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

Stochastic Boolean model of normal and aberrant cell cycles in budding yeast

The cell cycle of budding yeast is governed by an intricate protein regulatory network whose dysregulation can lead to lethal mistakes or aberrant cell division cycles. In this work, we model this network in a Boolean framework for stochastic simulations. Our model is sufficiently detailed to account for the phenotypes of 40 mutant yeast strains (83% of the experimentally characterized strains that we simulated) and also to simulate an endoreplicating strain (multiple rounds of DNA synthesis without mitosis) and a strain that exhibits 'Cdc14 endocycles' (periodic transitions between metaphase and anaphase). Because our model successfully replicates the observed properties of both wild-type yeast cells and many mutant strains, it provides a reasonable, validated starting point for more comprehensive stochastic-Boolean models of cell cycle controls. Such models may provide a better understanding of cell cycle anomalies in budding yeast and ultimately in mammalian cells.

The oscillation of mitotic kinase governs cell cycle latches in mammalian cells

The mammalian cell cycle alternates between two phases - S-G2-M with high levels of A- and B-type cyclins (CycA and CycB, respectively) bound to cyclin-dependent kinases (CDKs), and G1 with persistent degradation of CycA and CycB by an activated anaphase promoting complex/cyclosome (APC/C) bound to Cdh1 (also known as FZR1 in mammals; denoted APC/C:Cdh1). Because CDKs phosphorylate and inactivate Cdh1, these two phases are mutually exclusive. This 'toggle switch' is flipped from G1 to S by cyclin-E bound to a CDK (CycE:CDK), which is not degraded by APC/C:Cdh1, and from M to G1 by Cdc20-bound APC/C (APC/C:Cdc20), which is not inactivated by CycA:CDK or CycB:CDK. After flipping the switch, cyclin E is degraded and APC/C:Cdc20 is inactivated. Combining mathematical modelling with single-cell timelapse imaging, we show that dysregulation of CycB:CDK disrupts strict alternation of the G1-S and M-G1 switches. Inhibition of CycB:CDK results in Cdc20-independent Cdh1 'endocycles', and sustained activity of CycB:CDK drives Cdh1-independent Cdc20 endocycles. Our model provides a mechanistic explanation for how whole-genome doubling can arise, a common event in tumorigenesis that can drive tumour evolution.

A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets

Modeling biological systems holds great promise for speeding up the rate of discovery in systems biology by predicting experimental outcomes and suggesting targeted interventions. However, this process is dogged by an identifiability issue, in which network models and their parameters are not sufficiently constrained by coarse and noisy data to ensure unique solutions. In this work, we evaluated the capability of a simplified yeast cell-cycle network model to reproduce multiple observed transcriptomic behaviors under genomic mutations. We matched time-series data from both cycling and checkpoint arrested cells to model predictions using an asynchronous multi-level Boolean approach. We showed that this single network model, despite its simplicity, is capable of exhibiting dynamical behavior similar to the datasets in most cases, and we demonstrated the drop in severity of the identifiability issue that results from matching multiple datasets.

Temporal segregation of biosynthetic processes is responsible for metabolic oscillations during the budding yeast cell cycle

Many cell biological and biochemical mechanisms controlling the fundamental process of eukaryotic cell division have been identified; however, the temporal dynamics of biosynthetic processes during the cell division cycle are still elusive. Here, we show that key biosynthetic processes are temporally segregated along the cell cycle. Using budding yeast as a model and single-cell methods to dynamically measure metabolic activity, we observe two peaks in protein synthesis, in the G1 and S/G2/M phase, whereas lipid and polysaccharide synthesis peaks only once, during the S/G2/M phase. Integrating the inferred biosynthetic rates into a thermodynamic-stoichiometric metabolic model, we find that this temporal segregation in biosynthetic processes causes flux changes in primary metabolism, with an acceleration of glucose-uptake flux in G1 and phase-shifted oscillations of oxygen and carbon dioxide exchanges. Through experimental validation of the model predictions, we demonstrate that primary metabolism oscillates with cell-cycle periodicity to satisfy the changing demands of biosynthetic processes exhibiting unexpected dynamics during the cell cycle.

Genome-wide identification, evolutionary and expression analysis of the cyclin-dependent kinase gene family in peanut

BACKGROUND

Cyclin-dependent kinases (CDKs) are a predominant group of serine/threonine protein kinases that have multi-faceted functions in eukaryotes. The plant CDK members have well-known roles in cell cycle progression, transcriptional regulation, DNA repair, abiotic stress and defense responses, making them promising targets for developing stress adaptable high-yielding crops. There is relatively sparse information available on the CDK family genes of cultivated oilseed crop peanut and its diploid progenitors.

RESULTS

We have identified 52 putative cyclin-dependent kinases (CDKs) and CDK-like (CDKLs) genes in Arachis hypogaea (cultivated peanut) and total 26 genes in each diploid parent of cultivated peanut (Arachis duranensis and Arachis ipaensis). Both CDK and CDKL genes were classified into eight groups based on their cyclin binding motifs and their phylogenetic relationship with Arabidopsis counterparts. Genes in the same subgroup displayed similar exon-intron structure and conserved motifs. Further, gene duplication analysis suggested that segmental duplication events played major roles in the expansion and evolution of CDK and CDKL genes in cultivated peanuts. Identification of diverse cis-acting response elements in CDK and CDKL genes promoter indicated their potential fundamental roles in multiple biological processes. Various gene expression patterns of CDKs and CDKLs in different peanut tissues suggested their involvement during growth and development. In addition, qRT-PCR analysis demonstrated that most representing CDK and CDKL gene family members were significantly down-regulated under ABA, PEG and mannitol treatments.

CONCLUSIONS

Genome-wide analysis offers a comprehensive understanding of the classification, evolution, gene structure, and gene expression profiles of CDK and CDKL genes in cultivated peanut and their diploid progenitors. Additionally, it also provides cell cycle regulatory gene resources for further functional characterization to enhance growth, development and abiotic stress tolerance.

Repression of essential cell cycle genes increases cellular fitness

A network of transcription factors (TFs) coordinates transcription with cell cycle events in eukaryotes. Most TFs in the network are phosphorylated by cyclin-dependent kinase (CDK), which limits their activities during the cell cycle. Here, we investigate the physiological consequences of disrupting CDK regulation of the paralogous repressors Yhp1 and Yox1 in yeast. Blocking Yhp1/Yox1 phosphorylation increases their levels and decreases expression of essential cell cycle regulatory genes which, unexpectedly, increases cellular fitness in optimal growth conditions. Using synthetic genetic interaction screens, we find that Yhp1/Yox1 mutations improve the fitness of mutants with mitotic defects, including condensin mutants. Blocking Yhp1/Yox1 phosphorylation simultaneously accelerates the G1/S transition and delays mitotic exit, without decreasing proliferation rate. This mitotic delay partially reverses the chromosome segregation defect of condensin mutants, potentially explaining their increased fitness when combined with Yhp1/Yox1 phosphomutants. These findings reveal how altering expression of cell cycle genes leads to a redistribution of cell cycle timing and confers a fitness advantage to cells.

Mitotic kinase oscillation governs the latching of cell cycle switches

In 1996, Kim Nasmyth¹ proposed that the eukaryotic cell cycle is an alternating sequence of transitions from G₁ to S-G₂-M and back again. These two phases correlate to high activity of cyclin-dependent kinases (CDKs) that trigger S-G₂-M events and CDK antagonists that stabilize G₁ phase. We associated these “alternative phases” with the coexistence of two stable steady states of the biochemical reactions among CDKs and their antagonists.^2,3 Transitions between these steady states (G₁-to-S and M-to-G₁) are driven by “helper” proteins. The fact that the transitions are irreversible is guaranteed by a “latching” property of the molecular switches, as we have argued in previous publications.^4,5 Here, we show that if the latch is broken, then the biochemical reactions can swing back-and-forth across the transitions; either G₁-S-G₁-S … (periodic DNA replication without mitosis or cell division) or M-(G₁)-M-(G₁) … (periodic Cdc14 release, without fully exiting mitosis). Using mathematical modeling of the molecular control circuit in budding yeast, we provide a fresh account of aberrant cell cycles in mutant strains: endoreplication in the clb1-5Δ strain⁶ and periodic release and resequestration of Cdc14 (an “exit” phosphatase) in the CLB2kdΔ strain.^7,8 In our opinion, these “endocycles” are not autonomous oscillatory modules that must be entrained by the CDK oscillator^6,7 but rather inadvertent and deleterious oscillations that are normally suppressed by the CDK latching-gate mechanism.⁸

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Mitotic kinases enforce once-per-cycle occurrence of cell cycle events

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Many cell cycle events can occur periodically in the absence of mitosis

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Are they autonomous oscillators or accidental consequences of faulty regulation?

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Regulatory mutations allow negative feedback loops to sustain oscillations

Time-keeping and decision-making in living cells: Part I

To survive and reproduce, a cell must process information from its environment and its own internal state and respond accordingly, in terms of metabolic activity, gene expression, movement, growth, division and differentiation. These signal–response decisions are made by complex networks of interacting genes and proteins, which function as biochemical switches and clocks, and other recognizable information-processing circuitry. This theme issue of Interface Focus (in two parts) brings together articles on time-keeping and decision-making in living cells—work that uses precise mathematical modelling of underlying molecular regulatory networks to understand important features of cell physiology. Part I focuses on time-keeping: mechanisms and dynamics of biological oscillators and modes of synchronization and entrainment of oscillators, with special attention to circadian clocks.

Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes

Background

Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptional programs are controlled by gene regulatory networks (GRNs), and it has been shown through genetics and chromosome mapping approaches in model systems that at the core of these GRNs are small sets of genes that drive the transcript dynamics of the GRNs. However, it is unlikely that we have identified all of these core genes, even in model organisms. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming, or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available.

Results

This study shows that a unified set of quantified dynamic features of high-throughput time series gene expression data are more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, in multiple organism, even in the presence of external periodic stimuli. Additionally, we observe that the power to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features.

Conclusions

There are practical applications of the approach presented in this study for network inference, since the result is a ranking of genes that is enriched for core regulatory elements driving a periodic phenotype. In this way, the method provides a prioritization of follow-up genetic experiments. Furthermore, these findings reveal something unexpected—that there are shared dynamic features of the transcript abundance of core components of unrelated GRNs that control disparate periodic phenotypes.

Cyclin/Forkhead-mediated coordination of cyclin waves: an autonomous oscillator rationalizing the quantitative model of Cdk control for budding yeast

Networks of interacting molecules organize topology, amount, and timing of biological functions. Systems biology concepts required to pin down 'network motifs' or 'design principles' for time-dependent processes have been developed for the cell division cycle, through integration of predictive computer modeling with quantitative experimentation. A dynamic coordination of sequential waves of cyclin-dependent kinases (cyclin/Cdk) with the transcription factors network offers insights to investigate how incompatible processes are kept separate in time during the eukaryotic cell cycle. Here this coordination is discussed for the Forkhead transcription factors in light of missing gaps in the current knowledge of cell cycle control in budding yeast. An emergent design principle is proposed where cyclin waves are synchronized by a cyclin/Cdk-mediated feed-forward regulation through the Forkhead as a transcriptional timer. This design is rationalized by the bidirectional interaction between mitotic cyclins and the Forkhead transcriptional timer, resulting in an autonomous oscillator that may be instrumental for a well-timed progression throughout the cell cycle. The regulation centered around the cyclin/Cdk-Forkhead axis can be pivotal to timely coordinate cell cycle dynamics, thereby to actuate the quantitative model of Cdk control.

Dynamic genome plasticity during unisexual reproduction in the human fungal pathogen Cryptococcus deneoformans

Genome copy number variation occurs during each mitotic and meiotic cycle and it is crucial for organisms to maintain their natural ploidy. Defects in ploidy transitions can lead to chromosome instability, which is a hallmark of cancer. Ploidy in the haploid human fungal pathogen Cryptococcus neoformans is exquisitely orchestrated and ranges from haploid to polyploid during sexual development and under various environmental and host conditions. However, the mechanisms controlling these ploidy transitions are largely unknown. During C. deneoformans (formerly C. neoformans var. neoformans, serotype D) unisexual reproduction, ploidy increases prior to the onset of meiosis, can be independent from cell-cell fusion and nuclear fusion, and likely occurs through an endoreplication pathway. To elucidate the molecular mechanisms underlying this ploidy transition, we identified twenty cell cycle-regulating genes encoding cyclins, cyclin-dependent kinases (CDK), and CDK regulators. We characterized four cyclin genes and two CDK regulator genes that were differentially expressed during unisexual reproduction and contributed to diploidization. To detect ploidy transition events, we generated a ploidy reporter, called NURAT, which can detect copy number increases via double selection for nourseothricin-resistant, uracil-prototrophic cells. Utilizing this ploidy reporter, we showed that ploidy transition from haploid to diploid can be detected during the early phases of unisexual reproduction. Interestingly, selection for the NURAT reporter revealed several instances of segmental aneuploidy of multiple chromosomes, which conferred azole resistance in some isolates. These findings provide further evidence of ploidy plasticity in fungi with significant biological and public health implications.

Ploidy is an intrinsic fundamental feature of all eukaryotic organisms, and ploidy variation and maintenance are critical to the organism survival and evolution. Fungi exhibit exquisite plasticity in ploidy variation in adaptation to various environmental stresses. For example, the haploid opportunistic human fungal pathogen C. deneoformans can generate diploid blastospores during unisexual reproduction and also forms polyploid titan cells during host infection; however, the mechanisms underlying these ploidy transitions are largely unknown. In this study, we elucidated the genetic regulatory circuitry governing ploidy duplication during C. deneoformans unisexual reproduction through the identification and characterization of cell cycle regulators that are differentially expressed during unisexual reproduction. We showed that four cyclin and two cyclin-dependent kinase regulator genes function in concert to orchestrate ploidy transition during unisexual reproduction. To trace and track ploidy transition events, we also generated a ploidy reporter and revealed the formation of segmental aneuploidy in addition to diploidization, illustrating the diverse mechanisms of genome plasticity in C. deneoformans.

Inference of Gene Regulatory Network Uncovers the Linkage between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi

The circadian clock drives time-specific gene expression, enabling biological processes to be temporally controlled. Plants that conduct crassulacean acid metabolism (CAM) photosynthesis represent an interesting case of circadian regulation of gene expression as stomatal movement is temporally inverted relative to stomatal movement in C3 plants. The mechanisms behind how the circadian clock enabled physiological differences at the molecular level is not well understood. Recently, the rescheduling of gene expression was reported as a mechanism to explain how CAM evolved from C3. Therefore, we investigated whether core circadian clock genes in CAM plants were re-phased during evolution, or whether networks of phase-specific genes were simply re-wired to different core clock genes. We identified candidate core clock genes based on gene expression features and then applied the Local Edge Machine (LEM) algorithm to infer regulatory relationships between this new set of core candidates and known core clock genes in Kalanchoë fedtschenkoi. We further inferred stomata-related gene targets for known and candidate core clock genes and constructed a gene regulatory network for core clock and stomata-related genes. Our results provide new insight into the mechanism of circadian control of CAM-related genes in K. fedtschenkoi, facilitating the engineering of CAM machinery into non-CAM plants for sustainable crop production in water-limited environments.

CDK10 in Gastrointestinal Cancers: Dual Roles as a Tumor Suppressor and Oncogene

Cyclin-dependent kinase 10 (CDK10) is a CDC2-related serine/threonine kinase involved in cellular processes including cell proliferation, transcription regulation and cell cycle regulation. CDK10 has been identified as both a candidate tumor suppressor in hepatocellular carcinoma, biliary tract cancers and gastric cancer, and a candidate oncogene in colorectal cancer (CRC). CDK10 has been shown to be specifically involved in modulating cancer cell proliferation, motility and chemosensitivity. Specifically, in CRC, it may represent a viable biomarker and target for chemoresistance. The development of therapeutics targeting CDK10 has been hindered by lack a specific small molecule inhibitor for CDK10 kinase activity, due to a lack of a high throughput screening assay. Recently, a novel CDK10 kinase activity assay has been developed, which will aid in the development of small molecule inhibitors targeting CDK10 activity. Discovery of a small molecular inhibitor for CDK10 would facilitate further exploration of its biological functions and affirm its candidacy as a therapeutic target, specifically for CRC.

CDK4/6 Inhibitors in Melanoma: A Comprehensive Review

Historically, metastatic melanoma was considered a highly lethal disease. However, recent advances in drug development have allowed a significative improvement in prognosis. In particular, BRAF/MEK inhibitors and anti-PD1 antibodies have completely revolutionized the management of this disease. Nonetheless, not all patients derive a benefit or a durable benefit from these therapies. To overtake this challenges, new clinically active compounds are being tested in the context of clinical trials. CDK4/6 inhibitors are drugs already available in clinical practice and preliminary evidence showed a promising activity also in melanoma. Herein we review the available literature to depict a comprehensive landscape about CDK4/6 inhibitors in melanoma. We present the molecular and genetic background that might justify the usage of these drugs, the preclinical evidence, the clinical available data, and the most promising ongoing clinical trials.

Peroxiredoxins couple metabolism and cell division in an ultradian cycle

Redox cycles have been reported in ultradian, circadian and cell cycle-synchronized systems. Redox cycles persist in the absence of transcription and cyclin-CDK activity, indicating that cells harbor multiple coupled oscillators. Nonetheless, the causal relationships and molecular mechanisms by which redox cycles are embedded within ultradian, circadian or cell division cycles remain largely elusive. Yeast harbor an ultradian oscillator, the yeast metabolic cycle (YMC), which comprises metabolic/redox cycles, transcriptional cycles and synchronized cell division. Here, we reveal the existence of robust cycling of H₂O₂ and peroxiredoxin oxidation during the YMC and show that peroxiredoxin inactivation disrupts metabolic cycling and abolishes coupling with cell division. We find that thiol-disulfide oxidants and reductants predictably modulate the switching between different YMC metabolic states, which in turn predictably perturbs cell cycle entry and exit. We propose that oscillatory H₂O₂-dependent protein thiol oxidation is a key regulator of metabolic cycling and its coordination with cell division.

An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum

The blood stage of the infection of the malaria parasite Plasmodium falciparum exhibits a 48-hour developmental cycle that culminates in the synchronous release of parasites from red blood cells, which triggers 48-hour fever cycles in the host. This cycle could be driven extrinsically by host circadian processes or by a parasite-intrinsic oscillator. To distinguish between these hypotheses, we examine the P. falciparum cycle in an in vitro culture system and show that the parasite has molecular signatures associated with circadian and cell cycle oscillators. Each of the four strains examined has a different period, which indicates strain-intrinsic period control. Finally, we demonstrate that parasites have low cell-to-cell variance in cycle period, on par with a circadian oscillator. We conclude that an intrinsic oscillator maintains Plasmodium's rhythmic life cycle.

Using extremal events to characterize noisy time series

Experimental time series provide an informative window into the underlying dynamical system, and the timing of the extrema of a time series (or its derivative) contains information about its structure. However, the time series often contain significant measurement errors. We describe a method for characterizing a time series for any assumed level of measurement error [Formula: see text] by a sequence of intervals, each of which is guaranteed to contain an extremum for any function that [Formula: see text]-approximates the time series. Based on the merge tree of a continuous function, we define a new object called the normalized branch decomposition, which allows us to compute intervals for any level [Formula: see text]. We show that there is a well-defined total order on these intervals for a single time series, and that it is naturally extended to a partial order across a collection of time series comprising a dataset. We use the order of the extracted intervals in two applications. First, the partial order describing a single dataset can be used to pattern match against switching model output (Cummins et al. in SIAM J Appl Dyn Syst 17(2):1589-1616, 2018), which allows the rejection of a network model. Second, the comparison between graph distances of the partial orders of different datasets can be used to quantify similarity between biological replicates.

Quantitative Studies for Cell-Division Cycle Control

The cell-division cycle (CDC) is driven by cyclin-dependent kinases (CDKs). Mathematical models based on molecular networks, as revealed by molecular and genetic studies, have reproduced the oscillatory behavior of CDK activity. Thus, one basic system for representing the CDC is a biochemical oscillator (CDK oscillator). However, genetically clonal cells divide with marked variability in their total duration of a single CDC round, exhibiting non-Gaussian statistical distributions. Therefore, the CDK oscillator model does not account for the statistical nature of cell-cycle control. Herein, we review quantitative studies of the statistical properties of the CDC. Over the past 70 years, studies have shown that the CDC is driven by a cluster of molecular oscillators. The CDK oscillator is coupled to transcriptional and mitochondrial metabolic oscillators, which cause deterministic chaotic dynamics for the CDC. Recent studies in animal embryos have raised the possibility that the dynamics of molecular oscillators underlying CDC control are affected by allometric volume scaling among the cellular compartments. Considering these studies, we discuss the idea that a cluster of molecular oscillators embedded in different cellular compartments coordinates cellular physiology and geometry for successful cell divisions.

The cell-cycle transcriptional network generates and transmits a pulse of transcription once each cell cycle

Multiple studies have suggested the critical roles of cyclin-dependent kinases (CDKs) as well as a transcription factor (TF) network in generating the robust cell-cycle transcriptional program. However, the precise mechanisms by which these components function together in the gene regulatory network remain unclear. Here we show that the TF network can generate and transmit a "pulse" of transcription independently of CDK oscillations. The premature firing of the transcriptional pulse is prevented by early G1 inhibitors, including transcriptional corepressors and the E3 ubiquitin ligase complex APCCdh1. We demonstrate that G1 cyclin-CDKs facilitate the activation and accumulation of TF proteins in S/G2/M phases through inhibiting G1 transcriptional corepressors (Whi5 and Stb1) and APCCdh1, thereby promoting the initiation and propagation of the pulse by the TF network. These findings suggest a unique oscillatory mechanism in which global phase-specific transcription emerges from a pulse-generating network that fires once-and-only-once at the start of the cycle.

Layers of regulation of cell-cycle gene expression in the budding yeast Saccharomyces cerevisiae

In the budding yeast Saccharomyces cerevisiae, transcription factors (TFs) regulate the periodic expression of many genes during the cell cycle, including gene products required for progression through cell-cycle events. Experimental evidence coupled with quantitative models suggests that a network of interconnected TFs is capable of regulating periodic genes over the cell cycle. Importantly, these dynamical models were built on transcriptomics data and assumed that TF protein levels and activity are directly correlated with mRNA abundance. To ask whether TF transcripts match protein expression levels as cells progress through the cell cycle, we applied a multiplexed targeted mass spectrometry approach (parallel reaction monitoring) to synchronized populations of cells. We found that protein expression of many TFs and cell-cycle regulators closely followed their respective mRNA transcript dynamics in cycling wild-type cells. Discordant mRNA/protein expression dynamics was also observed for a subset of cell-cycle TFs and for proteins targeted for degradation by E3 ubiquitin ligase complexes such as SCF (Skp1/Cul1/F-box) and APC/C (anaphase-promoting complex/cyclosome). We further profiled mutant cells lacking B-type cyclin/CDK activity ( clb1-6) where oscillations in ubiquitin ligase activity, cyclin/CDKs, and cell-cycle progression are halted. We found that a number of proteins were no longer periodically degraded in clb1-6 mutants compared with wild type, highlighting the importance of posttranscriptional regulation. Finally, the TF complexes responsible for activating G1/S transcription (SBF and MBF) were more constitutively expressed at the protein level than at periodic mRNA expression levels in both wild-type and mutant cells. This comprehensive investigation of cell-cycle regulators reveals that multiple layers of regulation (transcription, protein stability, and proteasome targeting) affect protein expression dynamics during the cell cycle.

Model Rejection and Parameter Reduction via Time Series

We show how a graph algorithm for finding matching labeled paths in pairs of labeled directed graphs can be used to perform model invalidation for a class of dynamical systems including regulatory network models of relevance to systems biology. In particular, given a partial order of events describing local minima and local maxima of observed quantities from experimental time series data, we produce a labeled directed graph we call the pattern graph for which every path from root to leaf corresponds to a plausible sequence of events. We then consider the regulatory network model, which can itself be rendered into a labeled directed graph we call the search graph via techniques previously developed in computational dynamics. Labels on the pattern graph correspond to experimentally observed events, while labels on the search graph correspond to mathematical facts about the model. We give a theoretical guarantee that failing to find a match invalidates the model. As an application we consider gene regulatory models for the yeast S. cerevisiae.

Reconciling conflicting models for global control of cell-cycle transcription

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

Oscillatory stimuli differentiate adapting circuit topologies

Adapting pathways consist of negative feedback loops (NFLs) or incoherent feedforward loops (IFFLs), which we show can be differentiated using oscillatory stimulation: NFLs but not IFFLs generically show ‘refractory period stabilization’ or ‘period skipping’. Using these signatures and genetic rewiring we identified the circuit dominating cell cycle timing in yeast. In C. elegans AWA neurons we uncovered a Ca2+-NFL, diffcult to find by other means, especially in wild-type, intact animals. (70 words)

Connecting virulence pathways to cell-cycle progression in the fungal pathogen Cryptococcus neoformans

Proliferation and host evasion are critical processes to understand at a basic biological level for improving infectious disease treatment options. The human fungal pathogen Cryptococcus neoformans causes fungal meningitis in immunocompromised individuals by proliferating in cerebrospinal fluid. Current antifungal drugs target "virulence factors" for disease, such as components of the cell wall and polysaccharide capsule in C. neoformans. However, mechanistic links between virulence pathways and the cell cycle are not as well studied. Recently, cell-cycle synchronized C. neoformans cells were profiled over time to identify gene expression dynamics (Kelliher et al., PLoS Genet 12(12):e1006453, 2016). Almost 20% of all genes in the C. neoformans genome were periodically activated during the cell cycle in rich media, including 40 genes that have previously been implicated in virulence pathways. Here, we review important findings about cell-cycle-regulated genes in C. neoformans and provide two examples of virulence pathways-chitin synthesis and G-protein coupled receptor signaling-with their putative connections to cell division. We propose that a "comparative functional genomics" approach, leveraging gene expression timing during the cell cycle, orthology to genes in other fungal species, and previous experimental findings, can lead to mechanistic hypotheses connecting the cell cycle to fungal virulence.

A Clb/Cdk1-mediated regulation of Fkh2 synchronizes CLB expression in the budding yeast cell cycle

Precise timing of cell division is achieved by coupling waves of cyclin-dependent kinase (Cdk) activity with a transcriptional oscillator throughout cell cycle progression. Although details of transcription of cyclin genes are known, it is unclear which is the transcriptional cascade that modulates their expression in a timely fashion. Here, we demonstrate that a Clb/Cdk1-mediated regulation of the Fkh2 transcription factor synchronizes the temporal mitotic CLB expression in budding yeast. A simplified kinetic model of the cyclin/Cdk network predicts a linear cascade where a Clb/Cdk1-mediated regulation of an activator molecule drives CLB3 and CLB2 expression. Experimental validation highlights Fkh2 as modulator of CLB3 transcript levels, besides its role in regulating CLB2 expression. A Boolean model based on the minimal number of interactions needed to capture the information flow of the Clb/Cdk1 network supports the role of an activator molecule in the sequential activation, and oscillatory behavior, of mitotic Clb cyclins. This work illustrates how transcription and phosphorylation networks can be coupled by a Clb/Cdk1-mediated regulation that synchronizes them.

A dynamic coupling of cyclin-dependent kinase with transcription factors in yeast offers insights into the timely cell cycle progression. An international team lead by Matteo Barberis from University of Amsterdam in The Netherlands studied the molecular mechanisms responsible for the coordination of DNA replication with cell division. The researchers have demonstrated how the sequential order of waves of mitotic cyclins activating cyclin-dependent kinase, or Cdk, is achieved by synchronizing Cdk with transcriptional activities. They have generated a mathematical model that predicts a cyclin/Cdk-mediated regulation of an activator molecule to stimulate mitotic cyclin expression. This prediction was successfully validated experimentally, identifying Forkhead transcription factors, or Fkh, as pivotal molecules. Cyclin waves are temporally synchronized by Fkh, and a mitotic Clb/Cdk1-mediated regulation of Fkh modulates cyclin expression. The findings reveal a novel principle of design, with kinase and transcription activities interlocked to guarantee a timely cell cycle.

Epigallocatechin-3-gallate inhibits adipogenesis through down-regulation of PPARγ and FAS expression mediated by PI3K-AKT signaling in 3T3-L1 cells

Epigallocatechin-3-gallate (EGCG), a major component in green tea, functions as extensive bioactivities including anti-inflammation, anti-oxidation, and anti-cancer. However, little is known about its anti-adipogenesis and underlying mechanisms. The purport of this study sought to investigate effects of EGCG on 3T3-L1 preadipocyte differentiation and to explore its possible mechanisms. The 3T3-L1 cells were induced to differentiate under the condition of pro-adipogenic cocktail with or without indicated EGCG concentrations (10, 50, 100, 200µM) for 2, 4, 6 and 8 days, respectively. Also, another batch of 3T3-L1 cells was induced under the optimal EGCG concentration (100µM) with or without SC3036 (PI3K activator, 10µM) or SC79 (AKT activator, 0.5µM) for 8 days. Subsequently, the cell viability was examined by MTT assay and the cell morphology was visualized by Oil red O staining. Finally, the mRNA levels including peroxisome proliferator activated receptor γ (PPARγ) and fatty acid synthase (FAS) were detected by quantitative real time PCR, while the protein levels of PPARγ, FAS, phosphatidylinositol 3 kinase (PI3K), insulin receptor substrate1(IRS1), AKT, and p-AKT were measured by immunoblotting analysis. Our results showed that EGCG inhibited adipogenesis of 3T3-L1 preadipocyte in a concentration-dependent manner. Moreover, the inhibitory effects were reversed by SC3036 or SC79, suggesting that the inhibitory effects of EGCG are mediated by PI3K-AKT signaling to down-regulate PPARγ and FAS expression levels. The findings shed light on EGCG anti-adipogenic effects and its underlying mechanism and provide a novel preventive-therapeutic potential for obesity subjects as a compound from Chinese green tea.

Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans

The pathogenic yeast Cryptococcus neoformans causes fungal meningitis in immune-compromised patients. Cell proliferation in the budding yeast form is required for C. neoformans to infect human hosts, and virulence factors such as capsule formation and melanin production are affected by cell-cycle perturbation. Thus, understanding cell-cycle regulation is critical for a full understanding of virulence factors for disease. Our group and others have demonstrated that a large fraction of genes in Saccharomyces cerevisiae is expressed periodically during the cell cycle, and that proper regulation of this transcriptional program is important for proper cell division. Despite the evolutionary divergence of the two budding yeasts, we found that a similar percentage of all genes (~20%) is periodically expressed during the cell cycle in both yeasts. However, the temporal ordering of periodic expression has diverged for some orthologous cell-cycle genes, especially those related to bud emergence and bud growth. Genes regulating DNA replication and mitosis exhibited a conserved ordering in both yeasts, suggesting that essential cell-cycle processes are conserved in periodicity and in timing of expression (i.e. duplication before division). In S. cerevisiae cells, we have proposed that an interconnected network of periodic transcription factors (TFs) controls the bulk of the cell-cycle transcriptional program. We found that temporal ordering of orthologous network TFs was not always maintained; however, the TF network topology at cell-cycle commitment appears to be conserved in C. neoformans. During the C. neoformans cell cycle, DNA replication genes, mitosis genes, and 40 genes involved in virulence are periodically expressed. Future work toward understanding the gene regulatory network that controls cell-cycle genes is critical for developing novel antifungals to inhibit pathogen proliferation.

Silencing of CDK2, but not CDK1, separates mitogenic from anti-apoptotic signaling, sensitizing p53 defective cells for synthetic lethality

Small molecule inhibitors targeting CDK1/CDK2 have been clinically proven effective against a variety of tumors, albeit at the cost of profound off target toxicities. To separate potential therapeutic from toxic effects, we selectively knocked down CDK1 or CDK2 in p53 mutated HACAT cells by siRNA silencing. Using dynamic, cell cycle wide proteome arrays, we observed minor changes in overall abundance of proteins critically involved in cell cycle transition despite profound G₂/M or G₁/S arrest, respectively. Employing phospho site specific analyses, we identified uncoupled mitogenic, yet pro-apoptotic signaling from counter balancing anti-apoptotic activity in CDK2 disrupted cells. Moreover, a crucial role of CDK2 activity in early serum response was observed, extending well-established roles of CDKs outside their cell cycle regulating functions. In contrast, disruption of CDK1 only marginally affected phosphorylation events of crucial signaling nodes prior to G₂/S transition. The data presented here suggest that the temporal separation of pro- and anti-apoptotic pathways by selective inhibition of CDK2 disrupts coherent signaling modules and may synergize with anti-proliferative drugs, averting toxic side effects from CDK1 inhibition.

The Local Edge Machine: inference of dynamic models of gene regulation

We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.

Role of the prostaglandin E2 receptor agonists in TGF-β1-induced mesangial cell damage

PGE₂ exerts its biological effect through binding to various EP receptors that result inactivation of various signal transduction pathways. It also plays an important role in mice glomerular mesangial cells (MCs) damage induced by transforming growth factor-β1 (TGF-β1); however, the molecular mechanisms remain unknown. In the present study, we tested the efficacy of four selective agonists of PGE₂ receptor, EP₁A (17-phenyl trinor prostaglandin E₂ ethyl amid), EP₂A (butaprost), EP₃A (sulprostone) and EP₄A (cay10580), on mice MCs. Compared with the cAMP produced by TGF-β1, additional pretreatment of EP₃A decreased the cAMP level. MCs treated with EP₁A and EP₃A augmented PGE₂, cyclooxygenase-2 (COX-2), membrane-bound PGE synthase 1 (mPGES1), laminin (LN), connective tissue growth factor (CTGF) and cyclin D1 expression stimulated by TGFβ1. EP₁A and EP₃A increased the number of cells in S+G2/M phase and reduced cells in G0/G1 phase. EP₁ and EP₃ agonists also strengthened TGFβ1-induced mitogen-activated protein kinase (p38MAPK) and extracellular-signal-regulated kinase 1/2 (ERK1/2) phosphorylation. Whereas MCs treated with EP₂A and EP₄A weakened PGE₂, COX-2, mPGES1, LN, CTGF and cyclin D1 expression stimulated by TGFβ1. EP₂A and EP₄A decreased the number of cells in S+G2/M phase and increased cells in G0/G1 phase. EP₂ and EP₄ agonists weakened TGFβ1-induced p38MAPK and ERK1/2 phosphorylation. These findings suggest that PGE₂ has an important role in the progression of kidney disease via the EP₁/EP₃ receptor, whereas EP₂ and EP₄ receptors are equally important in preserving the progression of chronic kidney failure. Thus, agonists of EP₂ and EP₄ receptors may provide a basis for treating kidney damage induced by TGF-β1.

Inference of gene regulation functions from dynamic transcriptome data

To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a ‘gene regulation function’ (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the CLB2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.

DOI:http://dx.doi.org/10.7554/eLife.12188.001

Living cells rely on networks of genes to control their behavior, including how they grow, develop and respond to stress. Genes encode instructions needed to make proteins and other molecules, and much of the control is exerted at the first stage of protein production, known as transcription. During this process, a gene is copied to make molecules known as transcripts that may later be used as templates to make proteins.

Many genes encode proteins that act to regulate transcription. Therefore, an individual gene may receive inputs from other genes, and these inputs affect how much transcript the gene produces, which can be considered as the gene’s output. While these inputs and outputs can often be wired together to form a network, it is less clear exactly how all the different inputs at a gene interact to determine its output. These interactions are known as “gene regulation functions”, and knowing them would be an important step towards understanding gene networks, which would help us to predict how cells will behave in different situations.

Gene regulation functions are difficult to measure directly, so researchers would like to find other ways to assess them indirectly. A recently developed experimental technique called “dynamic transcriptome analysis” seemed promising as it measures both the inputs and outputs of all genes in a cell over time. Hillenbrand et al. used this technique to infer gene regulation functions with one or two inputs in yeast cells.

Comparing these estimates with experimental data from previous studies showed that these inferred gene regulation functions could successfully predict the output of a gene based on its inputs. Hillenbrand et al. then used these estimates to search and model a well-known genetic network that is thought to be part of the molecular clockwork that controls the timing of events that cause a cell to divide.

Currently, the approach used by Hillenbrand et al. treats gene regulation functions like “black boxes”. This means that, while an output can be predicted if the inputs are known, it cannot reveal all of the detailed mechanisms behind it. Gaining insights into the inner workings of these black boxes will require taking more data into account, such as how abundant the proteins that regulate transcription are, where they are located within cells or whether they are active or not. Therefore, the next challenge is to incorporate these kinds of data to gain a fuller picture of how gene networks operate within cells.

DOI:http://dx.doi.org/10.7554/eLife.12188.002

The CDK-APC/C Oscillator Predominantly Entrains Periodic Cell-Cycle Transcription

Throughout cell-cycle progression, the expression of multiple transcripts oscillate, and whether these are under the centralized control of the CDK-APC/C proteins or can be driven by a de-centralized transcription factor (TF) cascade is a fundamental question for understanding cell-cycle regulation. In budding yeast, we find that the transcription of nearly all genes, as assessed by RNA-seq or fluorescence microscopy in single cells, is dictated by CDK-APC/C. Three exceptional genes are transcribed in a pulsatile pattern in a variety of CDK-APC/C arrests. Pursuing one of these transcripts, the SIC1 inhibitor of B-type cyclins, we use a combination of mathematical modeling and experimentation to provide evidence that, counter-intuitively, Sic1 provides a failsafe mechanism promoting nuclear division when levels of mitotic cyclins are low.

Fluorescence activated cell sorting followed by small RNA sequencing reveals stable microRNA expression during cell cycle progression

BACKGROUND

Previously, drug-based synchronization procedures were used for characterizing the cell cycle dependent transcriptional program. However, these synchronization methods result in growth imbalance and alteration of the cell cycle machinery. DNA content-based fluorescence activated cell sorting (FACS) is able to sort the different cell cycle phases without perturbing the cell cycle. MiRNAs are key transcriptional regulators of the cell cycle, however, their expression dynamics during cell cycle has not been explored.

METHODS

Following an optimized FACS, a complex initiative of high throughput platforms (microarray, Taqman Low Density Array, small RNA sequencing) were performed to study gene and miRNA expression profiles of cell cycle sorted human cells originating from different tissues. Validation of high throughput data was performed using quantitative real time PCR. Protein expression was detected by Western blot. Complex statistics and pathway analysis were also applied.

RESULTS

Beyond confirming the previously described cell cycle transcriptional program, cell cycle dependently expressed genes showed a higher expression independently from the cell cycle phase and a lower amplitude of dynamic changes in cancer cells as compared to untransformed fibroblasts. Contrary to mRNA changes, miRNA expression was stable throughout the cell cycle.

CONCLUSIONS

Cell cycle sorting is a synchronization-free method for the proper analysis of cell cycle dynamics. Altered dynamic expression of universal cell cycle genes in cancer cells reflects the transformed cell cycle machinery. Stable miRNA expression during cell cycle progression may suggest that dynamical miRNA-dependent regulation may be of less importance in short term regulations during the cell cycle.

Cdk1 activity acts as a quantitative platform for coordinating cell cycle progression with periodic transcription

Cell proliferation is regulated by cyclin-dependent kinases (Cdks) and requires the periodic expression of particular gene clusters in different cell cycle phases. However, the interplay between the networks that generate these transcriptional oscillations and the core cell cycle machinery remains largely unexplored. In this work, we use a synthetic regulable Cdk1 module to demonstrate that periodic expression is governed by quantitative changes in Cdk1 activity, with different clusters directly responding to specific activity levels. We further establish that cell cycle events neither participate in nor interfere with the Cdk1-driven transcriptional program, provided that cells are exposed to the appropriate Cdk1 activities. These findings contrast with current models that propose self-sustained and Cdk1-independent transcriptional oscillations. Our work therefore supports a model in which Cdk1 activity serves as a quantitative platform for coordinating cell cycle transitions with the expression of critical genes to bring about proper cell cycle progression.

Cell proliferation is regulated by cyclin-dependent kinases (Cdks) and relies on periodic gene cluster expression according to cell cycle phases. Here the authors use a synthetic regulatable Cdk1 module to demonstrate that periodic expression is governed by quantitative changes in Cdk1 activity.

Role of Computational Modeling in Understanding Cell Cycle Oscillators

The periodic oscillations in the activity of the cell cycle regulatory program, drives the timely activation of key cell cycle events. Interesting dynamical systems, such as oscillators, have been investigated by various theoretical and computational modeling methods. Thanks to the insights achieved by these modeling efforts we have gained considerable insights about the underlying molecular regulatory networks that can drive cell cycle oscillations. Here we review the basic features and characteristics of biological oscillators, discussing from a computational modeling point of view their specific architectures and the current knowledge about the dynamics that the life evolution selected to drive cell cycle oscillations.

Create, activate, destroy, repeat: Cdk1 controls proliferation by limiting transcription factor activity

Progression through the cell cycle is controlled by a network of transcription factors that coordinate gene expression with cell-cycle events. One transcriptional activator in this network in budding yeast is the forkhead protein Hcm1, which controls the expression of genes that are transcribed during S-phase. Hcm1 activity is coordinated with the cell cycle via its regulation by cyclin-dependent kinase (Cdk1), which both activates Hcm1 and targets it for degradation, through phosphorylation of distinct sites. The mechanisms controlling the differential phosphorylation timing of the activating and destabilizing phosphosites are not clear. However, a recent study shows that the phosphatase calcineurin specifically removes activating phosphates from Hcm1 when cells are exposed to environmental stress, thus extinguishing its activity and slowing proliferation under unfavorable growth conditions. This regulatory mechanism, whereby a phosphatase actively alters the distribution of phosphosites on a cell cycle-regulatory transcription factor to elicit a change in cellular proliferation, adds an additional layer of complexity to the regulatory network controlling the cell cycle. Furthermore, this regulatory paradigm is likely to be a conserved mode of phosphoregulation that controls the cell cycle in diverse systems.

Analyzing transcription dynamics during the budding yeast cell cycle

Assaying global cell cycle-regulated transcription in budding yeast involves extracting RNA from a synchronous population and proper normalization of detected transcript levels. Here, we describe synchronization of Saccharomyces cerevisiae cell populations by centrifugal elutriation, followed by the isolation of RNA for microarray analysis. Further, we outline the computational methods required to directly compare RNA abundance from individual time points within an experiment and to compare independent experiments. Together, these methods describe the complete workflow necessary to observe RNA abundance during the cell cycle.

Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis

BACKGROUND

The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.

RESULTS

In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other.

CONCLUSIONS

The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.

Checkpoints couple transcription network oscillator dynamics to cell-cycle progression

BACKGROUND

The coupling of cyclin dependent kinases (CDKs) to an intrinsically oscillating network of transcription factors has been proposed to control progression through the cell cycle in budding yeast, Saccharomyces cerevisiae. The transcription network regulates the temporal expression of many genes, including cyclins, and drives cell-cycle progression, in part, by generating successive waves of distinct CDK activities that trigger the ordered program of cell-cycle events. Network oscillations continue autonomously in mutant cells arrested by depletion of CDK activities, suggesting the oscillator can be uncoupled from cell-cycle progression. It is not clear what mechanisms, if any, ensure that the network oscillator is restrained when progression in normal cells is delayed or arrested. A recent proposal suggests CDK acts as a master regulator of cell-cycle processes that have the potential for autonomous oscillatory behavior.

RESULTS

Here we find that mitotic CDK is not sufficient for fully inhibiting transcript oscillations in arrested cells. We do find that activation of the DNA replication and spindle assembly checkpoints can fully arrest the network oscillator via overlapping but distinct mechanisms. Further, we demonstrate that the DNA replication checkpoint effector protein, Rad53, acts to arrest a portion of transcript oscillations in addition to its role in halting cell-cycle progression.

CONCLUSIONS

Our findings indicate that checkpoint mechanisms, likely via phosphorylation of network transcription factors, maintain coupling of the network oscillator to progression during cell-cycle arrest.

Topology and control of the cell-cycle-regulated transcriptional circuitry

Nearly 20% of the budding yeast genome is transcribed periodically during the cell division cycle. The precise temporal execution of this large transcriptional program is controlled by a large interacting network of transcriptional regulators, kinases, and ubiquitin ligases. Historically, this network has been viewed as a collection of four coregulated gene clusters that are associated with each phase of the cell cycle. Although the broad outlines of these gene clusters were described nearly 20 years ago, new technologies have enabled major advances in our understanding of the genes comprising those clusters, their regulation, and the complex regulatory interplay between clusters. More recently, advances are being made in understanding the roles of chromatin in the control of the transcriptional program. We are also beginning to discover important regulatory interactions between the cell-cycle transcriptional program and other cell-cycle regulatory mechanisms such as checkpoints and metabolic networks. Here we review recent advances and contemporary models of the transcriptional network and consider these models in the context of eukaryotic cell-cycle controls.

Regulation of a transcription factor network by Cdk1 coordinates late cell cycle gene expression

To maintain genome stability, regulators of chromosome segregation must be expressed in coordination with mitotic events. Expression of these late cell cycle genes is regulated by cyclin-dependent kinase (Cdk1), which phosphorylates a network of conserved transcription factors (TFs). However, the effects of Cdk1 phosphorylation on many key TFs are not known. We find that elimination of Cdk1-mediated phosphorylation of four S-phase TFs decreases expression of many late cell cycle genes, delays mitotic progression, and reduces fitness in budding yeast. Blocking phosphorylation impairs degradation of all four TFs. Consequently, phosphorylation-deficient mutants of the repressors Yox1 and Yhp1 exhibit increased promoter occupancy and decreased expression of their target genes. Interestingly, although phosphorylation of the transcriptional activator Hcm1 on its N-terminus promotes its degradation, phosphorylation on its C-terminus is required for its activity, indicating that Cdk1 both activates and inhibits a single TF. We conclude that Cdk1 promotes gene expression by both activating transcriptional activators and inactivating transcriptional repressors. Furthermore, our data suggest that coordinated regulation of the TF network by Cdk1 is necessary for faithful cell division.

Cell cycle-regulated transcription: effectively using a genomics toolbox

The cell cycle comprises a series of temporally ordered events that occur sequentially, including DNA replication, centrosome duplication, mitosis, and cytokinesis. What are the regulatory mechanisms that ensure proper timing and coordination of events during the cell cycle? Biochemical and genetic screens have identified a number of cell-cycle regulators, and it was recognized early on that many of the genes encoding cell-cycle regulators, including cyclins, were transcribed only in distinct phases of the cell cycle. Thus, "just in time" expression is likely an important part of the mechanism that maintains the proper temporal order of cell cycle events. New high-throughput technologies for measuring transcript levels have revealed that a large percentage of the Saccharomyces cerevisiae transcriptome (~20 %) is cell cycle regulated. Similarly, a substantial fraction of the mammalian transcriptome is cell cycle-regulated. Over the past 25 years, many studies have been undertaken to determine how gene expression is regulated during the cell cycle. In this review, we discuss contemporary models for the control of cell cycle-regulated transcription, and how this transcription program is coordinated with other cell cycle events in S. cerevisiae. In addition, we address the genomic approaches and analytical methods that enabled contemporary models of cell cycle transcription. Finally, we address current and future technologies that will aid in further understanding the role of periodic transcription during cell cycle progression.

Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression

During the cell cycle, the levels of hundreds of mRNAs change in a periodic manner, but how this is achieved by alterations in the rates of mRNA synthesis and degradation has not been studied systematically. Here, we used metabolic RNA labeling and comparative dynamic transcriptome analysis (cDTA) to derive mRNA synthesis and degradation rates every 5 min during three cell cycle periods of the yeast Saccharomyces cerevisiae. A novel statistical model identified 479 genes that show periodic changes in mRNA synthesis and generally also periodic changes in their mRNA degradation rates. Peaks of mRNA degradation generally follow peaks of mRNA synthesis, resulting in sharp and high peaks of mRNA levels at defined times during the cell cycle. Whereas the timing of mRNA synthesis is set by upstream DNA motifs and their associated transcription factors (TFs), the synthesis rate of a periodically expressed gene is apparently set by its core promoter.

Identifying Cis-Regulatory Elements and Modules Using Conditional Random Fields

Accurate identification of cis-regulatory elements and their correlated modules is essential for analysis of transcriptional regulation, which is a challenging problem in computational biology. Unsupervised learning has the advantage of compensating for missing annotated data, and is thus promising to be effective to identify cis-regulatory elements and modules. We introduced a Conditional Random Fields model, referred to as CRFEM, to integrate sequence features and long-range dependency of genomic sequences such as epigenetic features to identify cis-regulatory elements and modules at the same time. The proposed method is able to automatically learn model parameters with no labeled data and explicitly optimize the predictive probability of cis-regulatory elements and modules. In comparison with existing methods, our method is more accurate and can be used for genome-wide studies of gene regulation.

Feedback loops and reciprocal regulation: recurring motifs in the systems biology of the cell cycle

The study of eukaryotic cell cycle regulation over the last several decades has led to a remarkably detailed understanding of the complex regulatory system that drives this fundamental process. This allows us to now look for recurring motifs in the regulatory system. Among these are negative feedback loops, which underpin checkpoints and generate cell cycle oscillations; positive feedback loops, which promote oscillations and make cell cycle transitions switch-like and unidirectional; and reciprocal regulation, which can increase the control a key regulator exerts. These simple motifs are found at multiple points in the cell cycle (e.g. S-phase and M-phase control) and are conserved in diverse organisms. These findings argue for an underlying unity in the principles of cell cycle control.

Radiation-induced cellular senescence results from a slippage of long-term G2 arrested cells into G1 phase

Diploid cells undergoing senescence and mitotic slippage have been reported in the literature. However, the mechanisms triggering senescence in long-term G2-arrested cells are currently unclear. Previously, we reported that the cell cycle of the human uveal melanoma cell line, 92-1, is suspended for up to 6 d upon exposure to 10 Gy ionizing radiation (IR), followed by senescence. In the current study, we initially distinguished senescence in long-term blocked 92-1 cells from mitotic slippage by confirming the blockage of cells in the G2 phase. We subsequently showed that the genes essential for G2-M transition are prematurely downregulated at both the transcriptional and translational levels. Furthermore, levels of the G1-specific markers, Cyclin D1 and Caveolin-1, were distinctly increased, while S/G2-specific markers, Cyclin B1 and Aurora A, were significantly downregulated. These findings collectively imply that long-term G2-arrested cells undergo senescence via G2 slippage. To our knowledge, this is the first study to report that the cellular process of G2 slippage is the mechanism responsible for senescence of cells under long-term G2 arrest.

Human height genes and cancer

Body development requires the ability to control cell proliferation and metabolism, together with selective 'invasive' cell migration for organogenesis. These requirements are shared with cancer. Human height-associated loci have been recently identified by genome-wide SNP-association studies. Strikingly, most of the more than 100 genes found associated to height appear linked to neoplastic growth, and impose a higher risk for cancer. Height-associated genes drive the HH/PTCH and BMP/TGFβ pathways, with p53, c-Myc, ERα, HNF4A and SMADs as central network nodes. Genetic analysis of body-size-affecting diseases and evidence from genetically-modified animals support this model. The finding that cancer is deeply linked to normal, body-plan master genes may profoundly affect current paradigms on tumor development.