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.
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 α 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 (α 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
, 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 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).
Example scores and statistics for a collection of genes
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 A 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.
Figure 1 Gene expression during the yeast cell cycle. Genes correspond to the rows, and the time points of each experiment are the columns. The ratio of induction/repression is shown for each gene such that the magnitude is indicated by the intensity of the colors (more ...)
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 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.
Figure 2 Binding site frequencies. The distribution of various promoter elements in the upstream regions of each the five cell cycle–regulated groups and a control group of 279 non-cell cycle–regulated genes that do not respond to either Cln3p (more ...)
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 B 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 . The locations of these subclusters in the main cluster are indicated on Figure B.
The G1 Clusters
The “CLN2” cluster is the largest subcluster and contains 76 genes. Genes in this cluster include CLN1
, and many other genes involved in DNA replication. A portion of this cluster is shown in Figure A. 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).
Figure 3 The G1 clusters. The transcription profiles are displayed as described in the legend to Figure . (A) CLN2 cluster. A fraction of the genes regulated similarly to the G1 cyclin CLN2, which reaches peak expression in the G1 phase of the (more ...)
The “Y′” cluster (Figure B) 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
, 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 C. 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 A 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
. However it has been shown that the level and periodicity of HTA2
mRNA accumulation are not noticeably affected by single mutation of SWI4
, 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
Figure 4 The S and M clusters. The transcription profiles are displayed as described in the legend to Figure . (A) Histone cluster. The eight genes encoding histones and the yeast histone H1 homologue cluster very tightly and are expressed during (more ...)
The “MET” cluster (20 genes, Figure B) 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 C) contains 35 genes and includes many genes involved in mitosis such as CLB2
, and SWI5
. There are also many other less tightly clustered genes that appear to be regulated in a similar manner, including WSC4
, and the major plasma membrane proton pumps PMA1
. The CLB2
cluster is highly regulated with a peak in M, and the genes are very strongly induced by GAL
, whereas GAL
appears somewhat repressive. It was previously known that four of the genes found in this cluster, CLB1
, 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
, 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
, 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 .
Figure 5 The MCM1 + SFF consensus. By aligning promoter elements of several coregulated genes found in the CLB2 cluster (see our web site for the alignment), we developed a matrix for a new MCM1 + SFF consensus. The number of times each base was (more ...)
The M/G1 Clusters
The “MCM” cluster (Figure A) contains 34 genes, including all six MCM
genes that are directly involved in DNA replication (MCM2
, and CDC47
; reviewed by Chevalier and Blow, 1996
) as well as FAR1
, 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
, 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
Figure 6 The M/G1 clusters. The transcription profiles are displayed as described in the legend to Figure . (A) MCM cluster. The MCM genes are involved in initiation of DNA replication and are coregulated during the M/G1 transition of the cell (more ...)
The “SIC1” cluster comprises 27 genes, including EGT2
, and CTS1
. These genes are strongly cell cycle regulated (Figure B) 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 C. Some of these genes (MFα1,
, and STE3
) are specific for MATα cells (Jarvis et al., 1988
) and so are significantly expressed only in the cdc15
experiment, which was done with a MATα strain. Other genes in the cluster (KAR4, AGA1, SST2
, and FUS1
) are induced by α factor and so are very strongly expressed at the beginning of the α factor experiment. However, these four genes oscillate in the other experiments when no α factor is present. We found MCM1 binding sites in the upstream regions of several of these genes, including MFα1
. Furthermore, as discussed below, we found MATα1, the transcription factor that cooperates with Mcm1p to induce α-specific genes, is itself cell cycle regulated, and this may largely explain the oscillation of the α specific genes in this cluster.
Other Genes and Regulators
The nine clusters or near clusters summarized in Table 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 we arrange 294 named genes in our set, according to both a functional class and the phase group to which they belong.
Figure 7 Cell cycle–regulated genes with characterized functions. Two hundred ninety-seven of the cell cycle–regulated genes are grouped by both function and phase of peak expression. Several functional groups are split into subgroups, which reflect (more ...)
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 ). 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 , 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
, 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 β 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 α factor and elutriation experiments did not contain DNA complementary to either the major (TUB1
) or minor (TUB3
) α 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
are moderately cell cycle regulated, but TUB3
appears less so (Figure ). 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 illustrates the point that a score near the threshold can be the result either of inadequate data or weak regulation.
Tubulin message levels. The mRNA levels for TUB1, TUB2, and TUB3, relative to those of PPA1, were determined during synchronous division after release from an α factor arrest, using the TAQman assay as described in MATERIALS AND METHODS.
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.
At least 19 genes directly involved in mating are cell cycle regulated. These include both mating pheromones (a-factor and α-factor) and, perhaps most interestingly, include the central mating-type transcription factor MATα1 itself. MATα1 binds to DNA in cooperation with Mcm1 (Sengupta and Cochran, 1991
) and induces expression of α-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 MATα1 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 MATα1. 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 α-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
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, is available in limiting amounts and for some reason is titrated during or just before S phase, causing coordinate derepression of this set of genes. Data supporting this idea are that Met30p is involved in cell cycle regulation as an F-box protein that targets Swe1 for degradation (Kaiser et al., 1998
; Patton et al., 1998
transcription is cell cycle regulated (Ma et al., 1996
) (our analyses recapitulate this observation), peaking at the G1/S phase boundary. Therefore, the concentration of a known Met30p substrate increases just before the derepression of genes involved in methionine biosynthesis that Met30p is known to repress. Thus, Met30p may become limiting, allowing expression of the MET genes. We do not know whether the cell cycle regulation of these genes is important for their function.
Interestingly, another F-box protein, Grr1, which is also involved in cell cycle regulation, regulates the expression of the HXT genes (hexose transporters). The HXT genes are members of a cluster of very weakly cell cycle–regulated genes peaking in M/G1 that also includes PHD1 and RGA1 (visible in Figure B on our web site). Thus, two different F-box proteins involved in cell cycle control also regulate genes involved in providing nutrients, and these nutrient-related genes are weakly cell cycle regulated. It is possible that these F-box proteins somehow coordinate nutrient availability with the cell cycle.
Other Nutritional Genes
A very large fraction of the genes involved in nutrition that are cell cycle regulated are involved in transport of essential minerals and organic compounds across the cell membrane. Some of the compounds that are moved by these transporters are amino acids (GAP1), ammonia (AUA1 and MEP3), sugars (e.g., HXT1 and RGT2), and iron (FET3 and FTR1). We also identified the acid phosphatases (e.g., PHO3 and PHO8). Nearly all of these genes reach peak expression late in the cell cycle during M and M/G1.
Developmental Pathway Genes: Sporulation and Pseudohyphal Growth
A number of genes associated with functions in specialized developmental pathways show cell cycle regulation. These include the apparently sporulation-specific genes SPS4
, which have peak expression in M and M/G1, respectively. These might represent cases such as SPO12
, which has known function in both mitotic and meiotic pathways (Klapholz and Esposito, 1980
; Toyn and Johnston, 1993
The Y′ Genes
Although not strictly a functional category, the Y′ genes form an interesting group of coregulated genes. The Y′ sequences are repeated sequences found just centromere proximal to the telomere itself on many chromosomes. Within the Y′ elements are two open reading frames, and there are appropriate splicing signals that suggest that they form one large product, although it has not been shown experimentally that these sites are functional (Louis and Haber, 1992
; Louis, 1995
). The larger (telomere proximal) of these two ORFs shows similarity to RNA helicases, containing all the motifs known to be necessary for helicase activity (Louis and Haber, 1992
). However, sequence similarities among these ORFs are very high, and we cannot distinguish whether one, a few, or all of these elements are cell cycle regulated.
The GAL-CLN3 and GAL-CLB2 Experiments
Our experiments to investigate the transcriptional effects of Cln3p and Clb2p provide an excellent corroborative data set that supports cell cycle regulation for more half of the genes in our list. Of the genes that are cell cycle regulated, there are 116 genes that are induced more than twofold by Cln3p, and are repressed by Clb2p. Eighty-seven percent of these peak in either G1 or S phase. In contrast there are only eight cell cycle–regulated genes that are induced by Cln3p and not repressed by Clb2p. There are 33 genes induced greater than twofold by Clb2p that are repressed by Cln3p, whereas only five genes induced by Clb2 are not repressed by Cln3p. All cell cycle–regulated genes responsive to Clb2p peak in either M or M/G1 phases.
There were also genes that responded to Clb2p or Cln3p (or both) that we did not identify as cell cycle regulated. For instance there are 53 genes induced by Cln3p and repressed by Clb2p that are not on our cell cycle–regulated list. Many of these are involved in functions for which we know many genes are cell cycle regulated, e.g., secretion (PMT2, PMT4, SEC53, and SEC21), chitin synthesis (CHS3), and nucleotide biosynthesis (ADE3, RNR2). However, we have no other evidence to suggest that these may be false negatives. Indeed, by visual inspection, none of these genes displays convincing signs of periodicity. This observation reinforces the notion that using many types of experiments is crucial to drawing legitimate conclusions.
Our experiments on the transcriptional effects of Cln3p and Clb2p help us dissect the transcriptional regulators of each gene (see above). In addition, they support the notion that mechanistically two opposing oscillators drive the cell cycle. This is particularly well illustrated in some of the subclusters, for instance, the CLN2 cluster, where the effects of CLN3 induction are almost exactly mirrored by the opposite effects of CLB2 induction. For other subclusters we see that the genes respond to only one of the cyclins, (e.g., the Y′ cluster is induced by CLN3 yet relatively unchanged by CLB2).
Finally, we found that CLN3
can repress the transcription of certain genes, particularly a group of genes involved in mating. This was not entirely unexpected, because it had previously been demonstrated that FAR1
transcription (McKinney et al., 1993
) is negatively regulated by start, although a direct link to Cln3p activity has not been previously demonstrated.