Periodic transcription: a cycle within a cycle

The APSES transcription factor CfSwi6 is required for growth, cell wall integrity, and pathogenicity of Ceratocystis fimbriata

Cell wall integrity (CWI) is crucial for the growth, development, and host invasion of pathogenic fungi. The APSES transcription factor Swi6 in fungi plays a role in mediating cell wall integrity through the mitogen-activated protein kinase (MAPK) signaling pathway. Ceratocystis fimbriata is a notorious pathogenic fungus responsible for causing black rot in sweet potatoes. In this study, an orthologous APSES transcription factor Swi6 (CfSwi6) downstream of the CWI regulatory pathway in C. fimbriata was characterized. Deletion of CfSWI6 leads to impaired hyphal development, conidiation, and compromised cell wall integrity, resulting in a significant reduction in virulence. Transcriptome analysis revealed the involvement of CfSWI6 in various pathways, including the MAPK pathway, DNA synthesis and stress response. ChIP-seq data provided predictions of potential target genes regulated by CfSwi6. Through yeast one-hybrid, we confirmed the direct binding of CfSwi6 to the promoter of the chitin synthetase gene. In summary, these findings indicated that CfSwi6 plays an important role in the growth, development, and pathogenicity of C. fimbriata. This study provides new insights into the pathogenic mechanism of C. fimbriata in sweet potato and inspires potential strategies to control sweet potato black rot.

Prevalent and dynamic binding of the cell cycle checkpoint kinase Rad53 to gene promoters

Replication of the genome must be coordinated with gene transcription and cellular metabolism, especially following replication stress in the presence of limiting deoxyribonucleotides. The Saccharomyces cerevisiae Rad53 (CHEK2 in mammals) checkpoint kinase plays a major role in cellular responses to DNA replication stress. Cell cycle regulated, genome-wide binding of Rad53 to chromatin was examined. Under replication stress, the kinase bound to sites of active DNA replication initiation and fork progression, but unexpectedly to the promoters of about 20% of genes encoding proteins involved in multiple cellular functions. Rad53 promoter binding correlated with changes in expression of a subset of genes. Rad53 promoter binding to certain genes was influenced by sequence-specific transcription factors and less by checkpoint signaling. However, in checkpoint mutants, untimely activation of late-replicating origins reduces the transcription of nearby genes, with concomitant localization of Rad53 to their gene bodies. We suggest that the Rad53 checkpoint kinase coordinates genome-wide replication and transcription under replication stress conditions.

G1/S restriction point coordinates phasic gene expression and cell differentiation

Pluripotent embryonic stem cells have a unique cell cycle structure with a suppressed G1/S restriction point and little differential expression across the cell cycle phases. Here, we evaluate the link between G1/S restriction point activation, phasic gene expression, and cellular differentiation. Expression analysis reveals a gain in phasic gene expression across lineages between embryonic days E7.5 and E9.5. Genetic manipulation of the G1/S restriction point regulators miR-302 and P27 respectively accelerates or delays the onset of phasic gene expression in mouse embryos. Loss of miR-302-mediated p21 or p27 suppression expedites embryonic stem cell differentiation, while a constitutive Cyclin E mutant blocks it. Together, these findings uncover a causal relationship between emergence of the G1/S restriction point with a gain in phasic gene expression and cellular differentiation.

Control of Gene Expression via the Yeast CWI Pathway

Living cells exposed to stressful environmental situations can elicit cellular responses that guarantee maximal cell survival. Most of these responses are mediated by mitogen-activated protein kinase (MAPK) cascades, which are highly conserved from yeast to humans. Cell wall damage conditions in the yeast Saccharomyces cerevisiae elicit rescue mechanisms mainly associated with reprogramming specific transcriptional responses via the cell wall integrity (CWI) pathway. Regulation of gene expression by this pathway is coordinated by the MAPK Slt2/Mpk1, mainly via Rlm1 and, to a lesser extent, through SBF (Swi4/Swi6) transcription factors. In this review, we summarize the molecular mechanisms controlling gene expression upon cell wall stress and the role of chromatin structure in these processes. Some of these mechanisms are also discussed in the context of other stresses governed by different yeast MAPK pathways. Slt2 regulates both transcriptional initiation and elongation by interacting with chromatin at the promoter and coding regions of CWI-responsive genes but using different mechanisms for Rlm1- and SBF-dependent genes. Since MAPK pathways are very well conserved in eukaryotic cells and are essential for controlling cellular physiology, improving our knowledge regarding how they regulate gene expression could impact the future identification of novel targets for therapeutic intervention.

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

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

Regulation of sphingolipid synthesis by the G1/S transcription factor Swi4

SBF (Swi4/Swi6 Binding Factor) complex is a crucial regulator of G1/S transition in Saccharomyces cerevisiae. Here, we show that SBF complex is required for myriocin resistance, an inhibitor of sphingolipid synthesis. This phenotype was not shared with MBF complex mutants nor with deletion of the Swi4p downstream targets, CLN1/CLN2. Based on data mining results, we selected putative Swi4p targets related to sphingolipid metabolism and studied their gene transcription as well as metabolite levels during progression of the cell cycle. Genes which encode key enzymes for the synthesis of long chain bases (LCBs) and ceramides were periodically transcribed during the mitotic cell cycle, having a peak at G1/S, and required SWI4 for full transcription at this stage. In addition, HPLC-MS/MS data indicated that swi4Δ cells have decreased levels of sphingolipids during progression of the cell cycle, particularly, dihydrosphingosine (DHS), C24-phytoceramides and C24-inositolphosphoryl ceramide (IPC) while it had increased levels of mannosylinositol phosphorylceramide (MIPC). Furthermore, we demonstrated that both inhibition of de novo sphingolipid synthesis by myriocin or SWI4 deletion caused partial arrest at the G2/M phase. Importantly, our lipidomic data demonstrated that the sphingolipid profile of WT cells treated with myriocin resembled that of swi4Δ cells, with lower levels of DHS, IPC and higher levels of MIPC. Taken together, these results show that SBF complex plays an essential role in the regulation of sphingolipid homeostasis, which reflects in the correct progression through the G2/M phase of the cell cycle.

The Role of Metabolic Engineering Technologies for the Production of Fatty Acids in Yeast

Simple Summary

Metabolic engineering involves the sustainable production of high-value products. E. coli and yeast, in particular, are used for such processes. Using metabolic engineering, the biosynthetic pathways of these cells are altered to obtain a high production of desired products. Fatty acids (FAs) and their derivatives are products produced using metabolic engineering. However, classical methods used for engineering yeast metabolic pathways for the production of fatty acids and their derivatives face problems such as the low supply of key precursors and product tolerance. This review introduces the different ways FAs are being produced in E. coli and yeast and the genetic manipulations for enhanced production of FAs. The review also summarizes the latest techniques (i.e., CRISPR–Cas and synthetic biology) for developing FA-producing yeast cell factories.

Abstract

Metabolic engineering is a cutting-edge field that aims to produce simple, readily available, and inexpensive biomolecules by applying different genetic engineering and molecular biology techniques. Fatty acids (FAs) play an important role in determining the physicochemical properties of membrane lipids and are precursors of biofuels. Microbial production of FAs and FA-derived biofuels has several advantages in terms of sustainability and cost. Conventional yeast Saccharomyces cerevisiae is one of the models used for FA synthesis. Several genetic manipulations have been performed to enhance the citrate accumulation and its conversation into acetyl-CoA, a precursor for FA synthesis. Success has been achieved in producing different chemicals, including FAs and their derivatives, through metabolic engineering. However, several hurdles such as slow growth rate, low oleaginicity, and cytotoxicity are still need to be resolved. More robust research needs to be conducted on developing microbes capable of resisting diverse environments, chemicals, and cost-effective feed requirements. Redesigning microbes to produce FAs with cutting-edge synthetic biology and CRISPR techniques can solve these problems. Here, we reviewed the technological progression of metabolic engineering techniques and genetic studies conducted on S. cerevisiae, making it suitable as a model organism and a great candidate for the production of biomolecules, especially FAs.

Critical slowing down and attractive manifold: A mechanism for dynamic robustness in the yeast cell-cycle process

Biological processes that execute complex multiple functions, such as the cell cycle, must ensure the order of sequential events and maintain dynamic robustness against various fluctuations. Here, we examine the mechanisms and fundamental structure that achieve these properties in the cell cycle of the budding yeast Saccharomyces cerevisiae. We show that this process behaves like an excitable system containing three well-decoupled saddle-node bifurcations to execute DNA replication and mitosis events. The yeast cell-cycle regulatory network can be divided into three modules-the G1/S phase, early M phase, and late M phase-wherein both positive feedback loops in each module and interactions among modules play important roles. Specifically, when the cell-cycle process operates near the critical points of the saddle-node bifurcations, a critical slowing down effect takes place. Such interregnum then allows for an attractive manifold and sufficient duration for cell-cycle events, within which to assess the completion of DNA replication and mitosis, e.g., spindle assembly. Moreover, such arrangement ensures that any fluctuation in an early module or event will not transmit to a later module or event. Thus, our results suggest a possible dynamical mechanism of the cell-cycle process to ensure event order and dynamic robustness and give insight into the evolution of eukaryotic cell-cycle processes.

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

BACKGROUND

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task.

RESULTS

We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4).

CONCLUSION

Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

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

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

Periodic expression of cell-cycle regulators: A laboratory experiment proposal for students in molecular and cell biology

This article describes a laboratory exercise designed for undergraduate students in the subject of "Regulation of cell proliferation" which allows the students to carry out a research experiment in an important field such as cell cycle control, and to be introduced to a widely used technique in molecular biology laboratories such as the western blot. The cell cycle is regulated by the succession of cyclin-CDK kinase activities. Activation and inactivation of different cyclin-CDK complexes depend on the control of their positive and negative regulators, cyclins and CDK inhibitors (CKIs), respectively. In this experiment, fluctuations in the level of mitotic cyclin Clb2 and CDK inhibitor Sic1 throughout the cell cycle of Saccharomyces cerevisiae are analyzed, particularly in the context of the control of mitotic exit and Start, two of the most important cell cycle transitions. In order to do this, a cdc15 mutant strain is used to block cells in telophase and, upon release from this blocking, the variation in the levels of Clb2 and Sic1 proteins are analyzed by western blot. Progress along the cell cycle is also evaluated by microscopic analysis of cell morphology and nuclear staining. This practical illustrates the experimental basis of theoretical concepts worked in the classroom and it is a good framework for an in-depth discussion of these concepts based on experimental data analysis. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):527-535, 2018.

Whi7 is an unstable cell-cycle repressor of the Start transcriptional program

Start is the main decision point in eukaryotic cell cycle in which cells commit to a new round of cell division. It involves the irreversible activation of a transcriptional program by G1 CDK-cyclin complexes through the inactivation of Start transcriptional repressors, Whi5 in yeast or Rb in mammals. Here we provide novel keys of how Whi7, a protein related at sequence level to Whi5, represses Start. Whi7 is an unstable protein, degraded by the SCF^Grr1 ubiquitin-ligase, whose stability is cell cycle regulated by CDK1 phosphorylation. Importantly, Whi7 associates to G1/S gene promoters in late G1 acting as a repressor of SBF-dependent transcription. Our results demonstrate that Whi7 is a genuine paralog of Whi5. In fact, both proteins collaborate in Start repression bringing to light that yeast cells, as occurs in mammalian cells, rely on the combined action of multiple transcriptional repressors to block Start transition.

The commitment of cells to a new cycle of division involves inactivation of the Start transcriptional repressor Whi5. Here the authors show that the sequence related protein Whi7 associates to G1/S gene promoters in late G1 and collaborates with Whi5 in Start repression.

Methods of Synchronization of Yeast Cells for the Analysis of Cell Cycle Progression

Cell division is a fascinating and fundamental process that sustains life. By this process, unicellular organisms reproduce and multicellular organisms sustain development, growth, and tissue repair. Division of a mother cell gives rise to two daughter cells according to an ordered set of events within four successive phases called G1 (gap1), S (DNA Synthesis), G2 (gap2), and M (Mitosis) phase. How these different phases are orchestrated to ensure the physical separation of the two daughter cells is a tightly regulated process. Indeed, inappropriate cell division could lead to uncontrolled cell proliferation and ultimately to cancer. Saccharomyces cerevisiae is an excellent model system for unraveling the secrets of cell division. A large community of researchers has chosen budding yeast as a model because of its advantages: rapid growth in simple and economical media, tractable genetics, powerful biochemistry, cell biology, and proteomics approaches. Furthermore, the cell cycle mechanisms, as elucidated in yeast, are conserved in higher eukaryotes. The ability to synchronize and get large numbers of cells in a particular stage of the cell cycle is crucial to properly explore the mechanisms of the cell cycle. An overview of the most common yeast synchronization techniques has been compiled in this chapter.

A comparative study of the degradation of yeast cyclins Cln1 and Cln2

The yeast cyclins Cln1 and Cln2 are very similar in both sequence and function, but some differences in their functionality and localization have been recently described. The control of Cln1 and Cln2 cellular levels is crucial for proper cell cycle initiation. In this work, we analyzed the degradation patterns of Cln1 and Cln2 in order to further investigate the possible differences between them. Both cyclins show the same half-life but, while Cln2 degradation depends on ubiquitin ligases SCF^Grr1 and SCF^Cdc4, Cln1 is affected only by SCF^Grr1. Degradation analysis of chimeric cyclins, constructed by combining fragments from Cln1 and Cln2, identifies the N-terminal sequence of the proteins as responsible of the cyclin degradation pattern. In particular, the N-terminal region of Cln2 is required to mediate degradation by SCF^Cdc4. This region is involved in nuclear import of Cln1 and Cln2, which suggests that differences in degradation may be due to differences in localization. Moreover, a comparison of the cyclins that differ only in the presence of the Cln2 nuclear export signal indicates a greater instability of exported cyclins, thus reinforcing the idea that cyclin stability is influenced by their localization.

DREAMs make plant cells to cycle or to become quiescent

Cell cycle phase specific oscillation of gene transcription has long been recognized as an underlying principle for ordered processes during cell proliferation. The G1/S-specific and G2/M-specific cohorts of genes in plants are regulated by the E2F and the MYB3R transcription factors. Mutant analysis suggests that activator E2F functions might not be fully required for cell cycle entry. In contrast, the two activator-type MYB3Rs are part of positive feedback loops to drive the burst of mitotic gene expression, which is necessary at least to accomplish cytokinesis. Repressor MYB3Rs act outside the mitotic time window during cell cycle progression, and are important for the shutdown of mitotic genes to impose quiescence in mature organs. The two distinct classes of E2Fs and MYB3Rs together with the RETINOBLATOMA RELATED are part of multiprotein complexes that may be evolutionary related to what is known as DREAM complex in animals. In plants, there are multiple such complexes with distinct compositions and functions that may be involved in the coordinated cell cycle and developmental regulation of E2F targets and mitotic genes.

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

Cooperation between SAGA and SWI/SNF complexes is required for efficient transcriptional responses regulated by the yeast MAPK Slt2

The transcriptional response of Saccharomyces cerevisiae to cell wall stress is mainly mediated by the cell wall integrity (CWI) pathway through the MAPK Slt2 and the transcription factor Rlm1. Once activated, Rlm1 interacts with the chromatin remodeling SWI/SNF complex which locally alters nucleosome positioning at the target promoters. Here we show that the SAGA complex plays along with the SWI/SNF complex an important role for eliciting both early induction and sustained gene expression upon stress. Gcn5 co-regulates together with Swi3 the majority of the CWI transcriptional program, except for a group of genes which are only dependent on the SWI/SNF complex. SAGA subunits are recruited to the promoter of CWI-responsive genes in a Slt2, Rlm1 and SWI/SNF-dependent manner. However, Gcn5 mediates acetylation and nucleosome eviction only at the promoters of the SAGA-dependent genes. This process is not essential for pre-initiation transcriptional complex assembly but rather increase the extent of the remodeling mediated by SWI/SNF. As a consequence, H3 eviction and Rlm1 recruitment is completely blocked in a swi3Δ gcn5Δ double mutant. Therefore, SAGA complex, through its histone acetylase activity, cooperates with the SWI/SNF complex for the mandatory nucleosome displacement required for full gene expression through the CWI pathway.

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

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

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

Regulation of Ace2-dependent genes requires components of the PBF complex in Schizosaccharomyces pombe

The division cycle of unicellular yeasts is completed with the activation of a cell separation program that results in the dissolution of the septum assembled during cytokinesis between the 2 daughter cells, allowing them to become independent entities. Expression of the eng1(+) and agn1(+) genes, encoding the hydrolytic enzymes responsible for septum degradation, is activated at the end of each cell cycle by the transcription factor Ace2. Periodic ace2(+) expression is regulated by the transcriptional complex PBF (PCB Binding Factor), composed of the forkhead-like proteins Sep1 and Fkh2 and the MADS box-like protein Mbx1. In this report, we show that Ace2-dependent genes contain several combinations of motifs for Ace2 and PBF binding in their promoters. Thus, Ace2, Fkh2 and Sep1 were found to bind in vivo to the eng1(+) promoter. Ace2 binding was coincident with maximum level of eng1(+) expression, whereas Fkh2 binding was maximal when mRNA levels were low, supporting the notion that they play opposing roles. In addition, we found that the expression of eng1(+) and agn1(+) was differentially affected by mutations in PBF components. Interestingly, agn1(+) was a major target of Mbx1, since its ectopic expression resulted in the suppression of Mbx1 deletion phenotypes. Our results reveal a complex regulation system through which the transcription factors Ace2, Fkh2, Sep1 and Mbx1 in combination control the expression of the genes involved in separation at the end of the cell division cycle.

Kinetics effects and modeling of mRNA turnover

Broader comprehension of gene expression regulatory mechanisms can be gained from a global analysis of how transcription and degradation are coordinated to orchestrate complex cell responses. The role of messenger RNA (mRNA) turnover modulation in gene expression levels has become increasingly recognized. From such perspective, in this review we briefly illustrate how a simple but effective mathematical model of mRNA turnover and some experimental findings, may together shed light on the molecular mechanisms underpinning the major role of mRNA decay rates in shaping the kinetics of gene activation and repression.

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.

Multiple MAPK cascades regulate the transcription of IME1, the master transcriptional activator of meiosis in Saccharomyces cerevisiae

The choice between alternative developmental pathways is primarily controlled at the level of transcription. Induction of meiosis in budding yeasts in response to nutrient levels provides a system to investigate the molecular basis of cellular decision-making. In Saccharomyces cerevisiae, entry into meiosis depends on multiple signals converging upon IME1, the master transcriptional activator of meiosis. Here we studied the regulation of the cis-acting regulatory element Upstream Activation Signal (UAS)ru, which resides within the IME1 promoter. Guided by our previous data acquired using a powerful high-throughput screening system, here we provide evidence that UASru is regulated by multiple stimuli that trigger distinct signal transduction pathways as follows: (i) The glucose signal inhibited UASru activity through the cyclic AMP (cAMP/protein kinase A (PKA) pathway, targeting the transcription factors (TFs), Com2 and Sko1; (ii) high osmolarity activated UASru through the Hog1/mitogen-activated protein kinase (MAPK) pathway and its corresponding TF Sko1; (iii) elevated temperature increased the activity of UASru through the cell wall integrity pathway and the TFs Swi4/Mpk1 and Swi4/Mlp1; (iv) the nitrogen source repressed UASru activity through Sum1; and (v) the absence of a nitrogen source was detected and transmitted to UASru by the Kss1 and Fus3 MAPK pathways through their respective downstream TFs, Ste12/Tec1 and Ste12/Ste12 as well as by their regulators Dig1/2. These signaling events were specific to UASru; they did not affect the mating and filamentation response elements that are regulated by MAPK pathways. The complex regulation of UASru through all the known vegetative MAPK pathways is unique to S. cerevisiae and is specific for IME1, likely because it is the master regulator of gametogenesis.

Identification of yeast cell cycle regulated genes based on genomic features

Background

Time-course microarray experiments have been widely used to identify cell cycle regulated genes. However, the method is not effective for lowly expressed genes and is sensitive to experimental conditions. To complement microarray experiments, we propose a computational method to predict cell cycle regulated genes based on their genomic features – transcription factor binding and motif profiles.

Results

Through integrating gene-expression data with ChIP-chip binding and putative binding sites of transcription factors, our method shows high accuracy in discriminating yeast cell cycle regulated genes from non-cell cycle regulated ones. We predict 211 novel cell cycle regulated genes. Our model rediscovers the main cell cycle transcription factors and provides new insights into the regulatory mechanisms. The model also reveals a regulatory circuit mediated by a number of key cell cycle regulators.

Conclusions

Our model suggests that the periodical pattern of cell cycle genes is largely coded in their promoter regions, which can be captured by motif and transcription factor binding data. Cell cycle is controlled by a relatively small number of master transcription factors. The concept of genomic feature based method can be readily extended to human cell cycle process and other transcriptionally regulated processes, such as tissue-specific expression.

Binding specificity of the G1/S transcriptional regulators in budding yeast

BACKGROUND

G1/S transcriptional regulation in the budding yeast Saccharomyces cerevisiae depends on three main transcriptional components, Swi4, Swi6 and Mbp1. These proteins constitute two transcription factor complexes that regulate over 300 G1/S transcripts, namely SBF (Swi4-Swi6) and MBF (Mbp1-Swi6). SBF and MBF are involved in regulating largely non-overlapping sets of G1/S genes via clearly distinct mechanisms.

METHODOLOGY/PRINCIPAL FINDINGS

Here we establish and confirm protein-protein and protein-DNA interactions using specific polyclonal antisera to whole Swi6 and to the C-terminal domains of related proteins Swi4 and Mbp1. Our data confirm the protein-protein binding specificity of Swi4 and Mbp1 to Swi6 but not to each other, and support the binding specificity of the transcriptional inhibitor Whi5 to SBF and of the corepressor Nrm1 to MBF. We also show the DNA binding preference of Swi4 to the CLN2 promoter and Mbp1 to the RNR1 promoter, while Swi6 binds both promoters. Finally, we establish the binding dynamics of Swi4 and Whi5 to the CLN2 promoter during the cell cycle.

CONCLUSIONS/SIGNIFICANCE

These data confirm the binding specificity of the G1/S transcriptional regulators. Whereas previous observations were made using tagged Swi4, Swi6 and Mbp1, here we use specific polyclonal antisera to reestablish the protein-protein and protein-DNA interactions of these G1/S transcriptional components. Our data also reveal the dynamic changes in promoter binding of Swi4 during the cell cycle, which suggests a possible positive feedback loop involving Swi4.

Repression of G1/S transcription is mediated via interaction of the GTB motifs of Nrm1 and Whi5 with Swi6

In Saccharomyces cerevisiae, G1/S transcription factors MBF and SBF regulate a large family of genes important for entry to the cell cycle and DNA replication and repair. Their regulation is crucial for cell viability, and it is conserved throughout evolution. MBF and SBF consist of a common component, Swi6, and a DNA-specific binding protein, Mbp1 and Swi4, respectively. Transcriptional repressors bind to and regulate the activity of both transcription factors. Whi5 binds to SBF and represses its activity at the beginning of the G1 phase to prevent early activation. Nrm1 binds to MBF to repress transcription as cells progress through S phase. Here, we describe a protein motif, the GTB motif (for G1/S transcription factor binding), in Nrm1 and Whi5 that is required to bind to the transcription factors. We also identify a region of the carboxy terminus of Swi6 that is required for Nrm1 and Whi5 binding to their target transcription factors and show that mutation of this region overrides the repression of MBF- and SBF-regulated genes by Nrm1 and Whi5. Finally, we show that the GTB motif is the core of a functional module that is necessary and sufficient for targeting of the transcription factors by their cognate repressors.

Detection and decomposition: treatment-induced cyclic gene expression disruption in high-throughput time-series datasets

Higher organisms possess many genes which cycle under normal conditions, to allow the organism to adapt to expected environmental conditions throughout the course of a day. However, treatment-induced disruption of regular cyclic gene expression patterns presents a significant challenge in novel gene discovery experiments because these disruptions can induce strong differential regulation events for genes that are not involved in an adaptive response to the treatment. To address this cycle disruption problem, we reviewed the state-of-art periodic pattern detection algorithms and a pattern decomposition algorithm (PRIISM), which is a knowledge-based Fourier analysis algorithm designed to distinguish the cyclic patterns from the rest gene expression patterns, and discussed potential future improvements.

Rho signaling participates in membrane fluidity homeostasis

Preservation of both the integrity and fluidity of biological membranes is a critical cellular homeostatic function. Signaling pathways that govern lipid bilayer fluidity have long been known in bacteria, yet no such pathways have been identified in eukaryotes. Here we identify mutants of the yeast Saccharomyces cerevisiae whose growth is differentially influenced by its two principal unsaturated fatty acids, oleic and palmitoleic acid. Strains deficient in the core components of the cell wall integrity (CWI) pathway, a MAP kinase pathway dependent on both Pkc1 (yeast's sole protein kinase C) and Rho1 (the yeast RhoA-like small GTPase), were among those inhibited by palmitoleate yet stimulated by oleate. A single GEF (Tus1) and a single GAP (Sac7) of Rho1 were also identified, neither of which participate in the CWI pathway. In contrast, key components of the CWI pathway, such as Rom2, Bem2 and Rlm1, failed to influence fatty acid sensitivity. The differential influence of palmitoleate and oleate on growth of key mutants correlated with changes in membrane fluidity measured by fluorescence anisotropy of TMA-DPH, a plasma membrane-bound dye. This work provides the first evidence for the existence of a signaling pathway that enables eukaryotic cells to control membrane fluidity, a requirement for division, differentiation and environmental adaptation.

BaSAR-A tool in R for frequency detection

Many biological processes are periodic, for example cell cycle expression, circadian rhythms and calcium oscillations. However, measured time series from these processes are commonly short and noisy, and finding frequencies in such data can be challenging. Here we present BaSAR, Bayesian Spectrum Analysis in R, a package for extracting frequency information from time series data. The software uses advanced techniques of Bayesian inference that are well suited for handling typical biological data. The core functions are designed for detecting a single key frequency, without the need for data pre-processing such as detrending. The package is freely available at CRAN – The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/BaSAR.

The checkpoint transcriptional response: make sure to turn it off once you are satisfied

The replication checkpoint signaling network monitors the presence of replication-induced lesions to DNA and coordinates an elaborate cellular response that includes ample transcriptional reprogramming. Recent work has established two major groups of replication stress-induced genes in Saccharomyces cerevisiae, the DNA damage response (DDR) genes and G 1/S cell cycle (CC) genes. In both cases, transcriptional activation is mediated via checkpoint-dependent inhibition of a transcriptional repressor (Crt1 for DDR and Nrm1 for CC) that participates in negative feedback regulation. This repressor-mediated regulation enables transcription to be rapidly repressed once cells have dealt with the replication stress. The recent finding of a new class of CC genes, named "switch genes," further uncovers a mode of transcription regulation that prevents overexpression of replication stress induced genes during G 1. Collectively, these findings highlight the need for mechanisms that tightly control replication stress-induced transcription, allowing rapid transcriptional activation during replication stress but also avoiding long-term hyperaccumulation of the induced protein product that may be detrimental to cell proliferation.

Driving the cell cycle through metabolism

For unicellular organisms, the decision to enter the cell cycle can be viewed most fundamentally as a metabolic problem. A cell must assess its nutritional and metabolic status to ensure it can synthesize sufficient biomass to produce a new daughter cell. The cell must then direct the appropriate metabolic outputs to ensure completion of the division process. Herein, we discuss the changes in metabolism that accompany entry to, and exit from, the cell cycle for the unicellular eukaryote Saccharomyces cerevisiae. Studies of budding yeast under continuous, slow-growth conditions have provided insights into the essence of these metabolic changes at unprecedented temporal resolution. Some of these mechanisms by which cell growth and proliferation are coordinated with metabolism are likely to be conserved in multicellular organisms. An improved understanding of the metabolic basis of cell cycle control promises to reveal fundamental principles governing tumorigenesis, metazoan development, niche expansion, and many additional aspects of cell and organismal growth control.

The MuvB complex sequentially recruits B-Myb and FoxM1 to promote mitotic gene expression

Cell cycle progression is dependent on two major waves of gene expression. Early cell cycle gene expression occurs during G1/S to generate factors required for DNA replication, while late cell cycle gene expression begins during G2 to prepare for mitosis. Here we demonstrate that the MuvB complex-comprised of LIN9, LIN37, LIN52, LIN54, and RBBP4-serves an essential role in three distinct transcription complexes to regulate cell cycle gene expression. The MuvB complex, together with the Rb-like protein p130, E2F4, and DP1, forms the DREAM complex during quiescence and represses expression of both early and late genes. Upon cell cycle entry, the MuvB complex dissociates from p130/DREAM, binds to B-Myb, and reassociates with the promoters of late genes during S phase. MuvB and B-Myb are required for the subsequent recruitment of FoxM1 to late gene promoters during G2. The MuvB complex remains bound to FoxM1 during peak late cell cycle gene expression, while B-Myb binding is lost when it undergoes phosphorylation-dependent, proteasome-mediated degradation during late S phase. Our results reveal a novel role for the MuvB complex in recruiting B-Myb and FoxM1 to promote late cell cycle gene expression and in regulating cell cycle gene expression from quiescence through mitosis.

Regulation of cell wall biogenesis in Saccharomyces cerevisiae: the cell wall integrity signaling pathway

The yeast cell wall is a strong, but elastic, structure that is essential not only for the maintenance of cell shape and integrity, but also for progression through the cell cycle. During growth and morphogenesis, and in response to environmental challenges, the cell wall is remodeled in a highly regulated and polarized manner, a process that is principally under the control of the cell wall integrity (CWI) signaling pathway. This pathway transmits wall stress signals from the cell surface to the Rho1 GTPase, which mobilizes a physiologic response through a variety of effectors. Activation of CWI signaling regulates the production of various carbohydrate polymers of the cell wall, as well as their polarized delivery to the site of cell wall remodeling. This review article centers on CWI signaling in Saccharomyces cerevisiae through the cell cycle and in response to cell wall stress. The interface of this signaling pathway with other pathways that contribute to the maintenance of cell wall integrity is also discussed.

Linking DNA replication checkpoint to MBF cell-cycle transcription reveals a distinct class of G1/S genes

Reprogramming gene expression is crucial for DNA replication stress response. We used quantitative proteomics to establish how the transcriptional response results in changes in protein levels. We found that expression of G1/S cell-cycle targets are strongly up-regulated upon replication stress, and show that MBF, but not SBF genes, are up-regulated via Rad53-dependent inactivation of the MBF co-repressor Nrm1. A subset of G1/S genes was found to undergo an SBF-to-MBF switch at the G1/S transition, enabling replication stress-induced transcription of genes targeted by SBF during G1. This subset of G1/S genes is characterized by an overlapping Swi4/Mbp1-binding site and is enriched for genes that cause a cell cycle and/or growth defect when overexpressed. Analysis of the prototypical switch gene TOS4 (Target Of SBF 4) reveals its role as a checkpoint effector supporting the importance of this distinct class of G1/S genes for the DNA replication checkpoint response. Our results reveal that replication stress induces expression of G1/S genes via the Rad53-MBF pathway and that an SBF-to-MBF switch characterizes a new class of genes that can be induced by replication stress.

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

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

How to cluster gene expression dynamics in response to environmental signals

Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.

Predicting housekeeping genes based on Fourier analysis

Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs.

Mpk1 MAPK association with the Paf1 complex blocks Sen1-mediated premature transcription termination

The Mpk1 MAPK of the yeast cell wall integrity pathway uses a noncatalytic mechanism to activate transcription of stress-induced genes by recruitment of initiation factors to target promoters. We show here that Mpk1 additionally serves a function in transcription elongation that is also independent of its catalytic activity. This function is mediated by an interaction between Mpk1 and the Paf1 subunit of the Paf1C elongation complex. A mutation in Paf1 that blocks this interaction causes a specific defect in transcription elongation of an Mpk1-induced gene, which results from Sen1-dependent premature termination through a Nab3-binding site within the promoter-proximal region of the gene. Our findings reveal a regulatory mechanism in which Mpk1 overcomes transcriptional attenuation by blocking recruitment of the Sen1-Nrd1-Nab3 termination complex to the elongating polymerase. Finally, we demonstrate that this mechanism is conserved in an interaction between the human ERK5 MAPK and human Paf1.

Changes in selective effects over time facilitate turnover of enhancer sequences

Correct gene expression is often critical and consequently stabilizing selection on expression is widespread. Yet few genes possess highly conserved regulatory DNA, and for the few enhancers that have been carefully characterized, substantial functional reorganization has often occurred. Given that natural selection removes mutations of even very small deleterious effect, how can transcription factor binding evolve so readily when it underlies a conserved phenotype? As a first step toward addressing this question, I combine a computational model for regulatory function that incorporates many aspects of our present biological knowledge with a model for the fitness effects of misexpression. I then use this model to study the evolution of enhancers. Several robust behaviors emerge: First, the selective effects of mutations at a site change dramatically over time due to substitutions elsewhere in the enhancer, and even the overall degree of constraint across the enhancer can change considerably. Second, many of the substitutions responsible for changes in binding occur at sites where previously the mutation would have been strongly deleterious, suggesting that fluctuations in selective effects at a site are important for functional turnover. Third, most substitutions contributing to the repatterning of binding and constraint are effectively neutral, highlighting the importance of genetic drift-even for enhancers underlying conserved phenotypes. These findings have important implications for phylogenetic inference of function and for interpretations of selection coefficients estimated for regulatory DNA.

Genetic alterations leading to increases in internal potassium concentrations are detrimental for DNA integrity in Saccharomyces cerevisiae

We have investigated the effects of alterations in potassium homeostasis on cell cycle progression and genome stability in Saccharomyces cerevisiae. Yeast strains lacking the PPZ1 and PPZ2 phosphatase genes, which aberrantly accumulate potassium, are sensitive to agents causing replicative stress or DNA damage and present a cell cycle delay in the G(1) /S phase. A synthetic slow growth phenotype was identified in a subset of DNA repair mutants upon inhibition of Ppz activity. Moreover, we observe that this slow growth phenotype observed in cdc7(ts) mutants with reduced Ppz activity is reverted by disrupting the TRK1 potassium transporter gene. As over-expression of a mammalian potassium transporter leads to similar phenotypes, we conclude that these defects can be attributed to potassium accumulation. As we reported previously, internal potassium accumulation activates the Slt2 MAP kinase pathway. We show that the removal of SLT2 in ppz1 ppz2 mutants ameliorates sensitivity to agents causing replication stress and DNA damage, whereas over-activation of the pathway leads to similar cell cycle-related defects. Taken together, these results are consistent with inappropriate potassium accumulation reducing DNA replication efficiency, negatively influencing DNA integrity and leading to the requirement of mismatch repair, the MRX complex, or homologous recombination pathways for normal growth.

Cell cycle sensing of oxidative stress in Saccharomyces cerevisiae by oxidation of a specific cysteine residue in the transcription factor Swi6p

Yeast cells begin to bud and enter the S phase when growth conditions are favorable during the G(1) phase. When subjected to some oxidative stresses, cells delay entry at G(1), allowing repair of cellular damage. Hence, oxidative stress sensing is coordinated with the regulation of cell cycle. We identified a novel function of the cell cycle regulator of Saccharomyces cerevisiae, Swi6p, as a redox sensor through its cysteine residue at position 404. When alanine was substituted at this position, the resultant mutant, C404A, was sensitive to several reactive oxygen species and oxidants including linoleic acid hydroperoxide, the superoxide anion, and diamide. This mutant lost the ability to arrest in G(1) phase upon treatment with lipid hydroperoxide. The Cys-404 residue of Swi6p in wild-type cells was oxidized to a sulfenic acid when cells were subjected to linoleic acid hydroperoxide. Mutation of Cys-404 to Ala abolished the down-regulation of expression of the G(1) cyclin genes CLN1, CLN2, PCL1, and PCL2 that occurred when cells of the wild type were exposed to the lipid hydroperoxide. In conclusion, oxidative stress signaling for cell cycle regulation occurs through oxidation of the G(1)/S-specific transcription factor Swi6p and consequently leads to suppression of the expression of G(1) cyclins and a delay in cells entering the cell cycle.

A novel function of the mitochondrial transcription factor Mtf1 in fission yeast; Mtf1 regulates the nuclear transcription of srk1

In eukaryotic cells, Mtf1 and its homologues function as mitochondrial transcription factors for the mitochondrial RNA polymerase in the mitochondrion. Here we show that in fission yeast Mtf1 exerts a non-mitochondrial function as a nuclear factor that regulates transcription of srk1, which is a kinase involved in the stress response and cell cycle progression. We first found Mtf1 expression in the nucleus. A ChIP-chip approach identified srk1 as a putative Mtf1 target gene. Over expression of Mtf1 induced transcription of the srk1 gene and Mtf1 deletion led to a reduction in transcription of the srk1 gene in vivo. Mtf1 overexpression causes cell elongation in a srk1 dependent manner. Mtf1 overexpression can cause cytoplasmic accumulation of Cdc25. We also provide biochemical evidence that Mtf1 binds to the upstream sequence of srk1. This is the first evidence that a mitochondrial transcription factor Mtf1 can regulate a nuclear gene. Mtf1 may also have a role in cell cycle progression.

Spt10 and Swi4 control the timing of histone H2A/H2B gene activation in budding yeast

The expression of the histone genes is regulated during the cell cycle to provide histones for nucleosome assembly during DNA replication. In budding yeast, histones H2A and H2B are expressed from divergent promoters at the HTA1-HTB1 and HTA2-HTB2 loci. Here, we show that the major activator of HTA1-HTB1 is Spt10, a sequence-specific DNA binding protein with a putative histone acetyltransferase (HAT) domain. Spt10 binds to two pairs of upstream activation sequence (UAS) elements in the HTA1-HTB1 promoter: UAS1 and UAS2 drive HTA1 expression, and UAS3 and UAS4 drive HTB1 expression. UAS3 and UAS4 also contain binding sites for the cell cycle regulator SBF (an Swi4-Swi6 heterodimer), which overlap the Spt10 binding sites. The binding of Spt10 and binding of SBF to UAS3 and UAS4 are mutually exclusive in vitro. Both SBF and Spt10 are bound in cells arrested with α-factor, apparently awaiting a signal to activate transcription. Soon after the removal of α-factor, SBF initiates a small, early peak of HTA1 and HTB1 transcription, which is followed by a much larger peak due to Spt10. Both activators dissociate from the HTA1-HTB1 promoter after expression has been activated. Thus, SBF and Spt10 cooperate to control the timing of HTA1-HTB1 expression.

The cell wall integrity checkpoint: coordination between cell wall synthesis and the cell cycle

The cell wall is an essential cellular component for the survival of fungi. The cell wall of the budding yeast Saccharomyces cerevisiae undergoes remodelling during the cell cycle. Evidence is accumulating that there are regulatory mechanisms that link cell wall remodelling and cell cycle progression. Here, we review one such mechanism, known as the 'cell wall integrity checkpoint', which functions to control cell cycle progression in response to cell wall perturbation.