High functional overlap between MluI cell-cycle box binding factor and Swi4/6 cell-cycle box binding factor in the G1/S transcriptional program in Saccharomyces cerevisiae

Molecular basis of the functional distinction between Cln1 and Cln2 cyclins

Cln1 and Cln2 are very similar but not identical cyclins. In this work, we tried to describe the molecular basis of the functional distinction between Cln1 and Cln2. We constructed chimeric cyclins containing different fragments of Cln1 and Cln2 and performed several functional analysis that make it possible to distinguish between Cln1 or Cln2. We identified that region between amino acids 225 and 299 of Cln2 is not only necessary but also sufficient to confer Cln2 specific functionality compared with Cln1. We also studied Cln1 and Cln2 subcellular localization identifying additional differences between them. Both cyclins are distributed between the nucleus and the cytoplasm, but Cln1 shows stronger nuclear accumulation. Nuclear import of both cyclins is mediated by the classical nuclear import pathway and by sequences in the N-terminal end of the proteins. For Cln2, but not for Cln1, a nuclear export mechanism mediated by karyopherin Msn5 has been identified. Strikingly, Cln2 export depends on a Msn5-dependent NES between amino acids 225 and 299. In fact, the introduction of this region confers to Cln1 an export mechanism dependent on Msn5; importantly, this causes the gain of Cln2-specific cytosolic functions and the impairment of nuclear function. In short, a region from Cln2 controlling an Msn5-dependent nuclear export mechanism confers a specific functionality to Cln2 compared with Cln1.

Topology of transcriptional regulatory networks: testing and improving

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

Pph3 dephosphorylation of Rad53 is required for cell recovery from MMS-induced DNA damage in Candida albicans

The pathogenic fungus Candida albicans switches from yeast growth to filamentous growth in response to genotoxic stresses, in which phosphoregulation of the checkpoint kinase Rad53 plays a crucial role. Here we report that the Pph3/Psy2 phosphatase complex, known to be involved in Rad53 dephosphorylation, is required for cellular responses to the DNA-damaging agent methyl methanesulfonate (MMS) but not the DNA replication inhibitor hydroxyurea (HU) in C. albicans. Deletion of either PPH3 or PSY2 resulted in enhanced filamentous growth during MMS treatment and continuous filamentous growth even after MMS removal. Moreover, during this growth, Rad53 remained hyperphosphorylated, MBF-regulated genes were downregulated, and hypha-specific genes were upregulated. We have also identified S461 and S545 on Rad53 as potential dephosphorylation sites of Pph3/Psy2 that are specifically involved in cellular responses to MMS. Therefore, our studies have identified a novel molecular mechanism mediating DNA damage response to MMS in C. albicans.

Role of a Candida albicans Nrm1/Whi5 homologue in cell cycle gene expression and DNA replication stress response

To explore cell cycle regulation in the dimorphic fungus Candida albicans, we identified and characterized CaNrm1, a C. albicans homologue of the Saccharomyces cerevisiae Whi5 and Nrm1 transcription inhibitors that, analogous to mammalian Rb, regulate the cell cycle transcription programme during the G1 phase. CaNRM1 is able to complement the phenotypes of both whi5 and nrm1 mutants in S. cerevisiae. In C. albicans, global transcription analysis of the CaNRM1 deletion mutant reveals a preferential induction of G1- and G1/S-specific genes. CaNrm1 interacts genetically with the C. albicans MBF functional homologue, and physically with its subunit CaSwi4. Similar to S. cerevisiae Whi5, CaNrm1 subcellular localization oscillates with the cell cycle between the nucleus and the cytoplasm. Deletion of CaNRM1 further results in increased resistance to hydroxyurea, an inhibitor of DNA replication; analysis of the expression of ribonucleotide reductase, the target of hydroxyurea, suggests that its transcriptional induction in response to hydroxyurea is regulated via CaNrm1, and biochemical analysis shows that hydroxyurea causes disruption of the interaction of CaNrm1 with CaSwi4. Furthermore, induction of the hyphal-specific genes is dampened under certain conditions in the Canrm1(-/-) mutant, suggesting that the cell cycle transcription programme can influence the morphogenetic transcription programme of C. albicans.

Saccharomyces cerevisiae Cmr1 protein preferentially binds to UV-damaged DNA in vitro

DNA metabolic processes such as DNA replication, recombination, and repair are fundamentally important for the maintenance of genome integrity and cell viability. Although a large number of proteins involved in these pathways have been extensively studied, many proteins still remain to be identified. In this study, we isolated DNA-binding proteins from Saccharomyces cerevisiae using DNA-cellulose columns. By analyzing the proteins using mass spectrometry, an uncharacterized protein, Cmr1/YDL156W, was identified. Cmr1 showed sequence homology to human Damaged-DNA binding protein 2 in its C-terminal WD40 repeats. Consistent with this finding, the purified recombinant Cmr1 protein was found to be intrinsically associated with DNA-binding activity and exhibited higher affinity to UV-damaged DNA substrates. Chromatin isolation experiments revealed that Cmr1 localized in both the chromatin and supernatant fractions, and the level of Cmr1 in the chromatin fraction increased when yeast cells were irradiated with UV. These results suggest that Cmr1 may be involved in DNA-damage responses in yeast.

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.

Evolution of networks and sequences in eukaryotic cell cycle control

The molecular networks regulating the G1-S transition in budding yeast and mammals are strikingly similar in network structure. However, many of the individual proteins performing similar network roles appear to have unrelated amino acid sequences, suggesting either extremely rapid sequence evolution, or true polyphyly of proteins carrying out identical network roles. A yeast/mammal comparison suggests that network topology, and its associated dynamic properties, rather than regulatory proteins themselves may be the most important elements conserved through evolution. However, recent deep phylogenetic studies show that fungal and animal lineages are relatively closely related in the opisthokont branch of eukaryotes. The presence in plants of cell cycle regulators such as Rb, E2F and cyclins A and D, that appear lost in yeast, suggests cell cycle control in the last common ancestor of the eukaryotes was implemented with this set of regulatory proteins. Forward genetics in non-opisthokonts, such as plants or their green algal relatives, will provide direct information on cell cycle control in these organisms, and may elucidate the potentially more complex cell cycle control network of the last common eukaryotic ancestor.

A flucytosine-responsive Mbp1/Swi4-like protein, Mbs1, plays pleiotropic roles in antifungal drug resistance, stress response, and virulence of Cryptococcus neoformans

Cryptococcosis, caused by the basidiomycetous fungus Cryptococcus neoformans, is responsible for more than 600,000 deaths annually in AIDS patients. Flucytosine is one of the most commonly used antifungal drugs for its treatment, but its resistance and regulatory mechanisms have never been investigated at the genome scale in C. neoformans. In the present study, we performed comparative transcriptome analysis by employing two-component system mutants (tco1Δ and tco2Δ) exhibiting opposing flucytosine susceptibility. As a result, a total of 177 flucytosine-responsive genes were identified, and many of them were found to be regulated by Tco1 or Tco2. Among these, we discovered an APSES-like transcription factor, Mbs1 (Mbp1- and Swi4-like protein 1). Expression analysis revealed that MBS1 was regulated in response to flucytosine in a Tco2/Hog1-dependent manner. Supporting this, C. neoformans with the deletion of MBS1 exhibited increased susceptibility to flucytosine. Intriguingly, Mbs1 played pleiotropic roles in diverse cellular processes of C. neoformans. Mbs1 positively regulated ergosterol biosynthesis and thereby affected polyene and azole drug susceptibility. Mbs1 was also involved in genotoxic and oxidative stress responses. Furthermore, Mbs1 promoted production of melanin and capsule and thereby was required for full virulence of C. neoformans. In conclusion, Mbs1 is considered to be a novel antifungal therapeutic target for treatment of cryptococcosis.

Multiple sequence-specific factors generate the nucleosome-depleted region on CLN2 promoter

Nucleosome-depleted regions (NDRs) are ubiquitous on eukaryotic promoters. The formation of many NDRs cannot be readily explained by previously proposed mechanisms. Here, we carry out a focused study on a physiologically important NDR in the yeast CLN2 promoter (CLN2pr). We show that this NDR does not result from intrinsically unfavorable histone-DNA interaction. Instead, we identified eight conserved factor binding sites, including that of Reb1, Mcm1, and Rsc3, that cause the local nucleosome depletion. These nucleosome-depleting factors (NDFs) work redundantly, and simultaneously mutating all their binding sites eliminates CLN2pr NDR. The loss of the NDR induces unreliable "on/off" expression in individual cell cycles, but in the presence of the NDR, NDFs have little direct effect on transcription. We present bioinformatic evidence that the formation of many NDRs across the genome involves multiple NDFs. Our findings also provide significant insight into the composition and spatial organization of functional promoters.

G1/S transcription factor orthologues Swi4p and Swi6p are important but not essential for cell proliferation and influence hyphal development in the fungal pathogen Candida albicans

The G(1)/S transition is a critical control point for cell proliferation and involves essential transcription complexes termed SBF and MBF in Saccharomyces cerevisiae or MBF in Schizosaccharomyces pombe. In the fungal pathogen Candida albicans, G(1)/S regulation is not clear. To gain more insight into the G(1)/S circuitry, we characterized Swi6p, Swi4p and Mbp1p, the closest orthologues of SBF (Swi6p and Swi4p) and MBF (Swi6p and Mbp1p) components in S. cerevisiae. The mbp1Δ/Δ cells showed minor growth defects, whereas swi4Δ/Δ and swi6Δ/Δ yeast cells dramatically increased in size, suggesting a G(1) phase delay. Gene set enrichment analysis (GSEA) of transcription profiles revealed that genes associated with G(1)/S phase were significantly enriched in cells lacking Swi4p and Swi6p. These expression patterns suggested that Swi4p and Swi6p have repressing as well as activating activity. Intriguingly, swi4Δ/Δ swi6Δ/Δ and swi4Δ/Δ mbp1Δ/Δ strains were viable, in contrast to the situation in S. cerevisiae, and showed pleiotropic phenotypes that included multibudded yeast, pseudohyphae, and intriguingly, true hyphae. Consistently, GSEA identified strong enrichment of genes that are normally modulated during C. albicans-host cell interactions. Since Swi4p and Swi6p influence G(1) phase progression and SBF binding sites are lacking in the C. albicans genome, these factors may contribute to MBF activity. Overall, the data suggest that the putative G(1)/S regulatory machinery of C. albicans contains novel features and underscore the existence of a relationship between G(1) phase and morphogenetic switching, including hyphal development, in the pathogen.

Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

BACKGROUND

Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.

RESULTS

We applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.

CONCLUSIONS

These results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.

Yeast karyopherin Kap95 is required for cell cycle progression at Start

Background

The control of the subcellular localization of cell cycle regulators has emerged as a crucial mechanism in cell division regulation. The active transport of proteins between the nucleus and the cytoplasm is mediated by the transport receptors of the β-karyopherin family. In this work we characterized the terminal phenotype of a mutant strain in β-karyopherin Kap95, a component of the classical nuclear import pathway.

Results

When KAP95 was inactivated, most cells arrested at the G2/M phase of the cell cycle, which is in agreement with the results observed in mutants in the other components of this pathway. However, a number of cells accumulate at G1, suggesting a novel role of Kap95 and the classical import pathway at Start. We investigated the localization of Start transcription factors. It is known that Swi6 contains a classical NLS that interacts with importin α. Here we show that the in vivo nuclear import of Swi6 depends on Kap95. For Swi4, we identified a functional NLS between amino acids 371 and 376 that is sufficient and necessary for Swi4 to enter the nucleus. The nuclear import driven by this NLS is mediated by karyopherins Kap95 and Srp1. Inactivation of Kap95 also produces a dramatic change in the localization of Mbp1 since the protein is mainly detected in the cytoplasm. Two functionally redundant Kap95- and Srp1-dependent NLSs were identified in Mbp1 between amino acids 27-30 and 166-181. Nuclear accumulation was not completely abolished in a kap95 mutant or in the Mbp1 mutated in the two NLSs, suggesting that alternative pathways might contribute to the Mbp1 nuclear import to a lesser extent.

Conclusions

Kap95 plays an essential role at the initiation of the cell cycle by driving the nuclear import of Swi4, Swi6 and Mbp1, the three transcription factors responsible for the gene expression at Start. This transport depends on the specific nuclear localization signals present in cargo proteins.

The transcriptional network activated by Cln3 cyclin at the G1-to-S transition of the yeast cell cycle

Background

The G1-to-S transition of the cell cycle in the yeast Saccharomyces cerevisiae involves an extensive transcriptional program driven by transcription factors SBF (Swi4-Swi6) and MBF (Mbp1-Swi6). Activation of these factors ultimately depends on the G1 cyclin Cln3.

Results

To determine the transcriptional targets of Cln3 and their dependence on SBF or MBF, we first have used DNA microarrays to interrogate gene expression upon Cln3 overexpression in synchronized cultures of strains lacking components of SBF and/or MBF. Secondly, we have integrated this expression dataset together with other heterogeneous data sources into a single probabilistic model based on Bayesian statistics. Our analysis has produced more than 200 transcription factor-target assignments, validated by ChIP assays and by functional enrichment. Our predictions show higher internal coherence and predictive power than previous classifications. Our results support a model whereby SBF and MBF may be differentially activated by Cln3.

Conclusions

Integration of heterogeneous genome-wide datasets is key to building accurate transcriptional networks. By such integration, we provide here a reliable transcriptional network at the G1-to-S transition in the budding yeast cell cycle. Our results suggest that to improve the reliability of predictions we need to feed our models with more informative experimental data.

Multiple signals converge on a differentiation MAPK pathway

An important emerging question in the area of signal transduction is how information from different pathways becomes integrated into a highly coordinated response. In budding yeast, multiple pathways regulate filamentous growth, a complex differentiation response that occurs under specific environmental conditions. To identify new aspects of filamentous growth regulation, we used a novel screening approach (called secretion profiling) that measures release of the extracellular domain of Msb2p, the signaling mucin which functions at the head of the filamentous growth (FG) MAPK pathway. Secretion profiling of complementary genomic collections showed that many of the pathways that regulate filamentous growth (RAS, RIM101, OPI1, and RTG) were also required for FG pathway activation. This regulation sensitized the FG pathway to multiple stimuli and synchronized it to the global signaling network. Several of the regulators were required for MSB2 expression, which identifies the MSB2 promoter as a target “hub” where multiple signals converge. Accessibility to the MSB2 promoter was further regulated by the histone deacetylase (HDAC) Rpd3p(L), which positively regulated FG pathway activity and filamentous growth. Our findings provide the first glimpse of a global regulatory hierarchy among the pathways that control filamentous growth. Systems-level integration of signaling circuitry is likely to coordinate other regulatory networks that control complex behaviors.

Signal integration is an essential feature of information flow through signal transduction pathways. The mechanisms by which signals from multiple pathways become integrated into a coordinated response remain unclear. We show that multiple pathways that regulate filamentous growth converge on a differentiation-dependent MAPK pathway. Our findings indicate that more extensive communication occurs between signaling pathways that control the filamentation response than has previously been appreciated. We suggest that global communication hierarchies regulate information flow in other systems, particularly higher eukaryotes where multiple pathways typically function simultaneously to modulate a complex response.

Logical modelling of cell cycle control in eukaryotes: a comparative study

Dynamical modelling is at the core of the systems biology paradigm. However, the development of comprehensive quantitative models is complicated by the daunting complexity of regulatory networks controlling crucial biological processes such as cell division, the paucity of currently available quantitative data, as well as the limited reproducibility of large-scale experiments. In this context, qualitative modelling approaches offer a useful alternative or complementary framework to build and analyse simplified, but still rigorous dynamical models. This point is illustrated here by analysing recent logical models of the molecular network controlling mitosis in different organisms, from yeasts to mammals. After a short introduction covering cell cycle and logical modelling, we compare the assumptions and properties underlying these different models. Next, leaning on their transposition into a common logical framework, we compare their functional structure in terms of regulatory circuits. Finally, we discuss assets and prospects of qualitative approaches for the modelling of the cell cycle.

Linking cell cycle to histone modifications: SBF and H2B monoubiquitination machinery and cell-cycle regulation of H3K79 dimethylation

To identify regulators involved in determining the differential pattern of H3K79 methylation by Dot1, we screened the entire yeast gene deletion collection by GPS for genes required for normal levels of H3K79 di- but not trimethylation. We identified the cell cycle-regulated SBF protein complex required for H3K79 dimethylation. We also found that H3K79 di- and trimethylation are mutually exclusive, with M/G1 cell cycle-regulated genes significantly enriched for H3K79 dimethylation. Since H3K79 trimethylation requires prior monoubiquitination of H2B, we performed genome-wide profiling of H2BK123 monoubiquitination and showed that H2BK123 monoubiquitination is not detected on cell cycle-regulated genes and sites containing H3K79me2, but is found on H3K79me3-containing regions. A screen for genes responsible for the establishment/removal of H3K79 dimethylation resulted in identification of NRM1 and WHI3, both of which impact the transcription by the SBF and MBF protein complexes, further linking the regulation of methylation status of H3K79 to the cell cycle.

Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data

Transcriptional cooperativity among several transcription factors (TFs) is believed to be the main mechanism of complexity and precision in transcriptional regulatory programs. Here, we present a Bayesian network framework to reconstruct a high-confidence whole-genome map of transcriptional cooperativity in Saccharomyces cerevisiae by integrating a comprehensive list of 15 genomic features. We design a Bayesian network structure to capture the dominant correlations among features and TF cooperativity, and introduce a supervised learning framework with a well-constructed gold-standard dataset. This framework allows us to assess the predictive power of each genomic feature, validate the superior performance of our Bayesian network compared to alternative methods, and integrate genomic features for optimal TF cooperativity prediction. Data integration reveals 159 high-confidence predicted cooperative relationships among 105 TFs, most of which are subsequently validated by literature search. The existing and predicted transcriptional cooperativities can be grouped into three categories based on the combination patterns of the genomic features, providing further biological insights into the different types of TF cooperativity. Our methodology is the first supervised learning approach for predicting transcriptional cooperativity, compares favorably to alternative unsupervised methodologies, and can be applied to other genomic data integration tasks where high-quality gold-standard positive data are scarce.

Role of the cell wall integrity and filamentous growth mitogen-activated protein kinase pathways in cell wall remodeling during filamentous growth

Many fungal species including pathogens exhibit filamentous growth (FG) as a means of foraging for nutrients. Genetic screens were performed to identify genes required for FG in the budding yeast Saccharomyces cerevisiae. Genes encoding proteins with established functions in transcriptional activation (MCM1, MATalpha2, PHD1, MSN2, SIR4, and HMS2), cell wall integrity (MPT5, WSC2, and MID2), and cell polarity (BUD5) were identified as potential regulators of FG. The transcription factors MCM1 and MATalpha2 induced invasive growth by promoting diploid-specific bipolar budding in haploid cells. Components of the cell wall integrity pathway including the cell surface proteins Slg1p/Wsc1p, Wsc2p, Mid2p, and the mitogen-activated protein kinase (MAPK) Slt2p/Mpk1p contributed to multiple aspects of the FG response including cell elongation, cell-cell adherence, and agar invasion. Mid2p and Wsc2p stimulated the FG MAPK pathway through the signaling mucin Msb2p and components of the MAPK cascade. The FG pathway contributed to cell wall integrity in parallel with the cell wall integrity pathway and in opposition with the high osmolarity glycerol response pathway. Mass spectrometry approaches identified components of the filamentous cell wall including the mucin-like proteins Msb2p, Flo11p, and subtelomeric (silenced) mucin Flo10p. Secretion of Msb2p, which occurs as part of the maturation of the protein, was inhibited by the ss-1,3-glucan layer of the cell wall, which highlights a new regulatory aspect to cell wall remodeling in this organism. Disruption of ss-1,3-glucan linkages induced mucin shedding and resulted in defects in cell-cell adhesion and invasion of cells into the agar matrix.

Stb1 collaborates with other regulators to modulate the G1-specific transcriptional circuit

G(1)-specific transcription in the budding yeast Saccharomyces cerevisiae depends upon SBF and MBF. Whereas inactivation of SBF-regulated genes during the G(1)/S transition depends upon mitotic B-type cyclins, inactivation of MBF has been reported to involve multiple regulators, Nrm1 and Stb1. Nrm1 is a transcriptional corepressor that inactivates MBF-regulated transcription via negative feedback as cells exit G(1) phase. Cln/cyclin-dependent kinase (CDK)-dependent inactivation of Stb1, identified via its interaction with the histone deacetylase (HDAC) component Sin3, has also been reported to inactivate MBF-regulated transcription. This report shows that Stb1 is a stable component of both SBF and MBF that binds G(1)-specific promoters via Swi6 during G(1) phase. It is important for the growth of cells in which SBF or MBF is inactive. Although dissociation of Stb1 from promoters as cells exit G(1) correlates with Stb1 phosphorylation, phosphorylation is only partially dependent upon Cln1/2 and is not involved in transcription inactivation. Inactivation depends upon Nrm1 and Clb/CDK activity. Stb1 inactivation dampens maximal transcriptional induction during late G(1) phase and also derepresses gene expression in G(1)-phase cells prior to Cln3-dependent transcriptional activation. The repression during G(1) also depends upon Sin3. We speculate that the interaction between Stb1 and Sin3 regulates the Sin3/HDAC complex at G(1)-specific promoters.

Positive feedback of G1 cyclins ensures coherent cell cycle entry

In budding yeast, Saccharomyces cerevisiae, the Start checkpoint integrates multiple internal and external signals into an all-or-none decision to enter the cell cycle. Here we show that Start behaves like a switch due to systems-level feedback in the regulatory network. In contrast to current models proposing a linear cascade of Start activation, transcriptional positive feedback of the G1 cyclins Cln1 and Cln2 induces the near-simultaneous expression of the approximately 200-gene G1/S regulon. Nuclear Cln2 drives coherent regulon expression, whereas cytoplasmic Cln2 drives efficient budding. Cells with the CLN1 and CLN2 genes deleted frequently arrest as unbudded cells, incurring a large fluctuation-induced fitness penalty due to both the lack of cytoplasmic Cln2 and insufficient G1/S regulon expression. Thus, positive-feedback-amplified expression of Cln1 and Cln2 simultaneously drives robust budding and rapid, coherent regulon expression. A similar G1/S regulatory network in mammalian cells, comprised of non-orthologous genes, suggests either conservation of regulatory architecture or convergent evolution.

A novel genetic screen implicates Elm1 in the inactivation of the yeast transcription factor SBF

Background

Despite extensive large scale analyses of expression and protein-protein interactions (PPI) in the model organism Saccharomyces cerevisiae, over a thousand yeast genes remain uncharacterized. We have developed a novel strategy in yeast that directly combines genetics with proteomics in the same screen to assign function to proteins based on the observation of genetic perturbations of sentinel protein interactions (GePPI). As proof of principle of the GePPI screen, we applied it to identify proteins involved in the regulation of an important yeast cell cycle transcription factor, SBF that activates gene expression during G1 and S phase.

Methodology/Principle Findings

The principle of GePPI is that if a protein is involved in a pathway of interest, deletion of the corresponding gene will result in perturbation of sentinel PPIs that report on the activity of the pathway. We created a fluorescent protein-fragment complementation assay (PCA) to detect the interaction between Cdc28 and Swi4, which leads to the inactivation of SBF. The PCA signal was quantified by microscopy and image analysis in deletion strains corresponding to 25 candidate genes that are periodically expressed during the cell cycle and are substrates of Cdc28. We showed that the serine-threonine kinase Elm1 plays a role in the inactivation of SBF and that phosphorylation of Elm1 by Cdc28 may be a mechanism to inactivate Elm1 upon completion of mitosis.

Conclusions/Significance

Our findings demonstrate that GePPI is an effective strategy to directly link proteins of known or unknown function to a specific biological pathway of interest. The ease in generating PCA assays for any protein interaction and the availability of the yeast deletion strain collection allows GePPI to be applied to any cellular network. In addition, the high degree of conservation between yeast and mammalian proteins and pathways suggest GePPI could be used to generate insight into human disease.

Protein kinase A, TOR, and glucose transport control the response to nutrient repletion in Saccharomyces cerevisiae

Nutrient repletion leads to substantial restructuring of the transcriptome in Saccharomyces cerevisiae. The expression levels of approximately one-third of all S. cerevisiae genes are altered at least twofold when a nutrient-depleted culture is transferred to fresh medium. Several nutrient-sensing pathways are known to play a role in this process, but the relative contribution that each pathway makes to the total response has not been determined. To better understand this, we used a chemical-genetic approach to block the protein kinase A (PKA), TOR (target of rapamycin), and glucose transport pathways, alone and in combination. Of the three pathways, we found that loss of PKA produced the largest effect on the transcriptional response; however, many genes required both PKA and TOR for proper nutrient regulation. Those genes that did not require PKA or TOR for nutrient regulation were dependent on glucose transport for either nutrient induction or repression. Therefore, loss of these three pathways is sufficient to prevent virtually the entire transcriptional response to fresh medium. In the absence of fresh medium, activation of the cyclic AMP/PKA pathway does not induce cellular growth; nevertheless, PKA activation induced a substantial fraction of the PKA-dependent genes. In contrast, the absence of fresh medium strongly limited gene repression by PKA. These results account for the signals needed to generate the transcriptional responses to glucose, including induction of growth genes required for protein synthesis and repression of stress genes, as well as the classical glucose repression and hexose transporter responses.

Inferring activity changes of transcription factors by binding association with sorted expression profiles

BACKGROUND

The identification of transcription factors (TFs) associated with a biological process is fundamental to understanding its regulatory mechanisms. From microarray data, however, the activity changes of TFs often cannot be directly observed due to their relatively low expression levels, post-transcriptional modifications, and other complications. Several approaches have been proposed to infer TF activity changes from microarray data. In some models, a linear relationship between gene expression and TF-gene binding strength is assumed. In some other models, the target genes of a TF are first determined by a significance cutoff to binding affinity scores, and then expression differentiation is checked between the target and other genes.

RESULTS

We propose a novel method, referred to as BASE (binding association with sorted expression), to infer TF activity changes from microarray expression profiles with the help of binding affinity data. It searches the maximum association between bind affinity profile of a TF and expression change profile along the direction of sorted differentiation. The method does not make hard target gene selection, rather, the significances of TF activity changes are evaluated by permutation tests of binding association at the end. To show the effectiveness of this method, we apply it to three typical examples using different kinds of binding affinity data, namely, ChIP-chip data, motif discovery data, and positional weighted matrix scanning data, respectively. The implications obtained from all three examples are consistent with established biological results. Moreover, the inferences suggest new and biological meaningful hypotheses for further investigation.

CONCLUSION

The proposed method makes transcription inference from profiles of expression and binding affinity. The same machinery can be used to deal with various kinds of binding affinity data. The method does not require a linear assumption, and has the desirable property of scale-invariance with respect to TF-specific binding affinity. This method is easy to implement and can be routinely applied for transcriptional inferences in microarray studies.

A dinoflagellate AAA family member rescues a conditional yeast G1/S phase cyclin mutant through increased CLB5 accumulation

An AAA protein from the dinoflagellate Gonyaulax polyedra (GpAAA) with the unusual ability to rescue the phenotype of a yeast mutant lacking G1/S phase cyclins (cln1cln2cln3) has been isolated and the mechanism of rescue was characterized. We find that GpAAA is not a cyclin and has no similarity to any known cell cycle regulators. Instead, GpAAA forms a novel and strongly supported clade with bacterial spoIIIAA proteins and an Arabidopsis gene of unknown function. Since dinoflagellates cannot be transformed, we took advantage of the powerful genetic tools available for yeast. We find that rescue of the cln1cln2cln3 phenotype does not involve an effect on the CDK-inhibitor (CKI) Sic1p, as GpAAA does not alter the sensitivity to an inducible SIC1. Instead, Northern blot analyses show that GpAAA expression increases levels of CLB5, in agreement with the observation that GpAAA is unable to rescue the quadruple mutant cln1cln2cln3clb5. We propose that the increased transcription of CLB5 may be due to a protein remodeling function of GpAAA alleviating inhibition of the transcription factor SBF. Thus, although no known equivalents to the yeast SBF have been documented in dinoflagellates, we conclude that dinoflagellates could indeed utilize GpAAA as a cell cycle regulator.

Multiple levels of cyclin specificity in cell-cycle control

Cyclins regulate the cell cycle by binding to and activating cyclin-dependent kinases (Cdks). Phosphorylation of specific targets by cyclin-Cdk complexes sets in motion different processes that drive the cell cycle in a timely manner. In budding yeast, a single Cdk is activated by multiple cyclins. The ability of these cyclins to target specific proteins and to initiate different cell-cycle events might, in some cases, reflect the timing of the expression of the cyclins; in others, it might reflect intrinsic properties of the cyclins that render them better suited to target particular proteins.

Connectivity in the yeast cell cycle transcription network: inferences from neural networks

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.

The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle

Transcription patterns shift dramatically as cells transit from one phase of the cell cycle to another. To better define this transcriptional circuitry, we collected new microarray data across the cell cycle of budding yeast. The combined analysis of these data with three other cell cycle data sets identifies hundreds of new highly periodic transcripts and provides a weighted average peak time for each transcript. Using these data and phylogenetic comparisons of promoter sequences, we have identified a late S-phase-specific promoter element. This element is the binding site for the forkhead protein Hcm1, which is required for its cell cycle-specific activity. Among the cell cycle-regulated genes that contain conserved Hcm1-binding sites, there is a significant enrichment of genes involved in chromosome segregation, spindle dynamics, and budding. This may explain why Hcm1 mutants show 10-fold elevated rates of chromosome loss and require the spindle checkpoint for viability. Hcm1 also induces the M-phase-specific transcription factors FKH1, FKH2, and NDD1, and two cell cycle-specific transcriptional repressors, WHI5 and YHP1. As such, Hcm1 fills a significant gap in our understanding of the transcriptional circuitry that underlies the cell cycle.

Constraining G1-specific transcription to late G1 phase: the MBF-associated corepressor Nrm1 acts via negative feedback

G1-specific transcription in yeast depends upon SBF and MBF. We have identified Nrm1 (negative regulator of MBF targets 1), as a stable component of MBF. NRM1 (YNR009w), an MBF-regulated gene expressed during late G1 phase, associates with G1-specific promoters via MBF. Transcriptional repression upon exit from G1 phase requires both Nrm1 and MBF. Inactivation of Nrm1 results in prolonged expression of MBF-regulated transcripts and leads to hydroxyurea (HU) resistance and enhanced bypass of rad53Delta- and mec1Delta-associated lethality. Constitutive expression of a stabilized form of Nrm1 represses MBF targets and leads to HU sensitivity. The fission yeast homolog SpNrm1, encoded by the MBF target gene nrm1(+) (SPBC16A3.07c), binds to MBF target genes and acts as a corepressor. In both yeasts, MBF represses G1-specific transcription outside of G1 phase. A negative feedback loop involving Nrm1 bound to MBF leads to transcriptional repression as cells exit G1 phase.

Few crucial links assure checkpoint efficiency in the yeast cell-cycle network

MOTIVATION

The ability of cells to complete mitosis with high fidelity relies on elaborate checkpoint mechanisms. We study S- and M-phase checkpoint responses in silico in the budding yeast with a stochastic dynamical model for the cell-cycle. We aim to provide an unbiased functional classification of network interactions that reflect the contribution of each link to checkpoint efficiency in the presence of cellular fluctuations.

RESULTS

We developed an algorithm BNetDyn to compute stochastic dynamical trajectories for an input gene network and its structural perturbations. User specified output measures like the mutual information between trigger and output nodes are then evaluated on the stationary state of the Markov process. Systematic perturbations of the yeast cell-cycle model by Li et al. classify each link according to its effect on checkpoint efficiencies and stabilities of the main cell-cycle phases. This points to the crosstalk in the cascades downstream of the SBF/MBF transcription activator complexes as determinant for checkpoint optimality; a finding that consistently reflects recent experiments. Finally our stochastic analysis emphasizes how dynamical stability in the yeast cell-cycle network crucially relies on backward inhibitory circuits next to forward induction.

AVAILABILITY

C++ source code and network models can be downloaded at http://www.vital-it.ch/Software/

Gene expression from random libraries of yeast promoters

Genomewide techniques to assay gene expression and transcription factor binding are in widespread use, but are far from providing predictive rules for the function of regulatory DNA. To investigate more intensively the grammar rules for active regulatory sequence, we made libraries from random ligations of a very restricted set of sequences. Working with the yeast Saccharomyces cerevisiae, we developed a novel screen based on the sensitivity of ascospores lacking dityrosine to treatment with lytic enzymes. We tested two separate libraries built by random ligation of a single type of activator site either for a well-characterized sporulation factor, Ndt80, or for a new sporulation-specific regulatory site that we identified and several neutral spacer elements. This selective system achieved up to 1:10(4) enrichment of the artificial sequences that were active during sporulation, allowing a high-throughput analysis of large libraries of synthetic promoters. This is not practical with methods involving direct screening for expression, such as those based on fluorescent reporters. There were very few false positives, since active promoters always passed the screen when retested. The survival rate of our libraries containing roughly equal numbers of spacers and activators was a few percent that of libraries made from activators alone. The sequences of approximately 100 examples of active and inactive promoters could not be distinguished by simple binary rules; instead, the best model for the data was a linear regression fit of a quantitative measure of gene activity to multiple features of the regulatory sequence.

Coherence and Timing of Cell Cycle Start Examined at Single-Cell Resolution

Cell cycle "Start" in budding yeast involves induction of a large battery of G1/S-regulated genes, coordinated with bud morphogenesis. It is unknown how intra-Start coherence of these events and inter-Start timing regularity are achieved. We developed quantitative time-lapse fluorescence microscopy on a multicell-cycle timescale, for following expression of unstable GFP under control of the G1 cyclin CLN2 promoter. Swi4, a major activator of the G1/S regulon, was required for a robustly coherent Start, as swi4 cells exhibited highly variable loss of cooccurrence of regular levels of CLN2pr-GFP expression with budding. In contrast, other known Start regulators Mbp1 and Cln3 are not needed for coherence but ensure regular timing of Start onset. The interval of nuclear retention of Whi5, a Swi4 repressor, largely accounts for wild-type mother-daughter asymmetry and for variable Start timing in cln3 mbp1 cells. Thus, multiple pathways may independently suppress qualitatively different kinds of noise at Start.