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

A transcriptome-wide analysis deciphers distinct roles of G1 cyclins in temporal organization of the yeast cell cycle

Oscillating gene expression is crucial for correct timing and progression through cell cycle. In Saccharomyces cerevisiae, G1 cyclins Cln1-3 are essential drivers of the cell cycle and have an important role for temporal fine-tuning. We measured time-resolved transcriptome-wide gene expression for wild type and cyclin single and double knockouts over cell cycle with and without osmotic stress. Clustering of expression profiles, peak time detection of oscillating genes, integration with transcription factor network dynamics, and assignment to cell cycle phases allowed us to quantify the effect of genetic or stress perturbations on the duration of cell cycle phases. Cln1 and Cln2 showed functional differences, especially affecting later phases. Deletion of Cln3 led to a delay of START followed by normal progression through later phases. Our data and network analysis suggest mutual effects of cyclins with the transcriptional regulators SBF and MBF.

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.

Cell cycle regulation of condensin Smc4

The condensin complex is a conserved ATPase which promotes the compaction of chromatin during mitosis in eukaryotic cells. Condensin complexes have in addition been reported to contribute to interphase processes including sister chromatid cohesion. It is not understood how condensins specifically become competent to facilitate chromosome condensation in preparation for chromosome segregation in anaphase. Here we describe evidence that core condensin subunits are regulated at the level of protein stability in budding yeast. In particular, Smc2 and Smc4 abundance is cell cycle regulated, peaking at mitosis and falling to low levels in interphase. Smc4 degradation at the end of mitosis is dependent on the Anaphase Promoting Complex/Cyclosome and is mediated by the proteasome. Overproduction of Smc4 results in delayed decondensation, but has a limited ability to promote premature condensation in interphase. Unexpectedly, the Mad2 spindle checkpoint protein is required for mitotic Smc4 degradation. These studies have revealed the novel finding that condensin protein levels are cell cycle regulated and have identified the factors necessary for Smc4 proteolysis.

Knockdown of arsenic resistance protein 2 inhibits human glioblastoma cell proliferation through the MAPK/ERK pathway

It is generally known that glioblastoma is the most common primary malignant brain tumor and that it is highly aggressive and deadly. Although surgical and pharmacological therapies have made long‑term progress, glioblastoma remains extremely lethal and has an uncommonly low survival rate. Therefore, further elucidation of the molecular mechanisms of glioblastoma initiation and its pathological processes are urgent. Arsenic resistance protein 2 (Ars2) is a highly conserved gene, and it has been found to play an important role in microRNA biosynthesis and cell proliferation in recent years. Furthermore, absence of Ars2 results in developmental death in Drosophila, zebrafish and mice. However, there are few studies on the role of Ars2 in regulating tumor development, and the mechanism of its action is mostly unknown. In the present study, we revealed that Ars2 is involved in glioblastoma proliferation and we identified a potential mechanistic role for it in cell cycle control. Our data demonstrated that Ars2 knockdown significantly repressed the proliferation and tumorigenesis abilities of glioblastoma cells in vitro and in vivo. Further investigation clarified that Ars2 deficiency inhibited the activation of the MAPK/ERK pathway, leading to cell cycle arrest in the G1 phase, resulting in suppression of cell proliferation. These findings support the conclusion that Ars2 is a key regulator of glioblastoma progression.

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.

A feature-based integrated scoring scheme for cell cycle-regulated genes prioritization

Prioritization of cell cycle-regulated genes from expression time-profiles is still an open problem. The point at issue is the surprisingly poor overlap among ranked lists obtained from different experimental protocols. Instead of developing a general-purpose computational methodology for detecting periodic signals, we focus on the budding yeast mitotic cell cycle. The reason being that the current availability of a total of 12 datasets, produced by 6 independent groups using 4 different synchronization methods, permits a re-analysis and re-consideration of this problem in a more reliable and extensive data domain. Notably, budding yeast is a model organism for studying cancer and testing new drugs. Here we propose a novel multi-feature score (called PERLA, PERiodicity, Regulation and Lag-Autocorrelation) that integrates different features of cell cycle-regulated gene expression time-profiles. We obtained increased performances on a wide range of benchmarks and, most importantly, a substantially increased overlap of the top ranking genes among different datasets, thus proving the effectiveness of the proposed prioritization algorithm. Examples on how to use PERLA to gain new insight into the biology of the cell cycle, are provided in a final dedicated section.

Dual gene deficient models of ApcMin/+ mouse in assessing molecular mechanisms of intestinal carcinogenesis

The Apc^Min/+ mouse, carrying an inactivated allele of the adenomatous polyposis coli (Apc) gene, is a widely used animal model of human colorectal tumorigenesis. While crossed with other gene knockout or knock-in mice, these mice possess advantages in investigation of human intestinal tumorigenesis. Intestinal tumor pathogenesis involves multiple gene alterations; thus, various double gene deficiency models could provide novel insights into molecular mechanisms of tumor biology, as well as gene-gene interactions involved in intestinal tumor development and assessment of novel strategies for preventing and treating intestinal cancer. This review discusses approximately 100 double gene deficient mice and their associated intestinal tumor development and progression phenotypes. The dual gene knockouts based on the Apc mutation background consist of inflammation and immune-related, cell cycle-related, Wnt/β-catenin signaling-related, tumor growth factor (TGF)-signaling-related, drug metabolism-related, and transcription factor genes, as well as some oncogenes and tumor suppressors. Future studies should focus on conditional or inducible dual or multiple mouse gene knockout models to investigate the molecular mechanisms underlying intestinal tumor development, as well as potential drug targets.

Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks

Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae.

Chloroform fraction of Serratulae chinensis S. Moore suppresses proliferation and induces apoptosis via the phosphatidylinositide 3-kinase/Akt pathway in human gastric cancer cells

The chloroform fraction of the folk Chinese medicine, Serratulae chinensis S. Moore (CSC) and its anti-inflammatory activity is well recognized. However, the molecular mechanisms underlying the beneficial anticancer effects of CSC remain largely unknown. The aim of the present study was to examine the effects of CSC on the regulation of cell proliferation and apoptosis in SGC-7901 gastric cancer cells, as well as to investigate the underlying molecular mechanisms involved. The results from the present study demonstrated that CSC treatment inhibited SGC-7901 cell viability and survival in a dose- and/or time-dependent manner. CSC treatment further induced the apoptosis of SGC-7901 cells, characterized by distinct chromatin condensation and fragmented nuclear morphology. In addition, CSC treatment suppressed protein kinase-B (Akt) phosphorylation and phosphatidylinositide 3-kinase (PI3K) expression in SGC-7901 cells, which in turn promoted cancer cell apoptosis and inhibited cell proliferation. Furthermore, CSC treatment altered the expression pattern of several key target genes of the PI3K/Akt signaling pathway through the downregulation of Cyclin D1, cyclin-dependent kinase-4 and B-cell lymphoma-2 and the upregulation of Bcl-2-associated X protein. Therefore, the results from the present study demonstrated that CSC suppressed cell survival and induced apoptosis in human gastric cancer cells, via targeting the PI3K/Akt pathway.

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.

BTNET : boosted tree based gene regulatory network inference algorithm using time-course measurement data

Background

Identifying gene regulatory networks is an important task for understanding biological systems. Time-course measurement data became a valuable resource for inferring gene regulatory networks. Various methods have been presented for reconstructing the networks from time-course measurement data. However, existing methods have been validated on only a limited number of benchmark datasets, and rarely verified on real biological systems.

Results

We first integrated benchmark time-course gene expression datasets from previous studies and reassessed the baseline methods. We observed that GENIE3-time, a tree-based ensemble method, achieved the best performance among the baselines. In this study, we introduce BTNET, a boosted tree based gene regulatory network inference algorithm which improves the state-of-the-art. We quantitatively validated BTNET on the integrated benchmark dataset. The AUROC and AUPR scores of BTNET were higher than those of the baselines. We also qualitatively validated the results of BTNET through an experiment on neuroblastoma cells treated with an antidepressant. The inferred regulatory network from BTNET showed that brachyury, a transcription factor, was regulated by fluoxetine, an antidepressant, which was verified by the expression of its downstream genes.

Conclusions

We present BTENT that infers a GRN from time-course measurement data using boosting algorithms. Our model achieved the highest AUROC and AUPR scores on the integrated benchmark dataset. We further validated BTNET qualitatively through a wet-lab experiment and showed that BTNET can produce biologically meaningful results.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0547-0) contains supplementary material, which is available to authorized users.

Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates

Motivation

Computational models in biology are frequently underdetermined, due to limits in our capacity to measure biological systems. In particular, mechanistic models often contain parameters whose values are not constrained by a single type of measurement. It may be possible to achieve better model determination by combining the information contained in different types of measurements. Bayesian statistics provides a convenient framework for this, allowing a quantification of the reduction in uncertainty with each additional measurement type. We wished to explore whether such integration is feasible and whether it can allow computational models to be more accurately determined.

Results

We created an ordinary differential equation model of cell cycle regulation in budding yeast and integrated data from 13 different studies covering different experimental techniques. We found that for some parameters, a single type of measurement, relative time course mRNA expression, is sufficient to constrain them. Other parameters, however, were only constrained when two types of measurements were combined, namely relative time course and absolute transcript concentration. Comparing the estimates to measurements from three additional, independent studies, we found that the degradation and transcription rates indeed matched the model predictions in order of magnitude. The predicted translation rate was incorrect however, thus revealing a deficiency in the model. Since this parameter was not constrained by any of the measurement types separately, it was only possible to falsify the model when integrating multiple types of measurements. In conclusion, this study shows that integrating multiple measurement types can allow models to be more accurately determined.

Availability and implementation

The models and files required for running the inference are included in the Supplementary information.

Contact

l.wessels@nki.nl.

Supplementary information

Supplementary data are available at Bioinformatics online.

dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data

The elucidation of gene regulatory networks is one of the major challenges of systems biology. Measurements about genes that are exploited by network inference methods are typically available either in the form of steady-state expression vectors or time series expression data. In our previous work, we proposed the GENIE3 method that exploits variable importance scores derived from Random forests to identify the regulators of each target gene. This method provided state-of-the-art performance on several benchmark datasets, but it could however not specifically be applied to time series expression data. We propose here an adaptation of the GENIE3 method, called dynamical GENIE3 (dynGENIE3), for handling both time series and steady-state expression data. The proposed method is evaluated extensively on the artificial DREAM4 benchmarks and on three real time series expression datasets. Although dynGENIE3 does not systematically yield the best performance on each and every network, it is competitive with diverse methods from the literature, while preserving the main advantages of GENIE3 in terms of scalability.

A Robust Molecular Network Motif for Period-Doubling Devices

Life is sustained by a variety of cyclic processes such as cell division, muscle contraction, and neuron firing. The periodic signals powering these processes often direct a variety of other downstream systems, which operate at different time scales and must have the capacity to divide or multiply the period of the master clock. Period modulation is also an important challenge in synthetic molecular systems, where slow and fast components may have to be coordinated simultaneously by a single oscillator whose frequency is often difficult to tune. Circuits that can multiply the period of a clock signal (frequency dividers), such as binary counters and flip-flops, are commonly encountered in electronic systems, but design principles to obtain similar devices in biological systems are still unclear. We take inspiration from the architecture of electronic flip-flops, and we propose to build biomolecular period-doubling networks by combining a bistable switch with negative feedback modules that preprocess the circuit inputs. We identify a network motif and we show it can be "realized" using different biomolecular components; two of the realizations we propose rely on transcriptional gene networks and one on nucleic acid strand displacement systems. We examine the capacity of each realization to perform period-doubling by studying how bistability of the motif is affected by the presence of the input; for this purpose, we employ mathematical tools from algebraic geometry that provide us with valuable insights on the input/output behavior as a function of the realization parameters. We show that transcriptional network realizations operate correctly also in a stochastic regime when processing oscillations from the repressilator, a canonical synthetic in vivo oscillator. Finally, we compare the performance of different realizations in a range of realistic parameters via numerical sensitivity analysis of the period-doubling region, computed with respect to the input period and amplitude. Our mathematical and computational analysis suggests that the motif we propose is generally robust with respect to specific implementation details: functionally equivalent circuits can be built as long as the species-interaction topology is respected. This indicates that experimental construction of the circuit is possible with a variety of components within the rapidly expanding libraries available in synthetic biology.

Curcumol induces cell cycle arrest in colon cancer cells via reactive oxygen species and Akt/ GSK3β/cyclin D1 pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Curcuma kwangsiensis S. G. Lee & C. F. Liang (Guangxi ezhu, in Chinese) belongs to the Zingiberaceae family, has been used as a traditionally Chinese medicine nearly 2000 year. Curcumol is one of the guaiane-type sesquiterpenoid hemiketal isolated from medicine plant Curcuma kwangsiensis S. G. Lee & C. F. Liang, which has been reported possesses anti-cancer effects. Our previous study found that the most contribution to inhibit nasopharyngeal carcinoma cell growth was curcumol.

AIM OF THE STUDY

To assess the effect of curcumol on cell cycle arrest against human colon cancer cells (CRC) cells (LoVo and SW480) and explore its mechanism in vitro and in vivo.

MATERIALS AND METHODS

Curcumol was dissolved in absolute ethyl alcohol. The concentration of absolute ethyl alcohol in the control group or in experimental samples was always 1/500 (v/v) of the final medium volume. LoVo and SW480 cells were treated with different concentrations of curcumol (0, 53, 106, 212 and 424μM). And then the cell cycle of each group was examined by flow cytometry. The protein levels of PI3K, p-Akt, cyclin D1, cyclin E, CDK2, CDK4 and GSK3β were determined by Western blot. The mRNA expression of PI3K, Akt, cyclin D1, CDK4, P27, p21, and P16 in the treated cells were analyzed by real-time RT-PCR. In addition, the antitumor activity of curcumol was evaluated in nude mice bearing orthotopic tumor implants.

RESULTS

Curcumol induced cell cycle arrest in G1/S phase. RT-qPCR and Western blot data showed that curcumol enhanced the expression of GSK3β, P27, p21 and P16, and decreased the levels of PI3K, phosphorylated Akt (p-Akt), cyclin D1, CDK4, cyclin E and CDK2. Furthermore, curcumol induced reactive oxygen species (ROS) generation in LoVo cells, and ROS scavenger N-acetylcysteine (NAC) significantly reversed curcumol-induced cell growth inhibition. Besides, curcumol also prevented the growth of human colon cancer cells xenografts in nude mouse, accompanied by the reduction of PI3K, Akt, cyclin D1, CDK4, cycln E and significant increase of GSK3β.

CONCLUSIONS

Curcumol caused cell cycle arrest at the G0/G1 phase by ROS production and Akt/ GSK3β/cyclin D1 pathways inactivation, indicating the potential of curcumol in the prevention of colon cancer carcinogenesis.

shRNA-induced silencing of Ras-related C3 botulinum toxin substrate 1 inhibits the proliferation of colon cancer cells through upregulation of BAD and downregulation of cyclin D1

Ras-related C3 botulinum toxin substrate 1 (RAC1) is a member of the Rho family of small GTPases. Recent studies have reported that RAC1 serves an important role in colon cancer cell proliferation. The present study aimed to investigate the effects of RAC1 knockdown on cell proliferation, cell cycle progression and apoptosis of colon cancer cells. Lentivirus-mediated short hairpin RNA (shRNA) was used to knockdown RAC1 expression in colon cancer cell lines, and cell proliferation, apoptosis, cell cycle progression were evaluated by MTT assays and flow cytometry. The differences in mRNAs expression were identified between RAC1-knockdown cells and control cells using a mRNA microarray, following which quantitative PCR (qPCR) and western blot were employed to confirm the results of the mRNA microarray. The proliferative ability of colon cancer cells was significantly decreased following RAC1 knockdown, and RAC1 knockdown increased the apoptotic rate and enhanced cell cycle arrest at G₁ phase in colon cancer cells. In addition, >1,200 known genes were demonstrated to be involved in RAC1-associated tumorigenic functions in SW620 colon cancer cells, as determined by gene chip analysis; these genes were associated with cell proliferation, cell cycle, apoptosis and metastasis. Furthermore, western blot analysis indicated that cyclin D1 was downregulated, whereas B-cell lymphoma 2-associated agonist of cell death (BAD) was upregulated following RAC1 knockdown in colon cancer cells. In conclusion, RAC1 silencing may suppress the proliferation of colon cancer cells by inducing apoptosis and cell cycle arrest. In addition, a large number of genes were revealed to be involved in the process, including BAD, which was upregulated and cyclin D1, which was downregulated. Further studies on these differentially expressed genes may provide a better understanding of the potential roles of RAC1 in colon carcinogenesis.

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.

Form and function in gene regulatory networks: the structure of network motifs determines fundamental properties of their dynamical state space

Network motifs have been studied extensively over the past decade, and certain motifs, such as the feed-forward loop, play an important role in regulatory networks. Recent studies have used Boolean network motifs to explore the link between form and function in gene regulatory networks and have found that the structure of a motif does not strongly determine its function, if this is defined in terms of the gene expression patterns the motif can produce. Here, we offer a different, higher-level definition of the ‘function’ of a motif, in terms of two fundamental properties of its dynamical state space as a Boolean network. One is the basin entropy, which is a complexity measure of the dynamics of Boolean networks. The other is the diversity of cyclic attractor lengths that a given motif can produce. Using these two measures, we examine all 104 topologically distinct three-node motifs and show that the structural properties of a motif, such as the presence of feedback loops and feed-forward loops, predict fundamental characteristics of its dynamical state space, which in turn determine aspects of its functional versatility. We also show that these higher-level properties have a direct bearing on real regulatory networks, as both basin entropy and cycle length diversity show a close correspondence with the prevalence, in neural and genetic regulatory networks, of the 13 connected motifs without self-interactions that have been studied extensively in the literature.

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)

High resolution temporal transcriptomics of mouse embryoid body development reveals complex expression dynamics of coding and noncoding loci

Cellular responses to stimuli are rapid and continuous and yet the vast majority of investigations of transcriptional responses during developmental transitions typically use long interval time courses; limiting the available interpretive power. Moreover, such experiments typically focus on protein-coding transcripts, ignoring the important impact of long noncoding RNAs. We therefore evaluated coding and noncoding expression dynamics at unprecedented temporal resolution (6-hourly) in differentiating mouse embryonic stem cells and report new insight into molecular processes and genome organization. We present a highly resolved differentiation cascade that exhibits coding and noncoding transcriptional alterations, transcription factor network interactions and alternative splicing events, little of which can be resolved by long-interval developmental time-courses. We describe novel short lived and cycling patterns of gene expression and dissect temporally ordered gene expression changes in response to transcription factors. We elucidate patterns in gene co-expression across the genome, describe asynchronous transcription at bidirectional promoters and functionally annotate known and novel regulatory lncRNAs. These findings highlight the complex and dynamic molecular events underlying mammalian differentiation that can only be observed though a temporally resolved time course.

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.

Rapamycin treatment dose-dependently improves the cystic kidney in a new ADPKD mouse model via the mTORC1 and cell-cycle-associated CDK1/cyclin axis

Although translational research into autosomal dominant polycystic kidney disease (ADPKD) and its pathogenesis has made considerable progress, there is presently lack of standardized animal model for preclinical trials. In this study, we developed an orthologous mouse model of human ADPKD by cross‐mating Pkd2 conditional‐knockout mice (Pkd2^f3) to Cre transgenic mice in which Cre is driven by a spectrum of kidney‐related promoters. By systematically characterizing the mouse model, we found that Pkd2^f3/f3 mice with a Cre transgene driven by the mouse villin‐1 promoter (Vil‐Cre;Pkd2^f3/f3) develop overt cysts in the kidney, liver and pancreas and die of end‐stage renal disease (ESRD) at 4–6 months of age. To determine whether these Vil‐Cre;Pkd2^f3/f3 mice were suitable for preclinical trials, we treated the mice with the high‐dose mammalian target of rapamycin (mTOR) inhibitor rapamycin. High‐dose rapamycin significantly increased the lifespan, lowered the cystic index and kidney/body weight ratio and improved renal function in Vil‐Cre;Pkd2^f3/f3 mice in a time‐ and dose‐dependent manner. In addition, we further found that rapamycin arrested aberrant epithelial‐cell proliferation in the ADPKD kidney by down‐regulating the cell‐cycle‐associated cyclin‐dependent kinase 1 (CDK1) and cyclins, namely cyclin A, cyclin B, cyclin D1 and cyclin E, demonstrating a direct link between mTOR signalling changes and the polycystin‐2 dysfunction in cystogenesis. Our newly developed ADPKD model provides a practical platform for translating in vivo preclinical results into ADPKD therapies. The newly defined molecular mechanism by which rapamycin suppresses proliferation via inhibiting abnormally elevated CDK1 and cyclins offers clues to new molecular targets for ADPKD treatment.

Identifying ultrasensitive HGF dose-response functions in a 3D mammalian system for synthetic morphogenesis

Nonlinear responses to signals are widespread natural phenomena that affect various cellular processes. Nonlinearity can be a desirable characteristic for engineering living organisms because it can lead to more switch-like responses, similar to those underlying the wiring in electronics. Steeper functions are described as ultrasensitive, and can be applied in synthetic biology by using various techniques including receptor decoys, multiple co-operative binding sites, and sequential positive feedbacks. Here, we explore the inherent non-linearity of a biological signaling system to identify functions that can potentially be exploited using cell genome engineering. For this, we performed genome-wide transcription profiling to identify genes with ultrasensitive response functions to Hepatocyte Growth Factor (HGF). We identified 3,527 genes that react to increasing concentrations of HGF, in Madin-Darby canine kidney (MDCK) cells, grown as cysts in 3D collagen cell culture. By fitting a generic Hill function to the dose-responses of these genes we obtained a measure of the ultrasensitivity of HGF-responsive genes, identifying a subset with higher apparent Hill coefficients (e.g. MMP1, TIMP1, SNORD75, SNORD86 and ERRFI1). The regulatory regions of these genes are potential candidates for future engineering of synthetic mammalian gene circuits requiring nonlinear responses to HGF signalling.

Autonomous Metabolic Oscillations Robustly Gate the Early and Late Cell Cycle

Eukaryotic cell division is known to be controlled by the cyclin/cyclin dependent kinase (CDK) machinery. However, eukaryotes have evolved prior to CDKs, and cells can divide in the absence of major cyclin/CDK components. We hypothesized that an autonomous metabolic oscillator provides dynamic triggers for cell-cycle initiation and progression. Using microfluidics, cell-cycle reporters, and single-cell metabolite measurements, we found that metabolism of budding yeast is a CDK-independent oscillator that oscillates across different growth conditions, both in synchrony with and also in the absence of the cell cycle. Using environmental perturbations and dynamic single-protein depletion experiments, we found that the metabolic oscillator and the cell cycle form a system of coupled oscillators, with the metabolic oscillator separately gating and maintaining synchrony with the early and late cell cycle. Establishing metabolism as a dynamic component within the cell-cycle network opens new avenues for cell-cycle research and therapeutic interventions for proliferative disorders.

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.

An Algorithm for Finding the Singleton Attractors and Pre-Images in Strong-Inhibition Boolean Networks

The detection of the singleton attractors is of great significance for the systematic study of genetic regulatory network. In this paper, we design an algorithm to compute the singleton attractors and pre-images of the strong-inhibition Boolean networks which is a biophysically plausible gene model. Our algorithm can not only identify accurately the singleton attractors, but also find easily the pre-images of the network. Based on extensive computational experiments, we show that the computational time of the algorithm is proportional to the number of the singleton attractors, which indicates the algorithm has much advantage in finding the singleton attractors for the networks with high average degree and less inhibitory interactions. Our algorithm may shed light on understanding the function and structure of the strong-inhibition Boolean networks.

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.

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.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

MetaCycle: an integrated R package to evaluate periodicity in large scale data

Detecting periodicity in large scale data remains a challenge. While efforts have been made to identify best of breed algorithms, relatively little research has gone into integrating these methods in a generalizable method. Here, we present MetaCycle, an R package that incorporates ARSER, JTK_CYCLE and Lomb-Scargle to conveniently evaluate periodicity in time-series data. MetaCycle has two functions, meta2d and meta3d, designed to analyze two-dimensional and three-dimensional time-series datasets, respectively. Meta2d implements N-version programming concepts using a suite of algorithms and integrating their results.

AVAILABILITY AND IMPLEMENTATION

MetaCycle package is available on the CRAN repository (https://cran.r-project.org/web/packages/MetaCycle/index.html) and GitHub (https://github.com/gangwug/MetaCycle).

CONTACT

hogenesch@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

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.

Roles for the Histone Modifying and Exchange Complex NuA4 in Cell Cycle Progression in Drosophila melanogaster

Robust and synchronous repression of E2F-dependent gene expression is critical to the proper timing of cell cycle exit when cells transition to a postmitotic state. Previously NuA4 was suggested to act as a barrier to proliferation in Drosophila by repressing E2F-dependent gene expression. Here we show that NuA4 activity is required for proper cell cycle exit and the repression of cell cycle genes during the transition to a postmitotic state in vivo However, the delay of cell cycle exit caused by compromising NuA4 is not due to additional proliferation or effects on E2F activity. Instead NuA4 inhibition results in slowed cell cycle progression through late S and G2 phases due to aberrant activation of an intrinsic p53-independent DNA damage response. A reduction in NuA4 function ultimately produces a paradoxical cell cycle gene expression program, where certain cell cycle genes become derepressed in cells that are delayed during the G2 phase of the final cell cycle. Bypassing the G2 delay when NuA4 is inhibited leads to abnormal mitoses and results in severe tissue defects. NuA4 physically and genetically interacts with components of the E2F complex termed D: rosophila, R: bf, E: 2F A: nd M: yb/ M: ulti-vulva class B: (DREAM/MMB), and modulates a DREAM/MMB-dependent ectopic neuron phenotype in the posterior wing margin. However, this effect is also likely due to the cell cycle delay, as simply reducing Cdk1 is sufficient to generate a similar phenotype. Our work reveals that the major requirement for NuA4 in the cell cycle in vivo is to suppress an endogenous DNA damage response, which is required to coordinate proper S and G2 cell cycle progression with differentiation and cell cycle gene expression.

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.

In vitro toxicological evaluation of ethyl carbamate in human HepG2 cells

Ethyl carbamate (EC) is a multi-site carcinogen in experiment animals and probably carcinogenic to humans (IARC group 2A). The present study was designed to investigate the cytotoxicity effect of EC on human hepatoma G2 (HepG2) cells. The results revealed that EC inhibited the viability of HepG2 cells significantly in a dose-dependent manner. Further analysis indicated that high concentration of EC induced cell apoptosis, inhibited the G1 to S phase transition along with increased expression of p53 and p21 and decreased the expression of cyclin E and Cdk 2, but no significant change in p27 expression was observed, which were evidenced by both real time PCR and western blotting analyses. Moreover, the results of the DCFH-DA assay suggested that oxidative stress was involved in the cytotoxic effects of EC. Altogether, the present work indicated that p21, cyclin E and Cdk2, which were regulated by p53, might account for the effect of EC on cell viability and cell cycle arrest, but p27 was not involved in the pathway in HepG2 cells treated with EC.

Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments

Cell cycle progression is carefully coordinated with a cell's intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments.

The Late S-Phase Transcription Factor Hcm1 Is Regulated through Phosphorylation by the Cell Wall Integrity Checkpoint

The cell wall integrity (CWI) checkpoint in the budding yeast Saccharomyces cerevisiae coordinates cell wall construction and cell cycle progression. In this study, we showed that the regulation of Hcm1, a late-S-phase transcription factor, arrests the cell cycle via the cell wall integrity checkpoint. Although the HCM1 mRNA level remained unaffected when the cell wall integrity checkpoint was induced, the protein level decreased. The overproduction of Hcm1 resulted in the failure of the cell wall integrity checkpoint. We identified 39 Hcm1 phosphorylation sites, including 26 novel sites, by tandem mass spectrometry analysis. A mutational analysis revealed that phosphorylation of Hcm1 at S61, S65, and S66 is required for the proper onset of the cell wall integrity checkpoint by regulating the timely decrease in its protein level. Hyperactivation of the CWI mitogen-activated protein kinase (MAPK) signaling pathway significantly reduced the Hcm1 protein level, and the deletion of CWI MAPK Slt2 resulted in a failure to decrease Hcm1 protein levels in response to stress, suggesting that phosphorylation is regulated by CWI MAPK. In conclusion, we suggest that Hcm1 is regulated negatively by the cell wall integrity checkpoint through timely phosphorylation and degradation under stress to properly control budding yeast proliferation.

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.

The Saccharomyces Genome Database: Advanced Searching Methods and Data Mining

At the core of the Saccharomyces Genome Database (SGD) are chromosomal features that encode a product. These include protein-coding genes and major noncoding RNA genes, such as tRNA and rRNA genes. The basic entry point into SGD is a gene or open-reading frame name that leads directly to the locus summary information page. A keyword describing function, phenotype, selective condition, or text from abstracts will also provide a door into the SGD. A DNA or protein sequence can be used to identify a gene or a chromosomal region using BLAST. Protein and DNA sequence identifiers, PubMed and NCBI IDs, author names, and function terms are also valid entry points. The information in SGD has been gathered and is maintained by a group of scientific biocurators and software developers who are devoted to providing researchers with up-to-date information from the published literature, connections to all the major research resources, and tools that allow the data to be explored. All the collected information cannot be represented or summarized for every possible question; therefore, it is necessary to be able to search the structured data in the database. This protocol describes the YeastMine tool, which provides an advanced search capability via an interactive tool. The SGD also archives results from microarray expression experiments, and a strategy designed to explore these data using the SPELL (Serial Pattern of Expression Levels Locator) tool is provided.

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.

Cyclin-Dependent Kinase Regulation of Diurnal Transcription in Chlamydomonas

We analyzed global transcriptome changes during synchronized cell division in the green alga Chlamydomonas reinhardtii. The Chlamydomonas cell cycle consists of a long G1 phase, followed by an S/M phase with multiple rapid, alternating rounds of DNA replication and segregation. We found that the S/M period is associated with strong induction of ∼2300 genes, many with conserved roles in DNA replication or cell division. Other genes, including many involved in photosynthesis, are reciprocally downregulated in S/M, suggesting a gene expression split correlating with the temporal separation between G1 and S/M. The Chlamydomonas cell cycle is synchronized by light-dark cycles, so in principle, these transcriptional changes could be directly responsive to light or to metabolic cues. Alternatively, cell-cycle-periodic transcription may be directly regulated by cyclin-dependent kinases. To distinguish between these possibilities, we analyzed transcriptional profiles of mutants in the kinases CDKA and CDKB, as well as other mutants with distinct cell cycle blocks. Initial cell-cycle-periodic expression changes are largely CDK independent, but later regulation (induction and repression) is under differential control by CDKA and CDKB. Deviation from the wild-type transcriptional program in diverse cell cycle mutants will be an informative phenotype for further characterization of the Chlamydomonas cell cycle.

Jianpi Huayu Decoction Inhibits Proliferation in Human Colorectal Cancer Cells (SW480) by Inducing G0/G1-Phase Cell Cycle Arrest and Apoptosis

Jianpi Huayu Decoction (JHD), a Chinese medicine formula, is a typical prescription against multiple tumors in the clinical treatment, which can raise quality of life and decrease complications. The aim of this study is to assess the efficacy of JHD against human colorectal carcinoma cells (SW480) and explore its mechanism. MTT assay showed that JHD decreased the cellular viability of SW480 cells in dose-dependent and time-dependent manner. Flow cytometry analysis revealed that JHD induced G0/G1-phase cell cycle arrest in SW480 cells and had a strong apoptosis-inducing effect on SW480 cells. Meanwhile it enhanced the expression of p27, cleaved PARP, cleaved caspase-3, and Bax and decreased the levels of PARP, caspase-3, Bcl-2, CDK2, CDK4, CDK6, cyclin D1, cyclin D2, cyclin D3, and cyclin E1, which was evidenced by RT-qPCR and Western blot analysis. In conclusion, these results indicated that JHD inhibited proliferation in SW480 cells by inducing G0/G1-phase cell cycle arrest and apoptosis, providing a practicaltherapeutic strategy against colorectal cancer.

A Dynamic Gene Regulatory Network Model That Recovers the Cyclic Behavior of Arabidopsis thaliana Cell Cycle

Cell cycle control is fundamental in eukaryotic development. Several modeling efforts have been used to integrate the complex network of interacting molecular components involved in cell cycle dynamics. In this paper, we aimed at recovering the regulatory logic upstream of previously known components of cell cycle control, with the aim of understanding the mechanisms underlying the emergence of the cyclic behavior of such components. We focus on Arabidopsis thaliana, but given that many components of cell cycle regulation are conserved among eukaryotes, when experimental data for this system was not available, we considered experimental results from yeast and animal systems. We are proposing a Boolean gene regulatory network (GRN) that converges into only one robust limit cycle attractor that closely resembles the cyclic behavior of the key cell-cycle molecular components and other regulators considered here. We validate the model by comparing our in silico configurations with data from loss- and gain-of-function mutants, where the endocyclic behavior also was recovered. Additionally, we approximate a continuous model and recovered the temporal periodic expression profiles of the cell-cycle molecular components involved, thus suggesting that the single limit cycle attractor recovered with the Boolean model is not an artifact of its discrete and synchronous nature, but rather an emergent consequence of the inherent characteristics of the regulatory logic proposed here. This dynamical model, hence provides a novel theoretical framework to address cell cycle regulation in plants, and it can also be used to propose novel predictions regarding cell cycle regulation in other eukaryotes.

In multicellular organisms, cells undergo a cyclic behavior of DNA duplication and delivery of a copy to daughter cells during cell division. In each of the main cell-cycle (CC) stages different sets of proteins are active and genes are expressed. Understanding how such cycling cellular behavior emerges and is robustly maintained in the face of changing developmental and environmental conditions, remains a fundamental challenge of biology. The molecular components that cycle through DNA duplication and citokinesis are interconnected in a complex regulatory network. Several models of such network have been proposed, although the regulatory network that robustly recovers a limit-cycle steady state that resembles the behavior of CC molecular components has been recovered only in a few cases, and no comprehensive model exists for plants. In this paper we used the plant Arabidopsis thaliana, as a study system to propose a core regulatory network to recover a cyclic attractor that mimics the oscillatory behavior of the key CC components. Our analyses show that the proposed GRN model is robust to transient alterations, and is validated with the loss- and gain-of-function mutants of the CC components. The interactions proposed for Arabidopsis thaliana CC can inspire predictions for further uncovering regulatory motifs in the CC of other organisms including human.

SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data

BACKGROUND

Identifying periodically expressed genes across different processes (e.g. the cell and metabolic cycles, circadian rhythms, etc) is a central problem in computational biology. Biological time series may contain (multiple) unknown signal shapes of systemic relevance, imperfections like noise, damping, and trending, or limited sampling density. While there exist methods for detecting periodicity, their design biases (e.g. toward a specific signal shape) can limit their applicability in one or more of these situations.

METHODS

We present in this paper a novel method, SW1PerS, for quantifying periodicity in time series in a shape-agnostic manner and with resistance to damping. The measurement is performed directly, without presupposing a particular pattern, by evaluating the circularity of a high-dimensional representation of the signal. SW1PerS is compared to other algorithms using synthetic data and performance is quantified under varying noise models, noise levels, sampling densities, and signal shapes. Results on biological data are also analyzed and compared.

RESULTS

On the task of periodic/not-periodic classification, using synthetic data, SW1PerS outperforms all other algorithms in the low-noise regime. SW1PerS is shown to be the most shape-agnostic of the evaluated methods, and the only one to consistently classify damped signals as highly periodic. On biological data, and for several experiments, the lists of top 10% genes ranked with SW1PerS recover up to 67% of those generated with other popular algorithms. Moreover, the list of genes from data on the Yeast metabolic cycle which are highly-ranked only by SW1PerS, contains evidently non-cosine patterns (e.g. ECM33, CDC9, SAM1,2 and MSH6) with highly periodic expression profiles. In data from the Yeast cell cycle SW1PerS identifies genes not preferred by other algorithms, hence not previously reported as periodic, but found in other experiments such as the universal growth rate response of Slavov. These genes are BOP3, CDC10, YIL108W, YER034W, MLP1, PAC2 and RTT101.

CONCLUSIONS

In biological systems with low noise, i.e. where periodic signals with interesting shapes are more likely to occur, SW1PerS can be used as a powerful tool in exploratory analyses. Indeed, by having an initial set of periodic genes with a rich variety of signal types, pattern/shape information can be included in the study of systems and the generation of hypotheses regarding the structure of gene regulatory networks.