Conserved homeodomain proteins interact with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cell cycle

'Organ'ising Floral Organ Development

Flowers are plant structures characteristic of the phylum Angiosperms composed of organs thought to have emerged from homologous structures to leaves in order to specialize in a distinctive function: reproduction. Symmetric shapes, colours, and scents all play important functional roles in flower biology. The evolution of flower symmetry and the morphology of individual flower parts (sepals, petals, stamens, and carpels) has significantly contributed to the diversity of reproductive strategies across flowering plant species. This diversity facilitates attractiveness for pollination, protection of gametes, efficient fertilization, and seed production. Symmetry, the establishment of body axes, and fate determination are tightly linked. The complex genetic networks underlying the establishment of organ, tissue, and cellular identity, as well as the growth regulators acting across the body axes, are steadily being elucidated in the field. In this review, we summarise the wealth of research already at our fingertips to begin weaving together how separate processes involved in specifying organ identity within the flower may interact, providing a functional perspective on how identity determination and axial regulation may be coordinated to inform symmetrical floral organ structures.

Gene Expression Analysis of Yeast Strains with a Nonsense Mutation in the eRF3-Coding Gene Highlights Possible Mechanisms of Adaptation

In yeast Saccharomyces cerevisiae, there are two translation termination factors, eRF1 (Sup45) and eRF3 (Sup35), which are essential for viability. Previous studies have revealed that presence of nonsense mutations in these genes leads to amplification of mutant alleles (sup35-n and sup45-n), which appears to be necessary for the viability of such cells. However, the mechanism of this phenomenon remained unclear. In this study, we used RNA-Seq and proteome analysis to reveal the complete set of gene expression changes that occur during cellular adaptation to the introduction of the sup35-218 nonsense allele. Our analysis demonstrated significant changes in the transcription of genes that control the cell cycle: decreases in the expression of genes of the anaphase promoting complex APC/C (APC9, CDC23) and their activator CDC20, and increases in the expression of the transcription factor FKH1, the main cell cycle kinase CDC28, and cyclins that induce DNA biosynthesis. We propose a model according to which yeast adaptation to nonsense mutations in the translation termination factor genes occurs as a result of a delayed cell cycle progression beyond the G2-M stage, which leads to an extension of the S and G2 phases and an increase in the number of copies of the mutant sup35-n allele.

CFHTF2 Is Needed for Vegetative Growth, Conidial Morphogenesis and the Osmotic Stress Response in the Tea Plant Anthracnose (Colletotrichum fructicola)

Tea is an important cash crop worldwide, and its nutritional value has led to its high economic benefits. Tea anthracnose is a common disease of tea plants that seriously affects food safety and yield and has a far-reaching impact on the sustainable development of the tea industry. In this study, phenotypic analysis and pathogenicity analysis were performed on knockout and complement strains of HTF2-the transcriptional regulator of tea anthracnose homeobox-and the pathogenic mechanism of these strains was explored via RNA-seq. The MoHox1 gene sequence of the rice blast fungus was indexed, and the anthracnose genome was searched for CfHTF2. Evolutionary analysis recently reported the affinity of HTF2 for C. fructicola and C. higginsianum. The loss of CfHTF2 slowed the vegetative growth and spore-producing capacity of C. fructicola and weakened its resistance and pathogenesis to adverse conditions. The transcriptome sequencing of wild-type N425 and CfHTF2 deletion mutants was performed, and a total of 3144 differentially expressed genes (DEGs) were obtained, 1594 of which were upregulated and 1550 of which were downregulated. GO and KEGG enrichment analyses of DEGs mainly focused on signaling pathways such as the biosynthesis of secondary metabolites. In conclusion, this study lays a foundation for further study of the pathogenic mechanism of tea anthracnose and provides a molecular basis for the analysis of the pathogenic molecular mechanism of CfHTF2.

Functional characterization and comparative analysis of gene repression-mediating domains interacting with yeast pleiotropic corepressors Sin3, Cyc8 and Tup1

Transcriptional corepressors Sin3, Cyc8 and Tup1 are important for downregulation of gene expression by recruiting various histone deacetylases once they gain access to defined genomic locations by interaction with pathway-specific repressor proteins. In this work we systematically investigated whether 17 yeast repressor proteins (Cti6, Dal80, Fkh1, Gal80, Mig1, Mot3, Nrg1, Opi1, Rdr1, Rox1, Sko1, Ume6, Ure2, Xbp1, Yhp1, Yox1 and Whi5) representing several unrelated regulatory pathways are able to bind to Sin3, Cyc8 and Tup1. Our results show that paired amphipathic helices 1 and 2 (PAH1 and PAH2) of Sin3 are functionally redundant for some regulatory pathways. WD40 domains of Tup1 proved to be sufficient for interaction with repressor proteins. Using length variants of selected repressors, we mapped corepressor interaction domains (CIDs) in vitro and assayed gene repression in vivo. Systematic comparison of CID minimal sequences allowed us to define several related positional patterns of hydrophobic amino acids some of which could be confirmed as functionally supported by site-directed mutagenesis. Although structural predictions indicated that certain CIDs may be α-helical, most repression domains appear to be randomly structured and must be considered as intrinsically disordered regions (IDR) adopting a defined conformation only by interaction with a corepressor.

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.

Active Module Identification From Multilayer Weighted Gene Co-Expression Networks: A Continuous Optimization Approach

Searching for active modules, i.e., regions showing striking changes in molecular activity in biological networks is important to reveal regulatory and signaling mechanisms of biological systems. Most existing active modules identification methods are based on protein-protein interaction networks or metabolic networks, which require comprehensive and accurate prior knowledge. On the other hand, weighted gene co-expression networks (WGCNs) are purely constructed from gene expression profiles. However, existing WGCN analysis methods are designed for identifying functional modules but not capable of identifying active modules. There is an urgent need to develop an active module identification algorithm for WGCNs to discover regulatory and signaling mechanism associating with a given cellular response. To address this urgent need, we propose a novel algorithm called active modules on the multi-layer weighted (co-expression gene) network, based on a continuous optimization approach (AMOUNTAIN). The algorithm is capable of identifying active modules not only from single-layer WGCNs but also from multilayer WGCNs such as cross-species and dynamic WGCNs. We first validate AMOUNTAIN on a synthetic benchmark dataset. We then apply AMOUNTAIN to WGCNs constructed from Th17 differentiation gene expression datasets of human and mouse, which include a single layer, a cross-species two-layer and a multilayer dynamic WGCNs. The identified active modules from WGCNs are enriched by known protein-protein interactions, and more importantly, they reveal some interesting and important regulatory and signaling mechanisms of Th17 cell differentiation.

Expression and Prognostic Value of MCM Family Genes in Osteosarcoma

We performed a detailed cancer VS normal analysis to explore the expression and prognostic value of minichromosome maintenance (MCM) proteinsin human sarcoma. The mRNA expression levels of the MCM family genes in sarcoma were analyzed using data from ONCOMINE, GEPIA and CCLE databases. KEGG database was used to analyze the function of MCM2–7 complex in DNA replication and cell cycle. QRT-PCR and western blot were used to confirm the differential expression of key MCMs in osteosarcoma cell lines. Cell Counting Kit-8 and flow cytometry method were used to detect the cell proliferation and apoptosis of hFOB1.19 cells. The results showed that MCM1–7 and MCM10 were all upregulated in sarcoma in ONCOMINE database. MCM2, and MCM4–7 were highly expressed in sarcoma in GEPIA database. Moreover, all these ten factors were highly expressed in sarcoma cell lines. Furthermore, we analyzed the prognostic value of MCMs for sarcoma in GEPIA and found that MCM2, MCM3, MCM4, and MCM10 are prognostic biomarkers for human sarcoma. Analysis results using KEGG datasets showed that MCM4 and MCM6–7 constituted a core structure of MCM2-7 hexamers. We found that AzadC treatment and overexpression of MCM4 significantly promoted hFOB1.19 cell proliferation and inhibited apoptosis. The present study implied that MCM2–4 and 10 are potential biomarkers for the prognosis of sarcoma. The prognostic role of MCM4 may be attributable to the change in its DNA methylation patterns.

Improving Acetic Acid and Furfural Resistance of Xylose-Fermenting Saccharomyces cerevisiae Strains by Regulating Novel Transcription Factors Revealed via Comparative Transcriptomic Analysis

Acetic acid and furfural are the two prevalent inhibitors coexisting with glucose and xylose in lignocellulosic hydrolysate. The transcriptional regulations of Saccharomyces cerevisiae in response to acetic acid (Aa), furfural (Fur), and the mixture of acetic acid and furfural (Aa_Fur) were revealed during mixed glucose and xylose fermentation. Carbohydrate metabolism pathways were significantly enriched in response to Aa, while pathways of xenobiotic biodegradation and metabolism were significantly enriched in response to Fur. In addition to these pathways, other pathways were activated in response to Aa_Fur, i.e., cofactor and vitamin metabolism and lipid metabolism. Overexpression of Haa1p or Tye7p improved xylose consumption rates by nearly 50%, while the ethanol yield was enhanced by nearly 8% under acetic acid and furfural stress conditions. Co-overexpression of Haa1p and Tye7p resulted in a 59% increase in xylose consumption rate and a 12% increase in ethanol yield, revealing the beneficial effects of Haa1p and Tye7p on improving the tolerance of yeast to mixed acetic acid and furfural.IMPORTANCE Inhibitor tolerance is essential for S. cerevisiae when fermenting lignocellulosic hydrolysate with various inhibitors, including weak acids, furans, and phenols. The details regarding how xylose-fermenting S. cerevisiae strains respond to multiple inhibitors during fermenting mixed glucose and xylose are still unknown. This study revealed the transcriptional regulation mechanism of an industrial xylose-fermenting S. cerevisiae strain in response to acetic acid and furfural. The transcription factor Haa1p was found to be involved in both acetic acid and furfural tolerance. In addition to Haa1p, four other transcription factors, Hap4p, Yox1p, Tye7p, and Mga1p, were identified as able to improve the resistance of yeast to these two inhibitors. This study underscores the feasibility of uncovering effective transcription factors for constructing robust strains for lignocellulosic bioethanol production.

Identification of mini-chromosome maintenance 8 as a potential prognostic marker and its effects on proliferation and apoptosis in gastric cancer

Mini-chromosome maintenance (MCM) proteins play important roles in initiating eukaryotic genome replication. The MCM family of proteins includes several members associated with the development and progression of certain cancers. We performed online data mining to assess the expression of MCMs in gastric cancer (GC) and the correlation between their expression and survival in patients with GC. Notably, MCM8 expression was undoubtedly up-regulated in GC, and higher expression correlated with shorter overall survival (OS) and progression-free survival (PFS) in patients with GC. However, the role of MCM8 in GC has not been previously explored. Our in vitro experiments revealed that MCM8 knockdown inhibited cell growth and metastasis. Moreover, MCM8 knockdown induced apoptosis. Mechanistically, the expression levels of Bax and cleaved caspase-3 were increased, whereas Bcl-2 expression decreased. Additionally, we demonstrated that MCM8 knockdown suppressed tumorigenesis in vivo. Overall, these results suggest that MCM8 plays a significant role in GC progression.

The Molecular Mechanism of Perillaldehyde Inducing Cell Death in Aspergillus flavus by Inhibiting Energy Metabolism Revealed by Transcriptome Sequencing

Perillaldehyde (PAE), an essential oil in Perilla plants, serves as a safe flavor ingredient in foods, and shows an effectively antifungal activity. Reactive oxygen species (ROS) accumulation in Aspergillus flavus plays a critical role in initiating a metacaspase-dependent apoptosis. However, the reason for ROS accumulation in A. flavus is not yet clear. Using transcriptome sequencing of A. flavus treated with different concentrations of PAE, our data showed that the ROS accumulation might have been as a result of an inhibition of energy metabolism with less production of reducing power. By means of GO and KEGG enrichment analysis, we screened four key pathways, which were divided into two distinct groups: a downregulated group that was made up of the glycolysis and pentose phosphate pathway, and an upregulated group that consisted of MAPK signaling pathway and GSH metabolism pathway. The inhibition of dehydrogenase gene expression in two glycometabolism pathways might play a crucial role in antifungal mechanism of PAE. Also, in our present study, we systematically showed a gene interaction network of how genes of four subsets are effected by PAE stress on glycometabolism, oxidant damage repair, and cell cycle control. This research may contribute to explaining an intrinsic antifungal mechanism of PAE against A. flavus.

A comprehensive, mechanistically detailed, and executable model of the cell division cycle in Saccharomyces cerevisiae

Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models-and eventually whole-cell models-of human cells.

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.

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.

Disruption of the transcription factors Thi2p and Nrm1p alleviates the post-glucose effect on xylose utilization in Saccharomyces cerevisiae

BACKGROUND

The recombinant Saccharomyces cerevisiae strains that acquired the ability to utilize xylose through metabolic and evolutionary engineering exhibit good performance when xylose is the sole carbon source in the medium (designated the X stage in the present work). However, the xylose consumption rate of strains is generally low after glucose depletion during glucose-xylose co-fermentation, despite the presence of xylose in the medium (designated the GX stage in the present work). Glucose fermentation appears to reduce the capacity of these strains to "recognize" xylose during the GX stage, a phenomenon termed the post-glucose effect on xylose metabolism.

RESULTS

Two independent xylose-fermenting S. cerevisiae strains derived from a haploid laboratory strain and a diploid industrial strain were used in the present study. Their common characteristics were investigated to reveal the mechanism underlying the post-glucose effect and to develop methods to alleviate this effect. Both strains showed lower growth and specific xylose consumption rates during the GX stage than during the X stage. Glycolysis, the pentose phosphate pathway, and translation-related gene expression were reduced; meanwhile, genes in the tricarboxylic acid cycle and glyoxylic acid cycle demonstrated higher expression during the GX stage than during the X stage. The effects of 11 transcription factors (TFs) whose expression levels significantly differed between the GX and X stages in both strains were investigated. Knockout of THI2 promoted ribosome synthesis, and the growth rate, specific xylose utilization rate, and specific ethanol production rate of the strain increased by 17.4, 26.8, and 32.4%, respectively, in the GX stage. Overexpression of the ribosome-related genes RPL9A, RPL7B, and RPL7A also enhanced xylose utilization in a corresponding manner. Furthermore, the overexpression of NRM1, which is related to the cell cycle, increased the growth rate by 8.7%, the xylose utilization rate by 30.0%, and the ethanol production rate by 76.6%.

CONCLUSIONS

The TFs Thi2p and Nrm1p exerted unexpected effects on the post-glucose effect, enhancing ribosome synthesis and altering the cell cycle, respectively. The results of this study will aid in maintaining highly efficient xylose metabolism during glucose-xylose co-fermentation, which is utilized for lignocellulosic bioethanol production.

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.

Mitotic degradation of yeast Fkh1 by the Anaphase Promoting Complex is required for normal longevity, genomic stability and stress resistance

The Saccharomyces cerevisiae Forkhead Box (Fox) orthologs, Forkheads (Fkh) 1 and 2, are conserved transcription factors required for stress response, cell cycle progression and longevity. These yeast proteins play a key role in mitotic progression through activation of the ubiquitin E3 ligase Anaphase Promoting Complex (APC) via transcriptional control. Here, we used genetic and molecular analyses to demonstrate that the APC E3 activity is necessary for mitotic Fkh1 protein degradation and subsequent cell cycle progression. We report that Fkh1 protein degradation occurs specifically during mitosis, requires APCCdc20 and proteasome activity, and that a stable Fkh1 mutant reduces normal chronological lifespan, increases genomic instability, and increases sensitivity to stress. Our data supports a model whereby cell cycle progression through mitosis and G1 requires the targeted degradation of Fkh1 by the APC. This is significant to many fields as these results impact our understanding of the mechanisms underpinning the control of aging and cancer.

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.

P-body proteins regulate transcriptional rewiring to promote DNA replication stress resistance

mRNA-processing (P-) bodies are cytoplasmic granules that form in eukaryotic cells in response to numerous stresses to serve as sites of degradation and storage of mRNAs. Functional P-bodies are critical for the DNA replication stress response in yeast, yet the repertoire of P-body targets and the mechanisms by which P-bodies promote replication stress resistance are unknown. In this study we identify the complete complement of mRNA targets of P-bodies during replication stress induced by hydroxyurea treatment. The key P-body protein Lsm1 controls the abundance of HHT1, ACF4, ARL3, TMA16, RRS1 and YOX1 mRNAs to prevent their toxic accumulation during replication stress. Accumulation of YOX1 mRNA causes aberrant downregulation of a network of genes critical for DNA replication stress resistance and leads to toxic acetaldehyde accumulation. Our data reveal the scope and the targets of regulation by P-body proteins during the DNA replication stress response.

P-bodies form in response to stress and act as sites of mRNA storage and degradation. Here the authors identify the mRNA targets of P-bodies during DNA replication stress, and show that P-body proteins act to prevent toxic accumulation of these target transcripts.

Local network component analysis for quantifying transcription factor activities

Transcription factors (TFs) could regulate physiological transitions or determine stable phenotypic diversity. The accurate estimation on TF regulatory signals or functional activities is of great significance to guide biological experiments or elucidate molecular mechanisms, but still remains challenging. Traditional methods identify TF regulatory signals at the population level, which masks heterogeneous regulation mechanisms in individuals or subgroups, thus resulting in inaccurate analyses. Here, we propose a novel computational framework, namely local network component analysis (LNCA), to exploit data heterogeneity and automatically quantify accurate transcription factor activity (TFA) in practical terms, through integrating the partitioned expression sets (i.e., local information) and prior TF-gene regulatory knowledge. Specifically, LNCA adopts an adaptive optimization strategy, which evaluates the local similarities of regulation controls and corrects biases during data integration, to construct the TFA landscape. In particular, we first numerically demonstrate the effectiveness of LNCA for the simulated data sets, compared with traditional methods, such as FastNCA, ROBNCA and NINCA. Then, we apply our model to two real data sets with implicit temporal or spatial regulation variations. The results show that LNCA not only recognizes the periodic mode along the S. cerevisiae cell cycle process, but also substantially outperforms over other methods in terms of accuracy and consistency. In addition, the cross-validation study for glioblastomas multiforme (GBM) indicates that the TFAs, identified by LNCA, can better distinguish clinically distinct tumor groups than the expression values of the corresponding TFs, thus opening a new way to classify tumor subtypes and also providing a novel insight into cancer heterogeneity.

AVAILABILITY

LNCA was implemented as a Matlab package, which is available at http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm/LNCApackage_0.1.rar.

Design of experiment for nonlinear dynamic gene regulatory network identification

The gene regulatory network (GRN) is critical for understanding the regulatory interaction between genes. Time-course microarray experiments provide ample information for constructing GRN. The designs for microarray experiments serve different purposes. However, the experiment design specifically for GRN identification is still sparse. In this article, we use a simulation-based approach to deal with design problems in the framework of nonparametric differential equations. We investigate a number of feasible designs. In particular, we evaluate whether earlier samplings can result in more useful information for GRN identification. We also evaluate the effectiveness of two strategies: more frequent samplings per replicate with fewer replicates versus fewer samplings per replicate with more replicates while keeping the total number of samplings constant. The results of our investigation provide quantitative guidance for designing and selecting microarray experiments for the purpose of GRN identification.

Functional Analysis of Kinases and Transcription Factors in Saccharomyces cerevisiae Using an Integrated Overexpression Library

Kinases and transcription factors (TFs) are key modulators of important signaling pathways and their activities underlie the proper function of many basic cellular processes such as cell division, differentiation, and development. Changes in kinase and TF dosage are often associated with disease, yet a systematic assessment of the cellular phenotypes caused by the combined perturbation of kinases and TFs has not been undertaken. We used a reverse-genetics approach to study the phenotypic consequences of kinase and TF overexpression (OE) in the budding yeast, Saccharomyces cerevisiae. We constructed a collection of strains expressing stably integrated inducible alleles of kinases and TFs and used a variety of assays to characterize the phenotypes caused by TF and kinase OE. We used the Synthetic Genetic Array (SGA) method to examine dosage-dependent genetic interactions (GIs) between 239 gain-of-function (OE) alleles of TFs and six loss-of-function (LOF) and seven OE kinase alleles, the former identifying Synthetic Dosage Lethal (SDL) interactions and the latter testing a GI we call Double Dosage Lethality (DDL). We identified and confirmed 94 GIs between 65 OE alleles of TFs and 9 kinase alleles. Follow-up experiments validated regulatory relationships between genetically interacting pairs (Cdc28–Stb1 and Pho85–Pdr1), suggesting that GI studies involving OE alleles of regulatory proteins will be a rich source of new functional information.

Transcriptional and post-transcriptional regulation of Cdc20 during the spindle assembly checkpoint in S. cerevisiae

The anaphase-promoting complex (APC) is a ubiquitin ligase responsible for promoting the degradation of many cell cycle regulators. One of the activators and substrate-binding proteins for the APC is Cdc20. It has been shown previously that Cdc20 can promote its own degradation by the APC in normal cycling cells mainly through a cis-degradation mode (i.e. via an intramolecular mechanism). However, how Cdc20 is degraded during the spindle assembly checkpoint (SAC) is still not fully clear. In this study, we used a dual-Cdc20 system to investigate this issue and found that the cis-degradation mode is also the major pathway responsible for Cdc20 degradation during the SAC. In addition, we found that there is an inverse relationship between APC^Cdc20 activity and the transcriptional activity of the CDC20 promoter, which likely occurs through feedback regulation by APC^Cdc20 substrates, such as the cyclins Clb2 and Clb5. These findings contribute to our understanding of how the inhibition of APC^Cdc20 activity and enhanced Cdc20 degradation are required for proper spindle checkpoint arrest.

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.

The Expression and Prognostic Roles of MCMs in Pancreatic Cancer

OBJECTIVES

Minichromosome maintenance (MCM) proteins play important roles in DNA replication by interacting with other factors which participate in the regulation of DNA synthesis. Abnormal over-expression of MCMs was observed in numerous malignancies, such as colorectal cancer. However, the expression of MCMs in pancreatic cancer (PC) was less investigated so far. This study was designed to analyze the expression and prognostic roles of MCM1-10 in PC based on the data provided by The Cancer Genome Atlas (TCGA).

METHODS

Pearson χ2 test was applied to evaluate the association of MCMs expression with clinicopathologic indicators, and biomarkers for tumor biological behaviors. Kaplan-Meier plots and log-rank tests were used to assess survival analysis, and univariate and multivariate Cox proportional hazard regression models were used to recognize independent prognostic factors.

RESULTS

MCM1-10 were generally expressed in PC samples. The levels of some molecules were markedly correlated with that of biomarkers for S phase, proliferation, gemcitabine resistance. And part of these molecules over-expression was significantly associated with indicators of disease progression, such as depth of tumor invasion and lymph node metastasis. Furthermore, MCM2, 4, 6, 8, and 10 over-expression was remarkably associated with shorter disease free survival time, and MCM2, 4,8, and 10 over-expression was associated with shorter overall survival time. Further multivariate analysis suggested that MCM8 was an independent prognostic factor for PC.

CONCLUSION

MCMs abnormal over-expression was significantly associated with PC progression and prognosis. These molecules could be regarded as prognostic and therapeutic biomarkers for PC. The roles of MCMs may be vitally important and the underlying mechanisms need to be furtherinvestigated.

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

Msa1 and Msa2 Modulate G1-Specific Transcription to Promote G1 Arrest and the Transition to Quiescence in Budding Yeast

Yeast that naturally exhaust their glucose source can enter a quiescent state that is characterized by reduced cell size, and high cell density, stress tolerance and longevity. The transition to quiescence involves highly asymmetric cell divisions, dramatic reprogramming of transcription and global changes in chromatin structure and chromosome topology. Cells enter quiescence from G1 and we find that there is a positive correlation between the length of G1 and the yield of quiescent cells. The Swi4 and Swi6 transcription factors, which form the SBF transcription complex and promote the G1 to S transition in cycling cells, are also critical for the transition to quiescence. Swi6 forms a second complex with Mbp1 (MBF), which is not required for quiescence. These are the functional analogues of the E2F complexes of higher eukaryotes. Loss of the RB analogue, Whi5, and the related protein Srl3/Whi7, delays G1 arrest, but it also delays recovery from quiescence. Two MBF- and SBF-Associated proteins have been identified that have little effect on SBF or MBF activity in cycling cells. We show that these two related proteins, Msa1 and Msa2, are specifically required for the transition to quiescence. Like the E2F complexes that are quiescence-specific, Msa1 and Msa2 are required to repress the transcription of many SBF target genes, including SWI4, the CLN2 cyclin and histones, specifically after glucose is exhausted from the media. They also activate transcription of many MBF target genes. msa1msa2 cells fail to G1 arrest and rapidly lose viability upon glucose exhaustion. msa1msa2 mutants that survive this transition are very large, but they attain the same thermo-tolerance and longevity of wild type quiescent cells. This indicates that Msa1 and Msa2 are required for successful transition to quiescence, but not for the maintenance of that state.

In spite of the many differences between yeast and humans, the basic strategies that regulate the cell division cycle are fundamentally conserved. In this study, we extend these parallels to include a common strategy by which cells transition from proliferation to quiescence. The decision to divide is made in the G1 phase of the cell cycle. During G1, the genes that drive DNA replication are repressed by the E2F/RB complex. When a signal to divide is received, RB is removed and the complex is activated. When cells commit to a long term, but reversible G1 arrest, or quiescence, they express a novel E2F/RB-like complex, which promotes and maintains a stable repressive state. Yeast cells contain a functional analog of E2F/RB, called SBF/Whi5, which is activated by a similar mechanism in proliferating yeast cells. In this study, we identify two novel components of the SBF/Whi5 complex whose activity is specific to the transition to quiescence. These factors, Msa1 and Msa2, repress SBF targets and are required for the long term, but reversible G1 arrest that is critical for achieving a quiescent state.

Using Formal Concept Analysis to Identify Negative Correlations in Gene Expression Data

Recently, many biological studies reported that two groups of genes tend to show negatively correlated or opposite expression tendency in many biological processes or pathways. The negative correlation between genes may imply an important biological mechanism. In this study, we proposed a FCA-based negative correlation algorithm (NCFCA) that can effectively identify opposite expression tendency between two gene groups in gene expression data. After applying it to expression data of cell cycle-regulated genes in yeast, we found that six minichromosome maintenance family genes showed the opposite changing tendency with eight core histone family genes. Furthermore, we confirmed that the negative correlation expression pattern between these two families may be conserved in the cell cycle. Finally, we discussed the reasons underlying the negative correlation of six minichromosome maintenance (MCM) family genes with eight core histone family genes. Our results revealed that negative correlation is an important and potential mechanism that maintains the balance of biological systems by repressing some genes while inducing others. It can thus provide new understanding of gene expression and regulation, the causes of diseases, etc.

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

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.

Functional Analysis and Characterization of Differential Coexpression Networks

Differential coexpression analysis is emerging as a complement to conventional differential gene expression analysis. The identified differential coexpression links can be assembled into a differential coexpression network (DCEN) in response to environmental stresses or genetic changes. Differential coexpression analyses have been successfully used to identify condition-specific modules; however, the structural properties and biological significance of general DCENs have not been well investigated. Here, we analyzed two independent Saccharomyces cerevisiae DCENs constructed from large-scale time-course gene expression profiles in response to different situations. Topological analyses show that DCENs are tree-like networks possessing scale-free characteristics, but not small-world. Functional analyses indicate that differentially coexpressed gene pairs in DCEN tend to link different biological processes, achieving complementary or synergistic effects. Furthermore, the gene pairs lacking common transcription factors are sensitive to perturbation and hence lead to differential coexpression. Based on these observations, we integrated transcriptional regulatory information into DCEN and identified transcription factors that might cause differential coexpression by gain or loss of activation in response to different situations. Collectively, our results not only uncover the unique structural characteristics of DCEN but also provide new insights into interpretation of DCEN to reveal its biological significance and infer the underlying gene regulatory dynamics.

Dynamic single cell measurements of kinase activity by synthetic kinase activity relocation sensors

BACKGROUND

Mitogen activated protein kinases (MAPK) play an essential role in integrating extra-cellular signals and intra-cellular cues to allow cells to grow, adapt to stresses, or undergo apoptosis. Budding yeast serves as a powerful system to understand the fundamental regulatory mechanisms that allow these pathways to combine multiple signals and deliver an appropriate response. To fully comprehend the variability and dynamics of these signaling cascades, dynamic and quantitative single cell measurements are required. Microscopy is an ideal technique to obtain these data; however, novel assays have to be developed to measure the activity of these cascades.

RESULTS

We have generated fluorescent biosensors that allow the real-time measurement of kinase activity at the single cell level. Here, synthetic MAPK substrates were engineered to undergo nuclear-to-cytoplasmic relocation upon phosphorylation of a nuclear localization sequence. Combination of fluorescence microscopy and automated image analysis allows the quantification of the dynamics of kinase activity in hundreds of single cells. A large heterogeneity in the dynamics of MAPK activity between individual cells was measured. The variability in the mating pathway can be accounted for by differences in cell cycle stage, while, in the cell wall integrity pathway, the response to cell wall stress is independent of cell cycle stage.

CONCLUSIONS

These synthetic kinase activity relocation sensors allow the quantification of kinase activity in live single cells. The modularity of the architecture of these reporters will allow their application in many other signaling cascades. These measurements will allow to uncover new dynamic behaviour that previously could not be observed in population level measurements.

Replication factor C3 is a CREB target gene that regulates cell cycle progression through the modulation of chromatin loading of PCNA

CREB (cyclic AMP response element-binding protein) is a transcription factor overexpressed in normal and neoplastic myelopoiesis and regulates cell cycle progression, although its oncogenic mechanism has not been well characterized. Replication factor C3 (RFC3) is required for chromatin loading of proliferating cell nuclear antigen (PCNA) which is a sliding clamp platform for recruiting numerous proteins in the DNA metabolism. CREB1 expression, which was activated by E2F, was coupled with RFC3 expression during the G1/S progression in the KG-1 acute myeloid leukemia (AML) cell line. There was also a direct correlation between the expression of RFC3 and CREB1 in human AML cell lines as well as in the AML cells from the patients. CREB interacted directly with the CRE site in RFC3 promoter region. CREB-knockdown inhibited primarily G1/S cell cycle transition by decreasing the expression of RFC3 as well as PCNA loading onto the chromatin. Exogenous expression of RFC3 was sufficient to rescue the impaired G1/S progression and PCNA chromatin loading caused by CREB knockdown. These studies suggest that RFC3 may have a role in neoplastic myelopoiesis by promoting the G1/S progression and its expression is regulated by CREB.

Promoter-Autonomous Functioning in a Controlled Environment using Single Molecule FISH

Transcription is a highly regulated biological process, initiated through the assembly of complexes at the promoter that contain both the general transcriptional machinery and promoter-specific factors. Despite the abundance of studies focusing on transcription, certain questions have remained unanswered. It is not clear how the transcriptional profile of a promoter is affected by genomic context. Also, there is no single cell method to directly compare transcriptional profiles independent of gene length and sequence. In this work, we employ a single genetic site for isolating the transcriptional kinetics of yeast promoters. Utilizing single molecule FISH, we directly compare the transcriptional activity of different promoters, considering both synthesis and cell-to-cell variability. With this approach, we provide evidence suggesting promoters autonomously encode their associated transcriptional profiles, independent of genomic locus, gene length and gene sequence.

A new transcription factor for mitosis: in Schizosaccharomyces pombe, the RFX transcription factor Sak1 works with forkhead factors to regulate mitotic expression

Mitotic genes are one of the most strongly oscillating groups of genes in the eukaryotic cell cycle. Understanding the regulation of mitotic gene expression is a key issue in cell cycle control but is poorly understood in most organisms. Here, we find a new mitotic transcription factor, Sak1, in the fission yeast Schizosaccharomyces pombe. Sak1 belongs to the RFX family of transcription factors, which have not previously been connected to cell cycle control. Sak1 binds upstream of mitotic genes in close proximity to Fkh2, a forkhead transcription factor previously implicated in regulation of mitotic genes. We show that Sak1 is the major activator of mitotic gene expression and also confirm the role of Fkh2 as the opposing repressor. Sep1, another forkhead transcription factor, is an activator for a small subset of mitotic genes involved in septation. From yeasts to humans, forkhead transcription factors are involved in mitotic gene expression and it will be interesting to see whether RFX transcription factors may also be involved in other organisms.

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.

PP2ARts1 is a master regulator of pathways that control cell size

Cell size checkpoints ensure that passage through G1 and mitosis occurs only when sufficient growth has occurred. The mechanisms by which these checkpoints work are largely unknown. PP2A associated with the Rts1 regulatory subunit (PP2A(Rts1)) is required for cell size control in budding yeast, but the relevant targets are unknown. In this paper, we used quantitative proteome-wide mass spectrometry to identify proteins controlled by PP2A(Rts1). This revealed that PP2A(Rts1) controls the two key checkpoint pathways thought to regulate the cell cycle in response to cell growth. To investigate the role of PP2A(Rts1) in these pathways, we focused on the Ace2 transcription factor, which is thought to delay cell cycle entry by repressing transcription of the G1 cyclin CLN3. Diverse experiments suggest that PP2A(Rts1) promotes cell cycle entry by inhibiting the repressor functions of Ace2. We hypothesize that control of Ace2 by PP2A(Rts1) plays a role in mechanisms that link G1 cyclin accumulation to cell growth.

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.

Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques –an unsupervised artificial neural network called a Self-Organizing Map (SOM)–which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

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.

Finding gene clusters for a replicated time course study

Background

Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies.

Findings

In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast.

Conclusions

The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism.

A nutrient dependant switch explains mutually exclusive existence of meiosis and mitosis initiation in budding yeast

Nutrients from living environment are vital for the survival and growth of any organism. Budding yeast diploid cells decide to grow by mitosis type cell division or decide to create unique, stress resistant spores by meiosis type cell division depending on the available nutrient conditions. To gain a molecular systems level understanding of the nutrient dependant switching between meiosis and mitosis initiation in diploid cells of budding yeast, we develop a theoretical model based on ordinary differential equations (ODEs) including the mitosis initiator and its relations to budding yeast meiosis initiation network. Our model accurately and qualitatively predicts the experimentally revealed temporal variations of related proteins under different nutrient conditions as well as the diverse mutant studies related to meiosis and mitosis initiation. Using this model, we show how the meiosis and mitosis initiators form an all-or-none type bistable switch in response to available nutrient level (mainly nitrogen). The transitions to and from meiosis or mitosis initiation states occur via saddle node bifurcation. This bidirectional switch helps the optimal usage of available nutrients and explains the mutually exclusive existence of meiosis and mitosis pathways.

Rad53 downregulates mitotic gene transcription by inhibiting the transcriptional activator Ndd1

The 33 genes in the Saccharomyces cerevisiae mitotic CLB2 transcription cluster have been known to be downregulated by the DNA damage checkpoint for many years. Here, we show that this is mediated by the checkpoint kinase Rad53 and the dedicated transcriptional activator of the cluster, Ndd1. Ndd1 is phosphorylated in response to DNA damage, which blocks recruitment to promoters and leads to the transcriptional downregulation of the CLB2 cluster. Finally, we show that downregulation of Ndd1 is an essential function of Rad53, as a hypomorphic ndd1 allele rescues RAD53 deletion.

Xbp1 directs global repression of budding yeast transcription during the transition to quiescence and is important for the longevity and reversibility of the quiescent state

Pure populations of quiescent yeast can be obtained from stationary phase cultures that have ceased proliferation after exhausting glucose and other carbon sources from their environment. They are uniformly arrested in the G1 phase of the cell cycle, and display very high thermo-tolerance and longevity. We find that G1 arrest is initiated before all the glucose has been scavenged from the media. Maintaining G1 arrest requires transcriptional repression of the G1 cyclin, CLN3, by Xbp1. Xbp1 is induced as glucose is depleted and it is among the most abundant transcripts in quiescent cells. Xbp1 binds and represses CLN3 transcription and in the absence of Xbp1, or with extra copies of CLN3, cells undergo ectopic divisions and produce very small cells. The Rad53-mediated replication stress checkpoint reinforces the arrest and becomes essential when Cln3 is overproduced. The XBP1 transcript also undergoes metabolic oscillations under glucose limitation and we identified many additional transcripts that oscillate out of phase with XBP1 and have Xbp1 binding sites in their promoters. Further global analysis revealed that Xbp1 represses 15% of all yeast genes as they enter the quiescent state and over 500 of these transcripts contain Xbp1 binding sites in their promoters. Xbp1-repressed transcripts are highly enriched for genes involved in the regulation of cell growth, cell division and metabolism. Failure to repress some or all of these targets leads xbp1 cells to enter a permanent arrest or senescence with a shortened lifespan.

Complex organisms depend on populations of non-dividing quiescent cells for their controlled growth, development and tissue renewal. These quiescent cells are maintained in a resting state, and divide only when stimulated to do so. Unscheduled exit or failure to enter this quiescent state results in uncontrolled proliferation and cancer. Yeast cells also enter a stable, protected and reversible quiescent state. As with higher cells, they exit the cell cycle from G1, reduce growth, conserve and recycle cellular contents. These similarities, and the fact that the mechanisms that start and stop the cell cycle are fundamentally conserved lead us to think that understanding how yeast enter, maintain and reverse quiescence could give important leads into the same processes in complex organisms. We show that yeast cells maintain G1 arrest by expressing a transcription factor that represses conserved activators (cyclins) and hundreds of other genes that are important for cell division and cell growth. Failure to repress some or all of these targets leads to extra cell divisions, prevents reversible arrest and shortens life span. Many Xbp1 targets are conserved cell cycle regulators and may also be actively repressed in the quiescent cells of more complex organisms.