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

Mid1p-dependent regulation of the M–G1 transcription wave in fission yeast

The control of gene expression at certain times during the mitotic cell division cycle is a common feature in eukaryotes. In fission yeast, at least five waves of gene expression have been described, with one transcribed at the M–G1 interval under the control of the PBF transcription factor complex. PBF consists of at least three transcription factors, two forkhead-like proteins Sep1p and Fkh2p, and a MADS box-like protein Mbx1p, and binds to PCB motifs found in the gene promoters. Mbx1p is under the direct control of the polo-like kinase Plo1p and the Cdc14p-like phosphatase Clp1p (Flp1p). Here, we show that M–G1 gene expression in fission yeast is also regulated by the anillin-like protein, Mid1p (Dmf1p). Mid1p binds in vivo to both Fkh2p and Sep1p, and to the promoter regions of M–G1 transcribed genes. Mid1p promoter binding is dependent on Fkh2p, Plo1p and Clp1p. The absence of mid1+ in cells results in partial loss of M–G1 specific gene expression, suggesting that it has a negative role in controlling gene expression. This phenotype is exacerbated by also removing clp1+, suggesting that Mid1p and Clp1p have overlapping functions in controlling transcription. As mid1+ is itself expressed at M–G1, these observations offer a new mechanism whereby Mid1p contributes to controlling cell cycle-specific gene expression as part of a feedback loop.

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 fission yeast rDNA-binding protein Reb1 regulates G1 phase under nutritional stress

Yeast Reb1 and its mammalian ortholog TTF1 are conserved Myb-type DNA-binding proteins that bind to specific sites near the 3'-end of rRNA genes (rDNA). Here, they participate in the termination of transcription driven by RNA polymerase I and block DNA replication forks approaching in the opposite direction. We found that Schizosaccharomyces pombe Reb1 also upregulates transcription of the ste9(+) gene that is required for nitrogen-starvation-induced growth arrest with a G1 DNA content and sexual differentiation. Ste9 activates the anaphase-promoting complex or cyclosome ('APC/C') in G1, targeting B-cyclin for proteasomal degradation in response to nutritional stress. Reb1 binds in vivo and in vitro to a specific DNA sequence at the promoter of ste9(+), similar to the sequence recognized in the rDNA, and this binding is required for ste9(+) transcriptional activation and G1 arrest. This suggests that Reb1 acts as a link between rDNA metabolism and cell cycle control in response to nutritional stress. In agreement with this new role for Reb1 in the regulation of the G1-S transition, reb1Δ and wee1(ts) mutations are synthetically lethal owing to the inability of these cells to lengthen G1 before entering S phase. Similarly, reb1Δ cdc10(ts) cells are unable to arrest in G1 and die at the semi-permissive temperature.

Regulation of cell cycle-specific gene expression in fission yeast by the Cdc14p-like phosphatase Clp1p

Regulated gene expression makes an important contribution to cell cycle control mechanisms. In fission yeast, a group of genes is coordinately expressed during a late stage of the cell cycle (M phase and cytokinesis) that is controlled by common cis-acting promoter motifs named pombe cell cycle boxes (PCBs), which are bound by a trans-acting transcription factor complex, PCB binding factor (PBF). PBF contains at least three transcription factors, a MADS box protein Mbx1p and two forkhead transcription factors, Sep1p and Fkh2p. Here we show that the fission yeast Cdc14p-like phosphatase Clp1p (Flp1p) controls M-G1 specific gene expression through PBF. Clp1p binds in vivo both to Mbx1p, a MADS box-like transcription factor, and to the promoters of genes transcribed at this cell cycle time. Because Clp1p dephosphorylates Mbx1p in vitro, and is required for Mbx1p cell cycle-specific dephosphorylation in vivo, our observations suggest that Clp1p controls cell cycle-specific gene expression through binding to and dephosphorylating Mbx1p.

Drosophila melanogaster in the study of human neurodegeneration

Human neurodegenerative diseases are devastating illnesses that predominantly affect elderly people. The majority of the diseases are associated with pathogenic oligomers from misfolded proteins, eventually causing the formation of aggregates and the progressive loss of neurons in the brain and nervous system. Several of these proteinopathies are sporadic and the cause of pathogenesis remains elusive. Heritable forms are associated with genetic defects, suggesting that the affected protein is causally related to disease formation and/or progression. The limitations of human genetics, however, make it necessary to use model systems to analyse affected genes and pathways in more detail. During the last two decades, research using the genetically amenable fruitfly has established Drosophila melanogaster as a valuable model system in the study of human neurodegeneration. These studies offer reliable models for Alzheimer's, Parkinson's, and motor neuron diseases, as well as models for trinucleotide repeat expansion diseases, including ataxias and Huntington's disease. As a result of these studies, several signalling pathways including phosphatidylinositol 3-kinase (PI3K)/Akt and target of rapamycin (TOR), c-Jun N-terminal kinase (JNK) and bone morphogenetic protein (BMP) signalling, have been shown to be deregulated in models of proteinopathies, suggesting that two or more initiating events may trigger disease formation in an age-related manner. Moreover, these studies also demonstrate that the fruitfly can be used to screen chemical compounds for their potential to prevent or ameliorate the disease, which in turn can directly guide clinical research and the development of novel therapeutic strategies for the treatment of human neurodegenerative diseases.

Discovering transcriptional modules by Bayesian data integration

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets.

Results: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs.

Availability: If interested in the code for the work presented in this article, please contact the authors.

Contact:d.l.wild@warwick.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Coordinated progression through two subtranscriptomes underlies the tachyzoite cycle of Toxoplasma gondii

BACKGROUND

Apicomplexan parasites replicate by varied and unusual processes where the typically eukaryotic expansion of cellular components and chromosome cycle are coordinated with the biosynthesis of parasite-specific structures essential for transmission.

METHODOLOGY/PRINCIPAL FINDINGS

Here we describe the global cell cycle transcriptome of the tachyzoite stage of Toxoplasma gondii. In dividing tachyzoites, more than a third of the mRNAs exhibit significant cyclical profiles whose timing correlates with biosynthetic events that unfold during daughter parasite formation. These 2,833 mRNAs have a bimodal organization with peak expression occurring in one of two transcriptional waves that are bounded by the transition into S phase and cell cycle exit following cytokinesis. The G1-subtranscriptome is enriched for genes required for basal biosynthetic and metabolic functions, similar to most eukaryotes, while the S/M-subtranscriptome is characterized by the uniquely apicomplexan requirements of parasite maturation, development of specialized organelles, and egress of infectious daughter cells. Two dozen AP2 transcription factors form a series through the tachyzoite cycle with successive sharp peaks of protein expression in the same timeframes as their mRNA patterns, indicating that the mechanisms responsible for the timing of protein delivery might be mediated by AP2 domains with different promoter recognition specificities.

CONCLUSION/SIGNIFICANCE

Underlying each of the major events in apicomplexan cell cycles, and many more subordinate actions, are dynamic changes in parasite gene expression. The mechanisms responsible for cyclical gene expression timing are likely crucial to the efficiency of parasite replication and may provide new avenues for interfering with parasite growth.

ODE, RDE and SDE models of cell cycle dynamics and clustering in yeast

Biologists have long observed periodic-like oxygen consumption oscillations in yeast populations under certain conditions, and several unsatisfactory explanations for this phenomenon have been proposed. These ‘autonomous oscillations’ have often appeared with periods that are nearly integer divisors of the calculated doubling time of the culture. We hypothesize that these oscillations could be caused by a form of cell cycle synchronization that we call clustering. We develop some novel ordinary differential equation models of the cell cycle. For these models, and for random and stochastic perturbations, we give both rigorous proofs and simulations showing that both positive and negative growth rate feedback within the cell cycle are possible agents that can cause clustering of populations within the cell cycle. It occurs for a variety of models and for a broad selection of parameter values. These results suggest that the clustering phenomenon is robust and is likely to be observed in nature. Since there are necessarily an integer number of clusters, clustering would lead to periodic-like behaviour with periods that are nearly integer divisors of the period of the cell cycle. Related experiments have shown conclusively that cell cycle clustering occurs in some oscillating yeast cultures.

Mitotic cell-cycle progression is regulated by CPEB1 and CPEB4-dependent translational control

Meiotic and early-embryonic cell divisions in vertebrates take place in the absence of transcription and rely on the translational regulation of stored maternal messenger RNAs. Most of these mRNAs are regulated by the cytoplasmic-polyadenylation-element-binding protein (CPEB), which mediates translational activation and repression through cytoplasmic changes in their poly(A) tail length. It was unknown whether translational regulation by cytoplasmic polyadenylation and CPEB can also regulate mRNAs at specific points of mitotic cell-cycle divisions. Here we show that CPEB-mediated post-transcriptional regulation by phase-specific changes in poly(A) tail length is required for cell proliferation and specifically for entry into M phase in mitotically dividing cells. This translational control is mediated by two members of the CPEB family of proteins, CPEB1 and CPEB4. We conclude that regulation of poly(A) tail length is not only required to compensate for the lack of transcription in specialized cell divisions but also acts as a general mechanism to control mitosis.

High-resolution transcription atlas of the mitotic cell cycle in budding yeast

BACKGROUND

Extensive transcription of non-coding RNAs has been detected in eukaryotic genomes and is thought to constitute an additional layer in the regulation of gene expression. Despite this role, their transcription through the cell cycle has not been studied; genome-wide approaches have only focused on protein-coding genes. To explore the complex transcriptome architecture underlying the budding yeast cell cycle, we used 8 bp tiling arrays to generate a 5 minute-resolution, strand-specific expression atlas of the whole genome.

RESULTS

We discovered 523 antisense transcripts, of which 80 cycle or are located opposite periodically expressed mRNAs, 135 unannotated intergenic non-coding RNAs, of which 11 cycle, and 109 cell-cycle-regulated protein-coding genes that had not previously been shown to cycle. We detected periodic expression coupling of sense and antisense transcript pairs, including antisense transcripts opposite of key cell-cycle regulators, like FAR1 and TAF2.

CONCLUSIONS

Our dataset presents the most comprehensive resource to date on gene expression during the budding yeast cell cycle. It reveals periodic expression of both protein-coding and non-coding RNA and profiles the expression of non-annotated RNAs throughout the cell cycle for the first time. This data enables hypothesis-driven mechanistic studies concerning the functions of non-coding RNAs.

Adenovirus type 5 E1A and E6 proteins of low-risk cutaneous beta-human papillomaviruses suppress cell transformation through interaction with FOXK1/K2 transcription factors

The adenovirus (Adv) oncoprotein E1A stimulates cell proliferation and inhibits differentiation. These activities are primarily linked to the N-terminal region (exon 1) of E1A, which interacts with multiple cellular protein complexes. The C terminus (exon 2) of E1A antagonizes these processes, mediated in part through interaction with C-terminal binding proteins 1 and 2 (CtBP1/2). To identify additional cellular E1A targets that are involved in the modulation of E1A C-terminus-mediated activities, we undertook tandem affinity purification of E1A-associated proteins. Through mass spectrometric analysis, we identified several known E1A-interacting proteins as well as novel E1A targets, such as the forkhead transcription factors, FOXK1/K2. We identified a Ser/Thr-containing sequence motif in E1A that mediated interaction with FOXK1/K2. We demonstrated that the E6 proteins of two beta-human papillomaviruses (HPV14 and HPV21) associated with epidermodysplasia verruciformis also interacted with FOXK1/K2 through a motif similar to that of E1A. The E1A mutants deficient in interaction with FOXK1/K2 induced enhanced cell proliferation and oncogenic transformation. The hypertransforming activity of the mutant E1A was suppressed by HPV21 E6. An E1A-E6 chimeric protein containing the Ser/Thr domain of the E6 protein in E1A interacted efficiently with FOXK1/K2 and inhibited cell transformation. Our results suggest that targeting FOXK1/K2 may be a common mechanism for certain beta-HPVs and Adv5. E1A exon 2 mutants deficient in interaction with the dual-specificity kinases DYRK1A/1B and their cofactor HAN11 also induced increased cell proliferation and transformation. Our results suggest that the E1A C-terminal region may suppress cell proliferation and oncogenic transformation through interaction with three different cellular protein complexes: FOXK1/K2, DYRK(1A/1B)/HAN11, and CtBP1/2.

Systematic localization of the Arabidopsis core cell cycle proteins reveals novel cell division complexes

Cell division depends on the correct localization of the cyclin-dependent kinases that are regulated by phosphorylation, cyclin proteolysis, and protein-protein interactions. Although immunological assays can define cell cycle protein abundance and localization, they are not suitable for detecting the dynamic rearrangements of molecular components during cell division. Here, we applied an in vivo approach to trace the subcellular localization of 60 Arabidopsis (Arabidopsis thaliana) core cell cycle proteins fused to green fluorescent proteins during cell division in tobacco (Nicotiana tabacum) and Arabidopsis. Several cell cycle proteins showed a dynamic association with mitotic structures, such as condensed chromosomes and the preprophase band in both species, suggesting a strong conservation of targeting mechanisms. Furthermore, colocalized proteins were shown to bind in vivo, strengthening their localization-function connection. Thus, we identified unknown spatiotemporal territories where functional cell cycle protein interactions are most likely to occur.

Overexpression of septin 7 suppresses glioma cell growth

Our previous study demonstrated that SEPT7 was downregulated at mRNA level in human gliomas. This study is to further examine the expression of SEPT7 in glioma samples and characterizes its role on cell cycle progression and growth of glioma cells. mRNA and protein expression of SEPT7 were detected by RT-PCR, immunohistochemical staining, and western blot analysis in human glioma specimens and normal brain tissues. A pcDNA3-SEPT7 expression plasmid was constructed and transfected into human glioblastoma cell line U251, and cell proliferation and apoptosis were examined. The growth of established U251 and TJ905 subcutaneous xenograft gliomas was measured in nude mice treated with pcDNA3-SEPT7 and U251 xenograft tumors treated with SEPT7 siRNA. SEPT7 expression is negatively correlated with the increase of glioma grade. Overexpression of SEPT7 is able to inhibit cell proliferation and arrest cell cycle progression in the G0/G1 phase both in vitro and in vivo. Knocking down further the already low endogenous expression of SEPT7 in U251 xenograft tumors with siRNA leads to faster tumor growth compared with control tumors. This study demonstrates that SEPT7 is involved in gliomagenesis and suppresses glioma cell growth.

A general life-death selection strategy for dissecting protein functions

Clonal selection strategies are central tools in molecular biology. We developed a general strategy to dissect protein functions through positive and negative clonal selection for protein-protein interactions, based on a protein-fragment complementation assay using Saccharomyces cerevisiae cytosine deaminase as a reporter. We applied this method to mutational or chemical disruption of protein-protein interactions in yeast and to dissection of the functions of an allosterically activated transcription factor, Swi6.

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.

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.

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

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

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

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

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

Sin3 is involved in cell size control at Start in Saccharomyces cerevisiae

Saccharomyces cerevisiae cells control their cell size at a point in late G(1) called Start. Here, we describe a negative role for the Sin3/Rpd3 histone deacetylase complex in the regulation of cell size at Start. Initiation of G(1)/S-specific transcription of CLN1, CLN2 and PCL1 in a sin3Delta strain occurs at a reduced cell size compared with a wild-type strain. In addition, inactivation of the transcriptional regulator SIN3 partially suppressed a cln3Delta mutant, causing sin3Deltacln3Delta double mutants to start the cell cycle at wild-type size. Chromatin immunoprecipitation results demonstrate that Sin3 and Rpd3 are recruited to promoters of SBF (Swi4/Swi6)-regulated genes, and reveal that binding of Sin3 to SBF-specific promoters is cell-cycle regulated. We observe that transcriptional repression of SBF-dependent genes in early G(1) coincides with the recruitment of Sin3 to specific promoters, whereas binding of Sin3 is abolished from Swi4/Swi6-regulated promoters when transcription is activated at the G(1) to S phase transition. We conclude that the Sin3/Rpd3 histone deacetylase complex helps to prevent premature activation of the S phase in daughter cells.

Identification of yeast transcriptional regulation networks using multivariate random forests

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

Cyclin-dependent kinase inhibits reinitiation of a normal S-phase program during G2 in fission yeast

To achieve faithful replication of the genome once in each cell cycle, reinitiation of S phase is prevented in G(2) and origins are restricted from refiring within S phase. We have investigated the block to rereplication during G(2) in fission yeast. The DNA synthesis that occurs when G(2)/M cyclin-dependent kinase (CDK) activity is depleted has been assumed to be repeated rounds of S phase without mitosis, but this has not been demonstrated to be the case. We show here that on G(2)/M CDK depletion in G(2), repeated S phases are induced, which are correlated with normal G(1)/S transcription and attainment of doublings in cell size. Mostly normal mitotic S-phase origins are utilized, although at different efficiencies, and replication is essentially equal across the genome. We conclude that CDK inhibits reinitiation of S phase during G(2), and if G(2)/M CDK is depleted, replication results from induction of a largely normal S-phase program with only small differences in origin usage and efficiency.

Transcriptional analysis of the Candida albicans cell cycle

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

A complex-centric view of protein network evolution

The recent availability of protein-protein interaction networks for several species makes it possible to study protein complexes in an evolutionary context. In this article, we present a novel network-based framework for reconstructing the evolutionary history of protein complexes. Our analysis is based on generalizing evolutionary measures for single proteins to the level of whole subnetworks, comprehensively considering a broad set of computationally derived complexes and accounting for both sequence and interaction changes. Specifically, we compute sets of orthologous complexes across species, and use these to derive evolutionary rate and age measures for protein complexes. We observe significant correlations between the evolutionary properties of a complex and those of its member proteins, suggesting that protein complexes form early in evolution and evolve as coherent units. Additionally, our approach enables us to directly quantify the extent to which gene duplication has played a role in the evolution of complexes. We find that about one quarter of the sets of orthologous complexes have originated from evolutionary cores of homodimers that underwent duplication and divergence, testifying to the important role of gene duplication in protein complex evolution.

BRNI: Modular analysis of transcriptional regulatory programs

BACKGROUND

Transcriptional responses often consist of regulatory modules - sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program.

RESULTS

Here, we present a novel approach for the analysis of modular regulatory programs. Our method - Biochemical Regulatory Network Inference (BRNI) - is based on an algorithm that learns from expression data a biochemically-motivated regulatory program. It describes the expression profiles of gene modules consisting of hundreds of genes using a small number of regulators and affinity parameters. We developed an ensemble learning algorithm that ensures the robustness of the learned model. We then use the topology of the learned regulatory program to guide the discovery of a library of cis-regulatory motifs, and determined the motif compositions associated with each module.We test our method on the cell cycle regulatory program of the fission yeast. We discovered 16 coherent modules, covering diverse processes from cell division to metabolism and associated them with 18 learned regulatory elements, including both known cell-cycle regulatory elements (MCB, Ace2, PCB, ACCCT box) and novel ones, some of which are associated with G2 modules. We integrate the regulatory relations from the expression- and motif-based models into a single network, highlighting specific topologies that result in distinct dynamics of gene expression in the fission yeast cell cycle.

CONCLUSION

Our approach provides a biologically-driven, principled way for deconstructing a set of genes into meaningful transcriptional modules and identifying their associated cis-regulatory programs. Our analysis sheds light on the architecture and function of the regulatory network controlling the fission yeast cell cycle, and a similar approach can be applied to the regulatory underpinnings of other modular transcriptional responses.

Creating reference datasets for systems biology applications using text mining

High-throughput experimental techniques are generating large data collections with the aim of identifying novel entities involved in fundamental cellular processes as well as drawing a systematic picture of the relationships between individual components. Determining the accuracy of the resulting data and the selection of a subset of targets for more careful characterizations often requires relying on information provided by manually annotated data repositories. These repositories are incomplete and cover only a small fraction of the knowledge contained in the literature. We propose in this paper the use of text-mining technologies to extract, organize, and present information relevant for a particular biological topic. The aims of the resulting approach are (1) to enable topic-centric biological literature navigation, (2) to assist in the construction of manually revised data repositories, (3) to provide prioritization of biological entities for experimental studies, and (4) to enable human interpretation of large-scale experiments by providing direct links of bio-entities to relevant descriptions in the literature.

Logical analysis of the budding yeast cell cycle

The budding yeast Saccharomyces cerevisiae is a model organism that is commonly used to investigate control of the eukaryotic cell cycle. Moreover, because of the extensive experimental data on wild type and mutant phenotypes, it is also particularly suitable for mathematical modelling and analysis. Here, I present a new Boolean model of the budding yeast cell cycle. This model is consistent with a wide range of wild type and mutant phenotypes and shows remarkable robustness against perturbations, both to reaction times and the states of component genes/proteins. Because of its simple logical nature, the model is suitable for sub-network analysis, which can be used to identify a four node core regulatory circuit underlying cell cycle regulation. Sub-network analysis can also be used to identify key sub-dynamics that are essential for viable cell cycle control, as well as identifying the sub-dynamics that are most variable between different mutants.

The impact of time delays on the robustness of biological oscillators and the effect of bifurcations on the inverse problem

Differential equation models for biological oscillators are often not robust with respect to parameter variations. They are based on chemical reaction kinetics, and solutions typically converge to a fixed point. This behavior is in contrast to real biological oscillators, which work reliably under varying conditions. Moreover, it complicates network inference from time series data. This paper investigates differential equation models for biological oscillators from two perspectives. First, we investigate the effect of time delays on the robustness of these oscillator models. In particular, we provide sufficient conditions for a time delay to cause oscillations by destabilizing a fixed point in two-dimensional systems. Moreover, we show that the inclusion of a time delay also stabilizes oscillating behavior in this way in larger networks. The second part focuses on the inverse problem of estimating model parameters from time series data. Bifurcations are related to nonsmoothness and multiple local minima of the objective function.

Genome-wide expression profiling and bioinformatics analysis of diurnally regulated genes in the mouse prefrontal cortex

Microarray analysis shows that approximately 10% of transcripts in the mouse prefrontal cortex have diurnally regulated expression patterns.

Background

The prefrontal cortex is important in regulating sleep and mood. Diurnally regulated genes in the prefrontal cortex may be controlled by the circadian system, by sleep:wake states, or by cellular metabolism or environmental responses. Bioinformatics analysis of these genes will provide insights into a wide-range of pathways that are involved in the pathophysiology of sleep disorders and psychiatric disorders with sleep disturbances.

Results

We examined gene expression in the mouse prefrontal cortex at four time points during a 24 hour (12 hour light:12 hour dark) cycle using microarrays, and identified 3,890 transcripts corresponding to 2,927 genes with diurnally regulated expression patterns. We show that 16% of the genes identified in our study are orthologs of identified clock, clock controlled or sleep/wakefulness induced genes in the mouse liver and suprachiasmatic nucleus, rat cortex and cerebellum, or Drosophila head. The diurnal expression patterns were confirmed for 16 out of 18 genes in an independent set of RNA samples. The diurnal genes fall into eight temporal categories with distinct functional attributes, as assessed by Gene Ontology classification and analysis of enriched transcription factor binding sites.

Conclusion

Our analysis demonstrates that approximately 10% of transcripts have diurnally regulated expression patterns in the mouse prefrontal cortex. Functional annotation of these genes will be important for the selection of candidate genes for behavioral mutants in the mouse and for genetic studies of disorders associated with anomalies in the sleep:wake cycle and circadian rhythm.

DNA replication checkpoint promotes G1-S transcription by inactivating the MBF repressor Nrm1

The cell cycle transcriptional program imposes order on events of the cell-cycle and is a target for signals that regulate cell-cycle progression, including checkpoints required to maintain genome integrity. Neither the mechanism nor functional significance of checkpoint regulation of the cell-cycle transcription program are established. We show that Nrm1, an MBF-specific transcriptional repressor acting at the transition from G(1) to S phase of the cell cycle, is at the nexus between the cell cycle transcriptional program and the DNA replication checkpoint in fission yeast. Phosphorylation of Nrm1 by the Cds1 (Chk2) checkpoint protein kinase, which is activated in response to DNA replication stress, promotes its dissociation from the MBF transcription factor. This leads to the expression of genes encoding components that function in DNA replication and repair pathways important for cell survival in response to arrested DNA replication.

Embedding mRNA stability in correlation analysis of time-series gene expression data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R² that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R² for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R² value among genes coding for a transient complex.

Microarrays provide snapshots of the transcriptional state of the cell at some point in time. Multiple snapshots can be taken sequentially in time, thus providing insight into the dynamics of change. Since genome-wide expression data report on the abundance of mRNA, not on the underlying activity of genes, we developed a novel method to relate the expression pattern of genes, detected in a time-series experiment, using a similarity measure that incorporates mRNA decay and called lead-lag R². We used the lead-lag R² similarity measure to predict the presence of common transcription factors between gene pairs using an integrated dataset consisting of 13 yeast cell-cycles. The method was benchmarked against six well-established similarity measures and obtained the best true positive rate result, around 95%. We believe that the lead-lag analysis can be successfully used also to predict the presence of a common mechanism able to modulate the degradation rate of specific transcripts. Finally, we envisage the possibility to extend our analysis to different experimental conditions and organisms, thus providing a simple off-the-shelf computational tool to support the understanding of the transcriptional and post-transcriptional regulation layer and its role in many diseases, such as cancer.

The DNA replication checkpoint directly regulates MBF-dependent G1/S transcription

The DNA replication checkpoint transcriptionally upregulates genes that allow cells to adapt to and survive replication stress. Our results show that, in the fission yeast Schizosaccharomyces pombe, the replication checkpoint regulates the entire G(1)/S transcriptional program by directly regulating MBF, the G(1)/S transcription factor. Instead of initiating a checkpoint-specific transcriptional program, the replication checkpoint targets MBF to maintain the normal G(1)/S transcriptional program during replication stress. We propose a mechanism for this regulation, based on in vitro phosphorylation of the Cdc10 subunit of MBF by the Cds1 replication-checkpoint kinase. Replacement of two potential phosphorylation sites with phosphomimetic amino acids suffices to promote the checkpoint transcriptional program, suggesting that Cds1 phosphorylation directly regulates MBF-dependent transcription. The conservation of MBF between fission and budding yeast, and recent results implicating MBF as a target of the budding yeast replication checkpoint, suggests that checkpoint regulation of the MBF transcription factor is a conserved strategy for coping with replication stress. Furthermore, the structural and regulatory similarity between MBF and E2F, the metazoan G(1)/S transcription factor, suggests that this checkpoint mechanism may be broadly conserved among eukaryotes.

Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

MOTIVATION

Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.

RESULTS

A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells.

AVAILABILITY

The software used in this article is available from http://sbie.kaist.ac.kr/software

Transcriptional regulatory network for sexual differentiation in fission yeast

Microarray analysis of the transcriptome of fission yeast after genetic perturbation of 6 genes known to have a role in sexual differentiation reveals insights into the regulatory principles controlling the gene expression program driving this process.

Background

Changes in gene expression are hallmarks of cellular differentiation. Sexual differentiation in fission yeast (Schizosaccharomyces pombe) provides a model system for gene expression programs accompanying and driving cellular specialization. The expression of hundreds of genes is modulated in successive waves during meiosis and sporulation in S. pombe, and several known transcription factors are critical for these processes.

Results

We used DNA microarrays to investigate meiotic gene regulation by examining transcriptomes after genetic perturbations (gene deletion and/or overexpression) of rep1, mei4, atf21 and atf31, which encode known transcription factors controlling sexual differentiation. This analysis reveals target genes at a genome-wide scale and uncovers combinatorial control by Atf21p and Atf31p. We also studied two transcription factors not previously implicated in sexual differentiation whose meiotic induction depended on Mei4p: Rsv2p induces stress-related genes during spore formation, while Rsv1p represses glucose-metabolism genes. Our data further reveal negative feedback interactions: both Rep1p and Mei4p not only activate specific gene expression waves (early and middle genes, respectively) but are also required for repression of genes induced in the previous waves (Ste11p-dependent and early genes, respectively).

Conclusion

These data give insight into regulatory principles controlling the extensive gene expression program driving sexual differentiation and highlight sophisticated interactions and combinatorial control among transcription factors. Besides triggering simultaneous expression of gene waves, transcription factors also repress genes in the previous wave and induce other factors that in turn regulate a subsequent wave. These dependencies ensure an ordered and timely succession of transcriptional waves during cellular differentiation.

Cohesin complex promotes transcriptional termination between convergent genes in S. pombe

Transcription analyses reported in these studies reveal that convergent genes in S. pombe generate overlapping transcripts in the G1 phase of the cell cycle. We show that this double-strand (ds) RNA induces localized RNAi (Dicer and RITS) dependent transient heterochromatin structures including histone H3 lysine 9 trimethylation marks and Swi6 association. Consequently cohesin is recruited to these chromosomal positions through interaction with Swi6. In G2, localized cohesin is further concentrated into the intergenic regions of the convergent genes tested. This results in a block to further dsRNA formation by promoting gene-proximal transcription termination between the convergent genes. Cohesin release at mitosis leads to a new G1 phase with repeated dsRNA formation, transient heterochromatin, and cohesin recruitment. Our results uncover a hitherto unanticipated role for cohesin and further suggest a widespread role for the selective formation of dsRNA, heterochromatin, and subsequent cohesin recruitment in regulated transcriptional termination.

Systematic identification of cell cycle regulated transcription factors from microarray time series data

Background

The cell cycle has long been an important model to study the genome-wide transcriptional regulation. Although several methods have been introduced to identify cell cycle regulated genes from microarray data, they can not be directly used to investigate cell cycle regulated transcription factors (CCRTFs), because for many transcription factors (TFs) it is their activities instead of expressions that are periodically regulated across the cell cycle. To overcome this problem, it is useful to infer TF activities across the cell cycle by integrating microarray expression data with ChIP-chip data, and then examine the periodicity of the inferred activities. For most species, however, large-scale ChIP-chip data are still not available.

Results

We propose a two-step method to identify the CCRTFs by integrating microarray cell cycle data with ChIP-chip data or motif discovery data. In S. cerevisiae, we identify 42 CCRTFs, among which 23 have been verified experimentally. The cell cycle related behaviors (e.g. at which cell cycle phase a TF achieves the highest activity) predicted by our method are consistent with the well established knowledge about them. We also find that the periodical activity fluctuation of some TFs can be perturbed by the cell synchronization treatment. Moreover, by integrating expression data with in-silico motif discovery data, we identify 8 cell cycle associated regulatory motifs, among which 7 are binding sites for well-known cell cycle related TFs.

Conclusion

Our method is effective to identify CCRTFs by integrating microarray cell cycle data with TF-gene binding information. In S. cerevisiae, the TF-gene binding information is provided by the systematic ChIP-chip experiments. In other species where systematic ChIP-chip data is not available, in-silico motif discovery and analysis provide us with an alternative method. Therefore, our method is ready to be implemented to the microarray cell cycle data sets from different species. The C++ program for AC score calculation is available for download from URL .

Combined analysis reveals a core set of cycling genes

The simultaneous analysis of expression data from multiple species reveals a core set of conserved cycling genes that is much larger than previously thought.

Background

Global transcript levels throughout the cell cycle have been characterized using microarrays in several species. Early analysis of these experiments focused on individual species. More recently, a number of studies have concluded that a surprisingly small number of genes conserved in two or more species are periodically transcribed in these species. Combining and comparing data from multiple species is challenging because of noise in expression data, the different synchronization and scoring methods used, and the need to determine an accurate set of homologs.

Results

To solve these problems, we developed and applied a new algorithm to analyze expression data from multiple species simultaneously. Unlike previous studies, we find that more than 20% of cycling genes in budding yeast have cycling homologs in fission yeast and 5% to 7% of cycling genes in each of four species have cycling homologs in all other species. These conserved cycling genes display much stronger cell cycle characteristics in several complementary high throughput datasets.

Essentiality analysis for yeast and human genes confirms these findings. Motif analysis indicates conservation in the corresponding regulatory mechanisms. Gene Ontology analysis and analysis of the genes in the conserved sets sheds light on the evolution of specific subfunctions within the cell cycle.

Conclusion

Our results indicate that the conservation in cyclic expression patterns is much greater than was previously thought. These genes are highly enriched for most cell cycle categories, and a large percentage of them are essential, supporting our claim that cross-species analysis can identify the core set of cycling genes.

Regulation of gene expression during M-G1-phase in fission yeast through Plo1p and forkhead transcription factors

In fission yeast the expression of several genes during M-G1 phase is controlled by binding of the PCB binding factor (PBF) transcription factor complex to Pombe cell cycle box (PCB) promoter motifs. Three components of PBF have been identified, including two forkhead-like proteins Sep1p and Fkh2p, and a MADS-box-like protein, Mbx1p. Here, we examine how PBF is controlled and reveal a role for the Polo kinase Plo1p. plo1+ shows genetic interactions with sep1+, fkh2+ and mbx1+, and overexpression of a kinase-domain mutant of plo1 abolishes M-G1-phase transcription. Plo1p binds to and directly phosphorylates Mbx1p, the first time a Polo kinase has been shown to phosphorylate a MADS box protein in any organism. Fkh2p and Sep1p interact in vivo and in vitro, and Fkh2p, Sep1p and Plo1p contact PCB promoters in vivo. However, strikingly, both Fkh2p and Plo1p bind to PCB promoters only when PCB-controlled genes are not expressed during S- and G2-phase, whereas by contrast Sep1p contacts PCBs coincident with M-G1-phase transcription. Thus, Plo1p, Fkh2p and Sep1p control M-G1-phase gene transcription through a combination of phosphorylation and cell-cycle-specific DNA binding to PCBs.

Cdc2p controls the forkhead transcription factor Fkh2p by phosphorylation during sexual differentiation in fission yeast

In most eukaryotes, cyclin-dependent kinases (Cdks) play a central role in control of cell-cycle progression. Cdks are inactivated from the end of mitosis to the start of the next cell cycle as well as during sexual differentiation. The forkhead-type transcription factor Fkh2p is required for the periodic expression of many genes and for efficient mating in the fission yeast Schizosaccharomyces pombe. However, the mechanism responsible for coordination of cell-cycle progression with sexual differentiation is still unknown. We now show that Fkh2p is phosphorylated by Cdc2p (Cdk1) and that phosphorylation of Fkh2p on T314 or S462 by this Cdk blocks mating in S. pombe by preventing the induction of ste11+ transcription, which is required for the onset of sexual development. We propose that functional interaction between Cdks and forkhead transcription factors may link the mitotic cell cycle and sexual differentiation.

Genome-wide analysis of mRNA decay rates and their determinants in Arabidopsis thaliana

To gain a global view of mRNA decay in Arabidopsis thaliana, suspension cell cultures were treated with a transcriptional inhibitor, and microarrays were used to measure transcript abundance over time. The deduced mRNA half-lives varied widely, from minutes to >24 h. Three features of the transcript displayed a correlation with decay rates: (1) genes possessing at least one intron produce mRNA transcripts significantly more stable than those of intronless genes, and this was not related to overall length, sequence composition, or number of introns; (2) various sequence elements in the 3' untranslated region are enriched among short- and long-lived transcripts, and their multiple occurrence suggests combinatorial control of transcript decay; and (3) transcripts that are microRNA targets generally have short half-lives. The decay rate of transcripts correlated with subcellular localization and function of the encoded proteins. Analysis of transcript decay rates for genes encoding orthologous proteins between Arabidopsis, yeast, and humans indicated that yeast and humans had a higher percentage of transcripts with shorter half-lives and that the relative stability of transcripts from genes encoding proteins involved in cell cycle, transcription, translation, and energy metabolism is conserved. Comparison of decay rates with changes in transcript abundance under a variety of abiotic stresses reveal that a set of transcription factors are downregulated with similar kinetics to decay rates, suggesting that inhibition of their transcription is an important early response to abiotic stress.

Inferring gene regulatory networks from temporal expression profiles under time-delay and noise

Ordinary differential equations (ODE) have been widely used for modeling and analysis of dynamic gene networks in systems biology. In this paper, we propose an optimization method that can infer a gene regulatory network from time-series gene expression data. Specifically, the following four cases are considered: (1) reconstruction of a gene network from synthetic gene expression data with noise, (2) reconstruction of a gene network from synthetic gene expression data with time-delay, (3) reconstruction of a gene network from synthetic gene expression data with noise and time-delay, and (4) reconstruction of a gene network from experimental time-series data in budding yeast cell cycle.

Transcriptome changes and cAMP oscillations in an archaeal cell cycle

BACKGROUND

The cell cycle of all organisms includes mass increase by a factor of two, replication of the genetic material, segregation of the genome to different parts of the cell, and cell division into two daughter cells. It is tightly regulated and typically includes cell cycle-specific oscillations of the levels of transcripts, proteins, protein modifications, and signaling molecules. Until now cell cycle-specific transcriptome changes have been described for four eukaryotic species ranging from yeast to human, but only for two prokaryotic species. Similarly, oscillations of small signaling molecules have been identified in very few eukaryotic species, but not in any prokaryote.

RESULTS

A synchronization procedure for the archaeon Halobacterium salinarum was optimized, so that nearly 100% of all cells divide in a time interval that is 1/4th of the generation time of exponentially growing cells. The method was used to characterize cell cycle-dependent transcriptome changes using a genome-wide DNA microarray. The transcript levels of 87 genes were found to be cell cycle-regulated, corresponding to 3% of all genes. They could be clustered into seven groups with different transcript level profiles. Cluster-specific sequence motifs were detected around the start of the genes that are predicted to be involved in cell cycle-specific transcriptional regulation. Notably, many cell cycle genes that have oscillating transcript levels in eukaryotes are not regulated on the transcriptional level in H. salinarum. Synchronized cultures were also used to identify putative small signaling molecules. H. salinarum was found to contain a basal cAMP concentration of 200 microM, considerably higher than that of yeast. The cAMP concentration is shortly induced directly prior to and after cell division, and thus cAMP probably is an important signal for cell cycle progression.

CONCLUSION

The analysis of cell cycle-specific transcriptome changes of H. salinarum allowed to identify a strategy of transcript level regulation that is different from all previously characterized species. The transcript levels of only 3% of all genes are regulated, a fraction that is considerably lower than has been reported for four eukaryotic species (6%-28%) and for the bacterium C. crescentus (19%). It was shown that cAMP is present in significant concentrations in an archaeon, and the phylogenetic profile of the adenylate cyclase indicates that this signaling molecule is widely distributed in archaea. The occurrence of cell cycle-dependent oscillations of the cAMP concentration in an archaeon and in several eukaryotic species indicates that cAMP level changes might be a phylogenetically old signal for cell cycle progression.

Bayesian model-based inference of transcription factor activity

Background

In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed.

Results

We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model.

Conclusion

We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.

Hormonal control of the inflated calyx syndrome, a morphological novelty, in Physalis

The 'Chinese lantern' phenotype or inflated calyx syndrome (ICS)--inflated sepals encapsulating the mature berry of Physalis floridana--is a morphological novelty within the Solanaceae. ICS is associated with heterotopic expression of MPF2, which codes for a MADS-box transcription factor otherwise involved in leaf formation and male fertility. In accordance with this finding, the MPF2 promoter sequence differs significantly from that of its orthologue STMADS16 in the related Solanum tuberosum, which does not exhibit ICS. However, heterotopic expression of MPF2 is not sufficient for ICS formation in P. floridana- fertilization is also important. Here we report that the hormones cytokinin and gibberellin are essential for ICS formation. MPF2 controls sepal cell division, but the resulting cells are small. Calyx size increases substantially only if gibberellin and cytokinin are available to promote cell elongation and further cell division. Transient expression of appropriate MPF2-/STMADS16-GFP fusions in leaf tissues in the presence of hormones revealed that cytokinin, but not gibberellin, facilitated transport of the transcription factor into the nucleus. Furthermore, an ICS-like structure can be induced in transgenic S. tuberosum by ectopic expression of STMADS16 and simultaneous treatment with cytokinin and gibberellin. Strikingly, transgenic Arabidopsis ectopically expressing solanaceous MPF2-like proteins display enhanced sepal growth when exposed to cytokinin only, while orthologous proteins from non-solanaceous plants did not require cytokinin for this function. These data are incorporated into a detailed model for ICS formation in P. floridana.

A genome-wide analysis in Saccharomyces cerevisiae demonstrates the influence of chromatin modifiers on transcription

Chromatin structure is important in transcription regulation. Many factors influencing chromatin structure have been identified, but the transcriptional programs in which they participate are still poorly understood. Chromatin modifiers participate in transcriptional control together with DNA-bound transcription factors. High-throughput experimental methods allow the genome-wide identification of binding sites for transcription factors as well as quantification of gene expression under various environmental and genetic conditions. We have developed a new methodology that uses the vast amount of available data to dissect the contribution of chromatin structure to transcription. We measure and characterize the dependence of transcription factor function on specific chromatin modifiers. We apply our methodology to S. cerevisiae, using a compendium of 170 gene expression profiles of strains defective for chromatin modifiers, taken from 26 different studies. Our method succeeds in identifying known intricate genetic interactions between chromatin modifiers and transcription factors and uncovers many previously unknown genetic interactions, giving the first genome-wide picture of the contribution of chromatin structure to transcription in a eukaryote.