The more the merrier: comparative analysis of microarray studies on cell cycle-regulated genes in fission yeast

The Rtf1/Prf1-dependent histone modification axis counteracts multi-drug resistance in fission yeast

RNA polymerase II transcription elongation directs an intricate pattern of histone modifications. This pattern includes a regulatory cascade initiated by the elongation factor Rtf1, leading to monoubiquitylation of histone H2B, and subsequent methylation of histone H3 on lysine 4. Previous studies have defined the molecular basis for these regulatory relationships, but it remains unclear how they regulate gene expression. To address this question, we investigated a drug resistance phenotype that characterizes defects in this axis in the model eukaryote Schizosaccharomyces pombe (fission yeast). The mutations caused resistance to the ribonucleotide reductase inhibitor hydroxyurea (HU) that correlated with a reduced effect of HU on dNTP pools, reduced requirement for the S-phase checkpoint, and blunting of the transcriptional response to HU treatment. Mutations in the C-terminal repeat domain of the RNA polymerase II large subunit Rpb1 led to similar phenotypes. Moreover, all the HU-resistant mutants also exhibited resistance to several azole-class antifungal agents. Our results suggest a novel, shared gene regulatory function of the Rtf1-H2Bub1-H3K4me axis and the Rpb1 C-terminal repeat domain in controlling fungal drug tolerance.

The GATA Transcription Factor Gaf1 Represses tRNAs, Inhibits Growth, and Extends Chronological Lifespan Downstream of Fission Yeast TORC1

Target of Rapamycin Complex 1 (TORC1) signaling promotes growth and aging. Inhibition of TORC1 leads to reduced protein translation, which promotes longevity. TORC1-dependent post-transcriptional regulation of protein translation has been well studied, while analogous transcriptional regulation is less understood. Here we screen fission yeast mutants for resistance to Torin1, which inhibits TORC1 and cell growth. Cells lacking the GATA factor Gaf1 (gaf1Δ) grow normally even in high doses of Torin1. The gaf1Δ mutation shortens the chronological lifespan of non-dividing cells and diminishes Torin1-mediated longevity. Expression profiling and genome-wide binding experiments show that upon TORC1 inhibition, Gaf1 directly upregulates genes for small-molecule metabolic pathways and indirectly represses genes for protein translation. Surprisingly, Gaf1 binds to and downregulates the tRNA genes, so it also functions as a transcription factor for RNA polymerase III. Thus, Gaf1 controls the transcription of both protein-coding and tRNA genes to inhibit translation and growth downstream of TORC1.

Unbiased Boolean analysis of public gene expression data for cell cycle gene identification

Cell proliferation is essential for the development and maintenance of all organisms and is dysregulated in cancer. Using synchronized cells progressing through the cell cycle, pioneering microarray studies defined cell cycle genes based on cyclic variation in their expression. However, the concordance of the small number of synchronized cell studies has been limited, leading to discrepancies in definition of the transcriptionally regulated set of cell cycle genes within and between species. Here we present an informatics approach based on Boolean logic to identify cell cycle genes. This approach used the vast array of publicly available gene expression data sets to query similarity to CCNB1, which encodes the cyclin subunit of the Cdk1-cyclin B complex that triggers the G2-to-M transition. In addition to highlighting conservation of cell cycle genes across large evolutionary distances, this approach identified contexts where well-studied genes known to act during the cell cycle are expressed and potentially acting in nondivision contexts. An accessible web platform enables a detailed exploration of the cell cycle gene lists generated using the Boolean logic approach. The methods employed are straightforward to extend to processes other than the cell cycle.

Rethinking cell-cycle-dependent gene expression in Schizosaccharomyces pombe

Three studies of gene expression during the division cycle of Schizosaccharomyces pombe led to the proposal that a large number of genes are expressed at particular times during the S. pombe cell cycle. Yet only a small fraction of genes proposed to be expressed in a cell-cycle-dependent manner are reproducible in all three published studies. In addition to reproducibility problems, questions about expression amplitudes, cell-cycle timing of expression, synchronization artifacts, and the problem with methods for synchronizing cells must be considered. These problems and complications prompt the idea that caution should be used before accepting the conclusion that there are a large number of genes expressed in a cell-cycle-dependent manner in S. pombe.

Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types

BACKGROUND

Cell division is central to the physiology and pathology of all eukaryotic organisms. The molecular machinery underpinning the cell cycle has been studied extensively in a number of species and core aspects of it have been found to be highly conserved. Similarly, the transcriptional changes associated with this pathway have been studied in different organisms and different cell types. In each case hundreds of genes have been reported to be regulated, however there seems to be little consensus in the genes identified across different studies. In a recent comparison of transcriptomic studies of the cell cycle in different human cell types, only 96 cell cycle genes were reported to be the same across all studies examined.

RESULTS

Here we perform a systematic re-examination of published human cell cycle expression data by using a network-based approach to identify groups of genes with a similar expression profile and therefore function. Two clusters in particular, containing 298 transcripts, showed patterns of expression consistent with cell cycle occurrence across the four human cell types assessed.

CONCLUSIONS

Our analysis shows that there is a far greater conservation of cell cycle-associated gene expression across human cell types than reported previously, which can be separated into two distinct transcriptional networks associated with the G₁/S-S and G₂-M phases of the cell cycle. This work also highlights the benefits of performing a re-analysis on combined datasets.

CSL protein regulates transcription of genes required to prevent catastrophic mitosis in fission yeast

For every eukaryotic cell to grow and divide, intricately coordinated action of numerous proteins is required to ensure proper cell-cycle progression. The fission yeast Schizosaccharomyces pombe has been instrumental in elucidating the fundamental principles of cell-cycle control. Mutations in S. pombe 'cut' (cell untimely torn) genes cause failed coordination between cell and nuclear division, resulting in catastrophic mitosis. Deletion of cbf11, a fission yeast CSL transcription factor gene, triggers a 'cut' phenotype, but the precise role of Cbf11 in promoting mitotic fidelity is not known. We report that Cbf11 directly activates the transcription of the acetyl-coenzyme A carboxylase gene cut6, and the biotin uptake/biosynthesis genes vht1 and bio2, with the former 2 implicated in mitotic fidelity. Cbf11 binds to a canonical, metazoan-like CSL response element (GTGGGAA) in the cut6 promoter. Expression of Cbf11 target genes shows apparent oscillations during the cell cycle using temperature-sensitive cdc25-22 and cdc10-M17 block-release experiments, but not with other synchronization methods. The penetrance of catastrophic mitosis in cbf11 and cut6 mutants is nutrient-dependent. We also show that drastic decrease in biotin availability arrests cell proliferation but does not cause mitotic defects. Taken together, our results raise the possibility that CSL proteins play conserved roles in regulating cell-cycle progression, and they could guide experiments into mitotic CSL functions in mammals.

Cdk1 activity acts as a quantitative platform for coordinating cell cycle progression with periodic transcription

Cell proliferation is regulated by cyclin-dependent kinases (Cdks) and requires the periodic expression of particular gene clusters in different cell cycle phases. However, the interplay between the networks that generate these transcriptional oscillations and the core cell cycle machinery remains largely unexplored. In this work, we use a synthetic regulable Cdk1 module to demonstrate that periodic expression is governed by quantitative changes in Cdk1 activity, with different clusters directly responding to specific activity levels. We further establish that cell cycle events neither participate in nor interfere with the Cdk1-driven transcriptional program, provided that cells are exposed to the appropriate Cdk1 activities. These findings contrast with current models that propose self-sustained and Cdk1-independent transcriptional oscillations. Our work therefore supports a model in which Cdk1 activity serves as a quantitative platform for coordinating cell cycle transitions with the expression of critical genes to bring about proper cell cycle progression.

Cell proliferation is regulated by cyclin-dependent kinases (Cdks) and relies on periodic gene cluster expression according to cell cycle phases. Here the authors use a synthetic regulatable Cdk1 module to demonstrate that periodic expression is governed by quantitative changes in Cdk1 activity.

AnGeLi: A Tool for the Analysis of Gene Lists from Fission Yeast

Genome-wide assays and screens typically result in large lists of genes or proteins. Enrichments of functional or other biological properties within such lists can provide valuable insights and testable hypotheses. To systematically detect these enrichments can be challenging and time-consuming, because relevant data to compare against query gene lists are spread over many different sources. We have developed AnGeLi (Analysis of Gene Lists), an intuitive, integrated web-tool for comprehensive and customized interrogation of gene lists from the fission yeast, Schizosaccharomyces pombe. AnGeLi searches for significant enrichments among multiple qualitative and quantitative information sources, including gene and phenotype ontologies, genetic and protein interactions, numerous features of genes, transcripts, translation, and proteins such as copy numbers, chromosomal positions, genetic diversity, RNA polymerase II and ribosome occupancy, localization, conservation, half-lives, domains, and molecular weight among others, as well as diverse sets of genes that are co-regulated or lead to the same phenotypes when mutated. AnGeLi uses robust statistics which can be tailored to specific needs. It also provides the option to upload user-defined gene sets to compare against the query list. Through an integrated data submission form, AnGeLi encourages the community to contribute additional curated gene lists to further increase the usefulness of this resource and to get the most from the ever increasing large-scale experiments. AnGeLi offers a rigorous yet flexible statistical analysis platform for rich insights into functional enrichments and biological context for query gene lists, thus providing a powerful exploratory tool through which S. pombe researchers can uncover fresh perspectives and unexpected connections from genomic data. AnGeLi is freely available at: www.bahlerlab.info/AnGeLi.

Fission Yeast CSL Transcription Factors: Mapping Their Target Genes and Biological Roles

BACKGROUND

Cbf11 and Cbf12, the fission yeast CSL transcription factors, have been implicated in the regulation of cell-cycle progression, but no specific roles have been described and their target genes have been only partially mapped.

METHODOLOGY/PRINCIPAL FINDINGS

Using a combination of transcriptome profiling under various conditions and genome-wide analysis of CSL-DNA interactions, we identify genes regulated directly and indirectly by CSL proteins in fission yeast. We show that the expression of stress-response genes and genes that are expressed periodically during the cell cycle is deregulated upon genetic manipulation of cbf11 and/or cbf12. Accordingly, the coordination of mitosis and cytokinesis is perturbed in cells with genetically manipulated CSL protein levels, together with other specific defects in cell-cycle progression. Cbf11 activity is nutrient-dependent and Δcbf11-associated defects are mitigated by inactivation of the protein kinase A (Pka1) and stress-activated MAP kinase (Sty1p38) pathways. Furthermore, Cbf11 directly regulates a set of lipid metabolism genes and Δcbf11 cells feature a stark decrease in the number of storage lipid droplets.

CONCLUSIONS/SIGNIFICANCE

Our results provide a framework for a more detailed understanding of the role of CSL proteins in the regulation of cell-cycle progression in fission yeast.

Mediator kinase module and human tumorigenesis

Mediator is a conserved multi-subunit signal processor through which regulatory informatiosn conveyed by gene-specific transcription factors is transduced to RNA Polymerase II (Pol II). In humans, MED13, MED12, CDK8 and Cyclin C (CycC) comprise a four-subunit "kinase" module that exists in variable association with a 26-subunit Mediator core. Genetic and biochemical studies have established the Mediator kinase module as a major ingress of developmental and oncogenic signaling through Mediator, and much of its function in signal-dependent gene regulation derives from its resident CDK8 kinase activity. For example, CDK8-targeted substrate phosphorylation impacts transcription factor half-life, Pol II activity and chromatin chemistry and functional status. Recent structural and biochemical studies have revealed a precise network of physical and functional subunit interactions required for proper kinase module activity. Accordingly, pathologic change in this activity through altered expression or mutation of constituent kinase module subunits can have profound consequences for altered signaling and tumor formation. Herein, we review the structural organization, biological function and oncogenic potential of the Mediator kinase module. We focus principally on tumor-associated alterations in kinase module subunits for which mechanistic relationships as opposed to strictly correlative associations are established. These considerations point to an emerging picture of the Mediator kinase module as an oncogenic unit, one in which pathogenic activation/deactivation through component change drives tumor formation through perturbation of signal-dependent gene regulation. It follows that therapeutic strategies to combat CDK8-driven tumors will involve targeted modulation of CDK8 activity or pharmacologic manipulation of dysregulated CDK8-dependent signaling pathways.

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

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

Meta-analysis of bivariate P values

AIM: To propose a new meta-analysis method for bivariate P value which account for the paired structure.

METHODS: Studies that look to test two different features from the same sample gives rise to bivariate P value. A relevant example of this is testing for periodicity as well expression from time-course gene expression studies. Kocak et al (2010) uses George and Mudholkar’ (1983) “Difference of Two Logit-Sums” method to pool bivariate P value across independent experiments, assuming independence within a pair. As bivariate P value need not to be independent within a given study, we propose a new meta-analysis approach for pooling bivariate P value across independent experiments, which accounts for potential correlation between paired P-values. We compare the “Difference of Two Logit Sums”method with our novel approach in terms of their sensitivity and specificity through extensive simulations by generating P value samples from most commonly used tests namely, Z test, t test, chi-square test, and F test, with varying sample sizes and correlation structure.

RESULTS: The simulations results showed that our new meta-analysis approach for correlated and uncorrelated bivariate P value has much more desirable sensitivity and specificity features compared to the existing method, which treats each member of the paired P value as independent. We also compare these meta-analysis approaches on bivariate P value from periodicity and expression tests of 4936 S.Pombe genes from 10 independent time-course experiments and we showed that our new approach ranks the periodic, conserved, and cycling genes significantly higher, and detects many more periodic, “conserved” and “cycling” genes among the top 100 genes, compared to the ‘Difference of Two Logit-Sums’ method. Finally, we used our meta-analytic approach to compare the relative evidence in the association of pre-term birth with preschool wheezing versus pre-school asthma.

CONCLUSION: The new meta-analysis method has much better sensitivity and specific characteristics compared to the “Difference of Two-Logit Sums” method and it is not computationally more expensive.

Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition

Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomposition (EMD) to examine this effect. We applied EMD to a yeast microarray dataset and extracted a series of intrinsic mode function (IMF) oscillations from the time-series data. Our analysis indicated that many periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. By validating a protein complex coexpression analysis, we revealed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. This approach can be applied to other time-series microarray studies.

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.

Contributions of transcription and mRNA decay to gene expression dynamics of fission yeast in response to oxidative stress

The cooperation of transcriptional and post-transcriptional levels of control to shape gene regulation is only partially understood. Here we show that a combination of two simple and non-invasive genomic techniques, coupled with kinetic mathematical modeling, afford insight into the intricate dynamics of RNA regulation in response to oxidative stress in the fission yeast Schizosaccharomyces pombe. This study reveals a dominant role of transcriptional regulation in response to stress, but also points to the first minutes after stress induction as a critical time when the coordinated control of mRNA turnover can support the control of transcription for rapid gene regulation. In addition, we uncover specialized gene expression strategies associated with distinct functional gene groups, such as simultaneous transcriptional repression and mRNA destabilization for genes encoding ribosomal proteins, delayed mRNA destabilization with varying contribution of transcription for ribosome biogenesis genes, dominant roles of mRNA stabilization for genes functioning in protein degradation, and adjustment of both transcription and mRNA turnover during the adaptation to stress. We also show that genes regulated independently of the bZIP transcription factor Atf1p are predominantly controlled by mRNA turnover, and identify putative cis-regulatory sequences that are associated with different gene expression strategies during the stress response. This study highlights the intricate and multi-faceted interplay between transcription and RNA turnover during the dynamic regulatory response to stress.

A novel histone deacetylase complex in the control of transcription and genome stability

The acetylation state of histones, controlled by histone acetyltransferases (HATs) and deacetylases (HDACs), profoundly affects DNA transcription and repair by modulating chromatin accessibility to the cellular machinery. The Schizosaccharomyces pombe HDAC Clr6 (human HDAC1) binds to different sets of proteins that define functionally distinct complexes: I, I', and II. Here, we determine the composition, architecture, and functions of a new Clr6 HDAC complex, I'', delineated by the novel proteins Nts1, Mug165, and Png3. Deletion of nts1 causes increased sensitivity to genotoxins and deregulated expression of Tf2 elements, long noncoding RNA, and subtelomeric and stress-related genes. Similar, but more pervasive, phenotypes are observed upon Clr6 inactivation, supporting the designation of complex I'' as a mediator of a key subset of Clr6 functions. We also reveal that with the exception of Tf2 elements, the genome-wide loading sites and loci regulated by Clr6 I″ do not correlate. Instead, Nts1 loads at genes that are expressed in midmeiosis, following oxidative stress, or are periodically expressed. Collective data suggest that Clr6 I'' has (i) indirect effects on gene expression, conceivably by mediating higher-order chromatin organization of subtelomeres and Tf2 elements, and (ii) direct effects on the transcription of specific genes in response to certain cellular or environmental stimuli.

Purification of G1 daughter cells from different Saccharomycetes species through an optimized centrifugal elutriation procedure

Centrifugal elutriation discriminates cells according to their sedimentation coefficients, generating homogeneous samples well suited for genomic comparative approaches. It can, for instance, isolate G1 daughter cells from a Saccharomyces cerevisiae unsynchronized population, alleviating ageing and cell-cycle biases when conducting genome-wide/single-cell studies. The present report describes a straightforward and robust procedure to determine whether a cell population of virtually any yeast species can be efficiently elutriated, while offering solutions to optimize success. This approach was used to characterize elutriation parameters and S-phase progression of four yeast species (S. cerevisiae, Candida glabrata, Lachancea kluyveri and Pichia sorbitophila) and could theoretically be applied to any culture of single, individual cells.

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.

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.

Regulation of transcriptome, translation, and proteome in response to environmental stress in fission yeast

Background

Gene expression is controlled globally and at multiple levels in response to environmental stress, but the relationships among these dynamic regulatory changes are not clear. Here we analyzed global regulation during different stress conditions in fission yeast, Schizosaccharomyces pombe, combining dynamic genome-wide data on mRNA, translation, and protein profiles.

Results

We observed a strong overall concordance between changes in mRNAs and co-directional changes in translation, for both induced and repressed genes, in response to three conditions: oxidative stress, heat shock, and DNA damage. However, approximately 200 genes each under oxidative and heat stress conditions showed discordant regulation with respect to mRNA and translation profiles, with genes and patterns of regulation being stress-specific. For oxidative stress, we also measured dynamic profiles for 2,147 proteins, comprising 43% of the proteome. The mRNAs induced during oxidative stress strongly correlated with increased protein expression, while repressed mRNAs did not relate to the corresponding protein profiles. Overall changes in relative protein expression correlated better with changes in mRNA expression than with changes in translational efficiency.

Conclusions

These data highlight a global coordination and fine-tuning of gene regulation during stress that mostly acts in the same direction at the levels of transcription and translation. In the oxidative stress condition analyzed, transcription dominates translation to control protein abundance. The concordant regulation of transcription and translation leads to the expected adjustment in protein expression only for up-regulated mRNAs. These patterns of control might reflect the need to balance protein production for stress survival given a limited translational capacity.

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.

Clustering of gene expression profiles: creating initialization-independent clusterings by eliminating unstable genes

Clustering is an important approach in the analysis of biological data, and often a first step to identify interesting patterns of coexpression in gene expression data. Because of the high complexity and diversity of gene expression data, many genes cannot be easily assigned to a cluster, but even if the dissimilarity of these genes with all other gene groups is large, they will finally be forced to become member of a cluster. In this paper we show how to detect such elements, called unstable elements. We have developed an approach for iterative clustering algorithms in which unstable elements are deleted, making the iterative algorithm less dependent on initial centers. Although the approach is unsupervised, it is less likely that the clusters into which the reduced data set is subdivided contain false positives. This clustering yields a more differentiated approach for biological data, since the cluster analysis is divided into two parts: the pruned data set is divided into highly consistent clusters in an unsupervised way and the removed, unstable elements for which no meaningful cluster exists in unsupervised terms can be given a cluster with the use of biological knowledge and information about the likelihood of cluster membership. We illustrate our framework on both an artificial and real biological data set.

A genetic engineering solution to the "arginine conversion problem" in stable isotope labeling by amino acids in cell culture (SILAC)

Stable isotope labeling by amino acids in cell culture (SILAC) provides a straightforward tool for quantitation in proteomics. However, one problem associated with SILAC is the in vivo conversion of labeled arginine to other amino acids, typically proline. We found that arginine conversion in the fission yeast Schizosaccharomyces pombe occurred at extremely high levels, such that labeling cells with heavy arginine led to undesired incorporation of label into essentially all of the proline pool as well as a substantial portion of glutamate, glutamine, and lysine pools. We found that this can be prevented by deleting genes involved in arginine catabolism using methods that are highly robust yet simple to implement. Deletion of both fission yeast arginase genes or of the single ornithine transaminase gene, together with a small modification to growth medium that improves arginine uptake in mutant strains, was sufficient to abolish essentially all arginine conversion. We demonstrated the usefulness of our approach in a large scale quantitative analysis of proteins before and after cell division; both up- and down-regulated proteins, including a novel protein involved in septation, were successfully identified. This strategy for addressing the “arginine conversion problem” may be more broadly applicable to organisms amenable to genetic manipulation.

Time warping of evolutionary distant temporal gene expression data based on noise suppression

Background

Comparative analysis of genome wide temporal gene expression data has a broad potential area of application, including evolutionary biology, developmental biology, and medicine. However, at large evolutionary distances, the construction of global alignments and the consequent comparison of the time-series data are difficult. The main reason is the accumulation of variability in expression profiles of orthologous genes, in the course of evolution.

Results

We applied Pearson distance matrices, in combination with other noise-suppression techniques and data filtering to improve alignments. This novel framework enhanced the capacity to capture the similarities between the temporal gene expression datasets separated by large evolutionary distances. We aligned and compared the temporal gene expression data in budding (Saccharomyces cerevisiae) and fission (Schizosaccharomyces pombe) yeast, which are separated by more then ~400 myr of evolution. We found that the global alignment (time warping) properly matched the duration of cell cycle phases in these distant organisms, which was measured in prior studies. At the same time, when applied to individual ortholog pairs, this alignment procedure revealed groups of genes with distinct alignments, different from the global alignment.

Conclusion

Our alignment-based predictions of differences in the cell cycle phases between the two yeast species were in a good agreement with the existing data, thus supporting the computational strategy adopted in this study. We propose that the existence of the alternative alignments, specific to distinct groups of genes, suggests presence of different synchronization modes between the two organisms and possible functional decoupling of particular physiological gene networks in the course of evolution.

Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast

BACKGROUND

Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction.

RESULTS

Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes.

CONCLUSION

A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.

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.

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.

Maternal diabetes alters transcriptional programs in the developing embryo

Background

Maternal diabetes is a well-known risk factor for birth defects, such as heart defects and neural tube defects. The causative molecular mechanisms in the developing embryo are currently unknown, and the pathogenesis of developmental abnormalities during diabetic pregnancy is not well understood. We hypothesized that the developmental defects are due to alterations in critical developmental pathways, possibly as a result of altered gene expression. We here report results from gene expression profiling of exposed embryos from a mouse diabetes model.

Results

In comparison to normal embryos at mid-gestation, we find significantly altered gene expression levels in diabetes-exposed embryos. Independent validation of altered expression was obtained by quantitative Real Time Polymerase Chain Reaction. Sequence motifs in the promoters of diabetes-affected genes suggest potential binding of transcription factors that are involved in responses to oxidative stress and/or to hypoxia, two conditions known to be associated with diabetic pregnancies. Functional annotation shows that a sixth of the de-regulated genes have known developmental phenotypes in mouse mutants. Over 30% of the genes we have identified encode transcription factors and chromatin modifying proteins or components of signaling pathways that impinge on transcription.

Conclusion

Exposure to maternal diabetes during pregnancy alters transcriptional profiles in the developing embryo. The enrichment, within the set of de-regulated genes, of those encoding transcriptional regulatory molecules provides support for the hypothesis that maternal diabetes affects specific developmental programs.

The Cell Cycle Ontology: an application ontology for the representation and integrated analysis of the cell cycle process

A software resource for the analysis of cell cycle related molecular networks.

The Cell Cycle Ontology ( is an application ontology that automatically captures and integrates detailed knowledge on the cell cycle process. Cell Cycle Ontology is enabled by semantic web technologies, and is accessible via the web for browsing, visualizing, advanced querying, and computational reasoning. Cell Cycle Ontology facilitates a detailed analysis of cell cycle-related molecular network components. Through querying and automated reasoning, it may provide new hypotheses to help steer a systems biology approach to biological network building.

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.

A novel meta-analysis method exploiting consistency of high-throughput experiments

MOTIVATION

Large-scale biological experiments provide snapshots into the huge number of processes running in parallel within the organism. These processes depend on a large number of (hidden) (epi)genetic, social, environmental and other factors that are out of experimentalists' control. This makes it extremely difficult to identify the dominant processes and the elements involved in them based on a single experiment. It is therefore desirable to use multiple sets of experiments targeting the same phenomena while differing in some experimental parameters (hidden or controllable). Although such datasets are becoming increasingly common, their analysis is complicated by the fact that the various biological elements could be influenced by different sets of factors.

RESULTS

The central hypothesis of this article is that biologically related elements and processes are affected by changes in similar ways while unrelated ones are affected differently. Thus, the relations between related elements are more consistent across experiments. The method outlined here looks for groups of elements with robust intra-group relationships in the expectation that they are related. The major groups of elements may be identified in this way. The strengths of relationships per se are not valued, just their consistency. This represents a completely novel and unutilized source of information. In the analysis of time course microarray experiments, I found cell cycle- and ribosome-related genes to be the major groups. Despite not looking for these groups in particular, the identification of these genes rivals that of methods designed specifically for this purpose.

AVAILABILITY

A C++ implementation is available at http://www.rinst.org/ICS/ICS_Programs.tar.gz.

Circular reasoning rather than cyclic expression

A response to Combined analysis reveals a core set of cycling genes by Y Lu, S Mahony, PV Benos, R Rosenfeld, I Simon, LL Breeden and Z Bar-Joseph. Genome Biol 2007, 8:R146.

Genomics and functional genomics with haloarchaea

The first haloarchaeal genome was published in 2000 and today five genome sequences are available. Transcriptome and proteome analyses have been established for two and three haloarchaeal species, respectively, and more than 20 studies using these functional genomic approaches have been published in the last two years. These studies gave global overviews of metabolic regulation (aerobic and anaerobic respiration, phototrophy, carbon source usage), stress response (UV, X-rays, transition metals, osmotic and temperature stress), cell cycle-dependent transcript level regulation, and transcript half-lives. The only translatome analysis available for any prokaryotic species revealed that 10 and 20% of all transcripts are translationally regulated in Haloferax volcanii and Halobacterium salinarum, respectively. Very effective methods for the construction of in frame deletion mutants have been established recently for haloarchaea and are intensively used to unravel the biological roles of genes in this group. Bioinformatic analyses include both cross-genome comparisons as well as integration of genomic data with experimental results. The first systems biology approaches have been performed that used experimental data to construct predictive models of gene expression and metabolism, respectively. In this contribution the current status of genomics, functional genomics, and molecular genetics of haloarchaea is summarized and selected examples are discussed.

urg1: a uracil-regulatable promoter system for fission yeast with short induction and repression times

Background

The fission yeast Schizosaccharomyces pombe is a popular genetic model organism with powerful experimental tools. The thiamine-regulatable nmt1 promoter and derivatives, which take >15 hours for full induction, are most commonly used for controlled expression of ectopic genes. Given the short cell cycle of fission yeast, however, a promoter system that can be rapidly regulated, similar to the GAL system for budding yeast, would provide a key advantage for many experiments.

Methodology/Principal Findings

We used S. pombe microarrays to identify three neighbouring genes (urg1, urg2, and urg3) whose transcript levels rapidly and strongly increased in response to uracil, a condition which otherwise had little effect on global gene expression. We cloned the promoter of urg1 (uracil-regulatable gene) to create several PCR-based gene targeting modules for replacing native promoters with the urg1 promoter (Purg1) in the normal chromosomal locations of genes of interest. The kanMX6 and natMX6 markers allow selection under urg1 induced and repressed conditions, respectively. Some modules also allow N-terminal tagging of gene products placed under urg1 control. Using pom1 as a proof-of-principle, we observed a maximal increase of Purg1-pom1 transcripts after uracil addition within less than 30 minutes, and a similarly rapid decrease after uracil removal. The induced and repressed transcriptional states remained stable over 24-hour periods. RT-PCR comparisons showed that both induced and repressed Purg1-pom1 transcript levels were lower than corresponding P3nmt1-pom1 levels (wild-type nmt1 promoter) but higher than P81nmt1-pom1 levels (weak nmt1 derivative).

Conclusions/Significance

We exploited the urg1 promoter system to rapidly induce pom1 expression at defined cell-cycle stages, showing that ectopic pom1 expression leads to cell branching in G2-phase but much less so in G1-phase. The high temporal resolution provided by the urg1 promoter should facilitate experimental design and improve the genetic toolbox for the fission yeast community.

Cyclebase.org--a comprehensive multi-organism online database of cell-cycle experiments

The past decade has seen the publication of a large number of cell-cycle microarray studies and many more are in the pipeline. However, data from these experiments are not easy to access, combine and evaluate. We have developed a centralized database with an easy-to-use interface, Cyclebase.org, for viewing and downloading these data. The user interface facilitates searches for genes of interest as well as downloads of genome-wide results. Individual genes are displayed with graphs of expression profiles throughout the cell cycle from all available experiments. These expression profiles are normalized to a common timescale to enable inspection of the combined experimental evidence. Furthermore, state-of-the-art computational analyses provide key information on both individual experiments and combined datasets such as whether or not a gene is periodically expressed and, if so, the time of peak expression. Cyclebase is available at http://www.cyclebase.org.

Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors

Background

We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.

Results

We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.

Conclusion

The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

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.

Bottleneck genes and community structure in the cell cycle network of S. pombe

The identification of cell cycle-related genes is still a difficult task, even for organisms with relatively few genes such as the fission yeast. Several gene expression studies have been published on S. pombe showing similarities but also discrepancies in their results. We introduce a network in which the weight of each link is a function of the phase difference between the expression peaks of two genes. The analysis of the stability of the clustering through the computation of an entropy parameter reveals a structure made of four clusters, the first one corresponding to a robustly connected M-G1 component, the second to genes in the S phase, and the third and fourth to two G2 components. They are separated by bottleneck structures that appear to correspond to cell cycle checkpoints. We identify a number of genes that are located on these bottlenecks. They represent a novel group of cell cycle regulatory genes. They all show interesting functions, and they are supposed to be involved in the regulation of the transition from one phase to the next. We therefore present a comparison of the available studies on the fission yeast cell cycle and a general statistical bioinformatics methodology to find bottlenecks and gene community structures based on recent developments in network theory.

A network of multiple regulatory layers shapes gene expression in fission yeast

Gene expression is controlled at multiple layers, and cells may integrate different regulatory steps for coherent production of proper protein levels. We applied various microarray-based approaches to determine key gene-expression intermediates in exponentially growing fission yeast, providing genome-wide data for translational profiles, mRNA steady-state levels, polyadenylation profiles, start-codon sequence context, mRNA half-lives, and RNA polymerase II occupancy. We uncovered widespread and unexpected relationships between distinct aspects of gene expression. Translation and polyadenylation are aligned on a global scale with both the lengths and levels of mRNAs: efficiently translated mRNAs have longer poly(A) tails and are shorter, more stable, and more efficiently transcribed on average. Transcription and translation may be independently but congruently optimized to streamline protein production. These rich data sets, all acquired under a standardized condition, reveal a substantial coordination between regulatory layers and provide a basis for a systems-level understanding of multilayered gene-expression programs.

How to get the most from fission yeast genome data: a report from the 2006 European Fission Yeast Meeting computing workshop

A fission yeast computing workshop 'How to get the most from the fission yeast genome data' was run as a satellite to the European Fission Yeast Meeting. The broad aims of the workshop were to provide fission yeast bench biologists with a set of tools and protocols to query the fission yeast genome data in specific ways, in order to extract biologically meaningful information of interest, which can be tailored to the needs of individual research projects. A description of the workshop content is provided and a selection of the tools presented are reviewed.

Comparative proteomic and transcriptomic profiling of the fission yeast Schizosaccharomyces pombe

The fission yeast Schizosaccharomyces pombe is a widely used model organism to study basic mechanisms of eukaryotic biology, but unlike other model organisms, its proteome remains largely uncharacterized. Using a shotgun proteomics approach based on multidimensional prefractionation and tandem mass spectrometry, we have detected ∼30% of the theoretical fission yeast proteome. Applying statistical modelling to normalize spectral counts to the number of predicted tryptic peptides, we have performed label-free quantification of 1465 proteins. The fission yeast protein data showed considerable correlations with mRNA levels and with the abundance of orthologous proteins in budding yeast. Functional pathway analysis indicated that the mRNA–protein correlation is strong for proteins involved in signalling and metabolic processes, but increasingly discordant for components of protein complexes, which clustered in groups with similar mRNA–protein ratios. Self-organizing map clustering of large-scale protein and mRNA data from fission and budding yeast revealed coordinate but not always concordant expression of components of functional pathways and protein complexes. This finding reaffirms at the protein level the considerable divergence in gene expression patterns of the two model organisms that was noticed in previous transcriptomic studies.

Co-evolution of transcriptional and post-translational cell-cycle regulation

DNA microarray studies have shown that hundreds of genes are transcribed periodically during the mitotic cell cycle of humans, budding yeast, fission yeast and the plant Arabidopsis thaliana. Here we show that despite the fact the protein complexes involved in this process are largely the same among all eukaryotes, their regulation has evolved considerably. Our comparative analysis of several large-scale data sets reveals that although the regulated subunits of each protein complex are expressed just before its time of action, the identity of the periodically expressed proteins differs significantly between organisms. Moreover, we show that these changes in transcriptional regulation have co-evolved with post-translational control independently in several lineages; loss or gain of cell-cycle-regulated transcription of specific genes is often mirrored by changes in phosphorylation of the proteins that they encode. Our results indicate that many different solutions have evolved for assembling the same molecular machines at the right time during the cell cycle, involving both transcriptional and post-translational layers that jointly control the dynamics of biological systems.