Periodic gene expression program of the fission yeast cell cycle

Systematic analysis of the role of RNA-binding proteins in the regulation of RNA stability

mRNA half-lives are transcript-specific and vary over a range of more than 100-fold in eukaryotic cells. mRNA stabilities can be regulated by sequence-specific RNA-binding proteins (RBPs), which bind to regulatory sequence elements and modulate the interaction of the mRNA with the cellular RNA degradation machinery. However, it is unclear if this kind of regulation is sufficient to explain the large range of mRNA stabilities. To address this question, we examined the transcriptome of 74 Schizosaccharomyces pombe strains carrying deletions in non-essential genes encoding predicted RBPs (86% of all such genes). We identified 25 strains that displayed changes in the levels of between 4 and 104 mRNAs. The putative targets of these RBPs formed biologically coherent groups, defining regulons involved in cell separation, ribosome biogenesis, meiotic progression, stress responses and mitochondrial function. Moreover, mRNAs in these groups were enriched in specific sequence motifs in their coding sequences and untranslated regions, suggesting that they are coregulated at the posttranscriptional level. We performed genome-wide RNA stability measurements for several RBP mutants, and confirmed that the altered mRNA levels were caused by changes in their stabilities. Although RBPs regulate the decay rates of multiple regulons, only 16% of all S. pombe mRNAs were affected in any of the 74 deletion strains. This suggests that other players or mechanisms are required to generate the observed range of RNA half-lives of a eukaryotic transcriptome.

Chromosome conformation maps in fission yeast reveal cell cycle dependent sub nuclear structure

Successful progression through the cell cycle requires spatial and temporal regulation of gene transcript levels and the number, positions and condensation levels of chromosomes. Here we present a high resolution survey of genome interactions in Schizosaccharomyces pombe using synchronized cells to investigate cell cycle dependent changes in genome organization and transcription. Cell cycle dependent interactions were captured between and within S. pombe chromosomes. Known features of genome organization (e.g. the clustering of telomeres and retrotransposon long terminal repeats (LTRs)) were observed throughout the cell cycle. There were clear correlations between transcript levels and chromosomal interactions between genes, consistent with a role for interactions in transcriptional regulation at specific stages of the cell cycle. In silico reconstructions of the chromosome organization within the S. pombe nuclei were made by polymer modeling. These models suggest that groups of genes with high and low, or differentially regulated transcript levels have preferred positions within the S. pombe nucleus. We conclude that the S. pombe nucleus is spatially divided into functional sub-nuclear domains that correlate with gene activity. The observation that chromosomal interactions are maintained even when chromosomes are fully condensed in M phase implicates genome organization in epigenetic inheritance and bookmarking.

Cell-cycle regulated transcription associates with DNA replication timing in yeast and human

Background

Eukaryotic DNA replication follows a specific temporal program, with some genomic regions consistently replicating earlier than others, yet what determines this program is largely unknown. Highly transcribed regions have been observed to replicate in early S-phase in all plant and animal species studied to date, but this relationship is thought to be absent from both budding yeast and fission yeast. No association between cell-cycle regulated transcription and replication timing has been reported for any species.

Results

Here I show that in budding yeast, fission yeast, and human, the genes most highly transcribed during S-phase replicate early, whereas those repressed in S-phase replicate late. Transcription during other cell-cycle phases shows either the opposite correlation with replication timing, or no relation. The relationship is strongest near late-firing origins of replication, which is not consistent with a previously proposed model—that replication timing may affect transcription—and instead suggests a potential mechanism involving the recruitment of limiting replication initiation factors during S-phase.

Conclusions

These results suggest that S-phase transcription may be an important determinant of DNA replication timing across eukaryotes, which may explain the well-established association between transcription and replication timing.

Topology and control of the cell-cycle-regulated transcriptional circuitry

Nearly 20% of the budding yeast genome is transcribed periodically during the cell division cycle. The precise temporal execution of this large transcriptional program is controlled by a large interacting network of transcriptional regulators, kinases, and ubiquitin ligases. Historically, this network has been viewed as a collection of four coregulated gene clusters that are associated with each phase of the cell cycle. Although the broad outlines of these gene clusters were described nearly 20 years ago, new technologies have enabled major advances in our understanding of the genes comprising those clusters, their regulation, and the complex regulatory interplay between clusters. More recently, advances are being made in understanding the roles of chromatin in the control of the transcriptional program. We are also beginning to discover important regulatory interactions between the cell-cycle transcriptional program and other cell-cycle regulatory mechanisms such as checkpoints and metabolic networks. Here we review recent advances and contemporary models of the transcriptional network and consider these models in the context of eukaryotic cell-cycle controls.

Mediator can regulate mitotic entry and direct periodic transcription in fission yeast

Cdk8 is required for correct timing of mitotic progression in fission yeast. How the activity of Cdk8 is regulated is unclear, since the kinase is not activated by T-loop phosphorylation and its partner, CycC, does not oscillate. Cdk8 is, however, a component of the multiprotein Mediator complex, a conserved coregulator of eukaryotic transcription that is connected to a number of intracellular signaling pathways. We demonstrate here that other Mediator components regulate the activity of Cdk8 in vivo and thereby direct the timing of mitotic entry. Deletion of Mediator components Med12 and Med13 leads to higher cellular Cdk8 protein levels, premature phosphorylation of the Cdk8 target Fkh2, and earlier entry into mitosis. We also demonstrate that Mediator is recruited to clusters of mitotic genes in a periodic fashion and that the complex is required for the transcription of these genes. We suggest that Mediator functions as a hub for coordinated regulation of mitotic progression and cell cycle-dependent transcription. The many signaling pathways and activator proteins shown to function via Mediator may influence the timing of these cell cycle events.

Absolute proteome and phosphoproteome dynamics during the cell cycle of Schizosaccharomyces pombe (Fission Yeast)

To quantify cell cycle-dependent fluctuations on a proteome-wide scale, we performed integrative analysis of the proteome and phosphoproteome during the four major phases of the cell cycle in Schizosaccharomyces pombe. In highly synchronized cells, we identified 3753 proteins and 3682 phosphorylation events and relatively quantified 65% of the data across all phases. Quantitative changes during the cell cycle were infrequent and weak in the proteome but prominent in the phosphoproteome. Protein phosphorylation peaked in mitosis, where the median phosphorylation site occupancy was 44%, about 2-fold higher than in other phases. We measured copy numbers of 3178 proteins, which together with phosphorylation site stoichiometry enabled us to estimate the absolute amount of protein-bound phosphate, as well as its change across the cell cycle. Our results indicate that 23% of the average intracellular ATP is utilized by protein kinases to phosphorylate their substrates to drive regulatory processes during cell division. Accordingly, we observe that phosphate transporters and phosphate-metabolizing enzymes are phosphorylated and therefore likely to be regulated in mitosis.

isocir: An R Package for Constrained Inference using Isotonic Regression for Circular Data, with an Application to Cell Biology

In many applications one may be interested in drawing inferences regarding the order of a collection of points on a unit circle. Due to the underlying geometry of the circle standard constrained inference procedures developed for Euclidean space data are not applicable. Recently, statistical inference for parameters under such order constraints on a unit circle was discussed in Rueda et al. (2009); Fernández et al. (2012). In this paper we introduce an R package called isocir which provides a set of functions that can be used for analyzing angular data subject to order constraints on a unit circle. Since this work is motivated by applications in cell biology, we illustrate the proposed package using a relevant cell cycle data.

The KASH protein Kms2 coordinates mitotic remodeling of the spindle pole body

Defects in the biogenesis of the spindle pole body (SPB), the yeast centrosome equivalent, can lead to monopolar spindles and mitotic catastrophe. The KASH domain protein Kms2 and the SUN domain protein Sad1 colocalize within the nuclear envelope at the site of SPB attachment during interphase and at the spindle poles during mitosis in Schizosaccharomyces pombe. We show that Kms2 interacts with the essential SPB components Cut12 and Pcp1 and the Polo kinase Plo1. Depletion of Kms2 delays mitotic entry and leads to defects in the insertion of the SPB into the nuclear envelope, disrupting stable bipolar spindle formation. These effects are mediated in part by a delay in the recruitment of Plo1 to the SPB at mitotic entry. Plo1 activity supports mitotic SPB remodeling by driving a burst of incorporation of Cut12 and Pcp1. Thus, a fission yeast SUN-KASH complex plays an important role in supporting the remodeling of the SPB at mitotic entry.

A formal concept analysis approach to consensus clustering of multi-experiment expression data

Background

Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them.

Results

We propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group.

These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological signals.

Conclusions

The proposed FCA-enhanced consensus clustering technique is a general approach to the combination of clustering algorithms with FCA for deriving clustering solutions from multiple gene expression matrices. The experimental results presented herein demonstrate that it is a robust data integration technique able to produce good quality clustering solution that is representative for the whole set of expression matrices.

Does a shift to limited glucose activate checkpoint control in fission yeast?

Here we review cell cycle control in the fission yeast, Schizosaccharomyces pombe, in response to an abrupt reduction of glucose concentration in culture media. S. pombe arrests cell cycle progression when transferred from media containing 2.0% glucose to media containing 0.1%. After a delay, S. pombe resumes cell division at a surprisingly fast rate, comparable to that observed in 2% glucose. We found that a number of genes, including zinc-finger transcription factor Scr1, CaMKK-like protein kinase Ssp1, and glucose transporter Ght5, enable rapid cell division in low glucose. In this article, we examine whether cell cycle checkpoint-like control operates during the delay and after resumption of cell division in limited-glucose. Using microarray analysis and genetic screening, we identified several candidate genes that may be involved in controlling this low-glucose adaptation.

Phylogenetic and comparative functional analysis of the cell-separation α-glucanase Agn1p in Schizosaccharomyces

The post-cytokinetic separation of cells in cell-walled organisms involves enzymic processes that degrade a specific layer of the division septum and the region of the mother cell wall that edges the septum. In the fission yeast Schizosaccharomyces pombe, the 1,3-α-glucanase Agn1p, originally identified as a mutanase-like glycoside hydrolase family 71 (GH71) enzyme, dissolves the mother cell wall around the septum edge. Our search in the genomes of completely sequenced fungi identified GH71 hydrolases in Basidiomycota, Taphrinomycotina and Pezizomycotina, but not in Saccharomycotina. The most likely Agn1p orthologues in Pezizomycotina species are not mutanases having mutanase-binding domains, but experimentally non-characterized hypothetical proteins that have no carbohydrate-binding domains. The analysis of the GH71 domains corroborated the phylogenetic relationships of the Schizosaccharomyces species determined by previous studies, but suggested a closer relationship to the Basidiomycota proteins than to the Ascomycota proteins. In the Schizosaccharomyces genus, the Agn1p proteins are structurally conserved: their GH71 domains are flanked by N-terminal secretion signals and C-terminal sequences containing the conserved block YNFNA(Y)/HTG. The inactivation of the agn1(Sj) gene in Schizosaccharomyces japonicus, the only true dimorphic member of the genus, caused a severe cell-separation defect in its yeast phase, but had no effect on the hyphal growth and yeast-to-mycelium transition. It did not affect the mycelium-to-yeast transition either, only delaying the separation of the yeast cells arising from the fragmenting hyphae. The heterologous expression of agn1(Sj) partially rescued the separation defect of the agn1Δ cells of Schizosaccharomyces pombe. The results presented indicate that the fission yeast Agn1p 1,3-α-glucanases of Schizosaccharomyces japonicus and Schizosaccharomyces pombe share conserved functions in the yeast phase.

Cell cycle-regulated transcription: effectively using a genomics toolbox

The cell cycle comprises a series of temporally ordered events that occur sequentially, including DNA replication, centrosome duplication, mitosis, and cytokinesis. What are the regulatory mechanisms that ensure proper timing and coordination of events during the cell cycle? Biochemical and genetic screens have identified a number of cell-cycle regulators, and it was recognized early on that many of the genes encoding cell-cycle regulators, including cyclins, were transcribed only in distinct phases of the cell cycle. Thus, "just in time" expression is likely an important part of the mechanism that maintains the proper temporal order of cell cycle events. New high-throughput technologies for measuring transcript levels have revealed that a large percentage of the Saccharomyces cerevisiae transcriptome (~20 %) is cell cycle regulated. Similarly, a substantial fraction of the mammalian transcriptome is cell cycle-regulated. Over the past 25 years, many studies have been undertaken to determine how gene expression is regulated during the cell cycle. In this review, we discuss contemporary models for the control of cell cycle-regulated transcription, and how this transcription program is coordinated with other cell cycle events in S. cerevisiae. In addition, we address the genomic approaches and analytical methods that enabled contemporary models of cell cycle transcription. Finally, we address current and future technologies that will aid in further understanding the role of periodic transcription during cell cycle progression.

Functional genomic analysis of the periodic transcriptome in the developing Drosophila wing

The eukaryotic cell cycle, driven by both transcriptional and posttranslational mechanisms, is the central molecular oscillator underlying tissue growth throughout animals. Although genome-wide studies have investigated cell-cycle-associated transcription in unicellular systems, global patterns of periodic transcription in multicellular tissues remain largely unexplored. Here we define the cell-cycle-associated transcriptome of the developing Drosophila wing epithelium and compare it with that of cultured Drosophila S2 cells, revealing a core set of periodic genes and a surprising degree of context specificity in periodic transcription. We further employ RNAi-mediated phenotypic profiling to define functional requirements for more than 300 periodic genes, with a focus on those required for cell proliferation in vivo. Finally, we investigate uncharacterized genes required for interkinetic nuclear migration. Combined, these findings provide a global perspective on cell-cycle control in vivo, and they highlight a critical need to understand the context-specific regulation of cell proliferation.

Replication origin selection regulates the distribution of meiotic recombination

The program of DNA replication, defined by the temporal and spatial pattern of origin activation, is altered during development and in cancers. However, whether changes in origin usage play a role in regulating specific biological processes remains unknown. We investigated the consequences of modifying origin selection on meiosis in fission yeast. Genome-wide changes in the replication program of premeiotic S phase do not affect meiotic progression, indicating that meiosis neither activates nor requires a particular origin pattern. In contrast, local changes in origin efficiencies between different replication programs lead to changes in Rad51 recombination factor binding and recombination frequencies in these domains. We observed similar results for Rad51 when changes in efficiencies were generated by directly targeting expression of the Cdc45 replication factor. We conclude that origin selection is a key determinant for organizing meiotic recombination, providing evidence that genome-wide modifications in replication program can modulate cellular physiology.

Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression

During the cell cycle, the levels of hundreds of mRNAs change in a periodic manner, but how this is achieved by alterations in the rates of mRNA synthesis and degradation has not been studied systematically. Here, we used metabolic RNA labeling and comparative dynamic transcriptome analysis (cDTA) to derive mRNA synthesis and degradation rates every 5 min during three cell cycle periods of the yeast Saccharomyces cerevisiae. A novel statistical model identified 479 genes that show periodic changes in mRNA synthesis and generally also periodic changes in their mRNA degradation rates. Peaks of mRNA degradation generally follow peaks of mRNA synthesis, resulting in sharp and high peaks of mRNA levels at defined times during the cell cycle. Whereas the timing of mRNA synthesis is set by upstream DNA motifs and their associated transcription factors (TFs), the synthesis rate of a periodically expressed gene is apparently set by its core promoter.

Linear mixed effects models under inequality constraints with applications

Constraints arise naturally in many scientific experiments/studies such as in, epidemiology, biology, toxicology, etc. and often researchers ignore such information when analyzing their data and use standard methods such as the analysis of variance (ANOVA). Such methods may not only result in a loss of power and efficiency in costs of experimentation but also may result poor interpretation of the data. In this paper we discuss constrained statistical inference in the context of linear mixed effects models that arise naturally in many applications, such as in repeated measurements designs, familial studies and others. We introduce a novel methodology that is broadly applicable for a variety of constraints on the parameters. Since in many applications sample sizes are small and/or the data are not necessarily normally distributed and furthermore error variances need not be homoscedastic (i.e. heterogeneity in the data) we use an empirical best linear unbiased predictor (EBLUP) type residual based bootstrap methodology for deriving critical values of the proposed test. Our simulation studies suggest that the proposed procedure maintains the desired nominal Type I error while competing well with other tests in terms of power. We illustrate the proposed methodology by re-analyzing a clinical trial data on blood mercury level. The methodology introduced in this paper can be easily extended to other settings such as nonlinear and generalized regression models.

Crowding-induced formation and structural alteration of nuclear compartments: insights from computer simulations

Our understanding of the structural and dynamical characteristics of nuclear structures such as chromosomes and nuclear bodies (NBs) has increased significantly in recent days owing to advances in biophysical and biochemical techniques. These techniques include the use of computer simulations, which have provided further physical insights complementary to findings from experiments. In this chapter, we review recent computer simulation studies on the structural alteration of chromosome subcompartments and the formation and maintenance of NBs in the highly crowded cell nucleus. It is found that because of macromolecular crowding, the degree of chromosome compaction changes significantly and the formation of NBs is facilitated. We further discuss the physical consequences of these phenomena, which may be of critical importance in understanding genome processes.

Systematic genetic analysis of transcription factors to map the fission yeast transcription-regulatory network

Mapping transcriptional-regulatory networks requires the identification of target genes, binding specificities and signalling pathways of transcription factors. However, the characterization of each transcription factor sufficiently for deciphering such networks remains laborious. The recent availability of overexpression and deletion strains for almost all of the transcription factor genes in the fission yeast Schizosaccharomyces pombe provides a valuable resource to better investigate transcription factors using systematic genetics. In the present paper, I review and discuss the utility of these strain collections combined with transcriptome profiling and genome-wide chromatin immunoprecipitation to identify the target genes of transcription factors.

Global regulatory architecture of human, mouse and rat tissue transcriptomes

Background

Predicting molecular responses in human by extrapolating results from model organisms requires a precise understanding of the architecture and regulation of biological mechanisms across species.

Results

Here, we present a large-scale comparative analysis of organ and tissue transcriptomes involving the three mammalian species human, mouse and rat. To this end, we created a unique, highly standardized compendium of tissue expression. Representative tissue specific datasets were aggregated from more than 33,900 Affymetrix expression microarrays. For each organism, we created two expression datasets covering over 55 distinct tissue types with curated data from two independent microarray platforms. Principal component analysis (PCA) revealed that the tissue-specific architecture of transcriptomes is highly conserved between human, mouse and rat. Moreover, tissues with related biological function clustered tightly together, even if the underlying data originated from different labs and experimental settings. Overall, the expression variance caused by tissue type was approximately 10 times higher than the variance caused by perturbations or diseases, except for a subset of cancers and chemicals. Pairs of gene orthologs exhibited higher expression correlation between mouse and rat than with human. Finally, we show evidence that tissue expression profiles, if combined with sequence similarity, can improve the correct assignment of functionally related homologs across species.

Conclusion

The results demonstrate that tissue-specific regulation is the main determinant of transcriptome composition and is highly conserved across mammalian species.

Identification of cell cycle-regulated genes periodically expressed in U2OS cells and their regulation by FOXM1 and E2F transcription factors

Characterization of the cell cycle–regulated transcripts in U2OS cells yielded 1871 unique genes. FOXM1 targets were identified via ChIP-seq, and novel targets in G2/M and S phases were verified using a real-time luciferase assay. ChIP-seq data were used to map cell cycle transcriptional regulators of cell cycle–regulated gene expression in U2OS cells.

We identify the cell cycle–regulated mRNA transcripts genome-wide in the osteosarcoma-derived U2OS cell line. This results in 2140 transcripts mapping to 1871 unique cell cycle–regulated genes that show periodic oscillations across multiple synchronous cell cycles. We identify genomic loci bound by the G2/M transcription factor FOXM1 by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) and associate these with cell cycle–regulated genes. FOXM1 is bound to cell cycle–regulated genes with peak expression in both S phase and G2/M phases. We show that ChIP-seq genomic loci are responsive to FOXM1 using a real-time luciferase assay in live cells, showing that FOXM1 strongly activates promoters of G2/M phase genes and weakly activates those induced in S phase. Analysis of ChIP-seq data from a panel of cell cycle transcription factors (E2F1, E2F4, E2F6, and GABPA) from the Encyclopedia of DNA Elements and ChIP-seq data for the DREAM complex finds that a set of core cell cycle genes regulated in both U2OS and HeLa cells are bound by multiple cell cycle transcription factors. These data identify the cell cycle–regulated genes in a second cancer-derived cell line and provide a comprehensive picture of the transcriptional regulatory systems controlling periodic gene expression in the human cell division cycle.

Impaired coenzyme A synthesis in fission yeast causes defective mitosis, quiescence-exit failure, histone hypoacetylation and fragile DNA

Biosynthesis of coenzyme A (CoA) requires a five-step process using pantothenate and cysteine in the fission yeast Schizosaccharomyces pombe. CoA contains a thiol (SH) group, which reacts with carboxylic acid to form thioesters, giving rise to acyl-activated CoAs such as acetyl-CoA. Acetyl-CoA is essential for energy metabolism and protein acetylation, and, in higher eukaryotes, for the production of neurotransmitters. We isolated a novel S. pombe temperature-sensitive strain ppc1-537 mutated in the catalytic region of phosphopantothenoylcysteine synthetase (designated Ppc1), which is essential for CoA synthesis. The mutant becomes auxotrophic to pantothenate at permissive temperature, displaying greatly decreased levels of CoA, acetyl-CoA and histone acetylation. Moreover, ppc1-537 mutant cells failed to restore proliferation from quiescence. Ppc1 is thus the product of a super-housekeeping gene. The ppc1-537 mutant showed combined synthetic lethal defects with five of six histone deacetylase mutants, whereas sir2 deletion exceptionally rescued the ppc1-537 phenotype. In synchronous cultures, ppc1-537 cells can proceed to the S phase, but lose viability during mitosis failing in sister centromere/kinetochore segregation and nuclear division. Additionally, double-strand break repair is defective in the ppc1-537 mutant, producing fragile broken DNA, probably owing to diminished histone acetylation. The CoA-supported metabolism thus controls the state of chromosome DNA.

Identification of yeast cell cycle regulated genes based on genomic features

Background

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

Results

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

Conclusions

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

Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways

The fission yeast Schizosaccharomyces pombe has more metazoan-like features than the budding yeast Saccharomyces cerevisiae, yet it has similarly facile genetics. We present a large-scale verified binary protein-protein interactome network, "StressNet," based on high-throughput yeast two-hybrid screens of interacting proteins classified as part of stress response and signal transduction pathways in S. pombe. We performed systematic, cross-species interactome mapping using StressNet and a protein interactome network of orthologous proteins in S. cerevisiae. With cross-species comparative network studies, we detected a previously unidentified component (Snr1) of the S. pombe mitogen-activated protein kinase Sty1 pathway. Coimmunoprecipitation experiments showed that Snr1 interacted with Sty1 and that deletion of snr1 increased the sensitivity of S. pombe cells to stress. Comparison of StressNet with the interactome network of orthologous proteins in S. cerevisiae showed that most of the interactions among these stress response and signaling proteins are not conserved between species but are "rewired"; orthologous proteins have different binding partners in both species. In particular, transient interactions connecting proteins in different functional modules were more likely to be rewired than conserved. By directly testing interactions between proteins in one yeast species and their corresponding binding partners in the other yeast species with yeast two-hybrid assays, we found that about half of the interactions that are traditionally considered "conserved" form modified interaction interfaces that may potentially accommodate novel functions.

Functional characterization of fission yeast transcription factors by overexpression analysis

In Schizosaccharomyces pombe, over 90% of transcription factor genes are nonessential. Moreover, the majority do not exhibit significant growth defects under optimal conditions when deleted, complicating their functional characterization and target gene identification. Here, we systematically overexpressed 99 transcription factor genes with the nmt1 promoter and found that 64 transcription factor genes exhibited reduced fitness when ectopically expressed. Cell cycle defects were also often observed. We further investigated three uncharacterized transcription factor genes (toe1(+)-toe3(+)) that displayed cell elongation when overexpressed. Ectopic expression of toe1(+) resulted in a G1 delay while toe2(+) and toe3(+) overexpression produced an accumulation of septated cells with abnormalities in septum formation and nuclear segregation, respectively. Transcriptome profiling and ChIP-chip analysis of the transcription factor overexpression strains indicated that Toe1 activates target genes of the pyrimidine-salvage pathway, while Toe3 regulates target genes involved in polyamine synthesis. We also found that ectopic expression of the putative target genes SPBC3H7.05c, and dad5(+) and SPAC11D3.06 could recapitulate the cell cycle phenotypes of toe2(+) and toe3(+) overexpression, respectively. Furthermore, single deletions of the putative target genes urg2(+) and SPAC1399.04c, and SPBC3H7.05c, SPACUNK4.15, and rds1(+), could suppress the phenotypes of toe1(+) and toe2(+) overexpression, respectively. This study implicates new transcription factors and metabolism genes in cell cycle regulation and demonstrates the potential of systematic overexpression analysis to elucidate the function and target genes of transcription factors in S. pombe.

Transcriptional regulatory network shapes the genome structure of Saccharomyces cerevisiae

Among cellular processes gene transcription is central. More and more evidence is mounting that transcription is tightly connected with the spatial organization of the chromosomes. Spatial proximity of genes sharing transcriptional machinery is one of the consequences of this organization. Motivated by information on the physical relationship among genes identified via chromosomal conformation capture methods, we complement the spatial organization with the idea that genes under similar transcription factor control, but possible scattered throughout the genome, might be in physically proximity to facilitate the access of their commonly used transcription factors. Unlike the transcription factory model, "interacting" genes in our "Gene Proximity Model" are not necessarily immediate physical neighbors but are in spatial proximity. Considering the stochastic nature of TF-promoter binding, this local condensation mechanism could serve as a tie to recruit co-regulated genes to guarantee the swiftness of biological reactions. We tested this idea with a simple eukaryotic organism, Saccharomyces cerevisiae. Chromosomal interaction patterns and folding behavior generated by our model re-construct those obtained from experiments. We show that the transcriptional regulatory network has a close linkage with the genome organization in budding yeast, which is fundamental and instrumental to later studies on other more complex eukaryotes.

Paralogous ribosomal protein l32-1 and l32-2 in fission yeast may function distinctively in cellular proliferation and quiescence by changing the ratio of rpl32 paralogs

Fission yeast cells express Rpl32-2 highly while Rpl32-1 lowly in log phase; in contrast, expression of Rpl32-1 raises and reaches a peak level while Rpl32-2 is downregulated to a low basic level when cells enter into stationary phase. Overexpression of Rpl32-1 inhibits cell growth while overexpression of Rpl32-2 does not. Deleting rpl32-2 impairs cell growth more severely than deleting rpl32-1 does. Cell growth impaired by deleting either paralog can be rescued completely by reintroducing rpl32-2, but only partly by rpl32-1. Overexpression of Rpl32-1 inhibits cell division, yielding 4c DNA and multiple septa, while overexpressed Rpl32-2 promotes it. Transcriptomics analysis proved that Rpl32 paralogs regulate expression of a subset of genes related with cell division and stress response in a distinctive way. This functional difference of the two paralogs is due to their difference of 95^th amino acid residue. The significance of a competitive inhibition between Rpl32 paralogs on their expression is discussed.

Spectral analysis on time-course expression data: detecting periodic genes using a real-valued iterative adaptive approach

Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities, which are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the proposed methods suffer from restrictions and large false positives to a certain extent. Additionally, in some experiments, arbitrarily irregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task. A novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive approach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then analyzed using Fisher's hypothesis test. With a proper p-value threshold, periodic genes can be detected. A periodic signal, two nonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition, two yeast real datasets were applied for validation. The simulations and real data analysis reveal that RIAA can perform competitively with the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and when the number of cycles covered by the sampling time points is very reduced.

Matching experiments across species using expression values and textual information

Motivation: With the vast increase in the number of gene expression datasets deposited in public databases, novel techniques are required to analyze and mine this wealth of data. Similar to the way BLAST enables cross-species comparison of sequence data, tools that enable cross-species expression comparison will allow us to better utilize these datasets: cross-species expression comparison enables us to address questions in evolution and development, and further allows the identification of disease-related genes and pathways that play similar roles in humans and model organisms. Unlike sequence, which is static, expression data changes over time and under different conditions. Thus, a prerequisite for performing cross-species analysis is the ability to match experiments across species.

Results: To enable better cross-species comparisons, we developed methods for automatically identifying pairs of similar expression datasets across species. Our method uses a co-training algorithm to combine a model of expression similarity with a model of the text which accompanies the expression experiments. The co-training method outperforms previous methods based on expression similarity alone. Using expert analysis, we show that the new matches identified by our method indeed capture biological similarities across species. We then use the matched expression pairs between human and mouse to recover known and novel cycling genes as well as to identify genes with possible involvement in diabetes. By providing the ability to identify novel candidate genes in model organisms, our method opens the door to new models for studying diseases.

Availability: Source code and supplementary information is available at: www.andrew.cmu.edu/user/aaronwis/cotrain12.

Contact:zivbj@cs.cmu.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Transcription profiling during the cell cycle shows that a subset of Polycomb-targeted genes is upregulated during DNA replication

Genome-wide gene expression analyses of the human somatic cell cycle have indicated that the set of cycling genes differ between primary and cancer cells. By identifying genes that have cell cycle dependent expression in HaCaT human keratinocytes and comparing these with previously identified cell cycle genes, we have identified three distinct groups of cell cycle genes. First, housekeeping genes enriched for known cell cycle functions; second, cell type-specific genes enriched for HaCaT-specific functions; and third, Polycomb-regulated genes. These Polycomb-regulated genes are specifically upregulated during DNA replication, and consistent with being epigenetically silenced in other cell cycle phases, these genes have lower expression than other cell cycle genes. We also find similar patterns in foreskin fibroblasts, indicating that replication-dependent expression of Polycomb-silenced genes is a prevalent but unrecognized regulatory mechanism.

Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells

Data on absolute molecule numbers will empower the modeling, understanding, and comparison of cellular functions and biological systems. We quantified transcriptomes and proteomes in fission yeast during cellular proliferation and quiescence. This rich resource provides the first comprehensive reference for all RNA and most protein concentrations in a eukaryote under two key physiological conditions. The integrated data set supports quantitative biology and affords unique insights into cell regulation. Although mRNAs are typically expressed in a narrow range above 1 copy/cell, most long, noncoding RNAs, except for a distinct subset, are tightly repressed below 1 copy/cell. Cell-cycle-regulated transcription tunes mRNA numbers to phase-specific requirements but can also bring about more switch-like expression. Proteins greatly exceed mRNAs in abundance and dynamic range, and concentrations are regulated to functional demands. Upon transition to quiescence, the proteome changes substantially, but, in stark contrast to mRNAs, proteins do not uniformly decrease but scale with cell volume.

Comprehensive proteomics analysis reveals new substrates and regulators of the fission yeast clp1/cdc14 phosphatase

The conserved family of Cdc14 phosphatases targets cyclin-dependent kinase substrates in yeast, mediating late mitotic signaling events. To discover substrates and regulators of the Schizosaccharomyces pombe Cdc14 phosphatase Clp1, TAP-tagged Clp1, and a substrate trapping mutant (Clp1-C286S) were purified from asynchronous and mitotic (prometaphase and anaphase) cells and binding partners were identified by 2D-LC-MS/MS. Over 100 Clp1-interacting proteins were consistently identified, over 70 of these were enriched in Clp1-C286S-TAP (potential substrates) and we and others detected Cdk1 phosphorylation sites in over half (44/73) of these potential substrates. According to GO annotations, Clp1-interacting proteins are involved in many essential cellular processes including mitosis, cytokinesis, ribosome biogenesis, transcription, and trafficking among others. We confirmed association and dephosphorylation of multiple candidate substrates, including a key scaffolding component of the septation initiation network called Cdc11, an essential kinase of the conserved morphogenesis-related NDR kinase network named Shk1, and multiple Mlu1-binding factor transcriptional regulators. In addition, we identified Sal3, a nuclear β-importin, as the sole karyopherin required for Clp1 nucleoplasmic shuttling, a key mode of Cdc14 phosphatase regulation. Finally, a handful of proteins were more abundant in wild type Clp1-TAP versus Clp1-C286S-TAP, suggesting that they may directly regulate Clp1 signaling or serve as scaffolding platforms to localize Clp1 activity.

Approaches to modeling gene regulatory networks: a gentle introduction

This chapter is split into two main sections; first, I will present an introduction to gene networks. Second, I will discuss various approaches to gene network modeling which will include some examples for using different data sources. Computational modeling has been used for many different biological systems and many approaches have been developed addressing the different needs posed by the different application fields. The modeling approaches presented here are not limited to gene regulatory networks and occasionally I will present other examples. The material covered here is an update based on several previous publications by Thomas Schlitt and Alvis Brazma (FEBS Lett 579(8),1859-1866, 2005; Philos Trans R Soc Lond B Biol Sci 361(1467), 483-494, 2006; BMC Bioinformatics 8(suppl 6), S9, 2007) that formed the foundation for a lecture on gene regulatory networks at the In Silico Systems Biology workshop series at the European Bioinformatics Institute in Hinxton.

Transcriptional networks controlling the cell cycle

In this work, we map the transcriptional targets of 107 previously identified Drosophila genes whose loss caused the strongest cell-cycle phenotypes in a genome-wide RNA interference screen and mine the resulting data computationally. Besides confirming existing knowledge, the analysis revealed several regulatory systems, among which were two highly-specific and interconnected feedback circuits, one between the ribosome and the proteasome that controls overall protein homeostasis, and the other between the ribosome and Myc/Max that regulates the protein synthesis capacity of cells. We also identified a set of genes that alter the timing of mitosis without affecting gene expression, indicating that the cyclic transcriptional program that produces the components required for cell division can be partially uncoupled from the cell division process itself. These genes all have a function in a pathway that regulates the phosphorylation state of Cdk1. We provide evidence showing that this pathway is involved in regulation of cell size, indicating that a Cdk1-regulated cell size checkpoint exists in metazoans.

Deciphering the transcriptional-regulatory network of flocculation in Schizosaccharomyces pombe

In the fission yeast Schizosaccharomyces pombe, the transcriptional-regulatory network that governs flocculation remains poorly understood. Here, we systematically screened an array of transcription factor deletion and overexpression strains for flocculation and performed microarray expression profiling and ChIP-chip analysis to identify the flocculin target genes. We identified five transcription factors that displayed novel roles in the activation or inhibition of flocculation (Rfl1, Adn2, Adn3, Sre2, and Yox1), in addition to the previously-known Mbx2, Cbf11, and Cbf12 regulators. Overexpression of mbx2(+) and deletion of rfl1(+) resulted in strong flocculation and transcriptional upregulation of gsf2(+)/pfl1(+) and several other putative flocculin genes (pfl2(+)-pfl9(+)). Overexpression of the pfl(+) genes singly was sufficient to trigger flocculation, and enhanced flocculation was observed in several combinations of double pfl(+) overexpression. Among the pfl1(+) genes, only loss of gsf2(+) abrogated the flocculent phenotype of all the transcription factor mutants and prevented flocculation when cells were grown in inducing medium containing glycerol and ethanol as the carbon source, thereby indicating that Gsf2 is the dominant flocculin. In contrast, the mild flocculation of adn2(+) or adn3(+) overexpression was likely mediated by the transcriptional activation of cell wall-remodeling genes including gas2(+), psu1(+), and SPAC4H3.03c. We also discovered that Mbx2 and Cbf12 displayed transcriptional autoregulation, and Rfl1 repressed gsf2(+) expression in an inhibitory feed-forward loop involving mbx2(+). These results reveal that flocculation in S. pombe is regulated by a complex network of multiple transcription factors and target genes encoding flocculins and cell wall-remodeling enzymes. Moreover, comparisons between the flocculation transcriptional-regulatory networks of Saccharomyces cerevisiae and S. pombe indicate substantial rewiring of transcription factors and cis-regulatory sequences.

Mathematical modeling of fission yeast Schizosaccharomyces pombe cell cycle: exploring the role of multiple phosphatases

Cell cycle is the central process that regulates growth and division in all eukaryotes. Based on the environmental condition sensed, the cell lies in a resting phase G0 or proceeds through the cyclic cell division process (G1→S→G2→M). These series of events and phase transitions are governed mainly by the highly conserved Cyclin dependent kinases (Cdks) and its positive and negative regulators. The cell cycle regulation of fission yeast Schizosaccharomyces pombe is modeled in this study. The study exploits a detailed molecular interaction map compiled based on the published model and experimental data. There are accumulating evidences about the prominent regulatory role of specific phosphatases in cell cycle regulations. The current study emphasizes the possible role of multiple phosphatases that governs the cell cycle regulation in fission yeast S. pombe. The ability of the model to reproduce the reported regulatory profile for the wild-type and various mutants was verified though simulations.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11693-011-9090-7) contains supplementary material, which is available to authorized users.

An empirical Bayes approach for analysis of diverse periodic trends in time-course gene expression data

MOTIVATION

There is a substantial body of works in the biology literature that seeks to characterize the cyclic behavior of genes during cell division. Gene expression microarrays made it possible to measure the expression profiles of thousands of genes simultaneously in time-course experiments to assess changes in the expression levels of genes over time. In this context, the commonly used procedures for testing include the permutation test by de Lichtenberg et al. and the Fisher's G-test, both of which are designed to evaluate periodicity against noise. However, it is possible that a gene of interest may have expression that is neither cyclic nor just noise. Thus, there is a need for a new test for periodicity that can identify cyclic patterns against not only noise but also other non-cyclic patterns such as linear, quadratic or higher order polynomial patterns.

RESULTS

To address this weakness, we have introduced an empirical Bayes approach to test for periodicity and compare its performance in terms of sensitivity and specificity with that of the permutation test and Fisher's G-test through extensive simulations and by application to a set of time-course experiments on the Schizosaccharomyces pombe cell-cycle gene expression. We use 'conserved' and 'cycling' genes by Lu et al. to assess the sensitivity and CESR genes by Chenet al. to assess the specificity of our new empirical Bayes method.

AVAILABILITY AND IMPLEMENTATION

The SAS Macro for our empirical Bayes test for periodicity is included in the supplementary materials along with a sample run of the MACRO program.

CONTACT

mkocak1@uthsc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dynamic proteomics of human protein level and localization across the cell cycle

Regulation of proteins across the cell cycle is a basic process in cell biology. It has been difficult to study this globally in human cells due to lack of methods to accurately follow protein levels and localizations over time. Estimates based on global mRNA measurements suggest that only a few percent of human genes have cell-cycle dependent mRNA levels. Here, we used dynamic proteomics to study the cell-cycle dependence of proteins. We used 495 clones of a human cell line, each with a different protein tagged fluorescently at its endogenous locus. Protein level and localization was quantified in individual cells over 24h of growth using time-lapse microscopy. Instead of standard chemical or mechanical methods for cell synchronization, we employed in-silico synchronization to place protein levels and localization on a time axis between two cell divisions. This non-perturbative synchronization approach, together with the high accuracy of the measurements, allowed a sensitive assay of cell-cycle dependence. We further developed a computational approach that uses texture features to evaluate changes in protein localizations. We find that 40% of the proteins showed cell cycle dependence, of which 11% showed changes in protein level and 35% in localization. This suggests that a broader range of cell-cycle dependent proteins exists in human cells than was previously appreciated. Most of the cell-cycle dependent proteins exhibit changes in cellular localization. Such changes can be a useful tool in the regulation of the cell-cycle being fast and efficient.

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

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

Linkers of cell polarity and cell cycle regulation in the fission yeast protein interaction network

The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify ‘linker’ proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key role in controlling the temporal changes in the localization of the polarity machinery. Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation. Detailed bibliographic inspection of other predicted ‘linkers’ also confirms the predictive power of the method. As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.

Analysis of protein interaction networks has been of use as a means to grapple with the complexity of the interactome of biological organisms. So far, network based approaches have only been used in a limited number of organisms due to the lack of high-throughput experiments. In this study, we investigate by graph theoretical network analysis approaches the protein-protein interaction network of fission yeast, and present a new network measure, linkerity, that predicts the ability of certain proteins to function as bridges between diverse cellular processes. We apply this linkerity measure to a highly conserved and coupled subset of the fission yeast network, consisting of the proteins that regulate cell cycle, polarized cell growth, and cell division. In depth literature analysis confirms that several proteins identified as linkers of cell polarity regulation are indeed also associated with cell cycle and/or cell division control. Similarly, experimental testing confirms that a mostly uncharacterized polarity regulator identified by the method as an important linker is regulated by the cell cycle, as predicted.

A checkpoints capturing timing-robust Boolean model of the budding yeast cell cycle regulatory network

Background

Cell cycle process of budding yeast (Saccharomyces cerevisiae) consists of four phases: G1, S, G2 and M. Initiated by stimulation of the G1 phase, cell cycle returns to the G1 stationary phase through a sequence of the S, G2 and M phases. During the cell cycle, a cell verifies whether necessary conditions are satisfied at the end of each phase (i.e., checkpoint) since damages of any phase can cause severe cell cycle defect. The cell cycle can proceed to the next phase properly only if checkpoint conditions are met. Over the last decade, there have been several studies to construct Boolean models that capture checkpoint conditions. However, they mostly focused on robustness to network perturbations, and the timing robustness has not been much addressed. Only recently, some studies suggested extension of such models towards timing-robust models, but they have not considered checkpoint conditions.

Results

To construct a timing-robust Boolean model that preserves checkpoint conditions of the budding yeast cell cycle, we used a model verification technique, ‘model checking’. By utilizing automatic and exhaustive verification of model checking, we found that previous models cannot properly capture essential checkpoint conditions in the presence of timing variations. In particular, such models violate the M phase checkpoint condition so that it allows a division of a budding yeast cell into two before the completion of its full DNA replication and synthesis. In this paper, we present a timing-robust model that preserves all the essential checkpoint conditions properly against timing variations. Our simulation results show that the proposed timing-robust model is more robust even against network perturbations and can better represent the nature of cell cycle than previous models.

Conclusions

To our knowledge this is the first work that rigorously examined the timing robustness of the cell cycle process of budding yeast with respect to checkpoint conditions using Boolean models. The proposed timing-robust model is the complete state-of-the-art model that guarantees no violation in terms of checkpoints known to date.

Hpz1 modulates the G1-S transition in fission yeast

Here we characterize a novel protein in S. pombe. It has a high degree of homology with the Zn-finger domain of the human Poly(ADP-ribose) polymerase (PARP). Surprisingly, the gene for this protein is, in many fungi, fused with and in the same reading frame as that encoding Rad3, the homologue of the human ATR checkpoint protein. We name the protein Hpz1 (Homologue of PARP-type Zn-finger). Hpz1 does not possess PARP activity, but is important for resistance to ultraviolet light in the G1 phase and to treatment with hydroxyurea, a drug that arrests DNA replication forks in the S phase. However, we find no evidence of a checkpoint function of Hpz1. Furthermore, absence of Hpz1 results in an advancement of S-phase entry after a G1 arrest as well as earlier recovery from a hydroxyurea block. The hpz1 gene is expressed mainly in the G1 phase and Hpz1 is localized to the nucleus. We conclude that Hpz1 regulates the initiation of the S phase and may cooperate with Rad3 in this function.

Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species

A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.

CENP-B cooperates with Set1 in bidirectional transcriptional silencing and genome organization of retrotransposons

Regulation of transposable elements (TEs) is critical to the integrity of the host genome. The fission yeast Schizosaccharomyces pombe homologs of mammalian CENP-B perform a host genome surveillance role by controlling Tf2 long terminal repeat (LTR) retrotransposons. However, the mechanisms by which CENP-Bs effect their functions are ill defined. Here, we show that the multifaceted roles of Abp1, the prominent member of fission yeast CENP-Bs, are mediated in part via recognition of a 10-bp AT-rich motif present in most LTRs and require the DNA-binding, transposase, and dimerization domains of Abp1 to maintain transcriptional repression and genome organization. Expression profiling analyses indicated that Abp1 recruits class I/II histone deacetylases (HDACs) to repress Tf2 retrotransposons and genes activated in response to stresses. We demonstrate that class I/II HDACs and sirtuins mediate the clustering of dispersed Tf2 retrotransposons into Tf bodies. Intriguingly, we uncovered an unexpected cooperation between Abp1 and the histone H3K4 methyltransferase Set1 in regulating sense and antisense transcriptional silencing of Tf2 retrotransposons and Tf body integrity. Moreover, Set1-mediated regulation of Tf2 expression and nuclear organization appears to be largely independent of H3K4 methylation. Our study illuminates a molecular pathway involving a transposase-containing transcription factor that cooperates with chromatin modifiers to regulate TE activities.

BaSAR-A tool in R for frequency detection

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

Studying and modelling dynamic biological processes using time-series gene expression data

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

Frequency-based time-series gene expression recomposition using PRIISM

Background

Circadian rhythm pathways influence the expression patterns of as much as 31% of the Arabidopsis genome through complicated interaction pathways, and have been found to be significantly disrupted by biotic and abiotic stress treatments, complicating treatment-response gene discovery methods due to clock pattern mismatches in the fold change-based statistics. The PRIISM (Pattern Recomposition for the Isolation of Independent Signals in Microarray data) algorithm outlined in this paper is designed to separate pattern changes induced by different forces, including treatment-response pathways and circadian clock rhythm disruptions.

Results

Using the Fourier transform, high-resolution time-series microarray data is projected to the frequency domain. By identifying the clock frequency range from the core circadian clock genes, we separate the frequency spectrum to different sections containing treatment-frequency (representing up- or down-regulation by an adaptive treatment response), clock-frequency (representing the circadian clock-disruption response) and noise-frequency components. Then, we project the components’ spectra back to the expression domain to reconstruct isolated, independent gene expression patterns representing the effects of the different influences.

By applying PRIISM on a high-resolution time-series Arabidopsis microarray dataset under a cold treatment, we systematically evaluated our method using maximum fold change and principal component analyses. The results of this study showed that the ranked treatment-frequency fold change results produce fewer false positives than the original methodology, and the 26-hour timepoint in our dataset was the best statistic for distinguishing the most known cold-response genes. In addition, six novel cold-response genes were discovered. PRIISM also provides gene expression data which represents only circadian clock influences, and may be useful for circadian clock studies.

Conclusion

PRIISM is a novel approach for overcoming the problem of circadian disruptions from stress treatments on plants. PRIISM can be integrated with any existing analysis approach on gene expression data to separate circadian-influenced changes in gene expression, and it can be extended to apply to any organism with regular oscillations in gene expression patterns across a large portion of the genome.

Transcription factor oscillations induce differential gene expressions

Intracellular protein levels of diverse transcription factors (TFs) vary periodically with time. However, the effects of TF oscillations on gene expression, the primary role of TFs, are poorly understood. In this study, we determined these effects by comparing gene expression levels induced in the presence and in the absence of TF oscillations under same mean intracellular protein level of TF. For all the nonlinear TF transcription kinetics studied, an oscillatory TF is predicted to induce gene expression levels that are distinct from a nonoscillatory TF. The conditions dictating whether TF oscillations induce either higher or lower average gene expression levels were elucidated. Subsequently, the predicted effects from an oscillatory TF, which follows sigmoid transcription kinetics, were applied to demonstrate how oscillatory dynamics provide a mechanism for differential target gene transactivation. Generally, the mean TF concentration at which oscillations occur relative to the promoter binding affinity of a target gene determines whether the gene is up- or downregulated whereas the oscillation amplitude amplifies the magnitude of the differential regulation. Notably, the predicted trends of differential gene expressions induced by oscillatory NF-κB and glucocorticoid receptor match the reported experimental observations. Furthermore, the biological function of p53 oscillations is predicted to prime the cell for death upon DNA damage via differential upregulation of apoptotic genes. Lastly, given N target genes, an oscillatory TF can generate between (N-1) and (2N-1) distinct patterns of differential transactivation. This study provides insights into the mechanism for TF oscillations to induce differential gene expressions, and underscores the importance of TF oscillations in biological regulations.