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Vol. 16, Issue 3, 1026-1042, March 2005

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Identification of Cell Cycle-regulated Genes in Fission Yeast

Xu Peng ^* , R. Krishna Murthy Karuturi ^* , Lance D. Miller ^*, Kui Lin ^* , Yonghui Jia ^*, Pinar Kondu , Long Wang ^*, Lim-Soon Wong ^||, Edison T. Liu ^*, Mohan K. Balasubramanian ^¶ #, and Jianhua Liu ^* @

^* Genome Institute of Singapore, Singapore 138672, Singapore; Bioinformatics Institute, Singapore 138671, Singapore; ^|| Institute for Infocomm Research, Singapore 119613, Singapore; ^¶ Temasek Life Sciences Laboratory, 1 Research Link, NUS, Singapore 117604, Singapore; ^# Department of Biological Sciences, National University of Singapore, Singapore 117597, Singapore; and ^@ Department of Biochemistry, National University of Singapore, Singapore 117597, Singapore

Submitted April 10, 2004; Accepted December 2, 2004
Monitoring Editor: John Pringle

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
Cell cycle progression is both regulated and accompanied by periodic changes in the expression levels of a large number of genes. To investigate cell cycle-regulated transcriptional programs in the fission yeast Schizosaccharomyces pombe, we developed a whole-genome oligonucleotide-based DNA microarray. Microarray analysis of both wild-type and cdc25 mutant cell cultures was performed to identify transcripts whose levels oscillated during the cell cycle. Using an unsupervised algorithm, we identified 747 genes that met the criteria for cell cycle-regulated expression. Peaks of gene expression were found to be distributed throughout the entire cell cycle. Furthermore, we found that four promoter motifs exhibited strong association with cell cycle phase-specific expression. Examination of the regulation of MCB motif-containing genes through the perturbation of DNA synthesis control/MCB-binding factor (DSC/MBF)-mediated transcription in arrested synchronous cdc10 mutant cell cultures revealed a subset of functional targets of the DSC/MBF transcription factor complex, as well as certain gene promoter requirements. Finally, we compared our data with those for the budding yeast Saccharomyces cerevisiae and found 140 genes that are cell cycle regulated in both yeasts, suggesting that these genes may play an evolutionarily conserved role in regulation of cell cycle-specific processes. Our complete data sets are available at http://giscompute.gis.a-star.edu.sg/~gisljh/CDC.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
The mitotic cell cycle of eukaryotic cells is a complex biological process in which many cellular events are carried out in a sequential manner to ensure precise duplication and faithful segregation of essential components into daughter cells. Cell cycle progression is thus regulated by a wide range of mechanisms such as protein modification, targeted proteolytic degradation, and cell cycle-specific transcription, which has been found to play a pivotal role in controlling cell cycle progression (Maqbool et al., 2003). In the budding yeast Saccharomyces cerevisiae, among a total of 6000 genes, 400–800 have been found whose transcript levels oscillate during the mitotic cell cycle (Cho et al., 1998; Spellman et al., 1998). In a human cancer cell line (HeLa), >1000 transcripts were found whose levels exhibited periodic changes during the cell cycle (Cho et al., 2001; Whitfield et al., 2002). Genes whose expression levels peak at specific stages of the cell cycle are often required for regulation of the processes that occur at these stages (Futcher, 2000).

The fission yeast Schizosaccharomyces pombe has been an excellent model for the study of mechanisms regulating the eukaryotic cell cycle (Nurse, 1990). As in other eukaryotic cells, the fission yeast cell cycle can be divided into four discrete phases: G1, S, G2, and M (Mitchison, 1970). A primary characteristic of the G1 phase is the synthesis and accumulation of active proteins required for DNA replication in the subsequent S phase, during which an entire complement of chromosomal DNA is synthesized. In rapidly growing fission yeast, the G1 and S phases are relatively short, each occupying 10% of the cell cycle (Nasmyth et al., 1979). After S phase, cells enter G2, which is the lengthiest phase and occupies 70% of the cell cycle. Increase of cell mass in wild-type cells occurs largely in G2 under exponential growth conditions. On reaching a critical cell mass and/or cell volume, cells progress into M phase, which is marked by chromosome condensation, formation of the mitotic spindle, and the segregation of chromosomes to opposite ends of the cells (Robinow, 1977; Hagan and Hyams, 1988). After nuclear division, cytokinesis occurs associated with the formation of a medial division septum that is then partially digested to physically separate the two daughter cells. Because the M, G1, and S phases typically occur before physical separation of the daughter cells, newborn fission yeast cells have typically already entered early G2 phase (MacNeill and Fantes, 1997).

About 40 of the 4970 genes in the S. pombe genome (Wood et al., 2002) had been reported previously to display cell cycle-regulated transcription (Table 2). Some of these genes contain MCB consensus sequences in their promoters. It has been shown that DNA synthesis control (DSC) transcriptional complexes (also known as MCB-binding factors, or MBF) can bind MCB motifs and activate transcription of genes that are required for the G1/S transition (Whitehall et al., 1999). The DSC complexes contain the products of the genes cdc10, res1, res2, rep1, and rep2 (Lowndes et al., 1992; Caligiuri and Beach, 1993; Miyamoto et al., 1994; Sugiyama et al., 1994; Zhu et al., 1994; Nakashima et al., 1995; White et al., 2001). Recently, another transcriptional complex, named the pombe cell cycle box (PCB)-binding factor (PBF), has been proposed to regulate cell cycle-specific transcription that peaks at the M-G1 boundary (Anderson et al., 2002). Although the composition of the PBF complex is not clear, it has been shown that it binds to PCB consensus sequences and regulates transcription activities in a Plo1p-dependent manner (Anderson et al., 2002). Many genes have been identified that contain PCB promoter consensus motifs and exhibit an expression peak at the M-G1 interval (Anderson et al., 2002). These include cdc15 (Fankhauser et al., 1995); fin1 (Krien et al., 2002); and mid1, plo1, sid2, and two genes previously characterized as G1-S genes, mcm2 and ppb1. Expression of three genes (cdc25, rum1, and wis3) has been shown to peak in the G2 phase (Ducommun et al., 1990; Benito et al., 1998; Samuel et al., 2000).

Although a number of studies have focused on the expression of individual genes that function in various cell cycle-regulatory processes, a systematic approach had not been applied to study expression of all genes through the cell cycle. We report here a genome-wide analysis of cell cycle-regulated transcription in S. pombe by using synchronous cultures generated by two different techniques and an oligonucleotide-based DNA microarray representing >99% of the open reading frames (ORFs) in the annotated genome. We have identified many cell cycle-regulated genes and used this set of genes to identify the promoter elements that may control their expression. We have also studied the extent of evolutionary conservation of cell cycle-regulated transcription by comparing our results to those obtained with S. cerevisiae. The primary data sets presented in this article can be found at http://giscompute.gis.a-star.edu.sg/~gisljh/CDC.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
Strains and Media
The strains used in this study are listed in Table 1. Cells were cultured in YES medium (0.5% yeast extract, 3.0% glucose, 225 mg/l uracil and leucine) at the temperatures indicated. ace2 deletion strain JLY1063 was constructed in strain JLY188 by using a polymerase chain reaction (PCR)-mediated approach (Bähler et al., 1998) with ura4⁺ as the selective marker. The deletion allele in strain JLY1603 was validated by PCR by using sequence-specific primer pairs (our unpublished data).

Microarray Manufacture
Based on the annotated fission yeast genome (Wood et al., 2002), we used a cDNA-specific oligonucleotide selection algorithm (Lin et al., 2004) to select two unique 50-mer sequences for each of 4929 ORFs (>99% of the ORFs in the genome). In brief, 50-mer sequences were first selected from ORF sequences based on a set of criteria that included GC content of 45–65%, no inverted repeat of more than five consecutive nucleotides, no single nucleotide run of >11 consecutive nucleotides, and no more than 50% of the same nucleotide. ORF-specific oligonucleotides were then selected to have no more than 68% similarity to other ORF sequences in the genome to ensure ORF specificity, a criterion more stringent than the maximum allowable similarity of 75% estimated by others (Kane et al., 2000). More than 99% of the oligonucleotides selected were <1 kb from the 3' end of the ORF. All the oligos were synthesized (Proligo, Hamburg, Germany), resuspended in 0.3x SSC buffer, and subsequently spotted onto poly-lysine–coated glass slides by using a spotting machine (GeneMachines, San Carlos, CA). Spotted glass slides were postprocessed according to DeRisi's protocol (http://derisilab.ucsf.edu/microarray/protocols.html) and used directly in hybridization or stored at room temperature in a dry cabinet for later use.

Nuclear Staining and Fluorescence-activated Cell Sorting (FACS) Analysis
To stain nuclei, 1-ml culture samples were collected and fixed either by the addition of 100 µl of concentrated formaldehyde (Sigma-Aldrich, St. Louis, MO) or by brief centrifugation followed by the addition of 1 ml of cold 70% ethanol. Fixed cells were washed twice with phosphate-buffered saline buffer and stained with 4,6-diamidino-2-phenylindole (Sigma-Aldrich) to visualize nuclear DNA. For each sample, at least 500 cells were scored using a fluorescence microscope (Leica, Wetzlar, Germany).

To analyze DNA content, cells were collected by brief centrifugation and fixed by resuspension in ice-cold 70% ethanol and then treated overnight with RNase A in 50 mM sodium citrate buffer, pH 7.0. After staining with propidium iodine, the fluorescence intensities of individual cells were measured by flow cytometry using a BD FACScan (BD Biosciences, Franklin Lakes, NJ).

Synchronization
For size-based synchronization, cells from 10 liters of early log phase culture were sonicated briefly and then subjected to elutriation centrifugation (Beckman Coulter, Fullerton, CA) at 8–10°C and 3000–4000 rpm with a constant flow rate of 100 ml/min. Synchronized wild-type cells (composed of newborn cells at early G2 phase) were grown at 30°C, and samples were collected every 10 min for a period of 6 h. Synchronized wild-type and cdc10-V50 mutant cells also were released to 36°C and sampled every 15 min for 5 h. Cell samples were centrifuged at 2500 rpm for 2 min, and the pellets were chilled in liquid nitrogen and stored at -80°C for RNA extraction later.

Temperature-sensitive cdc25-22 mutant cells were synchronized using the block-and-release method (Mitchison et al., 1991). Early log phase cells growing at 24°C were shifted to the restrictive temperature of 36°C for 4 h. The culture was then shifted back to 24°C to start synchronous growth. Samples were taken at 10-min intervals as described above.

RNA Preparation, cDNA Fluorescence Labeling, and Microarray Hybridization
Acid phenol was added to the frozen cell pellets, which were then incubated at 65°C for 15 min with occasional shaking using a thermomixer (Eppendorf, Hamburg, Germany). The aqueous phase was recovered by centrifuging at 5000 rpm for 5 min at 4°C and reextracted using phenol:chloroform (1:1). RNA was precipitated by adding an equal volume of isopropanol and recovered by centrifugation at 10,000 rpm for 20 min at 4°C. Fluorescence-labeled cDNA was synthesized using a SuperScript II kit (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions, by using oligo(dT)₂₀ in the presence of either Cy5- or Cy3-coupled dUTP. The reaction was stopped by the addition of EDTA, and the cDNA was briefly hydrolyzed by adding NaOH and subsequently neutralized by adding HCl. Neutralized fluorescent cDNA was washed and concentrated using a microcon-YM30 spin-column (Millipore, Billerica, MA).

Spotted microarray slides were prehybridized using Roche hybridization buffer (Cat. no.: 1603588, Roche Diagnostics, Basel, Switzerland) with a raised coverslip (Erie Scientific, Portsmouth, NH) in a slide hybridization chamber (GeneMachines) for 1 h at 42°C. Slides were washed first in distilled H₂O for 2 min, then in isopropanol for 2 min, and finally spin-dried. Slides were then hybridized using Roche hybridization buffer containing Cy5-labeled (samples from the synchronous cultures) and Cy3-labeled (asynchronous-culture reference samples) cDNA overnight at 42°C. Hybridized slides were washed consecutively in 2x SSC/0.1% SDS for 1 min, 1x SSC for 4 min, 0.2x SSC for 4 min, and 0.05x SSC for 1 min, and then spin-dried before scanning.

Data Acquisition and Processing
Microarray slides were scanned using a GenePix scanner (Axon Instruments, Union City, CA) at wavelengths of 635 and 532 nm and a resolution of 10 µm by using GenePix Pro3 or Pro4 software (Axon Instruments). GenePix Results files were generated by the program and subsequently normalized based on a median of ratios. To ensure well measured data, the ratio for a spot was collected if its intensity in either channel was twofold or greater than that of the background.

To detect cell cycle-regulated genes, we used a modified Fourier-transform method. This algorithm can be applied even with little or no prior knowledge of cell cycle-regulated genes, and it has been shown to be competitive with supervised methods by using S. cerevisiae and HeLa cell cycle data (Karuturi and Liu, 2004). This algorithm is described briefly below as a sequence of steps.

Filling Missing Values. A missing value was replaced by the average of the values of its immediate neighbors. If one of the neighbors did not exist or also had its value missing, then the missing value was replaced by the average of the average value of the whole profile and the value of the nonmissing neighbor. These filling steps were carried out repeatedly until all the missing values were filled for each profile. This step was required for the algorithm to effectively retrieve true positives even in the presence of missing values in their profiles, because missing values would reduce the support for retrieval of genes. Filling the missing values aided in proper clustering of genes and robust estimations of peaks and phases of gene expression. As shown below, in our data, only 0.95 and 3.75% of all profiles had more than four missing values in the cdc25 data and elutriation data, respectively; moreover, of the 747 genes predicted to be cell cycle regulated, no genes in the cdc25 data and only 11 genes in the elutriation data had more than four missing values in their profiles. We filtered out all profiles that had missing values for more than half of the time-course length. This filtering resulted in the removal of one gene from the cdc25 data and 16 genes from the elutriation data.

Gaussian Smoothing. The value F_ix (expression of gene i at time t = x) was replaced by

where was set to be equal to 1/20 of the cell cycle period. This step was performed to remove the high-frequency noise in the profiles that could otherwise affect the cell division cycle (CDC) score that we describe below. This choice of ensures that it will not introduce any artificial oscillation of period close to the cell cycle period.

Zero-Mean Local Normalization. Normalizing the time-course profile F_i of gene i such that its mean is zero is common practice. But the expression profile of a gene may have an additive linear component apart from the periodic component (Rifkin and Kim, 2002), which must be removed for a better estimate of the Fourier transform of the periodic component. We achieved this using local normalization by replacing F_it by (F_it–m_it), where

in which and T are the total profile period and the cell cycle period, respectively, and n_t is the number of values being averaged. m_it represents the local mean of profile i at time t. This step removes the additive linear component (represented by at+c, where a, t, and c are the rate of change of the component, time, and average expression, respectively), based on the fact that the average of values of a linear function symmetrically sampled around point t = x is ax+ c, whereas it is 0 for the function

if the period of averaging is T (the period of oscillation). Thus, the above-mentioned method calculates the local mean of the profile over a symmetric window of CDC period and subtracts it from the original profile value. The first and third equations take care of the border cases arising because our expression profiles are of finite length.

Fourier Transform. The Fourier transform is a method for finding the peak and phase of expression of a set of cell cycle-regulated genes by using their time-course profiles. We determined the periodicity of the profile F_i by fitting it with the function P_icos(t-_i), in which P_i is the peak value, = 2/T, and _i is the phase of expression. Both P_i and _i could be estimated by finding the coefficients A_i and B_i of the Fourier transform of the expression profile F_i, as follows:

P_i and _i were then calculated as follows:

These formulae assume that cos(t) and sin(t) are orthogonal over the duration of the profile, which may be violated if the number of cycles for which F_i is generated is not equal to an integral multiple of half cycles or sampling is at irregular intervals. In such cases, P_i and _i exhibit variation with the actual phase of F_i. To overcome this artifact, we introduced two corrective steps, as follows. First, after estimating _i, we searched for the phase of cos(t - ), which gave rise to the calculated phase if it were sampled at exactly the same time points as that of F_i. Then, the value of _i was replaced by that of . Second, after correcting for phase, the calculated peak was divided by the peak obtained for cos(t -) if it were sampled at the same time points as that of F_i. Hereafter, P_i and _i represent the corrected peak and phase of expression of gene i.

CDC Score. Spellman et al. (1998) obtained the "CDC score" for each gene by multiplying its estimated peak of expression (as obtained from the Fourier transform; P_i in the above-mentioned formulations) by the highest correlation value obtained between the data series for that gene and the expression profiles for known genes with peaks in the G₁, S, G₂, M, or M/G₁ phase. This strategy was also followed by Whitfield et al. (2002). However, in the unsupervised approach, peak correlation is not available. Hence, we defined two factors called mean square error and sign similarity for F_i as follows:

Mean square error

where N is the number of time points in the profile. E_i measures how much the model deviates from the original expression profile and helps to reduce the false discovery rate.

Sign similarity

where M_it = 1 if F_itcos(t - _i) > 0 and 0 if F_itcos(t - _i) 0, W_it is the maximum of 0 and (W_{i(t - 1)} + (2M_it - 1)) in which W_it = 0 if t 0. is an increment and was set to 0.1. M_ik indicates the agreement (takes a value of 1) or disagreement (takes a value of 0) of directionality between the profile and its model at t = k, and W_it is a measure of nonrandom occurrence of M_ik = 1 for 0 k t. C_i helps to deal with noise as well as deviation from the sinusoidal shape, and it thus improves the rate of true positives.

The CDC score of gene i was then defined as follows:

where and are positive numbers that were set to 1.0 and 0.25, respectively, based on the optimum performance with the S. cerevisiae cell cycle data (Spellman et al., 1998). These settings also were found to be optimal with the human cell cycle data (Whitfield et al., 2002).

Combining CDC Scores. The genes with the top 500 CDC scores in each data set were compared, and those present in both sets were used to calculate the average phase difference between the experiments. It was found that the wild-type elutriation experiment lagged behind the cdc25-mutant experiment by 0.27 rad, which was then added to the estimated phase of all genes in the cdc25 experiment. Then, the scores for each gene were combined to get a final score using the formula

where

are the scores and phases of gene expression in the cdc25 and the wild-type experiments, respectively.

Estimating False Discovery Rate (FDR)
The FDR was estimated using randomly generated data. For a given list of the top N (based on CDC score), transcripts whose minimum CDC score was S_min, the FDR was estimated as the number of random profiles that had CDC score > S_min divided by N. The random data were generated by shuffling either the whole data set or each profile individually. The FDR reported is the greater of the two estimates, because the FDR estimates for our algorithm are robust to the randomization procedure used, as shown elsewhere (Karuturi and Liu, 2004).

Identification of Cell Cycle-specific Promoter Motifs
Promoter sequences were extracted from intergenic regions (maximum of 1 kb) located immediately upstream of the start codons of the ORFs based on the published S. pombe genome sequence (Wood et al., 2002). For the 747 genes identified, 733 promoter sequences were available and therefore analyzed. The promoter motifs listed in the S. cerevisiae Promoter Database at http://cgsigma.cshl.org/jian/ were tested for their cell cycle phase specificity in S. pombe. All motifs that occurred in >300 genes were deleted from the analysis. The genes were then numbered in order of their average phase of expression. A method described by Tang and Lewontin (1999) was applied to search for cell cycle phases that were enriched for the motif-containing genes based on the ordered gene list. For the phase-specific motifs, the relative density of occurrence (fraction of motif-containing genes in a window of 100 genes divided by the average occurrence of the motif in all 733 genes) was plotted (Figure 2A).

View larger version (78K): [in this window] [in a new window]   Figure 2. The expression profiles of 747 cell cycle-regulated genes in S. pombe. Genes correspond to the rows, and the time points (10-min intervals) in each experiment form the columns. The intensities of the colors indicate the magnitudes of induction (red) or repression (green) for each gene (see key at bottom right). Black, no change of expression levels; gray, data not available. The color bars on top indicate the cell cycle phases according to Figure 1. (A) Genes are ordered by their times of peak expression; phases of peak expression are indicated by the colored bars on the right. The graph on the left shows the distributions of promoter motifs indicated as densities relative to the background density for that motif (see Materials and Methods). The distribution of the PCB motif (asterisk) was not significantly enriched at any stage of the cell cycle. (B) Genes that share similar expression profiles are grouped by cluster analysis as described in the text. The dendrogram on the left shows the structures of the clusters; the bars on the right indicate sets of genes sharing the indicated promoter motifs.

View larger version (26K): [in this window] [in a new window]   Figure 1. Characterization of the synchronous cultures. (A) FACS analysis of a synchronous wild-type culture. For each time point, 10,000 propidium iodine-stained cells were measured for fluorescence intensity. 2C indicates the DNA content of an interphase cell containing a G2 nucleus or a mitotic cell containing two G1 nuclei. 4C indicates the DNA content of a septated cell containing two G2 nuclei. (B) Mitotic (binucleate-cell) and septation indexes of synchronous cultures of wild-type (top) and cdc25–22 (bottom) cells. Black arrows, start times for the samples analyzed; red arrows, times of coincident peaks of 4C cells and septated cells in the wild-type culture; colored bars, cell cycle phases.

Statistical Analyses
To test the statistical significance of the occurrence of motifs in the promoters of a given cluster of n genes among a total of N genes, we used binomial analysis, as follows. Let p and k be the fraction of the N genes and the number of the n genes, respectively, that have the given motif. Then, the probability of having k occurrences of the motif among the n genes can be expressed as

To test the statistical significance of finding cell cycle regulation in common between S. pombe genes and their S. cerevisiae orthologues, we conducted simulations assuming that the orthologue map between the two yeasts is accurate and complete. We chose 747 and 800 genes at random from the S. pombe and the S. cerevisiae genomes, respectively, and ascertained the number of orthologues. We repeated this analysis 10⁶ times and found that the chance occurrence of 139 S. pombe genes being in the S. cerevisiae set was <10^-6. The converse analysis showed that the chance of 142 genes from that S. cerevisiae set having orthologues in the S. pombe set was also <10^-6.

To test the statistical significance of finding overlapping phases of peak expression for orthologous gene pairs, we proceeded as follows. The observed distribution is the distribution of 194 orthologue pairs among the 25 pairs of S. pombe and S. cerevisiae phases (i.e., G1, S, G2, G2/M, and M from each organism). The expected distribution is the distribution of orthologue pairs into phase pairs as calculated using the distribution of the orthologue pairs into the various phases in the respective organisms. The expected number of pairs in a phase pair (a and b) is calculated as 194PaPb, where Pa and Pb are the fractions of orthologue pairs having phase a in S. pombe and phase b in S. cerevisiae, respectively. The two distributions were considered to be independent to test (by ² test) whether the observed distribution shows any strong relationship between phase assignments in the two organisms.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
Synchronous Cultures and Cell Cycle Phases
The sensitivity for detecting periodic transcriptional events depends largely on the degree of synchrony of the cell cultures. We used two methods of synchronization. First, by elutriation centrifugation (see Materials and Methods), we physically selected small-sized or newborn daughter cells at early G2 phase to initiate synchronous wild-type cell cultures. Second, we used a block-and-release approach with a cdc25-22 temperature-sensitive mutant to generate cultures synchronized at the G2/M boundary (Mitchison et al., 1991). In both cases, samples were taken at 10-min intervals for a period of 6–7 h to ensure that at least two complete cell cycles were covered. Cellular DNA contents were analyzed by FACS. Either a 2C peak or both 2C and 4C peaks were observed in both wild-type (Figure 1A) and cdc25 mutant (our unpublished data) cultures. The 4C peak represents cells that contained two G2 nuclei separated by a division septum, a consequence of the daughter cells having completed G1 and S phases before physical separation. Thus, almost all newborn daughter cells were in early G2 phase. The synchrony of both types of cultures also was assessed by measuring the percentages of binucleate cells and septum-containing cells at each time point (Figure 1B). The peaks of binucleate cells indicate synchronous mitosis, whereas the peaks of septum-containing cells indicate synchronous septation, which should coincide with the peaks of histone transcript levels and DNA synthesis (Takahashi et al., 2000). Thus, we designated the period around the peak of binucleate cells as M phase and the period around the peak of septum-containing cells as S phase; G1 phase is the period between M and S, and G2 phase is the period between S and M (Figure 1B). The data shown in Figure 1 established that a high degree of synchrony was achieved with both methods.

Identification of Cell Cycle-regulated Transcripts
To identify cell cycle-regulated transcripts, RNA was isolated from synchronous cultures at 10-min intervals and analyzed by microarray hybridization as described in Materials and Methods. The data from the two cultures were analyzed separately using an unsupervised algorithm that generated the CDC score (i.e., a quantitative measurement of the degree to which gene expression levels oscillate during the cell cycle) and phase of peak expression for each of 4929 ORFs (see Materials and Methods). The final combined CDC scores and phases of peak expression from the two cultures were calculated and used to select the cell cycle-regulated genes (Figure 2A).

Previous studies had identified 43 genes whose transcript levels oscillate in a cell cycle-dependent manner (Table 2). In our study, one of these genes was not included in our microarray, four were filtered out because the cell cycle phase of their peak expression differed significantly between the two types of synchronous cultures, and three were excluded based on low CDC scores (Table 2). Thus, we identified correctly 35 of the 43 previously described cell cycle-regulated transcripts. Accordingly, the false negative rate was no more than 20% (even if all of the previous reports were accurate). In addition, we estimated a false discovery rate of <1.1% (see Materials and Methods). Except in four cases (mid1, rum1, sep1, and wis3), the phases of peak expression determined in our study agreed well with those reported previously (Table 2). In addition to the previously known genes, we identified an additional 712 cell cycle-regulated transcripts with similar phase-specific peak expression between the two types of synchronous cultures. The complete list of 747 cell cycle-regulated genes ordered on the basis of phase of peak expression is available at http://giscompute.gis.a-star.edu.sg/~gisljh/CDC.

Characterization of Known Transcripts by Cell Cycle Phases
Of the 747 cell cycle-regulated genes, >200 have been characterized functionally (and thus assigned gene symbols) based on the pombeDB (Incyte, Beverly, MA) and/or gene DB (Sanger Institute, Hinxton, United Kingdom) (Table 3). Many of these genes showed peaks in transcript abundance that correlated well with the times at which their products are thought to function during the cell cycle. For example, 11 genes whose products are implicated in cell cycle control had cell cycle-regulated transcripts (Table 3, line 1). Among these, wee1 and cdc25 showed peaks in G2 and G2/M, respectively, consistent with the role of Wee1p in negatively regulating Cdc2p during interphase and the role of Cdc25p in activating Cdc2p during the entry into mitosis (Russell and Nurse, 1986). In addition, many genes whose products function in DNA replication, nucleosome assembly, or chromosome organization had peaks of expression in G1 or S (Table 3, lines 2 and 3); several genes whose products function in the polarization of cell growth had peaks of expression in G1, S, or G2 (the phases in which polarized growth is established and carried out) (Table 3, line 4); several genes whose products probably function in the mitotic spindle had peaks of expression in G2/M or M (Table 3, line 5); and many genes whose products function in cytokinesis and/or septation had peaks of expression in M or G1 (which overlaps with the time of septum formation in S. pombe) (Table 3, line 6). Interestingly, many genes whose products function in sexual development also had peaks of expression in M or G1 (Table 3, line 7), consistent with the evidence that sexual development can only initiate from the G1 phase (Borgne et al., 2002).

G2 occupies 70% of the cell cycle in rapidly growing S. pombe cells, and it is accordingly when most increase in cell mass occurs (MacNeill and Nurse, 1997). Thus, it was not surprising to find that many genes whose products are involved in aspects of biosynthesis (transcription, amino acid and protein biosynthesis, protein transport and secretion, lipid metabolism, and cell wall biogenesis) had peaks of expression in G2 (Table 3, lines 8–13). Perhaps more surprising was the finding that many stress response genes had peaks of expression in G2 (Table 3, line 14). This may reflect a need to respond continually to stresses that arise as the plasma membrane and cell wall are remodeled as the cell grows during G2.

Cluster Analysis of Cell Cycle-regulated Genes
Many of the genes known previously to display cell cycle-regulated transcription have expression peaks around the G1-S or M-G1 interval and are thought to be regulated by the transcription factor complexes DSC (G1/S) and PBF (M/G1) (see Introduction). In addition, the S phase-specific transcription of the histone genes is thought to depend on the regulatory sequence ATCAMAACCCTAACCCT in their promoter regions (Choe et al., 1985; Matsumoto and Yanagida, 1985), although the transcription factors involved have not been identified. To identify additional members of these and other coregulated sets of genes, we analyzed the complete set of 747 genes by hierarchical clustering of transcripts with similar expression patterns. We used an uncentered Pearson correlation matrix with complete linkage (Eisen et al., 1998) (Figure 2B). As expected, we found a cluster of 31 genes that were expressed predominantly in G1 and were strongly associated (p value <2.7 x 10^-7) with the MCB (DSC-controlled) promoter motif. In addition, three other promoter motifs were found to have strong associations with sets of cell cycle-regulated genes (Figure 2A). One cluster of G1- and S-phase genes was found to contain the ACE2 motif (p value <3.5 x 10^-4); one cluster of G2-phase genes contains the ATF motif (p value <1.6 x 10^-8); and two clusters of M- and G1-phase genes were found to contain SFF motifs (p value <1.0 x 10^-8) (Figure 2B). We also identified several clusters enriched for genes that have common biological functions but lack known common promoter elements. Two such clusters contain largely G2-expressed genes encoding ribosomal proteins (RiP) (Figure 2B). Each of these clusters is discussed in more detail below. Surprisingly, we did not find a cluster of genes to be strongly associated with the PCB (PBF-controlled) promoter motif (Figure 2A; see, however, below).

G1-Phase Clusters
As noted above, we found an "MCB cluster" of 31 G1-expressed genes of which most (19) possessed one or more MCB motifs within 1 kb of the start codon (Figure 3A). A 20th gene, cig2, has MCB motifs that are >1 kb from its start codon but are still functional in controlling its expression (Ayte et al., 2001; see below). To ask whether expression of these genes is regulated by the DSC complex, we used mutations affecting Cdc10p, a component of the complex. Cells harboring the gain-of-function mutant allele cdc10-C4 allele have been shown to accumulate several MCB-motif-containing transcripts at permissive temperature (McInerny et al., 1995). Similarly, we found that 15 of the 20 genes with MCB motifs showed noticeable transcript accumulation relative to wild-type in at least two of three independent cultures (Figure 3A). We further tested DSC dependency by using the temperature-sensitive loss-of-function allele cdc10-V50 (Reymond et al., 1992; Rowley et al., 1992; see Materials and Methods). Analysis of mitotic and septation indices and of DNA contents confirmed that G1 arrest occurred as expected (Figure 3B, top). Fifteen of the 20 MCB motif-containing genes showed reduced expression in the cdc10-V50 culture, indicating DSC-dependent expression (Figure 3B, bottom). All of these genes contained two or more MCB motifs, and 14 of them also had been indicated to be DSC dependent by the cdc10-C4 experiment. Thus, only two of the MCB motif-containing genes, SPBC1709.12 and SPBC1442.01/ste6, gave inconsistent results in the two tests of DSC dependency. The reason for this discrepancy is not known, although we note that ste6 contains only one MCB motif. The genes identified here as DSC-dependent include some (cdc18, cdc22, cdt1, cig2, and mik1) that were previously known to be so and others that were not. Together, the data suggest that regulation of genes required for the G1/S transition or for DNA synthesis depends on double or multiple MCB motifs.

View larger version (63K): [in this window] [in a new window]   Figure 3. The G1-phase clusters. The transcription profiles are displayed as in Figure 2B. The systematic names plus the gene symbols (where available) are listed on the right. Genes possessing the indicated promoter motifs are marked by a "+" on the right. (A) Expanded view of the MCB cluster (see Figure 2B). The red + indicates that the MCB motifs are not within 1 kb of the start codon (see text). The expression profiles from three independent asynchronous cultures of the cdc10-C4 mutant at permissive temperature (vs. asynchronous wild-type cells as reference) are also shown. (B) Perturbation of the DSC transcriptional activity by use of a cdc10 mutation (see Materials and Methods). The top two panels show mitotic index (binuc), septation index (septa), and DNA contents of the elutriation-synchronized wild-type and cdc10-V50 mutant cultures at 36°C. The bottom panel shows the expression profiles of the MCB cluster genes. The solid boxes on the right indicate genes whose G1 expression was reduced in the cdc10 mutant and thus are classified as DSC dependent. The numbers of MCB and ACE2 motifs associated with each gene are also indicated. (C) Expanded view of the ACE2 cluster (see Figure 2B). The expression profiles from three independent asynchronous cultures of the ace2 mutant (vs. asynchronous wild-type cells as reference) also are shown.

In contrast, among the 11 genes that were originally identified as members of the MCB cluster but have no detectable MCB motifs, none seemed to be DSC dependent by both of the tests described above. In investigating further the regulation of these genes, we noted that many members of the MCB gene cluster (and 6 of the 11 seemingly DSB-independent genes) contained ACE2 motifs in their promoters, suggesting that transcription factors that use these motifs may regulate their G1-specific expression. In support of this hypothesis, we also found an ACE2 cluster of genes very close to the MCB cluster in the dendrogram of Figure 2B, as noted above. The genes in this cluster seemed to be DSC independent by both the cdc10-V50 and cdc10-C4 tests (our unpublished data). In contrast, profiling of a mutant lacking the putative transcription factor Ace2p (Martin-Cuadrado et al., 2003) indicated reduced expression of these genes, suggesting a cell cycle-specific transcription activity for Ace2p (Figure 3C). Most of the previously characterized genes in the ACE2 cluster encode proteins involved in cytokinesis and septation (such as Cdc4p, Bgs4p, and Mid2p). Together, the data suggest that there are at least two transcriptional machineries regulating gene expression around the G1 phase: one specific for the G1/S transition and DNA replication that uses MCB motifs and another specific for cytokinesis and septation that uses ACE2 motifs.

S- and G2-Phase Clusters
Large quantities of new histone proteins are required to form functional chromatin during S phase. Accordingly, transcription of histone genes has been found to be S phase specific in both S. pombe (Choe et al., 1985; Matsumoto et al., 1985) and a variety of other organisms. We found that eight of the nine S. pombe histone genes formed a very prominent S phase cluster (Figures 2B and 4A and Tables 2 and 3). One of the three histone H3 genes (hht2) was missing from the cluster, and only one other gene (sds22, encoding a subunit of protein phosphatase PP1) seemed to be coregulated.

View larger version (91K): [in this window] [in a new window]   Figure 4. The S- and G2-phase clusters. The transcription profiles and gene symbols are displayed as in Figures 2B and 3. (A) The S-phase cluster. (B and C) The two RiP clusters containing ribosomal protein genes. (D) The ATF cluster. The presence or absence of ATF motifs is indicated on the right.

As noted above, the importance of growth during the G2 phase is correlated with the G2-specific expression of many growth-associated genes such as ribosomal protein genes. This was reflected in the cluster analysis by the appearance of two clusters containing many of these genes, the RiP clusters (Figures 2B and 4, B and C). Interestingly, however, only 10 of the 130 ribosomal protein genes were found in our set of 747 cell cycle-regulated genes; eight of these were in one of the RiP clusters.

As noted above, another G2-phase gene cluster (Table 3, line 14, and Figures 2B and 4D) showed a strong association with ATF promoter motifs, which are targets of ATF/CREB-like transcriptional factors during cellular response to environmental stresses in S. cerevisiae (Sellers et al., 1990). Because several of the genes in this cluster are known to be involved in stress response pathways in S. pombe, they are probably also regulated by transcription factors similar to ATF/CREB.

M-Phase Clusters
We found two clusters of M- or M/G1-phase genes that were strongly associated with SFF promoter motifs (Figures 2B and 5). SFF (SWI5 factor) motifs (Lydall et al., 1991), together with MCM1 motifs, have previously been shown to regulate M-phase genes in S. cerevisiae (Spellman et al., 1998). However, we did not find an association with MCM1 motifs, suggesting that this regulatory role is restricted to SFF motifs in S. pombe. Interestingly, many genes that previously were shown to contain PCB motifs and to be regulated by the PBF transcription factors (Anderson et al., 2002) were found in our study to be members of the SFF clusters. Although the PCB motif did not seem to be associated with cell cycle-regulated genes in our analysis (Figure 2A), the PCB sequence GMAACR is very similar to the SFF motif GTMAACAA, suggesting that the SFF motif could be a variant of the PCB motif and thus used by PBF transcription factors. The forkhead domain-containing transcription factor Sep1p has been shown to regulate transcription of cdc15, one of the SFF cluster genes (Zilahi et al., 2000). We therefore investigated whether other genes in the cluster were regulated by Sep1p. In support of this hypothesis, many SFF-motif-containing genes were indeed down-regulated in a sep1 mutant (Figure 5, A and B).

View larger version (85K): [in this window] [in a new window]   Figure 5. The M-phase clusters. The transcription profiles and gene symbols are displayed as in Figures 2B and 3. The presence or absence of SFF motifs is indicated on the right. The expression profiles from three independent asynchronous sep1 mutant cultures (vs. asynchronous wild-type cells as reference) also are shown. (A) The SFF (1) cluster. Most genes have peak expression in M phase. (B) The SFF (2) cluster. Most genes have peak expression in either M or G1 phase.

Search for Cell Cycle-specific Promoter Motifs
To search for other promoter motifs that might regulate cell cycle-specific expression, we analyzed the distributions in the promoter regions of the 747 cell cycle-regulated genes of motif sequences that have been characterized previously in S. cerevisiae (see Materials and Methods). However, among 50 different motifs that we tested, only the four motifs described above (SFF, MCB, ACE2, and ATF) were found to exhibit statistically significant differential distributions among the 747 genes (Figure 2A). Although PCB motifs have been associated previously with several genes with peak transcription at M-G1 (Anderson et al., 2002), we found no such association, apparently because of the abundance of such motifs, which are present in 458 of the 747 cell cycle-regulated genes (see Materials and Methods). Thus, it seems unlikely that the PCB motif plays an important role in mediating cell cycle-specific gene expression.

Comparison of Cell Cycle-regulated Genes between Fission and Budding Yeast
S. pombe and S. cerevisiae are thought to have diverged evolutionarily 10⁹ yr ago (Heckman et al., 2001). Although the two yeasts share many cell cycle regulators, their cell cycles also show some differences in organization. For example, during rapid growth, S. pombe cells have a long G2 phase during which most growth occurs, and the major cell cycle checkpoint is at the G2/M transition. In contrast, S. cerevisiae daughter cells typically have an extended G1 phase during which growth to a critical size is achieved, and the major cell cycle checkpoint in both daughter and mother cells is at the G1/S transition. We therefore hypothesized the existence of two classes of cell cycle genes: those that are directly involved in cell cycle progression and those that are not (e.g., genes controlling metabolism and cell growth) but whose expression must be coordinated with certain cell cycle phases. To test this hypothesis, we compared the set of cell cycle-regulated genes identified here to those identified in S. cerevisiae (Spellman et al., 1998) by using a similar experimental approach. Using a current table of S. pombe and S. cerevisiae orthologues (Wood, personal communication), we found that 139 of the 747 cell cycle-regulated genes identified in S. pombe have 142 orthologues among the 800 cell cycle-regulated genes found in S. cerevisiae (Figure 6A). Of the 140 cell cycle-regulated genes common to both species, many (50) encode proteins involved in cell cycle-specific processes such as DNA replication, chromosome maintenance, and cell cycle control and may represent the core of transcriptionally regulated, cell cycle-specific genes that have been conserved over a large evolutionary distance.

View larger version (78K): [in this window] [in a new window]   Figure 6. S. pombe and S. cerevisiae orthologues showing cell cycle-regulated expression. (A) Venn diagram summarizing orthologies and cell cycle-regulated expression in the two yeasts. The thin red (blue) circle and the sum of red (blue) numbers represent the 4970 (6000) ORFs in S. pombe (S. cerevisiae). The thick circles and enclosed numbers represent the cell cycle-regulated genes. Thus, 139 S. pombe genes with 142 S. cerevisiae orthologues (including multiple-gene pairs, or co-orthologs) were cell cycle regulated in both yeasts; 417 S. pombe genes with 550 S. cerevisiae orthologues were cell cycle regulated in S. pombe but not in S. cerevisiae; and so on. (B) Expression profiles of the 140 orthologues as separated into 194 one-to-one pairs (see text). Expression profiles of the 139 S. pombe genes were displayed based on the elutriation experiment and ordered based on the timing of their peak expression (cf. Figure 2A). Expression profiles of the 142 S. cerevisiae genes were based on the -factor experiment (Spellman et al., 1998) and ordered according to their orthologues in S. pombe. (C) Distribution of the peak-expression phases for the 194 ortholog pairs. The numbers of gene pairs in each category are based on the assignment of peak-expression phases for the individual genes in S. pombe (this study) and S. cerevisiae (Spellman et al., 1998). Numbers in parentheses are the expected numbers of pairs based on the subtotals for each yeast (gray-shaded boxes) and the assumption of no correlation in peak-expression phases between the two yeasts. Pink-shaded and unshaded boxes indicate gene pairs with concordant and discordant expressions, respectively.

To compare the orthologue expression patterns between the two yeasts, we first generated a list of 194 one-to-one orthologue pairs between the 139 genes in S. pombe and the 142 genes in S. cerevisiae. Then, we compared the expression profiles of these gene pairs by using a phasogram format that should allow a systematic view of similarities in expression patterns (Figure 6B). Interestingly, such similarities were not readily apparent, suggesting that there are marked differences between orthologues in their phases of peak expression. Therefore, we grouped the orthologue pairs according to their assigned phases of peak expressions (Figure 6C), which revealed that the distribution of pairs with concordant phasing of peak expression was highly significant (p <2 x 10^-12) by the ² test. Thus, we then separated orthologue pairs that displayed concordant phasing of peak expression from those with discordant phasing. Of the 108 pairs with concordant phasing 25 were found to encode proteins involved in DNA replication or chromosome structure, as highlighted in blue in Figure 7.

View larger version (85K): [in this window] [in a new window]   Figure 7. Expression profiles of the 108 orthologue pairs with concordant phases of peak expression. The transcription profiles are displayed as in Figure 6B. The assigned phases of peak expression and functions in biological processes (where known) of the gene pairs are given. Genes encoding proteins involved in DNA replication or chromosome structure are shown in blue; genes encoding proteins involved in cell wall biogenesis or cytokinesis are shown in brown.

Comparative analysis of the expression patterns of the orthologue pairs with discordant phases of peak expression revealed that some of these genes encode proteins involved in cell metabolism and growth or in cell wall biogenesis (Figure 8). For example, S. pombe exg2 and SPAC19B12.02c were up-regulated at the end of G2 and down-regulated at the end of G1, whereas the S. cerevisiae orthologues EXG2 and GAS1 had nearly the opposite pattern. Such findings might reflect the differing roles of G1 and G2 in the two yeasts as noted above. Interestingly, however, several orthologue pairs thought to be involved in cell wall biogenesis also were found to exhibit concordant expression patterns, with peak expression in G1 (Figure 7). This may reflect specific functions for such proteins in G1 in both yeasts, such as the assembly of the division septum at cytokinesis.

View larger version (110K): [in this window] [in a new window]   Figure 8. Expression profiles of the 86 orthologues with discordant phases of peak expression. Information is displayed as described in Figure 7.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
We have identified 747 S. pombe genes whose expression levels oscillate during the cell cycle both in wild-type cultures synchronized by elutriation and in cdc25 mutant cultures synchronized by block-and-release. Of 43 genes shown previously to have cell cycle-regulated expression, 35 also were identified in our study, suggesting a false negative rate of 20%. Most of these genes displayed high dynamic expression ranges of greater than threefold compared with those of asynchronous references. We also estimated a low false discovery rate of 1%. Together, the data suggest that our approach is of high sensitivity and accuracy (cf. Spellman et al., 1998; Whitfield et al., 2002).

Of the 747 genes, 200 were previously known to be involved in cell cycle-specific processes such as DNA replication, chromosome structure and maintenance, cell wall biogenesis, cytokinesis, and cell cycle control. Cluster analyses of expression patterns identified groups containing both known genes and uncharacterized ORFs, which permits assigning possible functions to these ORFs based on those of the known genes in the same groups. In addition, perturbing the activities of the DSC, Ace2p, and Sep1p transcription factors revealed subsets of functional targets and helped to define the corresponding promoter requirements. Together, our results are consistent with the hypothesis that cell cycle-regulated genes play an important role in cell cycle progression (Spellman et al., 1998; Futcher, 2000; Whitfield et al., 2002; Maqbool et al., 2003).

Promoter-motif analysis revealed four motifs (of 50 different motifs tested) that were found preferentially associated with sets of cell cycle-regulated genes. These motifs were associated with expression at different phases: M-G1 (the SFF motifs that may be used by the putative transcription factor Sep1p); G1 (the MCB motifs used by the DSC transcription factor); G1-S (the ACE2 motifs that may be used by the putative transcription factor Ace2p); and G2 (the ATF motifs). Interestingly, each of these motifs, except for ATF, has been shown to be involved in the cell cycle transcriptional circuitry in S. cerevisiae (Spellman et al., 1998; Simon et al., 2001). However, experimental tests of the roles of the SFF, ACE2, and ATF motifs in regulation of cell cycle-specific expression in S. pombe have yet to be performed.

Based on a table of genes orthologous between S. pombe and S. cerevisiae and the data of Spellman et al. (1998) on the expression of S. cerevisiae genes, we found 140 genes whose orthologues in both yeasts exhibit cell cycle-regulated expression. Comparative analysis of these 140 genes revealed a subset of highly conserved genes that display concordant phases of peak expression; many of these genes encode proteins directly involved in aspects of the nuclear division cycle such as DNA replication and chromosome structure maintenance. In contrast, at least some orthologue pairs that did not display similar phases of peak expression seem to play roles in cell cycle-accompanying processes such as growth and aspects of cell wall biogenesis whose timing may be organism specific.

During the revision of our article, a similar study was reported by Rustici et al. (2004), in which the authors identified 407 cell cycle-regulated genes. By using a Fast Fourier Transformation algorithm, these investigators first generated a list of 1000 genes that met the criteria for cell cycle-modulated expression. This list was further trimmed to 407 genes by the use of additional criteria such as the removal of genes whose oscillation ranges were <1.5-fold (160 of 407, or 39%, had ranges >2-fold). In this study, we have uncovered 747 cell cycle-regulated genes, of which 313 (42%) displayed dynamic ranges of 2-fold, 387 (52%) had ranges of 1.5- to 2-fold, and 47 (6%) had ranges of 1.32- to 1.5-fold. About 60% (242) of the cell cycle-regulated genes found by Rustici et al. (2004) are also in our list of 747 genes (see Supplemental Table 1). Of these 242 genes, 207 were in the top 500 of our 747 genes based on our CDC scores and thus had relatively high dynamic oscillation ranges in our study. Thus, good matches of the two data sets were found for genes exhibiting high dynamic oscillation ranges, suggesting that the differences between the two data sets can probably be attributed to different sensitivities in detecting transcript levels. Our study used oligonucleotide-based microarray technology to analyze 36 time-points that covered two cell cycles (6 h at 10-min intervals), whereas Rustici et al. (2004) used cDNA microarray technology to analyze 24 time-points that also covered two cell cycles (6 h at 15-min intervals). It has been shown that the oligo-based microarray technology is more sensitive than the cDNA technology in detecting ORF-specific expression (Carter et al., 2003). In addition our higher sampling rate could have significantly increased the sensitivity of the algorithms for detecting transcripts whose levels oscillate.

We hope that our results will be useful for the further analysis of the genes with distinctive expression patterns and for comparisons with similar data from other systems. A complete data set from this study is available at http://giscompute.gis.a-star.edu.sg/~gisljh/CDC.

	ACKNOWLEDGMENTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
We thank Y. Kun and K. Gong for assistance in spotting microarrays, Y. Kuang for maintenance and update of databases, and Dr. P. Long for valuable comments on the algorithm for detecting cell cycle-regulated transcripts. We are indebted to Dr. V. Wood for providing the table of fission and budding yeast orthologues before publication. We thank Drs. C. J. McInerny and M. Sipiczki for strains and Drs. T. Lufkin, P. Robson, J. George, S. Gupta, and V. M. D'Souza for discussion and critical reading of the manuscript. We appreciate comments by the monitoring editor Dr. J. R. Pringle and three anonymous reviewers that significantly improved the manuscript. This work was supported by Temasek Life Sciences Laboratory, Singapore, the Agency for Science, Technology and Research, Singapore (A-STAR), and a Genome Institute of Singapore/A-STAR grant (#GIS/03-113401) to J. L.

	Footnotes

 
This article was published online ahead of print in MBC in Press (http://www.molbiolcell.org/cgi/doi/10.1091/mbc.E04-04-0299) on December 22, 2004.

The online version of this article contains supplemental material at MBC Online (http://www.molbiolcell.org).

These authors contributed equally to this work.

Present address: Beijing Normal University, Beijing, China.

Address correspondence to: Jianhua Liu (liujh{at}gis.a-star.edu.sg).

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