A novel Mcm1-dependent element in the SWI4, CLN3, CDC6, and CDC47 promoters activates M/G1-specific transcription

Control of meiotic entry by dual inhibition of a key mitotic transcription factor

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a functional homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

Control of meiotic entry by dual inhibition of a key mitotic transcription factor

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

The budding yeast GSK-3 homologue Mck1 is an essential component of the spindle position checkpoint

The spindle position checkpoint (SPOC) is a mitotic surveillance mechanism in Saccharomyces cerevisiae that prevents cells from completing mitosis in response to spindle misalignment, thereby contributing to genomic integrity. The kinase Kin4, one of the most downstream SPOC components, is essential to stop the mitotic exit network (MEN), a signalling pathway that promotes the exit from mitosis and cell division. Previous work, however, suggested that a Kin4-independent pathway contributes to SPOC, yet the underlying mechanisms remain elusive. Here, we established the glycogen-synthase-kinase-3 (GSK-3) homologue Mck1, as a novel component that works independently of Kin4 to engage SPOC. Our data indicate that both Kin4 and Mck1 work in parallel to counteract MEN activation by the Cdc14 early anaphase release (FEAR) network. We show that Mck1's function in SPOC is mediated by the pre-replication complex protein and mitotic cyclin-dependent kinase (M-Cdk) inhibitor, Cdc6, which is degraded in a Mck1-dependent manner prior to mitosis. Moderate overproduction of Cdc6 phenocopies MCK1 deletion and causes SPOC deficiency via its N-terminal, M-Cdk inhibitory domain. Our data uncover an unprecedented role of GSK-3 kinases in coordinating spindle orientation with cell cycle progression.

The budding yeast GSK-3 homologue Mck1 is an essential component of the spindle position checkpoint

The spindle position checkpoint (SPOC) is a mitotic surveillance mechanism in Saccharomyces cerevisiae that prevents cells from completing mitosis in response to spindle misalignment, thereby contributing to genomic integrity. The kinase Kin4, one of the most downstream SPOC components, is essential to stop the mitotic exit network (MEN), a signalling pathway that promotes the exit from mitosis and cell division. Previous work, however, suggested that a Kin4-independent pathway contributes to SPOC, yet the underlying mechanisms remain elusive. Here, we established the glycogen-synthase-kinase-3 (GSK-3) homologue Mck1, as a novel component that works independently of Kin4 to engage SPOC. Our data indicate that both Kin4 and Mck1 work in parallel to counteract MEN activation by the Cdc14 early anaphase release (FEAR) network. We show that Mck1's function in SPOC is mediated by the pre-replication complex protein and mitotic cyclin-dependent kinase (M-Cdk) inhibitor, Cdc6, which is degraded in a Mck1-dependent manner prior to mitosis. Moderate overproduction of Cdc6 phenocopies MCK1 deletion and causes SPOC deficiency via its N-terminal, M-Cdk inhibitory domain. Our data uncover an unprecedented role of GSK-3 kinases in coordinating spindle orientation with cell cycle progression.

The microprotein Nrs1 rewires the G1/S transcriptional machinery during nitrogen limitation in budding yeast

Commitment to cell division at the end of G1 phase, termed Start in the budding yeast Saccharomyces cerevisiae, is strongly influenced by nutrient availability. To identify new dominant activators of Start that might operate under different nutrient conditions, we screened a genome-wide ORF overexpression library for genes that bypass a Start arrest caused by absence of the G1 cyclin Cln3 and the transcriptional activator Bck2. We recovered a hypothetical gene YLR053c, renamed NRS1 for Nitrogen-Responsive Start regulator 1, which encodes a poorly characterized 108 amino acid microprotein. Endogenous Nrs1 was nuclear-localized, restricted to poor nitrogen conditions, induced upon TORC1 inhibition, and cell cycle-regulated with a peak at Start. NRS1 interacted genetically with SWI4 and SWI6, which encode subunits of the main G1/S transcription factor complex SBF. Correspondingly, Nrs1 physically interacted with Swi4 and Swi6 and was localized to G1/S promoter DNA. Nrs1 exhibited inherent transactivation activity, and fusion of Nrs1 to the SBF inhibitor Whi5 was sufficient to suppress other Start defects. Nrs1 appears to be a recently evolved microprotein that rewires the G1/S transcriptional machinery under poor nitrogen conditions.

Commitment to cell division at the end of G1 phase in the budding yeast Saccharomyces cerevisiae is strongly influenced by nutrient availability. This study identifies a micro-protein that promotes G1/S transcription activation and cell cycle entry in yeast under nitrogen-limited conditions.

Integrative Analysis of Minichromosome Maintenance Proteins and Their Prognostic Significance in Melanoma

Background

Minichromosome maintenance (MCM) is known for participating in cell cycle progression, as well as DNA replication. While the diverse expression patterns and prognostic values of MCMs in melanoma still remained unclear.

Methods

In the present study, the transcriptional and clinical profiles of MCMs were explored in patients with melanoma from multiple databases, including GEO, TCGA, ONCOMINE, GEPIA, UALCAN, cBioPortal, and TIMER databases.

Results

We found that the elevated expressions of MCM2–6 and MCM10 were significantly expressed in melanoma compared to normal skin. High mRNA levels of MCM4, MCM5, and MCM10 were closely related to worse prognosis in patients with melanoma. GSEA showed hallmark pathways were most involved in mTORC1 signaling, G2M checkpoint, E2F targets, and mitotic spindle. Furthermore, we found potential correlations between the MCM expression and the immune cell infiltration, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells.

Conclusion

Upregulated MCM gene expression in melanoma probably played a crucial part in the development and progression of melanoma. The upregulated MCM4/5/10 expressions could be used as potential prognostic markers to improve the poor outcome and prognostic accuracy in patients with melanoma. Our study might shed light on the selection of prognostic biomarkers as well as the underlying molecular pathogenesis of melanoma.

Interactome analysis of gene expression profiles identifies CDC6 as a potential therapeutic target modified by miR-215-5p in hepatocellular carcinoma

Background: Illustrating the pathogenesis of hepatocellular carcinoma (HCC) pathogenesis as well as identifying specific biomarkers are of great significance. Methods: The original CEL files were obtain from Gene Expression Omnibus, then affymetrix package was used to preprocess the CEL files, the function of DEGs were investigated by multiple bioinformatics approach. Finally, typical HCC cell lines and tissue samples were using to validate the role of CDC6 in vitro. Bioinformatics software was used to predict potential microRNA of CDC6. Luciferase assay was used to verify the interactions between CDC6 and microRNA. Results: A total of 445 DEGs were identified in HCC tissues based on two GEO datasets. GSEA results showed that the significant enriched gene sets were only associated with cell cycle signaling pathway. In the co-expression analysis, there were 370 hub genes from the blue modules were screened. We integrated DEGs, hub genes, TCGA cohort and GSEA analyses to further obtain 10 upregulated genes for validation. These genes were overexpressed in HCC tissues and negatively associated with overall and disease-free survival in HCC patients and related to immune cell infiltration in HCC microenvironments. Finally, Cell Division Cycle 6 (CDC6) was highlighted as one of the most probable genes among the 10 candidates participating in cancer process. The expression of CDC6 either in public datasets and HCC tissues sample were commonly high than the non-cancerous counterpart. Furthermore, we recognized that miR-215-5p, could directly bind to the 3'UTR of CDC6. In addition, CDC6 promoted proliferation via regulation of G1 phase checkpoint and was negative regulated by miR-215-5p to involve in the proliferation of HCC. Conclusion: Our study suggested that CDC6 served as a potential therapeutic target for HCC.

A processive phosphorylation circuit with multiple kinase inputs and mutually diversional routes controls G1/S decision

Studies on multisite phosphorylation networks of cyclin-dependent kinase (CDK) targets have opened a new level of signaling complexity by revealing signal processing routes encoded into disordered proteins. A model target, the CDK inhibitor Sic1, contains linear phosphorylation motifs, docking sites, and phosphodegrons to empower an N-to-C terminally directed phosphorylation process. Here, we uncover a signal processing mechanism involving multi-step competition between mutually diversional phosphorylation routes within the S-CDK-Sic1 inhibitory complex. Intracomplex phosphorylation plays a direct role in controlling Sic1 degradation, and provides a mechanism to sequentially integrate both the G1- and S-CDK activities while keeping S-CDK inhibited towards other targets. The competing phosphorylation routes prevent premature Sic1 degradation and demonstrate how integration of MAPK from the pheromone pathway allows one to tune the competition of alternative phosphorylation paths. The mutually diversional phosphorylation circuits may be a general way for processing multiple kinase signals to coordinate cellular decisions in eukaryotes.

The decision of whether and when a cell divides is tightly controlled. Here, the authors show in yeast that there is a multi-step competition between different phosphorylation states and sites in the S phase CDK-Sic1 complex, which controls Sic1 degradation and coordinates the precise timing of the G1/S transition.

Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast

In the unicellular eukaryote Saccharomyces cerevisiae, Cln3-cyclin-dependent kinase activity enables Start, the irreversible commitment to the cell division cycle. However, the concentration of Cln3 has been paradoxically considered to remain constant during G1, due to the presumed scaling of its production rate with cell size dynamics. Measuring metabolic and biosynthetic activity during cell cycle progression in single cells, we found that cells exhibit pulses in their protein production rate. Rather than scaling with cell size dynamics, these pulses follow the intrinsic metabolic dynamics, peaking around Start. Using a viral-based bicistronic construct and targeted proteomics to measure Cln3 at the single-cell and population levels, we show that the differential scaling between protein production and cell size leads to a temporal increase in Cln3 concentration, and passage through Start. This differential scaling causes Start in both daughter and mother cells across growth conditions. Thus, uncoupling between two fundamental physiological parameters drives cell cycle commitment.

Zebrafish cdc6 hypomorphic mutation causes Meier-Gorlin syndrome-like phenotype

Cell Division Cycle 6 (Cdc6) is a component of pre-replicative complex (preRC) forming on DNA replication origins in eukaryotes. Recessive mutations in ORC1, ORC4, ORC6, CDT1 or CDC6 of the preRC in human cause Meier-Gorlin syndrome (MGS) that is characterized by impaired post-natal growth, short stature and microcephaly. However, vertebrate models of MGS have not been reported. Through N-ethyl-N-nitrosourea mutagenesis and Cas9 knockout, we generate several cdc6 mutant lines in zebrafish. Loss-of-function mutations of cdc6, as manifested by cdc6tsu4305 and cdc6tsu7cd mutants, lead to embryonic lethality due to cell cycle arrest at the S phase and extensive apoptosis. Embryos homozygous for a cdc6 hypomorphic mutation, cdc6tsu21cd, develop normally during embryogenesis. Later on, compared with their wild-type (WT) siblings, cdc6tsu21cd mutant fish show growth retardation, and their body weight and length in adulthood are greatly reduced, which resemble human MGS. Surprisingly, cdc6tsu21cd mutant fish become males with a short life and fail to mate with WT females, suggesting defective reproduction. Overexpression of Cdc6 mutant forms, which mimic human CDC6(T323R) mutation found in a MGS patient, in zebrafish cdc6tsu4305 mutant embryos partially represses cell death phenotype, suggesting that the human CDC6(T323R) mutation is a hypomorph. cdc6tsu21cd mutant fish will be useful to detect more tissue defects and develop medical treatment strategies for MGS patients.

Yeast Cip1 is activated by environmental stress to inhibit Cdk1-G1 cyclins via Mcm1 and Msn2/4

Upon environmental changes, proliferating cells delay cell cycle to prevent further damage accumulation. Yeast Cip1 is a Cdk1 and Cln2-associated protein. However, the function and regulation of Cip1 are still poorly understood. Here we report that Cip1 expression is co-regulated by the cell-cycle-mediated factor Mcm1 and the stress-mediated factors Msn2/4. Overexpression of Cip1 arrests cell cycle through inhibition of Cdk1–G1 cyclin complexes at G1 stage and the stress-activated protein kinase-dependent Cip1 T65, T69, and T73 phosphorylation may strengthen the Cip1and Cdk1–G1 cyclin interaction. Cip1 accumulation mainly targets Cdk1–Cln3 complex to prevent Whi5 phosphorylation and inhibit early G1 progression. Under osmotic stress, Cip1 expression triggers transient G1 delay which plays a functionally redundant role with another hyperosmolar activated CKI, Sic1. These findings indicate that Cip1 functions similarly to mammalian p21 as a stress-induced CDK inhibitor to decelerate cell cycle through G1 cyclins to cope with environmental stresses.

A G1 cell cycle regulatory kinase Cip1 has been identified in budding yeast but how this is regulated is unclear. Here the authors identify cell cycle (Mcm1) and stress-mediated (Msn 2/4) transcription factors as regulating Cip1, causing stress induced CDK inhibition and delay in cell cycle progression.

A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability

The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally.

The cell division cycle—the process by which a living cell makes a new replica of itself—is fundamental to all aspects of biological growth, development and reproduction. If cells make mistakes in cell cycle progression, they may die or give birth to aberrant progeny. Such mistakes are the root cause of serious human diseases such as cancer. Hence, we would like to understand how cells control cell cycle events and correct mistakes before they do serious damage. Yeast cells are especially suited to studying cell cycle progression because so much is known about the underlying molecular control system, and because yeast cells—being so small—are especially vulnerable to random fluctuations in molecular regulators of the cell cycle. Experimental studies have identified feedback signals in the regulatory network that appear to keep these fluctuations within manageable limits. To place these proposals in a rigorous theoretical framework, we present a stochastic model of the major feedback controls in the yeast cell cycle. Our model accounts accurately for a range of observations about cell cycle variability in wild-type and mutant cells, and makes a host of verifiable predictions about mutant strains that are seriously compromised in cell cycle progression.

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.

Reliable cell cycle commitment in budding yeast is ensured by signal integration

Cell fate decisions are critical for life, yet little is known about how their reliability is achieved when signals are noisy and fluctuating with time. In this study, we show that in budding yeast, the decision of cell cycle commitment (Start) is determined by the time integration of its triggering signal Cln3. We further identify the Start repressor, Whi5, as the integrator. The instantaneous kinase activity of Cln3-Cdk1 is recorded over time on the phosphorylated Whi5, and the decision is made only when phosphorylated Whi5 reaches a threshold. Cells adjust the threshold by modulating Whi5 concentration in different nutrient conditions to coordinate growth and division. Our work shows that the strategy of signal integration, which was previously found in decision-making behaviors of animals, is adopted at the cellular level to reduce noise and minimize uncertainty.

DOI:http://dx.doi.org/10.7554/eLife.03977.001

Budding yeast and other single-celled organisms can reproduce by dividing to produce two daughter cells. The timing of the cell division is critical because if the cell is still small when it divides, the resulting daughter cells may not be big enough to survive.

In budding yeast, the irreversible decision to divide—known as the ‘Start’ checkpoint—is only made once a cell reaches a certain size and is triggered by a protein called Cln3. This protein controls the activity of another protein called Whi5, which normally prevents the cell from dividing by switching off particular genes. Cln3 adds phosphate groups to Whi5 to make ‘phosphorylated Whi5’, which allows the genes involved in cell division to be switched on.

It is commonly believed that the level of Cln3 reflects the size of the cell and the nutrient conditions. Therefore, one model of cell division proposes that the cell passes the Start checkpoint when the level of Cln3 reaches a threshold value. However, levels of the Cln3 protein in cells can naturally fluctuate, and computer simulations based on this model showed that this would not produce reliable decisions on when to divide. So how do cells manage to distinguish noise from the genuine signals that indicate it is the right time to divide?

To address this question, Liu et al. studied yeast cells containing an artificial version of the gene encoding the Cln3 protein whose levels could be adjusted by adding a particular chemical. This revealed that cells with higher levels of Cln3 passed through the Start checkpoint sooner than cells that had lower levels of Cln3.

The observation suggests that cells add up the amount of Cln3 present over a period of time to see if this reaches the threshold needed for the Start checkpoint. This could be possible if, instead of sensing Cln3 levels directly, the cell senses the accumulation of phosphorylated Whi5. To test this idea, Liu et al. carried out additional experiments and found that the decision to pass the Start checkpoint only occurs when the amount of phosphorylated Whi5 reaches a certain threshold.

The cells are able to coordinate their growth and division under different nutrient conditions by altering the threshold of phosphorylated Whi5. When the nutrient supply is poor, more phosphorylated Whi5 needs to be accumulated to allow the cell to pass the Start checkpoint. In this way, cells adjust when they divide according to nutrient conditions. Similar strategies may be found in other signaling or decision-making systems.

DOI:http://dx.doi.org/10.7554/eLife.03977.002

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.

MADS-box transcription factor SsMADS is involved in regulating growth and virulence in Sclerotinia sclerotiorum

MADS-box proteins, a well-conserved family of transcription factors in eukaryotic organisms, specifically regulate a wide range of cellular functions, including primary metabolism, cell cycle, and cell identity. However, little is known about roles of the MADS-box protein family in the fungal pathogen Sclerotinia sclerotiorum. In this research, the S. sclerotiorum MADS-box gene SsMADS was cloned; it encodes a protein that is highly similar to Mcm1 orthologs from Saccharomyces cerevisiae and other fungi, and includes a highly conserved DNA-binding domain. MADS is a member of the MADS box protein SRF (serum response factor) lineage. SsMADS function was investigated using RNA interference. Silenced strains were obtained using genetic transformation of the RNA interference vectors pS1-SsMADS and pSD-SsMADS. SsMADS expression levels in silenced strains were analyzed using RT-PCR. The results showed that SsMADS mRNA expression in these silenced strains was reduced to different degrees, and growth rate in these silenced strains was significantly decreased. Infecting tomato leaflets with silenced strains indicated that SsMADS was required for leaf pathogenesis in a susceptible host. Our results suggest that the MADS-box transcription factor SsMADS is involved in S. sclerotiorum growth and virulence.

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.

Dynamics of the cell-cycle network under genome-rewiring perturbations

The cell-cycle progression is regulated by a specific network enabling its ordered dynamics. Recent experiments supported by computational models have shown that a core of genes ensures this robust cycle dynamics. However, much less is known about the direct interaction of the cell-cycle regulators with genes outside of the cell-cycle network, in particular those of the metabolic system. Following our recent experimental work, we present here a model focusing on the dynamics of the cell-cycle core network under rewiring perturbations. Rewiring is achieved by placing an essential metabolic gene exclusively under the regulation of a cell-cycle's promoter, forcing the cell-cycle network to function under a multitasking challenging condition; operating in parallel the cell-cycle progression and a metabolic essential gene. Our model relies on simple rate equations that capture the dynamics of the relevant protein-DNA and protein-protein interactions, while making a clear distinction between these two different types of processes. In particular, we treat the cell-cycle transcription factors as limited 'resources' and focus on the redistribution of resources in the network during its dynamics. This elucidates the sensitivity of its various nodes to rewiring interactions. The basic model produces the correct cycle dynamics for a wide range of parameters. The simplicity of the model enables us to study the interface between the cell-cycle regulation and other cellular processes. Rewiring a promoter of the network to regulate a foreign gene, forces a multitasking regulatory load. The higher the load on the promoter, the longer is the cell-cycle period. Moreover, in agreement with our experimental results, the model shows that different nodes of the network exhibit variable susceptibilities to the rewiring perturbations. Our model suggests that the topology of the cell-cycle core network ensures its plasticity and flexible interface with other cellular processes, without a need for an optimal setting of the kinetic parameters.

Mitochondria influence CDR1 efflux pump activity, Hog1-mediated oxidative stress pathway, iron homeostasis, and ergosterol levels in Candida albicans

Mitochondrial dysfunction in Candida albicans is known to be associated with drug susceptibility, cell wall integrity, phospholipid homeostasis, and virulence. In this study, we deleted CaFZO1, a key component required during biogenesis of functional mitochondria. Cells with FZO1 deleted displayed fragmented mitochondria, mitochondrial genome loss, and reduced mitochondrial membrane potential and were rendered sensitive to azoles and peroxide. In order to understand the cellular response to dysfunctional mitochondria, genome-wide expression profiling of fzo1Δ/Δ cells was performed. Our results show that the increased susceptibility to azoles was likely due to reduced efflux activity of CDR efflux pumps, caused by the missorting of Cdr1p into the vacuole. In addition, fzo1Δ/Δ cells showed upregulation of genes involved in iron assimilation, in iron-sufficient conditions, characteristic of iron-starved cells. One of the consequent effects was downregulation of genes of the ergosterol biosynthesis pathway with a commensurate decrease in cellular ergosterol levels. We therefore connect deregulated iron metabolism to ergosterol biosynthesis pathway in response to dysfunctional mitochondria. Impaired activation of the Hog1 pathway in the mutant was the basis for increased susceptibility to peroxide and increase in reactive oxygen species, indicating the importance of functional mitochondria in controlling Hog1-mediated oxidative stress response. Mitochondrial phospholipid levels were also altered as indicated by an increase in phosphatidylserine and phosphatidylethanolamine and decrease in phosphatidylcholine in fzo1Δ/Δ cells. Collectively, these findings reinforce the connection between functional mitochondria and azole tolerance, oxidant-mediated stress, and iron homeostasis in C. albicans.

Bck2 acts through the MADS box protein Mcm1 to activate cell-cycle-regulated genes in budding yeast

The Bck2 protein is a potent genetic regulator of cell-cycle-dependent gene expression in budding yeast. To date, most experiments have focused on assessing a potential role for Bck2 in activation of the G1/S-specific transcription factors SBF (Swi4, Swi6) and MBF (Mbp1, Swi6), yet the mechanism of gene activation by Bck2 has remained obscure. We performed a yeast two-hybrid screen using a truncated version of Bck2 and discovered six novel Bck2-binding partners including Mcm1, an essential protein that binds to and activates M/G1 promoters through Early Cell cycle Box (ECB) elements as well as to G2/M promoters. At M/G1 promoters Mcm1 is inhibited by association with two repressors, Yox1 or Yhp1, and gene activation ensues once repression is relieved by an unknown activating signal. Here, we show that Bck2 interacts physically with Mcm1 to activate genes during G1 phase. We used chromatin immunoprecipitation (ChIP) experiments to show that Bck2 localizes to the promoters of M/G1-specific genes, in a manner dependent on functional ECB elements, as well as to the promoters of G1/S and G2/M genes. The Bck2-Mcm1 interaction requires valine 69 on Mcm1, a residue known to be required for interaction with Yox1. Overexpression of BCK2 decreases Yox1 localization to the early G1-specific CLN3 promoter and rescues the lethality caused by overexpression of YOX1. Our data suggest that Yox1 and Bck2 may compete for access to the Mcm1-ECB scaffold to ensure appropriate activation of the initial suite of genes required for cell cycle commitment.

Acetyl-CoA induces transcription of the key G1 cyclin CLN3 to promote entry into the cell division cycle in Saccharomyces cerevisiae

In budding yeast cells, nutrient repletion induces rapid exit from quiescence and entry into a round of growth and division. The G1 cyclin CLN3 is one of the earliest genes activated in response to nutrient repletion. Subsequent to its activation, hundreds of cell-cycle genes can then be expressed, including the cyclins CLN1/2 and CLB5/6. Although much is known regarding how CLN3 functions to activate downstream targets, the mechanism through which nutrients activate CLN3 transcription in the first place remains poorly understood. Here we show that a central metabolite of glucose catabolism, acetyl-CoA, induces CLN3 transcription by promoting the acetylation of histones present in its regulatory region. Increased rates of acetyl-CoA synthesis enable the Gcn5p-containing Spt-Ada-Gcn5-acetyltransferase transcriptional coactivator complex to catalyze histone acetylation at the CLN3 locus alongside ribosomal and other growth genes to promote entry into the cell division cycle.

A hybrid method for the exact planted (l, d) motif finding problem and its parallelization

Background

Given a set of DNA sequences s₁, ..., s_t, the (l, d) motif problem is to find an l-length motif sequence M , not necessary existing in any of the input sequences, such that for each sequence s_i, 1 ≤ i ≤ t, there is at least one subsequence differing with at most d mismatches from M. Many exact algorithms have been developed to solve the motif finding problem in the last three decades. However, the problem is still challenging and its solution is limited to small values of l and d.

Results

In this paper we present a new efficient method to improve the performance of the exact algorithms for the motif finding problem. Our method is composed of two main steps: First, we process q ≤ t sequences to find candidate motifs. Second, the candidate motifs are searched in the remaining sequences. For both steps, we use the best available algorithms. Our method is a hybrid one, because it integrates currently existing algorithms to achieve the best running time. In this paper, we show how the optimal value of q is determined to achieve the best running time. Our experimental results show that there is about 24% speed-up achieved by our method compared to the best existing algorithm. Furthermore, we also present a parallel version of our method running on shared memory architecture. Our experiments show that the performance of our algorithm scales linearly with the number of processors. Using the parallel version, we were able to solve the (21, 8) challenging instance using 8 processors in 20.42 hours instead of 6.68 days of the serial version.

Conclusions

Our method speeds up the solution of the exact motif problem. Our method is generic, because it can accommodate any new faster algorithm based on traditional methods. We expect that our method will help to discover longer motifs. The software we developed is available for free for academic research at http://www.nubios.nileu.edu.eg/tools/hymotif.

Ergodic sets as cell phenotype of budding yeast cell cycle

It has been suggested that irreducible sets of states in Probabilistic Boolean Networks correspond to cellular phenotype. In this study, we identify such sets of states for each phase of the budding yeast cell cycle. We find that these “ergodic sets” underly the cyclin activity levels during each phase of the cell cycle. Our results compare to the observations made in several laboratory experiments as well as the results of differential equation models. Dynamical studies of this model: (i) indicate that under stochastic external signals the continuous oscillating waves of cyclin activity and the opposing waves of CKIs emerge from the logic of a Boolean-based regulatory network without the need for specific biochemical/kinetic parameters; (ii) suggest that the yeast cell cycle network is robust to the varying behavior of cell size (e.g., cell division under nitrogen deprived conditions); (iii) suggest the irreversibility of the Start signal is a function of logic of the G1 regulon, and changing the structure of the regulatory network can render start reversible.

Topology of transcriptional regulatory networks: testing and improving

With the increasing amount and complexity of data generated in biological experiments it is becoming necessary to enhance the performance and applicability of existing statistical data analysis methods. This enhancement is needed for the hidden biological information to be better resolved and better interpreted. Towards that aim, systematic incorporation of prior information in biological data analysis has been a challenging problem for systems biology. Several methods have been proposed to integrate data from different levels of information most notably from metabolomics, transcriptomics and proteomics and thus enhance biological interpretation. However, in order not to be misled by the dominance of incorrect prior information in the analysis, being able to discriminate between competing prior information is required. In this study, we show that discrimination between topological information in competing transcriptional regulatory network models is possible solely based on experimental data. We use network topology dependent decomposition of synthetic gene expression data to introduce both local and global discriminating measures. The measures indicate how well the gene expression data can be explained under the constraints of the model network topology and how much each regulatory connection in the model refuses to be constrained. Application of the method to the cell cycle regulatory network of Saccharomyces cerevisiae leads to the prediction of novel regulatory interactions, improving the information content of the hypothesized network model.

Cell size control in yeast

Cell size is an important adaptive trait that influences nearly all aspects of cellular physiology. Despite extensive characterization of the cell-cycle regulatory network, the molecular mechanisms coupling cell growth to division, and thereby controlling cell size, have remained elusive. Recent work in yeast has reinvigorated the size control field and suggested provocative mechanisms for the distinct functions of setting and sensing cell size. Further examination of size-sensing models based on spatial gradients and molecular titration, coupled with elucidation of the pathways responsible for nutrient-modulated target size, may reveal the fundamental principles of eukaryotic cell size control.

An integrated framework to model cellular phenotype as a component of biochemical networks

Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.

Origin of irreversibility of cell cycle start in budding yeast

Budding yeast cells irreversibly commit to a new division cycle at a regulatory transition called Start. This essential decision-making step involves the activation of the SBF/MBF transcription factors. SBF/MBF promote expression of the G1 cyclins encoded by CLN1 and CLN2. Cln1,2 can activate their own expression by inactivating the Whi5 repressor of SBF/MBF. The resulting transcriptional positive feedback provides an appealing, but as yet unproven, candidate for generating irreversibility of Start. Here, we investigate the logic of the Start regulatory module by quantitative single-cell time-lapse microscopy, using strains in which expression of key regulators is efficiently controlled by changes of inducers in a microfluidic chamber. We show that Start activation is ultrasensitive to G1 cyclin. In the absence of CLN1,2-dependent positive feedback, we observe that Start transit is reversible, due to reactivation of the Whi5 transcriptional repressor. Introduction of the positive feedback loop makes Whi5 inactivation and Start activation irreversible, which therefore guarantees unidirectional entry into S phase. A simple mathematical model to describe G1 cyclin turn on at Start, entirely constrained by empirically measured parameters, shows that the experimentally measured ultrasensitivity and transcriptional positive feedback are necessary and sufficient dynamical characteristics to make the Start transition a bistable and irreversible switch. Our study thus demonstrates that Start irreversibility is a property that arises from the architecture of the system (Whi5/SBF/Cln2 loop), rather than the consequence of the regulation of a single component (e.g., irreversible protein degradation).

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

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

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

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

Dual regulation by pairs of cyclin-dependent protein kinases and histone deacetylases controls G1 transcription in budding yeast

START-dependent transcription in Saccharomyces cerevisiae is regulated by two transcription factors SBF and MBF, whose activity is controlled by the binding of the repressor Whi5. Phosphorylation and removal of Whi5 by the cyclin-dependent kinase (CDK) Cln3-Cdc28 alleviates the Whi5-dependent repression on SBF and MBF, initiating entry into a new cell cycle. This Whi5-SBF/MBF transcriptional circuit is analogous to the regulatory pathway in mammalian cells that features the E2F family of G1 transcription factors and the retinoblastoma tumor suppressor protein (Rb). Here we describe genetic and biochemical evidence for the involvement of another CDK, Pcl-Pho85, in regulating G1 transcription, via phosphorylation and inhibition of Whi5. We show that a strain deleted for both PHO85 and CLN3 has a slow growth phenotype, a G1 delay, and is severely compromised for SBF-dependent reporter gene expression, yet all of these defects are alleviated by deletion of WHI5. Our biochemical and genetic tests suggest Whi5 mediates repression in part through interaction with two histone deacetylases (HDACs), Hos3 and Rpd3. In a manner analogous to cyclin D/CDK4/6, which phosphorylates Rb in mammalian cells disrupting its association with HDACs, phosphorylation by the early G1 CDKs Cln3-Cdc28 and Pcl9-Pho85 inhibits association of Whi5 with the HDACs. Contributions from multiple CDKs may provide the precision and accuracy necessary to activate G1 transcription when both internal and external cues are optimal.

The fission yeast homeodomain protein Yox1p binds to MBF and confines MBF-dependent cell-cycle transcription to G1-S via negative feedback

The regulation of the G1- to S-phase transition is critical for cell-cycle progression. This transition is driven by a transient transcriptional wave regulated by transcription factor complexes termed MBF/SBF in yeast and E2F-DP in mammals. Here we apply genomic, genetic, and biochemical approaches to show that the Yox1p homeodomain protein of fission yeast plays a critical role in confining MBF-dependent transcription to the G1/S transition of the cell cycle. The yox1 gene is an MBF target, and Yox1p accumulates and preferentially binds to MBF-regulated promoters, via the MBF components Res2p and Nrm1p, when they are transcriptionally repressed during the cell cycle. Deletion of yox1 results in constitutively high transcription of MBF target genes and loss of their cell cycle–regulated expression, similar to deletion of nrm1. Genome-wide location analyses of Yox1p and the MBF component Cdc10p reveal dozens of genes whose promoters are bound by both factors, including their own genes and histone genes. In addition, Cdc10p shows promiscuous binding to other sites, most notably close to replication origins. This study establishes Yox1p as a new regulatory MBF component in fission yeast, which is transcriptionally induced by MBF and in turn inhibits MBF-dependent transcription. Yox1p may function together with Nrm1p to confine MBF-dependent transcription to the G1/S transition of the cell cycle via negative feedback. Compared to the orthologous budding yeast Yox1p, which indirectly functions in a negative feedback loop for cell-cycle transcription, similarities but also notable differences in the wiring of the regulatory circuits are evident.

Cells proliferate by growth and division, which is supported by different gene groups that are periodically induced at specific times when they are required during the cell cycle. These genes not only need to be induced at the right time but also repressed when they are no longer required; mistakes in gene regulation can lead to problems in cell proliferation and diseases such as cancer. A well-known regulatory complex functions just before cells replicate their DNA to induce genes required for this important transition. We show that in fission yeast this regulatory complex (MBF) induces a gene whose encoded protein (Yox1p) in turn binds to MBF and represses MBF-regulated genes. In the absence of Yox1p, the MBF-regulated genes do not fluctuate during the cell cycle but remain constantly induced. Thus, MBF sets up not only the induction but also the timely repression of its target genes via Yox1p. We also provide a global analysis of all the genes regulated by Yox1p and MBF. Together, our data uncover a new negative control loop, further highlighting the sophistication of gene regulation during the cell cycle, and illustrating regulatory similarities and differences between organisms.

Trypanosome prereplication machinery contains a single functional orc1/cdc6 protein, which is typical of archaea

In unicellular eukaryotes, such as Saccharomyces cerevisiae, and in multicellular organisms, the replication origin is recognized by the heterohexamer origin recognition complex (ORC) containing six proteins, Orc1 to Orc6, while in members of the domain Archaea, the replication origin is recognized by just one protein, Orc1/Cdc6; the sequence of Orc1/Cdc6 is highly related to those of Orc1 and Cdc6. Similar to Archaea, trypanosomatid genomes contain only one gene encoding a protein named Orc1. Since trypanosome Orc1 is also homologous to Cdc6, in this study we named the Orc1 protein from trypanosomes Orc1/Cdc6. Here we show that the recombinant Orc1/Cdc6 from Trypanosoma cruzi (TcOrc1/Cdc6) and from Trypanosoma brucei (TbOrc1/Cdc6) present ATPase activity, typical of prereplication machinery components. Also, TcOrc1/Cdc6 and TbOrc1/Cdc6 replaced yeast Cdc6 but not Orc1 in a phenotypic complementation assay. The induction of Orc1/Cdc6 silencing by RNA interference in T. brucei resulted in enucleated cells, strongly suggesting the involvement of Orc1/Cdc6 in DNA replication. Orc1/Cdc6 is expressed during the entire cell cycle in the nuclei of trypanosomes, remaining associated with chromatin in all stages of the cell cycle. These results allowed us to conclude that Orc1/Cdc6 is indeed a member of the trypanosome prereplication machinery and point out that trypanosomes carry a prereplication machinery that is less complex than other eukaryotes and closer to archaea.

Coupling phosphate homeostasis to cell cycle-specific transcription: mitotic activation of Saccharomyces cerevisiae PHO5 by Mcm1 and Forkhead proteins

Cells devote considerable resources to nutrient homeostasis, involving nutrient surveillance, acquisition, and storage at physiologically relevant concentrations. Many Saccharomyces cerevisiae transcripts coding for proteins with nutrient uptake functions exhibit peak periodic accumulation during M phase, indicating that an important aspect of nutrient homeostasis involves transcriptional regulation. Inorganic phosphate is a central macronutrient that we have previously shown oscillates inversely with mitotic activation of PHO5. The mechanism of this periodic cell cycle expression remains unknown. To date, only two sequence-specific activators, Pho4 and Pho2, were known to induce PHO5 transcription. We provide here evidence that Mcm1, a MADS-box protein, is essential for PHO5 mitotic activation. In addition, we found that cells simultaneously lacking the forkhead proteins, Fkh1 and Fkh2, exhibited a 2.5-fold decrease in PHO5 expression. The Mcm1-Fkh2 complex, first shown to transactivate genes within the CLB2 cluster that drive G(2)/M progression, also associated directly at the PHO5 promoter in a cell cycle-dependent manner in chromatin immunoprecipitation assays. Sds3, a component specific to the Rpd3L histone deacetylase complex, was also recruited to PHO5 in G(1). These findings provide (i) further mechanistic insight into PHO5 mitotic activation, (ii) demonstrate that Mcm1-Fkh2 can function combinatorially with other activators to yield late M/G(1) induction, and (iii) couple the mitotic cell cycle progression machinery to cellular phosphate homeostasis.

Budding yeast SSD1-V regulates transcript levels of many longevity genes and extends chronological life span in purified quiescent cells

Ssd1 is an RNA-binding protein that affects literally hundreds of different processes and is polymorphic in both wild and lab yeast strains. We have used transcript microarrays to compare mRNA levels in an isogenic pair of mutant (ssd1-d) and wild-type (SSD1-V) cells across the cell cycle. We find that 15% of transcripts are differentially expressed, but there is no correlation with those mRNAs bound by Ssd1. About 20% of cell cycle regulated transcripts are affected, and most show sharper amplitudes of oscillation in SSD1-V cells. Many transcripts whose gene products influence longevity are also affected, the largest class of which is involved in translation. Ribosomal protein mRNAs are globally down-regulated by SSD1-V. SSD1-V has been shown to increase replicative life span currency and we show that SSD1-V also dramatically increases chronological life span (CLS). Using a new assay of CLS in pure populations of quiescent prototrophs, we find that the CLS for SSD1-V cells is twice that of ssd1-d cells.

Identification of yeast transcriptional regulation networks using multivariate random forests

The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes.

Transcriptional analysis of the Candida albicans cell cycle

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

Systematic identification of yeast cell cycle transcription factors using multiple data sources

Background

Eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes.

Results

We developed a method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor binding site (TFBS), and cell cycle gene expression data. We identified 17 cell cycle TFs, 12 of which are known cell cycle TFs, while the remaining five (Ash1, Rlm1, Ste12, Stp1, Tec1) are putative novel cell cycle TFs. For each cell cycle TF, we assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. We also identified 178 novel cell cycle-regulated genes, among which 59 have unknown functions, but they may now be annotated as cell cycle-regulated genes. Most of our predictions are supported by previous experimental or computational studies. Furthermore, a high confidence TF-gene regulatory matrix is derived as a byproduct of our method. Each TF-gene regulatory relationship in this matrix is supported by at least three data sources: gene expression, TFBS, and ChIP-chip or/and mutant data. We show that our method performs better than four existing methods for identifying yeast cell cycle TFs. Finally, an application of our method to different cell cycle gene expression datasets suggests that our method is robust.

Conclusion

Our method is effective for identifying yeast cell cycle TFs and cell cycle-regulated genes. Many of our predictions are validated by the literature. Our study shows that integrating multiple data sources is a powerful approach to studying complex biological systems.

High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae

A novel approach for identifying condition-specific regulatory modules in yeast reveals functionally distinct coregulated submodules.

We present an approach for identifying condition-specific regulatory modules by using separate units of gene expression profiles along with ChIP-chip and motif data from Saccharomyces cerevisiae. By investigating the unique and common features of the obtained condition-specific modules, we detected several important properties of transcriptional network reorganization. Our approach reveals the functionally distinct coregulated submodules embedded in a coexpressed gene module and provides an effective method for identifying various condition-specific regulatory events at high resolution.

Inferring gene regulatory networks by integrating static and dynamic data

OBJECTIVES

The purpose of the paper is to propose a methodology for learning gene regulatory networks from DNA microarray data based on the integration of different data and knowledge sources. We applied our method to Saccharomyces cerevisiae experiments, focusing our attention on cell cycle regulatory mechanisms. We exploited data from deletion mutant experiments (static data), gene expression time series (dynamic data) and the knowledge encoded in the Gene Ontology.

METHODS

The proposed method is based on four phases. An initial gene network was derived from static data by means of a simple statistical approach. Then, the genes classified in the Gene Ontology as being involved in the cell cycle were selected. As a third step, the network structure was used to initialize a linear dynamic model of gene expression profiles. Finally, a genetic algorithm was applied to update the gene network exploiting data coming from an experiment on the yeast cell cycle.

RESULTS

We compared the network models provided by our approach with those obtained with a fully data-driven approach, by looking at their AIC scores and at the percentage of preserved connections in the best solutions. The results show that several nearly equivalent solutions, in terms of AIC scores, can be found. This problem is greatly mitigated by following our approach, which is able to find more robust models by fixing a portion of the network structure on the basis of prior knowledge. The best network structure was biologically evaluated on a set of 22 known cell cycle genes against independent knowledge sources.

CONCLUSIONS

An approach able to integrate several sources of information is needed to infer gene regulatory networks, as a fully data-driven search is in general prone to overfitting and to unidentifiability problems. The learned networks encode hypotheses on regulatory relationships that need to be verified by means of wet-lab experiments.

The effects of molecular noise and size control on variability in the budding yeast cell cycle

Molecular noise in gene expression can generate substantial variability in protein concentration. However, its effect on the precision of a natural eukaryotic circuit such as the control of cell cycle remains unclear. We use single-cell imaging of fluorescently labelled budding yeast to measure times from division to budding (G1) and from budding to the next division. The variability in G1 decreases with the square root of the ploidy through a 1N/2N/4N ploidy series, consistent with simple stochastic models for molecular noise. Also, increasing the gene dosage of G1 cyclins decreases the variability in G1. A new single-cell reporter for cell protein content allows us to determine the contribution to temporal G1 variability of deterministic size control (that is, smaller cells extending G1). Cell size control contributes significantly to G1 variability in daughter cells but not in mother cells. However, even in daughters, size-independent noise is the largest quantitative contributor to G1 variability. Exit of the transcriptional repressor Whi5 from the nucleus partitions G1 into two temporally uncorrelated and functionally distinct steps. The first step, which depends on the G1 cyclin gene CLN3, corresponds to noisy size control that extends G1 in small daughters, but is of negligible duration in mothers. The second step, whose variability decreases with increasing CLN2 gene dosage, is similar in mothers and daughters. This analysis decomposes the regulatory dynamics of the Start transition into two independent modules, a size sensing module and a timing module, each of which is predominantly controlled by a different G1 cyclin.

Cyclin Cln3 is retained at the ER and released by the J chaperone Ydj1 in late G1 to trigger cell cycle entry

G1 cyclin Cln3 plays a key role in linking cell growth and proliferation in budding yeast. It is generally assumed that Cln3, which is present throughout G1, accumulates passively in the nucleus until a threshold is reached to trigger cell cycle entry. We show here that Cln3 is retained bound to the ER in early G1 cells. ER retention requires binding of Cln3 to the cyclin-dependent kinase Cdc28, a fraction of which also associates to the ER. Cln3 contains a chaperone-regulatory Ji domain that counteracts Ydj1, a J chaperone essential for ER release and nuclear accumulation of Cln3 in late G1. Finally, Ydj1 is limiting for release of Cln3 and timely entry into the cell cycle. As protein synthesis and ribosome assembly rates compromise chaperone availability, we hypothesize that Ydj1 transmits growth capacity information to the cell cycle for setting efficient size/ploidy ratios.

Nascent transcription of MCM2-7 is important for nuclear localization of the minichromosome maintenance complex in G1

The minichromosome maintenance genes (MCM2-7) are transcribed at M/G1 just as the Mcm complex is imported into the nucleus to be assembled into prereplication complexes, during a period of low cyclin-dependent kinase (CDK) activity. The CDKs trigger DNA replication and prevent rereplication in part by exporting Mcm2-7 from the nucleus during S phase. We have found that repression of MCM2-7 transcription in a single cell cycle interferes with the nuclear import of Mcms in the subsequent M/G1 phase. This suggests that nascent Mcm proteins are preferentially imported into the nucleus. Consistent with this, we find that loss of CDK activity in G2/M is not sufficient for nuclear import, there is also a requirement for new protein synthesis. This requirement is not met by constitutive production of Cdc6 and does not involve synthesis of new transport machinery. The Mcm proteins generated in the previous cell cycle, which are unable to reaccumulate in the nucleus, are predominantly turned over by ubiquitin-mediated proteolysis in late mitosis/early G1. Therefore, the nuclear localization of Mcm2-7 is dependent on nascent transcription and translation of Mcm2-7 and the elimination of CDK activity which occurs simultaneously as cells enter G1.

Mitotic Cdc6 stabilizes anaphase-promoting complex substrates by a partially Cdc28-independent mechanism, and this stabilization is suppressed by deletion of Cdc55

Ectopic expression of Cdc6p results in mitotic delay, and this has been attributed to Cdc6p-mediated inhibition of Cdc28 protein kinase and failure to activate the anaphase-promoting complex (APC). Here we show that endogenous Cdc6p delays a specific subset of mitotic events and that Cdc28 inhibition is not sufficient to account for it. The depletion of Cdc6p in G(2)/M cells reveals that Cdc6p is rate limiting for the degradation of the APC/Cdc20 substrates Pds1p and Clb2p. Conversely, the premature expression of Cdc6p delays the degradation of APC/Cdc20 substrates. Abolishing Cdc6p/Cdc28p interaction does not eliminate the Cdc6-dependent delay of these anaphase events. To identify additional Cdc6-mediated, APC-inhibitory mechanisms, we looked for mutants that reversed the mitotic delay. The deletion of SWE1, RAD24, MAD2, or BUB2 had no effect. However, disrupting CDC55, a PP2A regulatory subunit, suppressed the Cdc6p-dependent delay of Pds1 and Clb2 destruction. A specific role for CDC55 was supported by demonstrating that the lethality of Cdc6 ectopic expression in a cdc16-264 mutant is suppressed by the deletion of CDC55, that endogenous Cdc6p coimmunoprecipitates with the Cdc55 and Tpd3 subunits of PP2A, that Cdc6p/Cdc55p/Tpd3 interaction occurs only during mitosis, and that Cdc6 affects PP2A-Cdc55 activity during anaphase. This demonstrates that the levels and timing of accumulation of Cdc6p in mitosis are appropriate for mediating the modulation of APC/Cdc20.