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

Inverse Modulation of Aurora Kinase A and Topoisomerase IIα in Normal and Tumor Breast Cells upon Knockdown of Mitochondrial ASncmtRNA

Breast cancer is currently the most diagnosed form of cancer and the leading cause of death by cancer among females worldwide. We described the family of long non-coding mitochondrial RNAs (ncmtRNAs), comprised of sense (SncmtRNA) and antisense (ASncmtRNA) members. Knockdown of ASncmtRNAs using antisense oligonucleotides (ASOs) induces proliferative arrest and apoptotic death of tumor cells, but not normal cells, from various tissue origins. In order to study the mechanisms underlying this selectivity, in this study we performed RNAseq in MDA-MB-231 breast cancer cells transfected with ASncmtRNA-specific ASO or control-ASO, or left untransfected. Bioinformatic analysis yielded several differentially expressed cell-cycle-related genes, from which we selected Aurora kinase A (AURKA) and topoisomerase IIα (TOP2A) for RT-qPCR and western blot validation in MDA-MB-231 and MCF7 breast cancer cells, as well as normal breast epithelial cells (HMEC). We observed no clear differences regarding mRNA levels but both proteins were downregulated in tumor cells and upregulated in normal cells. Since these proteins play a role in genomic integrity, this inverse effect of ASncmtRNA knockdown could account for tumor cell downfall whilst protecting normal cells, suggesting this approach could be used for genomic protection under cancer treatment regimens or other scenarios.

Universal prediction of cell-cycle position using transfer learning

BACKGROUND

The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data.

RESULTS

Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays.

CONCLUSIONS

Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.

Codon usage similarity between viral and some host genes suggests a codon-specific translational regulation

The codon usage pattern is a specific characteristic of each species; however, the codon usage of all of the genes in a genome is not uniform. Intriguingly, most viruses have codon usage patterns that are vastly different from the optimal codon usage of their hosts. How viral genes with different codon usage patterns are efficiently expressed during a viral infection is unclear. An analysis of the similarity between viral codon usage and the codon usage of the individual genes of a host genome has never been performed. In this study, we demonstrated that the codon usage of human RNA viruses is similar to that of some human genes, especially those involved in the cell cycle. This finding was substantiated by its concordance with previous reports of an upregulation at the protein level of some of these biological processes. It therefore suggests that some suboptimal viral codon usage patterns may actually be compatible with cellular translational machineries in infected conditions.

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.

A feature-based integrated scoring scheme for cell cycle-regulated genes prioritization

Prioritization of cell cycle-regulated genes from expression time-profiles is still an open problem. The point at issue is the surprisingly poor overlap among ranked lists obtained from different experimental protocols. Instead of developing a general-purpose computational methodology for detecting periodic signals, we focus on the budding yeast mitotic cell cycle. The reason being that the current availability of a total of 12 datasets, produced by 6 independent groups using 4 different synchronization methods, permits a re-analysis and re-consideration of this problem in a more reliable and extensive data domain. Notably, budding yeast is a model organism for studying cancer and testing new drugs. Here we propose a novel multi-feature score (called PERLA, PERiodicity, Regulation and Lag-Autocorrelation) that integrates different features of cell cycle-regulated gene expression time-profiles. We obtained increased performances on a wide range of benchmarks and, most importantly, a substantially increased overlap of the top ranking genes among different datasets, thus proving the effectiveness of the proposed prioritization algorithm. Examples on how to use PERLA to gain new insight into the biology of the cell cycle, are provided in a final dedicated section.

Comparison of structures among Saccharomyces cerevisiae Grxs proteins

Glutaredoxins (Grxs) comprise a group of glutathione (GSH)-dependent oxidoreductase enzymes that respond to oxidative stress and sustain redox homeostasis. Saccharomyces cerevisiae Grx has a similar interaction patterns through its residues between the residues and the environment. The glutaredoxin domain covers 100% of the entire mature Grx1 and Grx8, while the glutaredoxin domain covers ~ 52% of the entire mature Grx6 and Grx7, which have approximately 74 additional amino acids in their N-terminal regions, whereas Grx3 and Grx4 have two functional domains: glutaredoxin and thioredoxin. We have presented the prediction of disordered regions within these protein sequences. Multiple sequence alignment combined with a phylogenetic tree enabled us to specify the key residues contributing to the differences between Saccharomyces cerevisiae Grxs and the proportion symmetry.

The deubiquitylase USP15 regulates topoisomerase II alpha to maintain genome integrity

Ubiquitin-specific protease 15 (USP15) is a widely expressed deubiquitylase that has been implicated in diverse cellular processes in cancer. Here we identify topoisomerase II (TOP2A) as a novel protein that is regulated by USP15. TOP2A accumulates during G2 and functions to decatenate intertwined sister chromatids at prophase, ensuring the replicated genome can be accurately divided into daughter cells at anaphase. We show that USP15 is required for TOP2A accumulation, and that USP15 depletion leads to the formation of anaphase chromosome bridges. These bridges fail to decatenate, and at mitotic exit form micronuclei that are indicative of genome instability. We also describe the cell cycle-dependent behaviour for two major isoforms of USP15, which differ by a short serine-rich insertion that is retained in isoform-1 but not in isoform-2. Although USP15 is predominantly cytoplasmic in interphase, we show that both isoforms move into the nucleus at prophase, but that isoform-1 is phosphorylated on its unique S229 residue at mitotic entry. The micronuclei phenotype we observe on USP15 depletion can be rescued by either USP15 isoform and requires USP15 catalytic activity. Importantly, however, an S229D phospho-mimetic mutant of USP15 isoform-1 cannot rescue either the micronuclei phenotype, or accumulation of TOP2A. Thus, S229 phosphorylation selectively abrogates this role of USP15 in maintaining genome integrity in an isoform-specific manner. Finally, we show that USP15 isoform-1 is preferentially upregulated in a panel of non-small cell lung cancer cell lines, and propose that isoform imbalance may contribute to genome instability in cancer. Our data provide the first example of isoform-specific deubiquitylase phospho-regulation and reveal a novel role for USP15 in guarding genome integrity.

Regulation of the cell cycle and centrosome biology by deubiquitylases

Post-translational modification of proteins by ubiquitylation is increasingly recognised as a highly complex code that contributes to the regulation of diverse cellular processes. In humans, a family of almost 100 deubiquitylase enzymes (DUBs) are assigned to six subfamilies and many of these DUBs can remove ubiquitin from proteins to reverse signals. Roles for individual DUBs have been delineated within specific cellular processes, including many that are dysregulated in diseases, particularly cancer. As potentially druggable enzymes, disease-associated DUBs are of increasing interest as pharmaceutical targets. The biology, structure and regulation of DUBs have been extensively reviewed elsewhere, so here we focus specifically on roles of DUBs in regulating cell cycle processes in mammalian cells. Over a quarter of all DUBs, representing four different families, have been shown to play roles either in the unidirectional progression of the cell cycle through specific checkpoints, or in the DNA damage response and repair pathways. We catalogue these roles and discuss specific examples. Centrosomes are the major microtubule nucleating centres within a cell and play a key role in forming the bipolar mitotic spindle required to accurately divide genetic material between daughter cells during cell division. To enable this mitotic role, centrosomes undergo a complex replication cycle that is intimately linked to the cell division cycle. Here, we also catalogue and discuss DUBs that have been linked to centrosome replication or function, including centrosome clustering, a mitotic survival strategy unique to cancer cells with supernumerary centrosomes.

Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development

Invasive ductal carcinoma (IDC) is a major histo-morphologic type of breast cancer. Histological grading (HG) of IDC is widely adopted by oncologists as a prognostic factor. However, HG evaluation is highly subjective with only 50%-85% inter-observer agreements. Specifically, the subjectivity in the assignment of the intermediate grade (histologic grade 2, HG2) breast cancers (comprising ~50% of IDC cases) results in uncertain disease outcome prediction and sub-optimal systemic therapy. Despite several attempts to identify the mechanisms underlying the HG classification, their molecular bases are poorly understood.We performed integrative bioinformatics analysis of TCGA and several other cohorts (total 1246 patients). We identified a 22-gene tumor aggressiveness grading classifier (22g-TAG) that reflects global bifurcation in the IDC transcriptomes and reclassified patients with HG2 tumors into two genetically and clinically distinct subclasses: histological grade 1-like (HG1-like) and histological grade 3-like (HG3-like). The expression profiles and clinical outcomes of these subclasses were similar to the HG1 and HG3 tumors, respectively. We further reclassified IDC into low genetic grade (LGG = HG1+HG1-like) and high genetic grade (HGG = HG3-like+HG3) subclasses. For the HG1-like and HG3-like IDCs we found subclass-specific DNA alterations, somatic mutations, oncogenic pathways, cell cycle/mitosis and stem cell-like expression signatures that discriminate between these tumors. We found similar molecular patterns in the LGG and HGG tumor classes respectively.Our results suggest the existence of two genetically-predefined IDC classes, LGG and HGG, driven by distinct oncogenic pathways. They provide novel prognostic and therapeutic biomarkers and could open unique opportunities for personalized systemic therapies of IDC patients.

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.

Order restricted inference for oscillatory systems for detecting rhythmic signals

MOTIVATION

Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic.

AVAILABILITY AND IMPLEMENTATION

A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm CONTACT: peddada@niehs.nih.gov.

Principal component analysis based unsupervised feature extraction applied to budding yeast temporally periodic gene expression

Background

The recently proposed principal component analysis (PCA) based unsupervised feature extraction (FE) has successfully been applied to various bioinformatics problems ranging from biomarker identification to the screening of disease causing genes using gene expression/epigenetic profiles. However, the conditions required for its successful use and the mechanisms involved in how it outperforms other supervised methods is unknown, because PCA based unsupervised FE has only been applied to challenging (i.e. not well known) problems.

Results

In this study, PCA based unsupervised FE was applied to an extensively studied organism, i.e., budding yeast. When applied to two gene expression profiles expected to be temporally periodic, yeast metabolic cycle (YMC) and yeast cell division cycle (YCDC), PCA based unsupervised FE outperformed simple but powerful conventional methods, with sinusoidal fitting with regards to several aspects: (i) feasible biological term enrichment without assuming periodicity for YMC; (ii) identification of periodic profiles whose period was half as long as the cell division cycle for YMC; and (iii) the identification of no more than 37 genes associated with the enrichment of biological terms related to cell division cycle for the integrated analysis of seven YCDC profiles, for which sinusoidal fittings failed. The explantation for differences between methods used and the necessary conditions required were determined by comparing PCA based unsupervised FE with fittings to various periodic (artificial, thus pre-defined) profiles. Furthermore, four popular unsupervised clustering algorithms applied to YMC were not as successful as PCA based unsupervised FE.

Conclusions

PCA based unsupervised FE is a useful and effective unsupervised method to investigate YMC and YCDC. This study identified why the unsupervised method without pre-judged criteria outperformed supervised methods requiring human defined criteria.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0101-9) contains supplementary material, which is available to authorized users.

Redeployment of Myc and E2f1-3 drives Rb-deficient cell cycles

Robust mechanisms to control cell proliferation have evolved to maintain the integrity of organ architecture. Here, we investigated how two critical proliferative pathways, Myc and E2f, are integrated to control cell cycles in normal and Rb-deficient cells using a murine intestinal model. We show that Myc and E2f1-3 have little impact on normal G1-S transitions. Instead, they synergistically control an S-G2 transcriptional program required for normal cell divisions and maintaining crypt-villus integrity. Surprisingly, Rb deficiency results in the Myc-dependent accumulation of E2f3 protein and chromatin repositioning of both Myc and E2f3, leading to the 'super activation' of a G1-S transcriptional program, ectopic S phase entry and rampant cell proliferation. These findings reveal that Rb-deficient cells hijack and redeploy Myc and E2f3 from an S-G2 program essential for normal cell cycles to a G1-S program that re-engages ectopic cell cycles, exposing an unanticipated addiction of Rb-null cells on Myc.

Determination of Temporal Order among the Components of an Oscillatory System

Oscillatory systems in biology are tightly regulated process where the individual components (e.g. genes) express in an orderly manner by virtue of their functions. The temporal order among the components of an oscillatory system may potentially be disrupted for various reasons (e.g. environmental factors). As a result some components of the system may go out of order or even cease to participate in the oscillatory process. In this article, we develop a novel framework to evaluate whether the temporal order is unchanged in different populations (or experimental conditions). We also develop methodology to estimate the order among the components with a suitable notion of “confidence.” Using publicly available data on S. pombe, S. cerevisiae and Homo sapiens we discover that the temporal order among the genes cdc18; mik1; hhf1; hta2; fkh2 and klp5 is evolutionarily conserved from yeast to humans.

A novel role for lncRNAs in cell cycle control during stress adaptation

Eukaryotic cells have developed sophisticated systems to constantly monitor changes in the extracellular environment and to orchestrate a proper cellular response. To maximize survival, cells delay cell-cycle progression in response to environmental changes. In response to extracellular insults, stress-activated protein kinases (SAPKs) modulate cell-cycle progression and gene expression. In yeast, osmostress induces activation of the p38-related SAPK Hog1, which plays a key role in reprogramming gene expression upon osmostress. Genomic analysis has revealed the existence of a large number of long non-coding RNAs (lncRNAs) with different functions in a variety of organisms, including yeast. Upon osmostress, hundreds of lncRNAs are induced by the SAPK p38/Hog1. One gene that expresses Hog1-dependent lncRNA in an antisense orientation is the CDC28 gene, which encodes CDK1 kinase that controls the cell cycle in yeast. Cdc28 lncRNA mediates the induction of CDC28 expression and this increase in the level of Cdc28 results in more efficient re-entry of the cells into the cell cycle after stress. Thus, the control of lncRNA expression as a new mechanism for the regulation of cell-cycle progression opens new avenues to understand how stress adaptation can be accomplished in response to changing environments.

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.

Towards the discovery of novel genetic component involved in stress resistance in Arabidopsis thaliana

The exposure of plants to high concentrations of trace metallic elements such as copper involves a remodeling of the root system, characterized by a primary root growth inhibition and an increase in the lateral root density. These characteristics constitute easy and suitable markers for screening mutants altered in their response to copper excess. A forward genetic approach was undertaken in order to discover novel genetic factors involved in the response to copper excess. A Cu(2+) -sensitive mutant named copper modified resistance1 (cmr1) was isolated and a causative mutation in the CMR1 gene was identified by using positional cloning and next-generation sequencing. CMR1 encodes a plant-specific protein of unknown function. The analysis of the cmr1 mutant indicates that the CMR1 protein is required for optimal growth under normal conditions and has an essential role in the stress response. Impairment of the CMR1 activity alters root growth through aberrant activity of the root meristem, and modifies potassium concentration and hormonal balance (ethylene production and auxin accumulation). Our data support a putative role for CMR1 in cell division regulation and meristem maintenance. Research on the role of CMR1 will contribute to the understanding of the plasticity of plants in response to changing environments.

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.

Loss of heterozygosity preferentially occurs in early replicating regions in cancer genomes

Erroneous repair of DNA double-strand breaks by homologous recombination (HR) leads to loss of heterozygosity (LOH). Analysing 22,392 and 74,415 LOH events in 363 glioblastoma and 513 ovarian cancer samples, respectively, and using three different metrics, we report that LOH selectively occurs in early replicating regions; this pattern differs from the trends for point mutations and somatic deletions, which are biased toward late replicating regions. Our results are independent of BRCA1 and BRCA2 mutation status. The LOH events are significantly clustered near RNA polII-bound transcription start sites, consistent with the reports that slow replication near paused RNA polII might initiate HR-mediated repair. The frequency of LOH events is higher in the chromosomes with shorter inter-homolog distance inside the nucleus. We propose that during early replication, HR-mediated rescue of replication near paused RNA polII using homologous chromosomes as template leads to LOH. The difference in the preference for replication timing between different classes of genomic alterations in cancer genomes also provokes a testable hypothesis that replicating cells show changing preference between various DNA repair pathways, which have different levels of efficiency and fidelity, as the replication progresses.

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.

Cell cycle-regulated protein abundance changes in synchronously proliferating HeLa cells include regulation of pre-mRNA splicing proteins

Cell proliferation involves dramatic changes in DNA metabolism and cell division, and control of DNA replication, mitosis, and cytokinesis have received the greatest attention in the cell cycle field. To catalogue a wider range of cell cycle-regulated processes, we employed quantitative proteomics of synchronized HeLa cells. We quantified changes in protein abundance as cells actively progress from G1 to S phase and from S to G2 phase. We also describe a cohort of proteins whose abundance changes in response to pharmacological inhibition of the proteasome. Our analysis reveals not only the expected changes in proteins required for DNA replication and mitosis but also cell cycle-associated changes in proteins required for biological processes not known to be cell-cycle regulated. For example, many pre-mRNA alternative splicing proteins are down-regulated in S phase. Comparison of this dataset to several other proteomic datasets sheds light on global mechanisms of cell cycle phase transitions and underscores the importance of both phosphorylation and ubiquitination in cell cycle changes.

Temporal dynamics of protein complexes in PPI networks: a case study using yeast cell cycle dynamics

Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. With the advent of high-throughput techniques, significant amount of protein interaction (PPI) data has been catalogued for organisms such as yeast, which has in turn fueled computational methods for systematic identification and study of protein complexes. However, many complexes are dynamic entities - their subunits are known to assemble at a particular cellular space and time to perform a particular function and disassemble after that - and while current computational analyses have concentrated on studying the dynamics of individual or pairs of proteins in PPI networks, a crucial aspect overlooked is the dynamics of whole complex formations. In this work, using yeast as our model, we incorporate 'time' in the form of cell-cycle phases into the prediction of complexes from PPI networks and study the temporal phenomena of complex assembly and disassembly across phases. We hypothesize that 'staticness' (constitutive expression) of proteins might be related to their temporal "reusability" across complexes, and test this hypothesis using complexes predicted from large-scale PPI networks across the yeast cell cycle phases. Our results hint towards a biological design principle underlying cellular mechanisms - cells maintain generic proteins as 'static' to enable their "reusability" across multiple temporal complexes. We also demonstrate that these findings provide additional support and alternative explanations to findings from existing works on the dynamics in PPI networks.

Uncovering the molecular machinery of the human spindle--an integration of wet and dry systems biology

The mitotic spindle is an essential molecular machine involved in cell division, whose composition has been studied extensively by detailed cellular biology, high-throughput proteomics, and RNA interference experiments. However, because of its dynamic organization and complex regulation it is difficult to obtain a complete description of its molecular composition. We have implemented an integrated computational approach to characterize novel human spindle components and have analysed in detail the individual candidates predicted to be spindle proteins, as well as the network of predicted relations connecting known and putative spindle proteins. The subsequent experimental validation of a number of predicted novel proteins confirmed not only their association with the spindle apparatus but also their role in mitosis. We found that 75% of our tested proteins are localizing to the spindle apparatus compared to a success rate of 35% when expert knowledge alone was used. We compare our results to the previously published MitoCheck study and see that our approach does validate some findings by this consortium. Further, we predict so-called "hidden spindle hub", proteins whose network of interactions is still poorly characterised by experimental means and which are thought to influence the functionality of the mitotic spindle on a large scale. Our analyses suggest that we are still far from knowing the complete repertoire of functionally important components of the human spindle network. Combining integrated bio-computational approaches and single gene experimental follow-ups could be key to exploring the still hidden regions of the human spindle system.

Genes adopt non-optimal codon usage to generate cell cycle-dependent oscillations in protein levels

Most cell cycle-regulated genes adopt non-optimal codon usage, namely, their translation involves wobbly matching codons. Here, the authors show that tRNA expression is cyclic and that codon usage, therefore, can give rise to cell-cycle regulation of proteins.

Most cell cycle-regulated genes adopt non-optimal codon usage.

Non-optimal codon usage can give rise to cell-cycle dynamics at the protein level.

The high expression of transfer RNAs (tRNAs) observed in G2 phase enables cell cycle-regulated genes to adopt non-optimal codon usage, and conversely the lower expression of tRNAs at the end of G1 phase is associated with optimal codon usage.

The protein levels of aminoacyl-tRNA synthetases oscillate, peaking in G2/M phase, consistent with the observed cyclic expression of tRNAs.

The cell cycle is a temporal program that regulates DNA synthesis and cell division. When we compared the codon usage of cell cycle-regulated genes with that of other genes, we discovered that there is a significant preference for non-optimal codons. Moreover, genes encoding proteins that cycle at the protein level exhibit non-optimal codon preferences. Remarkably, cell cycle-regulated genes expressed in different phases display different codon preferences. Here, we show empirically that transfer RNA (tRNA) expression is indeed highest in the G2 phase of the cell cycle, consistent with the non-optimal codon usage of genes expressed at this time, and lowest toward the end of G1, reflecting the optimal codon usage of G1 genes. Accordingly, protein levels of human glycyl-, threonyl-, and glutamyl-prolyl tRNA synthetases were found to oscillate, peaking in G2/M phase. In light of our findings, we propose that non-optimal (wobbly) matching codons influence protein synthesis during the cell cycle. We describe a new mathematical model that shows how codon usage can give rise to cell-cycle regulation. In summary, our data indicate that cells exploit wobbling to generate cell cycle-dependent dynamics of proteins.

Identification of a core set of signature cell cycle genes whose relative order of time to peak expression is conserved across species

A cell division cycle is a well-coordinated process in eukaryotes with cell cycle genes exhibiting a periodic expression over time. There is considerable interest among cell biologists to determine genes that are periodic in multiple organisms and whether such genes are also evolutionarily conserved in their relative order of time to peak expression. Interestingly, periodicity is not well-conserved evolutionarily. A conservative estimate of a number of periodic genes common to fission yeast (Schizosaccharomyces pombe) and budding yeast (Saccharomyces cerevisiae) (‘core set FB’) is 35, while those common to fission yeast and humans (Homo sapiens) (‘core set FH’) is 24. Using a novel statistical methodology, we discover that the relative order of peak expression is conserved in ∼80% of FB genes and in ∼40% of FH genes. We also discover that the order is evolutionarily conserved in six genes which are potentially the core set of signature cell cycle genes. These include ace2 (a transcription factor) and polo-kinase plo1, which are well-known hubs of early M-phase clusters, cdc18 a key component of pre-replication complexes, mik1 which is critical for the establishment and maintenance of DNA damage check point, and histones hhf1 and hta2.

Integrating multiple types of data to predict novel cell cycle-related genes

Background

Cellular functions depend on genetic, physical and other types of interactions. As such, derived interaction networks can be utilized to discover novel genes involved in specific biological processes. Epistatic Miniarray Profile, or E-MAP, which is an experimental platform that measures genetic interactions on a genome-wide scale, has successfully recovered known pathways and revealed novel protein complexes in Saccharomyces cerevisiae (budding yeast).

Results

By combining E-MAP data with co-expression data, we first predicted a potential cell cycle related gene set. Using Gene Ontology (GO) function annotation as a benchmark, we demonstrated that the prediction by combining microarray and E-MAP data is generally >50% more accurate in identifying co-functional gene pairs than the prediction using either data source alone. We also used transcription factor (TF)–DNA binding data (Chip-chip) and protein phosphorylation data to construct a local cell cycle regulation network based on potential cell cycle related gene set we predicted. Finally, based on the E-MAP screening with 48 cell cycle genes crossing 1536 library strains, we predicted four unknown genes (YPL158C, YPR174C, YJR054W, and YPR045C) as potential cell cycle genes, and analyzed them in detail.

Conclusion

By integrating E-MAP and DNA microarray data, potential cell cycle-related genes were detected in budding yeast. This integrative method significantly improves the reliability of identifying co-functional gene pairs. In addition, the reconstructed network sheds light on both the function of known and predicted genes in the cell cycle process. Finally, our strategy can be applied to other biological processes and species, given the availability of relevant data.

The cell cycle regulated transcriptome of Trypanosoma brucei

Progression of the eukaryotic cell cycle requires the regulation of hundreds of genes to ensure that they are expressed at the required times. Integral to cell cycle progression in yeast and animal cells are temporally controlled, progressive waves of transcription mediated by cell cycle-regulated transcription factors. However, in the kinetoplastids, a group of early-branching eukaryotes including many important pathogens, transcriptional regulation is almost completely absent, raising questions about the extent of cell-cycle regulation in these organisms and the mechanisms whereby regulation is achieved. Here, we analyse gene expression over the Trypanosoma brucei cell cycle, measuring changes in mRNA abundance on a transcriptome-wide scale. We developed a “double-cut” elutriation procedure to select unperturbed, highly synchronous cell populations from log-phase cultures, and compared this to synchronization by starvation. Transcriptome profiling over the cell cycle revealed the regulation of at least 430 genes. While only a minority were homologous to known cell cycle regulated transcripts in yeast or human, their functions correlated with the cellular processes occurring at the time of peak expression. We searched for potential target sites of RNA-binding proteins in these transcripts, which might earmark them for selective degradation or stabilization. Over-represented sequence motifs were found in several co-regulated transcript groups and were conserved in other kinetoplastids. Furthermore, we found evidence for cell-cycle regulation of a flagellar protein regulon with a highly conserved sequence motif, bearing similarity to consensus PUF-protein binding motifs. RNA sequence motifs that are functional in cell-cycle regulation were more widespread than previously expected and conserved within kinetoplastids. These findings highlight the central importance of post-transcriptional regulation in the proliferation of parasitic kinetoplastids.

Interactional and functional centrality in transcriptional co-expression networks

MOTIVATION

The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets.

RESULTS

We propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. We demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures.

AVAILABILITY

ATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info).

The proteins of intra-nuclear bodies: a data-driven analysis of sequence, interaction and expression

Background

Cajal bodies, nucleoli, PML nuclear bodies, and nuclear speckles are morpohologically distinct intra-nuclear structures that dynamically respond to cellular cues. Such nuclear bodies are hypothesized to play important regulatory roles, e.g. by sequestering and releasing transcription factors in a timely manner. While the nucleolus and nuclear speckles have received more attention experimentally, the PML nuclear body and the Cajal body are still incompletely characterized in terms of their roles and protein complement.

Results

By collating recent experimentally verified data, we find that almost 1000 proteins in the mouse nuclear proteome are known to associate with one or more of the nuclear bodies. Their gene ontology terms highlight their regulatory roles: splicing is confirmed to be a core activity of speckles and PML nuclear bodies house a range of proteins involved in DNA repair. We train support-vector machines to show that nuclear proteins contain discriminative sequence features that can be used to identify their intra-nuclear body associations. Prediction accuracy is highest for nucleoli and nuclear speckles. The trained models are also used to estimate the full protein complement of each nuclear body. Protein interactions are found primarily to link proteins in the nuclear speckles with proteins from other compartments. Cell cycle expression data provide support for increased activity in nucleoli, nuclear speckles and PML nuclear bodies especially during S and G₂ phases.

Conclusions

The large-scale analysis of the mouse nuclear proteome sheds light on the functional organization of physically embodied intra-nuclear compartments. We observe partial support for the hypothesis that the physical organization of the nucleus mirrors functional modularity. However, we are unable to unambiguously identify proteins' intra-nuclear destination, suggesting that critical drivers behind of intra-nuclear translocation are yet to be identified.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

On the role of the chaperonin CCT in the just-in-time assembly process of APC/CCdc20

The just-in-time hypothesis relates to the assembly of large multi-protein complexes and their regulation of activation in the cell. Here I postulate that chaperonins may contribute to the timely assembly and activation of such complexes. For the case of anaphase promoting complex/cyclosome(Cdc20) assembly by the eukaryotic chaperonin chaperonin containing Tcp1 it is shown that just-in-time synthesis and chaperone-assisted folding can synergise to generate a highly regulated assembly process of a protein complex that is vital for cell cycle progression. Once dependency has been established transcriptional regulation and chaperonin-dependency may have co-evolved to safeguard the timely activation of important multi-protein complexes.

Cyclebase.org: version 2.0, an updated comprehensive, multi-species repository of cell cycle experiments and derived analysis results

Cell division involves a complex series of events orchestrated by thousands of molecules. To study this process, researchers have employed mRNA expression profiling of synchronously growing cell cultures progressing through the cell cycle. These experiments, which have been carried out in several organisms, are not easy to access, combine and evaluate. Complicating factors include variation in interdivision time between experiments and differences in relative duration of each cell-cycle phase across organisms. To address these problems, we created Cyclebase, an online resource of cell-cycle-related experiments. This database provides an easy-to-use web interface that facilitates visualization and download of genome-wide cell-cycle data and analysis results. Data from different experiments are normalized to a common timescale and are complimented with key cell-cycle information and derived analysis results. In Cyclebase version 2.0, we have updated the entire database to reflect changes to genome annotations, included information on cyclin-dependent kinase (CDK) substrates, predicted degradation signals and loss-of-function phenotypes from genome-wide screens. The web interface has been improved and provides a single, gene-centric graph summarizing the available cell-cycle experiments. Finally, key information and links to orthologous and paralogous genes are now included to further facilitate comparison of cell-cycle regulation across species. Cyclebase version 2.0 is available at http://www.cyclebase.org.

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.

Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs

Transfection of small RNAs (such as small interfering RNAs (siRNAs) and microRNAs (miRNAs)) into cells typically lowers expression of many genes. Unexpectedly, increased expression of genes also occurs. We investigated whether this upregulation results from a saturation effect--that is, competition among the transfected small RNAs and the endogenous pool of miRNAs for the intracellular machinery that processes small RNAs. To test this hypothesis, we analyzed genome-wide transcript responses from 151 published transfection experiments in seven different human cell types. We show that targets of endogenous miRNAs are expressed at significantly higher levels after transfection, consistent with impaired effectiveness of endogenous miRNA repression. This effect exhibited concentration and temporal dependence. Notably, the profile of endogenous miRNAs can be largely inferred by correlating miRNA sites with gene expression changes after transfections. The competition and saturation effects have practical implications for miRNA target prediction, the design of siRNA and short hairpin RNA (shRNA) genomic screens and siRNA therapeutics.

Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs

Transfection of small RNAs (such as small interfering RNAs (siRNAs) and microRNAs (miRNAs)) into cells typically lowers expression of many genes. Unexpectedly, increased expression of genes also occurs. We investigated whether this upregulation results from a saturation effect--that is, competition among the transfected small RNAs and the endogenous pool of miRNAs for the intracellular machinery that processes small RNAs. To test this hypothesis, we analyzed genome-wide transcript responses from 151 published transfection experiments in seven different human cell types. We show that targets of endogenous miRNAs are expressed at significantly higher levels after transfection, consistent with impaired effectiveness of endogenous miRNA repression. This effect exhibited concentration and temporal dependence. Notably, the profile of endogenous miRNAs can be largely inferred by correlating miRNA sites with gene expression changes after transfections. The competition and saturation effects have practical implications for miRNA target prediction, the design of siRNA and short hairpin RNA (shRNA) genomic screens and siRNA therapeutics.

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.

Predicting cell cycle regulated genes by causal interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

Intrinsic protein disorder and interaction promiscuity are widely associated with dosage sensitivity

Why are genes harmful when they are overexpressed? By testing possible causes of overexpression phenotypes in yeast, we identify intrinsic protein disorder as an important determinant of dosage sensitivity. Disordered regions are prone to make promiscuous molecular interactions when their concentration is increased, and we demonstrate that this is the likely cause of pathology when genes are overexpressed. We validate our findings in two animals, Drosophila melanogaster and Caenorhabditis elegans. In mice and humans the same properties are strongly associated with dosage-sensitive oncogenes, such that mass-action-driven molecular interactions may be a frequent cause of cancer. Dosage-sensitive genes are tightly regulated at the transcriptional, RNA, and protein levels, which may serve to prevent harmful increases in protein concentration under physiological conditions. Mass-action-driven interaction promiscuity is a single theoretical framework that can be used to understand, predict, and possibly treat the effects of increased gene expression in evolution and disease.

Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae

Background

Gene networks are a representation of molecular interactions among genes or products thereof and, hence, are forming causal networks. Despite intense studies during the last years most investigations focus so far on inferential methods to reconstruct gene networks from experimental data or on their structural properties, e.g., degree distributions. Their structural analysis to gain functional insights into organizational principles of, e.g., pathways remains so far under appreciated.

Results

In the present paper we analyze cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions and not just associations or correlations between genes, and a list of known periodic genes. No further data are used. Partitioning the transcriptional regulatory network according to a graph theoretical property leads to a hierarchy in the network and, hence, in the information flow allowing to identify two groups of periodic genes. This reveals a novel conceptual interpretation of the working mechanism of the cell cycle and the genes regulated by this pathway.

Conclusion

Aside from the obtained results for the cell cycle of yeast our approach could be exemplary for the analysis of general pathways by exploiting the rich causal structure of inferred and/or curated gene networks including protein or signaling networks.

Transcriptional analysis of the Candida albicans cell cycle

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

A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide

MOTIVATION

Approximately 9334 (37%) of human genes have no publications documenting their function and, for those that are published, the number of publications per gene is highly skewed. Furthermore, for reasons not clear, the entry of new gene names into the literature has slowed in recent years. If we are to better understand human/mammalian biology and complete the catalog of human gene function, it is important to finish predicting putative functions for these genes based upon existing experimental evidence.

RESULTS

A global meta-analysis (GMA) of all publicly available GEO two-channel human microarray datasets (3551 experiments total) was conducted to identify genes with recurrent, reproducible patterns of co-regulation across different conditions. Patterns of co-expression were divided into parallel (i.e. genes are up and down-regulated together) and anti-parallel. Several ranking methods to predict a gene's function based on its top 20 co-expressed gene pairs were compared. In the best method, 34% of predicted Gene Ontology (GO) categories matched exactly with the known GO categories for approximately 5000 genes analyzed versus only 3% for random gene sets. Only 2.4% of co-expressed gene pairs were found as co-occurring gene pairs in MEDLINE.

CONCLUSIONS

Via a GO enrichment analysis, genes co-expressed in parallel with the query gene were frequently associated with the same GO categories, whereas anti-parallel genes were not. Combining parallel and anti-parallel genes for analysis resulted in fewer significant GO categories, suggesting they are best analyzed separately. Expression databases contain much unexpected genetic knowledge that has not yet been reported in the literature. A total of 1642 Human genes with unknown function were differentially expressed in at least 30 experiments.

AVAILABILITY

Data matrix available upon request.

Computational systems biology of the cell cycle

One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.

Estimation of Parameters Subject to Order Restrictions on a Circle With Application to Estimation of Phase Angles of Cell Cycle Genes

Motivated by a problem encountered in the analysis of cell cycle gene expression data, this article deals with the estimation of parameters subject to order restrictions on a unit circle. A normal eukaryotic cell cycle has four major phases during cell division, and a cell cycle gene has its peak expression (phase angle) during the phase that may correspond to its biological function. Because the phases are ordered along a circle, the phase angles of cell cycle genes are ordered unknown parameters on a unit circle. The problem of interest is to estimate the phase angles using the information regarding the order among them. We address this problem by developing a circular version of the well-known isotonic regression for Euclidean data. Because of the underlying geometry, the standard pool adjacent violator algorithm (PAVA) cannot be used for deriving the circular isotonic regression estimator (CIRE). However, PAVA can be modified to obtain a computationally efficient algorithm for deriving the CIRE. We illustrate the CIRE by estimating the phase angles of some of well-known cell cycle genes using the unrestricted estimators obtained in the literature.

Cell cycle kinases predicted from conserved biophysical properties

Machine-learning techniques can classify functionally related proteins where homology-transfer as well as sequence and structure motifs fail. Here, we present a method that aimed at complementing homology-transfer in the identification of cell cycle control kinases from sequence alone. First, we identified functionally significant residues in cell cycle proteins through their high sequence conservation and biophysical properties. We then incorporated these residues and their features into support vector machines (SVM) to identify new kinases and more specifically to differentiate cell cycle kinases from other kinases and other proteins. As expected, the most informative residues tend to be highly conserved and tend to localize in the ATP binding regions of the kinases. Another observation confirmed that ATP binding regions are typically not found on the surface but in partially buried sites, and that this fact is correctly captured by accessibility predictions. Using these highly conserved, semi-buried residues and their biophysical properties, we could distinguish cell cycle S/T kinases from other kinase families at levels around 70-80% accuracy and 62-81% coverage. An application to the entire human proteome predicted at least 97 human proteins with limited previous annotations to be candidates for cell cycle kinases.

Robust discovery of periodically expressed genes using the laplace periodogram

Background

Time-course gene expression analysis has become important in recent developments due to the increasingly available experimental data. The detection of genes that are periodically expressed is an important step which allows us to study the regulatory mechanisms associated with the cell cycle.

Results

In this work, we present the Laplace periodogram which employs the least absolute deviation criterion to provide a more robust detection of periodic gene expression in the presence of outliers. The Laplace periodogram is shown to perform comparably to existing methods for the Sacharomyces cerevisiae and Arabidopsis time-course datasets, and to outperform existing methods when outliers are present.

Conclusion

Time-course gene expression data are often noisy due to the limitations of current technology, and may include outliers. These artifacts corrupt the available data and make the detection of periodicity difficult in many cases. The Laplace periodogram is shown to perform well for both data with and without the presence of outliers, and also for data that are non-uniformly sampled.

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.