Cell Cycle Progression In Yeast Research Articles

Effects of a novel DNA-damaging agent on the budding yeast Saccharomyces cerevisiae cell cycle.

We have investigated the effects on Saccharomyces cerevisiae of a novel antitumour agent (FCE24517 or Tallimustine) which causes selective alkylations to adenines in the minor groove of DNA. Tallimustine, added to wild-type cells for short periods, reduced the growth rate and increased the percentage of budded cells and delayed the cell cycle in the late S + G2 + M phases. In the rad9 delta null mutant cells, Tallimustine treatment did not affect growth rate and the percentage of budded cells but greatly reduced cell viability compared to isogenic cells. Consistent with a role of RAD9 in inducing a transient delay in G2 phase which preserves cell viability, the potent cytotoxic effect of the drug on rad9 delta cells was alleviated by treatment with nocodazole. Tallimustine was also found to delay the resumption from G1 arrest of wild-type but not of rad9 delta cells. These data indicate that the effects of Tallimustine on cell cycle progression in yeast are mediated by the RAD9 gene product. From our data it appears that yeast could be a valuable model system to study the mode of action of this alkylating drug and of minor groove alkylators in general.

From START to FINISH: computational analysis of cell cycle control in budding yeast.

In the cell division cycle of budding yeast, START refers to a set of tightly linked events that prepare a cell for budding and DNA replication, and FINISH denotes the interrelated events by which the cell exits from mitosis and divides into mother and daughter cells. On the basis of recent progress made by molecular biologists in characterizing the genes and proteins that control START and FINISH, we crafted a new mathematical model of cell cycle progression in yeast. Our model exploits a natural separation of time scales in the cell cycle control network to construct a system of differential-algebraic equations for protein synthesis and degradation, post-translational modifications, and rapid formation and dissociation of multimeric complexes. The model provides a unified account of the observed phenotypes of 257 mutant yeast strains (98% of the 263 strains in the data set used to constrain the model). We then use the model to predict the phenotypes of 30 novel combinations of mutant alleles. Our comprehensive model of the molecular events controlling cell cycle progression in budding yeast has both explanatory and predictive power. Future experimental tests of the model’s predictions will be useful to refine the underlying molecular mechanism, to constrain the adjustable parameters of the model, and to provide new insights into how the cell division cycle is regulated in budding yeast.

Systematic Analysis of Cell Cycle Effects of Common Drugs Leads to the Discovery of a Suppressive Interaction between Gemfibrozil and Fluoxetine

Screening chemical libraries to identify compounds that affect overall cell proliferation is common. However, in most cases, it is not known whether the compounds tested alter the timing of particular cell cycle transitions. Here, we evaluated an FDA-approved drug library to identify pharmaceuticals that alter cell cycle progression in yeast, using DNA content measurements by flow cytometry. This approach revealed strong cell cycle effects of several commonly used pharmaceuticals. We show that the antilipemic gemfibrozil delays initiation of DNA replication, while cells treated with the antidepressant fluoxetine severely delay progression through mitosis. Based on their effects on cell cycle progression, we also examined cell proliferation in the presence of both compounds. We discovered a strong suppressive interaction between gemfibrozil and fluoxetine. Combinations of interest among diverse pharmaceuticals are difficult to identify, due to the daunting number of possible combinations that must be evaluated. The novel interaction between gemfibrozil and fluoxetine suggests that identifying and combining drugs that show cell cycle effects might streamline identification of drug combinations with a pronounced impact on cell proliferation.

Impaired Manganese Metabolism Causes Mitotic Misregulation

Manganese is an essential trace element, whose intracellular levels need to be carefully regulated. Mn(2+) acts as a cofactor for many enzymes and excess of Mn(2+) is toxic. Alterations in Mn(2+) homeostasis affect metabolic functions and mutations in the human Mn(2+)/Ca(2+) transporter ATP2C1 have been linked to Hailey-Hailey disease. By deletion of the yeast orthologue PMR1 we have studied the impact of Mn(2+) on cell cycle progression and show that an excess of cytosolic Mn(2+) alters S-phase transit, induces transcriptional up-regulation of cell cycle regulators, bypasses the need for S-phase cell cycle checkpoints and predisposes to genomic instability. On the other hand, we find that depletion of the Golgi Mn(2+) pool requires a functional morphology checkpoint to avoid the formation of polyploid cells.

Parametric modeling of cellular state transitions as measured with flow cytometry

BackgroundGradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor.ResultsTo ensure precision and rigor, we propose a parametric modeling framework StateProfiler based on finite mixtures of skew t-Normal distributions that are robust against non-Gaussian features caused by asymmetry and outliers in data. Further, we present in StateProfiler a new greedy EM algorithm for fast and optimal model selection. The parsimonious approach of our greedy algorithm allows us to detect the genuine dynamic variation in the key features as and when they appear in time course data. We also present a procedure to construct a well-fitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model. This allows precise profiling of unusually shaped distributions and less well-separated features that may appear due to cellular heterogeneity even within clonal populations.ConclusionsBy modeling flow cytometric data measured over time course and marker space with StateProfiler, specific parametric characteristics of cellular states can be identified. The parameters are then tested statistically for learning global and local patterns of spatio-temporal change. We applied StateProfiler to identify the temporal features of yeast cell cycle progression based on knockout of S-phase triggering cyclins Clb5 and Clb6, and then compared the S-phase delay phenotypes due to differential regulation of the two cyclins. We also used StateProfiler to construct the temporal profile of clonal divergence underlying lineage selection in mammalian hematopoietic progenitor cells.

A Systematic Analysis of Cell Cycle Regulators in Yeast Reveals That Most Factors Act Independently of Cell Size to Control Initiation of Division

Upstream events that trigger initiation of cell division, at a point called START in yeast, determine the overall rates of cell proliferation. The identity and complete sequence of those events remain unknown. Previous studies relied mainly on cell size changes to identify systematically genes required for the timely completion of START. Here, we evaluated panels of non-essential single gene deletion strains for altered DNA content by flow cytometry. This analysis revealed that most gene deletions that altered cell cycle progression did not change cell size. Our results highlight a strong requirement for ribosomal biogenesis and protein synthesis for initiation of cell division. We also identified numerous factors that have not been previously implicated in cell cycle control mechanisms. We found that CBS, which catalyzes the synthesis of cystathionine from serine and homocysteine, advances START in two ways: by promoting cell growth, which requires CBS's catalytic activity, and by a separate function, which does not require CBS's catalytic activity. CBS defects cause disease in humans, and in animals CBS has vital, non-catalytic, unknown roles. Hence, our results may be relevant for human biology. Taken together, these findings significantly expand the range of factors required for the timely initiation of cell division. The systematic identification of non-essential regulators of cell division we describe will be a valuable resource for analysis of cell cycle progression in yeast and other organisms.

High-Dimensional ODEs Coupled With Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification

Gene regulation is a complicated process. The interaction of many genes and their products forms an intricate biological network. Identification of this dynamic network will help us understand the biological processes in a systematic way. However, the construction of a dynamic network is very challenging for a high-dimensional system. In this article we propose to use a set of ordinary differential equations (ODE), coupled with dimensional reduction by clustering and mixed-effects modeling techniques, to model the dynamic gene regulatory network (GRN). The ODE models allow us to quantify both positive and negative gene regulation as well as feedback effects of genes in a functional module on the dynamic expression changes of genes in another functional module, which results in a directed graph network. A five-step procedure—clustering, smoothing, regulation identification, parameter estimates refining, and function enrichment analysis (CSIEF)—is developed to identify the ODE-based dynamic GRN. In the proposed CSIEF procedure, a series of cutting-edge statistical methods and techniques are employed, that include nonparametric mixed-effects models with a mixture distribution for clustering, nonparametric mixed-effects smoothing-based methods for ODE models, the smoothly clipped absolute deviation (SCAD)-based variable selection, and stochastic approximation EM (SAEM) approach for mixed-effects ODE model parameter estimation. The key step, the SCAD-based variable selection, is justified by investigating its asymptotic properties and validated by Monte Carlo simulations. We apply the proposed method to identify the dynamic GRN for yeast cell cycle progression data. We are able to annotate the identified modules through function enrichment analyses. Some interesting biological findings are discussed. The proposed procedure is a promising tool for constructing a general dynamic GRN and more complicated dynamic networks. This article has supplementary material online.

The Cell Cycle Regulated Transcriptome of Trypanosoma brucei

Progression of the eukaryotic cell cycle requires the regulation of hundreds of genes to ensure that they are expressed at the required times. Integral to cell cycle progression in yeast and animal cells are temporally controlled, progressive waves of transcription mediated by cell cycle-regulated transcription factors. However, in the kinetoplastids, a group of early-branching eukaryotes including many important pathogens, transcriptional regulation is almost completely absent, raising questions about the extent of cell-cycle regulation in these organisms and the mechanisms whereby regulation is achieved. Here, we analyse gene expression over the Trypanosoma brucei cell cycle, measuring changes in mRNA abundance on a transcriptome-wide scale. We developed a “double-cut” elutriation procedure to select unperturbed, highly synchronous cell populations from log-phase cultures, and compared this to synchronization by starvation. Transcriptome profiling over the cell cycle revealed the regulation of at least 430 genes. While only a minority were homologous to known cell cycle regulated transcripts in yeast or human, their functions correlated with the cellular processes occurring at the time of peak expression. We searched for potential target sites of RNA-binding proteins in these transcripts, which might earmark them for selective degradation or stabilization. Over-represented sequence motifs were found in several co-regulated transcript groups and were conserved in other kinetoplastids. Furthermore, we found evidence for cell-cycle regulation of a flagellar protein regulon with a highly conserved sequence motif, bearing similarity to consensus PUF-protein binding motifs. RNA sequence motifs that are functional in cell-cycle regulation were more widespread than previously expected and conserved within kinetoplastids. These findings highlight the central importance of post-transcriptional regulation in the proliferation of parasitic kinetoplastids.

Structure/Function Implications in a Dynamic Complex of the Intrinsically Disordered Sic1 with the Cdc4 Subunit of an SCF Ubiquitin Ligase

Intrinsically disordered proteins can form highly dynamic complexes with partner proteins. One such dynamic complex involves the intrinsically disordered Sic1 with its partner Cdc4 in regulation of yeast cell cycle progression. Phosphorylation of six N-terminal Sic1 sites leads to equilibrium engagement of each phosphorylation site with the primary binding pocket in Cdc4, the substrate recognition subunit of a ubiquitin ligase. ENSEMBLE calculations using experimental nuclear magnetic resonance and small-angle X-ray scattering data reveal significant transient structure in both phosphorylation states of the isolated ensembles (Sic1 and pSic1) that modulates their electrostatic potential, suggesting a structural basis for the proposed strong contribution of electrostatics to binding. A structural model of the dynamic pSic1-Cdc4 complex demonstrates the spatial arrangements in the ubiquitin ligase complex. These results provide a physical picture of a protein that is predominantly disordered in both its free and bound states, enabling aspects of its structure/function relationship to be elucidated.

RFC3 regulates cell proliferation and pathogen resistance in Arabidopsis

Replication factor C subunit 3 (RFC3) is one of the small subunits of the RFC complex originally purified from the HeLa cells that is essential for the in vitro replication of Simian virus 40 (SV40). Although RFC has been reported to be involved in DNA replication, DNA repair and check-point control of cell cycle progression in yeast, little is known about the precise function of each subunit of the RFC in plants. We recently reported the identification of rfc3-1, which carries a point mutation leading to plants with enhanced expression of Pathogenesis-Related (PR) genes and resistance against the virulent oomycete Hyaloperonospora arabidopsidis (H. a.) Noco2. The mutant is hypersensitive to SA and has enhanced pathogen resistance independent of Nonexpressor of PR genes 1 (NPR1). The rfc3-1 mutation caused a substitution from a nonpolar aliphatic amino acid (Gly-84) to a negatively charged amino acid (Asp) in functional domain III, which is one of eight conserved domains in the RFC. This may interfere with the interaction between RFC3 and other subunits, compromising the function of the protein complex, and leading to cell proliferation defects in the leaves and roots of Arabidopsis. Furthermore, enhanced expression of PR genes and induction of systemic acquired resistance in rfc3-1 may be caused by a partial loss of RFC function through its involvement in replication-coupled chromatin assembling.

The yeast Cdc8 exhibits both deoxythymidine monophosphate and diphosphate kinase activities

The existence of multifunctional enzymes in the nucleotide biosynthesis pathways is believed to be one of the important mechanisms to facilitate the synthesis and the efficient supply of deoxyribonucleotides for DNA replication. Here, we used the bacterially expressed yeast thymidylate kinase (encoded by the CDC8 gene) to demonstrate that the purified Cdc8 protein possessed thymidylate-specific nucleoside diphosphate kinase activity in addition to thymidylate kinase activity. The yeast endogenous nucleoside diphosphate kinase is encoded by YNK1, which appears to be non-essential. Our results suggest that Cdc8 has dual enzyme activities and could duplicate the function of Ynk1 in thymidylate synthesis. We also discuss the importance of the coordinated expression of CDC8 during the cell cycle progression in yeast.

Cell cycle-dependent phosphorylation of human CDC5 regulates RNA processing

CDC5 proteins are components of the pre-mRNA splicing complex and essential for cell cycle progression in yeast, plants and mammals. Human CDC5 is phosphorylated in a mitogen-dependent manner, and its association with the spliceosome is ATP-dependent. Examination of the amino acid sequence suggests that CDC5L may be phosphorylated at up to 28 potential consensus recognition sequences for known kinases, however, the identity of actual phosphorylation sites, their role in regulating CDC5L activity, and the kinases responsible for their phosphorylation have not previously been determined. Using two-dimensional phosphopeptide mapping and nanoelectrospray mass spectrometry, we now show that CDC5L is phosphorylated on at least nine sites in vivo. We demonstrate that while CDC5L is capable of forming homodimers in vitro and in vivo, neither homodimerization nor nuclear localization is dependent on phosphorylation at these sites. Using an in vitro splicing assay, we show that phosphorylation of CDC5L at threonines 411 and 438 within recognition sequences for CDKs are required for CDC5L-mediated pre-mRNA splicing. We also demonstrate that a specific inhibitor of CDK2, CVT-313, inhibits CDC5L phosphorylation in both in vitro kinase assays and in vivo radiolabeling experiments in cycling cells. These studies represent the first demonstration of a regulatory role for phosphorylation of CDC5L, and suggest that targeting these sites or the implicated kinases may provide novel strategies for treating disorders of unguarded cellular proliferation, such as cancer.

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

BackgroundIntegrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.ResultsThe proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.ConclusionThe major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (e.g., LOOCV) and biologically (e.g., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.

Structural basis for SENP2 protease interactions with SUMO precursors and conjugated substrates

SUMO processing and deconjugation are essential proteolytic activities for nuclear metabolism and cell-cycle progression in yeast and higher eukaryotes. To elucidate the mechanisms used during substrate lysine deconjugation, SUMO isoform processing and SUMO isoform interactions, X-ray structures were determined for a catalytically inert SENP2 protease domain in complex with conjugated RanGAP1-SUMO-1 or RanGAP1-SUMO-2, or in complex with SUMO-2 or SUMO-3 precursors. Common features within the active site include a 90 degrees kink proximal to the scissile bond that forces C-terminal amino acid residues or the lysine side chain toward a protease surface that appears optimized for lysine deconjugation. Analysis of this surface reveals SENP2 residues, particularly Met497, that mediate, and in some instances reverse, in vitro substrate specificity. Mutational analysis and biochemistry provide a mechanism for SENP2 substrate preferences that explains why SENP2 catalyzes SUMO deconjugation more efficiently than processing.

Pas1, a G1 cyclin, regulates amino acid uptake and rescues a delay in G1 arrest in Tsc1 and Tsc2 mutants in Schizosaccharomyces pombe

Tuberous sclerosis complex is a tumor suppressor syndrome caused by mutations in either the TSC1 or the TSC2 gene. Previous studies have shown that deletion of the TSC1 or TSC2 ortholog in Schizosaccharomyces pombe results in an amino acid uptake defect, with conditional lethality. We identified a G1 cyclin, pas1+, as a high-copy suppressor of this defect in Deltatsc1. Disruption of pas1+ causes defects in arginine and leucine uptake that are remarkably similar to Deltatsc1 and Deltatsc2, whereas Deltapas1Deltatsc1 and Deltapas1Deltatsc2 double mutants have more severe amino acid uptake defects. In a second screen, we identified a novel G63D/S165 N mutant of the small GTPase Rhb1, the target of the Tsc1/Tsc2 protein complex. The Rhb1 mutant suppresses amino acid uptake in Deltatsc1 yeast, but not in Deltapas1 yeast. Hence, Pas1 does not regulate amino acid uptake through Rhb1. To determine whether Pas1 links nutrient availability to cell cycle progression downstream of the Tsc1/Tsc2 complex, we examined the kinetics of G1 arrest in single and double mutant strains. After nitrogen starvation, Deltatsc1 and Deltatsc2 yeast had a delay in G1 arrest when compared with wild-type, which was rescued by deletion of pas1+. In summary, we identified the G1 cyclin, Pas1, as a novel regulator of amino acid uptake. Our data support a model in which Pas1 inhibits G1 arrest downstream of Tsc1 and Tsc2, linking nutrient uptake and cell cycle progression in yeast.

Guanylic nucleotide starvation affects Saccharomyces cerevisiae mother-daughter separation and may be a signal for entry into quiescence

BackgroundGuanylic nucleotides are both macromolecules constituents and crucial regulators for a variety of cellular processes. Therefore, their intracellular concentration must be strictly controlled. Consistently both yeast and mammalian cells tightly correlate the transcription of genes encoding enzymes critical for guanylic nucleotides biosynthesis with the proliferation state of the cell population.ResultsTo gain insight into the molecular relationships connecting intracellular guanylic nucleotide levels and cellular proliferation, we have studied the consequences of guanylic nucleotide limitation on Saccharomyces cerevisiae cell cycle progression. We first utilized mycophenolic acid, an immunosuppressive drug that specifically inhibits inosine monophosphate dehydrogenase, the enzyme catalyzing the first committed step in de novo GMP biosynthesis. To approach this system physiologically, we next developed yeast mutants for which the intracellular guanylic nucleotide pools can be modulated through changes of growth conditions. In both the pharmacological and genetic approaches, we found that guanylic nucleotide limitation generated a mother-daughter separation defect, characterized by cells with two unseparated daughters. We then showed that this separation defect resulted from cell wall perturbations but not from impaired cytokinesis. Importantly, cells with similar separation defects were found in a wild type untreated yeast population entering quiescence upon nutrient limitation.ConclusionOur results demonstrate that guanylic nucleotide limitation slows budding yeast cell cycle progression, with a severe pause in telophase. At the cellular level, guanylic nucleotide limitation causes the emergence of cells with two unseparated daughters. By fluorescence and electron microscopy, we demonstrate that this phenotype arises from defects in cell wall partition between mother and daughter cells. Because cells with two unseparated daughters are also observed in a wild type population entering quiescence, our results reinforce the hypothesis that guanylic nucleotide intracellular pools contribute to a signal regulating both cell proliferation and entry into quiescence.

Mutational Analysis of the Stator Subunit E of the Yeast V-ATPase

Subunit E is a component of the peripheral stalk(s) that couples membrane and peripheral subunits of the V-ATPase complex. In order to elucidate the function of subunit E, site-directed mutations were performed at the amino terminus and carboxyl terminus. Except for S78A and D233A/T202A, which exhibited V(1)V(o) assembly defects, the function of subunit E was resistant to mutations. Most mutations complemented the growth phenotype of vma4Delta mutants, including T6A and D233A, which only had 25% of the wild-type ATPase activity. Residues Ser-78 and Thr-202 were essential for V(1)V(o) assembly and function. The mutation S78A destabilized subunit E and prevented assembly of V(1) subunits at the membranes. Mutant T202A membranes exhibited 2-fold increased V(max) and about 2-fold less of V(1)V(o) assembly; the mutation increased the specific activity of V(1)V(o) by enhancing the k(cat) of the enzyme 4-fold. Reduced levels of V(1)V(o) and V(o) complexes at T202A membranes suggest that the balance between V(1)V(o) and V(o) was not perturbed; instead, cells adjusted the amount of assembled V-ATPase complexes in order to compensate for the enhanced activity. These results indicated communication between subunit E and the catalytic sites at the A(3)B(3) hexamer and suggest potential regulatory roles for the carboxyl end of subunit E. At the carboxyl end, alanine substitution of Asp-233 significantly reduced ATP hydrolysis, although the truncation 229-233Delta and the point mutation K230A did not affect assembly and activity. The implication of these results for the topology and functions of subunit E within the V-ATPase complex are discussed.

A basis for SUMO protease specificity provided by analysis of human Senp2 and a Senp2-SUMO complex.

Modification of cellular proteins by the ubiquitin-like protein SUMO is essential for nuclear metabolism and cell cycle progression in yeast. X-ray structures of the human Senp2 catalytic protease domain and of a covalent thiohemiacetal transition-state complex obtained between the Senp2 catalytic domain and SUMO-1 revealed details of the respective protease and substrate surfaces utilized in interactions between these two proteins. Comparative biochemical and structural analysis between Senp2 and the yeast SUMO protease Ulp1 revealed differential abilities to process SUMO-1, SUMO-2, and SUMO-3 in maturation and deconjugation reactions. Further biochemical characterization of the three SUMO isoforms into which an additional Gly-Gly di-peptide was inserted, or whereby the respective SUMO tails from the three isoforms were swapped, suggests a strict dependence for SUMO isopeptidase activity on residues C-terminal to the conserved Gly-Gly motif and preferred cleavage site for SUMO proteases.

Cell Cycle-regulated Gene Expression inArabidopsis

Regulated gene expression is an important mechanism for controlling cell cycle progression in yeast and mammals, and genes involved in cell division-related processes often show transcriptional regulation dependent on cell cycle position. Analysis of cell cycle processes in plants has been hampered by the lack of synchronizable cell suspensions for Arabidopsis, and few cell cycle-regulated genes are known. Using a recently described synchrony system, we have analyzed RNA from sequential samples of Arabidopsis cells progressing through the cell cycle using Affymetrix Genearrays. We identify nearly 500 genes that robustly display significant fluctuation in expression, representing the first genomic analysis of cell cycle-regulated gene expression in any plant. In addition to the limited number of genes previously identified as cell cycle-regulated in plants, we also find specific patterns of regulation for genes known or suspected to be involved in signal transduction, transcriptional regulation, and hormonal regulation, including key genes of cytokinin response. Genes identified represent pathways that are cell cycle-regulated in other organisms and those involved in plant-specific processes. The range and number of cell cycle-regulated genes show the close integration of the plant cell cycle into a variety of cellular control and response pathways.

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