the
CLN2 cluster with this set suggests that expression from MCB
motifs may be activated somewhat before expression from SCB motifs, but both kinds of expression are induced by CLN3 (consistent
with previous studies) and repressed by CLB2. Earlier
studies demonstrated that repression of SCB-driven expression requires
CLB2, whereas repression of MCB-driven expression did not
(Amon et al., 1993
). Our results extend this by showing that
CLB2 can repress MCB-driven expression, even though there
may be additional repressive mechanisms. Many of the genes in this set
also had the motif AARAARAAG, which is similar to a motif found in the
CLN2 cluster (see above). However, because promoters generally are rich
in such sequences, the significance of this motif is unclear.
The S and M Clusters
The histone cluster in Figure 4A
forms the tightest cluster of any of the cell cycle genes. These nine
genes have very high peak-to-trough ratios and give aggregate scores of
~10. The histones have three known modes of regulation: first, there
are negative elements repressing transcription; second, there is an
element in the 3' region of the mRNAs that destabilizes the message
except during S phase; and third, there is a repeated positive element, which activates transcription (Freeman et al., 1992
). Part
of the core motif of the positive element is ATGCGAAR, which is similar to our degenerate SCB motif (ACRMSAAA). Consistent with this, histone
expression is induced by GAL-CLN3. However it has
been shown that the level and periodicity of
HTA2/HTB2 mRNA accumulation are not noticeably
affected by single mutation of SWI4, SWI6, or
MBP1 (Lowndes et al., 1992
; Cross et
al., 1994
). Additionally, histone levels are unaffected by
GAL-CLB2. The sharpness of the peak in histone regulation is worth
noting, both because it gives a good impression of the degree of
synchronization and because the histones were the first genes for which
periodic regulation was discovered (Hereford et al., 1981
).
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The "MET" cluster (20 genes, Figure 4B) was completely unexpected.
It contains 10 genes involved in the biosynthesis of methionine. Furthermore, two of the unnamed genes in this cluster show sequence similarity to human methionine synthetase, two are likely to be amino
acid transporters (with unidentified specificities), one is similar to
MET17, and one is on the opposite strand of MET2. Finally, ECM17, the only previously characterized gene in
the cluster that is not known to be part of the methionine biosynthetic pathway, is similar to a sulfite redoxin from human. Thus, nearly all
of the genes in this cluster are likely to be involved in methionine
metabolism. Expression of the genes in this cluster peaks just after
the histones, and at least some are inducible by CLN3. We
searched the upstream region of the genes in the MET cluster and found
that 15 of the genes had the consensus AAACTGTGG, which is identical to
the consensus found for Met31/Met32 binding (Blaiseau et
al., 1997
).
The "CLB2" cluster (Figure 4C) contains 35 genes and includes many
genes involved in mitosis such as CLB2, CDC5,
CDC20, and SWI5. There are also many other less
tightly clustered genes that appear to be regulated in a similar
manner, including WSC4, PMP1, and the major
plasma membrane proton pumps PMA1 and PMA2. The CLB2 cluster is highly regulated with a peak in M, and the
genes are very strongly induced by GAL-CLB2,
whereas GAL-CLN3 appears somewhat repressive. It
was previously known that four of the genes found in this cluster,
CLB1, CLB2, SWI5, and BUD4,
are regulated by a combination of two transcription factors, Mcm1p and
SFF (Althoefer et al., 1995
; Sanders and
Herskowitz, 1996
). Mcm1p binds to the consensus TTACCNAATTNGGTAA
(Acton et al., 1997
), whereas, on the basis of three of
these genes, SFF was thought to bind to the consensus sequence
GTMAACAA. Furthermore, transcription of CLB1, CLB2, and SWI5 was known to be induced by
Clb2p activity, possibly because of posttranslational activation of SFF
(Amon et al., 1993
). We compared the upstream regions of
genes in the CLB2 cluster and certain other coregulated
genes (e.g., ASE1, also thought to be a possible target of
SFF [Pellman et al., 1995
]) and found that most of them
contain an easily recognizable MCM1 + SFF motif. Of the 35 genes in the
cluster, only 9 genes (KIP2, MOB1,
NUM1, YCL012W, BUD3, CHA1,
YCL063W, YLR057W, and YML033W) did not
have an easily recognizable near match to the MCM1 + SFF consensus. An
alignment of the genes that contained this site can be viewed on our
web site, and on the basis of this alignment, we deduce a new consensus
for MCM1 + SFF binding, shown in Figure
5.
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The M/G1 Clusters
The "MCM" cluster (Figure
6A) contains 34 genes, including all six
MCM genes that are directly involved in DNA replication (MCM2, MCM3, CDC54, CDC46,
MCM6, and CDC47; reviewed by Chevalier and Blow,
1996
) as well as FAR1, DBF2, SPO12,
and KIN3. These genes peak late in the cycle, at about the
M/G1 boundary, and are induced by CLB2 and somewhat
repressed by CLN3. This cluster has similarities to the
CLB2 cluster, except that peak expression is slightly later.
Searches of the upstream regions reveal that the majority of these
genes contain binding sites for Mcm1p, as was previously shown for some
members of the cluster (McInerny et al., 1997
). Some, but
not all, of these MCM1 sites have nearby sites for SFF (e.g., in
FAR1, SPO12, KIN3, and
CDC47), although these presumptive SFF sites are of
varying quality. It has been suggested that some of the genes in this
cluster are regulated through the "ECB," a variant of the Mcm1p
binding site (McInerny et al., 1997
).
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The "SIC1" cluster comprises 27 genes, including EGT2,
PCL9, TEC1, ASH1, SIC1, and
CTS1. These genes are strongly cell cycle regulated (Figure
6B) and peak in late M or at the M/G1 boundary. GAL-CLN3 may
repress some of these genes, whereas GAL-CLB2 has no
consistent effect on the expression of these genes. Several of these
genes are known to be regulated by the transcription factor Swi5p,
which itself is a member of the CLB2 cluster (Dohrmann et al., 1992
; Bobola et al., 1996
; Knapp et
al., 1996
). Swi5p is thought to bind to a site with the consensus
ACCAGC (Knapp et al., 1996
), and indeed, when we searched
for common motifs in the 5' regions of the SIC1 cluster, we
found the consensus RRCCAGCR in many of the 27 genes. When all cell
cycle-regulated genes were examined for the presence of either the
original Swi5p consensus, or this new extended consensus, the extended
consensus was found to be much more specific for late M-phase genes.
This comparison is shown on our web site. The motif GCSCRGC was also found in ~40% of the genes in this cluster.
The "MAT" cluster contains 13 genes and is shown in Figure 6C. Some
of these genes (MF
1, MF
2, and
STE3) are specific for MAT
cells (Jarvis et
al., 1988
) and so are significantly expressed only in the
cdc15 experiment, which was done with a MAT
strain. Other
genes in the cluster (KAR4, AGA1, SST2, and FUS1)
are induced by
factor and so are very strongly expressed at the
beginning of the
factor experiment. However, these four genes
oscillate in the other experiments when no
factor is present. We
found MCM1 binding sites in the upstream regions of several of these genes, including MF
1 and MF
2. Furthermore,
as discussed below, we found MAT
1, the transcription factor that
cooperates with Mcm1p to induce
-specific genes, is itself cell
cycle regulated, and this may largely explain the oscillation of the
specific genes in this cluster.
Other Genes and Regulators
The nine clusters or near clusters summarized in Table 4 account for about half of the cell cycle-regulated genes. The remaining genes tend to be less strongly cell cycle regulated and cluster less tightly. We have attempted to find novel elements in the promoters of the remaining genes without great success. The best of these elements was the consensus GCAGNRNCCW, which we found in the upstream regions of CLB4, BUD3, CPR8, PRO2, YCL012W, YCL063W, YGL217C, YNL043C, YDR130C, and YOL030W; these genes appear to be moderately well coregulated (peak expression occurs in G2). There may be additional, novel, upstream elements that we are unable to find.
It is likely that many of the remaining genes are actually coregulated
with members of the clusters we have described, and their transcription
may be controlled by the same types of elements. Indeed, we know that
some of the remaining genes have recognizable elements (e.g., MCBs and
SCBs), whereas in other cases, the elements may be highly degenerate
versions of the known elements. This may explain why the cell cycle
regulation we observe is relatively weak and why the genes do not
cluster tightly. Finally, mRNA levels could oscillate, not because of
transcriptional control, but because of cell cycle control of mRNA
stability; the histone mRNAs are controlled partly in this way (Wang
et al., 1996
).
For the clusters we have identified, some of the genes in the cluster do not contain an obvious element; for instance, nine of the genes in the CLB2 cluster do not contain an obvious MCM1 + SFF site. We do not know whether these genes contain cryptic, degenerate sites that our algorithms fail to recognize, or whether these genes are regulated by an unknown factor.
The Functions of the Cell Cycle-regulated Genes
The major functions of the cell cycle regulated genes we identified are cell cycle control, DNA replication, DNA repair, budding, glycosylation, nuclear division and mitosis, structure of the cytoskeleton, and mating. In Figure 7 we arrange 294 named genes in our set, according to both a functional class and the phase group to which they belong.
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DNA Replication, Repair, and Chromosome Assembly
It is instructive to look at the pattern of expression of genes involved in a particular process. For instance, we can trace the expression of many genes somehow involved in DNA replication (as shown in Figure 7). Of the genes that peak in G1 there are 23 genes with known functions in DNA replication. These genes include subunits of the DNA polymerases and their accessory factors (e.g. CDC2, POL1, and POL2), genes involved in nucleotide synthesis (e.g. CDC21), and genes involved in initiation of DNA synthesis (e.g. CDC45). Many genes involved in DNA repair such as PMS1 and MSH2 reach peak expression in G1 phase, suggesting that repair of DNA lesions may be a normal part of S phase.
Later, when S phase is actually occurring, the histone genes reach peak expression. In late M phase or M/G1 all six MCM genes important for prereplicative complex formation (MCM2, MCM3, CDC54, CDC47, MCM6, CDC47, and CDC54) and CDC6 reach their peaks, presumably to help set up origins for the next cell cycle. Thus, many genes needed for replication and repair reach peak expression just before they are needed, the histones peak exactly at the time they are needed, and a few genes important for regulation of DNA synthesis peak well in advance of the next round of S phase. Only two known initiator genes, CDC45 and DBF4 (which we did not identify in our analysis; see below) peak just before S phase, suggesting these may be particularly important to trigger replication.
Bud Initiation and Bud Growth
Budding is a major metabolic activity for the cell and involves
several subprocesses. The cell must choose a site for the new bud
(initiation) and make components for an ever-increasing surface area
consisting of a new cell membrane (which requires lipids and integral
membrane proteins) and a new cell wall (composed largely of glucan,
chitin, and mannoproteins). All of these processes require delivery of
components, via the secretory apparatus, to the sites of new membrane
and cell wall synthesis, which, in normal conditions, occurs
exclusively in the bud (Kaiser et al., 1997
; for reviews,
see Lew et al., 1997
; Orlean, 1997
).
We found 17 genes that involved in bud site selection and cell polarization (e.g., BUD3, BUD4, BUD8, BUD9, BEM1, GIC1, MSB1, and MSB2). As indicated in Figure 7, none of these genes had been reported to be cell cycle regulated. Some of these (BUD9, CDC10, and RSR1) show peak expression in G1, consistent with roles in bud initiation. Others, (BUD4, BUD8, and BEM1) peak in M phase, suggesting roles in the following cell cycle, i.e., earlier in the budding pathway than the G1 group. We also identified many genes needed for secretion, glycosylation (needed for making mannoproteins), synthesis of lipids, and cell wall synthesis.
Cell Division and Mitosis
Another fundamental process of cell division, in which a large
number of the genes involved have their messages regulated by the cell
cycle, is the process of mitosis (for review of microtubule-related topics, see Botstein et al., 1997
). During the cell cycle
many events occur that allow mitosis to progress in a timely manner. This process begins in G1 when the spindle pole body (SPB) replicates. To facilitate this process six known components of the SPB reach peak
expression in G1 (CNM67, NUF1, SPC42,
SPC97, SPC98, and TUB4), one
(SPC34) peaks during S, and one (NUF2) peaks
during M phase. Some of these genes were already known to be cell cycle
regulated (NUF1, and SPC42) (Kilmartin et
al., 1993
; Donaldson and Kilmartin 1996
).
Once the mitotic program is entered the cell must create a spindle,
which is responsible for moving the nucleus to the bud neck so that
nuclear division can occur. This process requires microtubules and many
accessory proteins (to form the spindle) as well as kinesins (for
movements of the nucleus and the SPB). These genes reach peak
expression largely during the first half of the cell cycle. In G1
BIM1, BUB1, IPL1, KAR3, and SLK19
reach peak expression, and during S, five genes (CIN8,
KAR9, KIP1, STU2, and VIK1)
peak. Five genes peak during G2 (BUB2, CIK1,
KIP2, KIP3, and NUM1), as well as the
major
tubulin TUB2. Finally, one gene (ASE1)
reaches peak expression during M.
It was somewhat unexpected that tubulin messages would be regulated by
the cell cycle; unfortunately the microarrays that we used for the
factor and elutriation experiments did not contain DNA complementary to
either the major (TUB1) or minor (TUB3)
tubulins. Our data set suggested that that TUB1 might be
cell cycle regulated because it had a score just below our cutoff. We
wished to verify that the major tubulins were regulated in the cell
cycle by an independent method (quantitative real-time PCR [Heid
et al., 1996
]). This method allows determinations of relative mRNA levels with excellent reproducibility. We performed the
analysis as detailed in MATERIALS AND METHODS with the result that, as
we suspected, TUB1 and TUB2 are moderately cell
cycle regulated, but TUB3 appears less so (Figure
8). This suggests that the low score for
TUB1 may have been caused by their absence from some of the
arrays. It should be noted that TUB2 with a score of 2.33 is
clearly above the threshold we set for cell cycle regulation, but that
TUB1 with a score of 1.25 is just below, and TUB3
(score 0.53) is considerably below the threshold. Comparison with
Figure 8 illustrates the point that a score near the threshold can be the result either of inadequate data or weak regulation.
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One relatively small class of genes that displays tight temporal
regulation is a group of genes involved in chromatid cohesion. Five of
these genes (SMC1, SMC3, MCD1,
PDS1, and PDS5 [Strunnikov et al.,
1993
; Yamamoto et al., 1996
; Guacci et al., 1997
;
Michaelis et al., 1997
]) have peak expression during G1
just before the next round of DNA synthesis.
At the end of the cell cycle the cell must exit mitosis so that the
next round of division can occur. To do this, a system of proteins acts
to inhibit the activity of Clb-Cdc28p. One of these proteins is Sic1p,
whose expression is known to peak at this time (Donovan et
al., 1994
). Many of the proteins that inhibit Clb-Cdc28p or
prepare the cell to exit from mitosis are known to be cell cycle
regulated and peak in M phase. We also find that DBF20
(which is functionally related to DBF2) is cell cycle
regulated and peaks in G2.
Mating
At least 19 genes directly involved in mating are cell cycle
regulated. These include both mating pheromones (a-factor and
-factor) and, perhaps most interestingly, include the central mating-type transcription factor MAT
1 itself. MAT
1 binds to DNA
in cooperation with Mcm1 (Sengupta and Cochran, 1991
) and induces
expression of
-specific genes. It was previously shown that some
genes involved in mating were cell cycle regulated, and this regulation
was shown to be due to cooperative binding between Mcm1 and Ste12. The
fact that the MAT
1 transcription factor itself oscillates provides
yet another mechanism by which genes involved in mating might be cell
cycle regulated. We found Mcm1 sites in the upstream regions of several
of these genes, including MAT
1. The regulation of genes involved in
mating is clearly complex, and several transcription factors are
involved. However, it seems that most of these transcription factors
cooperate in one way or another with Mcm1. The fact that so many mating functions are cell cycle regulated, including an
-specific
transcription factor, helps explain the deep connection between mating,
start, and the cell cycle. For instance, if genes involved in mating are turned off at start by multiple mechanisms, it helps explain how
passage through start precludes mating.
Cell Cycle Control Genes
Of the 19 genes involved in cell cycle control we identified, 17 were already known to be cell cycle regulated. This set mainly includes
cyclins and transcription factors, whose activities and time of action
are well documented (see Koch and Nasmyth, 1994
; Andrews and Measday,
1998
). The only two cell cycle control genes that we identified newly
as regulated were WHI3 and HSL7.
Methionine Biosynthesis
It was an unexpected and somewhat surprising result that many
genes involved in methionine biosynthesis are cell cycle regulated. A
number of possibilities suggest themselves. First, the pool of
available cellular methionine is smaller than virtually any other amino
acid; thus, methionine is likely to be limiting (Jones and Fink, 1982
).
Indeed, Unger and Hartwell (1976)
noted that starvation for sulfur or
for methionine effectively causes G1 arrest, suggesting that cell cycle
progression is particularly sensitive to the availability of
methionine. They also found that a temperature-sensitive allele of
methionine tRNA synthetase causes G1 arrest, even in the presence of
methionine. These observations suggest that the cell cycle regulation
of methionine genes ensures sufficient capacity for protein synthesis
in that biosynthetic pathway for the next cell cycle; if there are
insufficient resources, G1 arrest ensues.
It is known that the more than 20 genes that constitute the sulfur
amino acid biosythesis pathway are coordinately regulated at the level
of transcription. This transcription is repressed in response to an
increase in the intracellular concentration of
S-adenosylmethionine, an end product of the pathway
(methionyl tRNA is another end product) (Thomas et al.,
1989
). A second possibility therefore is that the concentration of
S-adenosylmethionine is depleted as cells enter S phase,
causing derepression of these genes, which results in cell cycle regulation.
A third possibility is that the protein that actually represses these genes, Met30p,