Global control of cell-cycle transcription by coupled CDK and network oscillators

Transcriptional regulation underlying recovery from a DNA damage-induced arrest

When the DNA of a cell is damaged, cell cycle progression is arrested and cell cycle-specific transcription is inhibited. However, cell cycle-specific transcription is required for eventual recovery from the DNA damage-induced arrest. Here we discuss recent findings that demonstrate how transcription is fine-tuned during the DNA damage response and how this controls the capacity to recover from a DNA damage arrest in G(2) phase.

DNA replication stress differentially regulates G1/S genes via Rad53-dependent inactivation of Nrm1

MBF and SBF transcription factors regulate a large family of coordinately expressed G1/S genes required for early cell-cycle functions including DNA replication and repair. SBF is inactivated upon S-phase entry by Clb/CDK whereas MBF targets are repressed by the co-repressor, Nrm1. Using genome-wide expression analysis of cells treated with methyl methane sulfonate (MMS), hydroxyurea (HU) or camptothecin (CPT), we show that genotoxic stress during S phase specifically induces MBF-regulated genes. This occurs via direct phosphorylation of Nrm1 by Rad53, the effector checkpoint kinase, which prevents its binding to MBF target promoters. We conclude that MBF-regulated genes are distinguished from SBF-regulated genes by their sensitivity to activation by the S-phase checkpoint, thereby, providing an effective mechanism for enhancing DNA replication and repair and promoting genome stability.

Cyclin-dependent kinases are regulators and effectors of oscillations driven by a transcription factor network

During embryonic cell cycles, B-cyclin-CDKs function as the core component of an autonomous oscillator. Current models for the cell-cycle oscillator in nonembryonic cells are slightly more complex, incorporating multiple G1, S phase, and mitotic cyclin-CDK complexes. However, periodic events persist in yeast cells lacking all S phase and mitotic B-cyclin genes, challenging the assertion that cyclin-CDK complexes are essential for oscillations. These and other results led to the proposal that a network of sequentially activated transcription factors functions as an underlying cell-cycle oscillator. Here we examine the individual contributions of a transcription factor network and cyclin-CDKs to the maintenance of cell-cycle oscillations. Our findings suggest that while cyclin-CDKs are not required for oscillations, they do contribute to oscillation robustness. A model emerges in which cyclin expression (thereby, CDK activity) is entrained to an autonomous transcriptional oscillator. CDKs then modulate oscillator function and serve as effectors of the oscillator.

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 process in a systematic way. However, the construction of such 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 regulations as well as feedback effects of one set of genes in a functional module on the dynamic expression changes of the 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 non-parametric 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 of the proposed procedure 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.

Cell cycle synchronization by nutrient modulation

Living cells respond to changing environments by regulating their genes and activities. In unicellular organisms such as yeasts, the cell division cycle is coupled to the nutrient availability. However, it is unclear how tight this coupling is and how the intrinsic time scales of the different cell cycle processes respond to varying nutrient conditions. Here we study the cell cycle behavior of the budding yeast Saccharomyces cerevisiae in response to periodically modulated nutrient availability, using a microfluidic platform which allows for longtime cultivation, programmed medium switching, and automated time-lapse image acquisition. We observe that the division cycle of the yeast cells can follow a periodically modulated medium so that the whole population can be driven into synchrony. When the period of the nutrient modulation is optimized, as many as 80% of the cells in a population are continuously synchronized. The degree of synchronization as a function of the nutrient modulation period can be qualitatively captured by a stochastic phenomenological model. Our work may shed light on the coupling between the cell growth and cell division as well as provide a nontoxic and non-invasive method to continuously synchronize the cell cycle.

Transcriptional regulation is a major controller of cell cycle transition dynamics

DNA replication, mitosis and mitotic exit are critical transitions of the cell cycle which normally occur only once per cycle. A universal control mechanism was proposed for the regulation of mitotic entry in which Cdk helps its own activation through two positive feedback loops. Recent discoveries in various organisms showed the importance of positive feedbacks in other transitions as well. Here we investigate if a universal control system with transcriptional regulation(s) and post-translational positive feedback(s) can be proposed for the regulation of all cell cycle transitions. Through computational modeling, we analyze the transition dynamics in all possible combinations of transcriptional and post-translational regulations. We find that some combinations lead to ‘sloppy’ transitions, while others give very precise control. The periodic transcriptional regulation through the activator or the inhibitor leads to radically different dynamics. Experimental evidence shows that in cell cycle transitions of organisms investigated for cell cycle dependent periodic transcription, only the inhibitor OR the activator is under cyclic control and never both of them. Based on these observations, we propose two transcriptional control modes of cell cycle regulation that either STOP or let the cycle GO in case of a transcriptional failure. We discuss the biological relevance of such differences.

Metabolic cycling without cell division cycling in respiring yeast

Despite rapid progress in characterizing the yeast metabolic cycle, its connection to the cell division cycle (CDC) has remained unclear. We discovered that a prototrophic batch culture of budding yeast, growing in a phosphate-limited ethanol medium, synchronizes spontaneously and goes through multiple metabolic cycles, whereas the fraction of cells in the G1/G0 phase of the CDC increases monotonically from 90 to 99%. This demonstrates that metabolic cycling does not require cell division cycling and that metabolic synchrony does not require carbon-source limitation. More than 3,000 genes, including most genes annotated to the CDC, were expressed periodically in our batch culture, albeit a mere 10% of the cells divided asynchronously; only a smaller subset of CDC genes correlated with cell division. These results suggest that the yeast metabolic cycle reflects a growth cycle during G1/G0 and explains our previous puzzling observation that genes annotated to the CDC increase in expression at slow growth.

Commitment to a cellular transition precedes genome-wide transcriptional change

In budding yeast, commitment to cell division corresponds to activating the positive feedback loop of G1 cyclins controlled by the transcription factors SBF and MBF. This pair of transcription factors has over 200 targets, implying that cell-cycle commitment coincides with genome-wide changes in transcription. Here, we find that genes within this regulon have a well-defined distribution of transcriptional activation times. Combinatorial use of SBF and MBF results in a logical OR function for gene expression and partially explains activation timing. Activation of G1 cyclin expression precedes the activation of the bulk of the G1/S regulon, ensuring that commitment to cell division occurs before large-scale changes in transcription. Furthermore, we find similar positive feedback-first regulation in the yeasts S. bayanus and S. cerevisiae, as well as human cells. The widespread use of the feedback-first motif in eukaryotic cell-cycle control, implemented by nonorthologous proteins, suggests its frequent deployment at cellular transitions.

A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments

Motivation: To advance understanding of eukaryotic cell division, it is important to observe the process precisely. To this end, researchers monitor changes in dividing cells as they traverse the cell cycle, with the presence or absence of morphological or genetic markers indicating a cell's position in a particular interval of the cell cycle. A wide variety of marker data is available, including information-rich cellular imaging data. However, few formal statistical methods have been developed to use these valuable data sources in estimating how a population of cells progresses through the cell cycle. Furthermore, existing methods are designed to handle only a single binary marker of cell cycle progression at a time. Consequently, they cannot facilitate comparison of experiments involving different sets of markers.

Results: Here, we develop a new sampling model to accommodate an arbitrary number of different binary markers that characterize the progression of a population of dividing cells along a branching process. We engineer a strain of Saccharomyces cerevisiae with fluorescently labeled markers of cell cycle progression, and apply our new model to two image datasets we collected from the strain, as well as an independent dataset of different markers. We use our model to estimate the duration of post-cytokinetic attachment between a S.cerevisiae mother and daughter cell. The Java implementation is fast and extensible, and includes a graphical user interface. Our model provides a powerful and flexible cell cycle analysis tool, suitable to any type or combination of binary markers.

Availability: The software is available from: http://www.cs.duke.edu/~amink/software/cloccs/.

Contact:michael.mayhew@duke.edu; amink@cs.duke.edu

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements

Background

Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.

Results

We present a generative model-based Bayesian hierarchical clustering algorithm for microarray time series that employs Gaussian process regression to capture the structure of the data. By using a mixture model likelihood, our method permits a small proportion of the data to be modelled as outlier measurements, and adopts an empirical Bayes approach which uses replicate observations to inform a prior distribution of the noise variance. The method automatically learns the optimum number of clusters and can incorporate non-uniformly sampled time points. Using a wide variety of experimental data sets, we show that our algorithm consistently yields higher quality and more biologically meaningful clusters than current state-of-the-art methodologies. We highlight the importance of modelling outlier values by demonstrating that noisy genes can be grouped with other genes of similar biological function. We demonstrate the importance of including replicate information, which we find enables the discrimination of additional distinct expression profiles.

Conclusions

By incorporating outlier measurements and replicate values, this clustering algorithm for time series microarray data provides a step towards a better treatment of the noise inherent in measurements from high-throughput genomic technologies. Timeseries BHC is available as part of the R package 'BHC' (version 1.5), which is available for download from Bioconductor (version 2.9 and above) via http://www.bioconductor.org/packages/release/bioc/html/BHC.html?pagewanted=all.

Dikaryotic cell cycle in the phytopathogenic fungus Ustilago maydis is controlled by the DNA damage response cascade

In a large group of fungi, mating results in a dikaryon, a cell in which the two nuclei--one from each parent cell--share a single cytoplasm for a period of time without undergoing nuclear fusion. The dikaryon stage is typical in the life cycles of many fungal species primarily in the Basidiomycota, a large group that includes mushrooms, bracket fungi and many phytopathogenic fungi, such as the corn pathogen Ustilago maydis. Recently, we described that in U. maydis two conserved DNA-damage checkpoint kinases, Chk1 and Atr1, work together to control the dikaryon formation. However, how this pathway is activated during the dikaryon formation and how its activation/deactivation is coordinated with the different cell cycle phases is unknown. Here we propose and discuss several hypothesis to address these questions.

Dynamics of septin ring and collar formation in Saccharomyces cerevisiae

Although the septin ring and collar in budding yeast were described over 20 years ago, there is still controversy regarding the organization of septin filaments within these structures and about the way in which the ring first forms and about how it converts into a collar at the mother-bud neck. Here we present quantitative analyses of the recruitment of fluorescently-tagged septins to the ring and collar through the cell cycle. Septin ring assembly began several minutes after polarity establishment and this interval was longer in daughter than in mother cells, suggesting asymmetric inheritance of septin regulators. Septins formed an initial faint and irregular ring, which became more regular as septins were recruited at a constant rate. This steady rate of septin recruitment continued for several minutes after the ring converted to a collar at bud emergence. We did not detect a stepwise change in septin fluorescence during the ring-to-collar transition. After collar formation, septins continued to accumulate at the bud neck, though at a reduced rate, until the onset of cytokinesis when the amount of neck-localized septins rapidly decreased. Implications for the mechanism of septin ring assembly are discussed.

Rising cyclin-CDK levels order cell cycle events

Background

Diverse mitotic events can be triggered in the correct order and time by a single cyclin-CDK. A single regulator could confer order and timing on multiple events if later events require higher cyclin-CDK than earlier events, so that gradually rising cyclin-CDK levels can sequentially trigger responsive events: the “quantitative model” of ordering.

Methodology/Principal Findings

This ‘quantitative model’ makes predictions for the effect of locking cyclin at fixed levels for a protracted period: at low cyclin levels, early events should occur rapidly, while late events should be slow, defective, or highly variable (depending on threshold mechanism). We titrated the budding yeast mitotic cyclin Clb2 within its endogenous expression range to a stable, fixed level and measured time to occurrence of three mitotic events: growth depolarization, spindle formation, and spindle elongation, as a function of fixed Clb2 level. These events require increasingly more Clb2 according to their normal order of occurrence. Events occur efficiently and with low variability at fixed Clb2 levels similar to those observed when the events normally occur. A second prediction of the model is that increasing the rate of cyclin accumulation should globally advance timing of all events. Moderate (<2-fold) overexpression of Clb2 accelerates all events of mitosis, resulting in consistently rapid sequential cell cycles. However, this moderate overexpression also causes a significant frequency of premature mitoses leading to inviability, suggesting that Clb2 expression level is optimized to balance the fitness costs of variability and catastrophe.

Conclusions/Significance

We conclude that mitotic events are regulated by discrete cyclin-CDK thresholds. These thresholds are sequentially triggered as cyclin increases, yielding reliable order and timing. In many biological processes a graded input must be translated into discrete outputs. In such systems, expression of the central regulator is likely to be tuned to an optimum level, as we observe here for Clb2.

Modeling the cell cycle: why do certain circuits oscillate?

Computational modeling and the theory of nonlinear dynamical systems allow one to not simply describe the events of the cell cycle, but also to understand why these events occur, just as the theory of gravitation allows one to understand why cannonballs fly in parabolic arcs. The simplest examples of the eukaryotic cell cycle operate like autonomous oscillators. Here, we present the basic theory of oscillatory biochemical circuits in the context of the Xenopus embryonic cell cycle. We examine Boolean models, delay differential equation models, and especially ordinary differential equation (ODE) models. For ODE models, we explore what it takes to get oscillations out of two simple types of circuits (negative feedback loops and coupled positive and negative feedback loops). Finally, we review the procedures of linear stability analysis, which allow one to determine whether a given ODE model and a particular set of kinetic parameters will produce oscillations.

Regulated inactivation of the spindle assembly checkpoint without functional mitotic spindles

The spindle assembly checkpoint (SAC) arrests mitosis until bipolar attachment of spindle microtubules to all chromosomes is accomplished. However, when spindle formation is prevented and the SAC cannot be satisfied, mammalian cells can eventually overcome the mitotic arrest while the checkpoint is still activated. We find that Aspergillus nidulans cells, which are unable to satisfy the SAC, inactivate the checkpoint after a defined period of mitotic arrest. Such SAC inactivation allows normal nuclear reassembly and mitotic exit without DNA segregation. We demonstrate that the mechanisms, which govern such SAC inactivation, require protein synthesis and can occur independently of inactivation of the major mitotic regulator Cdk1/Cyclin B or mitotic exit. Moreover, in the continued absence of spindle function cells transit multiple cell cycles in which the SAC is reactivated each mitosis before again being inactivated. Such cyclic activation and inactivation of the SAC suggests that it is subject to cell-cycle regulation that is independent of bipolar spindle function.

Synchronized cluster formation in coupled laser networks

We experimentally investigate the phase dynamics of laser networks with homogenous time-delayed mutual coupling and establish the fundamental rules that govern their state of synchronization. We identified a specific substructure that imposes its synchronization state on the entire network and show that for any coupling configuration the network forms at most two synchronized clusters. Our results indicate that the synchronization state of the network is a nonlocal phenomenon and cannot be deduced by decomposing the network into smaller substructures, each with its individual synchronization state.

Reconstructing regulatory network transitions

Cellular responses often involve a transition of cells from one state to another. A transition from a stem cell to a differentiated cell state, for example, might occur in response to gene expression changes induced by a transcription factor, or to signaling cascades triggered by a hormone or pathogen. Regulatory networks are thought to control such cellular transitions. Thus, many researchers are interested in reconstructing regulatory networks, not only with the aim of gaining a deeper understanding of cellular transitions, but also of using networks to predict and potentially manipulate cellular transitions and outcomes. In this review, we highlight approaches to the reconstruction of regulatory networks underlying cellular transitions, with special attention to transcriptional regulatory networks. We describe recent regulatory network reconstructions in a variety of organisms, and discuss the success they share in identifying new regulatory components, shared relationships and phenotypic outcomes.

Inferring Boolean network structure via correlation

MOTIVATION

Accurate, context-specific regulation of gene expression is essential for all organisms. Accordingly, it is very important to understand the complex relations within cellular gene regulatory networks. A tool to describe and analyze the behavior of such networks are Boolean models. The reconstruction of a Boolean network from biological data requires identification of dependencies within the network. This task becomes increasingly computationally demanding with large amounts of data created by recent high-throughput technologies. Thus, we developed a method that is especially suited for network structure reconstruction from large-scale data. In our approach, we took advantage of the fact that a specific transcription factor often will consistently either activate or inhibit a specific target gene, and this kind of regulatory behavior can be modeled using monotone functions.

RESULTS

To detect regulatory dependencies in a network, we examined how the expression of different genes correlates to successive network states. For this purpose, we used Pearson correlation as an elementary correlation measure. Given a Boolean network containing only monotone Boolean functions, we prove that the correlation of successive states can identify the dependencies in the network. This method not only finds dependencies in randomly created artificial networks to very high percentage, but also reconstructed large fractions of both a published Escherichia coli regulatory network from simulated data and a yeast cell cycle network from real microarray data.

Regulation of modular Cyclin and CDK feedback loops by an E2F transcription oscillator in the mammalian cell cycle

The cell cycle is regulated by a large number of enzymes and transcription factors. We have developed a modular description of the cell cycle, based on a set of interleaved modular feedback loops, each leading to a cyclic behavior. The slowest loop is the E2F transcription and ubiquitination, which determines the cycling frequency of the entire cell cycle. Faster feedback loops describe the dynamics of each Cyclin by itself. Our model shows that the cell cycle progression as well as the checkpoints of the cell cycle can be understood through the interactions between the main E2F feedback loop and the driven Cyclin feedback loops. Multiple models were proposed for the cell cycle dynamics; each with differing basic mechanisms. We here propose a new generic formalism. In contrast with existing models, the proposed formalism allows a straightforward analysis and understanding of the dynamics, neglecting the details of each interaction. This model is not sensitive to small changes in the parameters used and it reproduces the observed behavior of the transcription factor E2F and different Cyclins in continuous or regulated cycling conditions. The modular description of the cell cycle resolves the gap between cyclic models, solely based on protein-protein reactions and transcription reactions based models. Beyond the explanation of existing observations, this model suggests the existence of unknown interactions, such as the need for a functional interaction between Cyclin B and retinoblastoma protein (Rb) de-phosphorylation.

A homeodomain transcription factor regulates the DNA replication checkpoint in yeast

Checkpoints monitor the successful completion of cell cycle processes, such as DNA replication, and also regulate the expression of cell cycle-dependent genes that are required for responses. In the model yeast Schizosaccharomyces pombe G 1/S phase-specific gene expression is regulated by the MBF (also known as DSC1) transcription factor complex and is also activated by the mammalian ATM/ATR-related Rad3 DNA replication checkpoint. Here, we show that the Yox1 homeodomain transcription factor acts to co-ordinate the expression of MBF-regulated genes during the cell division cycle. Moreover, our data suggests that Yox1 is inactivated by the Rad3 DNA replication checkpoint via phosphorylation by the conserved Cds1 checkpoint kinase. Collectively, our data has implications for understanding the mechanisms underlying the coordination of cell cycle processes in eukaryotes.

G1/S transcription factor orthologues Swi4p and Swi6p are important but not essential for cell proliferation and influence hyphal development in the fungal pathogen Candida albicans

The G(1)/S transition is a critical control point for cell proliferation and involves essential transcription complexes termed SBF and MBF in Saccharomyces cerevisiae or MBF in Schizosaccharomyces pombe. In the fungal pathogen Candida albicans, G(1)/S regulation is not clear. To gain more insight into the G(1)/S circuitry, we characterized Swi6p, Swi4p and Mbp1p, the closest orthologues of SBF (Swi6p and Swi4p) and MBF (Swi6p and Mbp1p) components in S. cerevisiae. The mbp1Δ/Δ cells showed minor growth defects, whereas swi4Δ/Δ and swi6Δ/Δ yeast cells dramatically increased in size, suggesting a G(1) phase delay. Gene set enrichment analysis (GSEA) of transcription profiles revealed that genes associated with G(1)/S phase were significantly enriched in cells lacking Swi4p and Swi6p. These expression patterns suggested that Swi4p and Swi6p have repressing as well as activating activity. Intriguingly, swi4Δ/Δ swi6Δ/Δ and swi4Δ/Δ mbp1Δ/Δ strains were viable, in contrast to the situation in S. cerevisiae, and showed pleiotropic phenotypes that included multibudded yeast, pseudohyphae, and intriguingly, true hyphae. Consistently, GSEA identified strong enrichment of genes that are normally modulated during C. albicans-host cell interactions. Since Swi4p and Swi6p influence G(1) phase progression and SBF binding sites are lacking in the C. albicans genome, these factors may contribute to MBF activity. Overall, the data suggest that the putative G(1)/S regulatory machinery of C. albicans contains novel features and underscore the existence of a relationship between G(1) phase and morphogenetic switching, including hyphal development, in the pathogen.

Cross-platform microarray data normalisation for regulatory network inference

BACKGROUND

Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

METHODS

We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.

CONCLUSIONS

Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.

YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT) information system (http://www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2010, this database contains over 48 200 regulatory associations between transcription factors (TFs) and target genes, including 298 specific DNA-binding sites for 110 characterized TFs. All regulatory associations stored in the database were revisited and detailed information on the experimental evidences that sustain those associations was added and classified as direct or indirect evidences. The inclusion of this new data, gathered in response to the requests of YEASTRACT users, allows the user to restrict its queries to subsets of the data based on the existence or not of experimental evidences for the direct action of the TFs in the promoter region of their target genes. Another new feature of this release is the availability of all data through a machine readable web-service interface. Users are no longer restricted to the set of available queries made available through the existing web interface, and can use the web service interface to query, retrieve and exploit the YEASTRACT data using their own implementation of additional functionalities. The YEASTRACT information system is further complemented with several computational tools that facilitate the use of the curated data when answering a number of important biological questions. Since its first release in 2006, YEASTRACT has been extensively used by hundreds of researchers from all over the world. We expect that by making the new data and services available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

Frequency control of cell cycle oscillators

The cell cycle oscillator, based on a core negative feedback loop and modified extensively by positive feedback, cycles with a frequency that is regulated by environmental and developmental programs to encompass a wide range of cell cycle times. We discuss how positive feedback allows frequency tuning, how size and morphogenetic checkpoints regulate oscillator frequency, and how extrinsic oscillators such as the circadian clock gate cell cycle frequency. The master cell cycle regulatory oscillator in turn controls the frequency of peripheral oscillators controlling essential events. A recently proposed phase-locking model accounts for this coupling.

The Ustilago maydis Clp1 protein orchestrates pheromone and b-dependent signaling pathways to coordinate the cell cycle and pathogenic development

Regulation of the cell cycle and morphogenetic switching during pathogenic and sexual development in Ustilago maydis is orchestrated by a concerted action of the a and b mating-type loci. Activation of either mating-type locus triggers the G2 cell cycle arrest that is a prerequisite for the formation of the infectious dikaryon; this cell cycle arrest is released only after penetration of the host plant. Here, we show that bW, one of the two homeodomain transcription factors encoded by the b mating-type locus, and the zinc-finger transcription factor Rbf1, a master regulator for pathogenic development, interact with Clp1 (clampless 1), a protein required for the distribution of nuclei during cell division of the dikaryon. In addition, we identify Cib1, a previously undiscovered bZIP transcription factor required for pathogenic development, as a Clp1-interacting protein. Clp1 interaction with bW blocks b-dependent functions, such as the b-dependent G2 cell cycle arrest and dimorphic switching. The interaction of Clp1 with Rbf1 results in the repression of the a-dependent pheromone pathway, conjugation tube formation, and the a-induced G2 cell cycle arrest. The concerted interaction of Clp1 with Rbf1 and bW coordinates a- and b-dependent cell cycle control and ensures cell cycle release and progression at the onset of biotrophic development.

Reliability of transcriptional cycles and the yeast cell-cycle oscillator

A recently published transcriptional oscillator associated with the yeast cell cycle provides clues and raises questions about the mechanisms underlying autonomous cyclic processes in cells. Unlike other biological and synthetic oscillatory networks in the literature, this one does not seem to rely on a constitutive signal or positive auto-regulation, but rather to operate through stable transmission of a pulse on a slow positive feedback loop that determines its period. We construct a continuous-time Boolean model of this network, which permits the modeling of noise through small fluctuations in the timing of events, and show that it can sustain stable oscillations. Analysis of simpler network models shows how a few building blocks can be arranged to provide stability against fluctuations. Our findings suggest that the transcriptional oscillator in yeast belongs to a new class of biological oscillators.

Technologies such as gene arrays enable acquisition of large amounts of data on gene expression variations, which reveal the structures of gene regulatory networks that govern the metabolic and developmental machinery in the cell. We study a model of an oscillatory gene regulatory network that has been recently suggested to play an integral role in maintaining the cell cycle in yeast. The oscillator differs from other known biological and synthetic oscillatory networks in that it seems to rely on a long positive feedback loop. We show that the presence of certain stabilizing sub-networks can account for the robustness and the unusual architecture of this oscillator. Our modeling approach elucidates both the logical structure of the system and the importance of the timing of update events.

Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

Background

Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels.

Results

We propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network.

Conclusions

The algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results [1]. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases.

The transcriptional network activated by Cln3 cyclin at the G1-to-S transition of the yeast cell cycle

Background

The G1-to-S transition of the cell cycle in the yeast Saccharomyces cerevisiae involves an extensive transcriptional program driven by transcription factors SBF (Swi4-Swi6) and MBF (Mbp1-Swi6). Activation of these factors ultimately depends on the G1 cyclin Cln3.

Results

To determine the transcriptional targets of Cln3 and their dependence on SBF or MBF, we first have used DNA microarrays to interrogate gene expression upon Cln3 overexpression in synchronized cultures of strains lacking components of SBF and/or MBF. Secondly, we have integrated this expression dataset together with other heterogeneous data sources into a single probabilistic model based on Bayesian statistics. Our analysis has produced more than 200 transcription factor-target assignments, validated by ChIP assays and by functional enrichment. Our predictions show higher internal coherence and predictive power than previous classifications. Our results support a model whereby SBF and MBF may be differentially activated by Cln3.

Conclusions

Integration of heterogeneous genome-wide datasets is key to building accurate transcriptional networks. By such integration, we provide here a reliable transcriptional network at the G1-to-S transition in the budding yeast cell cycle. Our results suggest that to improve the reliability of predictions we need to feed our models with more informative experimental data.

Detecting separate time scales in genetic expression data

Background

Biological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales.

Results

We introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions.

Conclusions

The spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.

Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model

RESULTS

We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression data. The network is specified by a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood. We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation.

AVAILABILITY

Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip

Using GeneReg to construct time delay gene regulatory networks

Background

Understanding gene expression and regulation is essential for understanding biological mechanisms. Because gene expression profiling has been widely used in basic biological research, especially in transcription regulation studies, we have developed GeneReg, an easy-to-use R package, to construct gene regulatory networks from time course gene expression profiling data; More importantly, this package can provide information about time delays between expression change in a regulator and that of its target genes.

Findings

The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases.

Conclusions

GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.

B-cyclin/CDKs regulate mitotic spindle assembly by phosphorylating kinesins-5 in budding yeast

Although it has been known for many years that B-cyclin/CDK complexes regulate the assembly of the mitotic spindle and entry into mitosis, the full complement of relevant CDK targets has not been identified. It has previously been shown in a variety of model systems that B-type cyclin/CDK complexes, kinesin-5 motors, and the SCF^Cdc4 ubiquitin ligase are required for the separation of spindle poles and assembly of a bipolar spindle. It has been suggested that, in budding yeast, B-type cyclin/CDK (Clb/Cdc28) complexes promote spindle pole separation by inhibiting the degradation of the kinesins-5 Kip1 and Cin8 by the anaphase-promoting complex (APC^Cdh1). We have determined, however, that the Kip1 and Cin8 proteins are present at wild-type levels in the absence of Clb/Cdc28 kinase activity. Here, we show that Kip1 and Cin8 are in vitro targets of Clb2/Cdc28 and that the mutation of conserved CDK phosphorylation sites on Kip1 inhibits spindle pole separation without affecting the protein's in vivo localization or abundance. Mass spectrometry analysis confirms that two CDK sites in the tail domain of Kip1 are phosphorylated in vivo. In addition, we have determined that Sic1, a Clb/Cdc28-specific inhibitor, is the SCF^Cdc4 target that inhibits spindle pole separation in cells lacking functional Cdc4. Based on these findings, we propose that Clb/Cdc28 drives spindle pole separation by direct phosphorylation of kinesin-5 motors.

The assembly of a bipolar mitotic spindle is essential for the accurate segregation of sister chromatids during mitosis and, hence, for successful cell division. Spindle assembly depends on the successful duplication of the spindle poles, followed by their separation to opposing ends of the cell. Although it has been known for many years that B-cyclin/CDK complexes regulate the assembly of the mitotic spindle, the relevant CDK targets have not been identified. Motor proteins of the kinesin-5 family generate movement on the microtubules that make up the spindle and are believed to power spindle pole separation. By employing the budding yeast Saccharomyces cerevisiae as a model, we have found evidence that cyclin/CDKs control spindle assembly by phosphorylating the kinesins-5 Kip1 and Cin8. When phosphorylation at a conserved CDK site in the motor domain of Kip1 is blocked, spindle pole separation is greatly diminished but neither protein abundance nor localization is affected. We have also obtained direct evidence by mass spectrometry that Kip1 and Cin8 are phosphorylated in vivo at consensus CDK sites in their tail domains. Our findings suggest that B-cyclin/CDKs regulate spindle assembly by regulating kinesin-5 motor activity.

Periodic cyclin-Cdk activity entrains an autonomous Cdc14 release oscillator

One oscillation of Cyclin-dependent kinase (Cdk) activity, largely driven by periodic synthesis and destruction of cyclins, is tightly coupled to a single complete eukaryotic cell division cycle. Tight linkage of different steps in diverse cell-cycle processes to Cdk activity has been proposed to explain this coupling. Here, we demonstrate an intrinsically oscillatory module controlling nucleolar release and resequestration of the Cdc14 phosphatase, which is essential for mitotic exit in budding yeast. We find that this Cdc14 release oscillator functions at constant and physiological cyclin-Cdk levels, and is therefore independent of the Cdk oscillator. However, the frequency of the release oscillator is regulated by cyclin-Cdk activity. This observation together with its mechanism suggests that the intrinsically autonomous Cdc14 release cycles are locked at once-per-cell-cycle through entrainment by the Cdk oscillator in wild-type cells. This concept may have broad implications for the structure and evolution of eukaryotic cell-cycle control.

Mitotic cell-cycle progression is regulated by CPEB1 and CPEB4-dependent translational control

Meiotic and early-embryonic cell divisions in vertebrates take place in the absence of transcription and rely on the translational regulation of stored maternal messenger RNAs. Most of these mRNAs are regulated by the cytoplasmic-polyadenylation-element-binding protein (CPEB), which mediates translational activation and repression through cytoplasmic changes in their poly(A) tail length. It was unknown whether translational regulation by cytoplasmic polyadenylation and CPEB can also regulate mRNAs at specific points of mitotic cell-cycle divisions. Here we show that CPEB-mediated post-transcriptional regulation by phase-specific changes in poly(A) tail length is required for cell proliferation and specifically for entry into M phase in mitotically dividing cells. This translational control is mediated by two members of the CPEB family of proteins, CPEB1 and CPEB4. We conclude that regulation of poly(A) tail length is not only required to compensate for the lack of transcription in specialized cell divisions but also acts as a general mechanism to control mitosis.

WebPARE: web-computing for inferring genetic or transcriptional interactions

Summary: Inferring genetic or transcriptional interactions, when done successfully, may provide insights into biological processes or biochemical pathways of interest. Unfortunately, most computational algorithms require a certain level of programming expertise. To provide a simple web interface for users to infer interactions from time course gene expression data, we present WebPARE, which is based on the pattern recognition algorithm (PARE). For expression data, in which each type of interaction (e.g. activator target) and the corresponding paired gene expression pattern are significantly associated, PARE uses a non-linear score to classify gene pairs of interest into a few subclasses of various time lags. In each subclass, PARE learns the parameters in the decision score using known interactions from biological experiments or published literature. Subsequently, the trained algorithm predicts interactions of a similar nature. Previously, PARE was shown to infer two sets of interactions in yeast successfully. Moreover, several predicted genetic interactions coincided with existing pathways; this indicates the potential of PARE in predicting partial pathway components. Given a list of gene pairs or genes of interest and expression data, WebPARE invokes PARE and outputs predicted interactions and their networks in directed graphs.

Availability: A web-computing service WebPARE is publicly available at: http://www.stat.sinica.edu.tw/WebPARE

Contact:gshieh@stat.sinica.edu.tw

Supplementary information:Supplementary data are available at Bioinformatics online.

Timing robustness in the budding and fission yeast cell cycles

Robustness of biological models has emerged as an important principle in systems biology. Many past analyses of Boolean models update all pending changes in signals simultaneously (i.e., synchronously), making it impossible to consider robustness to variations in timing that result from noise and different environmental conditions. We checked previously published mathematical models of the cell cycles of budding and fission yeast for robustness to timing variations by constructing Boolean models and analyzing them using model-checking software for the property of speed independence. Surprisingly, the models are nearly, but not totally, speed-independent. In some cases, examination of timing problems discovered in the analysis exposes apparent inaccuracies in the model. Biologically justified revisions to the model eliminate the timing problems. Furthermore, in silico random mutations in the regulatory interactions of a speed-independent Boolean model are shown to be unlikely to preserve speed independence, even in models that are otherwise functional, providing evidence for selection pressure to maintain timing robustness. Multiple cell cycle models exhibit strong robustness to timing variation, apparently due to evolutionary pressure. Thus, timing robustness can be a basis for generating testable hypotheses and can focus attention on aspects of a model that may need refinement.

Anomaly detection in gene expression via stochastic models of gene regulatory networks

Background

The steady-state behaviour of gene regulatory networks (GRNs) can provide crucial evidence for detecting disease-causing genes. However, monitoring the dynamics of GRNs is particularly difficult because biological data only reflects a snapshot of the dynamical behaviour of the living organism. Also most GRN data and methods are used to provide limited structural inferences.

Results

In this study, the theory of stochastic GRNs, derived from G-Networks, is applied to GRNs in order to monitor their steady-state behaviours. This approach is applied to a simulation dataset which is generated by using the stochastic gene expression model, and observe that the G-Network properly detects the abnormally expressed genes in the simulation study. In the analysis of real data concerning the cell cycle microarray of budding yeast, our approach finds that the steady-state probability of CLB2 is lower than that of other agents, while most of the genes have similar steady-state probabilities. These results lead to the conclusion that the key regulatory genes of the cell cycle can be expressed in the absence of CLB type cyclines, which was also the conclusion of the original microarray experiment study.

Conclusion

G-networks provide an efficient way to monitor steady-state of GRNs. Our method produces more reliable results then the conventional t-test in detecting differentially expressed genes. Also G-networks are successfully applied to the yeast GRNs. This study will be the base of further GRN dynamics studies cooperated with conventional GRN inference algorithms.

Analysis of the mitotic exit control system using locked levels of stable mitotic cyclin

Cyclin-dependent kinase (Cdk) both promotes mitotic entry (spindle assembly and anaphase) and inhibits mitotic exit (spindle disassembly and cytokinesis), leading to an elegant quantitative hypothesis that a single cyclin oscillation can function as a ratchet to order these events. This ratchet is at the core of a published ODE model for the yeast cell cycle. However, the ratchet model requires appropriate cyclin dose-response thresholds. Here, we test the inhibition of mitotic exit in budding yeast using graded levels of stable mitotic cyclin (Clb2). In opposition to the ratchet model, stable levels of Clb2 introduced dose-dependent delays, rather than hard thresholds, that varied by mitotic exit event. The ensuing cell cycle was highly abnormal, suggesting a novel reason for cyclin degradation. Cdc14 phosphatase antagonizes Clb2-Cdk, and Cdc14 is released from inhibitory nucleolar sequestration independently of stable Clb2. Thus, Cdc14/Clb2 balance may be the appropriate variable for mitotic regulation. Although our results are inconsistent with the aforementioned ODE model, revision of the model to allow Cdc14/Clb2 balance to control mitotic exit corrects these discrepancies, providing theoretical support for our conclusions.

Time warping of evolutionary distant temporal gene expression data based on noise suppression

Background

Comparative analysis of genome wide temporal gene expression data has a broad potential area of application, including evolutionary biology, developmental biology, and medicine. However, at large evolutionary distances, the construction of global alignments and the consequent comparison of the time-series data are difficult. The main reason is the accumulation of variability in expression profiles of orthologous genes, in the course of evolution.

Results

We applied Pearson distance matrices, in combination with other noise-suppression techniques and data filtering to improve alignments. This novel framework enhanced the capacity to capture the similarities between the temporal gene expression datasets separated by large evolutionary distances. We aligned and compared the temporal gene expression data in budding (Saccharomyces cerevisiae) and fission (Schizosaccharomyces pombe) yeast, which are separated by more then ~400 myr of evolution. We found that the global alignment (time warping) properly matched the duration of cell cycle phases in these distant organisms, which was measured in prior studies. At the same time, when applied to individual ortholog pairs, this alignment procedure revealed groups of genes with distinct alignments, different from the global alignment.

Conclusion

Our alignment-based predictions of differences in the cell cycle phases between the two yeast species were in a good agreement with the existing data, thus supporting the computational strategy adopted in this study. We propose that the existence of the alternative alignments, specific to distinct groups of genes, suggests presence of different synchronization modes between the two organisms and possible functional decoupling of particular physiological gene networks in the course of evolution.

Daughter-specific transcription factors regulate cell size control in budding yeast

The asymmetric localization of cell fate determinants results in asymmetric cell cycle control in budding yeast.

In budding yeast, asymmetric cell division yields a larger mother and a smaller daughter cell, which transcribe different genes due to the daughter-specific transcription factors Ace2 and Ash1. Cell size control at the Start checkpoint has long been considered to be a main regulator of the length of the G1 phase of the cell cycle, resulting in longer G1 in the smaller daughter cells. Our recent data confirmed this concept using quantitative time-lapse microscopy. However, it has been proposed that daughter-specific, Ace2-dependent repression of expression of the G1 cyclin CLN3 had a dominant role in delaying daughters in G1. We wanted to reconcile these two divergent perspectives on the origin of long daughter G1 times. We quantified size control using single-cell time-lapse imaging of fluorescently labeled budding yeast, in the presence or absence of the daughter-specific transcriptional regulators Ace2 and Ash1. Ace2 and Ash1 are not required for efficient size control, but they shift the domain of efficient size control to larger cell size, thus increasing cell size requirement for Start in daughters. Microarray and chromatin immunoprecipitation experiments show that Ace2 and Ash1 are direct transcriptional regulators of the G1 cyclin gene CLN3. Quantification of cell size control in cells expressing titrated levels of Cln3 from ectopic promoters, and from cells with mutated Ace2 and Ash1 sites in the CLN3 promoter, showed that regulation of CLN3 expression by Ace2 and Ash1 can account for the differential regulation of Start in response to cell size in mothers and daughters. We show how daughter-specific transcriptional programs can interact with intrinsic cell size control to differentially regulate Start in mother and daughter cells. This work demonstrates mechanistically how asymmetric localization of cell fate determinants results in cell-type-specific regulation of the cell cycle.

Asymmetric cell division is a universal mechanism for generating differentiated cells. The progeny of such divisions can often display differential cell cycle regulation. This study addresses how differential regulation of gene expression in the progeny of a single division can alter cell cycle control. In budding yeast, asymmetric cell division yields a bigger ‘mother’ cell and a smaller ‘daughter’ cell. Regulation of gene expression is also asymmetric because two transcription factors, Ace2 and Ash1, are specifically localized to the daughter. Cell size has long been proposed as important for the regulation of the cell cycle in yeast. Our work shows that Ace2 and Ash1 regulate size control in daughter cells: daughters ‘interpret’ their size as smaller, making size control more stringent and delaying cell cycle commitment relative to mother cells of the same size. This asymmetric interpretation of cell size is associated with differential regulation of the G1 cyclin CLN3 by Ace2 and Ash1, at least in part via direct binding of these factors to the CLN3 promoter. CLN3 is the most upstream regulator of Start, the initiation point of the yeast cell cycle, and differential regulation of CLN3 accounts for most or all asymmetric regulation of Start in budding yeast mother and daughter cells.

Trends in modeling Biomedical Complex Systems

In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.

Global effects of DNA replication and DNA replication origin activity on eukaryotic gene expression

This report provides a global view of how gene expression is affected by DNA replication. We analyzed synchronized cultures of Saccharomyces cerevisiae under conditions that prevent DNA replication initiation without delaying cell cycle progression. We use a higher-order singular value decomposition to integrate the global mRNA expression measured in the multiple time courses, detect and remove experimental artifacts and identify significant combinations of patterns of expression variation across the genes, time points and conditions. We find that, first, approximately 88% of the global mRNA expression is independent of DNA replication. Second, the requirement of DNA replication for efficient histone gene expression is independent of conditions that elicit DNA damage checkpoint responses. Third, origin licensing decreases the expression of genes with origins near their 3' ends, revealing that downstream origins can regulate the expression of upstream genes. This confirms previous predictions from mathematical modeling of a global causal coordination between DNA replication origin activity and mRNA expression, and shows that mathematical modeling of DNA microarray data can be used to correctly predict previously unknown biological modes of regulation.

Logical modelling of cell cycle control in eukaryotes: a comparative study

Dynamical modelling is at the core of the systems biology paradigm. However, the development of comprehensive quantitative models is complicated by the daunting complexity of regulatory networks controlling crucial biological processes such as cell division, the paucity of currently available quantitative data, as well as the limited reproducibility of large-scale experiments. In this context, qualitative modelling approaches offer a useful alternative or complementary framework to build and analyse simplified, but still rigorous dynamical models. This point is illustrated here by analysing recent logical models of the molecular network controlling mitosis in different organisms, from yeasts to mammals. After a short introduction covering cell cycle and logical modelling, we compare the assumptions and properties underlying these different models. Next, leaning on their transposition into a common logical framework, we compare their functional structure in terms of regulatory circuits. Finally, we discuss assets and prospects of qualitative approaches for the modelling of the cell cycle.

Nuclear transport of protein TTC4 depends on the cell cycle

TTC4 (tetratricopeptide repeat domain protein 4) is a putative tumor suppressor involved in the transformation of melanocytes. At present, the relationships between TTC4 and DNA replication proteins are largely unknown, as are the tissue distribution and subcellular localization of TTC4. Using reverse transcription with the polymerase chain reaction, we have observed that the murine TTC4 gene is ubiquitously expressed. Analysis of the TTC4 subcellular localization has shown that, upon overexpression, TTC4 localizes to the cytoplasm. Interestingly, co-expression with a known protein interaction partner, hampin/MSL1, results in the nuclear translocation of the TTC4 protein. The subcellular localization of endogenous TTC4 depends, however, on the cell cycle: it is mostly nuclear in the G1 and S phases and is evenly distributed between the nucleus and cytoplasm in G2. The nuclear transport of TTC4 is apparently a complex process dependent on interactions with other proteins during the progression of the cell cycle. Thus, the dynamic character of the nuclear accumulation of TTC4 might be a potential link with regard to its function in tumor suppression.

Transcriptional analysis of the Candida albicans cell cycle

We have examined the periodic expression of genes through the cell cycle in cultures of the human pathogenic fungus Candida albicans synchronized by mating pheromone treatment. Close to 500 genes show increased expression during the G1, S, G2, or M transitions of the C. albicans cell cycle. Comparisons of these C. albicans periodic genes with those already found in the budding and fission yeasts and in human cells reveal that of 2200 groups of homologous genes, close to 600 show periodicity in at least one organism, but only 11 are periodic in all four species. Overall, the C. albicans regulatory circuit most closely resembles that of Saccharomyces cerevisiae but contains a simplified structure. Although the majority of the C. albicans periodically regulated genes have homologues in the budding yeast, 20% (100 genes), most of which peak during the G1/S or M/G1 transitions, are unique to the pathogenic yeast.