Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization

The kinase Isr1 negatively regulates hexosamine biosynthesis in S. cerevisiae

The S. cerevisiae ISR1 gene encodes a putative kinase with no ascribed function. Here, we show that Isr1 acts as a negative regulator of the highly-conserved hexosamine biosynthesis pathway (HBP), which converts glucose into uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), the carbohydrate precursor to protein glycosylation, GPI-anchor formation, and chitin biosynthesis. Overexpression of ISR1 is lethal and, at lower levels, causes sensitivity to tunicamycin and resistance to calcofluor white, implying impaired protein glycosylation and reduced chitin deposition. Gfa1 is the first enzyme in the HBP and is conserved from bacteria and yeast to humans. The lethality caused by ISR1 overexpression is rescued by co-overexpression of GFA1 or exogenous glucosamine, which bypasses GFA1's essential function. Gfa1 is phosphorylated in an Isr1-dependent fashion and mutation of Isr1-dependent sites ameliorates the lethality associated with ISR1 overexpression. Isr1 contains a phosphodegron that is phosphorylated by Pho85 and subsequently ubiquitinated by the SCF-Cdc4 complex, largely confining Isr1 protein levels to the time of bud emergence. Mutation of this phosphodegron stabilizes Isr1 and recapitulates the overexpression phenotypes. As Pho85 is a cell cycle and nutrient responsive kinase, this tight regulation of Isr1 may serve to dynamically regulate flux through the HBP and modulate how the cell's energy resources are converted into structural carbohydrates in response to changing cellular needs.

Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis

Apoptotic cell death can be initiated through the extrinsic and intrinsic signaling pathways. While cell cycle progression promotes the responsiveness to intrinsic apoptosis induced by genotoxic stress or spindle poisons, this has not yet been studied conclusively for extrinsic apoptosis. Here, we combined fluorescence-based time-lapse monitoring of cell cycle progression and cell death execution by long-term time-lapse microscopy with sampling-based mathematical modeling to study cell cycle dependency of TRAIL-induced extrinsic apoptosis in NCI-H460/geminin cells. In particular, we investigated the interaction of cell death timing and progression of cell cycle states. We not only found that TRAIL prolongs cycle progression, but in reverse also that cell cycle progression affects the kinetics of TRAIL-induced apoptosis: Cells exposed to TRAIL in G1 died significantly faster than cells stimulated in S/G2/M. The connection between cell cycle state and apoptosis progression was captured by developing a mathematical model, for which parameter estimation revealed that apoptosis progression decelerates in the second half of the cell cycle. Similar results were also obtained when studying HCT-116 cells. Our results therefore reject the null hypothesis of independence between cell cycle progression and extrinsic apoptosis and, supported by simulations and experiments of synchronized cell populations, suggest that unwanted escape from TRAIL-induced apoptosis can be reduced by enriching the fraction of cells in G1 phase. Besides novel insight into the interrelation of cell cycle progression and extrinsic apoptosis signaling kinetics, our findings are therefore also relevant for optimizing future TRAIL-based treatment strategies.

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

BACKGROUND

Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information.

RESULTS

In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods.

CONCLUSIONS

This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.

Critical slowing down and attractive manifold: A mechanism for dynamic robustness in the yeast cell-cycle process

Biological processes that execute complex multiple functions, such as the cell cycle, must ensure the order of sequential events and maintain dynamic robustness against various fluctuations. Here, we examine the mechanisms and fundamental structure that achieve these properties in the cell cycle of the budding yeast Saccharomyces cerevisiae. We show that this process behaves like an excitable system containing three well-decoupled saddle-node bifurcations to execute DNA replication and mitosis events. The yeast cell-cycle regulatory network can be divided into three modules-the G1/S phase, early M phase, and late M phase-wherein both positive feedback loops in each module and interactions among modules play important roles. Specifically, when the cell-cycle process operates near the critical points of the saddle-node bifurcations, a critical slowing down effect takes place. Such interregnum then allows for an attractive manifold and sufficient duration for cell-cycle events, within which to assess the completion of DNA replication and mitosis, e.g., spindle assembly. Moreover, such arrangement ensures that any fluctuation in an early module or event will not transmit to a later module or event. Thus, our results suggest a possible dynamical mechanism of the cell-cycle process to ensure event order and dynamic robustness and give insight into the evolution of eukaryotic cell-cycle processes.

The Power of Resolution: Contextualized Understanding of Biological Responses to Liver Injury Chemicals Using High-throughput Transcriptomics and Benchmark Concentration Modeling

Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.

The coordinate actions of calcineurin and Hog1 mediate the stress response through multiple nodes of the cell cycle network

Upon exposure to environmental stressors, cells transiently arrest the cell cycle while they adapt and restore homeostasis. A challenge for all cells is to distinguish between stress signals and coordinate the appropriate adaptive response with cell cycle arrest. Here we investigate the role of the phosphatase calcineurin (CN) in the stress response and demonstrate that CN activates the Hog1/p38 pathway in both yeast and human cells. In yeast, the MAPK Hog1 is transiently activated in response to several well-studied osmostressors. We show that when a stressor simultaneously activates CN and Hog1, CN disrupts Hog1-stimulated negative feedback to prolong Hog1 activation and the period of cell cycle arrest. Regulation of Hog1 by CN also contributes to inactivation of multiple cell cycle-regulatory transcription factors (TFs) and the decreased expression of cell cycle-regulated genes. CN-dependent downregulation of G1/S genes is dependent upon Hog1 activation, whereas CN inactivates G2/M TFs through a combination of Hog1-dependent and -independent mechanisms. These findings demonstrate that CN and Hog1 act in a coordinated manner to inhibit multiple nodes of the cell cycle-regulatory network. Our results suggest that crosstalk between CN and stress-activated MAPKs helps cells tailor their adaptive responses to specific stressors.

In order to survive exposure to environmental stress, cells transiently arrest the cell division cycle while they adapt to the stress. Several kinases and phosphatases are known to control stress adaptation programs, but the extent to which these signaling pathways work together to tune the stress response is not well understood. This study investigates the role of the phosphatase calcineurin in the stress response and shows that calcineurin inhibits the cell cycle in part by stimulating the activity of the Hog1/p-38 stress-activated MAPK in both yeast and human cells. Crosstalk between stress response pathways may help cells mount specific responses to diverse stressors and to survive changes in their environment.

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Cellular heterogeneity in gene expression is driven by cellular processes, such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). By using these data, we developed a novel approach to characterize cell cycle progression. Although standard methods assign cells to discrete cell cycle stages, our method goes beyond this and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell's position on the cell cycle continuum to within 14% of the entire cycle and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

Regulation of intrinsic polarity establishment by a differentiation-type MAPK pathway in S. cerevisiae

All cells establish and maintain an axis of polarity that is critical for cell shape and progression through the cell cycle. A well-studied example of polarity establishment is bud emergence in the yeast Saccharomyces cerevisiae, which is controlled by the Rho GTPase Cdc42p. The prevailing view of bud emergence does not account for regulation by extrinsic cues. Here, we show that the filamentous growth mitogen activated protein kinase (fMAPK) pathway regulates bud emergence under nutrient-limiting conditions. The fMAPK pathway regulated the expression of polarity targets including the gene encoding a direct effector of Cdc42p, Gic2p. The fMAPK pathway also stimulated GTP-Cdc42p levels, which is a critical determinant of polarity establishment. The fMAPK pathway activity was spatially restricted to bud sites and active during the period of the cell cycle leading up to bud emergence. Time-lapse fluorescence microscopy showed that the fMAPK pathway stimulated the rate of bud emergence during filamentous growth. Unregulated activation of the fMAPK pathway induced multiple rounds of symmetry breaking inside the growing bud. Collectively, our findings identify a new regulatory aspect of bud emergence that sensitizes this essential cellular process to external cues.

Differential Scaling of Gene Expression with Cell Size May Explain Size Control in Budding Yeast

Yeast cells must grow to a critical size before committing to division. It is unknown how size is measured. We find that as cells grow, mRNAs for some cell-cycle activators scale faster than size, increasing in concentration, while mRNAs for some inhibitors scale slower than size, decreasing in concentration. Size-scaled gene expression could cause an increasing ratio of activators to inhibitors with size, triggering cell-cycle entry. Consistent with this, expression of the CLN2 activator from the promoter of the WHI5 inhibitor, or vice versa, interfered with cell size homeostasis, yielding a broader distribution of cell sizes. We suggest that size homeostasis comes from differential scaling of gene expression with size. Differential regulation of gene expression as a function of cell size could affect many cellular processes.

A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion

For some modeling problems a population may be better assessed as an aggregate of unknown subpopulations, each with a distinct relationship between a response and associated variables. The finite mixture of regressions (FMR) model, in which an outcome is derived from one of a finite number of linear regression models, is a natural tool in this setting. In this article, we first propose a new penalized regression approach. Then, we demonstrate how the proposed approach better identifies subpopulations and their corresponding models than a semiparametric FMR method does. Our new method fits models for each person via grouping pursuit, utilizing a new group-truncated L 1 penalty that shrinks the differences between estimated parameter vectors. The methodology causes the individuals' models to cluster into a few common models, in turn revealing previously unknown subpopulations. In fact, by varying the penalty strength, the new method can reveal a hierarchical structure among the subpopulations that can be useful in exploratory analyses. Simulations using FMR models and a real-data analysis show that the method performs promisingly well.

Time course analysis of large-scale gene expression in incised muscle using correspondence analysis

Studying the time course of gene expression in injured skeletal muscle would help to estimate the timing of injuries. In this study, we investigated large-scale gene expression in incision-injured mouse skeletal muscle by DNA microarray using correspondence analysis (CA). Biceps femoris muscle samples were collected 6, 12, and 24 hours after injury, and RNA was extracted and prepared for microarray analysis. On a 2-dimensional plot by CA, the genes (row score coordinate) located farther from each time series (column score coordinate) had more upregulation at particular times. Each gene was situated in 6 subdivided triangular areas according to the magnitude of the relationship of the fold change (FC) value at each time point compared to the control. In each area, genes for which the ratios of two particular FC values were close to 1 were distributed along the two border lines. There was a tendency for genes whose FC values were almost equal to be distributed near the intersection of these 6 areas. Therefore, the gene marker candidates for estimation of the timing of injuries were detectable according to the location on the CA plot. Moreover, gene sets created by a specific gene and its surrounding genes were composed of genes that showed similar or identical fluctuation patterns to the specific gene. In various analyses on these sets, significant gene ontology term and pathway activity may reflect changes in specific genes. In conclusion, analyses of gene sets based on CA plots is effective for investigation of the time-dependent fluctuation in gene expression after injury.

Building blocks are synthesized on demand during the yeast cell cycle

For cells to replicate, a sufficient supply of biosynthetic precursors is needed, necessitating the concerted action of metabolism and protein synthesis during progressive phases of cell division. A global understanding of which biosynthetic processes are involved and how they are temporally regulated during replication is, however, currently lacking. Here, quantitative multiomics analysis is used to generate a holistic view of the eukaryal cell cycle, using the budding yeast Saccharomyces cerevisiae Protein synthesis and central carbon pathways such as glycolysis and amino acid metabolism are shown to synchronize their respective abundance profiles with division, with pathway-specific changes in metabolite abundance also being reflected by a relative increase in mitochondrial volume, as shown by quantitative fluorescence microscopy. These results show biosynthetic precursor production to be temporally regulated to meet phase-specific demands of eukaryal cell division.

Abundances of transcripts, proteins, and metabolites in the cell cycle of budding yeast reveal coordinate control of lipid metabolism

Establishing the pattern of abundance of molecules of interest during cell division has been a long-standing goal of cell cycle studies. Here, for the first time in any system, we present experiment-matched datasets of the levels of RNAs, proteins, metabolites, and lipids from unarrested, growing, and synchronously dividing yeast cells. Overall, transcript and protein levels were correlated, but specific processes that appeared to change at the RNA level (e.g., ribosome biogenesis) did not do so at the protein level, and vice versa. We also found no significant changes in codon usage or the ribosome content during the cell cycle. We describe an unexpected mitotic peak in the abundance of ergosterol and thiamine biosynthesis enzymes. Although the levels of several metabolites changed in the cell cycle, by far the most significant changes were in the lipid repertoire, with phospholipids and triglycerides peaking strongly late in the cell cycle. Our findings provide an integrated view of the abundance of biomolecules in the eukaryotic cell cycle and point to a coordinate mitotic control of lipid metabolism.

Cell cycle transcriptomics of Capsaspora provides insights into the evolution of cyclin-CDK machinery

Progression through the cell cycle in eukaryotes is regulated on multiple levels. The main driver of the cell cycle progression is the periodic activity of cyclin-dependent kinase (CDK) complexes. In parallel, transcription during the cell cycle is regulated by a transcriptional program that ensures the just-in-time gene expression. Many core cell cycle regulators are widely conserved in eukaryotes, among them cyclins and CDKs; however, periodic transcriptional programs are divergent between distantly related species. In addition, many otherwise conserved cell cycle regulators have been lost and independently evolved in yeast, a widely used model organism for cell cycle research. For a better understanding of the evolution of the cell cycle regulation in opisthokonts, we investigated the transcriptional program during the cell cycle of the filasterean Capsaspora owczarzaki, a unicellular species closely related to animals. We developed a protocol for cell cycle synchronization in Capsaspora cultures and assessed gene expression over time across the entire cell cycle. We identified a set of 801 periodic genes that grouped into five clusters of expression over time. Comparison with datasets from other eukaryotes revealed that the periodic transcriptional program of Capsaspora is most similar to that of animal cells. We found that orthologues of cyclin A, B and E are expressed at the same cell cycle stages as in human cells and in the same temporal order. However, in contrast to human cells where these cyclins interact with multiple CDKs, Capsaspora cyclins likely interact with a single ancestral CDK1-3. Thus, the Capsaspora cyclin-CDK system could represent an intermediate state in the evolution of animal-like cyclin-CDK regulation. Overall, our results demonstrate that Capsaspora could be a useful unicellular model system for animal cell cycle regulation.

Model-based clustering of multi-tissue gene expression data

MOTIVATION

Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues.

RESULTS

We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals.

AVAILABILITY AND IMPLEMENTATION

Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bayesian Data Fusion of Gene Expression and Histone Modification Profiles for Inference of Gene Regulatory Network

Accurately reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data has been a major challenge in systems biology for decades. Many approaches have been proposed to solve this problem. However, there is still much room for the improvement of GRN inference. Integrating data from different sources is a promising strategy. Epigenetic modifications have a close relationship with gene regulation. Hence, epigenetic data such as histone modification profiles can provide useful information for uncovering regulatory interactions between genes. In this paper, we propose a method to integrate epigenetic data into the inference of GRNs. In particular, a dynamic Bayesian network (DBN) is employed to infer gene regulations from time-series gene expression data. Epigenetic data (histone modification profiles here) are integrated into the prior probability distribution of the Bayesian model. Our method has been validated on both synthetic and real datasets. Experimental results show that the integration of epigenetic data can significantly improve the performance of GRN inference. As more epigenetic datasets become available, our method would be useful for elucidating the gene regulatory mechanisms driving various cellular activities. The source code and testing datasets are available at https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior.

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

BACKGROUND

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task.

RESULTS

We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4).

CONCLUSION

Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

WormCat: An Online Tool for Annotation and Visualization of Caenorhabditis elegans Genome-Scale Data

The emergence of large gene expression datasets has revealed the need for improved tools to identify enriched gene categories and visualize enrichment patterns. While gene ontogeny (GO) provides a valuable tool for gene set enrichment analysis, it has several limitations. First, it is difficult to graph multiple GO analyses for comparison. Second, genes from some model systems are not well represented. For example, ∼30% of Caenorhabditis elegans genes are missing from the analysis in commonly used databases. To allow categorization and visualization of enriched C. elegans gene sets in different types of genome-scale data, we developed WormCat, a web-based tool that uses a near-complete annotation of the C. elegans genome to identify coexpressed gene sets and scaled heat map for enrichment visualization. We tested the performance of WormCat using a variety of published transcriptomic datasets, and show that it reproduces major categories identified by GO. Importantly, we also found previously unidentified categories that are informative for interpreting phenotypes or predicting biological function. For example, we analyzed published RNA-seq data from C. elegans treated with combinations of lifespan-extending drugs, where one combination paradoxically shortened lifespan. Using WormCat, we identified sterol metabolism as a category that was not enriched in the single or double combinations, but emerged in a triple combination along with the lifespan shortening. Thus, WormCat identified a gene set with potential. phenotypic relevance not found with previous GO analysis. In conclusion, WormCat provides a powerful tool for the analysis and visualization of gene set enrichment in different types of C. elegans datasets.

Non-coding RNAs as cell wall regulators in Saccharomyces cerevisiae

The cell wall of Saccharomyces cerevisiae is an extracellular organelle crucial for preserving its cellular integrity and detecting environmental cues. The cell wall is composed of mannoproteins attached to a polysaccharide network and is continuously remodelled as cells undergo cell division, mating, gametogenesis or adapt to stressors. This makes yeast an excellent model to study the regulation of genes important for cell wall formation and maintenance. Given that certain yeast strains are pathogenic, a better understanding of their life cycle is of clinical relevance. This is why transcriptional regulatory mechanisms governing genes involved in cell wall biogenesis or maintenance have been the focus of numerous studies. However, little is known about the roles of long non-coding RNAs (lncRNAs), a class of transcripts that are thought to possess little or no protein coding potential, in controlling the expression of cell wall-related genes. This review outlines currently known mechanisms of lncRNA-mediated regulation of gene expression in S. cerevisiae and describes examples of lncRNA-regulated genes encoding cell wall proteins. We suggest that the association of currently annotated lncRNAs with the coding sequences and/or promoters of cell wall-related genes highlights a potential role for lncRNAs as important regulators of the yeast cell wall structure.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

Does genome surveillance explain the global discrepancy between binding and effect of chromatin factors?

Knocking out a chromatin factor often does not alter the transcription of its binding targets. What explains the observed disconnect between binding and effect? We hypothesize that this discrepancy could be associated with the role of chromatin factors in maintaining genetic and epigenetic integrity at promoters, and not necessarily with transcription. Through re-analysis of published datasets, we present several lines of evidence that support our hypothesis and deflate the popular assumptions. We also tested the hypothesis through mutation accumulation assays on yeast knockouts of chromatin factors. Altogether, the proposed hypothesis presents a simple explanation for the global discord between chromatin factor binding and effect. Future work in this direction might fortify the hypothesis and elucidate the underlying mechanisms.

Comparative Transcriptome Analyses during the Vegetative Cell Cycle in the Mono-Cellular Organism Pseudokeronopsis erythrina (Alveolata, Ciliophora)

Studies focusing on molecular mechanisms of cell cycles have been lagging in unicellular eukaryotes compared to other groups. Ciliates, a group of unicellular eukaryotes, have complex cell division cycles characterized by multiple events. During their vegetative cell cycle, ciliates undergo macronuclear amitosis, micronuclear mitosis, stomatogenesis and somatic cortex morphogenesis, and cytokinesis. Herein, we used the hypotrich ciliate Pseudokeronopsis erythrina, whose morphogenesis has been well studied, to examine molecular mechanisms of ciliate vegetative cell cycles. Single-cell transcriptomes of the growth (G) and cell division (D) stages were compared. The results showed that (i) More than 2051 significantly differentially expressed genes (DEGs) were detected, among which 1545 were up-regulated, while 256 were down-regulated at the D stage. Of these, 11 randomly picked DEGs were validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR); (ii) Enriched DEGs during the D stage of the vegetative cell cycle of P. erythrina were involved in development, cortex modifications, and several organelle-related biological processes, showing correspondence of molecular evidence to morphogenetic changes for the first time; (iii) Several individual components of molecular mechanisms of ciliate vegetative division, the sexual cell cycle and cellular regeneration overlap; and (iv) The P. erythrina cell cycle and division have the same essential components as other eukaryotes, including cyclin-dependent kinases (CDKs), cyclins, and genes closely related to cell proliferation, indicating the conserved nature of this biological process. Further studies are needed focusing on detailed inventory and gene interactions that regulate specific ciliated cell-phase events.

Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast

BACKGROUND

Current technologies for understanding the transcriptional reprogramming in cells include the transcription factor (TF) chromatin immunoprecipitation (ChIP) experiments and the TF knockout experiments. The ChIP experiments show the binding targets of TFs against which the antibody directs while the knockout techniques find the regulatory gene targets of the knocked-out TFs. However, it was shown that these two complementary results contain few common targets. Researchers have used the concept of TF functional redundancy to explain the low overlap between these two techniques. But the detailed molecular mechanisms behind TF functional redundancy remain unknown. Without knowing the possible molecular mechanisms, it is hard for biologists to fully unravel the cause of TF functional redundancy.

RESULTS

To mine out the molecular mechanisms, a novel algorithm to extract TF regulatory modules that help explain the observed TF functional redundancy effect was devised and proposed in this research. The method first searched for candidate TF sets from the TF binding data. Then based on these candidate sets the method utilized the modified Steiner Tree construction algorithm to construct the possible TF regulatory modules from protein-protein interaction data and finally filtered out the noise-induced results by using confidence tests. The mined-out regulatory modules were shown to correlate to the concept of functional redundancy and provided testable hypotheses of the molecular mechanisms behind functional redundancy. And the biological significance of the mined-out results was demonstrated in three different biological aspects: ontology enrichment, protein interaction prevalence and expression coherence. About 23.5% of the mined-out TF regulatory modules were literature-verified. Finally, the biological applicability of the proposed method was shown in one detailed example of a verified TF regulatory module for pheromone response and filamentous growth in yeast.

CONCLUSION

In this research, a novel method that mined out the potential TF regulatory modules which elucidate the functional redundancy observed among TFs is proposed. The extracted TF regulatory modules not only correlate the molecular mechanisms to the observed functional redundancy among TFs, but also show biological significance in inferring TF functional binding target genes. The results provide testable hypotheses for biologists to further design subsequent research and experiments.

Induction of triacylglycerol synthesis in yeast by cell cycle arrest

In this study, we found that cell cycle arrest induced by alpha-factor mating pheromone (G1), hydroxyurea (S) or nocodazole (G2/M) was associated to increased lipid droplet (LD) content. To identify novel cell cycle genes involved in LD homeostasis, we screened a deletion library for strains with altered LD levels. Among the mutants related to mitotic cell cycle, we found 24 hits that displayed a significantly higher LD content. Ontology mapping showed that neither a biological process nor a specific cell cycle phase was enriched among the hits. We decided to further study the role of SWI4 on LD homeostasis as it is involved in G1/S transition, a stage where lipolysis is active. The high LD content of swi4Δ mutant was not due to inhibition of lipolysis, but due to an increase in triacylglycerol (TAG) synthesis. In addition, deletion of the AMP kinase gene SNF1 or inhibition of TORC1 activity, both known regulators of LD homeostasis, further increased the LD content of a swi4Δ mutant. These findings highlight a role of the cell cycle regulator SWI4 in the coordination of lipid metabolism which is independent of the TORC1 and SNF1/AMPK pathways.

ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast

The understanding of the multi-scale nature of molecular networks represents a major challenge. For example, regulation of a timely cell cycle must be coordinated with growth, during which changes in metabolism occur, and integrate information from the extracellular environment, e.g. signal transduction. Forkhead transcription factors are evolutionarily conserved among eukaryotes, and coordinate a timely cell cycle progression in budding yeast. Specifically, Fkh1 and Fkh2 are expressed during a lengthy window of the cell cycle, thus are potentially able to function as hubs in the multi-scale cellular environment that interlocks various biochemical networks. Here we report on a novel ChIP-exo dataset for Fkh1 and Fkh2 in both logarithmic and stationary phases, which is analyzed by novel and existing software tools. Our analysis confirms known Forkhead targets from available ChIP-chip studies and highlights novel ones involved in the cell cycle, metabolism and signal transduction. Target genes are analyzed with respect to their function, temporal expression during the cell cycle, correlation with Fkh1 and Fkh2 as well as signaling and metabolic pathways they occur in. Furthermore, differences in targets between Fkh1 and Fkh2 are presented. Our work highlights Forkhead transcription factors as hubs that integrate multi-scale networks to achieve proper timing of cell division in budding yeast.

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ('housekeeping genes') typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

Pol5 is an essential ribosome biogenesis factor required for 60S ribosomal subunit maturation in Saccharomyces cerevisiae

In Saccharomyces cerevisiae, more than 250 trans-acting factors are involved in the maturation of 40S and 60S ribosomal subunits. The expression of most of these factors is transcriptionally coregulated to ensure correct ribosome production under a wide variety of environmental and intracellular conditions. Here, we identified the essential nucleolar Pol5 protein as a novel trans-acting factor required for the synthesis of 60S ribosomal subunits. Pol5 weakly and/or transiently associates with early to medium pre-60S ribosomal particles. Depletion of and temperature-sensitive mutations in Pol5 result in a deficiency of 60S ribosomal subunits and accumulation of half-mer polysomes. Both processing of 27SB pre-rRNA to mature 25S rRNA and release of pre-60S ribosomal particles from the nucle(ol)us to the cytoplasm are impaired in the Pol5-depleted strain. Moreover, we identified the genes encoding ribosomal proteins uL23 and eL27A as multicopy suppressors of the slow growth of a temperature-sensitive pol5 mutant. These results suggest that Pol5 could function in ensuring the correct folding of 25S rRNA domain III; thus, favoring the correct assembly of these two ribosomal proteins at their respective binding sites into medium pre-60S ribosomal particles. Pol5 is homologous to the human tumor suppressor Myb-binding protein 1A (MYBBP1A). However, expression of MYBBP1A failed to complement the lethal phenotype of a pol5 null mutant strain though interfered with 60S ribosomal subunit biogenesis.

A Multi-Laplacian Prior and Augmented Lagrangian Approach to the Exploratory Analysis of Time-Varying Gene and Transcriptional Regulatory Networks for Gene Microarray Data

This paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional L₁-based penalties. Moreover, the ALM allows the resultant non-smooth L₁-based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.

Hyperacetylation of Cardiac Mitochondrial Proteins Is Associated with Metabolic Impairment and Sirtuin Downregulation after Chronic Total Body Irradiation of ApoE -/- Mice

Chronic exposure to low-dose ionizing radiation is associated with an increased risk of cardiovascular disease. Alteration in energy metabolism has been suggested to contribute to radiation-induced heart pathology, mitochondrial dysfunction being a hallmark of this disease. The goal of this study was to investigate the regulatory role of acetylation in heart mitochondria in the long-term response to chronic radiation. ApoE-deficient C57Bl/6J mice were exposed to low-dose-rate (20 mGy/day) gamma radiation for 300 days, resulting in a cumulative total body dose of 6.0 Gy. Heart mitochondria were isolated and analyzed using quantitative proteomics. Radiation-induced proteome and acetylome alterations were further validated using immunoblotting, enzyme activity assays, and ELISA. In total, 71 proteins showed peptides with a changed acetylation status following irradiation. The great majority (94%) of the hyperacetylated proteins were involved in the TCA cycle, fatty acid oxidation, oxidative stress response and sirtuin pathway. The elevated acetylation patterns coincided with reduced activity of mitochondrial sirtuins, increased the level of Acetyl-CoA, and were accompanied by inactivation of major cardiac metabolic regulators PGC-1 alpha and PPAR alpha. These observations suggest that the changes in mitochondrial acetylation after irradiation is associated with impairment of heart metabolism. We propose a novel mechanism involved in the development of late cardiac damage following chronic irradiation.

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process-such as the active-learning methods found in the machine-learning literature-will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.

PyBioNetFit and the Biological Property Specification Language

In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.

MADS-box transcription factor Mcm1 controls cell cycle, fungal development, cell integrity and virulence in the filamentous insect pathogenic fungus Beauveria bassiana

MADS-box transcription factor Mcm1 plays crucial roles in regulating mating processes and pathogenesis in some fungi. However, its roles are varied in fungal species, and its function remains unclear in entomopathogenic fungi. Here, Mcm1 orthologue, Bbmcm1, was characterized in a filamentous entomopathogenic fungus, Beauveria bassiana. Disruption of Bbmcm1 resulted in a distinct reduction in growth with abnormal conidiogenesis, and a significant decrease in conidial viability with abnormal germination. ΔBbmcm1 displayed impaired cell integrity, with distorted cell wall structure and altered cell wall component. Abnormal cell cycle was detected in ΔBbmcm1 with longer G₂ /M phase but shorter G₁ /G₀ and S phases in unicellular blastospores, and sparser septa in multicellular hyphae, which might be responsible for defects in development and differentiation due to the regulation of cell cycle-involved genes, as well as the corresponding cellular events-associated genes. Dramatically decreased virulence was examined in ΔBbmcm1, with impaired ability to escape haemocyte encapsulation, which was consistent with markedly reduced cuticle-degrading enzyme production by repressing their coding genes, and downregulated fungal effector protein-coding genes, suggesting a novel role of Mcm1 in interaction with host insect. These data indicate that Mcm1 is a crucial regulator of development, cell integrity, cell cycle and virulence in insect fungal pathogens.

Quantitative Studies for Cell-Division Cycle Control

The cell-division cycle (CDC) is driven by cyclin-dependent kinases (CDKs). Mathematical models based on molecular networks, as revealed by molecular and genetic studies, have reproduced the oscillatory behavior of CDK activity. Thus, one basic system for representing the CDC is a biochemical oscillator (CDK oscillator). However, genetically clonal cells divide with marked variability in their total duration of a single CDC round, exhibiting non-Gaussian statistical distributions. Therefore, the CDK oscillator model does not account for the statistical nature of cell-cycle control. Herein, we review quantitative studies of the statistical properties of the CDC. Over the past 70 years, studies have shown that the CDC is driven by a cluster of molecular oscillators. The CDK oscillator is coupled to transcriptional and mitochondrial metabolic oscillators, which cause deterministic chaotic dynamics for the CDC. Recent studies in animal embryos have raised the possibility that the dynamics of molecular oscillators underlying CDC control are affected by allometric volume scaling among the cellular compartments. Considering these studies, we discuss the idea that a cluster of molecular oscillators embedded in different cellular compartments coordinates cellular physiology and geometry for successful cell divisions.

Assembly of a parts list of the human mitotic cell cycle machinery

The set of proteins required for mitotic division remains poorly characterized. Here, an extensive series of correlation analyses of human and mouse transcriptomics data were performed to identify genes strongly and reproducibly associated with cells undergoing S/G2-M phases of the cell cycle. In so doing, 701 cell cycle-associated genes were defined and while it was shown that many are only expressed during these phases, the expression of others is also driven by alternative promoters. Of this list, 496 genes have known cell cycle functions, whereas 205 were assigned as putative cell cycle genes, 53 of which are functionally uncharacterized. Among these, 27 were screened for subcellular localization revealing many to be nuclear localized and at least three to be novel centrosomal proteins. Furthermore, 10 others inhibited cell proliferation upon siRNA knockdown. This study presents the first comprehensive list of human cell cycle proteins, identifying many new candidate proteins.

Quantitative proteomics reveals the key molecular events occurring at different cell cycle phases of the in situ blooming dinoflagellate cells

Dinoflagellate blooms are the results of rapid cell proliferation governed by cell cycle, a highly-ordered series of events that culminates in cell division. However, little is known about cell cycle progression of the in situ bloom cells. Here, we compared proteomes of the in situ blooming cells of a dinoflagellate Prorocentrum donghaiense collected at different cell cycle phases. The blooming P. donghaiense cells completed a cell cycle within 24 h with a high synchronization rate of 82.7%. Proteins associated with photosynthesis, porphyrin and chlorophyll synthesis, carbon, nitrogen and amino acid metabolisms exhibited high expressions at the G₁ phase; DNA replication and mismatch repair related proteins were more abundant at the S phase; while protein synthesis and oxidative phosphorylation were highly enriched at the G₂/M phase. Cell cycle proteins presented similar periodic diel patterns to other eukaryotic cells, and higher expressions of proliferating cell nuclear antigen and cyclin dependent kinase 2 at the S phase ensured the smooth S-G₂/M transition. Strikingly, four histones were first identified in P. donghaiense and highly expressed at the G₂/M phase, indicating their potential roles in regulating cell cycle. This study presents the first quantitative survey, to our knowledge, of proteome changes at different cell cycle phases of the in situ blooming cells in natural environment and provides insights into cell cycle regulation of the blooming dinoflagellate cells.

Transcription Factors Indirectly Regulate Genes through Nuclear Colocalization

Various types of data, including genomic sequences, transcription factor (TF) knockout data, TF-DNA interaction and expression profiles, have been used to decipher TF regulatory mechanisms. However, most of the genes affected by knockout of a particular TF are not bound by that factor. Here, I showed that this interesting result can be partially explained by considering the nuclear positioning of TF knockout affected genes and TF bound genes. I found that a statistically significant number of TF knockout affected genes show nuclear colocalization with genes bound by the corresponding TF. Although these TF knockout affected genes are not directly bound by the corresponding TF; the TF tend to be in the same cellular component with the TFs that directly bind these genes. TF knockout affected genes show co-expression and tend to be involved in the same biological process with the spatially adjacent genes that are bound by the corresponding TF. These results demonstrate that TFs can regulate genes through nuclear colocalization without direct DNA binding, complementing the conventional view that TFs directly bind DNA to regulate genes. My findings will have implications in understanding TF regulatory mechanisms.

Adaptive Elman Model of Gene Regulation Network Based on Time Series Data

Background:

Time series expression data of genes contain relations among different genes, which are difficult to model precisely. Slime-forming bacteria is one of the three major harmful bacteria types in industrial circulating cooling water systems.

Objective:

This study aimed at constructing gene regulation network(GRN) for slime-forming bacteria to understand the microbial fouling mechanism.

Methods:

For this purpose, an Adaptive Elman Neural Network (AENN) to reveal the relationships among genes using gene expression time series is proposed. The parameters of Elman neural network were optimized adaptively by a Genetic Algorithm (GA). And a Pearson correlation analysis is applied to discover the relationships among genes. In addition, the gene expression data of slime-forming bacteria by transcriptome gene sequencing was presented.

Results:

To evaluate our proposed method, we compared several alternative data-driven approaches, including a Neural Fuzzy Recurrent Network (NFRN), a basic Elman Neural Network (ENN), and an ensemble network. The experimental results of simulated and real datasets demonstrate that the proposed approach has a promising performance for modeling Gene Regulation Networks (GRNs). We also applied the proposed method for the GRN construction of slime-forming bacteria and at last a GRN for 6 genes was constructed.

Conclusion:

The proposed GRN construction method can effectively extract the regulations among genes. This is also the first report to construct the GRN for slime-forming bacteria.

Set2 methyltransferase facilitates cell cycle progression by maintaining transcriptional fidelity

Methylation of histone H3 lysine 36 (H3K36me) by yeast Set2 is critical for the maintenance of chromatin structure and transcriptional fidelity. However, we do not know the full range of Set2/H3K36me functions or the scope of mechanisms that regulate Set2-dependent H3K36 methylation. Here, we show that the APC/C^CDC20 complex regulates Set2 protein abundance during the cell cycle. Significantly, absence of Set2-mediated H3K36me causes a loss of cell cycle control and pronounced defects in the transcriptional fidelity of cell cycle regulatory genes, a class of genes that are generally long, hence highly dependent on Set2/H3K36me for their transcriptional fidelity. Because APC/C also controls human SETD2, and SETD2 likewise regulates cell cycle progression, our data imply an evolutionarily conserved cell cycle function for Set2/SETD2 that may explain why recurrent mutations of SETD2 contribute to human disease.

DNA double-strand breaks in telophase lead to coalescence between segregated sister chromatid loci

DNA double strand breaks (DSBs) pose a high risk for genome integrity. Cells repair DSBs through homologous recombination (HR) when a sister chromatid is available. HR is upregulated by the cycling dependent kinase (CDK) despite the paradox of telophase, where CDK is high but a sister chromatid is not nearby. Here we study in the budding yeast the response to DSBs in telophase, and find they activate the DNA damage checkpoint (DDC), leading to a telophase-to-G1 delay. Outstandingly, we observe a partial reversion of sister chromatid segregation, which includes approximation of segregated material, de novo formation of anaphase bridges, and coalescence between sister loci. We finally show that DSBs promote a massive change in the dynamics of telophase microtubules (MTs), together with dephosphorylation and relocalization of kinesin-5 Cin8. We propose that chromosome segregation is not irreversible and that DSB repair using the sister chromatid is possible in telophase.

Comprehensive and quantitative analysis of G1 cyclins. A tool for studying the cell cycle

In eukaryotes, the cell cycle is driven by the actions of several cyclin dependent kinases (CDKs) and an array of regulatory proteins called cyclins, due to the cyclical expression patterns of the latter. In yeast, the accepted pattern of cyclin waves is based on qualitative studies performed by different laboratories using different strain backgrounds, different growing conditions and media, and different kinds of genetic manipulation. Additionally, only the subset of cyclins regulating Cdc28 was included, while the Pho85 cyclins were excluded. We describe a comprehensive, quantitative and accurate blueprint of G₁ cyclins in the yeast Saccharomyces cerevisiae that, in addition to validating previous conclusions, yields new findings and establishes an accurate G₁ cyclin blueprint. For the purposes of this research, we produced a collection of strains with all G₁ cyclins identically tagged using the same and most respectful procedure possible. We report the contribution of each G₁ cyclin for a broad array of growing and stress conditions, describe an unknown role for Pcl2 in heat-stress conditions and demonstrate the importance of maintaining the 3’UTR sequence of cyclins untouched during the tagging process.

Combining learning and constraints for genome-wide protein annotation

BACKGROUND

The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.

RESULTS

We present OCELOT, a predictive pipeline which simultaneously addresses functional and interaction annotation of all proteins of a given genome. The system combines sequence-based predictors for functional and protein-protein interaction (PPI) prediction with a consistency layer enforcing (soft) constraints as fuzzy logic rules. The enforced rules represent the available prior knowledge about the classification task, including taxonomic constraints over each GO hierarchy (e.g. a protein labeled with a GO term should also be labeled with all ancestor terms) as well as rules combining interaction and function prediction. An extensive experimental evaluation on the Yeast genome shows that the integration of prior knowledge via rules substantially improves the quality of the predictions. The system largely outperforms GoFDR, the only high-ranking system at the last CAFA challenge with a readily available implementation, when GoFDR is given access to intra-genome information only (as OCELOT), and has comparable or better results (depending on the hierarchy and performance measure) when GoFDR is allowed to use information from other genomes. Our system also compares favorably to recent methods based on deep learning.

A Genetic XK-Means Algorithm with Empty Cluster Reassignment

K-Means is a well known and widely used classical clustering algorithm. It is easy to fall into local optimum and it is sensitive to the initial choice of cluster centers. XK-Means (eXploratory K-Means) has been introduced in the literature by adding an exploratory disturbance onto the vector of cluster centers, so as to jump out of the local optimum and reduce the sensitivity to the initial centers. However, empty clusters may appear during the iteration of XK-Means, causing damage to the efficiency of the algorithm. The aim of this paper is to introduce an empty-cluster-reassignment technique and use it to modify XK-Means, resulting in an EXK-Means clustering algorithm. Furthermore, we combine the EXK-Means with genetic mechanism to form a genetic XK-Means algorithm with empty-cluster-reassignment, referred to as GEXK-Means clustering algorithm. The convergence of GEXK-Means to the global optimum is theoretically proved. Numerical experiments on a few real world clustering problems are carried out, showing the advantage of EXK-Means over XK-Means, and the advantage of GEXK-Means over EXK-Means, XK-Means, K-Means and GXK-Means (genetic XK-Means).