Transcriptional regulatory networks and the yeast cell cycle

Exportin Crm1 is important for Swi6 nuclear shuttling and MBF transcription activation in Saccharomyces cerevisiae

BACKGROUND

Swi6 acts as a transcription factor in budding yeast, functioning in two different heterodimeric complexes, SBF and MBF, that activate the expression of distinct but overlapping sets of genes. Swi6 undergoes regulated changes in nucleocytoplasmic localization throughout the cell cycle that correlate with changes in gene expression. This study investigates how nucleocytoplasmic transport by multiple transport factors may influence specific Swi6 activities.

RESULTS

Here we show that the exportin Crm1 is important for Swi6 nuclear export and activity. Loss of a putative Crm1 NES or inhibition of Crm1 activity results in changes in nucleocytoplasmic Swi6 localization. Alteration of the Crm1 NES in Swi6 results in decreased MBF-mediated gene expression, but does not affect SBF reporter expression, suggesting that export of Swi6 by Crm1 regulates a subset of Swi6 transcription activation activity. Finally, alteration of the putative Crm1 NES in Swi6 results in cells that are larger than wild type, and this increase in cell size is exacerbated by deletion of Msn5.

CONCLUSIONS

These data provide evidence that Swi6 has at least two different exportins, Crm1 and Msn5, each of which interacts with a distinct nuclear export signal. We identify a putative nuclear export signal for Crm1 within Swi6, and observe that export by Crm1 or Msn5 independently influences Swi6-regulated expression of a different subset of Swi6-controlled genes. These findings provide new insights into the complex regulation of Swi6 transcription activation activity and the role of nucleocytoplasmic shuttling in regulated gene expression.

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.

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.

Transcriptional control of fungal cell cycle and cellular events by Fkh2, a forkhead transcription factor in an insect pathogen

Transcriptional control of the cell cycle by forkhead (Fkh) transcription factors is likely associated with fungal adaptation to host and environment. Here we show that Fkh2, an ortholog of yeast Fkh1/2, orchestrates cell cycle and many cellular events of Beauveria bassiana, a filamentous fungal insect pathogen. Deletion of Fkh2 in B. bassiana resulted in dramatic down-regulation of the cyclin-B gene cluster and hence altered cell cycle (longer G₂/M and S, but shorter G₀/G₁, phases) in unicellular blastospores. Consequently, ΔFkh2 produced twice as many, but smaller, blastospores than wild-type under submerged conditions, and formed denser septa and shorter/broader cells in aberrantly branched hyphae. In these hyphae, clustered genes required for septation and conidiation were remarkedly up-regulated, followed by higher yield and slower germination of aerial conidia. Moreover, ΔFkh2 displayed attenuated virulence and decreased tolerance to chemical and environmental stresses, accompanied with altered transcripts and activities of phenotype-influencing proteins or enzymes. All the changes in ΔFkh2 were restored by Fkh2 complementation. All together, Fkh2-dependent transcriptional control is vital for the adaptation of B. bassiana to diverse habitats of host insects and hence contributes to its biological control potential against arthropod pests.

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

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

Translate to divide: сontrol of the cell cycle by protein synthesis

Protein synthesis underpins much of cell growth and, consequently, cell multiplication. Understanding how proliferating cells commit and progress into the cell cycle requires knowing not only which proteins need to be synthesized, but also what determines their rate of synthesis during cell division.

Identifying cooperative transcription factors in yeast using multiple data sources

BACKGROUND

Transcriptional regulation of gene expression is usually accomplished by multiple interactive transcription factors (TFs). Therefore, it is crucial to understand the precise cooperative interactions among TFs. Various kinds of experimental data including ChIP-chip, TF binding site (TFBS), gene expression, TF knockout and protein-protein interaction data have been used to identify cooperative TF pairs in existing methods. The nucleosome occupancy data is not yet used for this research topic despite that several researches have revealed the association between nucleosomes and TFBSs.

RESULTS

In this study, we developed a novel method to infer the cooperativity between two TFs by integrating the TF-gene documented regulation, TFBS and nucleosome occupancy data. TF-gene documented regulation and TFBS data were used to determine the target genes of a TF, and the genome-wide nucleosome occupancy data was used to assess the nucleosome occupancy on TFBSs. Our method identifies cooperative TF pairs based on two biologically plausible assumptions. If two TFs cooperate, then (i) they should have a significantly higher number of common target genes than random expectation and (ii) their binding sites (in the promoters of their common target genes) should tend to be co-depleted of nucleosomes in order to make these binding sites simultaneously accessible to TF binding. Each TF pair is given a cooperativity score by our method. The higher the score is, the more likely a TF pair has cooperativity. Finally, a list of 27 cooperative TF pairs has been predicted by our method. Among these 27 TF pairs, 19 pairs are also predicted by existing methods. The other 8 pairs are novel cooperative TF pairs predicted by our method. The biological relevance of these 8 novel cooperative TF pairs is justified by the existence of protein-protein interactions and co-annotation in the same MIPS functional categories. Moreover, we adopted three performance indices to compare our predictions with 11 existing methods' predictions. We show that our method performs better than these 11 existing methods in identifying cooperative TF pairs in yeast. Finally, the cooperative TF network constructed from the 27 predicted cooperative TF pairs shows that our method has the power to find cooperative TF pairs of different biological processes.

CONCLUSION

Our method is effective in identifying cooperative TF pairs in yeast. Many of our predictions are validated by the literature, and our method outperforms 11 existing methods. We believe that our study will help biologists to understand the mechanisms of transcriptional regulation in eukaryotic cells.

Protein acetylation and acetyl coenzyme a metabolism in budding yeast

Cells sense and appropriately respond to the physical conditions and availability of nutrients in their environment. This sensing of the environment and consequent cellular responses are orchestrated by a multitude of signaling pathways and typically involve changes in transcription and metabolism. Recent discoveries suggest that the signaling and transcription machineries are regulated by signals which are derived from metabolism and reflect the metabolic state of the cell. Acetyl coenzyme A (CoA) is a key metabolite that links metabolism with signaling, chromatin structure, and transcription. Acetyl-CoA is produced by glycolysis as well as other catabolic pathways and used as a substrate for the citric acid cycle and as a precursor in synthesis of fatty acids and steroids and in other anabolic pathways. This central position in metabolism endows acetyl-CoA with an important regulatory role. Acetyl-CoA serves as a substrate for lysine acetyltransferases (KATs), which catalyze the transfer of acetyl groups to the epsilon-amino groups of lysines in histones and many other proteins. Fluctuations in the concentration of acetyl-CoA, reflecting the metabolic state of the cell, are translated into dynamic protein acetylations that regulate a variety of cell functions, including transcription, replication, DNA repair, cell cycle progression, and aging. This review highlights the synthesis and homeostasis of acetyl-CoA and the regulation of transcriptional and signaling machineries in yeast by acetylation.

Cell cycle: proteomics gives it a spin

The eukaryotic cell division cycle has been studied at the molecular level for over 30 years, most fruitfully in model organisms. In the past 5 years, developments in mass spectrometry-based proteomics have been applied to the study of protein interactions and post-translational modifications involving key cell cycle regulators such as cyclin-dependent kinases and the anaphase-promoting complex, as well as effectors such as centrosomes, the kinetochore and DNA replication forks. In addition, innovations in chemical biology, functional proteomics and bioinformatics have been employed to study the cell cycle at the proteome level. This review surveys the contributions of proteomics to cell cycle research. The near future should see the application of more quantitative proteomic approaches to probe the dynamic aspects of the molecular system that underlie the cell cycle in model organisms and in human cells.

Aneuploid yeast strains exhibit defects in cell growth and passage through START

Aneuploidy causes cell proliferation defects in budding yeast, with many aneuploid strains exhibiting a G1 delay. This study shows that the G1 delay in aneuploid budding yeast is caused by a growth defect and delayed passage through START due to delayed G1 cyclin accumulation.

Aneuploidy, a chromosome content that is not a multiple of the haploid karyotype, is associated with reduced fitness in all organisms analyzed to date. In budding yeast aneuploidy causes cell proliferation defects, with many different aneuploid strains exhibiting a delay in G1, a cell cycle stage governed by extracellular cues, growth rate, and cell cycle events. Here we characterize this G1 delay. We show that 10 of 14 aneuploid yeast strains exhibit a growth defect during G1. Furthermore, 10 of 14 aneuploid strains display a cell cycle entry delay that correlates with the size of the additional chromosome. This cell cycle entry delay is due to a delayed accumulation of G1 cyclins that can be suppressed by supplying cells with high levels of a G1 cyclin. Our results indicate that aneuploidy frequently interferes with the ability of cells to grow and, as with many other cellular stresses, entry into the cell cycle.

LEARNING GENE NETWORK USING TIME-DELAYED BAYESIAN NETWORK

Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Most research work in learning gene networks either assumes that there is no time delay in gene expression or that there is a constant time delay. This paper shows how Bayesian Networks can be applied to represent multi-time delay relationships as well as directed loops. The intractability of the network learning algorithm is handled by using an improved mutual information criterion. Also, a new structure learning algorithm, "Learning By Modification", is proposed to learn the sparse structure of a gene network. The experimental results on synthetic data and real data show that our method is more accurate in determining the gene structure as compared to the traditional methods. Even transcriptional loops spanning over the whole cell can be detected by our algorithm.

Protein phosphatase 2A (B55α) prevents premature activation of forkhead transcription factor FoxM1 by antagonizing cyclin A/cyclin-dependent kinase-mediated phosphorylation

The forkhead transcription factor FoxM1 controls expression of a large number of genes that are specifically expressed during the G(2) phase of the cell cycle. Throughout most of the cell cycle, FoxM1 activity is restrained by an autoinhibitory mechanism, involving a repressor domain present in the N-terminal part of the protein. Activation of FoxM1 in G(2) is achieved by Cyclin A/Cyclin-dependent kinase (Cdk)-mediated phosphorylation, which alleviates autoinhibition by the N-terminal repressor domain. Here, we show that FoxM1 interacts with B55α, a regulatory subunit of protein phosphatase 2A (PP2A). B55α binds the catalytic subunit of PP2A, and this promotes dephosphorylation and inactivation of FoxM1. Indeed, we find that overexpression of B55α results in decreased FoxM1 activity. Inversely, depletion of B55α results in premature activation of FoxM1. The activation of FoxM1 that is observed upon depletion of B55α is fully dependent on Cyclin A/Cdk-mediated phosphorylation of FoxM1. Taken together, these data demonstrate that B55α acts to antagonize Cyclin A/Cdk-dependent activation of FoxM1, to ensure that FoxM1 activity is restricted to the G(2) phase of the cell cycle.

A Markov-blanket-based model for gene regulatory network inference

An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.

Synchronization of yeast

The budding yeast Saccharomyces cerevisiae and fission yeast Schizosaccharomyces pombe are amongst the simplest and most powerful model systems for studying the genetics of cell cycle control. Because yeast grows very rapidly in simple and economical media, large numbers of cells can easily be obtained for genetic, molecular, and biochemical studies of the cell cycle. The use of synchronized cultures greatly aids in the ease and interpretation of cell cycle studies. In principle, there are two general methods for obtaining synchronized yeast populations. Block and release methods can be used to induce cell cycle synchrony. Alternatively, centrifugal elutriation can be used to select synchronous populations. Because each method has innate advantages and disadvantages, the use of multiple approaches helps in generalizing results. An overview of the most commonly used methods to generate synchronized yeast cultures is presented along with working Notes, a section that includes practical comments, experimental considerations and observations, and hints regarding the pros and cons innate to each approach.

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.

The wavelet-based cluster analysis for temporal gene expression data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.

Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model

RESULTS

We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression data. The network is specified by a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood. We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation.

AVAILABILITY

Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip

Gene regulatory networks modelling using a dynamic evolutionary hybrid

Background

Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.

Results

The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes.

Conclusions

The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks.

The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/.

Evolutionary tinkering with conserved components of a transcriptional regulatory network

A surprising level of evolutionary plasticity is revealed by analysis of differences between related yeasts in the mechanisms regulating the essential cellular process of ribosomal gene expression.

Gene expression variation between species is a major contributor to phenotypic diversity, yet the underlying flexibility of transcriptional regulatory networks remains largely unexplored. Transcription of the ribosomal regulon is a critical task for all cells; in S. cerevisiae the transcription factors Rap1, Fhl1, Ifh1, and Hmo1 form a multi-subunit complex that controls ribosomal gene expression, while in C. albicans this regulation is under the control of Tbf1 and Cbf1. Here, we analyzed, using full-genome transcription factor mapping, the roles, in both S. cerevisiae and C. albicans, of each orthologous component of this complete set of regulators. We observe dramatic changes in the binding profiles of the generalist regulators Cbf1, Hmo1, Rap1, and Tbf1, while the Fhl1-Ifh1 dimer is the only component involved in ribosomal regulation in both fungi: it activates ribosomal protein genes and rDNA expression in a Tbf1-dependent manner in C. albicans and a Rap1-dependent manner in S. cerevisiae. We show that the transcriptional regulatory network governing the ribosomal expression program of two related yeast species has been massively reshaped in cis and trans. Changes occurred in transcription factor wiring with cellular functions, movements in transcription factor hierarchies, DNA-binding specificity, and regulatory complexes assembly to promote global changes in the architecture of the fungal transcriptional regulatory network.

Conserved metabolic machineries direct energy production and investment in most life forms. However, variation in the transcriptional regulation of the genes that encode this machinery has been observed and shown to contribute to phenotypic differences between species. Here, we show that the regulatory circuits governing the expression of central metabolic components (in this case the ribosomes) in different yeast species have an unexpected level of evolutionary plasticity. Most transcription factors involved in the regulation of expression of ribosomal genes have in fact been reused in new ways during the evolutionary time separating S. cerevisiae and C. albicans to generate global changes in transcriptional network structures and new ribosomal regulatory complexes.

Towards a dynamical network view of brain ischemia and reperfusion. Part I: background and preliminaries

The general failure of neuroprotectants in clinical trials of ischemic stroke points to the possibility of a fundamental blind spot in the current conception of ischemic brain injury, the "ischemic cascade". This is the first in a series of four papers whose purpose is to work towards a revision of the concept of brain ischemia by applying network concepts to develop a bistable model of brain ischemia. This first paper sets the stage for developing the bistable model of brain ischemia. Necessary background in network theory is introduced using examples from developmental biology which, perhaps surprisingly, can be adapted to brain ischemia with only minor modification. Then, to move towards a network model, we extract two core generalizations about brain ischemia from the mass of empirical data. First, we conclude that all changes induced in the brain by ischemia can be classified as either damage mechanisms that contribute to cell death, or stress responses that contribute to cell survival. Second, we move towards formalizing the idea of the "amount of ischemia", I, as a continuous, nonnegative, monotonically increasing quantity. These two generalizations are necessary precursors to reformulating brain ischemia as a bistable network.

The Rts1 regulatory subunit of protein phosphatase 2A is required for control of G1 cyclin transcription and nutrient modulation of cell size

The key molecular event that marks entry into the cell cycle is transcription of G1 cyclins, which bind and activate cyclin-dependent kinases. In yeast cells, initiation of G1 cyclin transcription is linked to achievement of a critical cell size, which contributes to cell-size homeostasis. The critical cell size is modulated by nutrients, such that cells growing in poor nutrients are smaller than cells growing in rich nutrients. Nutrient modulation of cell size does not work through known critical regulators of G1 cyclin transcription and is therefore thought to work through a distinct pathway. Here, we report that Rts1, a highly conserved regulatory subunit of protein phosphatase 2A (PP2A), is required for normal control of G1 cyclin transcription. Loss of Rts1 caused delayed initiation of bud growth and delayed and reduced accumulation of G1 cyclins. Expression of the G1 cyclin CLN2 from an inducible promoter rescued the delayed bud growth in rts1Delta cells, indicating that Rts1 acts at the level of transcription. Moreover, loss of Rts1 caused altered regulation of Swi6, a key component of the SBF transcription factor that controls G1 cyclin transcription. Epistasis analysis revealed that Rts1 does not work solely through several known critical upstream regulators of G1 cyclin transcription. Cells lacking Rts1 failed to undergo nutrient modulation of cell size. Together, these observations demonstrate that Rts1 is a key player in pathways that link nutrient availability, cell size, and G1 cyclin transcription. Since Rts1 is highly conserved, it may function in similar pathways in vertebrates.

Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle

Background

Fission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast.

Results

By integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor SPBC19G7.04. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved cis-regulatory motifs. In particular, we show that SPBC19G7.04 has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor fkh2. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1) which constitute regulatory modules from different phases of the cell cycle, 2) whose phase order is coherent across the 10 time course experiments, and 3) which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in S. pombe. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs.

Conclusion

Using a systems-level analysis of the phase-specific nature of the S. pombe cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle. Based on this comprehensive gene regulatory network, we demonstrated how one can generate and investigate plausible hypotheses on fission yeast cell cycle regulation which can potentially be explored experimentally.

Genome-wide expression profiling of in vivo-derived bloodstream parasite stages and dynamic analysis of mRNA alterations during synchronous differentiation in Trypanosoma brucei

Background

Trypanosomes undergo extensive developmental changes during their complex life cycle. Crucial among these is the transition between slender and stumpy bloodstream forms and, thereafter, the differentiation from stumpy to tsetse-midgut procyclic forms. These developmental events are highly regulated, temporally reproducible and accompanied by expression changes mediated almost exclusively at the post-transcriptional level.

Results

In this study we have examined, by whole-genome microarray analysis, the mRNA abundance of genes in slender and stumpy forms of T.brucei AnTat1.1 cells, and also during their synchronous differentiation to procyclic forms. In total, five biological replicates representing the differentiation of matched parasite populations derived from five individual mouse infections were assayed, with RNAs being derived at key biological time points during the time course of their synchronous differentiation to procyclic forms. Importantly, the biological context of these mRNA profiles was established by assaying the coincident cellular events in each population (surface antigen exchange, morphological restructuring, cell cycle re-entry), thereby linking the observed gene expression changes to the well-established framework of trypanosome differentiation.

Conclusion

Using stringent statistical analysis and validation of the derived profiles against experimentally-predicted gene expression and phenotypic changes, we have established the profile of regulated gene expression during these important life-cycle transitions. The highly synchronous nature of differentiation between stumpy and procyclic forms also means that these studies of mRNA profiles are directly relevant to the changes in mRNA abundance within individual cells during this well-characterised developmental transition.

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

BACKGROUND

One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.

RESULTS

Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences.

CONCLUSION

The high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well.

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.

DYRK1A interacts with the REST/NRSF-SWI/SNF chromatin remodelling complex to deregulate gene clusters involved in the neuronal phenotypic traits of Down syndrome

The molecular mechanisms that lead to the cognitive defects characteristic of Down syndrome (DS), the most frequent cause of mental retardation, have remained elusive. Here we use a transgenic DS mouse model (152F7 line) to show that DYRK1A gene dosage imbalance deregulates chromosomal clusters of genes located near neuron-restrictive silencer factor (REST/NRSF) binding sites. We found that Dyrk1a binds the SWI/SNF complex known to interact with REST/NRSF. The mutation of a REST/NRSF binding site in the promoter of the REST/NRSF target gene L1cam modifies the transcriptional effect of Dyrk1a-dosage imbalance on L1cam. Dyrk1a dosage imbalance perturbs Rest/Nrsf levels with decreased Rest/Nrsf expression in embryonic neurons and increased expression in adult neurons. Using transcriptome analysis of embryonic brain subregions of transgenic 152F7 mouse line, we identified a coordinated deregulation of multiple genes that are responsible for dendritic growth impairment present in DS. Similarly, Dyrk1a overexpression in primary mouse cortical neurons induced severe reduction of the dendritic growth and dendritic complexity. We propose that DYRK1A overexpression-related neuronal gene deregulation via disturbance of REST/NRSF levels, and the REST/NRSF-SWI/SNF chromatin remodelling complex, significantly contributes to the neural phenotypic changes that characterize DS.

Construction and application of a protein and genetic interaction network (yeast interactome)

Cytoscape is a bioinformatic data analysis and visualization platform that is well-suited to the analysis of gene expression data. To facilitate the analysis of yeast microarray data using Cytoscape, we constructed an interaction network (interactome) using the curated interaction data available from the Saccharomyces Genome Database (www.yeastgenome.org) and the database of yeast transcription factors at YEASTRACT (www.yeastract.com). These data were formatted and imported into Cytoscape using semi-automated methods, including Linux-based scripts, that simplified the process while minimizing the introduction of processing errors. The methods described for the construction of this yeast interactome are generally applicable to the construction of any interactome. Using Cytoscape, we illustrate the use of this interactome through the analysis of expression data from a recent yeast diauxic shift experiment. We also report and briefly describe the complex associations among transcription factors that result in the regulation of thousands of genes through coordinated changes in expression of dozens of transcription factors. These cells are thus able to sensitively regulate cellular metabolism in response to changes in genetic or environmental conditions through relatively small changes in the expression of large numbers of genes, affecting the entire yeast metabolome.

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.

Genome-wide expression profiling, in vivo DNA binding analysis, and probabilistic motif prediction reveal novel Abf1 target genes during fermentation, respiration, and sporulation in yeast

The autonomously replicating sequence binding factor 1 (Abf1) was initially identified as an essential DNA replication factor and later shown to be a component of the regulatory network controlling mitotic and meiotic cell cycle progression in budding yeast. The protein is thought to exert its functions via specific interaction with its target site as part of distinct protein complexes, but its roles during mitotic growth and meiotic development are only partially understood. Here, we report a comprehensive approach aiming at the identification of direct Abf1-target genes expressed during fermentation, respiration, and sporulation. Computational prediction of the protein's target sites was integrated with a genome-wide DNA binding assay in growing and sporulating cells. The resulting data were combined with the output of expression profiling studies using wild-type versus temperature-sensitive alleles. This work identified 434 protein-coding loci as being transcriptionally dependent on Abf1. More than 60% of their putative promoter regions contained a computationally predicted Abf1 binding site and/or were bound by Abf1 in vivo, identifying them as direct targets. The present study revealed numerous loci previously unknown to be under Abf1 control, and it yielded evidence for the protein's variable DNA binding pattern during mitotic growth and meiotic development.

Comprehensive gene expression atlas for the Arabidopsis MAP kinase signalling pathways

* Mitogen activated protein kinase (MAPK) pathways are signal transduction modules with layers of protein kinases having c. 120 genes in Arabidopsis, but only a few have been linked experimentally to functions. * We analysed microarray expression data for 114 MAPK signalling genes represented on the ATH1 Affymetrix arrays; determined their expression patterns during development, and in a wide range of time-course microarray experiments for their signal-dependent transcriptional regulation and their coregulation with other signalling components and transcription factors. * Global expression correlation of the MAPK genes with each of the represented 21 692 Arabidopsis genes was determined by calculating Pearson correlation coefficients. To group MAPK signalling genes based on similarities in global regulation, we performed hierarchical clustering on the pairwise correlation values. This should allow inferring functional information from well-studied MAPK components to functionally uncharacterized ones. Statistical overrepresentation of specific gene ontology (GO) categories in the gene lists showing high expression correlation values with each of the MAPK components predicted biological themes for the gene functions. * The combination of these methods provides functional information for many uncharacterized MAPK genes, and a framework for complementary future experimental dissection of the function of this complex family.

A systematic approach to detecting transcription factors in response to environmental stresses

Background

Eukaryotic cells have developed mechanisms to respond to external environmental or physiological changes (stresses). In order to increase the activities of stress-protection functions in response to an environmental change, the internal cell mechanisms need to induce certain specific gene expression patterns and pathways by changing the expression levels of specific transcription factors (TFs). The conventional methods to find these specific TFs and their interactivities are slow and laborious. In this study, a novel efficient method is proposed to detect the TFs and their interactivities that regulate yeast genes that respond to any specific environment change.

Results

For each gene expressed in a specific environmental condition, a dynamic regulatory model is constructed in which the coefficients of the model represent the transcriptional activities and interactivities of the corresponding TFs. The proposed method requires only microarray data and information of all TFs that bind to the gene but it has superior resolution than the current methods. Our method not only can find stress-specific TFs but also can predict their regulatory strengths and interactivities. Moreover, TFs can be ranked, so that we can identify the major TFs to a stress. Similarly, it can rank the interactions between TFs and identify the major cooperative TF pairs. In addition, the cross-talks and interactivities among different stress-induced pathways are specified by the proposed scheme to gain much insight into protective mechanisms of yeast under different environmental stresses.

Conclusion

In this study, we find significant stress-specific and cell cycle-controlled TFs via constructing a transcriptional dynamic model to regulate the expression profiles of genes under different environmental conditions through microarray data. We have applied this TF activity and interactivity detection method to many stress conditions, including hyper- and hypo- osmotic shock, heat shock, hydrogen peroxide and cell cycle, because the available expression time profiles for these conditions are long enough. Especially, we find significant TFs and cooperative TFs responding to environmental changes. Our method may also be applicable to other stresses if the gene expression profiles have been examined for a sufficiently long time.

Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast

We studied the relationship between growth rate and genome-wide gene expression, cell cycle progression, and glucose metabolism in 36 steady-state continuous cultures limited by one of six different nutrients (glucose, ammonium, sulfate, phosphate, uracil, or leucine). The expression of more than one quarter of all yeast genes is linearly correlated with growth rate, independent of the limiting nutrient. The subset of negatively growth-correlated genes is most enriched for peroxisomal functions, whereas positively correlated genes mainly encode ribosomal functions. Many (not all) genes associated with stress response are strongly correlated with growth rate, as are genes that are periodically expressed under conditions of metabolic cycling. We confirmed a linear relationship between growth rate and the fraction of the cell population in the G0/G1 cell cycle phase, independent of limiting nutrient. Cultures limited by auxotrophic requirements wasted excess glucose, whereas those limited on phosphate, sulfate, or ammonia did not; this phenomenon (reminiscent of the "Warburg effect" in cancer cells) was confirmed in batch cultures. Using an aggregate of gene expression values, we predict (in both continuous and batch cultures) an "instantaneous growth rate." This concept is useful in interpreting the system-level connections among growth rate, metabolism, stress, and the cell cycle.

Activation of the G2/M-specific gene CLB2 requires multiple cell cycle signals

In budding yeast (Saccharomyces cerevisiae), the periodic expression of the G2/M-specific gene CLB2 depends on a DNA binding complex that mediates its repression during G1 and activation from the S phase to the exit of mitosis. The switch from low to high expression levels depends on the transcriptional activator Ndd1. We show that the inactivation of the Sin3 histone deacetylase complex bypasses the essential role of Ndd1 in cell cycle progression. Sin3 and its catalytic subunit Rpd3 associate with the CLB2 promoter during the G1 phase of the cell cycle. Both proteins dissociate from the promoter at the onset of the S phase and reassociate during G2 phase. Sin3 removal coincides with a transient increase in histone H4 acetylation followed by the expulsion of at least one nucleosome from the promoter region. Whereas the first step depends on Cdc28/Cln1 activity, Ndd1 function is required for the second step. Since the removal of Sin3 is independent of Ndd1 recruitment and Cdc28/Clb activity it represents a unique regulatory step which is distinct from transcriptional activation.

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.

The Isw2 chromatin-remodeling ATPase cooperates with the Fkh2 transcription factor to repress transcription of the B-type cyclin gene CLB2

Forkhead (Fkh) transcription factors influence cell death, proliferation, and differentiation and the cell cycle. In Saccharomyces cerevisiae, Fkh2 both activates and represses transcription of CLB2, encoding a B-type cyclin. CLB2 is expressed during G(2)/M phase and repressed during G(1). Fkh2 recruits the coactivator Ndd1, an interaction which is promoted by Clb2/Cdk1-dependent phosphorylation of Ndd1, suggesting that CLB2 is autoregulated. Ndd1 is proposed to function by antagonizing Fkh2-mediated repression, but nothing is known about the mechanism. Here we ask how Fkh2 represses CLB2. We show that Fkh2 controls a repressive chromatin structure that initiates in the early coding region of CLB2 and spreads up the promoter during the M and G(1) phases. The Isw2 chromatin-remodeling ATPase cooperates with Fkh2 to remodel the chromatin and repress CLB2 expression throughout the cell cycle. In addition, the related factors Isw1 and Fkh1 configure the chromatin at the early coding region and negatively regulate CLB2 expression but only during G(2)/M phase. Thus, the cooperative actions of two forkhead transcription factors and two chromatin-remodeling ATPases combine to regulate CLB2. We propose that chromatin-mediated repression by Isw1 and Isw2 may serve to limit activation of CLB2 expression by the Clb2/Cdk1 kinase during G(2)/M and to fully repress expression during G(1).

Polo kinase controls cell-cycle-dependent transcription by targeting a coactivator protein

Polo kinases have crucial conserved functions in controlling the eukaryotic cell cycle through orchestrating several events during mitosis. An essential element of cell cycle control is exerted by altering the expression of key regulators. Here we show an important function for the polo kinase Cdc5p in controlling cell-cycle-dependent gene expression that is crucial for the execution of mitosis in the model eukaryote Saccharomyces cerevisiae. In particular, we find that Cdc5p is temporally recruited to promoters of the cell-cycle-regulated CLB2 gene cluster, where it targets the Mcm1p-Fkh2p-Ndd1p transcription factor complex, through direct phosphorylation of the coactivator protein Ndd1p. This phosphorylation event is required for the normal temporal expression of cell-cycle-regulated genes such as CLB2 and SWI5 in G2/M phases. Furthermore, severe defects in cell division occur in the absence of Cdc5p-mediated phosphorylation of Ndd1p. Thus, polo kinase is required for the production of key mitotic regulators, in addition to previously defined roles in controlling other mitotic events.

Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle

BACKGROUND

A transcriptional regulatory module (TRM) is a set of genes that is regulated by a common set of transcription factors (TFs). By organizing the genome into TRMs, a living cell can coordinate the activities of many genes and carry out complex functions. Therefore, identifying TRMs is helpful for understanding gene regulation.

RESULTS

Integrating gene expression and ChIP-chip data, we develop a method, called MOdule Finding Algorithm (MOFA), for reconstructing TRMs of the yeast cell cycle. MOFA identified 87 TRMs, which together contain 336 distinct genes regulated by 40 TFs. Using various kinds of data, we validated the biological relevance of the identified TRMs. Our analysis shows that different combinations of a fairly small number of TFs are responsible for regulating a large number of genes involved in different cell cycle phases and that there may exist crosstalk between the cell cycle and other cellular processes. MOFA is capable of finding many novel TF-target gene relationships and can determine whether a TF is an activator or/and a repressor. Finally, MOFA refines some clusters proposed by previous studies and provides a better understanding of how the complex expression program of the cell cycle is regulated.

CONCLUSION

MOFA was developed to reconstruct TRMs of the yeast cell cycle. Many of these TRMs are in agreement with previous studies. Further, MOFA inferred many interesting modules and novel TF combinations. We believe that computational analysis of multiple types of data will be a powerful approach to studying complex biological systems when more and more genomic resources such as genome-wide protein activity data and protein-protein interaction data become available.

Identification of transcription factor cooperativity via stochastic system model

MOTIVATION

Transcription factor binding sites are known to co-occur in the same gene owing to cooperativity of the transcription factors (TFs) that bind to them. Genome-wide location data can help us understand how an individual TF regulates its target gene. Nevertheless, how TFs cooperate to regulate their target genes still needs further study. In this study, genome-wide location data and expression profiles are integrated to reveal how TFs cooperate to regulate their target genes from the stochastic system perspective.

RESULTS

Based on a stochastic dynamic model, a new measurement of TF cooperativity is developed according to the regulatory abilities of cooperative TF pairs and the number of their occurrences. Our method is employed to the yeast cell cycle and reveals successfully many cooperative TF pairs confirmed by previous experiments, e.g. Swi4-Swi6 in G1/S phase and Ndd1-Fkh2 in G2/M phase. Other TF pairs with potential cooperativity mentioned in our results can provide new directions for future experiments. Finally, a cooperative TF network of cell cycle is constructed from significant cooperative TF pairs.

CONTACT

bschen@ee.nthu.edu.tw

SUPPLEMENTARY INFORMATION

http://www.ee.nthu.edu.tw/~bschen/cooperativity/

Evolutionary relationships between Saccharomyces cerevisiae and other fungal species as determined from genome comparisons

The increasing number of fungal genomes whose sequence has been completed permits their comparison both at the nucleotide and protein levels. The information thus obtained improves our knowledge on evolutionary relationships between fungi. Comparison of the Saccharomyces cerevisiae genome with other Hemiascomycetes genomes confirms that a whole-genome duplication occurred before the diversification between Candida glabrata and the Saccharomyces sensu stricto species and after separation from the branch leading to the other Hemiascomycetes. Duplication was followed by individual gene losses and rearrangements affecting extensive DNA regions. Although S. cerevisiae and C. glabrata are two closely related yeast species at an evolutionary scale, their different habitats and life styles correlate with specific gene differences and with more extensive gene loses having occurred in the parasitic C. glabrata. At a closer evolutive scale, diversification among the sensu stricto species began with nucleotide changes at the intergenic regions affecting sequences that are not relevant for gene regulation, together with more extensive genome rearrangements involving transposons and telomeric regions. One important characteristic of fungal genomes that is shared with other eukaryotes is the fusion of gene sequences coding for separate protein modules into a single open reading frame. This allows diversification of protein functions while saving gene information.

Flexible promoter architecture requirements for coactivator recruitment

Background

The spatial organization of transcription factor binding sites in regulatory DNA, and the composition of intersite sequences, influences the assembly of the multiprotein complexes that regulate RNA polymerase recruitment and thereby affects transcription. We have developed a genetic approach to investigate how reporter gene transcription is affected by varying the spacing between transcription factor binding sites. We characterized the components of promoter architecture that govern the yeast transcription factors Cbf1 and Met31/32, which bind independently, but collaboratively recruit the coactivator Met4.

Results

A Cbf1 binding site was required upstream of a Met31/32 binding site for full reporter gene expression. Distance constraints on coactivator recruitment were more flexible than those for cooperatively binding transcription factors. Distances from 18 to 50 bp between binding sites support efficient recruitment of Met4, with only slight modulation by helical phasing. Intriguingly, we found that certain sequences located between the binding sites abolished gene expression.

Conclusion

These results yield insight to the influence of both binding site architecture and local DNA flexibility on gene expression, and can be used to refine computational predictions of gene expression from promoter sequences. In addition, our approach can be applied to survey promoter architecture requirements for arbitrary combinations of transcription factor binding sites.

Cell-cycle control of gene expression in budding and fission yeast

Cell-cycle control of transcription seems to be a universal feature of proliferating cells, although relatively little is known about its biological significance and conservation between organisms. The two distantly related yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe have provided valuable complementary insight into the regulation of periodic transcription as a function of the cell cycle. More recently, genome-wide studies of proliferating cells have identified hundreds of periodically expressed genes and underlying mechanisms of transcriptional control. This review discusses the regulation of three major transcriptional waves, which roughly coincide with three main cell-cycle transitions (initiation of DNA replication, entry into mitosis, and exit from mitosis). I also compare and contrast the transcriptional regulatory networks between the two yeasts and discuss the evolutionary conservation and possible roles for cell cycle-regulated transcription.

New probabilistic graphical models for genetic regulatory networks studies

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions may be preserved. The results of our proposed models were compared with several other commonly used models, and our models have shown to give superior performance. Additionally, we found the same common limitations in the prediction of genetic regulatory networks when using only DNA microarray data.

A role for KEM1 at the START of the cell cycle in Saccharomyces cerevisiae

KEM1 is a Saccharomyces cerevisiae gene, conserved in all eukaryotes, whose deletion leads to pleiotropic phenotypes. For the most part, these phenotypes are thought to arise from Kem1p's role in RNA turnover, because Kem1p is a major 5'-3' cytoplasmic exonuclease. For example, the exonuclease-dependent role of Kem1p is involved in the exit from mitosis, by degrading the mRNA of the mitotic cyclin CLB2. Here, we describe the identification of a KEM1 truncation, KEM1(1-975), that accelerated the G1 to S transition and initiation of DNA replication when over-expressed. Interestingly, although this truncated Kem1p lacked exonuclease activity, it could efficiently complement another function affected by the loss of KEM1, microtubule-dependent nuclear migration. Taken together, the results we report here suggest that Kem1p might have a previously unrecognized role at the G1 to S transition, but not through its exonuclease activity. Our findings also support the notion that Kem1p is a multifunctional protein with distinct and separable roles.